Setting up the R session and Loading packages

# environment setup to run ordered logit properly
options(contrasts = rep("contr.treatment", 2))

This chunk loads all the packages to use

#packages for ordered logit
library(ordinal) # package for ordinal logit regression
library(brant) # brant test for the parallel assumption for ordered logit
library(MASS) # models that work with the brant test

library(tidyverse) # package for data cleaning and plotting
library(readxl) # package for reading excel file
library(broom) # extracting model summary as data frame
library(modelsummary) # deriving model tables
library(scales) # label percent
library(lubridate) # working with dates
library(marginaleffects) #to calculate marginal effects

Import the cleaned data

merged <- read_csv("D:/Archive/Socialcare UK/data/spinout_2017.csv")

Prepare the data for ordinal regression

#select relevant columns, rename and relabel 
merged_cleaned <- merged %>% 
  # recode legal form types to be more readable / easier to present
  mutate(# inherited = ifelse(inherited == "Y", TRUE, FALSE),
         rating = recode(rating, 
                         "Insufficient evidence to rate" = "NA",
                         "Requires improvement" = "Req improv"),
         date = ymd(publication_date)) %>% 
  
  # assign order in the rating levels
  mutate(rating = ordered(rating, levels = c("Inadequate","Req improv", "Good", "Outstanding")),
         form_spinout = case_when(form == "GOV" ~ "GOV",
                           form == "CIC" & spin_out == "TRUE" ~ "SP_CIC",
                           form == "CIC" & spin_out == "FALSE" ~ "NSP_CIC"),
         socialcare = ifelse(type == "Social Care Org", TRUE, FALSE)) %>% 
  
  # creating a new dummy variable for facility category
  mutate(year = year(date),
         year2 = year-2013,
         Year = factor(year)) %>%
  
  # regroup care type

  mutate(service_type = case_when(
      str_detect(primary_cat, "Acute hospital") ~ "Acute hospital",
      str_detect(primary_cat, "Mental health") ~ "Mental health",
      TRUE ~ primary_cat  # Keep the original value if none of the conditions match
    )) %>%

  # converting the ordinal variable to numerical 
  mutate(rating_num = case_when(rating == "Inadequate" ~ 1,
                                rating == "Req improv" ~ 2,
                                rating == "Good" ~ 3,
                                rating == "Outstanding" ~ 4)) 
# show first several rows of the data set derived 
head(merged_cleaned)
## # A tibble: 6 × 22
##   project_id provider_name   location_name type  primary_cat form  region domain
##   <chr>      <chr>           <chr>         <chr> <chr>       <chr> <chr>  <chr> 
## 1 location1  Solihull Metro… Bluebell Cen… Soci… Community … GOV   West … Safe  
## 2 location2  Solihull Metro… Bluebell Cen… Soci… Community … GOV   West … Effec…
## 3 location3  Solihull Metro… Bluebell Cen… Soci… Community … GOV   West … Caring
## 4 location4  Solihull Metro… Bluebell Cen… Soci… Community … GOV   West … Respo…
## 5 location5  Solihull Metro… Bluebell Cen… Soci… Community … GOV   West … Well-…
## 6 location6  Solihull Metro… Bluebell Cen… Soci… Community … GOV   West … Overa…
## # ℹ 14 more variables: rating <ord>, publication_date <dttm>,
## #   report_type <chr>, std_name <chr>, level <chr>, spin_out <lgl>,
## #   date <date>, form_spinout <chr>, socialcare <lgl>, year <dbl>, year2 <dbl>,
## #   Year <fct>, service_type <chr>, rating_num <dbl>

drop GOV providers in extra primary_cad

cic_cats <- merged_cleaned %>%
  filter(form == "CIC") %>%
  distinct(primary_cat) %>% 
  pull(primary_cat)
merged_short <- merged_cleaned %>% 
  filter(primary_cat %in% cic_cats)
nrow(merged_cleaned)
## [1] 26473
nrow(merged_short)
## [1] 7619

Overall distribution

datasummary(form_spinout + socialcare  + region + Year ~ 1, data = merged_short, fmt = 0)
All
form_spinout GOV 5963
NSP_CIC 813
SP_CIC 843
socialcare FALSE 2561
TRUE 5058
region East Midlands 730
East of England 851
London 756
North East 381
North West 1304
South East 1204
South West 677
West Midlands 690
Yorkshire and The Humber 1026
Year 2013 0
2014 276
2015 786
2016 3459
2017 3098

Comparison table on Primary Inspection Category

datasummary(service_type + primary_cat ~ form, data = merged_cleaned, fmt = 0)
CIC GOV
service_type Acute hospital 24 14227
Ambulance service 0 361
Community based adult social care services 360 1944
Community health - NHS & Independent 300 954
Dentists 0 24
GP Practices 546 203
Hospice services 12 6
Independent consulting doctors 24 0
Mental health 156 4242
Out of hours 6 6
Prison Healthcare 30 0
Residential social care 186 2832
Urgent care services & mobile doctors 12 18
primary_cat Acute hospital - Independent specialist 24 0
Acute hospital - NHS non-specialist 0 13135
Acute hospital - NHS specialist 0 1092
Ambulance service 0 361
Community based adult social care services 360 1944
Community health - NHS & Independent 300 954
Dentists 0 24
GP Practices 546 203
Hospice services 12 6
Independent consulting doctors 24 0
Mental health - community & hospital - independent 156 0
Mental health - community & residential - NHS 0 4242
Out of hours 6 6
Prison Healthcare 30 0
Residential social care 186 2832
Urgent care services & mobile doctors 12 18
datasummary(service_type + primary_cat ~ form_spinout, data = merged_cleaned, fmt = 0)
GOV NSP_CIC SP_CIC
service_type Acute hospital 14227 0 24
Ambulance service 361 0 0
Community based adult social care services 1944 252 108
Community health - NHS & Independent 954 102 198
Dentists 24 0 0
GP Practices 203 285 261
Hospice services 6 6 6
Independent consulting doctors 0 0 24
Mental health 4242 24 132
Out of hours 6 6 0
Prison Healthcare 0 30 0
Residential social care 2832 102 84
Urgent care services & mobile doctors 18 6 6
primary_cat Acute hospital - Independent specialist 0 0 24
Acute hospital - NHS non-specialist 13135 0 0
Acute hospital - NHS specialist 1092 0 0
Ambulance service 361 0 0
Community based adult social care services 1944 252 108
Community health - NHS & Independent 954 102 198
Dentists 24 0 0
GP Practices 203 285 261
Hospice services 6 6 6
Independent consulting doctors 0 0 24
Mental health - community & hospital - independent 0 24 132
Mental health - community & residential - NHS 4242 0 0
Out of hours 6 6 0
Prison Healthcare 0 30 0
Residential social care 2832 102 84
Urgent care services & mobile doctors 18 6 6

whole models with continous year trend

Since service_type is inclusive of variation/information from socialcare and level, we drop this variable from the model

model_order_overall <- clm(rating ~ form_spinout  + service_type + region + year2,
                data = filter(merged_cleaned, domain == "Overall"),
                link = "logit")

model_order_safe <- clm(rating ~ form_spinout  + service_type+ region + year2,
                data = filter(merged_cleaned, domain == "Safe"),
                link = "logit")
model_order_effective <- clm(rating ~ form_spinout  + service_type + region + year2,
                data = filter(merged_cleaned, domain == "Effective"),
                link = "logit")
model_order_caring <- clm(rating ~ form_spinout  + service_type + region + year2,
                data = filter(merged_cleaned, domain == "Caring"),
                link = "logit")
model_order_well_led <- clm(rating ~ form_spinout + service_type + region + year2,
                data = filter(merged_cleaned, domain == "Well-led"),
                link = "logit")
model_order_responsive <- clm(rating ~ form_spinout + service_type + region + year2,
                data = filter(merged_cleaned, domain == "Responsive"),
                link = "logit")
ordinal_models <-
  modelsummary(
    list(
      "overall" = model_order_overall,
      "safe" = model_order_safe,
      "effective" = model_order_effective,
      "caring" = model_order_caring,
      "well-led" = model_order_well_led,
      "responsive" = model_order_responsive
    ),
    coef_omit = "region",
    exponentiate = F,
    statistic = "({p.value}) {stars}")
ordinal_models
overall safe effective caring well-led responsive
Inadequate|Req improv -4.279 -4.038 -5.485 -7.335 -3.881 -4.509
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
Req improv|Good -1.263 -0.881 -1.622 -3.441 -1.361 -1.242
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
Good|Outstanding 2.675 5.277 3.186 2.881 2.563 3.273
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
form_spinoutNSP_CIC 0.468 0.548 0.331 0.363 0.175 0.633
(0.009) ** (0.053) + (0.208) (0.285) (0.444) (0.014) *
form_spinoutSP_CIC 0.395 0.618 0.354 0.270 0.607 0.639
(0.026) * (0.014) * (0.160) (0.384) (0.008) ** (0.011) *
service_typeAmbulance service -0.482 -0.149 -0.466 0.883 -0.867 0.718
(0.064) + (0.574) (0.096) + (0.006) ** (<0.001) *** (0.012) *
service_typeCommunity based adult social care services 1.317 2.095 0.919 -0.768 0.869 1.500
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
service_typeCommunity health - NHS & Independent 0.473 0.370 0.098 0.207 0.168 0.662
(0.002) ** (0.018) * (0.576) (0.351) (0.283) (<0.001) ***
service_typeDentists 2.865 2.489 3.208 -1.393 2.838 3.060
(0.005) ** (0.126) (0.002) ** (0.390) (0.005) ** (0.004) **
service_typeGP Practices 1.326 1.693 1.567 -0.442 1.272 1.217
(<0.001) *** (<0.001) *** (<0.001) *** (0.338) (<0.001) *** (<0.001) ***
service_typeHospice services 1.571 2.825 1.181 -0.942 1.355 1.464
(0.206) (0.150) (0.426) (0.637) (0.275) (0.352)
service_typeIndependent consulting doctors 2.509 2.837 1.323 -1.021 0.991 2.412
(0.017) * (0.105) (0.313) (0.576) (0.430) (0.029) *
service_typeMental health 0.840 0.551 0.278 -0.055 0.660 1.082
(<0.001) *** (<0.001) *** (0.005) ** (0.680) (<0.001) *** (<0.001) ***
service_typeOut of hours -0.065 0.297 -2.733 -1.180 -0.443 1.601
(0.961) (0.828) (0.174) (0.641) (0.734) (0.338)
service_typePrison Healthcare 0.680 1.373 0.131 -0.030 0.480 0.482
(0.476) (0.221) (0.901) (0.979) (0.618) (0.639)
service_typeResidential social care 1.036 1.564 0.645 -1.080 0.534 1.345
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
service_typeUrgent care services & mobile doctors 1.880 3.374 1.609 -1.144 1.494 1.794
(0.051) + (0.026) * (0.161) (0.468) (0.119) (0.091) +
year2 -0.358 -0.369 -0.332 0.127 -0.271 -0.248
(<0.001) *** (<0.001) *** (<0.001) *** (0.019) * (<0.001) *** (<0.001) ***
Num.Obs. 4793 4405 4077 4387 4407 4404
AIC 8117.8 6623.4 5516.5 3606.5 7691.2 6634.8
BIC 8286.2 6789.5 5680.7 3772.6 7857.4 6800.9
RMSE 2.29 2.09 2.23 2.34 2.29 2.25
ordinal_models_exp <-
  modelsummary(
    list(
      "overall" = model_order_overall,
      "safe" = model_order_safe,
      "effective" = model_order_effective,
      "caring" = model_order_caring,
      "well-led" = model_order_well_led,
      "responsive" = model_order_responsive
    ),
    coef_omit = "region",
    exponentiate = T,
    statistic = "({p.value}) {stars}")
ordinal_models_exp
overall safe effective caring well-led responsive
Inadequate|Req improv 0.014 0.018 0.004 0.001 0.021 0.011
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
Req improv|Good 0.283 0.414 0.198 0.032 0.256 0.289
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
Good|Outstanding 14.505 195.756 24.192 17.841 12.974 26.390
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
form_spinoutNSP_CIC 1.596 1.730 1.392 1.437 1.191 1.884
(0.009) ** (0.053) + (0.208) (0.285) (0.444) (0.014) *
form_spinoutSP_CIC 1.484 1.855 1.424 1.309 1.835 1.895
(0.026) * (0.014) * (0.160) (0.384) (0.008) ** (0.011) *
service_typeAmbulance service 0.617 0.861 0.628 2.418 0.420 2.050
(0.064) + (0.574) (0.096) + (0.006) ** (<0.001) *** (0.012) *
service_typeCommunity based adult social care services 3.733 8.122 2.508 0.464 2.386 4.483
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
service_typeCommunity health - NHS & Independent 1.605 1.448 1.103 1.230 1.183 1.939
(0.002) ** (0.018) * (0.576) (0.351) (0.283) (<0.001) ***
service_typeDentists 17.549 12.045 24.731 0.248 17.082 21.329
(0.005) ** (0.126) (0.002) ** (0.390) (0.005) ** (0.004) **
service_typeGP Practices 3.767 5.433 4.791 0.643 3.567 3.375
(<0.001) *** (<0.001) *** (<0.001) *** (0.338) (<0.001) *** (<0.001) ***
service_typeHospice services 4.813 16.853 3.259 0.390 3.878 4.323
(0.206) (0.150) (0.426) (0.637) (0.275) (0.352)
service_typeIndependent consulting doctors 12.289 17.060 3.756 0.360 2.694 11.151
(0.017) * (0.105) (0.313) (0.576) (0.430) (0.029) *
service_typeMental health 2.317 1.736 1.320 0.947 1.935 2.950
(<0.001) *** (<0.001) *** (0.005) ** (0.680) (<0.001) *** (<0.001) ***
service_typeOut of hours 0.937 1.346 0.065 0.307 0.642 4.956
(0.961) (0.828) (0.174) (0.641) (0.734) (0.338)
service_typePrison Healthcare 1.974 3.946 1.140 0.970 1.616 1.619
(0.476) (0.221) (0.901) (0.979) (0.618) (0.639)
service_typeResidential social care 2.819 4.776 1.905 0.339 1.705 3.837
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
service_typeUrgent care services & mobile doctors 6.555 29.195 4.999 0.318 4.457 6.012
(0.051) + (0.026) * (0.161) (0.468) (0.119) (0.091) +
year2 0.699 0.691 0.717 1.135 0.762 0.781
(<0.001) *** (<0.001) *** (<0.001) *** (0.019) * (<0.001) *** (<0.001) ***
Num.Obs. 4793 4405 4077 4387 4407 4404
AIC 8117.8 6623.4 5516.5 3606.5 7691.2 6634.8
BIC 8286.2 6789.5 5680.7 3772.6 7857.4 6800.9
RMSE 2.29 2.09 2.23 2.34 2.29 2.25

Models with Year fixed effects

model_order_overall_fe <- clm(rating ~ form_spinout  + service_type + region + Year,
                data = filter(merged_cleaned, domain == "Overall"),
                link = "logit")

model_order_safe_fe <- clm(rating ~ form_spinout  + service_type + region + Year,
                data = filter(merged_cleaned, domain == "Safe"),
                link = "logit")
model_order_effective_fe <- clm(rating ~ form_spinout  + service_type + region + Year,
                data = filter(merged_cleaned, domain == "Effective"),
                link = "logit")
model_order_caring_fe <- clm(rating ~ form_spinout  + service_type + region + Year,
                data = filter(merged_cleaned, domain == "Caring"),
                link = "logit")
model_order_well_led_fe <- clm(rating ~ form_spinout + service_type + region + Year,
                data = filter(merged_cleaned, domain == "Well-led"),
                link = "logit")
model_order_responsive_fe <- clm(rating ~ form_spinout + service_type + region + Year,
                data = filter(merged_cleaned, domain == "Responsive"),
                link = "logit")
ordinal_models_fe <-
  modelsummary(
    list(
      "overall" = model_order_overall_fe,
      "safe" = model_order_safe_fe,
      "effective" = model_order_effective_fe,
      "caring" = model_order_caring_fe,
      "well-led" = model_order_well_led_fe,
      "responsive" = model_order_responsive_fe
    ),
    coef_omit = "region",
    exponentiate = F,
    statistic = "({p.value}) {stars}")
ordinal_models_fe
overall safe effective caring well-led responsive
Inadequate|Req improv -4.501 -4.952 -5.945 -7.769 -4.432 -5.677
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
Req improv|Good -1.472 -1.784 -2.081 -3.874 -1.907 -2.400
(0.035) * (0.010) ** (0.003) ** (<0.001) *** (<0.001) *** (<0.001) ***
Good|Outstanding 2.498 4.398 2.738 2.484 2.031 2.143
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
form_spinoutNSP_CIC 0.526 0.607 0.339 0.445 0.213 0.686
(0.003) ** (0.032) * (0.198) (0.192) (0.352) (0.008) **
form_spinoutSP_CIC 0.395 0.651 0.363 0.315 0.635 0.678
(0.026) * (0.010) * (0.151) (0.309) (0.005) ** (0.007) **
service_typeAmbulance service -0.519 -0.168 -0.479 0.855 -0.888 0.700
(0.049) * (0.530) (0.087) + (0.008) ** (<0.001) *** (0.014) *
service_typeCommunity based adult social care services 1.253 2.053 0.910 -0.853 0.819 1.440
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
service_typeCommunity health - NHS & Independent 0.479 0.360 0.070 0.252 0.158 0.658
(0.002) ** (0.023) * (0.689) (0.255) (0.315) (<0.001) ***
service_typeDentists 2.671 2.342 3.165 -1.621 2.691 2.884
(0.009) ** (0.152) (0.002) ** (0.326) (0.008) ** (0.007) **
service_typeGP Practices 1.325 1.686 1.566 -0.489 1.237 1.193
(<0.001) *** (<0.001) *** (<0.001) *** (0.290) (<0.001) *** (<0.001) ***
service_typeHospice services 1.272 2.600 1.140 -1.303 1.153 1.204
(0.308) (0.187) (0.444) (0.517) (0.354) (0.447)
service_typeIndependent consulting doctors 2.709 2.915 1.319 -0.883 1.046 2.503
(0.010) ** (0.097) + (0.316) (0.631) (0.406) (0.024) *
service_typeMental health 0.836 0.543 0.274 -0.081 0.648 1.072
(<0.001) *** (<0.001) *** (0.006) ** (0.542) (<0.001) *** (<0.001) ***
service_typeOut of hours 0.016 0.340 -2.754 -1.312 -0.508 1.508
(0.991) (0.804) (0.166) (0.607) (0.696) (0.357)
service_typePrison Healthcare 0.788 1.425 0.116 0.128 0.533 0.565
(0.411) (0.204) (0.912) (0.911) (0.580) (0.583)
service_typeResidential social care 1.027 1.560 0.644 -1.116 0.516 1.329
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
service_typeUrgent care services & mobile doctors 2.019 3.449 1.603 -0.994 1.553 1.881
(0.037) * (0.023) * (0.164) (0.533) (0.106) (0.077) +
Year2014 -0.511 -1.148 -0.566 -0.461 -0.719 -1.309
(0.465) (0.096) + (0.423) (0.519) (0.194) (0.035) *
Year2015 -1.167 -1.871 -1.302 -0.211 -1.269 -1.855
(0.093) + (0.006) ** (0.063) + (0.764) (0.021) * (0.003) **
Year2016 -0.989 -1.801 -1.415 0.303 -1.164 -1.640
(0.154) (0.008) ** (0.042) * (0.665) (0.034) * (0.008) **
Year2017 -1.737 -2.430 -1.781 -0.026 -1.669 -2.193
(0.012) * (<0.001) *** (0.011) * (0.970) (0.002) ** (<0.001) ***
Num.Obs. 4793 4405 4077 4387 4407 4404
AIC 8074.9 6600.5 5515.0 3591.9 7675.9 6608.6
BIC 8262.7 6785.8 5698.1 3777.1 7861.2 6793.9
RMSE 2.29 2.09 2.23 2.34 2.29 2.25
ordinal_models_fe_exp <-
  modelsummary(
    list(
      "overall" = model_order_overall_fe,
      "safe" = model_order_safe_fe,
      "effective" = model_order_effective_fe,
      "caring" = model_order_caring_fe,
      "well-led" = model_order_well_led_fe,
      "responsive" = model_order_responsive_fe
    ),
    coef_omit = "region",
    exponentiate = T,
    statistic = "({p.value}) {stars}")
ordinal_models_fe_exp
overall safe effective caring well-led responsive
Inadequate|Req improv 0.011 0.007 0.003 0.000 0.012 0.003
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
Req improv|Good 0.229 0.168 0.125 0.021 0.149 0.091
(0.035) * (0.010) ** (0.003) ** (<0.001) *** (<0.001) *** (<0.001) ***
Good|Outstanding 12.155 81.315 15.452 11.985 7.621 8.528
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
form_spinoutNSP_CIC 1.693 1.836 1.404 1.560 1.238 1.985
(0.003) ** (0.032) * (0.198) (0.192) (0.352) (0.008) **
form_spinoutSP_CIC 1.484 1.918 1.437 1.370 1.887 1.970
(0.026) * (0.010) * (0.151) (0.309) (0.005) ** (0.007) **
service_typeAmbulance service 0.595 0.845 0.620 2.352 0.411 2.014
(0.049) * (0.530) (0.087) + (0.008) ** (<0.001) *** (0.014) *
service_typeCommunity based adult social care services 3.500 7.792 2.485 0.426 2.269 4.219
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
service_typeCommunity health - NHS & Independent 1.615 1.433 1.073 1.287 1.171 1.932
(0.002) ** (0.023) * (0.689) (0.255) (0.315) (<0.001) ***
service_typeDentists 14.449 10.404 23.677 0.198 14.740 17.879
(0.009) ** (0.152) (0.002) ** (0.326) (0.008) ** (0.007) **
service_typeGP Practices 3.763 5.399 4.785 0.613 3.445 3.298
(<0.001) *** (<0.001) *** (<0.001) *** (0.290) (<0.001) *** (<0.001) ***
service_typeHospice services 3.569 13.466 3.128 0.272 3.169 3.335
(0.308) (0.187) (0.444) (0.517) (0.354) (0.447)
service_typeIndependent consulting doctors 15.010 18.446 3.739 0.414 2.846 12.218
(0.010) ** (0.097) + (0.316) (0.631) (0.406) (0.024) *
service_typeMental health 2.307 1.722 1.316 0.922 1.912 2.921
(<0.001) *** (<0.001) *** (0.006) ** (0.542) (<0.001) *** (<0.001) ***
service_typeOut of hours 1.016 1.405 0.064 0.269 0.601 4.519
(0.991) (0.804) (0.166) (0.607) (0.696) (0.357)
service_typePrison Healthcare 2.199 4.157 1.123 1.137 1.703 1.759
(0.411) (0.204) (0.912) (0.911) (0.580) (0.583)
service_typeResidential social care 2.793 4.759 1.904 0.328 1.676 3.779
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
service_typeUrgent care services & mobile doctors 7.529 31.480 4.968 0.370 4.725 6.563
(0.037) * (0.023) * (0.164) (0.533) (0.106) (0.077) +
Year2014 0.600 0.317 0.568 0.630 0.487 0.270
(0.465) (0.096) + (0.423) (0.519) (0.194) (0.035) *
Year2015 0.311 0.154 0.272 0.810 0.281 0.157
(0.093) + (0.006) ** (0.063) + (0.764) (0.021) * (0.003) **
Year2016 0.372 0.165 0.243 1.354 0.312 0.194
(0.154) (0.008) ** (0.042) * (0.665) (0.034) * (0.008) **
Year2017 0.176 0.088 0.169 0.974 0.188 0.112
(0.012) * (<0.001) *** (0.011) * (0.970) (0.002) ** (<0.001) ***
Num.Obs. 4793 4405 4077 4387 4407 4404
AIC 8074.9 6600.5 5515.0 3591.9 7675.9 6608.6
BIC 8262.7 6785.8 5698.1 3777.1 7861.2 6793.9
RMSE 2.29 2.09 2.23 2.34 2.29 2.25