DOH DAP parameters and targets
ZK, MM
2021-05-28
rm(list = ls())
library(dplyr)
library(data.table)
library(readxl)
library(tidyverse)
library(knitr)
library(kableExtra)
library(reshape2)
library(ggplot2)
library(plotly)
library(gt)
library(tiff)
library(grid)
# some plotting utility functions
layout_3x5 <- function(
gg, x = -0.15, y = -0.06,
x_legend=1.05, y_legend=0.95,
mar=list(l=50, r=150)){
# The 1 and 2 goes into the list that contains the options for the x and y axis labels respectively
gg[['x']][['layout']][['annotations']][[1]][['y']] <- x
gg[['x']][['layout']][['annotations']][[2]][['x']] <- y
gg[['x']][['layout']][['annotations']][[12]][['x']] <- x_legend
gg[['x']][['layout']][['legend']][['y']] <- y_legend
gg[['x']][['layout']][['legend']][['x']] <- x_legend
gg %>% layout(margin = mar)
}
layout_3x3 <- function(
gg, x = -0.15, y = -0.06,
x_legend=1.05, y_legend=0.95,
mar=list(l=50, r=150)){
# The 1 and 2 goes into the list that contains the options for the x and y axis labels respectively
gg[['x']][['layout']][['annotations']][[1]][['y']] <- x
gg[['x']][['layout']][['annotations']][[2]][['x']] <- y
gg[['x']][['layout']][['annotations']][[6]][['x']] <- x_legend
gg[['x']][['layout']][['legend']][['y']] <- y_legend
gg[['x']][['layout']][['legend']][['x']] <- x_legend
gg %>% layout(margin = mar)
}
layout_3x1 <- function(
gg, x = -0.08, y = -0.06,
x_legend=1.05, y_legend=0.95,
mar=list(l=50, r=150)){
# The 1 and 2 goes into the list that contains the options for the x and y axis labels respectively
gg[['x']][['layout']][['annotations']][[1]][['y']] <- x
gg[['x']][['layout']][['annotations']][[2]][['x']] <- y
gg[['x']][['layout']][['annotations']][[6]][['x']] <- x_legend
gg[['x']][['layout']][['legend']][['y']] <- y_legend
gg[['x']][['layout']][['legend']][['x']] <- x_legend
gg %>% layout(margin = mar)
}
layout_1x2 <- function(
gg, x = -0.08, y = -0.08,
x_legend=1.05, y_legend=0.95,
mar=list(l=50, r=150)){
# The 1 and 2 goes into the list that contains the options for the x and y axis labels respectively
gg[['x']][['layout']][['annotations']][[1]][['y']] <- x
gg[['x']][['layout']][['annotations']][[2]][['x']] <- y
gg[['x']][['layout']][['annotations']][[5]][['x']] <- x_legend
gg[['x']][['layout']][['legend']][['y']] <- y_legend
gg[['x']][['layout']][['legend']][['x']] <- x_legend
gg %>% layout(margin = mar)
}Back to Outline
Introduction
This report summarizes the parameters and targets used for simulating the ADAP and PDAP dynamics.
The parameters and targets constructed here are based on WADOH DAP participation data. The script is based on code and data sources originally developed by ZK (see Parameters and targets.Rmd).
- DAP data used here was originally constructed on the secure terminal server by ZK
DataManagment.Rperforms the data cleaningDAPoutputs.Rperforms the data analysis
Data
We read in data from
- WApopdata for population totals
- WHAMP survey for prep.targets, art.targets, dap.targets
- ZK’s ADAP and PDAP analyses (after race/region names updating)
# NOTE: MOST VARIABLE CONSTRUCTION IS DONE IN THIS CHUNK
# We now construct some of the variables that ZK constructed in the last
# chunk of her Parameters and targets.Rmd.
# But there are modifications to vars in chunks below, so the final updated
# versions of adap_data and pdap_data should be saved at the end.
#### Updated WApopdata ----
msm.pop.totals_2014 <- WApopdata::msm.pop.totals_2014
msm.pop.totals_2015 <- WApopdata::msm.pop.totals_2015
msm.pop.totals_2016 <- WApopdata::msm.pop.totals_2016
msm.pop.totals_2017 <- WApopdata::msm.pop.totals_2017
msm.pop.totals_2018 <- WApopdata::msm.pop.totals_2018
msm.pop.totals_2019 <- WApopdata::msm.pop.totals_2019
#### DOH on ART estimate ----
misc_targets <- readr::read_csv(here::here("Data", "Targets",
"misc_targets.csv"))
doh.on.art <- misc_targets$Value[misc_targets$Name == "on_art.df"]
#### WHAMP survey target objects ----
w.prepTargets <- readRDS(here::here("Data", "Targets", "prepTargets.RDS"))
w.artTargets <- readRDS(here::here("Data", "Targets", "artTargets.RDS"))
w.dapFractions <- readRDS(here::here("Data", "Params", "WhampDAPparam.RDS"))
# construct WHAMP ADAP frac of ART users for comparison to DOH DAP estimate
whamp_adap_frac <- w.dapFractions$adap.frac$art
semean = sqrt(whamp_adap_frac*(1-whamp_adap_frac)/
w.dapFractions$adap.frac$n.art)
adap.lb <- whamp_adap_frac - 2*semean
adap.ub <- whamp_adap_frac + 2*semean
# construct WHAMP PDAP frac of PrEP users for comparison to DOH DAP estimate
whamp_pdap_frac <- w.dapFractions$pdap.frac$prep
semean = sqrt(whamp_pdap_frac*(1-whamp_pdap_frac)/
w.dapFractions$pdap.frac$n.prep)
pdap.lb <- whamp_pdap_frac - 2*semean
pdap.ub <- whamp_pdap_frac + 2*semean
#### ZK's original DAP datafiles, after fixing the race/region names ----
#### using: source(here::here("MakeData", "MM", "Scripts", "update_dap_data.R"))
orig_adap_data <- readRDS(here::here("Data", "Clean", "DAPdata",
"adap_data.rds"))
orig_pdap_data <- readRDS(here::here("Data", "Clean", "DAPdata",
"pdap_data.rds"))
# Re-organize and update the ZK objects
# Goal is to get consistent naming and caps across ADAP and PDAP data
# and to add the DOH DAP fraction estimates
## ADAP ----
# from last chunk of ZK's rmd, to get something to hold the
# list element order, will be overwritten after transforming the
# adap_demog data later
init.demog <- orig_adap_data$adap_demog[enrollYear == 1]
# Rename
adap_data <- list(demog = orig_adap_data$adap_demog,
init.demog = init.demog,
ins = orig_adap_data$adap_ins,
new.clients = orig_adap_data$adap_new_clients,
cost.pday = orig_adap_data$ADAPcost_pd,
cost.pyr = orig_adap_data$ADAPcost_tot,
model.disenroll = orig_adap_data$surv_coef,
pred.disenroll = orig_adap_data$pred_disenroll,
vl.race = orig_adap_data$vl_race,
makefile = "make_DOHDAPdynamics.Rmd & DAPoutputs.R")
# compute 2018 DOH ADAP fractions -- For comparison to WHAMP survey
doh_adap_2018 <- sum(adap_data$demog$N[adap_data$demog$enrollYear==2018])
pop_pos_2018 <- sum(msm.pop.totals_2018$pop.pos$num_ub)
doh_adap_art_frac <- doh_adap_2018 / (pop_pos_2018 * doh.on.art)
doh_adap_hivpos_frac <- doh_adap_2018 / pop_pos_2018
adap.frac = list(art = doh_adap_art_frac,
hivpos = doh_adap_hivpos_frac)
# rebuild descTable
adap_desc_table <- tibble(
Component = c("adap.frac", names(adap_data)),
Description = c("ADAP fraction of ART users or HIV+",
"demographics of ADAP clients",
"demographics in first yr",
"insurance of ADAP clients",
"demographics of new ADAP clients",
"daily cost by component and total",
"annual cost by component and total",
"survival model calc outputs",
"pred prob ADAP disenroll/wk",
"prop virally suppressed",
"source files"),
Method = c("clients/(art_current or pop.pos)",
rep("Obs summary", 6),
rep("Exp + Weibull survival model", 2),
"Obs summary",
" "),
Levels = c("overall",
"enroll yr x race x region",
"race x region",
rep("enroll yr x race x region", 2),
rep("yr x race x insurance", 2),
"day x race x insurance x type",
"wk x race x insurance",
"race x ADAP status",
" "),
Source = c("ADAP claims, Census, DOH report",
rep("ADAP claims data", 8),
"ADAP claims data + EHARS",
" "),
Group = c(rep("ADAP-related", 10), " "))
# Reconstruct the object
adap_data <- c(adap.frac = list(adap.frac),
adap_data,
descTable = list(adap_desc_table))
## PDAP ----
# we have several preliminary changes to make:
# from last chunk of ZK's Parameters and Targets.rmd
pred.disenroll <- orig_pdap_data$drop_prob_dat[ , years := as.numeric(years)]
# construct initial enrollment demog distn (% of HIV-)
## init_pdap_dat gets a name change but no other transforms,
## so unlike adap init.demog, this is not overwritten later
## the init_pdap_dat table is a linelist with enrollment days
## for each client, restricted to clients with
## enrollYr=2016 so we use the 2016 WApopdata
## ZK transforms this to init_pdap_n, the count by race x region
## NOTE: we rename n_samp to N, and replace pop with popsize.neg
## to parallel ADAP init.demog
init_pdap_n <- orig_pdap_data$init_pdap_dat[, .N, by = c("race", "region")]
init_pdap_n <- merge(init_pdap_n, #pop.hboregion.neg, was original merge
msm.pop.totals_2016$pop.racexregion.neg,
by = c("race", "region")) %>%
mutate(popsize.neg = num_ub,
prop = N/popsize.neg) %>%
select(race, region, N, popsize.neg, prop) %>%
setkeyv(c("race", "region"))
# Rename, add placeholder for predicted insurance (model in PDAP section)
pdap_data <- list(demog = orig_pdap_data$pdap_demog,
init.demog = init_pdap_n,
ins = orig_pdap_data$pdap_ins,
ins.pred = "placeholder",
new.clients = orig_pdap_data$pdap_new_clients,
cost.pday = orig_pdap_data$PDAPcost_pd,
model.disenroll = orig_pdap_data$dropout_model,
pred.disenroll = pred.disenroll,
init.dur = orig_pdap_data$init_pdap_dat,
makefile = "make_DOHDAPdynamics.Rmd & DAPoutputs.R")
# compute DOH PDAP fraction -- use 2018 for comparison to WHAMP survey
doh_pdap_2018 <- sum(pdap_data$demog$N[pdap_data$demog$year==2018])
pop_neg_2018 <- sum(msm.pop.totals_2018$pop.neg$num_ub)
# PrepFrac -- we don't have a DOH estimate so use WHAMP
prepFrac <- w.prepTargets$all$wtd.mean[w.prepTargets$all$var == "prep.current"]
doh_pdap_prep_frac <- doh_pdap_2018 / (pop_neg_2018 * prepFrac)
doh_pdap_hivneg_frac <- doh_pdap_2018 / pop_neg_2018
pdap.frac = list(prep = doh_pdap_prep_frac,
hivneg = doh_pdap_hivneg_frac)
# rebuild descTable
pdap_desc_table <- tibble(
Component = c("pdap.frac", names(pdap_data)),
Description = c("PDAP fraction of PrEP users or HIV-",
"demographics of PDAP clients",
"demographics before first yr (2016)",
"observed insurance of PDAP clients",
"predicted insurance for PDAP clients",
"demographics of new PDAP clients",
"daily cost by component and total",
"disenroll model fit object",
"pred prob PDAP disenroll/wk",
"time on PDAP at start of first yr (days)",
"source files"),
Method = c("clients/(prep.current or pop.neg)",
rep("Obs summary", 3),
"multinomial regression",
rep("Obs summary", 2),
rep("Logistic regression", 2),
"Obs summary",
" "),
Levels = c("overall",
"enroll yr x race x region",
"race x region",
"enroll yr x race x region",
"race x region",
"enroll yr x race x region",
"enroll yr x race x insurance",
rep("yr x race x insurance", 2),
"race x insurance x region",
" "),
Source = c("PDAP claims, Census, WHAMP survey",
rep("PDAP claims data", 9), " "),
Group = c(rep("PDAP-related", 10), " "))
# Reconstruct the object
pdap_data <- c(pdap.frac = list(pdap.frac),
pdap_data,
descTable = list(pdap_desc_table))Population estimates
The population estimates msm.pop.totals_20XX come from the WApopdata repository, for XX=year (14 thru 19). The objects are documented, so the details are available via ?WApopdata::msm.pop.totals_2019.
These are list objects, which include the MSM population estimates by demographic attributes (age, race, region) and HIV status. Summary of the 2019 object below.
summary(msm.pop.totals_2019)## Length Class Mode
## pop.all 5 tbl_df list
## pop.pos 8 tbl_df list
## pop.neg 6 tbl_df list
## newdx.all 8 tbl_df list
## pop.age.all 5 tbl_df list
## pop.age.neg 4 tbl_df list
## pop.age.pos 9 tbl_df list
## newdx.age 9 tbl_df list
## pop.race.all 5 tbl_df list
## pop.race.neg 4 tbl_df list
## pop.race.pos 9 tbl_df list
## newdx.race 9 tbl_df list
## pop.region.all 3 tbl_df list
## pop.region.neg 4 tbl_df list
## pop.region.pos 6 tbl_df list
## newdx.region 6 tbl_df list
## pop.racexregion.all 4 grouped_df list
## pop.racexregion.neg 5 grouped_df list
## pop.racexregion.pos 7 grouped_df list
## newdx.racexregion 7 grouped_df list
## MSM_PLWH_WA 5 tbl_df list
## msm.wts.all.age5 5 tbl_df list
## msm.wts.all.age10 5 tbl_df list
## msm.wts.15_65.age10 5 tbl_df listDOH DAP data files
These files are derived from the ADAP and PDAP program data. Construction workflow is on the secure terminal server: DataManagement.R followed by DAPoutputs.R.
NOTE: We have renamed the components of ZK’s DAP objects for clarity, consistency and brevity. The renames can be found in the code section at the top of this file where the objects are read in, and the new names are also listed in the descTable at the end of this report.
ADAP data
adap_data is a list including the following elements.
kable(adap_data$descTable,
caption= "ADAP data") %>%
kable_styling(full_width = F, position = "center",
bootstrap_options = c("striped"))| Component | Description | Method | Levels | Source | Group |
|---|---|---|---|---|---|
| adap.frac | ADAP fraction of ART users or HIV+ | clients/(art_current or pop.pos) | overall | ADAP claims, Census, DOH report | ADAP-related |
| demog | demographics of ADAP clients | Obs summary | enroll yr x race x region | ADAP claims data | ADAP-related |
| init.demog | demographics in first yr | Obs summary | race x region | ADAP claims data | ADAP-related |
| ins | insurance of ADAP clients | Obs summary | enroll yr x race x region | ADAP claims data | ADAP-related |
| new.clients | demographics of new ADAP clients | Obs summary | enroll yr x race x region | ADAP claims data | ADAP-related |
| cost.pday | daily cost by component and total | Obs summary | yr x race x insurance | ADAP claims data | ADAP-related |
| cost.pyr | annual cost by component and total | Obs summary | yr x race x insurance | ADAP claims data | ADAP-related |
| model.disenroll | survival model calc outputs | Exp + Weibull survival model | day x race x insurance x type | ADAP claims data | ADAP-related |
| pred.disenroll | pred prob ADAP disenroll/wk | Exp + Weibull survival model | wk x race x insurance | ADAP claims data | ADAP-related |
| vl.race | prop virally suppressed | Obs summary | race x ADAP status | ADAP claims data + EHARS | ADAP-related |
| makefile | source files |
PDAP data
pdap_data is a list including the following elements.
kable(pdap_data$descTable,
caption= "PDAP data") %>%
kable_styling(full_width=F, position="center",
bootstrap_options = c("striped"))| Component | Description | Method | Levels | Source | Group |
|---|---|---|---|---|---|
| pdap.frac | PDAP fraction of PrEP users or HIV- | clients/(prep.current or pop.neg) | overall | PDAP claims, Census, WHAMP survey | PDAP-related |
| demog | demographics of PDAP clients | Obs summary | enroll yr x race x region | PDAP claims data | PDAP-related |
| init.demog | demographics before first yr (2016) | Obs summary | race x region | PDAP claims data | PDAP-related |
| ins | observed insurance of PDAP clients | Obs summary | enroll yr x race x region | PDAP claims data | PDAP-related |
| ins.pred | predicted insurance for PDAP clients | multinomial regression | race x region | PDAP claims data | PDAP-related |
| new.clients | demographics of new PDAP clients | Obs summary | enroll yr x race x region | PDAP claims data | PDAP-related |
| cost.pday | daily cost by component and total | Obs summary | enroll yr x race x insurance | PDAP claims data | PDAP-related |
| model.disenroll | disenroll model fit object | Logistic regression | yr x race x insurance | PDAP claims data | PDAP-related |
| pred.disenroll | pred prob PDAP disenroll/wk | Logistic regression | yr x race x insurance | PDAP claims data | PDAP-related |
| init.dur | time on PDAP at start of first yr (days) | Obs summary | race x insurance x region | PDAP claims data | PDAP-related |
| makefile | source files |
WHAMP survey targets
These are based on the WHAMP survey data, and do not include the ARTnetWA cases.
ART
This is a list object with summary statistics on the ART continuum, overall and stratified by attributes (components are all, age, race, region). Sample is restricted to HIV+.
In general we can only use the overall sample summaries, as the cell sizes are too small when stratified by attribute to be reliable:
kable(w.artTargets$all,
caption= "ART summary stats for all HIV+",
digits = c(0,0,0,0,1,2,2,3,1)) %>%
kable_styling(full_width=F, position="center",
bootstrap_options = c("striped")) %>%
footnote("WHAMP survey data")| var | nobs | n.valid | n.missing | wtd.n | wtd.mean | wtd.sd | wtd.semean | wtd.median |
|---|---|---|---|---|---|---|---|---|
| mos.frst.art | 87 | 87 | 0 | 89.7 | 177.59 | 105.27 | 11.116 | 160 |
| art.int | 87 | 87 | 0 | 89.7 | 0.30 | 0.46 | 0.049 | 0 |
| art.int.mos | 87 | 24 | 63 | 25.2 | 11.37 | 19.36 | 2.044 | 6 |
| Note: | ||||||||
| WHAMP survey data |
PrEP
This is a list object with summary statistics on the PrEP continuum, overall and stratified by attributes (components are all, age, race, region, snap5). Sample is restricted to HIV-.
As with the WHAMP ART stats, we can only use the overall sample summaries, as the cell sizes are too small when stratified by attribute to be reliable:
kable(w.prepTargets$all,
caption= "PrEP summary stats for all HIV-",
digits = c(0,0,0,0,1,2,2,3,1)) %>%
kable_styling(full_width=F, position="center",
bootstrap_options = c("striped")) %>%
footnote("WHAMP survey data")| var | nobs | n.valid | n.missing | wtd.n | wtd.mean | wtd.sd | wtd.semean | wtd.median |
|---|---|---|---|---|---|---|---|---|
| prep.aware | 831 | 756 | 75 | 749.8 | 0.92 | 0.27 | 0.009 | 1 |
| prep.ever | 831 | 755 | 76 | 748.8 | 0.30 | 0.46 | 0.016 | 0 |
| prep.current | 831 | 755 | 76 | 748.8 | 0.22 | 0.42 | 0.014 | 0 |
| prep.discont | 831 | 222 | 609 | 225.2 | 0.39 | 0.49 | 0.017 | 0 |
| prep.stop | 831 | 222 | 609 | 225.2 | 0.26 | 0.44 | 0.015 | 0 |
| prep.reinit | 831 | 161 | 670 | 167.5 | 0.18 | 0.39 | 0.013 | 0 |
| prep.int.mos | 831 | 34 | 797 | 30.3 | 3.50 | 2.62 | 0.091 | 2 |
| Note: | ||||||||
| WHAMP survey data |
DAP participation
This is a list object with point estimates of the overall ADAP and PDAP enrollment rates, for both those engaged in care and the HIV status-relevant population, and the number of observations on which they are based.
Again we can only use the overall sample summaries, as the subgroups are too small when stratified by attribute to be reliable.
- ADAP enrollment fraction estimates
- of ART users: 29.9%
- of HIV+ : 28.2%
- PDAP enrollment fraction estimates
- of PrEP users: 16.7%
- of HIV- : 3.7%
kable(w.dapFractions$descTable,
caption= "WHAMP DAP participation rate estimates") %>%
kable_styling(full_width=F, position="center",
bootstrap_options = c("striped"))| Params | Description | Subset | Method | Levels | Notes |
|---|---|---|---|---|---|
| adap.frac | ADAP enrollment fractions & nobs | ART current or HIV+ | wtd proportions | overall | adap.frac art is starting value for calibration |
| pdap.frac | PDAP enrollment fractions & nobs | PrEP current or HIV- | wtd proportions | overall | pdap.frac prep is starting value for calibration |
| makefile | source file | WHAMP survey estimates |
cat("adap.frac object")## adap.frac objectglimpse(w.dapFractions$adap.frac)## List of 4
## $ art : num 0.299
## $ n.art : int 87
## $ hivpos: num 0.282
## $ n.pos : int 92cat("pdap.frac object")## pdap.frac objectglimpse(w.dapFractions$pdap.frac)## List of 4
## $ prep : num 0.167
## $ n.prep: int 161
## $ hivneg: num 0.0372
## $ n.neg : int 755Parameters
ADAP
demog
Demographics of ADAP clients.
Data inputs
- Number of ADAP clients by race x region x year from DAP data
- HIV+ popsize by race x region x year from WApopdata
- Overall 2019 engaged in care fraction of HIV+ from 2020 DOH surveillance report (see this GH issue)
Parameter outputs
Year 1 (2014) serves as the initial distribution of ADAP clients by race x region.
ADAP fraction of ART users (can be used as a target, or a parameter)
# ADAP data only for 2014-2018
# we are now using 2014-2018 data for pop estimates also
# and we use the upper bound estimate for number of HIV positive
year<- data.frame(year = c(rep(2014, 9), rep(2015,9), rep(2016,9),
rep(2017,9), rep(2018,9)))
pop.racexregion.pos <- bind_rows(msm.pop.totals_2014$pop.racexregion.pos,
msm.pop.totals_2015$pop.racexregion.pos,
msm.pop.totals_2016$pop.racexregion.pos,
msm.pop.totals_2017$pop.racexregion.pos,
msm.pop.totals_2018$pop.racexregion.pos) %>%
bind_cols(year, .) %>%
select(year, region, race, popsize.pos = num_ub) %>%
data.table()
# ZK modifies the data structure in place
adap_data$demog <- merge(adap_data$demog,
pop.racexregion.pos,
by.x = c("enrollYear", "race", "region"),
by.y = c("year", "race", "region"),
all.x = T) %>%
mutate(prop = N/popsize.pos,
year = enrollYear - 2013) %>%
select(-c(yr_bk, enrollYear)) %>%
setkeyv(c("year", "race", "region")) %>%
setcolorder(c("year", "race", "region"))
adap_data$init.demog <- adap_data$demog[year == 1] %>% # note we overwrite
setkeyv(c("race", "region"))
## Not used, but would be interesting
# adap_person_yrs <- sum(adap_data$demog$N)ADAP Prevalence estimate comparisons
We have 2 sources for estimating the fraction of ART users on ADAP: DOH DAP + surveillance data, and the WHAMP survey. The two estimates are consistent (given sampling error):
- DOH data: 34.3%
- WHAMP survey: 29.9% (95% CI: 20.1% - 39.7%)
DOH ADAP estimates
The plot and table below show the fraction of HIV- in ADAP, based on the DOH DAP and WApop data. The estimates are based on ADAP.clients(yr)/PopHIV-(yr) by attribute. Note the denominator is not ART users.
Plot
p <- ggplot(adap_data$demog %>% mutate(year=year+2013),
aes(x = year, y = prop, text = N)) +
geom_line(aes(color = race)) +
facet_wrap(~ region) +
labs(title = "ADAP participation rates for HIV+",
xlab = "year",
ylab = "proportion") +
theme(axis.text.x = element_text(size = 9, angle = 90, hjust = 1))
gp <- ggplotly(p, tooltip = "text")
gp %>% layout_3x3Table
kable(adap_data$demog, digits = c(0, 0, 0, 0, 1, 2),
caption= "ADAP by race and region") %>%
kable_styling(full_width=F, position="center",
bootstrap_options = c("striped"))| year | race | region | N | popsize.pos | prop |
|---|---|---|---|---|---|
| 1 | B | EasternWA | 6 | 34.3 | 0.17 |
| 1 | B | King | 165 | 498.1 | 0.33 |
| 1 | B | WesternWA | 55 | 220.2 | 0.25 |
| 1 | H | EasternWA | 72 | 194.1 | 0.37 |
| 1 | H | King | 261 | 668.1 | 0.39 |
| 1 | H | WesternWA | 117 | 321.7 | 0.36 |
| 1 | O | EasternWA | 242 | 780.8 | 0.31 |
| 1 | O | King | 1144 | 4141.0 | 0.28 |
| 1 | O | WesternWA | 674 | 2120.6 | 0.32 |
| 2 | B | EasternWA | 5 | 40.4 | 0.12 |
| 2 | B | King | 157 | 507.1 | 0.31 |
| 2 | B | WesternWA | 58 | 240.4 | 0.24 |
| 2 | H | EasternWA | 71 | 206.6 | 0.34 |
| 2 | H | King | 279 | 721.5 | 0.39 |
| 2 | H | WesternWA | 128 | 354.0 | 0.36 |
| 2 | O | EasternWA | 235 | 872.7 | 0.27 |
| 2 | O | King | 1074 | 4164.1 | 0.26 |
| 2 | O | WesternWA | 684 | 2191.6 | 0.31 |
| 3 | B | EasternWA | 3 | 40.5 | 0.07 |
| 3 | B | King | 178 | 530.2 | 0.34 |
| 3 | B | WesternWA | 59 | 248.6 | 0.24 |
| 3 | H | EasternWA | 72 | 210.9 | 0.34 |
| 3 | H | King | 300 | 771.7 | 0.39 |
| 3 | H | WesternWA | 150 | 367.6 | 0.41 |
| 3 | O | EasternWA | 257 | 913.0 | 0.28 |
| 3 | O | King | 1106 | 4139.0 | 0.27 |
| 3 | O | WesternWA | 743 | 2286.3 | 0.32 |
| 4 | B | EasternWA | 6 | 45.1 | 0.13 |
| 4 | B | King | 200 | 573.1 | 0.35 |
| 4 | B | WesternWA | 64 | 266.6 | 0.24 |
| 4 | H | EasternWA | 84 | 229.7 | 0.37 |
| 4 | H | King | 320 | 808.4 | 0.40 |
| 4 | H | WesternWA | 161 | 426.3 | 0.38 |
| 4 | O | EasternWA | 262 | 969.1 | 0.27 |
| 4 | O | King | 1121 | 4130.7 | 0.27 |
| 4 | O | WesternWA | 769 | 2421.9 | 0.32 |
| 5 | B | EasternWA | 8 | 52.5 | 0.15 |
| 5 | B | King | 212 | 604.4 | 0.35 |
| 5 | B | WesternWA | 67 | 278.0 | 0.24 |
| 5 | H | EasternWA | 103 | 243.0 | 0.42 |
| 5 | H | King | 329 | 850.7 | 0.39 |
| 5 | H | WesternWA | 167 | 445.7 | 0.37 |
| 5 | O | EasternWA | 284 | 996.6 | 0.28 |
| 5 | O | King | 1142 | 4094.8 | 0.28 |
| 5 | O | WesternWA | 794 | 2548.2 | 0.31 |
ins
Insurance among ADAP clients
Data source: DOH DAP data
- Number of ADAP clients by race x region x insurance x year
- 6 insurance cagetories: employer, individual, medicaid, medicare, none (uninsured), and WSHIP.
Parameter Output
- Probability of insurance category by race x region x year.
- Insurance of new ADAP clients was assigned using this data.table.
# ZK modifies the original data here
ins <- adap_data$ins
ins[, sum_N := sum(N), by = list(year, race, region)]
ins[, prop := N / sum_N]
ins[, `:=` (yr_bk = NULL,
sum_N = NULL,
year = year - 2013)]
adap_data$ins <- insPlot
p <- ggplot(adap_data$ins %>% mutate(year = year+2013),
aes(x = year, y = prop,
text = round(prop,2))) +
geom_line(aes(group = race, color = race)) +
facet_grid(region ~ insurance) +
labs(title = "Insurance type for ADAP clients") +
xlab("year") + ylab("proportion") +
theme(axis.text.x = element_text(size = 9, angle = 90, hjust = 1))
# This plot takes a lot of tweaking to display properly using
# ggplotly
gp <- ggplotly(p, tooltip = "text")
gp %>% layout_3x5Table
kable(adap_data$ins, digits = c(0, 0, 0, 0, 0, 3),
caption= "insurance of ADAP clients by race and region") %>%
kable_styling(full_width=F, position="center", bootstrap_options = c("striped"))| year | race | region | insurance | N | prop |
|---|---|---|---|---|---|
| 1 | B | EasternWA | employer | 0 | 0.000 |
| 1 | B | EasternWA | individual | 0 | 0.000 |
| 1 | B | EasternWA | medicaid | 1 | 0.167 |
| 1 | B | EasternWA | medicare | 2 | 0.333 |
| 1 | B | EasternWA | none | 3 | 0.500 |
| 1 | B | EasternWA | wship | 0 | 0.000 |
| 1 | B | King | employer | 0 | 0.000 |
| 1 | B | King | individual | 57 | 0.345 |
| 1 | B | King | medicaid | 23 | 0.139 |
| 1 | B | King | medicare | 63 | 0.382 |
| 1 | B | King | none | 22 | 0.133 |
| 1 | B | King | wship | 0 | 0.000 |
| 1 | B | WesternWA | employer | 0 | 0.000 |
| 1 | B | WesternWA | individual | 21 | 0.382 |
| 1 | B | WesternWA | medicaid | 3 | 0.055 |
| 1 | B | WesternWA | medicare | 24 | 0.436 |
| 1 | B | WesternWA | none | 7 | 0.127 |
| 1 | B | WesternWA | wship | 0 | 0.000 |
| 1 | H | EasternWA | employer | 0 | 0.000 |
| 1 | H | EasternWA | individual | 18 | 0.250 |
| 1 | H | EasternWA | medicaid | 1 | 0.014 |
| 1 | H | EasternWA | medicare | 14 | 0.194 |
| 1 | H | EasternWA | none | 18 | 0.250 |
| 1 | H | EasternWA | wship | 21 | 0.292 |
| 1 | H | King | employer | 0 | 0.000 |
| 1 | H | King | individual | 107 | 0.410 |
| 1 | H | King | medicaid | 4 | 0.015 |
| 1 | H | King | medicare | 38 | 0.146 |
| 1 | H | King | none | 45 | 0.172 |
| 1 | H | King | wship | 67 | 0.257 |
| 1 | H | WesternWA | employer | 0 | 0.000 |
| 1 | H | WesternWA | individual | 31 | 0.265 |
| 1 | H | WesternWA | medicaid | 2 | 0.017 |
| 1 | H | WesternWA | medicare | 25 | 0.214 |
| 1 | H | WesternWA | none | 24 | 0.205 |
| 1 | H | WesternWA | wship | 35 | 0.299 |
| 1 | O | EasternWA | employer | 0 | 0.000 |
| 1 | O | EasternWA | individual | 72 | 0.298 |
| 1 | O | EasternWA | medicaid | 28 | 0.116 |
| 1 | O | EasternWA | medicare | 134 | 0.554 |
| 1 | O | EasternWA | none | 8 | 0.033 |
| 1 | O | EasternWA | wship | 0 | 0.000 |
| 1 | O | King | employer | 0 | 0.000 |
| 1 | O | King | individual | 536 | 0.469 |
| 1 | O | King | medicaid | 68 | 0.059 |
| 1 | O | King | medicare | 401 | 0.351 |
| 1 | O | King | none | 131 | 0.115 |
| 1 | O | King | wship | 8 | 0.007 |
| 1 | O | WesternWA | employer | 0 | 0.000 |
| 1 | O | WesternWA | individual | 222 | 0.329 |
| 1 | O | WesternWA | medicaid | 33 | 0.049 |
| 1 | O | WesternWA | medicare | 359 | 0.533 |
| 1 | O | WesternWA | none | 55 | 0.082 |
| 1 | O | WesternWA | wship | 5 | 0.007 |
| 2 | B | EasternWA | employer | 0 | 0.000 |
| 2 | B | EasternWA | individual | 2 | 0.400 |
| 2 | B | EasternWA | medicaid | 0 | 0.000 |
| 2 | B | EasternWA | medicare | 3 | 0.600 |
| 2 | B | EasternWA | none | 0 | 0.000 |
| 2 | B | EasternWA | wship | 0 | 0.000 |
| 2 | B | King | employer | 0 | 0.000 |
| 2 | B | King | individual | 53 | 0.338 |
| 2 | B | King | medicaid | 26 | 0.166 |
| 2 | B | King | medicare | 61 | 0.389 |
| 2 | B | King | none | 17 | 0.108 |
| 2 | B | King | wship | 0 | 0.000 |
| 2 | B | WesternWA | employer | 0 | 0.000 |
| 2 | B | WesternWA | individual | 21 | 0.362 |
| 2 | B | WesternWA | medicaid | 3 | 0.052 |
| 2 | B | WesternWA | medicare | 26 | 0.448 |
| 2 | B | WesternWA | none | 8 | 0.138 |
| 2 | B | WesternWA | wship | 0 | 0.000 |
| 2 | H | EasternWA | employer | 0 | 0.000 |
| 2 | H | EasternWA | individual | 24 | 0.338 |
| 2 | H | EasternWA | medicaid | 3 | 0.042 |
| 2 | H | EasternWA | medicare | 12 | 0.169 |
| 2 | H | EasternWA | none | 12 | 0.169 |
| 2 | H | EasternWA | wship | 20 | 0.282 |
| 2 | H | King | employer | 0 | 0.000 |
| 2 | H | King | individual | 115 | 0.412 |
| 2 | H | King | medicaid | 4 | 0.014 |
| 2 | H | King | medicare | 37 | 0.133 |
| 2 | H | King | none | 55 | 0.197 |
| 2 | H | King | wship | 68 | 0.244 |
| 2 | H | WesternWA | employer | 0 | 0.000 |
| 2 | H | WesternWA | individual | 43 | 0.336 |
| 2 | H | WesternWA | medicaid | 1 | 0.008 |
| 2 | H | WesternWA | medicare | 28 | 0.219 |
| 2 | H | WesternWA | none | 21 | 0.164 |
| 2 | H | WesternWA | wship | 35 | 0.273 |
| 2 | O | EasternWA | employer | 0 | 0.000 |
| 2 | O | EasternWA | individual | 56 | 0.238 |
| 2 | O | EasternWA | medicaid | 29 | 0.123 |
| 2 | O | EasternWA | medicare | 143 | 0.609 |
| 2 | O | EasternWA | none | 7 | 0.030 |
| 2 | O | EasternWA | wship | 0 | 0.000 |
| 2 | O | King | employer | 0 | 0.000 |
| 2 | O | King | individual | 499 | 0.465 |
| 2 | O | King | medicaid | 74 | 0.069 |
| 2 | O | King | medicare | 396 | 0.369 |
| 2 | O | King | none | 97 | 0.090 |
| 2 | O | King | wship | 8 | 0.007 |
| 2 | O | WesternWA | employer | 0 | 0.000 |
| 2 | O | WesternWA | individual | 199 | 0.291 |
| 2 | O | WesternWA | medicaid | 53 | 0.077 |
| 2 | O | WesternWA | medicare | 381 | 0.557 |
| 2 | O | WesternWA | none | 45 | 0.066 |
| 2 | O | WesternWA | wship | 6 | 0.009 |
| 3 | B | EasternWA | employer | 0 | 0.000 |
| 3 | B | EasternWA | individual | 0 | 0.000 |
| 3 | B | EasternWA | medicaid | 1 | 0.333 |
| 3 | B | EasternWA | medicare | 2 | 0.667 |
| 3 | B | EasternWA | none | 0 | 0.000 |
| 3 | B | EasternWA | wship | 0 | 0.000 |
| 3 | B | King | employer | 10 | 0.056 |
| 3 | B | King | individual | 49 | 0.275 |
| 3 | B | King | medicaid | 31 | 0.174 |
| 3 | B | King | medicare | 64 | 0.360 |
| 3 | B | King | none | 24 | 0.135 |
| 3 | B | King | wship | 0 | 0.000 |
| 3 | B | WesternWA | employer | 4 | 0.068 |
| 3 | B | WesternWA | individual | 21 | 0.356 |
| 3 | B | WesternWA | medicaid | 5 | 0.085 |
| 3 | B | WesternWA | medicare | 25 | 0.424 |
| 3 | B | WesternWA | none | 4 | 0.068 |
| 3 | B | WesternWA | wship | 0 | 0.000 |
| 3 | H | EasternWA | employer | 3 | 0.042 |
| 3 | H | EasternWA | individual | 26 | 0.361 |
| 3 | H | EasternWA | medicaid | 4 | 0.056 |
| 3 | H | EasternWA | medicare | 12 | 0.167 |
| 3 | H | EasternWA | none | 9 | 0.125 |
| 3 | H | EasternWA | wship | 18 | 0.250 |
| 3 | H | King | employer | 10 | 0.033 |
| 3 | H | King | individual | 134 | 0.447 |
| 3 | H | King | medicaid | 6 | 0.020 |
| 3 | H | King | medicare | 37 | 0.123 |
| 3 | H | King | none | 44 | 0.147 |
| 3 | H | King | wship | 69 | 0.230 |
| 3 | H | WesternWA | employer | 3 | 0.020 |
| 3 | H | WesternWA | individual | 57 | 0.380 |
| 3 | H | WesternWA | medicaid | 1 | 0.007 |
| 3 | H | WesternWA | medicare | 32 | 0.213 |
| 3 | H | WesternWA | none | 21 | 0.140 |
| 3 | H | WesternWA | wship | 36 | 0.240 |
| 3 | O | EasternWA | employer | 5 | 0.019 |
| 3 | O | EasternWA | individual | 53 | 0.206 |
| 3 | O | EasternWA | medicaid | 42 | 0.163 |
| 3 | O | EasternWA | medicare | 143 | 0.556 |
| 3 | O | EasternWA | none | 14 | 0.054 |
| 3 | O | EasternWA | wship | 0 | 0.000 |
| 3 | O | King | employer | 37 | 0.033 |
| 3 | O | King | individual | 468 | 0.423 |
| 3 | O | King | medicaid | 97 | 0.088 |
| 3 | O | King | medicare | 391 | 0.354 |
| 3 | O | King | none | 105 | 0.095 |
| 3 | O | King | wship | 8 | 0.007 |
| 3 | O | WesternWA | employer | 36 | 0.048 |
| 3 | O | WesternWA | individual | 204 | 0.275 |
| 3 | O | WesternWA | medicaid | 68 | 0.092 |
| 3 | O | WesternWA | medicare | 393 | 0.529 |
| 3 | O | WesternWA | none | 36 | 0.048 |
| 3 | O | WesternWA | wship | 6 | 0.008 |
| 4 | B | EasternWA | employer | 1 | 0.167 |
| 4 | B | EasternWA | individual | 1 | 0.167 |
| 4 | B | EasternWA | medicaid | 4 | 0.667 |
| 4 | B | EasternWA | medicare | 0 | 0.000 |
| 4 | B | EasternWA | none | 0 | 0.000 |
| 4 | B | EasternWA | wship | 0 | 0.000 |
| 4 | B | King | employer | 46 | 0.230 |
| 4 | B | King | individual | 34 | 0.170 |
| 4 | B | King | medicaid | 61 | 0.305 |
| 4 | B | King | medicare | 38 | 0.190 |
| 4 | B | King | none | 21 | 0.105 |
| 4 | B | King | wship | 0 | 0.000 |
| 4 | B | WesternWA | employer | 19 | 0.297 |
| 4 | B | WesternWA | individual | 10 | 0.156 |
| 4 | B | WesternWA | medicaid | 13 | 0.203 |
| 4 | B | WesternWA | medicare | 15 | 0.234 |
| 4 | B | WesternWA | none | 7 | 0.109 |
| 4 | B | WesternWA | wship | 0 | 0.000 |
| 4 | H | EasternWA | employer | 19 | 0.226 |
| 4 | H | EasternWA | individual | 18 | 0.214 |
| 4 | H | EasternWA | medicaid | 11 | 0.131 |
| 4 | H | EasternWA | medicare | 6 | 0.071 |
| 4 | H | EasternWA | none | 12 | 0.143 |
| 4 | H | EasternWA | wship | 18 | 0.214 |
| 4 | H | King | employer | 65 | 0.203 |
| 4 | H | King | individual | 87 | 0.272 |
| 4 | H | King | medicaid | 24 | 0.075 |
| 4 | H | King | medicare | 18 | 0.056 |
| 4 | H | King | none | 59 | 0.184 |
| 4 | H | King | wship | 67 | 0.209 |
| 4 | H | WesternWA | employer | 27 | 0.168 |
| 4 | H | WesternWA | individual | 40 | 0.248 |
| 4 | H | WesternWA | medicaid | 12 | 0.075 |
| 4 | H | WesternWA | medicare | 18 | 0.112 |
| 4 | H | WesternWA | none | 27 | 0.168 |
| 4 | H | WesternWA | wship | 37 | 0.230 |
| 4 | O | EasternWA | employer | 30 | 0.115 |
| 4 | O | EasternWA | individual | 38 | 0.145 |
| 4 | O | EasternWA | medicaid | 96 | 0.366 |
| 4 | O | EasternWA | medicare | 89 | 0.340 |
| 4 | O | EasternWA | none | 9 | 0.034 |
| 4 | O | EasternWA | wship | 0 | 0.000 |
| 4 | O | King | employer | 237 | 0.211 |
| 4 | O | King | individual | 313 | 0.279 |
| 4 | O | King | medicaid | 239 | 0.213 |
| 4 | O | King | medicare | 251 | 0.224 |
| 4 | O | King | none | 73 | 0.065 |
| 4 | O | King | wship | 8 | 0.007 |
| 4 | O | WesternWA | employer | 129 | 0.168 |
| 4 | O | WesternWA | individual | 129 | 0.168 |
| 4 | O | WesternWA | medicaid | 202 | 0.263 |
| 4 | O | WesternWA | medicare | 264 | 0.343 |
| 4 | O | WesternWA | none | 39 | 0.051 |
| 4 | O | WesternWA | wship | 6 | 0.008 |
| 5 | B | EasternWA | employer | 1 | 0.125 |
| 5 | B | EasternWA | individual | 1 | 0.125 |
| 5 | B | EasternWA | medicaid | 3 | 0.375 |
| 5 | B | EasternWA | medicare | 0 | 0.000 |
| 5 | B | EasternWA | none | 3 | 0.375 |
| 5 | B | EasternWA | wship | 0 | 0.000 |
| 5 | B | King | employer | 51 | 0.241 |
| 5 | B | King | individual | 37 | 0.175 |
| 5 | B | King | medicaid | 62 | 0.292 |
| 5 | B | King | medicare | 40 | 0.189 |
| 5 | B | King | none | 22 | 0.104 |
| 5 | B | King | wship | 0 | 0.000 |
| 5 | B | WesternWA | employer | 17 | 0.254 |
| 5 | B | WesternWA | individual | 10 | 0.149 |
| 5 | B | WesternWA | medicaid | 14 | 0.209 |
| 5 | B | WesternWA | medicare | 16 | 0.239 |
| 5 | B | WesternWA | none | 10 | 0.149 |
| 5 | B | WesternWA | wship | 0 | 0.000 |
| 5 | H | EasternWA | employer | 27 | 0.262 |
| 5 | H | EasternWA | individual | 25 | 0.243 |
| 5 | H | EasternWA | medicaid | 13 | 0.126 |
| 5 | H | EasternWA | medicare | 9 | 0.087 |
| 5 | H | EasternWA | none | 11 | 0.107 |
| 5 | H | EasternWA | wship | 18 | 0.175 |
| 5 | H | King | employer | 70 | 0.213 |
| 5 | H | King | individual | 75 | 0.228 |
| 5 | H | King | medicaid | 27 | 0.082 |
| 5 | H | King | medicare | 16 | 0.049 |
| 5 | H | King | none | 76 | 0.231 |
| 5 | H | King | wship | 65 | 0.198 |
| 5 | H | WesternWA | employer | 24 | 0.144 |
| 5 | H | WesternWA | individual | 49 | 0.293 |
| 5 | H | WesternWA | medicaid | 13 | 0.078 |
| 5 | H | WesternWA | medicare | 20 | 0.120 |
| 5 | H | WesternWA | none | 26 | 0.156 |
| 5 | H | WesternWA | wship | 35 | 0.210 |
| 5 | O | EasternWA | employer | 32 | 0.113 |
| 5 | O | EasternWA | individual | 49 | 0.173 |
| 5 | O | EasternWA | medicaid | 110 | 0.387 |
| 5 | O | EasternWA | medicare | 81 | 0.285 |
| 5 | O | EasternWA | none | 12 | 0.042 |
| 5 | O | EasternWA | wship | 0 | 0.000 |
| 5 | O | King | employer | 256 | 0.224 |
| 5 | O | King | individual | 309 | 0.271 |
| 5 | O | King | medicaid | 239 | 0.209 |
| 5 | O | King | medicare | 260 | 0.228 |
| 5 | O | King | none | 71 | 0.062 |
| 5 | O | King | wship | 7 | 0.006 |
| 5 | O | WesternWA | employer | 134 | 0.169 |
| 5 | O | WesternWA | individual | 138 | 0.174 |
| 5 | O | WesternWA | medicaid | 209 | 0.263 |
| 5 | O | WesternWA | medicare | 267 | 0.336 |
| 5 | O | WesternWA | none | 40 | 0.050 |
| 5 | O | WesternWA | wship | 6 | 0.008 |
cost.pday
Average daily ADAP cost per person
Data inputs: DOH DAP data
- Number of clients
- Number of client-days enrolled in ADAP
- Costs for each category of spending
- race, insurance, year (2014-2015, 2016, and 2017-2018)
Parameter outputs:
- Costs stratified by race x insurance x year
- Total category costs = sum over all persons, all days (annual cost)
- Daily category cost per person = Total / sum(person-days of enrollment).
- Overall program costs sum the category costs.
# ZK modifies the original data here
tmpDF <- data.table(expand.grid(yr_bk = c("2014-2015", "2016", "2017-2018"),
race = c("B", "H", "O"),
insurance = c("none", "employer", "individual",
"medicaid", "medicare", "wship")))
adap_data$cost.pday <- merge(tmpDF, adap_data$cost.pday,
by = c("yr_bk", "race", "insurance"),
all.x = T)
adap_data$cost.pday[is.na(adap_data$cost.pday)] <- 0Plots
ART
p <- ggplot(adap_data$cost.pday,
aes(x = insurance, y = cART,
text = round(cART, 2))) +
geom_bar(aes(fill = race), stat = "identity",
position = position_dodge2(width = 0.9), alpha = 0.7) +
facet_wrap(~ yr_bk, nrow = 5) +
labs(title = "ART cost per person-day",
x = "insurance",
y = "cost ($)") +
theme(legend.position = "bottom")
gp <- ggplotly(p, tooltip = "text")
gp %>% layout_3x1Medical care
p <- ggplot(adap_data$cost.pday,
aes(x = insurance, y = cMedCare,
text = round(cMedCare, 2))) +
geom_bar(aes(fill = race), stat = "identity",
position = position_dodge2(width = 0.9), alpha = 0.7) +
facet_wrap(~ yr_bk, nrow = 5) +
labs(title = "Other Medical costs per person-day",
x = "insurance",
y = "cost ($)") +
theme(legend.position = "bottom")
gp <- ggplotly(p, tooltip = "text")
gp %>% layout_3x1Total
#ggplot(adap_data$cost.pday, aes(x = insurance, y = cTotCost))
p <- ggplot(adap_data$cost.pday,
aes(x = insurance, y = cTotCost,
text = round(cTotCost, 2))) +
geom_bar(aes(fill = race), stat = "identity",
position = position_dodge2(width = 0.9), alpha = 0.7) +
facet_wrap(~ yr_bk, nrow = 5) +
labs(title = "Total cost per person-day",
x = "insurance",
y = "cost ($)") +
theme(legend.position = "bottom")
gp <- ggplotly(p, tooltip = "text")
gp %>% layout_3x1Table
kable(adap_data$cost.pday, digits = c(0, 0, 0, 2, 2, 2, 2),
caption= "Daily ADAP cost by race and insurance") %>%
kable_styling(full_width=F, position="center",
bootstrap_options = c("striped"))| yr_bk | race | insurance | cPremium | cART | cOtherDrug | cMedCare | cTotCost |
|---|---|---|---|---|---|---|---|
| 2014-2015 | B | employer | 0.00 | 0.00 | 0.00 | 0.00 | 0 |
| 2014-2015 | B | individual | 4.46 | 7.13 | 0.23 | 0.76 | 13 |
| 2014-2015 | B | medicaid | 0.75 | 0.16 | 0.03 | 0.01 | 1 |
| 2014-2015 | B | medicare | 0.61 | 3.65 | 0.27 | 0.60 | 5 |
| 2014-2015 | B | none | 1.56 | 41.55 | 0.89 | 3.00 | 47 |
| 2014-2015 | B | wship | 0.00 | 0.00 | 0.00 | 0.00 | 0 |
| 2014-2015 | H | employer | 0.00 | 0.00 | 0.00 | 0.00 | 0 |
| 2014-2015 | H | individual | 8.16 | 6.37 | 0.85 | 0.92 | 16 |
| 2014-2015 | H | medicaid | 0.93 | 0.02 | 0.01 | 0.00 | 1 |
| 2014-2015 | H | medicare | 0.66 | 3.35 | 0.15 | 1.02 | 5 |
| 2014-2015 | H | none | 0.92 | 48.18 | 1.30 | 6.05 | 56 |
| 2014-2015 | H | wship | 35.10 | 1.05 | 0.05 | 1.39 | 38 |
| 2014-2015 | O | employer | 0.00 | 0.00 | 0.00 | 0.00 | 0 |
| 2014-2015 | O | individual | 6.27 | 6.58 | 0.51 | 0.92 | 14 |
| 2014-2015 | O | medicaid | 0.83 | 0.07 | 0.03 | 0.00 | 1 |
| 2014-2015 | O | medicare | 1.14 | 5.29 | 0.78 | 0.85 | 8 |
| 2014-2015 | O | none | 2.25 | 36.05 | 1.01 | 4.49 | 44 |
| 2014-2015 | O | wship | 32.58 | 2.06 | 0.30 | 1.19 | 36 |
| 2016 | B | employer | 1.53 | 3.39 | 0.12 | 0.44 | 5 |
| 2016 | B | individual | 9.08 | 3.12 | 0.19 | 0.94 | 13 |
| 2016 | B | medicaid | 0.86 | 0.35 | 0.03 | 0.28 | 2 |
| 2016 | B | medicare | 0.95 | 6.18 | 0.28 | 0.80 | 8 |
| 2016 | B | none | 2.32 | 39.27 | 0.37 | 4.46 | 46 |
| 2016 | B | wship | 0.00 | 0.00 | 0.00 | 0.00 | 0 |
| 2016 | H | employer | 1.99 | 3.22 | 0.07 | 0.65 | 6 |
| 2016 | H | individual | 10.27 | 6.29 | 0.14 | 0.92 | 18 |
| 2016 | H | medicaid | 0.38 | 0.01 | 0.01 | 0.60 | 1 |
| 2016 | H | medicare | 0.86 | 4.32 | 0.22 | 1.14 | 7 |
| 2016 | H | none | 1.58 | 57.53 | 3.97 | 6.00 | 69 |
| 2016 | H | wship | 42.57 | 1.09 | 0.04 | 1.24 | 45 |
| 2016 | O | employer | 2.11 | 3.32 | 0.30 | 0.85 | 7 |
| 2016 | O | individual | 10.17 | 2.92 | 0.29 | 0.91 | 14 |
| 2016 | O | medicaid | 0.92 | 0.04 | 0.01 | 0.35 | 1 |
| 2016 | O | medicare | 1.53 | 6.95 | 0.59 | 1.01 | 10 |
| 2016 | O | none | 2.37 | 40.01 | 0.34 | 3.64 | 46 |
| 2016 | O | wship | 37.71 | 2.48 | 0.22 | 1.75 | 42 |
| 2017-2018 | B | employer | 1.14 | 4.06 | 1.23 | 1.31 | 8 |
| 2017-2018 | B | individual | 14.11 | 4.63 | 0.48 | 0.95 | 20 |
| 2017-2018 | B | medicaid | 0.53 | 0.19 | 0.08 | 0.79 | 2 |
| 2017-2018 | B | medicare | 1.30 | 11.20 | 1.30 | 1.56 | 15 |
| 2017-2018 | B | none | 1.47 | 54.38 | 3.06 | 5.03 | 64 |
| 2017-2018 | B | wship | 0.00 | 0.00 | 0.00 | 0.00 | 0 |
| 2017-2018 | H | employer | 1.05 | 5.66 | 0.32 | 1.21 | 8 |
| 2017-2018 | H | individual | 15.29 | 10.25 | 0.26 | 2.32 | 28 |
| 2017-2018 | H | medicaid | 0.39 | 0.16 | 0.06 | 1.02 | 2 |
| 2017-2018 | H | medicare | 1.42 | 10.51 | 1.48 | 2.30 | 16 |
| 2017-2018 | H | none | 0.99 | 57.40 | 6.39 | 8.01 | 73 |
| 2017-2018 | H | wship | 53.91 | 1.75 | 0.07 | 1.73 | 57 |
| 2017-2018 | O | employer | 2.05 | 4.66 | 0.57 | 1.35 | 9 |
| 2017-2018 | O | individual | 15.08 | 5.15 | 0.64 | 1.27 | 22 |
| 2017-2018 | O | medicaid | 1.02 | 0.18 | 0.10 | 1.32 | 3 |
| 2017-2018 | O | medicare | 2.22 | 10.05 | 1.20 | 1.91 | 15 |
| 2017-2018 | O | none | 2.46 | 43.87 | 5.84 | 5.34 | 58 |
| 2017-2018 | O | wship | 47.87 | 2.63 | 1.75 | 2.81 | 55 |
pred.disenroll
Disenrollment probability from ADAP (weekly)
This is a model-based estimate that pieces together two different model specifications:
Incident client model: An exponential survival model based on ADAP clients who enrolled after 2014.
Established client model: A Weibull survival model based on ADAP clients who were already enrolled in 2014.
Both models controlled for race and insurance (public, private, and uninsured) at the time of first enrollment. The model code is in the repo here, but the analysis was done on the secure terminal server.
The models are pieced together by using the predicted disenrollment probabilities from the exponential model (a constant hazard) for the first 2 years, then switching over to the predicted values from the Weibull model for the subsequent enrollment period. The subsequent “established client” period has lower hazards that almost all decline over time.
Tables
Survival coefficients
coeff.est <- adap_data$model.disenroll
coeff.est <- coeff.est %>%
ungroup() %>%
group_by(RaceCategory, FirstInsuranceCategory, type) %>%
filter(row_number() == 1) %>%
select(Race = RaceCategory,
FirstIns = FirstInsuranceCategory,
type, shape, scale) %>%
ungroup() %>%
arrange(type, Race, FirstIns) %>%
mutate(model = ifelse(type == "incident", "exponential", "weibull"))
kable(coeff.est, digits = c(0, 0, 0, 2, 0, 0),
caption= "Shape and scale coefficients of survival model") %>%
kable_styling(full_width=F, position="center",
bootstrap_options = c("striped"))| Race | FirstIns | type | shape | scale | model |
|---|---|---|---|---|---|
| Other | Public | established | 0.99 | 5422 | weibull |
| Other | Private | established | 0.83 | 1456 | weibull |
| Other | None | established | 0.73 | 632 | weibull |
| Hispanic | Public | established | 0.91 | 4431 | weibull |
| Hispanic | Private | established | 0.97 | 1395 | weibull |
| Hispanic | None | established | 0.75 | 1882 | weibull |
| Black | Public | established | 1.13 | 3300 | weibull |
| Black | Private | established | 0.70 | 1514 | weibull |
| Black | None | established | 0.55 | 1118 | weibull |
| Other | Public | incident | 1.00 | 3244 | exponential |
| Other | Private | incident | 1.00 | 1036 | exponential |
| Other | None | incident | 1.00 | 962 | exponential |
| Hispanic | Public | incident | 1.00 | 2424 | exponential |
| Hispanic | Private | incident | 1.00 | 988 | exponential |
| Hispanic | None | incident | 1.00 | 1250 | exponential |
| Black | Public | incident | 1.00 | 1506 | exponential |
| Black | Private | incident | 1.00 | 643 | exponential |
| Black | None | incident | 1.00 | 824 | exponential |
Probability of disenrollment
- R code used to calculate probability of disenrollment included here, but not run.
for (i in 1:nrow(coeff.est)){
cumhazdiff <- rep(NA, 3650)
cumhaz <- rep(NA, 3650)
for(t in 1:3650){
a <- coeff.est$shape[i]
b <- coeff.est$scale[i]
cumhazdiff[t] <- -pweibull(t, a, b, lower = FALSE, log = TRUE) +
pweibull(t-1, a, b, lower = FALSE, log = TRUE)
cumhaz[t] <- -pweibull(t, a, b, lower = FALSE, log = TRUE)
}
dat <- data.frame(t = 1:3650,
cumhazdiff = cumhazdiff,
cumhaz = cumhaz,
Race = coeff.est$Race[i],
FirstIns = coeff.est$FirstIns[i],
type = coeff.est$type[i],
shape = coeff.est$shape[i],
scale = coeff.est$scale[i])
if(i==1) {plot.dat <- dat} else {plot.dat <- rbind(plot.dat, dat)}
}
model_disenroll <- plot.dat %>%
filter(type == "established") %>%
mutate(t = t + 730) %>%
filter(t <= 3650) %>%
full_join(plot.dat %>%
filter(type == "incident")) %>%
group_by(FirstIns, Race,t) %>%
arrange(FirstIns, Race, t, cumhazdiff) %>%
filter(row_number() == 1) %>%
ungroup() %>%
group_by(FirstIns, Race) %>%
arrange(t) %>%
mutate(cumhaz = cumsum(cumhazdiff)) %>%
ungroup() %>%
rowwise() %>%
mutate(surv = exp(-cumhaz)) - Probability of disenrollment by race and insurance type (data.table)
kable(adap_data$pred.disenroll, digits = c(0, 0, 0, 3),
caption= "probability of ADAP disenrollment") %>%
kable_styling(full_width=F, position="center", bootstrap_options = c("striped"))| week | insurance | race | disenroll_prob |
|---|---|---|---|
| 1 | private | B | 0.011 |
| 2 | private | B | 0.011 |
| 3 | private | B | 0.011 |
| 4 | private | B | 0.011 |
| 5 | private | B | 0.011 |
| 6 | private | B | 0.011 |
| 7 | private | B | 0.011 |
| 8 | private | B | 0.011 |
| 9 | private | B | 0.011 |
| 10 | private | B | 0.011 |
| 11 | private | B | 0.011 |
| 12 | private | B | 0.011 |
| 13 | private | B | 0.011 |
| 14 | private | B | 0.011 |
| 15 | private | B | 0.011 |
| 16 | private | B | 0.011 |
| 17 | private | B | 0.011 |
| 18 | private | B | 0.011 |
| 19 | private | B | 0.011 |
| 20 | private | B | 0.011 |
| 21 | private | B | 0.011 |
| 22 | private | B | 0.011 |
| 23 | private | B | 0.011 |
| 24 | private | B | 0.011 |
| 25 | private | B | 0.011 |
| 26 | private | B | 0.011 |
| 27 | private | B | 0.011 |
| 28 | private | B | 0.011 |
| 29 | private | B | 0.011 |
| 30 | private | B | 0.011 |
| 31 | private | B | 0.011 |
| 32 | private | B | 0.011 |
| 33 | private | B | 0.011 |
| 34 | private | B | 0.011 |
| 35 | private | B | 0.011 |
| 36 | private | B | 0.011 |
| 37 | private | B | 0.011 |
| 38 | private | B | 0.011 |
| 39 | private | B | 0.011 |
| 40 | private | B | 0.011 |
| 41 | private | B | 0.011 |
| 42 | private | B | 0.011 |
| 43 | private | B | 0.011 |
| 44 | private | B | 0.011 |
| 45 | private | B | 0.011 |
| 46 | private | B | 0.011 |
| 47 | private | B | 0.011 |
| 48 | private | B | 0.011 |
| 49 | private | B | 0.011 |
| 50 | private | B | 0.011 |
| 51 | private | B | 0.011 |
| 52 | private | B | 0.011 |
| 53 | private | B | 0.011 |
| 54 | private | B | 0.011 |
| 55 | private | B | 0.011 |
| 56 | private | B | 0.011 |
| 57 | private | B | 0.011 |
| 58 | private | B | 0.011 |
| 59 | private | B | 0.011 |
| 60 | private | B | 0.011 |
| 61 | private | B | 0.011 |
| 62 | private | B | 0.011 |
| 63 | private | B | 0.011 |
| 64 | private | B | 0.011 |
| 65 | private | B | 0.011 |
| 66 | private | B | 0.011 |
| 67 | private | B | 0.011 |
| 68 | private | B | 0.011 |
| 69 | private | B | 0.011 |
| 70 | private | B | 0.011 |
| 71 | private | B | 0.011 |
| 72 | private | B | 0.011 |
| 73 | private | B | 0.011 |
| 74 | private | B | 0.011 |
| 75 | private | B | 0.011 |
| 76 | private | B | 0.011 |
| 77 | private | B | 0.011 |
| 78 | private | B | 0.011 |
| 79 | private | B | 0.011 |
| 80 | private | B | 0.011 |
| 81 | private | B | 0.011 |
| 82 | private | B | 0.011 |
| 83 | private | B | 0.011 |
| 84 | private | B | 0.011 |
| 85 | private | B | 0.011 |
| 86 | private | B | 0.011 |
| 87 | private | B | 0.011 |
| 88 | private | B | 0.011 |
| 89 | private | B | 0.011 |
| 90 | private | B | 0.011 |
| 91 | private | B | 0.011 |
| 92 | private | B | 0.011 |
| 93 | private | B | 0.011 |
| 94 | private | B | 0.011 |
| 95 | private | B | 0.011 |
| 96 | private | B | 0.011 |
| 97 | private | B | 0.011 |
| 98 | private | B | 0.011 |
| 99 | private | B | 0.011 |
| 100 | private | B | 0.011 |
| 101 | private | B | 0.011 |
| 102 | private | B | 0.011 |
| 103 | private | B | 0.011 |
| 104 | private | B | 0.011 |
| 105 | private | B | 0.011 |
| 106 | private | B | 0.011 |
| 107 | private | B | 0.011 |
| 108 | private | B | 0.011 |
| 109 | private | B | 0.011 |
| 110 | private | B | 0.010 |
| 111 | private | B | 0.009 |
| 112 | private | B | 0.009 |
| 113 | private | B | 0.009 |
| 114 | private | B | 0.008 |
| 115 | private | B | 0.008 |
| 116 | private | B | 0.008 |
| 117 | private | B | 0.008 |
| 118 | private | B | 0.007 |
| 119 | private | B | 0.007 |
| 120 | private | B | 0.007 |
| 121 | private | B | 0.007 |
| 122 | private | B | 0.007 |
| 123 | private | B | 0.007 |
| 124 | private | B | 0.007 |
| 125 | private | B | 0.007 |
| 126 | private | B | 0.006 |
| 127 | private | B | 0.006 |
| 128 | private | B | 0.006 |
| 129 | private | B | 0.006 |
| 130 | private | B | 0.006 |
| 131 | private | B | 0.006 |
| 132 | private | B | 0.006 |
| 133 | private | B | 0.006 |
| 134 | private | B | 0.006 |
| 135 | private | B | 0.006 |
| 136 | private | B | 0.006 |
| 137 | private | B | 0.006 |
| 138 | private | B | 0.006 |
| 139 | private | B | 0.006 |
| 140 | private | B | 0.006 |
| 141 | private | B | 0.006 |
| 142 | private | B | 0.005 |
| 143 | private | B | 0.005 |
| 144 | private | B | 0.005 |
| 145 | private | B | 0.005 |
| 146 | private | B | 0.005 |
| 147 | private | B | 0.005 |
| 148 | private | B | 0.005 |
| 149 | private | B | 0.005 |
| 150 | private | B | 0.005 |
| 151 | private | B | 0.005 |
| 152 | private | B | 0.005 |
| 153 | private | B | 0.005 |
| 154 | private | B | 0.005 |
| 155 | private | B | 0.005 |
| 156 | private | B | 0.005 |
| 157 | private | B | 0.005 |
| 158 | private | B | 0.005 |
| 159 | private | B | 0.005 |
| 160 | private | B | 0.005 |
| 161 | private | B | 0.005 |
| 162 | private | B | 0.005 |
| 163 | private | B | 0.005 |
| 164 | private | B | 0.005 |
| 165 | private | B | 0.005 |
| 166 | private | B | 0.005 |
| 167 | private | B | 0.005 |
| 168 | private | B | 0.005 |
| 169 | private | B | 0.005 |
| 170 | private | B | 0.005 |
| 171 | private | B | 0.005 |
| 172 | private | B | 0.005 |
| 173 | private | B | 0.005 |
| 174 | private | B | 0.005 |
| 175 | private | B | 0.005 |
| 176 | private | B | 0.005 |
| 177 | private | B | 0.004 |
| 178 | private | B | 0.004 |
| 179 | private | B | 0.004 |
| 180 | private | B | 0.004 |
| 181 | private | B | 0.004 |
| 182 | private | B | 0.004 |
| 183 | private | B | 0.004 |
| 184 | private | B | 0.004 |
| 185 | private | B | 0.004 |
| 186 | private | B | 0.004 |
| 187 | private | B | 0.004 |
| 188 | private | B | 0.004 |
| 189 | private | B | 0.004 |
| 190 | private | B | 0.004 |
| 191 | private | B | 0.004 |
| 192 | private | B | 0.004 |
| 193 | private | B | 0.004 |
| 194 | private | B | 0.004 |
| 195 | private | B | 0.004 |
| 196 | private | B | 0.004 |
| 197 | private | B | 0.004 |
| 198 | private | B | 0.004 |
| 199 | private | B | 0.004 |
| 200 | private | B | 0.004 |
| 201 | private | B | 0.004 |
| 202 | private | B | 0.004 |
| 203 | private | B | 0.004 |
| 204 | private | B | 0.004 |
| 205 | private | B | 0.004 |
| 206 | private | B | 0.004 |
| 207 | private | B | 0.004 |
| 208 | private | B | 0.004 |
| 209 | private | B | 0.004 |
| 210 | private | B | 0.004 |
| 211 | private | B | 0.004 |
| 212 | private | B | 0.004 |
| 213 | private | B | 0.004 |
| 214 | private | B | 0.004 |
| 215 | private | B | 0.004 |
| 216 | private | B | 0.004 |
| 217 | private | B | 0.004 |
| 218 | private | B | 0.004 |
| 219 | private | B | 0.004 |
| 220 | private | B | 0.004 |
| 221 | private | B | 0.004 |
| 222 | private | B | 0.004 |
| 223 | private | B | 0.004 |
| 224 | private | B | 0.004 |
| 225 | private | B | 0.004 |
| 226 | private | B | 0.004 |
| 227 | private | B | 0.004 |
| 228 | private | B | 0.004 |
| 229 | private | B | 0.004 |
| 230 | private | B | 0.004 |
| 231 | private | B | 0.004 |
| 232 | private | B | 0.004 |
| 233 | private | B | 0.004 |
| 234 | private | B | 0.004 |
| 235 | private | B | 0.004 |
| 236 | private | B | 0.004 |
| 237 | private | B | 0.004 |
| 238 | private | B | 0.004 |
| 239 | private | B | 0.004 |
| 240 | private | B | 0.004 |
| 241 | private | B | 0.004 |
| 242 | private | B | 0.004 |
| 243 | private | B | 0.004 |
| 244 | private | B | 0.004 |
| 245 | private | B | 0.004 |
| 246 | private | B | 0.004 |
| 247 | private | B | 0.004 |
| 248 | private | B | 0.004 |
| 249 | private | B | 0.004 |
| 250 | private | B | 0.004 |
| 251 | private | B | 0.004 |
| 252 | private | B | 0.004 |
| 253 | private | B | 0.004 |
| 254 | private | B | 0.004 |
| 255 | private | B | 0.004 |
| 256 | private | B | 0.004 |
| 257 | private | B | 0.004 |
| 258 | private | B | 0.004 |
| 259 | private | B | 0.004 |
| 260 | private | B | 0.004 |
| 261 | private | B | 0.004 |
| 262 | private | B | 0.004 |
| 263 | private | B | 0.004 |
| 264 | private | B | 0.004 |
| 265 | private | B | 0.004 |
| 266 | private | B | 0.004 |
| 267 | private | B | 0.004 |
| 268 | private | B | 0.004 |
| 269 | private | B | 0.003 |
| 270 | private | B | 0.003 |
| 271 | private | B | 0.003 |
| 272 | private | B | 0.003 |
| 273 | private | B | 0.003 |
| 274 | private | B | 0.003 |
| 275 | private | B | 0.003 |
| 276 | private | B | 0.003 |
| 277 | private | B | 0.003 |
| 278 | private | B | 0.003 |
| 279 | private | B | 0.003 |
| 280 | private | B | 0.003 |
| 281 | private | B | 0.003 |
| 282 | private | B | 0.003 |
| 283 | private | B | 0.003 |
| 284 | private | B | 0.003 |
| 285 | private | B | 0.003 |
| 286 | private | B | 0.003 |
| 287 | private | B | 0.003 |
| 288 | private | B | 0.003 |
| 289 | private | B | 0.003 |
| 290 | private | B | 0.003 |
| 291 | private | B | 0.003 |
| 292 | private | B | 0.003 |
| 293 | private | B | 0.003 |
| 294 | private | B | 0.003 |
| 295 | private | B | 0.003 |
| 296 | private | B | 0.003 |
| 297 | private | B | 0.003 |
| 298 | private | B | 0.003 |
| 299 | private | B | 0.003 |
| 300 | private | B | 0.003 |
| 301 | private | B | 0.003 |
| 302 | private | B | 0.003 |
| 303 | private | B | 0.003 |
| 304 | private | B | 0.003 |
| 305 | private | B | 0.003 |
| 306 | private | B | 0.003 |
| 307 | private | B | 0.003 |
| 308 | private | B | 0.003 |
| 309 | private | B | 0.003 |
| 310 | private | B | 0.003 |
| 311 | private | B | 0.003 |
| 312 | private | B | 0.003 |
| 313 | private | B | 0.003 |
| 314 | private | B | 0.003 |
| 315 | private | B | 0.003 |
| 316 | private | B | 0.003 |
| 317 | private | B | 0.003 |
| 318 | private | B | 0.003 |
| 319 | private | B | 0.003 |
| 320 | private | B | 0.003 |
| 321 | private | B | 0.003 |
| 322 | private | B | 0.003 |
| 323 | private | B | 0.003 |
| 324 | private | B | 0.003 |
| 325 | private | B | 0.003 |
| 326 | private | B | 0.003 |
| 327 | private | B | 0.003 |
| 328 | private | B | 0.003 |
| 329 | private | B | 0.003 |
| 330 | private | B | 0.003 |
| 331 | private | B | 0.003 |
| 332 | private | B | 0.003 |
| 333 | private | B | 0.003 |
| 334 | private | B | 0.003 |
| 335 | private | B | 0.003 |
| 336 | private | B | 0.003 |
| 337 | private | B | 0.003 |
| 338 | private | B | 0.003 |
| 339 | private | B | 0.003 |
| 340 | private | B | 0.003 |
| 341 | private | B | 0.003 |
| 342 | private | B | 0.003 |
| 343 | private | B | 0.003 |
| 344 | private | B | 0.003 |
| 345 | private | B | 0.003 |
| 346 | private | B | 0.003 |
| 347 | private | B | 0.003 |
| 348 | private | B | 0.003 |
| 349 | private | B | 0.003 |
| 350 | private | B | 0.003 |
| 351 | private | B | 0.003 |
| 352 | private | B | 0.003 |
| 353 | private | B | 0.003 |
| 354 | private | B | 0.003 |
| 355 | private | B | 0.003 |
| 356 | private | B | 0.003 |
| 357 | private | B | 0.003 |
| 358 | private | B | 0.003 |
| 359 | private | B | 0.003 |
| 360 | private | B | 0.003 |
| 361 | private | B | 0.003 |
| 362 | private | B | 0.003 |
| 363 | private | B | 0.003 |
| 364 | private | B | 0.003 |
| 365 | private | B | 0.003 |
| 366 | private | B | 0.003 |
| 367 | private | B | 0.003 |
| 368 | private | B | 0.003 |
| 369 | private | B | 0.003 |
| 370 | private | B | 0.003 |
| 371 | private | B | 0.003 |
| 372 | private | B | 0.003 |
| 373 | private | B | 0.003 |
| 374 | private | B | 0.003 |
| 375 | private | B | 0.003 |
| 376 | private | B | 0.003 |
| 377 | private | B | 0.003 |
| 378 | private | B | 0.003 |
| 379 | private | B | 0.003 |
| 380 | private | B | 0.003 |
| 381 | private | B | 0.003 |
| 382 | private | B | 0.003 |
| 383 | private | B | 0.003 |
| 384 | private | B | 0.003 |
| 385 | private | B | 0.003 |
| 386 | private | B | 0.003 |
| 387 | private | B | 0.003 |
| 388 | private | B | 0.003 |
| 389 | private | B | 0.003 |
| 390 | private | B | 0.003 |
| 391 | private | B | 0.003 |
| 392 | private | B | 0.003 |
| 393 | private | B | 0.003 |
| 394 | private | B | 0.003 |
| 395 | private | B | 0.003 |
| 396 | private | B | 0.003 |
| 397 | private | B | 0.003 |
| 398 | private | B | 0.003 |
| 399 | private | B | 0.003 |
| 400 | private | B | 0.003 |
| 401 | private | B | 0.003 |
| 402 | private | B | 0.003 |
| 403 | private | B | 0.003 |
| 404 | private | B | 0.003 |
| 405 | private | B | 0.003 |
| 406 | private | B | 0.003 |
| 407 | private | B | 0.003 |
| 408 | private | B | 0.003 |
| 409 | private | B | 0.003 |
| 410 | private | B | 0.003 |
| 411 | private | B | 0.003 |
| 412 | private | B | 0.003 |
| 413 | private | B | 0.003 |
| 414 | private | B | 0.003 |
| 415 | private | B | 0.003 |
| 416 | private | B | 0.003 |
| 417 | private | B | 0.003 |
| 418 | private | B | 0.003 |
| 419 | private | B | 0.003 |
| 420 | private | B | 0.003 |
| 421 | private | B | 0.003 |
| 422 | private | B | 0.003 |
| 423 | private | B | 0.003 |
| 424 | private | B | 0.003 |
| 425 | private | B | 0.003 |
| 426 | private | B | 0.003 |
| 427 | private | B | 0.003 |
| 428 | private | B | 0.003 |
| 429 | private | B | 0.003 |
| 430 | private | B | 0.003 |
| 431 | private | B | 0.003 |
| 432 | private | B | 0.003 |
| 433 | private | B | 0.003 |
| 434 | private | B | 0.003 |
| 435 | private | B | 0.003 |
| 436 | private | B | 0.003 |
| 437 | private | B | 0.003 |
| 438 | private | B | 0.003 |
| 439 | private | B | 0.003 |
| 440 | private | B | 0.003 |
| 441 | private | B | 0.003 |
| 442 | private | B | 0.003 |
| 443 | private | B | 0.003 |
| 444 | private | B | 0.003 |
| 445 | private | B | 0.003 |
| 446 | private | B | 0.003 |
| 447 | private | B | 0.003 |
| 448 | private | B | 0.003 |
| 449 | private | B | 0.003 |
| 450 | private | B | 0.003 |
| 451 | private | B | 0.003 |
| 452 | private | B | 0.003 |
| 453 | private | B | 0.003 |
| 454 | private | B | 0.003 |
| 455 | private | B | 0.003 |
| 456 | private | B | 0.003 |
| 457 | private | B | 0.003 |
| 458 | private | B | 0.003 |
| 459 | private | B | 0.003 |
| 460 | private | B | 0.003 |
| 461 | private | B | 0.003 |
| 462 | private | B | 0.003 |
| 463 | private | B | 0.003 |
| 464 | private | B | 0.003 |
| 465 | private | B | 0.003 |
| 466 | private | B | 0.003 |
| 467 | private | B | 0.003 |
| 468 | private | B | 0.003 |
| 469 | private | B | 0.003 |
| 470 | private | B | 0.003 |
| 471 | private | B | 0.003 |
| 472 | private | B | 0.003 |
| 473 | private | B | 0.003 |
| 474 | private | B | 0.003 |
| 475 | private | B | 0.003 |
| 476 | private | B | 0.003 |
| 477 | private | B | 0.003 |
| 478 | private | B | 0.003 |
| 479 | private | B | 0.003 |
| 480 | private | B | 0.003 |
| 481 | private | B | 0.003 |
| 482 | private | B | 0.003 |
| 483 | private | B | 0.003 |
| 484 | private | B | 0.003 |
| 485 | private | B | 0.003 |
| 486 | private | B | 0.003 |
| 487 | private | B | 0.003 |
| 488 | private | B | 0.003 |
| 489 | private | B | 0.003 |
| 490 | private | B | 0.003 |
| 491 | private | B | 0.003 |
| 492 | private | B | 0.003 |
| 493 | private | B | 0.003 |
| 494 | private | B | 0.003 |
| 495 | private | B | 0.003 |
| 496 | private | B | 0.003 |
| 497 | private | B | 0.003 |
| 498 | private | B | 0.003 |
| 499 | private | B | 0.003 |
| 500 | private | B | 0.003 |
| 501 | private | B | 0.003 |
| 502 | private | B | 0.003 |
| 503 | private | B | 0.003 |
| 504 | private | B | 0.003 |
| 505 | private | B | 0.003 |
| 506 | private | B | 0.003 |
| 507 | private | B | 0.003 |
| 508 | private | B | 0.003 |
| 509 | private | B | 0.003 |
| 510 | private | B | 0.003 |
| 511 | private | B | 0.003 |
| 512 | private | B | 0.003 |
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| 514 | private | B | 0.003 |
| 515 | private | B | 0.003 |
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| 518 | private | B | 0.003 |
| 519 | private | B | 0.003 |
| 520 | private | B | 0.003 |
| 521 | private | B | 0.003 |
| 1 | public | B | 0.005 |
| 2 | public | B | 0.005 |
| 3 | public | B | 0.005 |
| 4 | public | B | 0.005 |
| 5 | public | B | 0.005 |
| 6 | public | B | 0.005 |
| 7 | public | B | 0.005 |
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| 10 | public | B | 0.005 |
| 11 | public | B | 0.005 |
| 12 | public | B | 0.005 |
| 13 | public | B | 0.005 |
| 14 | public | B | 0.005 |
| 15 | public | B | 0.005 |
| 16 | public | B | 0.005 |
| 17 | public | B | 0.005 |
| 18 | public | B | 0.005 |
| 19 | public | B | 0.005 |
| 20 | public | B | 0.005 |
| 21 | public | B | 0.005 |
| 22 | public | B | 0.005 |
| 23 | public | B | 0.005 |
| 24 | public | B | 0.005 |
| 25 | public | B | 0.005 |
| 26 | public | B | 0.005 |
| 27 | public | B | 0.005 |
| 28 | public | B | 0.005 |
| 29 | public | B | 0.005 |
| 30 | public | B | 0.005 |
| 31 | public | B | 0.005 |
| 32 | public | B | 0.005 |
| 33 | public | B | 0.005 |
| 34 | public | B | 0.005 |
| 35 | public | B | 0.005 |
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| 40 | public | B | 0.005 |
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| 44 | public | B | 0.005 |
| 45 | public | B | 0.005 |
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| 50 | public | B | 0.005 |
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| 55 | public | B | 0.005 |
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| 60 | public | B | 0.005 |
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| 70 | public | B | 0.005 |
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| 80 | public | B | 0.005 |
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| 84 | public | B | 0.005 |
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| 95 | public | B | 0.005 |
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| 100 | public | B | 0.005 |
| 101 | public | B | 0.005 |
| 102 | public | B | 0.005 |
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| 104 | public | B | 0.005 |
| 105 | public | B | 0.002 |
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| 110 | public | B | 0.001 |
| 111 | public | B | 0.001 |
| 112 | public | B | 0.001 |
| 113 | public | B | 0.001 |
| 114 | public | B | 0.001 |
| 115 | public | B | 0.001 |
| 116 | public | B | 0.001 |
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| 120 | public | B | 0.002 |
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| 200 | public | B | 0.002 |
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| 1 | uninsure | B | 0.008 |
| 2 | uninsure | B | 0.008 |
| 3 | uninsure | B | 0.008 |
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| 6 | uninsure | B | 0.008 |
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| 8 | uninsure | B | 0.008 |
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| 10 | uninsure | B | 0.008 |
| 11 | uninsure | B | 0.008 |
| 12 | uninsure | B | 0.008 |
| 13 | uninsure | B | 0.008 |
| 14 | uninsure | B | 0.008 |
| 15 | uninsure | B | 0.008 |
| 16 | uninsure | B | 0.008 |
| 17 | uninsure | B | 0.008 |
| 18 | uninsure | B | 0.008 |
| 19 | uninsure | B | 0.008 |
| 20 | uninsure | B | 0.008 |
| 21 | uninsure | B | 0.008 |
| 22 | uninsure | B | 0.008 |
| 23 | uninsure | B | 0.008 |
| 24 | uninsure | B | 0.008 |
| 25 | uninsure | B | 0.008 |
| 26 | uninsure | B | 0.008 |
| 27 | uninsure | B | 0.008 |
| 28 | uninsure | B | 0.008 |
| 29 | uninsure | B | 0.008 |
| 30 | uninsure | B | 0.008 |
| 31 | uninsure | B | 0.008 |
| 32 | uninsure | B | 0.008 |
| 33 | uninsure | B | 0.008 |
| 34 | uninsure | B | 0.008 |
| 35 | uninsure | B | 0.008 |
| 36 | uninsure | B | 0.008 |
| 37 | uninsure | B | 0.008 |
| 38 | uninsure | B | 0.008 |
| 39 | uninsure | B | 0.008 |
| 40 | uninsure | B | 0.008 |
| 41 | uninsure | B | 0.008 |
| 42 | uninsure | B | 0.008 |
| 43 | uninsure | B | 0.008 |
| 44 | uninsure | B | 0.008 |
| 45 | uninsure | B | 0.008 |
| 46 | uninsure | B | 0.008 |
| 47 | uninsure | B | 0.008 |
| 48 | uninsure | B | 0.008 |
| 49 | uninsure | B | 0.008 |
| 50 | uninsure | B | 0.008 |
| 51 | uninsure | B | 0.008 |
| 52 | uninsure | B | 0.008 |
| 53 | uninsure | B | 0.008 |
| 54 | uninsure | B | 0.008 |
| 55 | uninsure | B | 0.008 |
| 56 | uninsure | B | 0.008 |
| 57 | uninsure | B | 0.008 |
| 58 | uninsure | B | 0.008 |
| 59 | uninsure | B | 0.008 |
| 60 | uninsure | B | 0.008 |
| 61 | uninsure | B | 0.008 |
| 62 | uninsure | B | 0.008 |
| 63 | uninsure | B | 0.008 |
| 64 | uninsure | B | 0.008 |
| 65 | uninsure | B | 0.008 |
| 66 | uninsure | B | 0.008 |
| 67 | uninsure | B | 0.008 |
| 68 | uninsure | B | 0.008 |
| 69 | uninsure | B | 0.008 |
| 70 | uninsure | B | 0.008 |
| 71 | uninsure | B | 0.008 |
| 72 | uninsure | B | 0.008 |
| 73 | uninsure | B | 0.008 |
| 74 | uninsure | B | 0.008 |
| 75 | uninsure | B | 0.008 |
| 76 | uninsure | B | 0.008 |
| 77 | uninsure | B | 0.008 |
| 78 | uninsure | B | 0.008 |
| 79 | uninsure | B | 0.008 |
| 80 | uninsure | B | 0.008 |
| 81 | uninsure | B | 0.008 |
| 82 | uninsure | B | 0.008 |
| 83 | uninsure | B | 0.008 |
| 84 | uninsure | B | 0.008 |
| 85 | uninsure | B | 0.008 |
| 86 | uninsure | B | 0.008 |
| 87 | uninsure | B | 0.008 |
| 88 | uninsure | B | 0.008 |
| 89 | uninsure | B | 0.008 |
| 90 | uninsure | B | 0.008 |
| 91 | uninsure | B | 0.008 |
| 92 | uninsure | B | 0.008 |
| 93 | uninsure | B | 0.008 |
| 94 | uninsure | B | 0.008 |
| 95 | uninsure | B | 0.008 |
| 96 | uninsure | B | 0.008 |
| 97 | uninsure | B | 0.008 |
| 98 | uninsure | B | 0.008 |
| 99 | uninsure | B | 0.008 |
| 100 | uninsure | B | 0.008 |
| 101 | uninsure | B | 0.008 |
| 102 | uninsure | B | 0.008 |
| 103 | uninsure | B | 0.008 |
| 104 | uninsure | B | 0.008 |
| 105 | uninsure | B | 0.008 |
| 106 | uninsure | B | 0.008 |
| 107 | uninsure | B | 0.008 |
| 108 | uninsure | B | 0.008 |
| 109 | uninsure | B | 0.008 |
| 110 | uninsure | B | 0.008 |
| 111 | uninsure | B | 0.008 |
| 112 | uninsure | B | 0.008 |
| 113 | uninsure | B | 0.008 |
| 114 | uninsure | B | 0.008 |
| 115 | uninsure | B | 0.008 |
| 116 | uninsure | B | 0.008 |
| 117 | uninsure | B | 0.008 |
| 118 | uninsure | B | 0.008 |
| 119 | uninsure | B | 0.008 |
| 120 | uninsure | B | 0.008 |
| 121 | uninsure | B | 0.008 |
| 122 | uninsure | B | 0.008 |
| 123 | uninsure | B | 0.008 |
| 124 | uninsure | B | 0.008 |
| 125 | uninsure | B | 0.008 |
| 126 | uninsure | B | 0.008 |
| 127 | uninsure | B | 0.008 |
| 128 | uninsure | B | 0.008 |
| 129 | uninsure | B | 0.008 |
| 130 | uninsure | B | 0.008 |
| 131 | uninsure | B | 0.008 |
| 132 | uninsure | B | 0.008 |
| 133 | uninsure | B | 0.007 |
| 134 | uninsure | B | 0.007 |
| 135 | uninsure | B | 0.007 |
| 136 | uninsure | B | 0.007 |
| 137 | uninsure | B | 0.007 |
| 138 | uninsure | B | 0.007 |
| 139 | uninsure | B | 0.007 |
| 140 | uninsure | B | 0.007 |
| 141 | uninsure | B | 0.007 |
| 142 | uninsure | B | 0.007 |
| 143 | uninsure | B | 0.007 |
| 144 | uninsure | B | 0.006 |
| 145 | uninsure | B | 0.006 |
| 146 | uninsure | B | 0.006 |
| 147 | uninsure | B | 0.006 |
| 148 | uninsure | B | 0.006 |
| 149 | uninsure | B | 0.006 |
| 150 | uninsure | B | 0.006 |
| 151 | uninsure | B | 0.006 |
| 152 | uninsure | B | 0.006 |
| 153 | uninsure | B | 0.006 |
| 154 | uninsure | B | 0.006 |
| 155 | uninsure | B | 0.006 |
| 156 | uninsure | B | 0.006 |
| 157 | uninsure | B | 0.006 |
| 158 | uninsure | B | 0.006 |
| 159 | uninsure | B | 0.006 |
| 160 | uninsure | B | 0.006 |
| 161 | uninsure | B | 0.005 |
| 162 | uninsure | B | 0.005 |
| 163 | uninsure | B | 0.005 |
| 164 | uninsure | B | 0.005 |
| 165 | uninsure | B | 0.005 |
| 166 | uninsure | B | 0.005 |
| 167 | uninsure | B | 0.005 |
| 168 | uninsure | B | 0.005 |
| 169 | uninsure | B | 0.005 |
| 170 | uninsure | B | 0.005 |
| 171 | uninsure | B | 0.005 |
| 172 | uninsure | B | 0.005 |
| 173 | uninsure | B | 0.005 |
| 174 | uninsure | B | 0.005 |
| 175 | uninsure | B | 0.005 |
| 176 | uninsure | B | 0.005 |
| 177 | uninsure | B | 0.005 |
| 178 | uninsure | B | 0.005 |
| 179 | uninsure | B | 0.005 |
| 180 | uninsure | B | 0.005 |
| 181 | uninsure | B | 0.005 |
| 182 | uninsure | B | 0.005 |
| 183 | uninsure | B | 0.005 |
| 184 | uninsure | B | 0.005 |
| 185 | uninsure | B | 0.005 |
| 186 | uninsure | B | 0.005 |
| 187 | uninsure | B | 0.005 |
| 188 | uninsure | B | 0.005 |
| 189 | uninsure | B | 0.005 |
| 190 | uninsure | B | 0.005 |
| 191 | uninsure | B | 0.005 |
| 192 | uninsure | B | 0.005 |
| 193 | uninsure | B | 0.004 |
| 194 | uninsure | B | 0.004 |
| 195 | uninsure | B | 0.004 |
| 196 | uninsure | B | 0.004 |
| 197 | uninsure | B | 0.004 |
| 198 | uninsure | B | 0.004 |
| 199 | uninsure | B | 0.004 |
| 200 | uninsure | B | 0.004 |
| 201 | uninsure | B | 0.004 |
| 202 | uninsure | B | 0.004 |
| 203 | uninsure | B | 0.004 |
| 204 | uninsure | B | 0.004 |
| 205 | uninsure | B | 0.004 |
| 206 | uninsure | B | 0.004 |
| 207 | uninsure | B | 0.004 |
| 208 | uninsure | B | 0.004 |
| 209 | uninsure | B | 0.004 |
| 210 | uninsure | B | 0.004 |
| 211 | uninsure | B | 0.004 |
| 212 | uninsure | B | 0.004 |
| 213 | uninsure | B | 0.004 |
| 214 | uninsure | B | 0.004 |
| 215 | uninsure | B | 0.004 |
| 216 | uninsure | B | 0.004 |
| 217 | uninsure | B | 0.004 |
| 218 | uninsure | B | 0.004 |
| 219 | uninsure | B | 0.004 |
| 220 | uninsure | B | 0.004 |
| 221 | uninsure | B | 0.004 |
| 222 | uninsure | B | 0.004 |
| 223 | uninsure | B | 0.004 |
| 224 | uninsure | B | 0.004 |
| 225 | uninsure | B | 0.004 |
| 226 | uninsure | B | 0.004 |
| 227 | uninsure | B | 0.004 |
| 228 | uninsure | B | 0.004 |
| 229 | uninsure | B | 0.004 |
| 230 | uninsure | B | 0.004 |
| 231 | uninsure | B | 0.004 |
| 232 | uninsure | B | 0.004 |
| 233 | uninsure | B | 0.004 |
| 234 | uninsure | B | 0.004 |
| 235 | uninsure | B | 0.004 |
| 236 | uninsure | B | 0.004 |
| 237 | uninsure | B | 0.004 |
| 238 | uninsure | B | 0.004 |
| 239 | uninsure | B | 0.004 |
| 240 | uninsure | B | 0.004 |
| 241 | uninsure | B | 0.004 |
| 242 | uninsure | B | 0.004 |
| 243 | uninsure | B | 0.004 |
| 244 | uninsure | B | 0.004 |
| 245 | uninsure | B | 0.004 |
| 246 | uninsure | B | 0.004 |
| 247 | uninsure | B | 0.004 |
| 248 | uninsure | B | 0.004 |
| 249 | uninsure | B | 0.004 |
| 250 | uninsure | B | 0.004 |
| 251 | uninsure | B | 0.004 |
| 252 | uninsure | B | 0.004 |
| 253 | uninsure | B | 0.004 |
| 254 | uninsure | B | 0.004 |
| 255 | uninsure | B | 0.004 |
| 256 | uninsure | B | 0.004 |
| 257 | uninsure | B | 0.004 |
| 258 | uninsure | B | 0.004 |
| 259 | uninsure | B | 0.003 |
| 260 | uninsure | B | 0.003 |
| 261 | uninsure | B | 0.003 |
| 262 | uninsure | B | 0.003 |
| 263 | uninsure | B | 0.003 |
| 264 | uninsure | B | 0.003 |
| 265 | uninsure | B | 0.003 |
| 266 | uninsure | B | 0.003 |
| 267 | uninsure | B | 0.003 |
| 268 | uninsure | B | 0.003 |
| 269 | uninsure | B | 0.003 |
| 270 | uninsure | B | 0.003 |
| 271 | uninsure | B | 0.003 |
| 272 | uninsure | B | 0.003 |
| 273 | uninsure | B | 0.003 |
| 274 | uninsure | B | 0.003 |
| 275 | uninsure | B | 0.003 |
| 276 | uninsure | B | 0.003 |
| 277 | uninsure | B | 0.003 |
| 278 | uninsure | B | 0.003 |
| 279 | uninsure | B | 0.003 |
| 280 | uninsure | B | 0.003 |
| 281 | uninsure | B | 0.003 |
| 282 | uninsure | B | 0.003 |
| 283 | uninsure | B | 0.003 |
| 284 | uninsure | B | 0.003 |
| 285 | uninsure | B | 0.003 |
| 286 | uninsure | B | 0.003 |
| 287 | uninsure | B | 0.003 |
| 288 | uninsure | B | 0.003 |
| 289 | uninsure | B | 0.003 |
| 290 | uninsure | B | 0.003 |
| 291 | uninsure | B | 0.003 |
| 292 | uninsure | B | 0.003 |
| 293 | uninsure | B | 0.003 |
| 294 | uninsure | B | 0.003 |
| 295 | uninsure | B | 0.003 |
| 296 | uninsure | B | 0.003 |
| 297 | uninsure | B | 0.003 |
| 298 | uninsure | B | 0.003 |
| 299 | uninsure | B | 0.003 |
| 300 | uninsure | B | 0.003 |
| 301 | uninsure | B | 0.003 |
| 302 | uninsure | B | 0.003 |
| 303 | uninsure | B | 0.003 |
| 304 | uninsure | B | 0.003 |
| 305 | uninsure | B | 0.003 |
| 306 | uninsure | B | 0.003 |
| 307 | uninsure | B | 0.003 |
| 308 | uninsure | B | 0.003 |
| 309 | uninsure | B | 0.003 |
| 310 | uninsure | B | 0.003 |
| 311 | uninsure | B | 0.003 |
| 312 | uninsure | B | 0.003 |
| 313 | uninsure | B | 0.003 |
| 314 | uninsure | B | 0.003 |
| 315 | uninsure | B | 0.003 |
| 316 | uninsure | B | 0.003 |
| 317 | uninsure | B | 0.003 |
| 318 | uninsure | B | 0.003 |
| 319 | uninsure | B | 0.003 |
| 320 | uninsure | B | 0.003 |
| 321 | uninsure | B | 0.003 |
| 322 | uninsure | B | 0.003 |
| 323 | uninsure | B | 0.003 |
| 324 | uninsure | B | 0.003 |
| 325 | uninsure | B | 0.003 |
| 326 | uninsure | B | 0.003 |
| 327 | uninsure | B | 0.003 |
| 328 | uninsure | B | 0.003 |
| 329 | uninsure | B | 0.003 |
| 330 | uninsure | B | 0.003 |
| 331 | uninsure | B | 0.003 |
| 332 | uninsure | B | 0.003 |
| 333 | uninsure | B | 0.003 |
| 334 | uninsure | B | 0.003 |
| 335 | uninsure | B | 0.003 |
| 336 | uninsure | B | 0.003 |
| 337 | uninsure | B | 0.003 |
| 338 | uninsure | B | 0.003 |
| 339 | uninsure | B | 0.003 |
| 340 | uninsure | B | 0.003 |
| 341 | uninsure | B | 0.003 |
| 342 | uninsure | B | 0.003 |
| 343 | uninsure | B | 0.003 |
| 344 | uninsure | B | 0.003 |
| 345 | uninsure | B | 0.003 |
| 346 | uninsure | B | 0.003 |
| 347 | uninsure | B | 0.003 |
| 348 | uninsure | B | 0.003 |
| 349 | uninsure | B | 0.003 |
| 350 | uninsure | B | 0.003 |
| 351 | uninsure | B | 0.003 |
| 352 | uninsure | B | 0.003 |
| 353 | uninsure | B | 0.003 |
| 354 | uninsure | B | 0.003 |
| 355 | uninsure | B | 0.003 |
| 356 | uninsure | B | 0.003 |
| 357 | uninsure | B | 0.003 |
| 358 | uninsure | B | 0.003 |
| 359 | uninsure | B | 0.003 |
| 360 | uninsure | B | 0.003 |
| 361 | uninsure | B | 0.003 |
| 362 | uninsure | B | 0.003 |
| 363 | uninsure | B | 0.003 |
| 364 | uninsure | B | 0.003 |
| 365 | uninsure | B | 0.003 |
| 366 | uninsure | B | 0.003 |
| 367 | uninsure | B | 0.003 |
| 368 | uninsure | B | 0.003 |
| 369 | uninsure | B | 0.003 |
| 370 | uninsure | B | 0.003 |
| 371 | uninsure | B | 0.003 |
| 372 | uninsure | B | 0.003 |
| 373 | uninsure | B | 0.003 |
| 374 | uninsure | B | 0.003 |
| 375 | uninsure | B | 0.003 |
| 376 | uninsure | B | 0.003 |
| 377 | uninsure | B | 0.003 |
| 378 | uninsure | B | 0.003 |
| 379 | uninsure | B | 0.003 |
| 380 | uninsure | B | 0.003 |
| 381 | uninsure | B | 0.003 |
| 382 | uninsure | B | 0.003 |
| 383 | uninsure | B | 0.003 |
| 384 | uninsure | B | 0.003 |
| 385 | uninsure | B | 0.003 |
| 386 | uninsure | B | 0.003 |
| 387 | uninsure | B | 0.003 |
| 388 | uninsure | B | 0.003 |
| 389 | uninsure | B | 0.003 |
| 390 | uninsure | B | 0.003 |
| 391 | uninsure | B | 0.003 |
| 392 | uninsure | B | 0.003 |
| 393 | uninsure | B | 0.003 |
| 394 | uninsure | B | 0.003 |
| 395 | uninsure | B | 0.003 |
| 396 | uninsure | B | 0.003 |
| 397 | uninsure | B | 0.003 |
| 398 | uninsure | B | 0.003 |
| 399 | uninsure | B | 0.003 |
| 400 | uninsure | B | 0.003 |
| 401 | uninsure | B | 0.003 |
| 402 | uninsure | B | 0.003 |
| 403 | uninsure | B | 0.003 |
| 404 | uninsure | B | 0.003 |
| 405 | uninsure | B | 0.003 |
| 406 | uninsure | B | 0.003 |
| 407 | uninsure | B | 0.003 |
| 408 | uninsure | B | 0.003 |
| 409 | uninsure | B | 0.003 |
| 410 | uninsure | B | 0.003 |
| 411 | uninsure | B | 0.003 |
| 412 | uninsure | B | 0.003 |
| 413 | uninsure | B | 0.003 |
| 414 | uninsure | B | 0.003 |
| 415 | uninsure | B | 0.003 |
| 416 | uninsure | B | 0.003 |
| 417 | uninsure | B | 0.003 |
| 418 | uninsure | B | 0.003 |
| 419 | uninsure | B | 0.003 |
| 420 | uninsure | B | 0.003 |
| 421 | uninsure | B | 0.003 |
| 422 | uninsure | B | 0.003 |
| 423 | uninsure | B | 0.003 |
| 424 | uninsure | B | 0.003 |
| 425 | uninsure | B | 0.003 |
| 426 | uninsure | B | 0.003 |
| 427 | uninsure | B | 0.003 |
| 428 | uninsure | B | 0.003 |
| 429 | uninsure | B | 0.003 |
| 430 | uninsure | B | 0.003 |
| 431 | uninsure | B | 0.003 |
| 432 | uninsure | B | 0.002 |
| 433 | uninsure | B | 0.002 |
| 434 | uninsure | B | 0.002 |
| 435 | uninsure | B | 0.002 |
| 436 | uninsure | B | 0.002 |
| 437 | uninsure | B | 0.002 |
| 438 | uninsure | B | 0.002 |
| 439 | uninsure | B | 0.002 |
| 440 | uninsure | B | 0.002 |
| 441 | uninsure | B | 0.002 |
| 442 | uninsure | B | 0.002 |
| 443 | uninsure | B | 0.002 |
| 444 | uninsure | B | 0.002 |
| 445 | uninsure | B | 0.002 |
| 446 | uninsure | B | 0.002 |
| 447 | uninsure | B | 0.002 |
| 448 | uninsure | B | 0.002 |
| 449 | uninsure | B | 0.002 |
| 450 | uninsure | B | 0.002 |
| 451 | uninsure | B | 0.002 |
| 452 | uninsure | B | 0.002 |
| 453 | uninsure | B | 0.002 |
| 454 | uninsure | B | 0.002 |
| 455 | uninsure | B | 0.002 |
| 456 | uninsure | B | 0.002 |
| 457 | uninsure | B | 0.002 |
| 458 | uninsure | B | 0.002 |
| 459 | uninsure | B | 0.002 |
| 460 | uninsure | B | 0.002 |
| 461 | uninsure | B | 0.002 |
| 462 | uninsure | B | 0.002 |
| 463 | uninsure | B | 0.002 |
| 464 | uninsure | B | 0.002 |
| 465 | uninsure | B | 0.002 |
| 466 | uninsure | B | 0.002 |
| 467 | uninsure | B | 0.002 |
| 468 | uninsure | B | 0.002 |
| 469 | uninsure | B | 0.002 |
| 470 | uninsure | B | 0.002 |
| 471 | uninsure | B | 0.002 |
| 472 | uninsure | B | 0.002 |
| 473 | uninsure | B | 0.002 |
| 474 | uninsure | B | 0.002 |
| 475 | uninsure | B | 0.002 |
| 476 | uninsure | B | 0.002 |
| 477 | uninsure | B | 0.002 |
| 478 | uninsure | B | 0.002 |
| 479 | uninsure | B | 0.002 |
| 480 | uninsure | B | 0.002 |
| 481 | uninsure | B | 0.002 |
| 482 | uninsure | B | 0.002 |
| 483 | uninsure | B | 0.002 |
| 484 | uninsure | B | 0.002 |
| 485 | uninsure | B | 0.002 |
| 486 | uninsure | B | 0.002 |
| 487 | uninsure | B | 0.002 |
| 488 | uninsure | B | 0.002 |
| 489 | uninsure | B | 0.002 |
| 490 | uninsure | B | 0.002 |
| 491 | uninsure | B | 0.002 |
| 492 | uninsure | B | 0.002 |
| 493 | uninsure | B | 0.002 |
| 494 | uninsure | B | 0.002 |
| 495 | uninsure | B | 0.002 |
| 496 | uninsure | B | 0.002 |
| 497 | uninsure | B | 0.002 |
| 498 | uninsure | B | 0.002 |
| 499 | uninsure | B | 0.002 |
| 500 | uninsure | B | 0.002 |
| 501 | uninsure | B | 0.002 |
| 502 | uninsure | B | 0.002 |
| 503 | uninsure | B | 0.002 |
| 504 | uninsure | B | 0.002 |
| 505 | uninsure | B | 0.002 |
| 506 | uninsure | B | 0.002 |
| 507 | uninsure | B | 0.002 |
| 508 | uninsure | B | 0.002 |
| 509 | uninsure | B | 0.002 |
| 510 | uninsure | B | 0.002 |
| 511 | uninsure | B | 0.002 |
| 512 | uninsure | B | 0.002 |
| 513 | uninsure | B | 0.002 |
| 514 | uninsure | B | 0.002 |
| 515 | uninsure | B | 0.002 |
| 516 | uninsure | B | 0.002 |
| 517 | uninsure | B | 0.002 |
| 518 | uninsure | B | 0.002 |
| 519 | uninsure | B | 0.002 |
| 520 | uninsure | B | 0.002 |
| 521 | uninsure | B | 0.002 |
| 1 | wship | B | 0.004 |
| 2 | wship | B | 0.004 |
| 3 | wship | B | 0.004 |
| 4 | wship | B | 0.004 |
| 5 | wship | B | 0.004 |
| 6 | wship | B | 0.004 |
| 7 | wship | B | 0.004 |
| 8 | wship | B | 0.004 |
| 9 | wship | B | 0.004 |
| 10 | wship | B | 0.004 |
| 11 | wship | B | 0.004 |
| 12 | wship | B | 0.004 |
| 13 | wship | B | 0.004 |
| 14 | wship | B | 0.004 |
| 15 | wship | B | 0.004 |
| 16 | wship | B | 0.004 |
| 17 | wship | B | 0.004 |
| 18 | wship | B | 0.004 |
| 19 | wship | B | 0.004 |
| 20 | wship | B | 0.004 |
| 21 | wship | B | 0.004 |
| 22 | wship | B | 0.004 |
| 23 | wship | B | 0.004 |
| 24 | wship | B | 0.004 |
| 25 | wship | B | 0.004 |
| 26 | wship | B | 0.004 |
| 27 | wship | B | 0.004 |
| 28 | wship | B | 0.004 |
| 29 | wship | B | 0.004 |
| 30 | wship | B | 0.004 |
| 31 | wship | B | 0.004 |
| 32 | wship | B | 0.004 |
| 33 | wship | B | 0.004 |
| 34 | wship | B | 0.004 |
| 35 | wship | B | 0.004 |
| 36 | wship | B | 0.004 |
| 37 | wship | B | 0.004 |
| 38 | wship | B | 0.004 |
| 39 | wship | B | 0.004 |
| 40 | wship | B | 0.004 |
| 41 | wship | B | 0.004 |
| 42 | wship | B | 0.004 |
| 43 | wship | B | 0.004 |
| 44 | wship | B | 0.004 |
| 45 | wship | B | 0.004 |
| 46 | wship | B | 0.004 |
| 47 | wship | B | 0.004 |
| 48 | wship | B | 0.004 |
| 49 | wship | B | 0.004 |
| 50 | wship | B | 0.004 |
| 51 | wship | B | 0.004 |
| 52 | wship | B | 0.004 |
| 53 | wship | B | 0.004 |
| 54 | wship | B | 0.004 |
| 55 | wship | B | 0.004 |
| 56 | wship | B | 0.004 |
| 57 | wship | B | 0.004 |
| 58 | wship | B | 0.004 |
| 59 | wship | B | 0.004 |
| 60 | wship | B | 0.004 |
| 61 | wship | B | 0.004 |
| 62 | wship | B | 0.004 |
| 63 | wship | B | 0.004 |
| 64 | wship | B | 0.004 |
| 65 | wship | B | 0.004 |
| 66 | wship | B | 0.004 |
| 67 | wship | B | 0.004 |
| 68 | wship | B | 0.004 |
| 69 | wship | B | 0.004 |
| 70 | wship | B | 0.004 |
| 71 | wship | B | 0.004 |
| 72 | wship | B | 0.004 |
| 73 | wship | B | 0.004 |
| 74 | wship | B | 0.004 |
| 75 | wship | B | 0.004 |
| 76 | wship | B | 0.004 |
| 77 | wship | B | 0.004 |
| 78 | wship | B | 0.004 |
| 79 | wship | B | 0.004 |
| 80 | wship | B | 0.004 |
| 81 | wship | B | 0.004 |
| 82 | wship | B | 0.004 |
| 83 | wship | B | 0.004 |
| 84 | wship | B | 0.004 |
| 85 | wship | B | 0.004 |
| 86 | wship | B | 0.004 |
| 87 | wship | B | 0.004 |
| 88 | wship | B | 0.004 |
| 89 | wship | B | 0.004 |
| 90 | wship | B | 0.004 |
| 91 | wship | B | 0.004 |
| 92 | wship | B | 0.004 |
| 93 | wship | B | 0.004 |
| 94 | wship | B | 0.004 |
| 95 | wship | B | 0.004 |
| 96 | wship | B | 0.004 |
| 97 | wship | B | 0.004 |
| 98 | wship | B | 0.004 |
| 99 | wship | B | 0.004 |
| 100 | wship | B | 0.004 |
| 101 | wship | B | 0.004 |
| 102 | wship | B | 0.004 |
| 103 | wship | B | 0.004 |
| 104 | wship | B | 0.004 |
| 105 | wship | B | 0.004 |
| 106 | wship | B | 0.004 |
| 107 | wship | B | 0.004 |
| 108 | wship | B | 0.004 |
| 109 | wship | B | 0.004 |
| 110 | wship | B | 0.004 |
| 111 | wship | B | 0.004 |
| 112 | wship | B | 0.004 |
| 113 | wship | B | 0.004 |
| 114 | wship | B | 0.004 |
| 115 | wship | B | 0.004 |
| 116 | wship | B | 0.004 |
| 117 | wship | B | 0.004 |
| 118 | wship | B | 0.004 |
| 119 | wship | B | 0.004 |
| 120 | wship | B | 0.004 |
| 121 | wship | B | 0.004 |
| 122 | wship | B | 0.004 |
| 123 | wship | B | 0.004 |
| 124 | wship | B | 0.004 |
| 125 | wship | B | 0.004 |
| 126 | wship | B | 0.004 |
| 127 | wship | B | 0.004 |
| 128 | wship | B | 0.004 |
| 129 | wship | B | 0.004 |
| 130 | wship | B | 0.004 |
| 131 | wship | B | 0.004 |
| 132 | wship | B | 0.004 |
| 133 | wship | B | 0.004 |
| 134 | wship | B | 0.004 |
| 135 | wship | B | 0.004 |
| 136 | wship | B | 0.004 |
| 137 | wship | B | 0.004 |
| 138 | wship | B | 0.004 |
| 139 | wship | B | 0.004 |
| 140 | wship | B | 0.004 |
| 141 | wship | B | 0.004 |
| 142 | wship | B | 0.004 |
| 143 | wship | B | 0.004 |
| 144 | wship | B | 0.004 |
| 145 | wship | B | 0.004 |
| 146 | wship | B | 0.004 |
| 147 | wship | B | 0.004 |
| 148 | wship | B | 0.004 |
| 149 | wship | B | 0.004 |
| 150 | wship | B | 0.004 |
| 151 | wship | B | 0.004 |
| 152 | wship | B | 0.004 |
| 153 | wship | B | 0.004 |
| 154 | wship | B | 0.004 |
| 155 | wship | B | 0.004 |
| 156 | wship | B | 0.004 |
| 157 | wship | B | 0.004 |
| 158 | wship | B | 0.004 |
| 159 | wship | B | 0.004 |
| 160 | wship | B | 0.004 |
| 161 | wship | B | 0.004 |
| 162 | wship | B | 0.004 |
| 163 | wship | B | 0.004 |
| 164 | wship | B | 0.004 |
| 165 | wship | B | 0.004 |
| 166 | wship | B | 0.004 |
| 167 | wship | B | 0.004 |
| 168 | wship | B | 0.004 |
| 169 | wship | B | 0.004 |
| 170 | wship | B | 0.004 |
| 171 | wship | B | 0.004 |
| 172 | wship | B | 0.004 |
| 173 | wship | B | 0.004 |
| 174 | wship | B | 0.004 |
| 175 | wship | B | 0.004 |
| 176 | wship | B | 0.004 |
| 177 | wship | B | 0.004 |
| 178 | wship | B | 0.004 |
| 179 | wship | B | 0.004 |
| 180 | wship | B | 0.004 |
| 181 | wship | B | 0.004 |
| 182 | wship | B | 0.004 |
| 183 | wship | B | 0.004 |
| 184 | wship | B | 0.004 |
| 185 | wship | B | 0.004 |
| 186 | wship | B | 0.004 |
| 187 | wship | B | 0.004 |
| 188 | wship | B | 0.004 |
| 189 | wship | B | 0.004 |
| 190 | wship | B | 0.004 |
| 191 | wship | B | 0.004 |
| 192 | wship | B | 0.004 |
| 193 | wship | B | 0.004 |
| 194 | wship | B | 0.004 |
| 195 | wship | B | 0.004 |
| 196 | wship | B | 0.004 |
| 197 | wship | B | 0.004 |
| 198 | wship | B | 0.004 |
| 199 | wship | B | 0.004 |
| 200 | wship | B | 0.004 |
| 201 | wship | B | 0.004 |
| 202 | wship | B | 0.004 |
| 203 | wship | B | 0.004 |
| 204 | wship | B | 0.004 |
| 205 | wship | B | 0.004 |
| 206 | wship | B | 0.004 |
| 207 | wship | B | 0.004 |
| 208 | wship | B | 0.004 |
| 209 | wship | B | 0.004 |
| 210 | wship | B | 0.004 |
| 211 | wship | B | 0.004 |
| 212 | wship | B | 0.004 |
| 213 | wship | B | 0.004 |
| 214 | wship | B | 0.004 |
| 215 | wship | B | 0.004 |
| 216 | wship | B | 0.004 |
| 217 | wship | B | 0.004 |
| 218 | wship | B | 0.004 |
| 219 | wship | B | 0.004 |
| 220 | wship | B | 0.004 |
| 221 | wship | B | 0.004 |
| 222 | wship | B | 0.004 |
| 223 | wship | B | 0.004 |
| 224 | wship | B | 0.004 |
| 225 | wship | B | 0.004 |
| 226 | wship | B | 0.004 |
| 227 | wship | B | 0.004 |
| 228 | wship | B | 0.004 |
| 229 | wship | B | 0.004 |
| 230 | wship | B | 0.004 |
| 231 | wship | B | 0.004 |
| 232 | wship | B | 0.004 |
| 233 | wship | B | 0.004 |
| 234 | wship | B | 0.004 |
| 235 | wship | B | 0.004 |
| 236 | wship | B | 0.004 |
| 237 | wship | B | 0.004 |
| 238 | wship | B | 0.004 |
| 239 | wship | B | 0.004 |
| 240 | wship | B | 0.004 |
| 241 | wship | B | 0.004 |
| 242 | wship | B | 0.004 |
| 243 | wship | B | 0.004 |
| 244 | wship | B | 0.004 |
| 245 | wship | B | 0.004 |
| 246 | wship | B | 0.004 |
| 247 | wship | B | 0.004 |
| 248 | wship | B | 0.004 |
| 249 | wship | B | 0.004 |
| 250 | wship | B | 0.004 |
| 251 | wship | B | 0.004 |
| 252 | wship | B | 0.004 |
| 253 | wship | B | 0.004 |
| 254 | wship | B | 0.004 |
| 255 | wship | B | 0.004 |
| 256 | wship | B | 0.004 |
| 257 | wship | B | 0.004 |
| 258 | wship | B | 0.004 |
| 259 | wship | B | 0.004 |
| 260 | wship | B | 0.004 |
| 261 | wship | B | 0.004 |
| 262 | wship | B | 0.004 |
| 263 | wship | B | 0.004 |
| 264 | wship | B | 0.004 |
| 265 | wship | B | 0.004 |
| 266 | wship | B | 0.004 |
| 267 | wship | B | 0.004 |
| 268 | wship | B | 0.004 |
| 269 | wship | B | 0.004 |
| 270 | wship | B | 0.004 |
| 271 | wship | B | 0.004 |
| 272 | wship | B | 0.004 |
| 273 | wship | B | 0.004 |
| 274 | wship | B | 0.004 |
| 275 | wship | B | 0.004 |
| 276 | wship | B | 0.004 |
| 277 | wship | B | 0.004 |
| 278 | wship | B | 0.004 |
| 279 | wship | B | 0.004 |
| 280 | wship | B | 0.004 |
| 281 | wship | B | 0.004 |
| 282 | wship | B | 0.004 |
| 283 | wship | B | 0.004 |
| 284 | wship | B | 0.004 |
| 285 | wship | B | 0.004 |
| 286 | wship | B | 0.004 |
| 287 | wship | B | 0.004 |
| 288 | wship | B | 0.004 |
| 289 | wship | B | 0.004 |
| 290 | wship | B | 0.004 |
| 291 | wship | B | 0.004 |
| 292 | wship | B | 0.004 |
| 293 | wship | B | 0.004 |
| 294 | wship | B | 0.004 |
| 295 | wship | B | 0.004 |
| 296 | wship | B | 0.004 |
| 297 | wship | B | 0.004 |
| 298 | wship | B | 0.004 |
| 299 | wship | B | 0.004 |
| 300 | wship | B | 0.004 |
| 301 | wship | B | 0.004 |
| 302 | wship | B | 0.004 |
| 303 | wship | B | 0.004 |
| 304 | wship | B | 0.004 |
| 305 | wship | B | 0.004 |
| 306 | wship | B | 0.004 |
| 307 | wship | B | 0.004 |
| 308 | wship | B | 0.004 |
| 309 | wship | B | 0.004 |
| 310 | wship | B | 0.004 |
| 311 | wship | B | 0.004 |
| 312 | wship | B | 0.004 |
| 313 | wship | B | 0.004 |
| 314 | wship | B | 0.004 |
| 315 | wship | B | 0.004 |
| 316 | wship | B | 0.004 |
| 317 | wship | B | 0.004 |
| 318 | wship | B | 0.004 |
| 319 | wship | B | 0.004 |
| 320 | wship | B | 0.004 |
| 321 | wship | B | 0.004 |
| 322 | wship | B | 0.004 |
| 323 | wship | B | 0.004 |
| 324 | wship | B | 0.004 |
| 325 | wship | B | 0.004 |
| 326 | wship | B | 0.004 |
| 327 | wship | B | 0.004 |
| 328 | wship | B | 0.004 |
| 329 | wship | B | 0.004 |
| 330 | wship | B | 0.004 |
| 331 | wship | B | 0.004 |
| 332 | wship | B | 0.004 |
| 333 | wship | B | 0.004 |
| 334 | wship | B | 0.004 |
| 335 | wship | B | 0.004 |
| 336 | wship | B | 0.004 |
| 337 | wship | B | 0.004 |
| 338 | wship | B | 0.004 |
| 339 | wship | B | 0.004 |
| 340 | wship | B | 0.004 |
| 341 | wship | B | 0.004 |
| 342 | wship | B | 0.004 |
| 343 | wship | B | 0.004 |
| 344 | wship | B | 0.004 |
| 345 | wship | B | 0.004 |
| 346 | wship | B | 0.004 |
| 347 | wship | B | 0.004 |
| 348 | wship | B | 0.004 |
| 349 | wship | B | 0.004 |
| 350 | wship | B | 0.004 |
| 351 | wship | B | 0.004 |
| 352 | wship | B | 0.004 |
| 353 | wship | B | 0.004 |
| 354 | wship | B | 0.004 |
| 355 | wship | B | 0.004 |
| 356 | wship | B | 0.004 |
| 357 | wship | B | 0.004 |
| 358 | wship | B | 0.004 |
| 359 | wship | B | 0.004 |
| 360 | wship | B | 0.004 |
| 361 | wship | B | 0.004 |
| 362 | wship | B | 0.004 |
| 363 | wship | B | 0.004 |
| 364 | wship | B | 0.004 |
| 365 | wship | B | 0.004 |
| 366 | wship | B | 0.004 |
| 367 | wship | B | 0.004 |
| 368 | wship | B | 0.004 |
| 369 | wship | B | 0.004 |
| 370 | wship | B | 0.004 |
| 371 | wship | B | 0.004 |
| 372 | wship | B | 0.004 |
| 373 | wship | B | 0.004 |
| 374 | wship | B | 0.004 |
| 375 | wship | B | 0.004 |
| 376 | wship | B | 0.004 |
| 377 | wship | B | 0.004 |
| 378 | wship | B | 0.004 |
| 379 | wship | B | 0.004 |
| 380 | wship | B | 0.004 |
| 381 | wship | B | 0.004 |
| 382 | wship | B | 0.004 |
| 383 | wship | B | 0.004 |
| 384 | wship | B | 0.004 |
| 385 | wship | B | 0.004 |
| 386 | wship | B | 0.004 |
| 387 | wship | B | 0.004 |
| 388 | wship | B | 0.004 |
| 389 | wship | B | 0.004 |
| 390 | wship | B | 0.004 |
| 391 | wship | B | 0.004 |
| 392 | wship | B | 0.004 |
| 393 | wship | B | 0.004 |
| 394 | wship | B | 0.004 |
| 395 | wship | B | 0.004 |
| 396 | wship | B | 0.004 |
| 397 | wship | B | 0.004 |
| 398 | wship | B | 0.004 |
| 399 | wship | B | 0.004 |
| 400 | wship | B | 0.004 |
| 401 | wship | B | 0.004 |
| 402 | wship | B | 0.004 |
| 403 | wship | B | 0.004 |
| 404 | wship | B | 0.004 |
| 405 | wship | B | 0.004 |
| 406 | wship | B | 0.004 |
| 407 | wship | B | 0.004 |
| 408 | wship | B | 0.004 |
| 409 | wship | B | 0.004 |
| 410 | wship | B | 0.004 |
| 411 | wship | B | 0.004 |
| 412 | wship | B | 0.004 |
| 413 | wship | B | 0.004 |
| 414 | wship | B | 0.004 |
| 415 | wship | B | 0.004 |
| 416 | wship | B | 0.004 |
| 417 | wship | B | 0.004 |
| 418 | wship | B | 0.004 |
| 419 | wship | B | 0.004 |
| 420 | wship | B | 0.004 |
| 421 | wship | B | 0.004 |
| 422 | wship | B | 0.004 |
| 423 | wship | B | 0.004 |
| 424 | wship | B | 0.004 |
| 425 | wship | B | 0.004 |
| 426 | wship | B | 0.004 |
| 427 | wship | B | 0.004 |
| 428 | wship | B | 0.004 |
| 429 | wship | B | 0.004 |
| 430 | wship | B | 0.004 |
| 431 | wship | B | 0.004 |
| 432 | wship | B | 0.004 |
| 433 | wship | B | 0.004 |
| 434 | wship | B | 0.004 |
| 435 | wship | B | 0.004 |
| 436 | wship | B | 0.004 |
| 437 | wship | B | 0.004 |
| 438 | wship | B | 0.004 |
| 439 | wship | B | 0.004 |
| 440 | wship | B | 0.004 |
| 441 | wship | B | 0.004 |
| 442 | wship | B | 0.004 |
| 443 | wship | B | 0.004 |
| 444 | wship | B | 0.004 |
| 445 | wship | B | 0.004 |
| 446 | wship | B | 0.004 |
| 447 | wship | B | 0.004 |
| 448 | wship | B | 0.004 |
| 449 | wship | B | 0.004 |
| 450 | wship | B | 0.004 |
| 451 | wship | B | 0.004 |
| 452 | wship | B | 0.004 |
| 453 | wship | B | 0.004 |
| 454 | wship | B | 0.004 |
| 455 | wship | B | 0.004 |
| 456 | wship | B | 0.004 |
| 457 | wship | B | 0.004 |
| 458 | wship | B | 0.004 |
| 459 | wship | B | 0.004 |
| 460 | wship | B | 0.004 |
| 461 | wship | B | 0.004 |
| 462 | wship | B | 0.004 |
| 463 | wship | B | 0.004 |
| 464 | wship | B | 0.004 |
| 465 | wship | B | 0.004 |
| 466 | wship | B | 0.004 |
| 467 | wship | B | 0.004 |
| 468 | wship | B | 0.004 |
| 469 | wship | B | 0.004 |
| 470 | wship | B | 0.004 |
| 471 | wship | B | 0.004 |
| 472 | wship | B | 0.004 |
| 473 | wship | B | 0.004 |
| 474 | wship | B | 0.004 |
| 475 | wship | B | 0.004 |
| 476 | wship | B | 0.004 |
| 477 | wship | B | 0.004 |
| 478 | wship | B | 0.004 |
| 479 | wship | B | 0.004 |
| 480 | wship | B | 0.004 |
| 481 | wship | B | 0.004 |
| 482 | wship | B | 0.004 |
| 483 | wship | B | 0.004 |
| 484 | wship | B | 0.004 |
| 485 | wship | B | 0.004 |
| 486 | wship | B | 0.004 |
| 487 | wship | B | 0.004 |
| 488 | wship | B | 0.004 |
| 489 | wship | B | 0.004 |
| 490 | wship | B | 0.004 |
| 491 | wship | B | 0.004 |
| 492 | wship | B | 0.004 |
| 493 | wship | B | 0.004 |
| 494 | wship | B | 0.004 |
| 495 | wship | B | 0.004 |
| 496 | wship | B | 0.004 |
| 497 | wship | B | 0.004 |
| 498 | wship | B | 0.004 |
| 499 | wship | B | 0.004 |
| 500 | wship | B | 0.004 |
| 501 | wship | B | 0.004 |
| 502 | wship | B | 0.004 |
| 503 | wship | B | 0.004 |
| 504 | wship | B | 0.004 |
| 505 | wship | B | 0.004 |
| 506 | wship | B | 0.004 |
| 507 | wship | B | 0.004 |
| 508 | wship | B | 0.004 |
| 509 | wship | B | 0.004 |
| 510 | wship | B | 0.004 |
| 511 | wship | B | 0.004 |
| 512 | wship | B | 0.004 |
| 513 | wship | B | 0.004 |
| 514 | wship | B | 0.004 |
| 515 | wship | B | 0.004 |
| 516 | wship | B | 0.004 |
| 517 | wship | B | 0.004 |
| 518 | wship | B | 0.004 |
| 519 | wship | B | 0.004 |
| 520 | wship | B | 0.004 |
| 521 | wship | B | 0.004 |
| 1 | private | H | 0.007 |
| 2 | private | H | 0.007 |
| 3 | private | H | 0.007 |
| 4 | private | H | 0.007 |
| 5 | private | H | 0.007 |
| 6 | private | H | 0.007 |
| 7 | private | H | 0.007 |
| 8 | private | H | 0.007 |
| 9 | private | H | 0.007 |
| 10 | private | H | 0.007 |
| 11 | private | H | 0.007 |
| 12 | private | H | 0.007 |
| 13 | private | H | 0.007 |
| 14 | private | H | 0.007 |
| 15 | private | H | 0.007 |
| 16 | private | H | 0.007 |
| 17 | private | H | 0.007 |
| 18 | private | H | 0.007 |
| 19 | private | H | 0.007 |
| 20 | private | H | 0.007 |
| 21 | private | H | 0.007 |
| 22 | private | H | 0.007 |
| 23 | private | H | 0.007 |
| 24 | private | H | 0.007 |
| 25 | private | H | 0.007 |
| 26 | private | H | 0.007 |
| 27 | private | H | 0.007 |
| 28 | private | H | 0.007 |
| 29 | private | H | 0.007 |
| 30 | private | H | 0.007 |
| 31 | private | H | 0.007 |
| 32 | private | H | 0.007 |
| 33 | private | H | 0.007 |
| 34 | private | H | 0.007 |
| 35 | private | H | 0.007 |
| 36 | private | H | 0.007 |
| 37 | private | H | 0.007 |
| 38 | private | H | 0.007 |
| 39 | private | H | 0.007 |
| 40 | private | H | 0.007 |
| 41 | private | H | 0.007 |
| 42 | private | H | 0.007 |
| 43 | private | H | 0.007 |
| 44 | private | H | 0.007 |
| 45 | private | H | 0.007 |
| 46 | private | H | 0.007 |
| 47 | private | H | 0.007 |
| 48 | private | H | 0.007 |
| 49 | private | H | 0.007 |
| 50 | private | H | 0.007 |
| 51 | private | H | 0.007 |
| 52 | private | H | 0.007 |
| 53 | private | H | 0.007 |
| 54 | private | H | 0.007 |
| 55 | private | H | 0.007 |
| 56 | private | H | 0.007 |
| 57 | private | H | 0.007 |
| 58 | private | H | 0.007 |
| 59 | private | H | 0.007 |
| 60 | private | H | 0.007 |
| 61 | private | H | 0.007 |
| 62 | private | H | 0.007 |
| 63 | private | H | 0.007 |
| 64 | private | H | 0.007 |
| 65 | private | H | 0.007 |
| 66 | private | H | 0.007 |
| 67 | private | H | 0.007 |
| 68 | private | H | 0.007 |
| 69 | private | H | 0.007 |
| 70 | private | H | 0.007 |
| 71 | private | H | 0.007 |
| 72 | private | H | 0.007 |
| 73 | private | H | 0.007 |
| 74 | private | H | 0.007 |
| 75 | private | H | 0.007 |
| 76 | private | H | 0.007 |
| 77 | private | H | 0.007 |
| 78 | private | H | 0.007 |
| 79 | private | H | 0.007 |
| 80 | private | H | 0.007 |
| 81 | private | H | 0.007 |
| 82 | private | H | 0.007 |
| 83 | private | H | 0.007 |
| 84 | private | H | 0.007 |
| 85 | private | H | 0.007 |
| 86 | private | H | 0.007 |
| 87 | private | H | 0.007 |
| 88 | private | H | 0.007 |
| 89 | private | H | 0.007 |
| 90 | private | H | 0.007 |
| 91 | private | H | 0.007 |
| 92 | private | H | 0.007 |
| 93 | private | H | 0.007 |
| 94 | private | H | 0.007 |
| 95 | private | H | 0.007 |
| 96 | private | H | 0.007 |
| 97 | private | H | 0.007 |
| 98 | private | H | 0.007 |
| 99 | private | H | 0.007 |
| 100 | private | H | 0.007 |
| 101 | private | H | 0.007 |
| 102 | private | H | 0.007 |
| 103 | private | H | 0.007 |
| 104 | private | H | 0.007 |
| 105 | private | H | 0.006 |
| 106 | private | H | 0.006 |
| 107 | private | H | 0.006 |
| 108 | private | H | 0.005 |
| 109 | private | H | 0.005 |
| 110 | private | H | 0.005 |
| 111 | private | H | 0.005 |
| 112 | private | H | 0.005 |
| 113 | private | H | 0.005 |
| 114 | private | H | 0.005 |
| 115 | private | H | 0.005 |
| 116 | private | H | 0.005 |
| 117 | private | H | 0.005 |
| 118 | private | H | 0.005 |
| 119 | private | H | 0.005 |
| 120 | private | H | 0.005 |
| 121 | private | H | 0.005 |
| 122 | private | H | 0.005 |
| 123 | private | H | 0.005 |
| 124 | private | H | 0.005 |
| 125 | private | H | 0.005 |
| 126 | private | H | 0.005 |
| 127 | private | H | 0.005 |
| 128 | private | H | 0.005 |
| 129 | private | H | 0.005 |
| 130 | private | H | 0.005 |
| 131 | private | H | 0.005 |
| 132 | private | H | 0.005 |
| 133 | private | H | 0.005 |
| 134 | private | H | 0.005 |
| 135 | private | H | 0.005 |
| 136 | private | H | 0.005 |
| 137 | private | H | 0.005 |
| 138 | private | H | 0.005 |
| 139 | private | H | 0.005 |
| 140 | private | H | 0.005 |
| 141 | private | H | 0.005 |
| 142 | private | H | 0.005 |
| 143 | private | H | 0.005 |
| 144 | private | H | 0.005 |
| 145 | private | H | 0.005 |
| 146 | private | H | 0.005 |
| 147 | private | H | 0.005 |
| 148 | private | H | 0.005 |
| 149 | private | H | 0.005 |
| 150 | private | H | 0.005 |
| 151 | private | H | 0.005 |
| 152 | private | H | 0.005 |
| 153 | private | H | 0.005 |
| 154 | private | H | 0.005 |
| 155 | private | H | 0.005 |
| 156 | private | H | 0.005 |
| 157 | private | H | 0.005 |
| 158 | private | H | 0.005 |
| 159 | private | H | 0.005 |
| 160 | private | H | 0.005 |
| 161 | private | H | 0.005 |
| 162 | private | H | 0.005 |
| 163 | private | H | 0.005 |
| 164 | private | H | 0.005 |
| 165 | private | H | 0.005 |
| 166 | private | H | 0.005 |
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| 330 | uninsure | H | 0.003 |
| 331 | uninsure | H | 0.003 |
| 332 | uninsure | H | 0.003 |
| 333 | uninsure | H | 0.003 |
| 334 | uninsure | H | 0.003 |
| 335 | uninsure | H | 0.003 |
| 336 | uninsure | H | 0.003 |
| 337 | uninsure | H | 0.003 |
| 338 | uninsure | H | 0.003 |
| 339 | uninsure | H | 0.003 |
| 340 | uninsure | H | 0.003 |
| 341 | uninsure | H | 0.003 |
| 342 | uninsure | H | 0.003 |
| 343 | uninsure | H | 0.003 |
| 344 | uninsure | H | 0.003 |
| 345 | uninsure | H | 0.003 |
| 346 | uninsure | H | 0.003 |
| 347 | uninsure | H | 0.003 |
| 348 | uninsure | H | 0.003 |
| 349 | uninsure | H | 0.003 |
| 350 | uninsure | H | 0.003 |
| 351 | uninsure | H | 0.003 |
| 352 | uninsure | H | 0.003 |
| 353 | uninsure | H | 0.003 |
| 354 | uninsure | H | 0.003 |
| 355 | uninsure | H | 0.003 |
| 356 | uninsure | H | 0.003 |
| 357 | uninsure | H | 0.003 |
| 358 | uninsure | H | 0.003 |
| 359 | uninsure | H | 0.003 |
| 360 | uninsure | H | 0.003 |
| 361 | uninsure | H | 0.003 |
| 362 | uninsure | H | 0.003 |
| 363 | uninsure | H | 0.003 |
| 364 | uninsure | H | 0.003 |
| 365 | uninsure | H | 0.003 |
| 366 | uninsure | H | 0.003 |
| 367 | uninsure | H | 0.003 |
| 368 | uninsure | H | 0.003 |
| 369 | uninsure | H | 0.003 |
| 370 | uninsure | H | 0.003 |
| 371 | uninsure | H | 0.003 |
| 372 | uninsure | H | 0.003 |
| 373 | uninsure | H | 0.003 |
| 374 | uninsure | H | 0.003 |
| 375 | uninsure | H | 0.003 |
| 376 | uninsure | H | 0.003 |
| 377 | uninsure | H | 0.003 |
| 378 | uninsure | H | 0.003 |
| 379 | uninsure | H | 0.003 |
| 380 | uninsure | H | 0.003 |
| 381 | uninsure | H | 0.003 |
| 382 | uninsure | H | 0.003 |
| 383 | uninsure | H | 0.003 |
| 384 | uninsure | H | 0.003 |
| 385 | uninsure | H | 0.003 |
| 386 | uninsure | H | 0.003 |
| 387 | uninsure | H | 0.003 |
| 388 | uninsure | H | 0.003 |
| 389 | uninsure | H | 0.003 |
| 390 | uninsure | H | 0.003 |
| 391 | uninsure | H | 0.003 |
| 392 | uninsure | H | 0.003 |
| 393 | uninsure | H | 0.003 |
| 394 | uninsure | H | 0.003 |
| 395 | uninsure | H | 0.003 |
| 396 | uninsure | H | 0.003 |
| 397 | uninsure | H | 0.003 |
| 398 | uninsure | H | 0.003 |
| 399 | uninsure | H | 0.003 |
| 400 | uninsure | H | 0.003 |
| 401 | uninsure | H | 0.003 |
| 402 | uninsure | H | 0.003 |
| 403 | uninsure | H | 0.003 |
| 404 | uninsure | H | 0.003 |
| 405 | uninsure | H | 0.003 |
| 406 | uninsure | H | 0.003 |
| 407 | uninsure | H | 0.003 |
| 408 | uninsure | H | 0.003 |
| 409 | uninsure | H | 0.003 |
| 410 | uninsure | H | 0.003 |
| 411 | uninsure | H | 0.003 |
| 412 | uninsure | H | 0.003 |
| 413 | uninsure | H | 0.003 |
| 414 | uninsure | H | 0.003 |
| 415 | uninsure | H | 0.003 |
| 416 | uninsure | H | 0.003 |
| 417 | uninsure | H | 0.003 |
| 418 | uninsure | H | 0.003 |
| 419 | uninsure | H | 0.003 |
| 420 | uninsure | H | 0.003 |
| 421 | uninsure | H | 0.003 |
| 422 | uninsure | H | 0.003 |
| 423 | uninsure | H | 0.003 |
| 424 | uninsure | H | 0.003 |
| 425 | uninsure | H | 0.003 |
| 426 | uninsure | H | 0.003 |
| 427 | uninsure | H | 0.003 |
| 428 | uninsure | H | 0.003 |
| 429 | uninsure | H | 0.003 |
| 430 | uninsure | H | 0.003 |
| 431 | uninsure | H | 0.003 |
| 432 | uninsure | H | 0.003 |
| 433 | uninsure | H | 0.003 |
| 434 | uninsure | H | 0.003 |
| 435 | uninsure | H | 0.003 |
| 436 | uninsure | H | 0.003 |
| 437 | uninsure | H | 0.003 |
| 438 | uninsure | H | 0.003 |
| 439 | uninsure | H | 0.003 |
| 440 | uninsure | H | 0.003 |
| 441 | uninsure | H | 0.003 |
| 442 | uninsure | H | 0.003 |
| 443 | uninsure | H | 0.003 |
| 444 | uninsure | H | 0.003 |
| 445 | uninsure | H | 0.003 |
| 446 | uninsure | H | 0.003 |
| 447 | uninsure | H | 0.003 |
| 448 | uninsure | H | 0.003 |
| 449 | uninsure | H | 0.003 |
| 450 | uninsure | H | 0.003 |
| 451 | uninsure | H | 0.003 |
| 452 | uninsure | H | 0.003 |
| 453 | uninsure | H | 0.003 |
| 454 | uninsure | H | 0.003 |
| 455 | uninsure | H | 0.003 |
| 456 | uninsure | H | 0.003 |
| 457 | uninsure | H | 0.003 |
| 458 | uninsure | H | 0.003 |
| 459 | uninsure | H | 0.003 |
| 460 | uninsure | H | 0.003 |
| 461 | uninsure | H | 0.003 |
| 462 | uninsure | H | 0.003 |
| 463 | uninsure | H | 0.003 |
| 464 | uninsure | H | 0.003 |
| 465 | uninsure | H | 0.003 |
| 466 | uninsure | H | 0.003 |
| 467 | uninsure | H | 0.003 |
| 468 | uninsure | H | 0.003 |
| 469 | uninsure | H | 0.003 |
| 470 | uninsure | H | 0.003 |
| 471 | uninsure | H | 0.003 |
| 472 | uninsure | H | 0.003 |
| 473 | uninsure | H | 0.003 |
| 474 | uninsure | H | 0.003 |
| 475 | uninsure | H | 0.003 |
| 476 | uninsure | H | 0.003 |
| 477 | uninsure | H | 0.003 |
| 478 | uninsure | H | 0.003 |
| 479 | uninsure | H | 0.003 |
| 480 | uninsure | H | 0.003 |
| 481 | uninsure | H | 0.003 |
| 482 | uninsure | H | 0.003 |
| 483 | uninsure | H | 0.003 |
| 484 | uninsure | H | 0.003 |
| 485 | uninsure | H | 0.003 |
| 486 | uninsure | H | 0.003 |
| 487 | uninsure | H | 0.003 |
| 488 | uninsure | H | 0.003 |
| 489 | uninsure | H | 0.003 |
| 490 | uninsure | H | 0.003 |
| 491 | uninsure | H | 0.003 |
| 492 | uninsure | H | 0.003 |
| 493 | uninsure | H | 0.003 |
| 494 | uninsure | H | 0.003 |
| 495 | uninsure | H | 0.003 |
| 496 | uninsure | H | 0.003 |
| 497 | uninsure | H | 0.003 |
| 498 | uninsure | H | 0.003 |
| 499 | uninsure | H | 0.003 |
| 500 | uninsure | H | 0.003 |
| 501 | uninsure | H | 0.003 |
| 502 | uninsure | H | 0.003 |
| 503 | uninsure | H | 0.003 |
| 504 | uninsure | H | 0.003 |
| 505 | uninsure | H | 0.003 |
| 506 | uninsure | H | 0.003 |
| 507 | uninsure | H | 0.003 |
| 508 | uninsure | H | 0.003 |
| 509 | uninsure | H | 0.003 |
| 510 | uninsure | H | 0.003 |
| 511 | uninsure | H | 0.003 |
| 512 | uninsure | H | 0.003 |
| 513 | uninsure | H | 0.003 |
| 514 | uninsure | H | 0.003 |
| 515 | uninsure | H | 0.003 |
| 516 | uninsure | H | 0.003 |
| 517 | uninsure | H | 0.002 |
| 518 | uninsure | H | 0.002 |
| 519 | uninsure | H | 0.002 |
| 520 | uninsure | H | 0.002 |
| 521 | uninsure | H | 0.002 |
| 1 | wship | H | 0.004 |
| 2 | wship | H | 0.004 |
| 3 | wship | H | 0.004 |
| 4 | wship | H | 0.004 |
| 5 | wship | H | 0.004 |
| 6 | wship | H | 0.004 |
| 7 | wship | H | 0.004 |
| 8 | wship | H | 0.004 |
| 9 | wship | H | 0.004 |
| 10 | wship | H | 0.004 |
| 11 | wship | H | 0.004 |
| 12 | wship | H | 0.004 |
| 13 | wship | H | 0.004 |
| 14 | wship | H | 0.004 |
| 15 | wship | H | 0.004 |
| 16 | wship | H | 0.004 |
| 17 | wship | H | 0.004 |
| 18 | wship | H | 0.004 |
| 19 | wship | H | 0.004 |
| 20 | wship | H | 0.004 |
| 21 | wship | H | 0.004 |
| 22 | wship | H | 0.004 |
| 23 | wship | H | 0.004 |
| 24 | wship | H | 0.004 |
| 25 | wship | H | 0.004 |
| 26 | wship | H | 0.004 |
| 27 | wship | H | 0.004 |
| 28 | wship | H | 0.004 |
| 29 | wship | H | 0.004 |
| 30 | wship | H | 0.004 |
| 31 | wship | H | 0.004 |
| 32 | wship | H | 0.004 |
| 33 | wship | H | 0.004 |
| 34 | wship | H | 0.004 |
| 35 | wship | H | 0.004 |
| 36 | wship | H | 0.004 |
| 37 | wship | H | 0.004 |
| 38 | wship | H | 0.004 |
| 39 | wship | H | 0.004 |
| 40 | wship | H | 0.004 |
| 41 | wship | H | 0.004 |
| 42 | wship | H | 0.004 |
| 43 | wship | H | 0.004 |
| 44 | wship | H | 0.004 |
| 45 | wship | H | 0.004 |
| 46 | wship | H | 0.004 |
| 47 | wship | H | 0.004 |
| 48 | wship | H | 0.004 |
| 49 | wship | H | 0.004 |
| 50 | wship | H | 0.004 |
| 51 | wship | H | 0.004 |
| 52 | wship | H | 0.004 |
| 53 | wship | H | 0.004 |
| 54 | wship | H | 0.004 |
| 55 | wship | H | 0.004 |
| 56 | wship | H | 0.004 |
| 57 | wship | H | 0.004 |
| 58 | wship | H | 0.004 |
| 59 | wship | H | 0.004 |
| 60 | wship | H | 0.004 |
| 61 | wship | H | 0.004 |
| 62 | wship | H | 0.004 |
| 63 | wship | H | 0.004 |
| 64 | wship | H | 0.004 |
| 65 | wship | H | 0.004 |
| 66 | wship | H | 0.004 |
| 67 | wship | H | 0.004 |
| 68 | wship | H | 0.004 |
| 69 | wship | H | 0.004 |
| 70 | wship | H | 0.004 |
| 71 | wship | H | 0.004 |
| 72 | wship | H | 0.004 |
| 73 | wship | H | 0.004 |
| 74 | wship | H | 0.004 |
| 75 | wship | H | 0.004 |
| 76 | wship | H | 0.004 |
| 77 | wship | H | 0.004 |
| 78 | wship | H | 0.004 |
| 79 | wship | H | 0.004 |
| 80 | wship | H | 0.004 |
| 81 | wship | H | 0.004 |
| 82 | wship | H | 0.004 |
| 83 | wship | H | 0.004 |
| 84 | wship | H | 0.004 |
| 85 | wship | H | 0.004 |
| 86 | wship | H | 0.004 |
| 87 | wship | H | 0.004 |
| 88 | wship | H | 0.004 |
| 89 | wship | H | 0.004 |
| 90 | wship | H | 0.004 |
| 91 | wship | H | 0.004 |
| 92 | wship | H | 0.004 |
| 93 | wship | H | 0.004 |
| 94 | wship | H | 0.004 |
| 95 | wship | H | 0.004 |
| 96 | wship | H | 0.004 |
| 97 | wship | H | 0.004 |
| 98 | wship | H | 0.004 |
| 99 | wship | H | 0.004 |
| 100 | wship | H | 0.004 |
| 101 | wship | H | 0.004 |
| 102 | wship | H | 0.004 |
| 103 | wship | H | 0.004 |
| 104 | wship | H | 0.004 |
| 105 | wship | H | 0.004 |
| 106 | wship | H | 0.004 |
| 107 | wship | H | 0.004 |
| 108 | wship | H | 0.004 |
| 109 | wship | H | 0.004 |
| 110 | wship | H | 0.004 |
| 111 | wship | H | 0.004 |
| 112 | wship | H | 0.004 |
| 113 | wship | H | 0.004 |
| 114 | wship | H | 0.004 |
| 115 | wship | H | 0.004 |
| 116 | wship | H | 0.004 |
| 117 | wship | H | 0.004 |
| 118 | wship | H | 0.004 |
| 119 | wship | H | 0.004 |
| 120 | wship | H | 0.004 |
| 121 | wship | H | 0.004 |
| 122 | wship | H | 0.004 |
| 123 | wship | H | 0.004 |
| 124 | wship | H | 0.004 |
| 125 | wship | H | 0.004 |
| 126 | wship | H | 0.004 |
| 127 | wship | H | 0.004 |
| 128 | wship | H | 0.004 |
| 129 | wship | H | 0.004 |
| 130 | wship | H | 0.004 |
| 131 | wship | H | 0.004 |
| 132 | wship | H | 0.004 |
| 133 | wship | H | 0.004 |
| 134 | wship | H | 0.004 |
| 135 | wship | H | 0.004 |
| 136 | wship | H | 0.004 |
| 137 | wship | H | 0.004 |
| 138 | wship | H | 0.004 |
| 139 | wship | H | 0.004 |
| 140 | wship | H | 0.004 |
| 141 | wship | H | 0.004 |
| 142 | wship | H | 0.004 |
| 143 | wship | H | 0.004 |
| 144 | wship | H | 0.004 |
| 145 | wship | H | 0.004 |
| 146 | wship | H | 0.004 |
| 147 | wship | H | 0.004 |
| 148 | wship | H | 0.004 |
| 149 | wship | H | 0.004 |
| 150 | wship | H | 0.004 |
| 151 | wship | H | 0.004 |
| 152 | wship | H | 0.004 |
| 153 | wship | H | 0.004 |
| 154 | wship | H | 0.004 |
| 155 | wship | H | 0.004 |
| 156 | wship | H | 0.004 |
| 157 | wship | H | 0.004 |
| 158 | wship | H | 0.004 |
| 159 | wship | H | 0.004 |
| 160 | wship | H | 0.004 |
| 161 | wship | H | 0.004 |
| 162 | wship | H | 0.004 |
| 163 | wship | H | 0.004 |
| 164 | wship | H | 0.004 |
| 165 | wship | H | 0.004 |
| 166 | wship | H | 0.004 |
| 167 | wship | H | 0.004 |
| 168 | wship | H | 0.004 |
| 169 | wship | H | 0.004 |
| 170 | wship | H | 0.004 |
| 171 | wship | H | 0.004 |
| 172 | wship | H | 0.004 |
| 173 | wship | H | 0.004 |
| 174 | wship | H | 0.004 |
| 175 | wship | H | 0.004 |
| 176 | wship | H | 0.004 |
| 177 | wship | H | 0.004 |
| 178 | wship | H | 0.004 |
| 179 | wship | H | 0.004 |
| 180 | wship | H | 0.004 |
| 181 | wship | H | 0.004 |
| 182 | wship | H | 0.004 |
| 183 | wship | H | 0.004 |
| 184 | wship | H | 0.004 |
| 185 | wship | H | 0.004 |
| 186 | wship | H | 0.004 |
| 187 | wship | H | 0.004 |
| 188 | wship | H | 0.004 |
| 189 | wship | H | 0.004 |
| 190 | wship | H | 0.004 |
| 191 | wship | H | 0.004 |
| 192 | wship | H | 0.004 |
| 193 | wship | H | 0.004 |
| 194 | wship | H | 0.004 |
| 195 | wship | H | 0.004 |
| 196 | wship | H | 0.004 |
| 197 | wship | H | 0.004 |
| 198 | wship | H | 0.004 |
| 199 | wship | H | 0.004 |
| 200 | wship | H | 0.004 |
| 201 | wship | H | 0.004 |
| 202 | wship | H | 0.004 |
| 203 | wship | H | 0.004 |
| 204 | wship | H | 0.004 |
| 205 | wship | H | 0.004 |
| 206 | wship | H | 0.004 |
| 207 | wship | H | 0.004 |
| 208 | wship | H | 0.004 |
| 209 | wship | H | 0.004 |
| 210 | wship | H | 0.004 |
| 211 | wship | H | 0.004 |
| 212 | wship | H | 0.004 |
| 213 | wship | H | 0.004 |
| 214 | wship | H | 0.004 |
| 215 | wship | H | 0.004 |
| 216 | wship | H | 0.004 |
| 217 | wship | H | 0.004 |
| 218 | wship | H | 0.004 |
| 219 | wship | H | 0.004 |
| 220 | wship | H | 0.004 |
| 221 | wship | H | 0.004 |
| 222 | wship | H | 0.004 |
| 223 | wship | H | 0.004 |
| 224 | wship | H | 0.004 |
| 225 | wship | H | 0.004 |
| 226 | wship | H | 0.004 |
| 227 | wship | H | 0.004 |
| 228 | wship | H | 0.004 |
| 229 | wship | H | 0.004 |
| 230 | wship | H | 0.004 |
| 231 | wship | H | 0.004 |
| 232 | wship | H | 0.004 |
| 233 | wship | H | 0.004 |
| 234 | wship | H | 0.004 |
| 235 | wship | H | 0.004 |
| 236 | wship | H | 0.004 |
| 237 | wship | H | 0.004 |
| 238 | wship | H | 0.004 |
| 239 | wship | H | 0.004 |
| 240 | wship | H | 0.004 |
| 241 | wship | H | 0.004 |
| 242 | wship | H | 0.004 |
| 243 | wship | H | 0.004 |
| 244 | wship | H | 0.004 |
| 245 | wship | H | 0.004 |
| 246 | wship | H | 0.004 |
| 247 | wship | H | 0.004 |
| 248 | wship | H | 0.004 |
| 249 | wship | H | 0.004 |
| 250 | wship | H | 0.004 |
| 251 | wship | H | 0.004 |
| 252 | wship | H | 0.004 |
| 253 | wship | H | 0.004 |
| 254 | wship | H | 0.004 |
| 255 | wship | H | 0.004 |
| 256 | wship | H | 0.004 |
| 257 | wship | H | 0.004 |
| 258 | wship | H | 0.004 |
| 259 | wship | H | 0.004 |
| 260 | wship | H | 0.004 |
| 261 | wship | H | 0.004 |
| 262 | wship | H | 0.004 |
| 263 | wship | H | 0.004 |
| 264 | wship | H | 0.004 |
| 265 | wship | H | 0.004 |
| 266 | wship | H | 0.004 |
| 267 | wship | H | 0.004 |
| 268 | wship | H | 0.004 |
| 269 | wship | H | 0.004 |
| 270 | wship | H | 0.004 |
| 271 | wship | H | 0.004 |
| 272 | wship | H | 0.004 |
| 273 | wship | H | 0.004 |
| 274 | wship | H | 0.004 |
| 275 | wship | H | 0.004 |
| 276 | wship | H | 0.004 |
| 277 | wship | H | 0.004 |
| 278 | wship | H | 0.004 |
| 279 | wship | H | 0.004 |
| 280 | wship | H | 0.004 |
| 281 | wship | H | 0.004 |
| 282 | wship | H | 0.004 |
| 283 | wship | H | 0.004 |
| 284 | wship | H | 0.004 |
| 285 | wship | H | 0.004 |
| 286 | wship | H | 0.004 |
| 287 | wship | H | 0.004 |
| 288 | wship | H | 0.004 |
| 289 | wship | H | 0.004 |
| 290 | wship | H | 0.004 |
| 291 | wship | H | 0.004 |
| 292 | wship | H | 0.004 |
| 293 | wship | H | 0.004 |
| 294 | wship | H | 0.004 |
| 295 | wship | H | 0.004 |
| 296 | wship | H | 0.004 |
| 297 | wship | H | 0.004 |
| 298 | wship | H | 0.004 |
| 299 | wship | H | 0.004 |
| 300 | wship | H | 0.004 |
| 301 | wship | H | 0.004 |
| 302 | wship | H | 0.004 |
| 303 | wship | H | 0.004 |
| 304 | wship | H | 0.004 |
| 305 | wship | H | 0.004 |
| 306 | wship | H | 0.004 |
| 307 | wship | H | 0.004 |
| 308 | wship | H | 0.004 |
| 309 | wship | H | 0.004 |
| 310 | wship | H | 0.004 |
| 311 | wship | H | 0.004 |
| 312 | wship | H | 0.004 |
| 313 | wship | H | 0.004 |
| 314 | wship | H | 0.004 |
| 315 | wship | H | 0.004 |
| 316 | wship | H | 0.004 |
| 317 | wship | H | 0.004 |
| 318 | wship | H | 0.004 |
| 319 | wship | H | 0.004 |
| 320 | wship | H | 0.004 |
| 321 | wship | H | 0.004 |
| 322 | wship | H | 0.004 |
| 323 | wship | H | 0.004 |
| 324 | wship | H | 0.004 |
| 325 | wship | H | 0.004 |
| 326 | wship | H | 0.004 |
| 327 | wship | H | 0.004 |
| 328 | wship | H | 0.004 |
| 329 | wship | H | 0.004 |
| 330 | wship | H | 0.004 |
| 331 | wship | H | 0.004 |
| 332 | wship | H | 0.004 |
| 333 | wship | H | 0.004 |
| 334 | wship | H | 0.004 |
| 335 | wship | H | 0.004 |
| 336 | wship | H | 0.004 |
| 337 | wship | H | 0.004 |
| 338 | wship | H | 0.004 |
| 339 | wship | H | 0.004 |
| 340 | wship | H | 0.004 |
| 341 | wship | H | 0.004 |
| 342 | wship | H | 0.004 |
| 343 | wship | H | 0.004 |
| 344 | wship | H | 0.004 |
| 345 | wship | H | 0.004 |
| 346 | wship | H | 0.004 |
| 347 | wship | H | 0.004 |
| 348 | wship | H | 0.004 |
| 349 | wship | H | 0.004 |
| 350 | wship | H | 0.004 |
| 351 | wship | H | 0.004 |
| 352 | wship | H | 0.004 |
| 353 | wship | H | 0.004 |
| 354 | wship | H | 0.004 |
| 355 | wship | H | 0.004 |
| 356 | wship | H | 0.004 |
| 357 | wship | H | 0.004 |
| 358 | wship | H | 0.004 |
| 359 | wship | H | 0.004 |
| 360 | wship | H | 0.004 |
| 361 | wship | H | 0.004 |
| 362 | wship | H | 0.004 |
| 363 | wship | H | 0.004 |
| 364 | wship | H | 0.004 |
| 365 | wship | H | 0.004 |
| 366 | wship | H | 0.004 |
| 367 | wship | H | 0.004 |
| 368 | wship | H | 0.004 |
| 369 | wship | H | 0.004 |
| 370 | wship | H | 0.004 |
| 371 | wship | H | 0.004 |
| 372 | wship | H | 0.004 |
| 373 | wship | H | 0.004 |
| 374 | wship | H | 0.004 |
| 375 | wship | H | 0.004 |
| 376 | wship | H | 0.004 |
| 377 | wship | H | 0.004 |
| 378 | wship | H | 0.004 |
| 379 | wship | H | 0.004 |
| 380 | wship | H | 0.004 |
| 381 | wship | H | 0.004 |
| 382 | wship | H | 0.004 |
| 383 | wship | H | 0.004 |
| 384 | wship | H | 0.004 |
| 385 | wship | H | 0.004 |
| 386 | wship | H | 0.004 |
| 387 | wship | H | 0.004 |
| 388 | wship | H | 0.004 |
| 389 | wship | H | 0.004 |
| 390 | wship | H | 0.004 |
| 391 | wship | H | 0.004 |
| 392 | wship | H | 0.004 |
| 393 | wship | H | 0.004 |
| 394 | wship | H | 0.004 |
| 395 | wship | H | 0.004 |
| 396 | wship | H | 0.004 |
| 397 | wship | H | 0.004 |
| 398 | wship | H | 0.004 |
| 399 | wship | H | 0.004 |
| 400 | wship | H | 0.004 |
| 401 | wship | H | 0.004 |
| 402 | wship | H | 0.004 |
| 403 | wship | H | 0.004 |
| 404 | wship | H | 0.004 |
| 405 | wship | H | 0.004 |
| 406 | wship | H | 0.004 |
| 407 | wship | H | 0.004 |
| 408 | wship | H | 0.004 |
| 409 | wship | H | 0.004 |
| 410 | wship | H | 0.004 |
| 411 | wship | H | 0.004 |
| 412 | wship | H | 0.004 |
| 413 | wship | H | 0.004 |
| 414 | wship | H | 0.004 |
| 415 | wship | H | 0.004 |
| 416 | wship | H | 0.004 |
| 417 | wship | H | 0.004 |
| 418 | wship | H | 0.004 |
| 419 | wship | H | 0.004 |
| 420 | wship | H | 0.004 |
| 421 | wship | H | 0.004 |
| 422 | wship | H | 0.004 |
| 423 | wship | H | 0.004 |
| 424 | wship | H | 0.004 |
| 425 | wship | H | 0.004 |
| 426 | wship | H | 0.004 |
| 427 | wship | H | 0.004 |
| 428 | wship | H | 0.004 |
| 429 | wship | H | 0.004 |
| 430 | wship | H | 0.004 |
| 431 | wship | H | 0.004 |
| 432 | wship | H | 0.004 |
| 433 | wship | H | 0.004 |
| 434 | wship | H | 0.004 |
| 435 | wship | H | 0.004 |
| 436 | wship | H | 0.004 |
| 437 | wship | H | 0.004 |
| 438 | wship | H | 0.004 |
| 439 | wship | H | 0.004 |
| 440 | wship | H | 0.004 |
| 441 | wship | H | 0.004 |
| 442 | wship | H | 0.004 |
| 443 | wship | H | 0.004 |
| 444 | wship | H | 0.004 |
| 445 | wship | H | 0.004 |
| 446 | wship | H | 0.004 |
| 447 | wship | H | 0.004 |
| 448 | wship | H | 0.004 |
| 449 | wship | H | 0.004 |
| 450 | wship | H | 0.004 |
| 451 | wship | H | 0.004 |
| 452 | wship | H | 0.004 |
| 453 | wship | H | 0.004 |
| 454 | wship | H | 0.004 |
| 455 | wship | H | 0.004 |
| 456 | wship | H | 0.004 |
| 457 | wship | H | 0.004 |
| 458 | wship | H | 0.004 |
| 459 | wship | H | 0.004 |
| 460 | wship | H | 0.004 |
| 461 | wship | H | 0.004 |
| 462 | wship | H | 0.004 |
| 463 | wship | H | 0.004 |
| 464 | wship | H | 0.004 |
| 465 | wship | H | 0.004 |
| 466 | wship | H | 0.004 |
| 467 | wship | H | 0.004 |
| 468 | wship | H | 0.004 |
| 469 | wship | H | 0.004 |
| 470 | wship | H | 0.004 |
| 471 | wship | H | 0.004 |
| 472 | wship | H | 0.004 |
| 473 | wship | H | 0.004 |
| 474 | wship | H | 0.004 |
| 475 | wship | H | 0.004 |
| 476 | wship | H | 0.004 |
| 477 | wship | H | 0.004 |
| 478 | wship | H | 0.004 |
| 479 | wship | H | 0.004 |
| 480 | wship | H | 0.004 |
| 481 | wship | H | 0.004 |
| 482 | wship | H | 0.004 |
| 483 | wship | H | 0.004 |
| 484 | wship | H | 0.004 |
| 485 | wship | H | 0.004 |
| 486 | wship | H | 0.004 |
| 487 | wship | H | 0.004 |
| 488 | wship | H | 0.004 |
| 489 | wship | H | 0.004 |
| 490 | wship | H | 0.004 |
| 491 | wship | H | 0.004 |
| 492 | wship | H | 0.004 |
| 493 | wship | H | 0.004 |
| 494 | wship | H | 0.004 |
| 495 | wship | H | 0.004 |
| 496 | wship | H | 0.004 |
| 497 | wship | H | 0.004 |
| 498 | wship | H | 0.004 |
| 499 | wship | H | 0.004 |
| 500 | wship | H | 0.004 |
| 501 | wship | H | 0.004 |
| 502 | wship | H | 0.004 |
| 503 | wship | H | 0.004 |
| 504 | wship | H | 0.004 |
| 505 | wship | H | 0.004 |
| 506 | wship | H | 0.004 |
| 507 | wship | H | 0.004 |
| 508 | wship | H | 0.004 |
| 509 | wship | H | 0.004 |
| 510 | wship | H | 0.004 |
| 511 | wship | H | 0.004 |
| 512 | wship | H | 0.004 |
| 513 | wship | H | 0.004 |
| 514 | wship | H | 0.004 |
| 515 | wship | H | 0.004 |
| 516 | wship | H | 0.004 |
| 517 | wship | H | 0.004 |
| 518 | wship | H | 0.004 |
| 519 | wship | H | 0.004 |
| 520 | wship | H | 0.004 |
| 521 | wship | H | 0.004 |
| 1 | private | O | 0.007 |
| 2 | private | O | 0.007 |
| 3 | private | O | 0.007 |
| 4 | private | O | 0.007 |
| 5 | private | O | 0.007 |
| 6 | private | O | 0.007 |
| 7 | private | O | 0.007 |
| 8 | private | O | 0.007 |
| 9 | private | O | 0.007 |
| 10 | private | O | 0.007 |
| 11 | private | O | 0.007 |
| 12 | private | O | 0.007 |
| 13 | private | O | 0.007 |
| 14 | private | O | 0.007 |
| 15 | private | O | 0.007 |
| 16 | private | O | 0.007 |
| 17 | private | O | 0.007 |
| 18 | private | O | 0.007 |
| 19 | private | O | 0.007 |
| 20 | private | O | 0.007 |
| 21 | private | O | 0.007 |
| 22 | private | O | 0.007 |
| 23 | private | O | 0.007 |
| 24 | private | O | 0.007 |
| 25 | private | O | 0.007 |
| 26 | private | O | 0.007 |
| 27 | private | O | 0.007 |
| 28 | private | O | 0.007 |
| 29 | private | O | 0.007 |
| 30 | private | O | 0.007 |
| 31 | private | O | 0.007 |
| 32 | private | O | 0.007 |
| 33 | private | O | 0.007 |
| 34 | private | O | 0.007 |
| 35 | private | O | 0.007 |
| 36 | private | O | 0.007 |
| 37 | private | O | 0.007 |
| 38 | private | O | 0.007 |
| 39 | private | O | 0.007 |
| 40 | private | O | 0.007 |
| 41 | private | O | 0.007 |
| 42 | private | O | 0.007 |
| 43 | private | O | 0.007 |
| 44 | private | O | 0.007 |
| 45 | private | O | 0.007 |
| 46 | private | O | 0.007 |
| 47 | private | O | 0.007 |
| 48 | private | O | 0.007 |
| 49 | private | O | 0.007 |
| 50 | private | O | 0.007 |
| 51 | private | O | 0.007 |
| 52 | private | O | 0.007 |
| 53 | private | O | 0.007 |
| 54 | private | O | 0.007 |
| 55 | private | O | 0.007 |
| 56 | private | O | 0.007 |
| 57 | private | O | 0.007 |
| 58 | private | O | 0.007 |
| 59 | private | O | 0.007 |
| 60 | private | O | 0.007 |
| 61 | private | O | 0.007 |
| 62 | private | O | 0.007 |
| 63 | private | O | 0.007 |
| 64 | private | O | 0.007 |
| 65 | private | O | 0.007 |
| 66 | private | O | 0.007 |
| 67 | private | O | 0.007 |
| 68 | private | O | 0.007 |
| 69 | private | O | 0.007 |
| 70 | private | O | 0.007 |
| 71 | private | O | 0.007 |
| 72 | private | O | 0.007 |
| 73 | private | O | 0.007 |
| 74 | private | O | 0.007 |
| 75 | private | O | 0.007 |
| 76 | private | O | 0.007 |
| 77 | private | O | 0.007 |
| 78 | private | O | 0.007 |
| 79 | private | O | 0.007 |
| 80 | private | O | 0.007 |
| 81 | private | O | 0.007 |
| 82 | private | O | 0.007 |
| 83 | private | O | 0.007 |
| 84 | private | O | 0.007 |
| 85 | private | O | 0.007 |
| 86 | private | O | 0.007 |
| 87 | private | O | 0.007 |
| 88 | private | O | 0.007 |
| 89 | private | O | 0.007 |
| 90 | private | O | 0.007 |
| 91 | private | O | 0.007 |
| 92 | private | O | 0.007 |
| 93 | private | O | 0.007 |
| 94 | private | O | 0.007 |
| 95 | private | O | 0.007 |
| 96 | private | O | 0.007 |
| 97 | private | O | 0.007 |
| 98 | private | O | 0.007 |
| 99 | private | O | 0.007 |
| 100 | private | O | 0.007 |
| 101 | private | O | 0.007 |
| 102 | private | O | 0.007 |
| 103 | private | O | 0.007 |
| 104 | private | O | 0.007 |
| 105 | private | O | 0.007 |
| 106 | private | O | 0.007 |
| 107 | private | O | 0.007 |
| 108 | private | O | 0.007 |
| 109 | private | O | 0.007 |
| 110 | private | O | 0.007 |
| 111 | private | O | 0.007 |
| 112 | private | O | 0.007 |
| 113 | private | O | 0.007 |
| 114 | private | O | 0.007 |
| 115 | private | O | 0.007 |
| 116 | private | O | 0.007 |
| 117 | private | O | 0.006 |
| 118 | private | O | 0.006 |
| 119 | private | O | 0.006 |
| 120 | private | O | 0.006 |
| 121 | private | O | 0.006 |
| 122 | private | O | 0.006 |
| 123 | private | O | 0.006 |
| 124 | private | O | 0.006 |
| 125 | private | O | 0.006 |
| 126 | private | O | 0.006 |
| 127 | private | O | 0.006 |
| 128 | private | O | 0.006 |
| 129 | private | O | 0.006 |
| 130 | private | O | 0.006 |
| 131 | private | O | 0.006 |
| 132 | private | O | 0.006 |
| 133 | private | O | 0.006 |
| 134 | private | O | 0.006 |
| 135 | private | O | 0.006 |
| 136 | private | O | 0.005 |
| 137 | private | O | 0.005 |
| 138 | private | O | 0.005 |
| 139 | private | O | 0.005 |
| 140 | private | O | 0.005 |
| 141 | private | O | 0.005 |
| 142 | private | O | 0.005 |
| 143 | private | O | 0.005 |
| 144 | private | O | 0.005 |
| 145 | private | O | 0.005 |
| 146 | private | O | 0.005 |
| 147 | private | O | 0.005 |
| 148 | private | O | 0.005 |
| 149 | private | O | 0.005 |
| 150 | private | O | 0.005 |
| 151 | private | O | 0.005 |
| 152 | private | O | 0.005 |
| 153 | private | O | 0.005 |
| 154 | private | O | 0.005 |
| 155 | private | O | 0.005 |
| 156 | private | O | 0.005 |
| 157 | private | O | 0.005 |
| 158 | private | O | 0.005 |
| 159 | private | O | 0.005 |
| 160 | private | O | 0.005 |
| 161 | private | O | 0.005 |
| 162 | private | O | 0.005 |
| 163 | private | O | 0.005 |
| 164 | private | O | 0.005 |
| 165 | private | O | 0.005 |
| 166 | private | O | 0.005 |
| 167 | private | O | 0.005 |
| 168 | private | O | 0.005 |
| 169 | private | O | 0.005 |
| 170 | private | O | 0.005 |
| 171 | private | O | 0.005 |
| 172 | private | O | 0.005 |
| 173 | private | O | 0.005 |
| 174 | private | O | 0.005 |
| 175 | private | O | 0.005 |
| 176 | private | O | 0.005 |
| 177 | private | O | 0.005 |
| 178 | private | O | 0.005 |
| 179 | private | O | 0.005 |
| 180 | private | O | 0.005 |
| 181 | private | O | 0.005 |
| 182 | private | O | 0.005 |
| 183 | private | O | 0.005 |
| 184 | private | O | 0.005 |
| 185 | private | O | 0.005 |
| 186 | private | O | 0.005 |
| 187 | private | O | 0.005 |
| 188 | private | O | 0.005 |
| 189 | private | O | 0.005 |
| 190 | private | O | 0.005 |
| 191 | private | O | 0.005 |
| 192 | private | O | 0.005 |
| 193 | private | O | 0.005 |
| 194 | private | O | 0.005 |
| 195 | private | O | 0.005 |
| 196 | private | O | 0.005 |
| 197 | private | O | 0.005 |
| 198 | private | O | 0.005 |
| 199 | private | O | 0.005 |
| 200 | private | O | 0.005 |
| 201 | private | O | 0.005 |
| 202 | private | O | 0.005 |
| 203 | private | O | 0.005 |
| 204 | private | O | 0.005 |
| 205 | private | O | 0.005 |
| 206 | private | O | 0.005 |
| 207 | private | O | 0.004 |
| 208 | private | O | 0.004 |
| 209 | private | O | 0.004 |
| 210 | private | O | 0.004 |
| 211 | private | O | 0.004 |
| 212 | private | O | 0.004 |
| 213 | private | O | 0.004 |
| 214 | private | O | 0.004 |
| 215 | private | O | 0.004 |
| 216 | private | O | 0.004 |
| 217 | private | O | 0.004 |
| 218 | private | O | 0.004 |
| 219 | private | O | 0.004 |
| 220 | private | O | 0.004 |
| 221 | private | O | 0.004 |
| 222 | private | O | 0.004 |
| 223 | private | O | 0.004 |
| 224 | private | O | 0.004 |
| 225 | private | O | 0.004 |
| 226 | private | O | 0.004 |
| 227 | private | O | 0.004 |
| 228 | private | O | 0.004 |
| 229 | private | O | 0.004 |
| 230 | private | O | 0.004 |
| 231 | private | O | 0.004 |
| 232 | private | O | 0.004 |
| 233 | private | O | 0.004 |
| 234 | private | O | 0.004 |
| 235 | private | O | 0.004 |
| 236 | private | O | 0.004 |
| 237 | private | O | 0.004 |
| 238 | private | O | 0.004 |
| 239 | private | O | 0.004 |
| 240 | private | O | 0.004 |
| 241 | private | O | 0.004 |
| 242 | private | O | 0.004 |
| 243 | private | O | 0.004 |
| 244 | private | O | 0.004 |
| 245 | private | O | 0.004 |
| 246 | private | O | 0.004 |
| 247 | private | O | 0.004 |
| 248 | private | O | 0.004 |
| 249 | private | O | 0.004 |
| 250 | private | O | 0.004 |
| 251 | private | O | 0.004 |
| 252 | private | O | 0.004 |
| 253 | private | O | 0.004 |
| 254 | private | O | 0.004 |
| 255 | private | O | 0.004 |
| 256 | private | O | 0.004 |
| 257 | private | O | 0.004 |
| 258 | private | O | 0.004 |
| 259 | private | O | 0.004 |
| 260 | private | O | 0.004 |
| 261 | private | O | 0.004 |
| 262 | private | O | 0.004 |
| 263 | private | O | 0.004 |
| 264 | private | O | 0.004 |
| 265 | private | O | 0.004 |
| 266 | private | O | 0.004 |
| 267 | private | O | 0.004 |
| 268 | private | O | 0.004 |
| 269 | private | O | 0.004 |
| 270 | private | O | 0.004 |
| 271 | private | O | 0.004 |
| 272 | private | O | 0.004 |
| 273 | private | O | 0.004 |
| 274 | private | O | 0.004 |
| 275 | private | O | 0.004 |
| 276 | private | O | 0.004 |
| 277 | private | O | 0.004 |
| 278 | private | O | 0.004 |
| 279 | private | O | 0.004 |
| 280 | private | O | 0.004 |
| 281 | private | O | 0.004 |
| 282 | private | O | 0.004 |
| 283 | private | O | 0.004 |
| 284 | private | O | 0.004 |
| 285 | private | O | 0.004 |
| 286 | private | O | 0.004 |
| 287 | private | O | 0.004 |
| 288 | private | O | 0.004 |
| 289 | private | O | 0.004 |
| 290 | private | O | 0.004 |
| 291 | private | O | 0.004 |
| 292 | private | O | 0.004 |
| 293 | private | O | 0.004 |
| 294 | private | O | 0.004 |
| 295 | private | O | 0.004 |
| 296 | private | O | 0.004 |
| 297 | private | O | 0.004 |
| 298 | private | O | 0.004 |
| 299 | private | O | 0.004 |
| 300 | private | O | 0.004 |
| 301 | private | O | 0.004 |
| 302 | private | O | 0.004 |
| 303 | private | O | 0.004 |
| 304 | private | O | 0.004 |
| 305 | private | O | 0.004 |
| 306 | private | O | 0.004 |
| 307 | private | O | 0.004 |
| 308 | private | O | 0.004 |
| 309 | private | O | 0.004 |
| 310 | private | O | 0.004 |
| 311 | private | O | 0.004 |
| 312 | private | O | 0.004 |
| 313 | private | O | 0.004 |
| 314 | private | O | 0.004 |
| 315 | private | O | 0.004 |
| 316 | private | O | 0.004 |
| 317 | private | O | 0.004 |
| 318 | private | O | 0.004 |
| 319 | private | O | 0.004 |
| 320 | private | O | 0.004 |
| 321 | private | O | 0.004 |
| 322 | private | O | 0.004 |
| 323 | private | O | 0.004 |
| 324 | private | O | 0.004 |
| 325 | private | O | 0.004 |
| 326 | private | O | 0.004 |
| 327 | private | O | 0.004 |
| 328 | private | O | 0.004 |
| 329 | private | O | 0.004 |
| 330 | private | O | 0.004 |
| 331 | private | O | 0.004 |
| 332 | private | O | 0.004 |
| 333 | private | O | 0.004 |
| 334 | private | O | 0.004 |
| 335 | private | O | 0.004 |
| 336 | private | O | 0.004 |
| 337 | private | O | 0.004 |
| 338 | private | O | 0.004 |
| 339 | private | O | 0.004 |
| 340 | private | O | 0.004 |
| 341 | private | O | 0.004 |
| 342 | private | O | 0.004 |
| 343 | private | O | 0.004 |
| 344 | private | O | 0.004 |
| 345 | private | O | 0.004 |
| 346 | private | O | 0.004 |
| 347 | private | O | 0.004 |
| 348 | private | O | 0.004 |
| 349 | private | O | 0.004 |
| 350 | private | O | 0.004 |
| 351 | private | O | 0.004 |
| 352 | private | O | 0.004 |
| 353 | private | O | 0.004 |
| 354 | private | O | 0.004 |
| 355 | private | O | 0.004 |
| 356 | private | O | 0.004 |
| 357 | private | O | 0.004 |
| 358 | private | O | 0.004 |
| 359 | private | O | 0.004 |
| 360 | private | O | 0.004 |
| 361 | private | O | 0.004 |
| 362 | private | O | 0.004 |
| 363 | private | O | 0.004 |
| 364 | private | O | 0.004 |
| 365 | private | O | 0.004 |
| 366 | private | O | 0.004 |
| 367 | private | O | 0.004 |
| 368 | private | O | 0.004 |
| 369 | private | O | 0.004 |
| 370 | private | O | 0.004 |
| 371 | private | O | 0.004 |
| 372 | private | O | 0.004 |
| 373 | private | O | 0.004 |
| 374 | private | O | 0.004 |
| 375 | private | O | 0.004 |
| 376 | private | O | 0.004 |
| 377 | private | O | 0.004 |
| 378 | private | O | 0.004 |
| 379 | private | O | 0.004 |
| 380 | private | O | 0.004 |
| 381 | private | O | 0.004 |
| 382 | private | O | 0.004 |
| 383 | private | O | 0.004 |
| 384 | private | O | 0.004 |
| 385 | private | O | 0.004 |
| 386 | private | O | 0.004 |
| 387 | private | O | 0.004 |
| 388 | private | O | 0.004 |
| 389 | private | O | 0.004 |
| 390 | private | O | 0.004 |
| 391 | private | O | 0.004 |
| 392 | private | O | 0.004 |
| 393 | private | O | 0.004 |
| 394 | private | O | 0.004 |
| 395 | private | O | 0.004 |
| 396 | private | O | 0.004 |
| 397 | private | O | 0.004 |
| 398 | private | O | 0.004 |
| 399 | private | O | 0.004 |
| 400 | private | O | 0.004 |
| 401 | private | O | 0.004 |
| 402 | private | O | 0.004 |
| 403 | private | O | 0.004 |
| 404 | private | O | 0.004 |
| 405 | private | O | 0.004 |
| 406 | private | O | 0.004 |
| 407 | private | O | 0.004 |
| 408 | private | O | 0.004 |
| 409 | private | O | 0.004 |
| 410 | private | O | 0.004 |
| 411 | private | O | 0.004 |
| 412 | private | O | 0.004 |
| 413 | private | O | 0.004 |
| 414 | private | O | 0.004 |
| 415 | private | O | 0.004 |
| 416 | private | O | 0.004 |
| 417 | private | O | 0.004 |
| 418 | private | O | 0.004 |
| 419 | private | O | 0.004 |
| 420 | private | O | 0.004 |
| 421 | private | O | 0.004 |
| 422 | private | O | 0.004 |
| 423 | private | O | 0.004 |
| 424 | private | O | 0.004 |
| 425 | private | O | 0.004 |
| 426 | private | O | 0.004 |
| 427 | private | O | 0.004 |
| 428 | private | O | 0.004 |
| 429 | private | O | 0.004 |
| 430 | private | O | 0.004 |
| 431 | private | O | 0.004 |
| 432 | private | O | 0.004 |
| 433 | private | O | 0.004 |
| 434 | private | O | 0.004 |
| 435 | private | O | 0.004 |
| 436 | private | O | 0.004 |
| 437 | private | O | 0.004 |
| 438 | private | O | 0.004 |
| 439 | private | O | 0.004 |
| 440 | private | O | 0.004 |
| 441 | private | O | 0.004 |
| 442 | private | O | 0.004 |
| 443 | private | O | 0.004 |
| 444 | private | O | 0.004 |
| 445 | private | O | 0.004 |
| 446 | private | O | 0.004 |
| 447 | private | O | 0.004 |
| 448 | private | O | 0.004 |
| 449 | private | O | 0.004 |
| 450 | private | O | 0.004 |
| 451 | private | O | 0.004 |
| 452 | private | O | 0.004 |
| 453 | private | O | 0.004 |
| 454 | private | O | 0.004 |
| 455 | private | O | 0.004 |
| 456 | private | O | 0.004 |
| 457 | private | O | 0.004 |
| 458 | private | O | 0.004 |
| 459 | private | O | 0.004 |
| 460 | private | O | 0.004 |
| 461 | private | O | 0.004 |
| 462 | private | O | 0.004 |
| 463 | private | O | 0.004 |
| 464 | private | O | 0.004 |
| 465 | private | O | 0.004 |
| 466 | private | O | 0.004 |
| 467 | private | O | 0.004 |
| 468 | private | O | 0.004 |
| 469 | private | O | 0.004 |
| 470 | private | O | 0.004 |
| 471 | private | O | 0.004 |
| 472 | private | O | 0.004 |
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| 1 | uninsure | O | 0.007 |
| 2 | uninsure | O | 0.007 |
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| 30 | uninsure | O | 0.007 |
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| 48 | uninsure | O | 0.007 |
| 49 | uninsure | O | 0.007 |
| 50 | uninsure | O | 0.007 |
| 51 | uninsure | O | 0.007 |
| 52 | uninsure | O | 0.007 |
| 53 | uninsure | O | 0.007 |
| 54 | uninsure | O | 0.007 |
| 55 | uninsure | O | 0.007 |
| 56 | uninsure | O | 0.007 |
| 57 | uninsure | O | 0.007 |
| 58 | uninsure | O | 0.007 |
| 59 | uninsure | O | 0.007 |
| 60 | uninsure | O | 0.007 |
| 61 | uninsure | O | 0.007 |
| 62 | uninsure | O | 0.007 |
| 63 | uninsure | O | 0.007 |
| 64 | uninsure | O | 0.007 |
| 65 | uninsure | O | 0.007 |
| 66 | uninsure | O | 0.007 |
| 67 | uninsure | O | 0.007 |
| 68 | uninsure | O | 0.007 |
| 69 | uninsure | O | 0.007 |
| 70 | uninsure | O | 0.007 |
| 71 | uninsure | O | 0.007 |
| 72 | uninsure | O | 0.007 |
| 73 | uninsure | O | 0.007 |
| 74 | uninsure | O | 0.007 |
| 75 | uninsure | O | 0.007 |
| 76 | uninsure | O | 0.007 |
| 77 | uninsure | O | 0.007 |
| 78 | uninsure | O | 0.007 |
| 79 | uninsure | O | 0.007 |
| 80 | uninsure | O | 0.007 |
| 81 | uninsure | O | 0.007 |
| 82 | uninsure | O | 0.007 |
| 83 | uninsure | O | 0.007 |
| 84 | uninsure | O | 0.007 |
| 85 | uninsure | O | 0.007 |
| 86 | uninsure | O | 0.007 |
| 87 | uninsure | O | 0.007 |
| 88 | uninsure | O | 0.007 |
| 89 | uninsure | O | 0.007 |
| 90 | uninsure | O | 0.007 |
| 91 | uninsure | O | 0.007 |
| 92 | uninsure | O | 0.007 |
| 93 | uninsure | O | 0.007 |
| 94 | uninsure | O | 0.007 |
| 95 | uninsure | O | 0.007 |
| 96 | uninsure | O | 0.007 |
| 97 | uninsure | O | 0.007 |
| 98 | uninsure | O | 0.007 |
| 99 | uninsure | O | 0.007 |
| 100 | uninsure | O | 0.007 |
| 101 | uninsure | O | 0.007 |
| 102 | uninsure | O | 0.007 |
| 103 | uninsure | O | 0.007 |
| 104 | uninsure | O | 0.007 |
| 105 | uninsure | O | 0.007 |
| 106 | uninsure | O | 0.007 |
| 107 | uninsure | O | 0.007 |
| 108 | uninsure | O | 0.007 |
| 109 | uninsure | O | 0.007 |
| 110 | uninsure | O | 0.007 |
| 111 | uninsure | O | 0.007 |
| 112 | uninsure | O | 0.007 |
| 113 | uninsure | O | 0.007 |
| 114 | uninsure | O | 0.007 |
| 115 | uninsure | O | 0.007 |
| 116 | uninsure | O | 0.007 |
| 117 | uninsure | O | 0.007 |
| 118 | uninsure | O | 0.007 |
| 119 | uninsure | O | 0.007 |
| 120 | uninsure | O | 0.007 |
| 121 | uninsure | O | 0.007 |
| 122 | uninsure | O | 0.007 |
| 123 | uninsure | O | 0.007 |
| 124 | uninsure | O | 0.007 |
| 125 | uninsure | O | 0.007 |
| 126 | uninsure | O | 0.007 |
| 127 | uninsure | O | 0.007 |
| 128 | uninsure | O | 0.007 |
| 129 | uninsure | O | 0.007 |
| 130 | uninsure | O | 0.007 |
| 131 | uninsure | O | 0.007 |
| 132 | uninsure | O | 0.007 |
| 133 | uninsure | O | 0.007 |
| 134 | uninsure | O | 0.007 |
| 135 | uninsure | O | 0.007 |
| 136 | uninsure | O | 0.007 |
| 137 | uninsure | O | 0.007 |
| 138 | uninsure | O | 0.007 |
| 139 | uninsure | O | 0.007 |
| 140 | uninsure | O | 0.007 |
| 141 | uninsure | O | 0.007 |
| 142 | uninsure | O | 0.007 |
| 143 | uninsure | O | 0.007 |
| 144 | uninsure | O | 0.007 |
| 145 | uninsure | O | 0.007 |
| 146 | uninsure | O | 0.007 |
| 147 | uninsure | O | 0.007 |
| 148 | uninsure | O | 0.007 |
| 149 | uninsure | O | 0.007 |
| 150 | uninsure | O | 0.007 |
| 151 | uninsure | O | 0.007 |
| 152 | uninsure | O | 0.007 |
| 153 | uninsure | O | 0.007 |
| 154 | uninsure | O | 0.007 |
| 155 | uninsure | O | 0.007 |
| 156 | uninsure | O | 0.007 |
| 157 | uninsure | O | 0.007 |
| 158 | uninsure | O | 0.007 |
| 159 | uninsure | O | 0.007 |
| 160 | uninsure | O | 0.007 |
| 161 | uninsure | O | 0.007 |
| 162 | uninsure | O | 0.007 |
| 163 | uninsure | O | 0.007 |
| 164 | uninsure | O | 0.007 |
| 165 | uninsure | O | 0.007 |
| 166 | uninsure | O | 0.007 |
| 167 | uninsure | O | 0.007 |
| 168 | uninsure | O | 0.007 |
| 169 | uninsure | O | 0.007 |
| 170 | uninsure | O | 0.007 |
| 171 | uninsure | O | 0.007 |
| 172 | uninsure | O | 0.007 |
| 173 | uninsure | O | 0.007 |
| 174 | uninsure | O | 0.007 |
| 175 | uninsure | O | 0.007 |
| 176 | uninsure | O | 0.007 |
| 177 | uninsure | O | 0.007 |
| 178 | uninsure | O | 0.007 |
| 179 | uninsure | O | 0.007 |
| 180 | uninsure | O | 0.007 |
| 181 | uninsure | O | 0.007 |
| 182 | uninsure | O | 0.007 |
| 183 | uninsure | O | 0.007 |
| 184 | uninsure | O | 0.007 |
| 185 | uninsure | O | 0.007 |
| 186 | uninsure | O | 0.007 |
| 187 | uninsure | O | 0.007 |
| 188 | uninsure | O | 0.007 |
| 189 | uninsure | O | 0.007 |
| 190 | uninsure | O | 0.007 |
| 191 | uninsure | O | 0.007 |
| 192 | uninsure | O | 0.007 |
| 193 | uninsure | O | 0.007 |
| 194 | uninsure | O | 0.007 |
| 195 | uninsure | O | 0.007 |
| 196 | uninsure | O | 0.007 |
| 197 | uninsure | O | 0.007 |
| 198 | uninsure | O | 0.007 |
| 199 | uninsure | O | 0.007 |
| 200 | uninsure | O | 0.007 |
| 201 | uninsure | O | 0.007 |
| 202 | uninsure | O | 0.007 |
| 203 | uninsure | O | 0.007 |
| 204 | uninsure | O | 0.007 |
| 205 | uninsure | O | 0.007 |
| 206 | uninsure | O | 0.007 |
| 207 | uninsure | O | 0.007 |
| 208 | uninsure | O | 0.007 |
| 209 | uninsure | O | 0.007 |
| 210 | uninsure | O | 0.007 |
| 211 | uninsure | O | 0.007 |
| 212 | uninsure | O | 0.007 |
| 213 | uninsure | O | 0.007 |
| 214 | uninsure | O | 0.007 |
| 215 | uninsure | O | 0.007 |
| 216 | uninsure | O | 0.007 |
| 217 | uninsure | O | 0.007 |
| 218 | uninsure | O | 0.007 |
| 219 | uninsure | O | 0.007 |
| 220 | uninsure | O | 0.007 |
| 221 | uninsure | O | 0.007 |
| 222 | uninsure | O | 0.007 |
| 223 | uninsure | O | 0.007 |
| 224 | uninsure | O | 0.007 |
| 225 | uninsure | O | 0.007 |
| 226 | uninsure | O | 0.007 |
| 227 | uninsure | O | 0.007 |
| 228 | uninsure | O | 0.007 |
| 229 | uninsure | O | 0.007 |
| 230 | uninsure | O | 0.007 |
| 231 | uninsure | O | 0.007 |
| 232 | uninsure | O | 0.007 |
| 233 | uninsure | O | 0.007 |
| 234 | uninsure | O | 0.007 |
| 235 | uninsure | O | 0.007 |
| 236 | uninsure | O | 0.007 |
| 237 | uninsure | O | 0.007 |
| 238 | uninsure | O | 0.007 |
| 239 | uninsure | O | 0.007 |
| 240 | uninsure | O | 0.007 |
| 241 | uninsure | O | 0.007 |
| 242 | uninsure | O | 0.007 |
| 243 | uninsure | O | 0.007 |
| 244 | uninsure | O | 0.007 |
| 245 | uninsure | O | 0.007 |
| 246 | uninsure | O | 0.007 |
| 247 | uninsure | O | 0.007 |
| 248 | uninsure | O | 0.007 |
| 249 | uninsure | O | 0.007 |
| 250 | uninsure | O | 0.007 |
| 251 | uninsure | O | 0.007 |
| 252 | uninsure | O | 0.007 |
| 253 | uninsure | O | 0.007 |
| 254 | uninsure | O | 0.007 |
| 255 | uninsure | O | 0.007 |
| 256 | uninsure | O | 0.007 |
| 257 | uninsure | O | 0.007 |
| 258 | uninsure | O | 0.007 |
| 259 | uninsure | O | 0.007 |
| 260 | uninsure | O | 0.007 |
| 261 | uninsure | O | 0.007 |
| 262 | uninsure | O | 0.007 |
| 263 | uninsure | O | 0.007 |
| 264 | uninsure | O | 0.007 |
| 265 | uninsure | O | 0.007 |
| 266 | uninsure | O | 0.007 |
| 267 | uninsure | O | 0.007 |
| 268 | uninsure | O | 0.007 |
| 269 | uninsure | O | 0.007 |
| 270 | uninsure | O | 0.007 |
| 271 | uninsure | O | 0.007 |
| 272 | uninsure | O | 0.007 |
| 273 | uninsure | O | 0.007 |
| 274 | uninsure | O | 0.007 |
| 275 | uninsure | O | 0.007 |
| 276 | uninsure | O | 0.007 |
| 277 | uninsure | O | 0.007 |
| 278 | uninsure | O | 0.007 |
| 279 | uninsure | O | 0.007 |
| 280 | uninsure | O | 0.007 |
| 281 | uninsure | O | 0.007 |
| 282 | uninsure | O | 0.007 |
| 283 | uninsure | O | 0.007 |
| 284 | uninsure | O | 0.007 |
| 285 | uninsure | O | 0.007 |
| 286 | uninsure | O | 0.007 |
| 287 | uninsure | O | 0.007 |
| 288 | uninsure | O | 0.007 |
| 289 | uninsure | O | 0.007 |
| 290 | uninsure | O | 0.007 |
| 291 | uninsure | O | 0.007 |
| 292 | uninsure | O | 0.007 |
| 293 | uninsure | O | 0.007 |
| 294 | uninsure | O | 0.007 |
| 295 | uninsure | O | 0.007 |
| 296 | uninsure | O | 0.007 |
| 297 | uninsure | O | 0.007 |
| 298 | uninsure | O | 0.007 |
| 299 | uninsure | O | 0.006 |
| 300 | uninsure | O | 0.006 |
| 301 | uninsure | O | 0.006 |
| 302 | uninsure | O | 0.006 |
| 303 | uninsure | O | 0.006 |
| 304 | uninsure | O | 0.006 |
| 305 | uninsure | O | 0.006 |
| 306 | uninsure | O | 0.006 |
| 307 | uninsure | O | 0.006 |
| 308 | uninsure | O | 0.006 |
| 309 | uninsure | O | 0.006 |
| 310 | uninsure | O | 0.006 |
| 311 | uninsure | O | 0.006 |
| 312 | uninsure | O | 0.006 |
| 313 | uninsure | O | 0.006 |
| 314 | uninsure | O | 0.006 |
| 315 | uninsure | O | 0.006 |
| 316 | uninsure | O | 0.006 |
| 317 | uninsure | O | 0.006 |
| 318 | uninsure | O | 0.006 |
| 319 | uninsure | O | 0.006 |
| 320 | uninsure | O | 0.006 |
| 321 | uninsure | O | 0.006 |
| 322 | uninsure | O | 0.006 |
| 323 | uninsure | O | 0.006 |
| 324 | uninsure | O | 0.006 |
| 325 | uninsure | O | 0.006 |
| 326 | uninsure | O | 0.006 |
| 327 | uninsure | O | 0.006 |
| 328 | uninsure | O | 0.006 |
| 329 | uninsure | O | 0.006 |
| 330 | uninsure | O | 0.006 |
| 331 | uninsure | O | 0.006 |
| 332 | uninsure | O | 0.006 |
| 333 | uninsure | O | 0.006 |
| 334 | uninsure | O | 0.006 |
| 335 | uninsure | O | 0.006 |
| 336 | uninsure | O | 0.006 |
| 337 | uninsure | O | 0.006 |
| 338 | uninsure | O | 0.006 |
| 339 | uninsure | O | 0.006 |
| 340 | uninsure | O | 0.006 |
| 341 | uninsure | O | 0.006 |
| 342 | uninsure | O | 0.006 |
| 343 | uninsure | O | 0.006 |
| 344 | uninsure | O | 0.006 |
| 345 | uninsure | O | 0.006 |
| 346 | uninsure | O | 0.006 |
| 347 | uninsure | O | 0.006 |
| 348 | uninsure | O | 0.006 |
| 349 | uninsure | O | 0.006 |
| 350 | uninsure | O | 0.006 |
| 351 | uninsure | O | 0.006 |
| 352 | uninsure | O | 0.006 |
| 353 | uninsure | O | 0.006 |
| 354 | uninsure | O | 0.006 |
| 355 | uninsure | O | 0.006 |
| 356 | uninsure | O | 0.006 |
| 357 | uninsure | O | 0.006 |
| 358 | uninsure | O | 0.006 |
| 359 | uninsure | O | 0.006 |
| 360 | uninsure | O | 0.006 |
| 361 | uninsure | O | 0.006 |
| 362 | uninsure | O | 0.006 |
| 363 | uninsure | O | 0.006 |
| 364 | uninsure | O | 0.006 |
| 365 | uninsure | O | 0.006 |
| 366 | uninsure | O | 0.006 |
| 367 | uninsure | O | 0.006 |
| 368 | uninsure | O | 0.006 |
| 369 | uninsure | O | 0.006 |
| 370 | uninsure | O | 0.006 |
| 371 | uninsure | O | 0.006 |
| 372 | uninsure | O | 0.006 |
| 373 | uninsure | O | 0.006 |
| 374 | uninsure | O | 0.006 |
| 375 | uninsure | O | 0.006 |
| 376 | uninsure | O | 0.006 |
| 377 | uninsure | O | 0.006 |
| 378 | uninsure | O | 0.006 |
| 379 | uninsure | O | 0.006 |
| 380 | uninsure | O | 0.006 |
| 381 | uninsure | O | 0.006 |
| 382 | uninsure | O | 0.006 |
| 383 | uninsure | O | 0.006 |
| 384 | uninsure | O | 0.006 |
| 385 | uninsure | O | 0.006 |
| 386 | uninsure | O | 0.006 |
| 387 | uninsure | O | 0.006 |
| 388 | uninsure | O | 0.006 |
| 389 | uninsure | O | 0.006 |
| 390 | uninsure | O | 0.006 |
| 391 | uninsure | O | 0.006 |
| 392 | uninsure | O | 0.006 |
| 393 | uninsure | O | 0.006 |
| 394 | uninsure | O | 0.006 |
| 395 | uninsure | O | 0.006 |
| 396 | uninsure | O | 0.006 |
| 397 | uninsure | O | 0.006 |
| 398 | uninsure | O | 0.006 |
| 399 | uninsure | O | 0.006 |
| 400 | uninsure | O | 0.006 |
| 401 | uninsure | O | 0.006 |
| 402 | uninsure | O | 0.006 |
| 403 | uninsure | O | 0.006 |
| 404 | uninsure | O | 0.006 |
| 405 | uninsure | O | 0.006 |
| 406 | uninsure | O | 0.006 |
| 407 | uninsure | O | 0.006 |
| 408 | uninsure | O | 0.006 |
| 409 | uninsure | O | 0.006 |
| 410 | uninsure | O | 0.006 |
| 411 | uninsure | O | 0.006 |
| 412 | uninsure | O | 0.006 |
| 413 | uninsure | O | 0.006 |
| 414 | uninsure | O | 0.006 |
| 415 | uninsure | O | 0.006 |
| 416 | uninsure | O | 0.006 |
| 417 | uninsure | O | 0.006 |
| 418 | uninsure | O | 0.006 |
| 419 | uninsure | O | 0.006 |
| 420 | uninsure | O | 0.006 |
| 421 | uninsure | O | 0.006 |
| 422 | uninsure | O | 0.006 |
| 423 | uninsure | O | 0.006 |
| 424 | uninsure | O | 0.006 |
| 425 | uninsure | O | 0.006 |
| 426 | uninsure | O | 0.006 |
| 427 | uninsure | O | 0.006 |
| 428 | uninsure | O | 0.006 |
| 429 | uninsure | O | 0.006 |
| 430 | uninsure | O | 0.006 |
| 431 | uninsure | O | 0.006 |
| 432 | uninsure | O | 0.006 |
| 433 | uninsure | O | 0.006 |
| 434 | uninsure | O | 0.006 |
| 435 | uninsure | O | 0.006 |
| 436 | uninsure | O | 0.006 |
| 437 | uninsure | O | 0.006 |
| 438 | uninsure | O | 0.006 |
| 439 | uninsure | O | 0.006 |
| 440 | uninsure | O | 0.006 |
| 441 | uninsure | O | 0.006 |
| 442 | uninsure | O | 0.006 |
| 443 | uninsure | O | 0.006 |
| 444 | uninsure | O | 0.006 |
| 445 | uninsure | O | 0.006 |
| 446 | uninsure | O | 0.006 |
| 447 | uninsure | O | 0.006 |
| 448 | uninsure | O | 0.006 |
| 449 | uninsure | O | 0.006 |
| 450 | uninsure | O | 0.006 |
| 451 | uninsure | O | 0.006 |
| 452 | uninsure | O | 0.006 |
| 453 | uninsure | O | 0.006 |
| 454 | uninsure | O | 0.006 |
| 455 | uninsure | O | 0.006 |
| 456 | uninsure | O | 0.006 |
| 457 | uninsure | O | 0.006 |
| 458 | uninsure | O | 0.006 |
| 459 | uninsure | O | 0.006 |
| 460 | uninsure | O | 0.006 |
| 461 | uninsure | O | 0.005 |
| 462 | uninsure | O | 0.005 |
| 463 | uninsure | O | 0.005 |
| 464 | uninsure | O | 0.005 |
| 465 | uninsure | O | 0.005 |
| 466 | uninsure | O | 0.005 |
| 467 | uninsure | O | 0.005 |
| 468 | uninsure | O | 0.005 |
| 469 | uninsure | O | 0.005 |
| 470 | uninsure | O | 0.005 |
| 471 | uninsure | O | 0.005 |
| 472 | uninsure | O | 0.005 |
| 473 | uninsure | O | 0.005 |
| 474 | uninsure | O | 0.005 |
| 475 | uninsure | O | 0.005 |
| 476 | uninsure | O | 0.005 |
| 477 | uninsure | O | 0.005 |
| 478 | uninsure | O | 0.005 |
| 479 | uninsure | O | 0.005 |
| 480 | uninsure | O | 0.005 |
| 481 | uninsure | O | 0.005 |
| 482 | uninsure | O | 0.005 |
| 483 | uninsure | O | 0.005 |
| 484 | uninsure | O | 0.005 |
| 485 | uninsure | O | 0.005 |
| 486 | uninsure | O | 0.005 |
| 487 | uninsure | O | 0.005 |
| 488 | uninsure | O | 0.005 |
| 489 | uninsure | O | 0.005 |
| 490 | uninsure | O | 0.005 |
| 491 | uninsure | O | 0.005 |
| 492 | uninsure | O | 0.005 |
| 493 | uninsure | O | 0.005 |
| 494 | uninsure | O | 0.005 |
| 495 | uninsure | O | 0.005 |
| 496 | uninsure | O | 0.005 |
| 497 | uninsure | O | 0.005 |
| 498 | uninsure | O | 0.005 |
| 499 | uninsure | O | 0.005 |
| 500 | uninsure | O | 0.005 |
| 501 | uninsure | O | 0.005 |
| 502 | uninsure | O | 0.005 |
| 503 | uninsure | O | 0.005 |
| 504 | uninsure | O | 0.005 |
| 505 | uninsure | O | 0.005 |
| 506 | uninsure | O | 0.005 |
| 507 | uninsure | O | 0.005 |
| 508 | uninsure | O | 0.005 |
| 509 | uninsure | O | 0.005 |
| 510 | uninsure | O | 0.005 |
| 511 | uninsure | O | 0.005 |
| 512 | uninsure | O | 0.005 |
| 513 | uninsure | O | 0.005 |
| 514 | uninsure | O | 0.005 |
| 515 | uninsure | O | 0.005 |
| 516 | uninsure | O | 0.005 |
| 517 | uninsure | O | 0.005 |
| 518 | uninsure | O | 0.005 |
| 519 | uninsure | O | 0.005 |
| 520 | uninsure | O | 0.005 |
| 521 | uninsure | O | 0.005 |
| 1 | wship | O | 0.004 |
| 2 | wship | O | 0.004 |
| 3 | wship | O | 0.004 |
| 4 | wship | O | 0.004 |
| 5 | wship | O | 0.004 |
| 6 | wship | O | 0.004 |
| 7 | wship | O | 0.004 |
| 8 | wship | O | 0.004 |
| 9 | wship | O | 0.004 |
| 10 | wship | O | 0.004 |
| 11 | wship | O | 0.004 |
| 12 | wship | O | 0.004 |
| 13 | wship | O | 0.004 |
| 14 | wship | O | 0.004 |
| 15 | wship | O | 0.004 |
| 16 | wship | O | 0.004 |
| 17 | wship | O | 0.004 |
| 18 | wship | O | 0.004 |
| 19 | wship | O | 0.004 |
| 20 | wship | O | 0.004 |
| 21 | wship | O | 0.004 |
| 22 | wship | O | 0.004 |
| 23 | wship | O | 0.004 |
| 24 | wship | O | 0.004 |
| 25 | wship | O | 0.004 |
| 26 | wship | O | 0.004 |
| 27 | wship | O | 0.004 |
| 28 | wship | O | 0.004 |
| 29 | wship | O | 0.004 |
| 30 | wship | O | 0.004 |
| 31 | wship | O | 0.004 |
| 32 | wship | O | 0.004 |
| 33 | wship | O | 0.004 |
| 34 | wship | O | 0.004 |
| 35 | wship | O | 0.004 |
| 36 | wship | O | 0.004 |
| 37 | wship | O | 0.004 |
| 38 | wship | O | 0.004 |
| 39 | wship | O | 0.004 |
| 40 | wship | O | 0.004 |
| 41 | wship | O | 0.004 |
| 42 | wship | O | 0.004 |
| 43 | wship | O | 0.004 |
| 44 | wship | O | 0.004 |
| 45 | wship | O | 0.004 |
| 46 | wship | O | 0.004 |
| 47 | wship | O | 0.004 |
| 48 | wship | O | 0.004 |
| 49 | wship | O | 0.004 |
| 50 | wship | O | 0.004 |
| 51 | wship | O | 0.004 |
| 52 | wship | O | 0.004 |
| 53 | wship | O | 0.004 |
| 54 | wship | O | 0.004 |
| 55 | wship | O | 0.004 |
| 56 | wship | O | 0.004 |
| 57 | wship | O | 0.004 |
| 58 | wship | O | 0.004 |
| 59 | wship | O | 0.004 |
| 60 | wship | O | 0.004 |
| 61 | wship | O | 0.004 |
| 62 | wship | O | 0.004 |
| 63 | wship | O | 0.004 |
| 64 | wship | O | 0.004 |
| 65 | wship | O | 0.004 |
| 66 | wship | O | 0.004 |
| 67 | wship | O | 0.004 |
| 68 | wship | O | 0.004 |
| 69 | wship | O | 0.004 |
| 70 | wship | O | 0.004 |
| 71 | wship | O | 0.004 |
| 72 | wship | O | 0.004 |
| 73 | wship | O | 0.004 |
| 74 | wship | O | 0.004 |
| 75 | wship | O | 0.004 |
| 76 | wship | O | 0.004 |
| 77 | wship | O | 0.004 |
| 78 | wship | O | 0.004 |
| 79 | wship | O | 0.004 |
| 80 | wship | O | 0.004 |
| 81 | wship | O | 0.004 |
| 82 | wship | O | 0.004 |
| 83 | wship | O | 0.004 |
| 84 | wship | O | 0.004 |
| 85 | wship | O | 0.004 |
| 86 | wship | O | 0.004 |
| 87 | wship | O | 0.004 |
| 88 | wship | O | 0.004 |
| 89 | wship | O | 0.004 |
| 90 | wship | O | 0.004 |
| 91 | wship | O | 0.004 |
| 92 | wship | O | 0.004 |
| 93 | wship | O | 0.004 |
| 94 | wship | O | 0.004 |
| 95 | wship | O | 0.004 |
| 96 | wship | O | 0.004 |
| 97 | wship | O | 0.004 |
| 98 | wship | O | 0.004 |
| 99 | wship | O | 0.004 |
| 100 | wship | O | 0.004 |
| 101 | wship | O | 0.004 |
| 102 | wship | O | 0.004 |
| 103 | wship | O | 0.004 |
| 104 | wship | O | 0.004 |
| 105 | wship | O | 0.004 |
| 106 | wship | O | 0.004 |
| 107 | wship | O | 0.004 |
| 108 | wship | O | 0.004 |
| 109 | wship | O | 0.004 |
| 110 | wship | O | 0.004 |
| 111 | wship | O | 0.004 |
| 112 | wship | O | 0.004 |
| 113 | wship | O | 0.004 |
| 114 | wship | O | 0.004 |
| 115 | wship | O | 0.004 |
| 116 | wship | O | 0.004 |
| 117 | wship | O | 0.004 |
| 118 | wship | O | 0.004 |
| 119 | wship | O | 0.004 |
| 120 | wship | O | 0.004 |
| 121 | wship | O | 0.004 |
| 122 | wship | O | 0.004 |
| 123 | wship | O | 0.004 |
| 124 | wship | O | 0.004 |
| 125 | wship | O | 0.004 |
| 126 | wship | O | 0.004 |
| 127 | wship | O | 0.004 |
| 128 | wship | O | 0.004 |
| 129 | wship | O | 0.004 |
| 130 | wship | O | 0.004 |
| 131 | wship | O | 0.004 |
| 132 | wship | O | 0.004 |
| 133 | wship | O | 0.004 |
| 134 | wship | O | 0.004 |
| 135 | wship | O | 0.004 |
| 136 | wship | O | 0.004 |
| 137 | wship | O | 0.004 |
| 138 | wship | O | 0.004 |
| 139 | wship | O | 0.004 |
| 140 | wship | O | 0.004 |
| 141 | wship | O | 0.004 |
| 142 | wship | O | 0.004 |
| 143 | wship | O | 0.004 |
| 144 | wship | O | 0.004 |
| 145 | wship | O | 0.004 |
| 146 | wship | O | 0.004 |
| 147 | wship | O | 0.004 |
| 148 | wship | O | 0.004 |
| 149 | wship | O | 0.004 |
| 150 | wship | O | 0.004 |
| 151 | wship | O | 0.004 |
| 152 | wship | O | 0.004 |
| 153 | wship | O | 0.004 |
| 154 | wship | O | 0.004 |
| 155 | wship | O | 0.004 |
| 156 | wship | O | 0.004 |
| 157 | wship | O | 0.004 |
| 158 | wship | O | 0.004 |
| 159 | wship | O | 0.004 |
| 160 | wship | O | 0.004 |
| 161 | wship | O | 0.004 |
| 162 | wship | O | 0.004 |
| 163 | wship | O | 0.004 |
| 164 | wship | O | 0.004 |
| 165 | wship | O | 0.004 |
| 166 | wship | O | 0.004 |
| 167 | wship | O | 0.004 |
| 168 | wship | O | 0.004 |
| 169 | wship | O | 0.004 |
| 170 | wship | O | 0.004 |
| 171 | wship | O | 0.004 |
| 172 | wship | O | 0.004 |
| 173 | wship | O | 0.004 |
| 174 | wship | O | 0.004 |
| 175 | wship | O | 0.004 |
| 176 | wship | O | 0.004 |
| 177 | wship | O | 0.004 |
| 178 | wship | O | 0.004 |
| 179 | wship | O | 0.004 |
| 180 | wship | O | 0.004 |
| 181 | wship | O | 0.004 |
| 182 | wship | O | 0.004 |
| 183 | wship | O | 0.004 |
| 184 | wship | O | 0.004 |
| 185 | wship | O | 0.004 |
| 186 | wship | O | 0.004 |
| 187 | wship | O | 0.004 |
| 188 | wship | O | 0.004 |
| 189 | wship | O | 0.004 |
| 190 | wship | O | 0.004 |
| 191 | wship | O | 0.004 |
| 192 | wship | O | 0.004 |
| 193 | wship | O | 0.004 |
| 194 | wship | O | 0.004 |
| 195 | wship | O | 0.004 |
| 196 | wship | O | 0.004 |
| 197 | wship | O | 0.004 |
| 198 | wship | O | 0.004 |
| 199 | wship | O | 0.004 |
| 200 | wship | O | 0.004 |
| 201 | wship | O | 0.004 |
| 202 | wship | O | 0.004 |
| 203 | wship | O | 0.004 |
| 204 | wship | O | 0.004 |
| 205 | wship | O | 0.004 |
| 206 | wship | O | 0.004 |
| 207 | wship | O | 0.004 |
| 208 | wship | O | 0.004 |
| 209 | wship | O | 0.004 |
| 210 | wship | O | 0.004 |
| 211 | wship | O | 0.004 |
| 212 | wship | O | 0.004 |
| 213 | wship | O | 0.004 |
| 214 | wship | O | 0.004 |
| 215 | wship | O | 0.004 |
| 216 | wship | O | 0.004 |
| 217 | wship | O | 0.004 |
| 218 | wship | O | 0.004 |
| 219 | wship | O | 0.004 |
| 220 | wship | O | 0.004 |
| 221 | wship | O | 0.004 |
| 222 | wship | O | 0.004 |
| 223 | wship | O | 0.004 |
| 224 | wship | O | 0.004 |
| 225 | wship | O | 0.004 |
| 226 | wship | O | 0.004 |
| 227 | wship | O | 0.004 |
| 228 | wship | O | 0.004 |
| 229 | wship | O | 0.004 |
| 230 | wship | O | 0.004 |
| 231 | wship | O | 0.004 |
| 232 | wship | O | 0.004 |
| 233 | wship | O | 0.004 |
| 234 | wship | O | 0.004 |
| 235 | wship | O | 0.004 |
| 236 | wship | O | 0.004 |
| 237 | wship | O | 0.004 |
| 238 | wship | O | 0.004 |
| 239 | wship | O | 0.004 |
| 240 | wship | O | 0.004 |
| 241 | wship | O | 0.004 |
| 242 | wship | O | 0.004 |
| 243 | wship | O | 0.004 |
| 244 | wship | O | 0.004 |
| 245 | wship | O | 0.004 |
| 246 | wship | O | 0.004 |
| 247 | wship | O | 0.004 |
| 248 | wship | O | 0.004 |
| 249 | wship | O | 0.004 |
| 250 | wship | O | 0.004 |
| 251 | wship | O | 0.004 |
| 252 | wship | O | 0.004 |
| 253 | wship | O | 0.004 |
| 254 | wship | O | 0.004 |
| 255 | wship | O | 0.004 |
| 256 | wship | O | 0.004 |
| 257 | wship | O | 0.004 |
| 258 | wship | O | 0.004 |
| 259 | wship | O | 0.004 |
| 260 | wship | O | 0.004 |
| 261 | wship | O | 0.004 |
| 262 | wship | O | 0.004 |
| 263 | wship | O | 0.004 |
| 264 | wship | O | 0.004 |
| 265 | wship | O | 0.004 |
| 266 | wship | O | 0.004 |
| 267 | wship | O | 0.004 |
| 268 | wship | O | 0.004 |
| 269 | wship | O | 0.004 |
| 270 | wship | O | 0.004 |
| 271 | wship | O | 0.004 |
| 272 | wship | O | 0.004 |
| 273 | wship | O | 0.004 |
| 274 | wship | O | 0.004 |
| 275 | wship | O | 0.004 |
| 276 | wship | O | 0.004 |
| 277 | wship | O | 0.004 |
| 278 | wship | O | 0.004 |
| 279 | wship | O | 0.004 |
| 280 | wship | O | 0.004 |
| 281 | wship | O | 0.004 |
| 282 | wship | O | 0.004 |
| 283 | wship | O | 0.004 |
| 284 | wship | O | 0.004 |
| 285 | wship | O | 0.004 |
| 286 | wship | O | 0.004 |
| 287 | wship | O | 0.004 |
| 288 | wship | O | 0.004 |
| 289 | wship | O | 0.004 |
| 290 | wship | O | 0.004 |
| 291 | wship | O | 0.004 |
| 292 | wship | O | 0.004 |
| 293 | wship | O | 0.004 |
| 294 | wship | O | 0.004 |
| 295 | wship | O | 0.004 |
| 296 | wship | O | 0.004 |
| 297 | wship | O | 0.004 |
| 298 | wship | O | 0.004 |
| 299 | wship | O | 0.004 |
| 300 | wship | O | 0.004 |
| 301 | wship | O | 0.004 |
| 302 | wship | O | 0.004 |
| 303 | wship | O | 0.004 |
| 304 | wship | O | 0.004 |
| 305 | wship | O | 0.004 |
| 306 | wship | O | 0.004 |
| 307 | wship | O | 0.004 |
| 308 | wship | O | 0.004 |
| 309 | wship | O | 0.004 |
| 310 | wship | O | 0.004 |
| 311 | wship | O | 0.004 |
| 312 | wship | O | 0.004 |
| 313 | wship | O | 0.004 |
| 314 | wship | O | 0.004 |
| 315 | wship | O | 0.004 |
| 316 | wship | O | 0.004 |
| 317 | wship | O | 0.004 |
| 318 | wship | O | 0.004 |
| 319 | wship | O | 0.004 |
| 320 | wship | O | 0.004 |
| 321 | wship | O | 0.004 |
| 322 | wship | O | 0.004 |
| 323 | wship | O | 0.004 |
| 324 | wship | O | 0.004 |
| 325 | wship | O | 0.004 |
| 326 | wship | O | 0.004 |
| 327 | wship | O | 0.004 |
| 328 | wship | O | 0.004 |
| 329 | wship | O | 0.004 |
| 330 | wship | O | 0.004 |
| 331 | wship | O | 0.004 |
| 332 | wship | O | 0.004 |
| 333 | wship | O | 0.004 |
| 334 | wship | O | 0.004 |
| 335 | wship | O | 0.004 |
| 336 | wship | O | 0.004 |
| 337 | wship | O | 0.004 |
| 338 | wship | O | 0.004 |
| 339 | wship | O | 0.004 |
| 340 | wship | O | 0.004 |
| 341 | wship | O | 0.004 |
| 342 | wship | O | 0.004 |
| 343 | wship | O | 0.004 |
| 344 | wship | O | 0.004 |
| 345 | wship | O | 0.004 |
| 346 | wship | O | 0.004 |
| 347 | wship | O | 0.004 |
| 348 | wship | O | 0.004 |
| 349 | wship | O | 0.004 |
| 350 | wship | O | 0.004 |
| 351 | wship | O | 0.004 |
| 352 | wship | O | 0.004 |
| 353 | wship | O | 0.004 |
| 354 | wship | O | 0.004 |
| 355 | wship | O | 0.004 |
| 356 | wship | O | 0.004 |
| 357 | wship | O | 0.004 |
| 358 | wship | O | 0.004 |
| 359 | wship | O | 0.004 |
| 360 | wship | O | 0.004 |
| 361 | wship | O | 0.004 |
| 362 | wship | O | 0.004 |
| 363 | wship | O | 0.004 |
| 364 | wship | O | 0.004 |
| 365 | wship | O | 0.004 |
| 366 | wship | O | 0.004 |
| 367 | wship | O | 0.004 |
| 368 | wship | O | 0.004 |
| 369 | wship | O | 0.004 |
| 370 | wship | O | 0.004 |
| 371 | wship | O | 0.004 |
| 372 | wship | O | 0.004 |
| 373 | wship | O | 0.004 |
| 374 | wship | O | 0.004 |
| 375 | wship | O | 0.004 |
| 376 | wship | O | 0.004 |
| 377 | wship | O | 0.004 |
| 378 | wship | O | 0.004 |
| 379 | wship | O | 0.004 |
| 380 | wship | O | 0.004 |
| 381 | wship | O | 0.004 |
| 382 | wship | O | 0.004 |
| 383 | wship | O | 0.004 |
| 384 | wship | O | 0.004 |
| 385 | wship | O | 0.004 |
| 386 | wship | O | 0.004 |
| 387 | wship | O | 0.004 |
| 388 | wship | O | 0.004 |
| 389 | wship | O | 0.004 |
| 390 | wship | O | 0.004 |
| 391 | wship | O | 0.004 |
| 392 | wship | O | 0.004 |
| 393 | wship | O | 0.004 |
| 394 | wship | O | 0.004 |
| 395 | wship | O | 0.004 |
| 396 | wship | O | 0.004 |
| 397 | wship | O | 0.004 |
| 398 | wship | O | 0.004 |
| 399 | wship | O | 0.004 |
| 400 | wship | O | 0.004 |
| 401 | wship | O | 0.004 |
| 402 | wship | O | 0.004 |
| 403 | wship | O | 0.004 |
| 404 | wship | O | 0.004 |
| 405 | wship | O | 0.004 |
| 406 | wship | O | 0.004 |
| 407 | wship | O | 0.004 |
| 408 | wship | O | 0.004 |
| 409 | wship | O | 0.004 |
| 410 | wship | O | 0.004 |
| 411 | wship | O | 0.004 |
| 412 | wship | O | 0.004 |
| 413 | wship | O | 0.004 |
| 414 | wship | O | 0.004 |
| 415 | wship | O | 0.004 |
| 416 | wship | O | 0.004 |
| 417 | wship | O | 0.004 |
| 418 | wship | O | 0.004 |
| 419 | wship | O | 0.004 |
| 420 | wship | O | 0.004 |
| 421 | wship | O | 0.004 |
| 422 | wship | O | 0.004 |
| 423 | wship | O | 0.004 |
| 424 | wship | O | 0.004 |
| 425 | wship | O | 0.004 |
| 426 | wship | O | 0.004 |
| 427 | wship | O | 0.004 |
| 428 | wship | O | 0.004 |
| 429 | wship | O | 0.004 |
| 430 | wship | O | 0.004 |
| 431 | wship | O | 0.004 |
| 432 | wship | O | 0.004 |
| 433 | wship | O | 0.004 |
| 434 | wship | O | 0.004 |
| 435 | wship | O | 0.004 |
| 436 | wship | O | 0.004 |
| 437 | wship | O | 0.004 |
| 438 | wship | O | 0.004 |
| 439 | wship | O | 0.004 |
| 440 | wship | O | 0.004 |
| 441 | wship | O | 0.004 |
| 442 | wship | O | 0.004 |
| 443 | wship | O | 0.004 |
| 444 | wship | O | 0.004 |
| 445 | wship | O | 0.004 |
| 446 | wship | O | 0.004 |
| 447 | wship | O | 0.004 |
| 448 | wship | O | 0.004 |
| 449 | wship | O | 0.004 |
| 450 | wship | O | 0.004 |
| 451 | wship | O | 0.004 |
| 452 | wship | O | 0.004 |
| 453 | wship | O | 0.004 |
| 454 | wship | O | 0.004 |
| 455 | wship | O | 0.004 |
| 456 | wship | O | 0.004 |
| 457 | wship | O | 0.004 |
| 458 | wship | O | 0.004 |
| 459 | wship | O | 0.004 |
| 460 | wship | O | 0.004 |
| 461 | wship | O | 0.004 |
| 462 | wship | O | 0.004 |
| 463 | wship | O | 0.004 |
| 464 | wship | O | 0.004 |
| 465 | wship | O | 0.004 |
| 466 | wship | O | 0.004 |
| 467 | wship | O | 0.004 |
| 468 | wship | O | 0.004 |
| 469 | wship | O | 0.004 |
| 470 | wship | O | 0.004 |
| 471 | wship | O | 0.004 |
| 472 | wship | O | 0.004 |
| 473 | wship | O | 0.004 |
| 474 | wship | O | 0.004 |
| 475 | wship | O | 0.004 |
| 476 | wship | O | 0.004 |
| 477 | wship | O | 0.004 |
| 478 | wship | O | 0.004 |
| 479 | wship | O | 0.004 |
| 480 | wship | O | 0.004 |
| 481 | wship | O | 0.004 |
| 482 | wship | O | 0.004 |
| 483 | wship | O | 0.004 |
| 484 | wship | O | 0.004 |
| 485 | wship | O | 0.004 |
| 486 | wship | O | 0.004 |
| 487 | wship | O | 0.004 |
| 488 | wship | O | 0.004 |
| 489 | wship | O | 0.004 |
| 490 | wship | O | 0.004 |
| 491 | wship | O | 0.004 |
| 492 | wship | O | 0.004 |
| 493 | wship | O | 0.004 |
| 494 | wship | O | 0.004 |
| 495 | wship | O | 0.004 |
| 496 | wship | O | 0.004 |
| 497 | wship | O | 0.004 |
| 498 | wship | O | 0.004 |
| 499 | wship | O | 0.004 |
| 500 | wship | O | 0.004 |
| 501 | wship | O | 0.004 |
| 502 | wship | O | 0.004 |
| 503 | wship | O | 0.004 |
| 504 | wship | O | 0.004 |
| 505 | wship | O | 0.004 |
| 506 | wship | O | 0.004 |
| 507 | wship | O | 0.004 |
| 508 | wship | O | 0.004 |
| 509 | wship | O | 0.004 |
| 510 | wship | O | 0.004 |
| 511 | wship | O | 0.004 |
| 512 | wship | O | 0.004 |
| 513 | wship | O | 0.004 |
| 514 | wship | O | 0.004 |
| 515 | wship | O | 0.004 |
| 516 | wship | O | 0.004 |
| 517 | wship | O | 0.004 |
| 518 | wship | O | 0.004 |
| 519 | wship | O | 0.004 |
| 520 | wship | O | 0.004 |
| 521 | wship | O | 0.004 |
Hazard curves
Note the piece-wise structure. It looks odd at the individual hazard level, and some of the transitions are quite abrupt, but the population level survival curves in the next section are pretty smooth. The one concern is the Weibull piece for Black, public insurance, which suggests an increasing disenrollment rate over time.
ggplot(data = adap_data$pred.disenroll,
aes(x = week, y = disenroll_prob, color = insurance)) +
geom_line() + facet_wrap(~race) +
labs(title = "Piecewise hazard curves by race and insurance",
x = "Week",
y = "Prob of disenrolling") 
Survival curves for ADAP clients
Note: the figures are read in here, but are contructed on the secure terminal server in Scripts/DAPoutputs.R
Race
grid.raster(readTIFF(here::here("MakeData", "Zoe", "EconModelBook", "data",
"p_dropout1.tiff")))
Insurance
grid.raster(readTIFF(here::here("MakeData", "Zoe", "EconModelBook", "data",
"p_dropout2.tiff")))
PDAP
demog
Demographics of PDAP clients.
Data inputs
- Number of PDAP clients by year, race, and region from DOH DAP data
- Popsize of HIV- from WApopdata (
pop.racexregion.neg)
- Fraction of HIV- on PrEP from WHAMP survey
Parameter outputs
- prop - proportion of HIV- in PDAP, by race x region x year (year 4 used for intial distribution)
- prop.pdap - Client race x region distribution by year (for insurance assignment). NB: this is not included in ADAP demog
Target outputs (for comparison to WHAMP)
- pdap.frac - Estimated % of PrEP users on PDAP: PDAPclients / (popHIV- * WhampPrEPfrac). Calculated in data section above.
### PDAP
### we again use num_ub, which for HIV- will be a the lower value
pop.racexregion.neg <- bind_rows(msm.pop.totals_2014$pop.racexregion.neg,
msm.pop.totals_2015$pop.racexregion.neg,
msm.pop.totals_2016$pop.racexregion.neg,
msm.pop.totals_2017$pop.racexregion.neg,
msm.pop.totals_2018$pop.racexregion.neg) %>%
bind_cols(year, .) %>% #year is made in the adap section
select(year, region, race, popsize.neg = num_ub) %>%
data.table()
# ZK modifies the original data here, we use tidytools instead of data.table
# object will have the same structure as ADAP, with one extra column:
# prop.pdap: race x region distn of clients by year for insurance assignment
pdap_data$demog <- merge(pdap_data$demog,
pop.racexregion.neg,
by = c("year", "race", "region"),
all.x = T) %>%
mutate(prop = N / popsize.neg,
year = year - 2013) %>%
filter(year >= 4) %>% # 2017 & 18 only
group_by(year) %>%
mutate(prop.pdap = N/sum(N)) %>%
ungroup() %>%
data.table() %>%
setkeyv(c("year", "race", "region"))PDAP Prevalence estimates
As with ADAP participation rates, we have 2 sources for estimating the fraction of PrEP users on PDAP: DOH DAP data, and the WHAMP survey.
In this case, the DOH estimate is not completely independent of WHAMP, because DOH do not have a population estimate of number of MSM on PrEP (analogous to the number on ART). DOH do have an estimate of the prevalence of PrEP use among HIV- MSM from Darcy’s 2018 WHPP: 18%. That is a bit lower than the WHAMP 2019 survey estimate, (22.4%), so we rely on the WHAMP estimate of the PrEP fraction to adjust the denominator for the DOH estimate of the fraction of PrEP users in PDAP.
The two estimates are not consistent:
- DOH 2018 data: 6.1%
- WHAMP 2019 survey: 16.7% (95% CI, 10.8% - 22.5%)
The PDAP program, unlike ADAP, is changing rapidly as it scales up. The program size more than doubled from 2017 to 2018 (see the Targets section). So it’s possible that the discrepancy is due to the difference in observation years, and the WHAMP survey estimate is correct.
In any case, we will need to calibrate this input parameter to overall program size.
DOH PDAP estimates
The plot and table below show the fraction of HIV+ in PDAP, based on the DOH DAP and WApop data. The estimates are based on DAPclients(yr)/PopHIV+(yr) by attribute. Note the denominator is not PrEP users.
Plot
p <- ggplot(pdap_data$demog,
aes(x = region, y = prop, text = N)) +
geom_bar(aes(fill = race), alpha = 0.7,
stat = "identity", size = 1.2,
position = position_dodge2(width = 0.9)) +
labs(title = "Demographics of PDAP clients",
ylab = "proportion") +
facet_wrap(~year)
ggplotly(p, tooltip = "text")gp <- ggplotly(p, tooltip = "text")
gp %>% layout_1x2Table
kable(pdap_data$demog,
digits = c(0, 0, 0, 0, 0, 4),
caption= "Demographics of PDAP clients by year, race, and region") %>%
kable_styling(full_width=F, position="center", bootstrap_options = c("striped"))| year | race | region | N | popsize.neg | prop | prop.pdap |
|---|---|---|---|---|---|---|
| 4 | B | EasternWA | 0 | 252 | 0.0000 | 0 |
| 4 | B | King | 9 | 4658 | 0.0019 | 0 |
| 4 | B | WesternWA | 7 | 1980 | 0.0035 | 0 |
| 4 | H | EasternWA | 3 | 2191 | 0.0014 | 0 |
| 4 | H | King | 60 | 5478 | 0.0110 | 0 |
| 4 | H | WesternWA | 12 | 3390 | 0.0035 | 0 |
| 4 | O | EasternWA | 20 | 9676 | 0.0021 | 0 |
| 4 | O | King | 429 | 56734 | 0.0076 | 1 |
| 4 | O | WesternWA | 81 | 35822 | 0.0023 | 0 |
| 5 | B | EasternWA | 0 | 256 | 0.0000 | 0 |
| 5 | B | King | 43 | 4813 | 0.0089 | 0 |
| 5 | B | WesternWA | 13 | 2061 | 0.0063 | 0 |
| 5 | H | EasternWA | 18 | 2249 | 0.0080 | 0 |
| 5 | H | King | 276 | 5620 | 0.0491 | 0 |
| 5 | H | WesternWA | 66 | 3556 | 0.0186 | 0 |
| 5 | O | EasternWA | 50 | 9765 | 0.0051 | 0 |
| 5 | O | King | 1005 | 57656 | 0.0174 | 1 |
| 5 | O | WesternWA | 195 | 36230 | 0.0054 | 0 |
ins
Distribution of insurance among PDAP clients
Data source: DOH DAP data
Number of PDAP clients by race x region x insurance x year
3 insurance categories: individual, employer, and none.
The race x region x ins stratification leads to many empty cells for B PDAP clients if we restrict to 2017-18. So we use all yrs of data for the prediction.
Data notes:
In the original data, there were only 17 Medicare records so we removed the Medicare insurance in the data.
There were no Medicaid clients in the PDAP claims data
Parameter Output
- Model based predicted insurance category probs by race x region.
- Insurance of new PDAP clients was assigned using this data.table.
# ZK modifies the original data here
pdap_data$ins[, sum_N := sum(n), by = list(year, race, region)]
pdap_data$ins[, prop := n / sum_N]
pdap_data$ins[, sum_N := NULL]
pdap_data$ins[, year := year - 2013]
#pdap_data$ins <- pdap_data$ins[year >= 4]
ins.levels <- data.table(expand.grid(year = 1:5,
race = c("B", "H", "O"),
region = c("EasternWA", "King", "WesternWA"),
insurance = c("employer", "individual", "none")))
pdap_data$ins <- merge(ins.levels, pdap_data$ins,
by = c("year", "race", "region", "insurance"),
all.x = T)
pdap_data$ins[is.na(n)]$n <- 0
pdap_data$ins[is.na(prop)]$prop <- 0Tables
We compare all yrs to 2017-18, to see the impact on the distributions for non-B. Region is collapsed to KC vs. other WA to reduce empty cells. The distributions are close enough that we will use all years to estimate the multinomial model for insurance.
allyrs <- pdap_data$ins %>%
mutate(region2 = if_else(region == "King", "King", "Other WA")) %>%
group_by(race, region2, insurance) %>%
summarize(num = sum(n)) %>%
mutate(pct = 100*round(num/sum(num), 3))
last2yrs <- pdap_data$ins %>% filter(year > 3) %>%
mutate(region2 = if_else(region == "King", "King", "Other WA")) %>%
group_by(race, region2, insurance) %>%
summarize(num = sum(n)) %>%
mutate(pct = 100*round(num/sum(num), 3))
comp <- left_join(allyrs, last2yrs,
by = c("race", "region2", "insurance"),
suffix = c(".all", ".l2yr")) %>%
mutate(diff = pct.all - pct.l2yr)
comp %>%
kable(digits = c(0, 0, 0, 0, 1, 0, 1, 1),
caption= "All years: Insurance distn for PDAP clients") %>%
add_header_above(c(" " = 3, "All yrs" = 2, "2017-18" = 2, " " = 1)) %>%
kable_styling(full_width=F, position="center",
bootstrap_options = c("striped"))| race | region2 | insurance | num.all | pct.all | num.l2yr | pct.l2yr | diff |
|---|---|---|---|---|---|---|---|
| B | King | employer | 56 | 62.2 | 30 | 57.7 | 4.5 |
| B | King | individual | 12 | 13.3 | 5 | 9.6 | 3.7 |
| B | King | none | 22 | 24.4 | 17 | 32.7 | -8.3 |
| B | Other WA | employer | 21 | 72.4 | 14 | 70.0 | 2.4 |
| B | Other WA | individual | 4 | 13.8 | 4 | 20.0 | -6.2 |
| B | Other WA | none | 4 | 13.8 | 2 | 10.0 | 3.8 |
| H | King | employer | 241 | 47.2 | 177 | 52.7 | -5.5 |
| H | King | individual | 84 | 16.4 | 40 | 11.9 | 4.5 |
| H | King | none | 186 | 36.4 | 119 | 35.4 | 1.0 |
| H | Other WA | employer | 61 | 44.2 | 48 | 48.5 | -4.3 |
| H | Other WA | individual | 20 | 14.5 | 12 | 12.1 | 2.4 |
| H | Other WA | none | 57 | 41.3 | 39 | 39.4 | 1.9 |
| O | King | employer | 1463 | 62.4 | 923 | 64.4 | -2.0 |
| O | King | individual | 541 | 23.1 | 323 | 22.5 | 0.6 |
| O | King | none | 340 | 14.5 | 188 | 13.1 | 1.4 |
| O | Other WA | employer | 305 | 55.4 | 193 | 55.8 | -0.4 |
| O | Other WA | individual | 131 | 23.8 | 76 | 22.0 | 1.8 |
| O | Other WA | none | 115 | 20.9 | 77 | 22.3 | -1.4 |
Model and predicted probs
# PDAP insurance prediction
mult_dat <- pdap_data$ins %>%
mutate(region2 = if_else(region == "King", "King", "Other WA")) %>%
group_by(race, region2, insurance) %>%
summarize(n = sum(n))
mult_fit <- nnet::multinom(insurance ~ race + region2,
weights = n, data = mult_dat) ## # weights: 15 (8 variable)
## initial value 4024.216813
## iter 10 value 3457.259892
## final value 3454.716377
## convergedsummary(mult_fit)## Call:
## nnet::multinom(formula = insurance ~ race + region2, data = mult_dat,
## weights = n)
##
## Coefficients:
## (Intercept) raceH raceO region2Other WA
## individual -1.596415 0.5094920 0.6092373 0.1071063
## none -1.180985 0.8841925 -0.2518469 0.3665687
##
## Std. Errors:
## (Intercept) raceH raceO region2Other WA
## individual 0.2759613 0.2974104 0.2785476 0.1058176
## none 0.2291814 0.2432541 0.2334088 0.1055024
##
## Residual Deviance: 6909.433
## AIC: 6925.433pred.ins.probs <- bind_cols(
mult_dat[seq(1, nrow(mult_dat), by = 3), c("race", "region2")],
as.data.frame(predict(
mult_fit, newdata = mult_dat[seq(1, nrow(mult_dat), by = 3),
c("race", "region2")],
type = "probs"))) %>%
as.data.table() %>%
setkey("race", "region2")
pred.ins.probs %>%
kable(digits = c(0, 0, 3, 3, 3),
caption= "Predicted insurance types for PDAP clients") %>%
footnote("Multinomial model based on all years, 2014-18") %>%
kable_styling(full_width=F, position="center",
bootstrap_options = c("striped"))| race | region2 | employer | individual | none |
|---|---|---|---|---|
| B | King | 0.662 | 0.134 | 0.203 |
| B | Other WA | 0.599 | 0.135 | 0.265 |
| H | King | 0.481 | 0.162 | 0.357 |
| H | Other WA | 0.409 | 0.153 | 0.438 |
| O | King | 0.621 | 0.231 | 0.148 |
| O | Other WA | 0.568 | 0.236 | 0.196 |
| Note: | ||||
| Multinomial model based on all years, 2014-18 |
Plot
These are the predicted insurance types, by race and region.
tmp <- pred.ins.probs %>%
pivot_longer(c("employer":"none"), names_to = "insurance")
p <- ggplot(tmp,
aes(x = insurance, y = value,
group = region2, color = region2,
text = scales::percent(tmp$value, accuracy = .1))) +
geom_line(alpha = 0.7) +
facet_grid(~race) +
theme(axis.text.x = element_text(size = 9, angle = 40, hjust = 1)) +
labs(title = "Predicted insurance type for PDAP clients") +
xlab("race") + ylab("proportion")
ggplotly(p, tooltip = "text")# filter ins dt to yrs 4-5
pdap_data$ins <- pdap_data$ins[year >= 4]
# reformat pred dt for 3-region use; E&W WA will have same values
# new component in pdap list: ins.pred (replaces placeholder)
mult_dat <- pdap_data$ins %>%
mutate(region2 = if_else(region == "King", "King", "Other WA"))
pdap_data$ins.pred <- left_join(mult_dat, tmp,
by = c("race", "region2", "insurance")) %>%
select(-c(region2, n, prop)) %>%
pivot_wider(names_from = insurance)cost.pday
Average daily PDAP cost per person
The calculation method is very similar to the ADAP cost.pday. The primary differences are date range (2017 - 2018) and insurance categories (none, employer, and individual).
Data inputs: DOH DAP data
- Number of clients
- Number of enrollment days on PDAP
- Costs by category of spending
- By race x insurance
Parameter outputs:
- Costs stratified by race x insurance x year
- Total category costs = sum over all persons, all days (annual cost)
- Daily category cost per person = Total / sum(person-days of enrollment)
- Overall program costs summed the category costs.
tmpDF <- data.table(expand.grid(year = c(2017, 2018),
race = c("B", "H", "O"),
insurance = c("none", "employer", "individual")))
# ZK modifies the original data here
pdap_data$cost.pday <- pdap_data$cost.pday %>%
select(year, race, insurance, pdtruvCost, Overallpdcost) %>%
rename(cTruv = pdtruvCost,
cTotCost = Overallpdcost)
pdap_data$cost.pday <- merge(tmpDF, pdap_data$cost.pday,
by = c("year", "race", "insurance"),
all.x = T)
pdap_data$cost.pday[is.na(pdap_data$cost.pday)] <- 0Plots
Truvada
p <- ggplot(pdap_data$cost.pday,
aes(x = insurance, y = cTruv, text = round(cTruv,2))) +
geom_bar(aes(fill = race), stat = "identity",
alpha = 0.7, size = 1.2,
position = position_dodge2(width = 0.9)) +
facet_wrap(~ year, nrow = 2) +
labs(title = "Truvada cost per person-day", y = "cost ($)")
ggplotly(p, tooltip = "text")Total costs
p <- ggplot(pdap_data$cost.pday,
aes(x = insurance, y = cTotCost, text = round(cTotCost,2))) +
geom_bar(aes(fill = race), stat = "identity",
alpha = 0.7, size = 1.2,
position = position_dodge2(width = 0.9)) +
facet_wrap(~ year, nrow = 2) +
labs(title = "Total cost per person-day", y = "cost ($)")
ggplotly(p, tooltip = "text")Table
kable(pdap_data$cost.pday, digits = c(0, 0, 0, 2, 2, 2, 2),
caption = "Daily PDAP cost by race and insurance") %>%
kable_styling(full_width=F, position="center", bootstrap_options = c("striped"))| year | race | insurance | cTruv | cTotCost |
|---|---|---|---|---|
| 2017 | B | employer | 3.00 | 3.10 |
| 2017 | B | individual | 0.00 | 0.00 |
| 2017 | B | none | 0.09 | 0.09 |
| 2017 | H | employer | 5.47 | 5.48 |
| 2017 | H | individual | 11.52 | 11.52 |
| 2017 | H | none | 25.30 | 25.49 |
| 2017 | O | employer | 5.30 | 5.34 |
| 2017 | O | individual | 9.92 | 9.93 |
| 2017 | O | none | 10.59 | 10.65 |
| 2018 | B | employer | 1.15 | 1.63 |
| 2018 | B | individual | 2.02 | 2.02 |
| 2018 | B | none | 4.84 | 6.48 |
| 2018 | H | employer | 1.15 | 1.45 |
| 2018 | H | individual | 6.43 | 6.66 |
| 2018 | H | none | 3.35 | 5.90 |
| 2018 | O | employer | 1.82 | 2.19 |
| 2018 | O | individual | 5.46 | 5.62 |
| 2018 | O | none | 8.56 | 10.14 |
pred.disenroll_pdap
Disenrollment from PDAP
This is not used in the simulation, but we include it in the report.
It is likely that pausing/quitting PrEP is the primary reason for disenrollment. The DOH PDAP data include a field called endReason, but “stopped using PrEP” is unfortunately not one of the response options. The distribution of endReason is shown below (from secure terminal server analysis):
Overall
n 4621
endReason (%)
Address Update 6 ( 0.1)
Apple Health 7 ( 0.2)
Benefit Change 40 ( 0.9)
Benefit Level Change 251 ( 5.4)
Client's Request 1 ( 0.0)
Deceased 2 ( 0.0)
Did not recertify 621 (13.4)
Dropped out, no reason given 2 ( 0.0)
Eligibility expired 1345 (29.1)
End of year: client remains eligible 1237 (26.8)
Enrollment renewal 601 (13.0)
Health Benefits Update 491 (10.6)
Incomplete Application 4 ( 0.1)
Ineligible for PrEP 6 ( 0.1)
Ineligible for PrEP, no longer meets PrEP eligibility 6 ( 0.1)
PAP 1 ( 0.0)When the enrollGap variable is NA this signifies no subsequent enrollment (so, this person has dropped out of PDAP).
> round(100*proportions(table(PDAP$endReason, is.na(PDAP$enrollGap)), 2), 1)
FALSE TRUE
Address Update 0.2 0.0
Apple Health 0.1 0.2
Benefit Change 0.1 1.8
Benefit Level Change 0.0 11.7
Client's Request 0.0 0.0
Deceased 0.0 0.1
Did not recertify 2.3 26.3
Dropped out, no reason given 0.0 0.1
Eligibility expired 3.9 58.2
End of year: client remains eligible 49.8 0.1
Enrollment renewal 24.2 0.0
Health Benefits Update 19.2 0.7
Incomplete Application 0.0 0.1
Ineligible for PrEP 0.0 0.3
Ineligible for PrEP, no longer meets PrEP eligibility 0.0 0.3
PAP 0.0 0.0Based on this, it looks like 84% of the dropouts (TRUE column above) are coming from Did not recertify and Eligibility expired – categories which suggest a personal choice not to re-enroll.
We can’t be certain, but this does suggest the person may no longer have any need for PDAP because they are no longer taking PrEP.
Model
We are preserving ZK’s model for the probability of disenrollment, and it’s predictions, primarily for eventual comparison to WHAMP survey data predictions of the probability of stopping PrEP. These are not used as parameters in the EpiModel PrEP dynamics modules.
Logistic regression to predict the probability of PDAP disenrollment at the end of year on PDAP.
Covariates: years on PDAP (year 1, 2 or 3), race, and insurance type (none, individual, and employer).
summary(pdap_data$model.disenroll)##
## Call:
## glm(formula = event ~ factor(years) + RaceCat + Insurance, family = binomial(link = "logit"),
## data = PDAPexpand %>% filter(cateYear >= 2016 & Insurance !=
## "Public"))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0268 -1.2224 0.7484 1.0368 1.4471
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.58545 0.23153 2.529 0.0115 *
## factor(years)2 0.23476 0.08720 2.692 0.0071 **
## factor(years)3+ 0.77688 0.13538 5.739 9.55e-09 ***
## RaceCatHispanic 0.01032 0.24848 0.042 0.9669
## RaceCatOther -0.48017 0.23284 -2.062 0.0392 *
## InsuranceIndividual -0.72000 0.09900 -7.273 3.52e-13 ***
## InsuranceNone 0.54403 0.10480 5.191 2.09e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 3901.7 on 2864 degrees of freedom
## Residual deviance: 3728.0 on 2858 degrees of freedom
## AIC: 3742
##
## Number of Fisher Scoring iterations: 4Table
tmpDF <- pdap_data$pred.disenroll
kable(tmpDF, digits = c(0, 0, 0, 3),
caption = "Probability of PDAP disenrollment at year end") %>%
kable_styling(full_width=F, position="center", bootstrap_options = c("striped"))| race | insurance | years | Prob |
|---|---|---|---|
| B | employer | 1 | 0.642 |
| H | employer | 1 | 0.645 |
| O | employer | 1 | 0.526 |
| B | individual | 1 | 0.466 |
| H | individual | 1 | 0.469 |
| O | individual | 1 | 0.351 |
| B | none | 1 | 0.756 |
| H | none | 1 | 0.758 |
| O | none | 1 | 0.657 |
| B | employer | 2 | 0.694 |
| H | employer | 2 | 0.696 |
| O | employer | 2 | 0.584 |
| B | individual | 2 | 0.525 |
| H | individual | 2 | 0.528 |
| O | individual | 2 | 0.406 |
| B | none | 2 | 0.796 |
| H | none | 2 | 0.798 |
| O | none | 2 | 0.708 |
| B | employer | 3 | 0.796 |
| H | employer | 3 | 0.798 |
| O | employer | 3 | 0.707 |
| B | individual | 3 | 0.655 |
| H | individual | 3 | 0.658 |
| O | individual | 3 | 0.540 |
| B | none | 3 | 0.871 |
| H | none | 3 | 0.872 |
| O | none | 3 | 0.806 |
Plot
p <- ggplot(tmpDF,
aes(x = years, y = Prob,
text = scales::percent(Prob,1))) +
geom_line(aes(group=race, color = race)) +
facet_wrap(~ insurance) +
labs(title = "Probability of disenrollment from PDAP",
y = "probability")
ggplotly(p, tooltip = "text")Targets
Program size
ADAP
Data inputs:
- Number of ADAP clients from DOH DAP data
- Popsize HIV+ from WApopdata
Target outcomes:
- adap.size$prop – ADAP program size expressed as a % of the HIV+ population, by year
adap.size <- adap_data$demog %>%
group_by(year) %>%
summarize(adapN = sum(N),
popsize = sum(popsize.pos)) %>%
mutate(prop = adapN/popsize) %>%
data.table()
setkeyv(adap.size, c("year")) # check if we need this
p <- ggplot(data = adap.size %>% mutate(year = year+2013),
aes(x = factor(year), y = prop, text = adapN)) +
geom_bar(fill = "blue", alpha = 0.5,
stat="identity") +
labs(title = "ADAP program size: Proportion of HIV+ population",
x = "year", y = "proportion")
ggplotly(p, tooltip = "text")PDAP
Data inputs:
- Number of PDAP clients from DOH DAP data
- Popsize HIV- from WApopdata
Target outcomes:
- pdap.size$prop – PDAP program size expressed as a % of the HIV- population, by year. Note, given the dynamic nature of this program (it doubles in size from 2017 to 2018), this should not be used as an “equilibrium” target value. The estimate from the WHAMP survey in 2019 is substantially higher.
pdap.size <- pdap_data$demog %>%
group_by(year) %>%
summarize(pdapN = sum(N),
popsize = sum(popsize.neg)) %>%
mutate(prop = pdapN/popsize) %>%
data.table()
setkeyv(pdap.size, c("year")) # check if we need this
p <- ggplot(data = pdap.size %>% mutate(year = year+2013),
aes(x = factor(year), y = prop, text = pdapN)) +
geom_bar(fill = "blue", alpha = 0.5,
stat="identity") +
labs(title = "PDAP program size: Proportion of HIV- population",
x = "year", y = "proportion")
ggplotly(p, tooltip = "text")Program cost
To construct overall cost targets, the DOH DAP total costs need to be adjusted for population size, if the simulated population size is different than the estimated WApopdata size.
In general the scale factor will be:
DAP.total.cost * sim$pop.size/WApopdata$pop.sizewhich can be calculated overall, or stratified by group.
We leave the scaling adjustment to the EpiModel run. Here we just calculate the total annual cost.
ADAP
Data inputs:
- Total program costs from DOH ADAP data
- ZK uses 3 periods: 2014-15, 2016, and 2017-18, because there were changes in how WADOH recorded insurance type, esp. for “employer”.
Target outcomes:
- adap.annual.cost – ADAP annual total cost, for each period (the 2-yr periods are divided by 2)
adap.cost <- adap_data$cost.pyr %>%
mutate(year = case_when(yr_ix==1 ~ 1,
yr_ix==2 ~ 3,
yr_ix==3 ~ 5)) %>%
group_by(year, race) %>%
summarize(cost.tot = sum(cTotCost)) %>%
mutate(cost.tot = if_else(year==3, cost.tot, cost.tot/2)) %>%
data.table() %>%
setkeyv(c("year", "race"))
p <- ggplot(data = adap.cost %>% mutate(year = year+2013),
aes(x = factor(year), y = cost.tot,
group = race, fill = race,
text = scales::comma(cost.tot))) +
geom_bar(alpha = 0.5,
stat="identity") +
labs(title = "ADAP total annual cost",
x = "year", y = "$ cost")+
scale_y_continuous(labels = scales::comma)
ggplotly(p, tooltip = "text")PDAP
Data inputs:
- Total program costs from DOH PDAP data
- Here we just use the last year of data, 2018.
Target outcomes:
- pdap.annual.cost – PDAP annual total cost
pdap.cost <- orig_pdap_data$PDAPcost_pd %>%
filter(year == 2018) %>%
group_by(race) %>%
summarize(cost.tot = sum(Overalltcost)) %>%
data.table() %>%
setkeyv("race")
p <- ggplot(data = data.frame(year = "2018", pdap.cost),
aes(x = year, y = cost.tot,
group = race, fill = race,
text = scales::comma(cost.tot))) +
geom_bar(alpha = 0.5,
stat="identity") +
labs(title = "PDAP total annual cost",
x = "year", y = "$ cost") +
scale_y_continuous(labels = scales::comma)
ggplotly(p, tooltip = "text")Save out
DAP parameters
# Parameters -- just the updated adap_data and pdap_data
dap_params <- list(adap = adap_data,
pdap = pdap_data,
makefile = "make_DOHDAPdynamics.Rmd")
descTable <-
tibble(Objects = names(dap_params),
Description = c("Updated object from ZK analysis, incl. VL",
"Updated object from ZK analysis",
"source file"),
DataSource = c("DOH ADAP client data, EHARS, WApopdata",
"DOH PDAP client data, WApopdata",
" "))
dap_params <- c(dap_params, list(descTable = descTable))
kable(dap_params$descTable,
caption= "ADAP & PDAP related parameter object") %>%
kable_styling(position = "center",
bootstrap_options = c("striped"))| Objects | Description | DataSource |
|---|---|---|
| adap | Updated object from ZK analysis, incl. VL | DOH ADAP client data, EHARS, WApopdata |
| pdap | Updated object from ZK analysis | DOH PDAP client data, WApopdata |
| makefile | source file |
kable(dap_params$adap$descTable,
caption= "ADAP related parameter object") %>%
kable_styling(position = "center",
bootstrap_options = c("striped"))| Component | Description | Method | Levels | Source | Group |
|---|---|---|---|---|---|
| adap.frac | ADAP fraction of ART users or HIV+ | clients/(art_current or pop.pos) | overall | ADAP claims, Census, DOH report | ADAP-related |
| demog | demographics of ADAP clients | Obs summary | enroll yr x race x region | ADAP claims data | ADAP-related |
| init.demog | demographics in first yr | Obs summary | race x region | ADAP claims data | ADAP-related |
| ins | insurance of ADAP clients | Obs summary | enroll yr x race x region | ADAP claims data | ADAP-related |
| new.clients | demographics of new ADAP clients | Obs summary | enroll yr x race x region | ADAP claims data | ADAP-related |
| cost.pday | daily cost by component and total | Obs summary | yr x race x insurance | ADAP claims data | ADAP-related |
| cost.pyr | annual cost by component and total | Obs summary | yr x race x insurance | ADAP claims data | ADAP-related |
| model.disenroll | survival model calc outputs | Exp + Weibull survival model | day x race x insurance x type | ADAP claims data | ADAP-related |
| pred.disenroll | pred prob ADAP disenroll/wk | Exp + Weibull survival model | wk x race x insurance | ADAP claims data | ADAP-related |
| vl.race | prop virally suppressed | Obs summary | race x ADAP status | ADAP claims data + EHARS | ADAP-related |
| makefile | source files |
kable(dap_params$pdap$descTable,
caption= "PDAP related parameter object") %>%
kable_styling(position = "center",
bootstrap_options = c("striped"))| Component | Description | Method | Levels | Source | Group |
|---|---|---|---|---|---|
| pdap.frac | PDAP fraction of PrEP users or HIV- | clients/(prep.current or pop.neg) | overall | PDAP claims, Census, WHAMP survey | PDAP-related |
| demog | demographics of PDAP clients | Obs summary | enroll yr x race x region | PDAP claims data | PDAP-related |
| init.demog | demographics before first yr (2016) | Obs summary | race x region | PDAP claims data | PDAP-related |
| ins | observed insurance of PDAP clients | Obs summary | enroll yr x race x region | PDAP claims data | PDAP-related |
| ins.pred | predicted insurance for PDAP clients | multinomial regression | race x region | PDAP claims data | PDAP-related |
| new.clients | demographics of new PDAP clients | Obs summary | enroll yr x race x region | PDAP claims data | PDAP-related |
| cost.pday | daily cost by component and total | Obs summary | enroll yr x race x insurance | PDAP claims data | PDAP-related |
| model.disenroll | disenroll model fit object | Logistic regression | yr x race x insurance | PDAP claims data | PDAP-related |
| pred.disenroll | pred prob PDAP disenroll/wk | Logistic regression | yr x race x insurance | PDAP claims data | PDAP-related |
| init.dur | time on PDAP at start of first yr (days) | Obs summary | race x insurance x region | PDAP claims data | PDAP-related |
| makefile | source files |
### Params
saveRDS(dap_params, here::here("Data", "Params", "DohDapParam.RDS"))DAP targets
dap_targets = list(adap.size = adap.size,
adap.annual.cost = adap.cost,
pdap.size = pdap.size,
pdap.annual.cost = pdap.cost,
vl.suppressed = adap_data$vl.race,
makefile = "make_DOHDAPdynamics.Rmd")
descTable <-
tibble(Targets = names(dap_targets),
Description = c("ADAP program size",
"ADAP annual cost",
"PDAP program size",
"PDAP annual cost",
"VL suppression rates",
"source file"),
Subset = c(rep("ADAP clients",2),
rep("PDAP clients",2),
"Dx HIV+ on ART",
" "),
Method = c(rep("obs summaries", 5), " "),
Levels = c("yr", "yr x race",
"yr", "race (2018)",
"race x ADAP",
" "),
DataSource = c(rep("DOH ADAP data",2),
rep("DOH PDAP data",2),
"DOH ADAP data and EHARS",
" "))
kable(descTable,
caption= "ADAP & PDAP related targets") %>%
kable_styling(position = "center",
bootstrap_options = c("striped"))| Targets | Description | Subset | Method | Levels | DataSource |
|---|---|---|---|---|---|
| adap.size | ADAP program size | ADAP clients | obs summaries | yr | DOH ADAP data |
| adap.annual.cost | ADAP annual cost | ADAP clients | obs summaries | yr x race | DOH ADAP data |
| pdap.size | PDAP program size | PDAP clients | obs summaries | yr | DOH PDAP data |
| pdap.annual.cost | PDAP annual cost | PDAP clients | obs summaries | race (2018) | DOH PDAP data |
| vl.suppressed | VL suppression rates | Dx HIV+ on ART | obs summaries | race x ADAP | DOH ADAP data and EHARS |
| makefile | source file |
dap_targets <- c(dap_targets, list(descTable = descTable))
### Targets
saveRDS(dap_targets, here::here("Data", "Targets", "DohDapTargets.RDS"))