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[1] "dplyr" "stats" "graphics" "grDevices" "utils" "datasets"
[7] "methods" "base"
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[11] "utils" "datasets" "methods" "base"
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[1] "maps" "scales" "ggplot2" "sf" "tidycensus"
[6] "tidyr" "stringr" "dplyr" "stats" "graphics"
[11] "grDevices" "utils" "datasets" "methods" "base"
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[11] "graphics" "grDevices" "utils" "datasets" "methods"
[16] "base"
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[1] "ggmap" "mapdata" "maps" "scales" "ggplot2"
[6] "sf" "tidycensus" "tidyr" "stringr" "dplyr"
[11] "stats" "graphics" "grDevices" "utils" "datasets"
[16] "methods" "base"
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[1] "ggspatial" "ggmap" "mapdata" "maps" "scales"
[6] "ggplot2" "sf" "tidycensus" "tidyr" "stringr"
[11] "dplyr" "stats" "graphics" "grDevices" "utils"
[16] "datasets" "methods" "base"
[[12]]
[1] "rnaturalearth" "ggspatial" "ggmap" "mapdata"
[5] "maps" "scales" "ggplot2" "sf"
[9] "tidycensus" "tidyr" "stringr" "dplyr"
[13] "stats" "graphics" "grDevices" "utils"
[17] "datasets" "methods" "base"
[[13]]
[1] "rnaturalearthdata" "rnaturalearth" "ggspatial"
[4] "ggmap" "mapdata" "maps"
[7] "scales" "ggplot2" "sf"
[10] "tidycensus" "tidyr" "stringr"
[13] "dplyr" "stats" "graphics"
[16] "grDevices" "utils" "datasets"
[19] "methods" "base"
[[14]]
[1] "lubridate" "forcats" "purrr"
[4] "readr" "tibble" "tidyverse"
[7] "rnaturalearthdata" "rnaturalearth" "ggspatial"
[10] "ggmap" "mapdata" "maps"
[13] "scales" "ggplot2" "sf"
[16] "tidycensus" "tidyr" "stringr"
[19] "dplyr" "stats" "graphics"
[22] "grDevices" "utils" "datasets"
[25] "methods" "base"
[[15]]
[1] "tigris" "lubridate" "forcats"
[4] "purrr" "readr" "tibble"
[7] "tidyverse" "rnaturalearthdata" "rnaturalearth"
[10] "ggspatial" "ggmap" "mapdata"
[13] "maps" "scales" "ggplot2"
[16] "sf" "tidycensus" "tidyr"
[19] "stringr" "dplyr" "stats"
[22] "graphics" "grDevices" "utils"
[25] "datasets" "methods" "base"
[[16]]
[1] "plotly" "tigris" "lubridate"
[4] "forcats" "purrr" "readr"
[7] "tibble" "tidyverse" "rnaturalearthdata"
[10] "rnaturalearth" "ggspatial" "ggmap"
[13] "mapdata" "maps" "scales"
[16] "ggplot2" "sf" "tidycensus"
[19] "tidyr" "stringr" "dplyr"
[22] "stats" "graphics" "grDevices"
[25] "utils" "datasets" "methods"
[28] "base"
[[17]]
[1] "mapproj" "plotly" "tigris"
[4] "lubridate" "forcats" "purrr"
[7] "readr" "tibble" "tidyverse"
[10] "rnaturalearthdata" "rnaturalearth" "ggspatial"
[13] "ggmap" "mapdata" "maps"
[16] "scales" "ggplot2" "sf"
[19] "tidycensus" "tidyr" "stringr"
[22] "dplyr" "stats" "graphics"
[25] "grDevices" "utils" "datasets"
[28] "methods" "base"
[[18]]
[1] "ggtext" "mapproj" "plotly"
[4] "tigris" "lubridate" "forcats"
[7] "purrr" "readr" "tibble"
[10] "tidyverse" "rnaturalearthdata" "rnaturalearth"
[13] "ggspatial" "ggmap" "mapdata"
[16] "maps" "scales" "ggplot2"
[19] "sf" "tidycensus" "tidyr"
[22] "stringr" "dplyr" "stats"
[25] "graphics" "grDevices" "utils"
[28] "datasets" "methods" "base"
[[19]]
[1] "ggrepel" "ggtext" "mapproj"
[4] "plotly" "tigris" "lubridate"
[7] "forcats" "purrr" "readr"
[10] "tibble" "tidyverse" "rnaturalearthdata"
[13] "rnaturalearth" "ggspatial" "ggmap"
[16] "mapdata" "maps" "scales"
[19] "ggplot2" "sf" "tidycensus"
[22] "tidyr" "stringr" "dplyr"
[25] "stats" "graphics" "grDevices"
[28] "utils" "datasets" "methods"
[31] "base"
[[20]]
[1] "gstat" "ggrepel" "ggtext"
[4] "mapproj" "plotly" "tigris"
[7] "lubridate" "forcats" "purrr"
[10] "readr" "tibble" "tidyverse"
[13] "rnaturalearthdata" "rnaturalearth" "ggspatial"
[16] "ggmap" "mapdata" "maps"
[19] "scales" "ggplot2" "sf"
[22] "tidycensus" "tidyr" "stringr"
[25] "dplyr" "stats" "graphics"
[28] "grDevices" "utils" "datasets"
[31] "methods" "base"
[[21]]
[1] "knitr" "gstat" "ggrepel"
[4] "ggtext" "mapproj" "plotly"
[7] "tigris" "lubridate" "forcats"
[10] "purrr" "readr" "tibble"
[13] "tidyverse" "rnaturalearthdata" "rnaturalearth"
[16] "ggspatial" "ggmap" "mapdata"
[19] "maps" "scales" "ggplot2"
[22] "sf" "tidycensus" "tidyr"
[25] "stringr" "dplyr" "stats"
[28] "graphics" "grDevices" "utils"
[31] "datasets" "methods" "base"
[[22]]
[1] "rgdal" "sp" "knitr"
[4] "gstat" "ggrepel" "ggtext"
[7] "mapproj" "plotly" "tigris"
[10] "lubridate" "forcats" "purrr"
[13] "readr" "tibble" "tidyverse"
[16] "rnaturalearthdata" "rnaturalearth" "ggspatial"
[19] "ggmap" "mapdata" "maps"
[22] "scales" "ggplot2" "sf"
[25] "tidycensus" "tidyr" "stringr"
[28] "dplyr" "stats" "graphics"
[31] "grDevices" "utils" "datasets"
[34] "methods" "base"
[[23]]
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[4] "knitr" "gstat" "ggrepel"
[7] "ggtext" "mapproj" "plotly"
[10] "tigris" "lubridate" "forcats"
[13] "purrr" "readr" "tibble"
[16] "tidyverse" "rnaturalearthdata" "rnaturalearth"
[19] "ggspatial" "ggmap" "mapdata"
[22] "maps" "scales" "ggplot2"
[25] "sf" "tidycensus" "tidyr"
[28] "stringr" "dplyr" "stats"
[31] "graphics" "grDevices" "utils"
[34] "datasets" "methods" "base"
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[4] "sp" "knitr" "gstat"
[7] "ggrepel" "ggtext" "mapproj"
[10] "plotly" "tigris" "lubridate"
[13] "forcats" "purrr" "readr"
[16] "tibble" "tidyverse" "rnaturalearthdata"
[19] "rnaturalearth" "ggspatial" "ggmap"
[22] "mapdata" "maps" "scales"
[25] "ggplot2" "sf" "tidycensus"
[28] "tidyr" "stringr" "dplyr"
[31] "stats" "graphics" "grDevices"
[34] "utils" "datasets" "methods"
[37] "base"
[[25]]
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[4] "rgdal" "sp" "knitr"
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[10] "mapproj" "plotly" "tigris"
[13] "lubridate" "forcats" "purrr"
[16] "readr" "tibble" "tidyverse"
[19] "rnaturalearthdata" "rnaturalearth" "ggspatial"
[22] "ggmap" "mapdata" "maps"
[25] "scales" "ggplot2" "sf"
[28] "tidycensus" "tidyr" "stringr"
[31] "dplyr" "stats" "graphics"
[34] "grDevices" "utils" "datasets"
[37] "methods" "base"
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[16] "purrr" "readr" "tibble"
[19] "tidyverse" "rnaturalearthdata" "rnaturalearth"
[22] "ggspatial" "ggmap" "mapdata"
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[28] "sf" "tidycensus" "tidyr"
[31] "stringr" "dplyr" "stats"
[34] "graphics" "grDevices" "utils"
[37] "datasets" "methods" "base"
[[27]]
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[4] "leaflet" "raster" "rgdal"
[7] "sp" "knitr" "gstat"
[10] "ggrepel" "ggtext" "mapproj"
[13] "plotly" "tigris" "lubridate"
[16] "forcats" "purrr" "readr"
[19] "tibble" "tidyverse" "rnaturalearthdata"
[22] "rnaturalearth" "ggspatial" "ggmap"
[25] "mapdata" "maps" "scales"
[28] "ggplot2" "sf" "tidycensus"
[31] "tidyr" "stringr" "dplyr"
[34] "stats" "graphics" "grDevices"
[37] "utils" "datasets" "methods"
[40] "base"
[[28]]
[1] "htmltools" "mapview" "rgeos"
[4] "leafem" "leaflet" "raster"
[7] "rgdal" "sp" "knitr"
[10] "gstat" "ggrepel" "ggtext"
[13] "mapproj" "plotly" "tigris"
[16] "lubridate" "forcats" "purrr"
[19] "readr" "tibble" "tidyverse"
[22] "rnaturalearthdata" "rnaturalearth" "ggspatial"
[25] "ggmap" "mapdata" "maps"
[28] "scales" "ggplot2" "sf"
[31] "tidycensus" "tidyr" "stringr"
[34] "dplyr" "stats" "graphics"
[37] "grDevices" "utils" "datasets"
[40] "methods" "base"
[[29]]
[1] "RColorBrewer" "htmltools" "mapview"
[4] "rgeos" "leafem" "leaflet"
[7] "raster" "rgdal" "sp"
[10] "knitr" "gstat" "ggrepel"
[13] "ggtext" "mapproj" "plotly"
[16] "tigris" "lubridate" "forcats"
[19] "purrr" "readr" "tibble"
[22] "tidyverse" "rnaturalearthdata" "rnaturalearth"
[25] "ggspatial" "ggmap" "mapdata"
[28] "maps" "scales" "ggplot2"
[31] "sf" "tidycensus" "tidyr"
[34] "stringr" "dplyr" "stats"
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[40] "datasets" "methods" "base"
[[30]]
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[4] "htmltools" "mapview" "rgeos"
[7] "leafem" "leaflet" "raster"
[10] "rgdal" "sp" "knitr"
[13] "gstat" "ggrepel" "ggtext"
[16] "mapproj" "plotly" "tigris"
[19] "lubridate" "forcats" "purrr"
[22] "readr" "tibble" "tidyverse"
[25] "rnaturalearthdata" "rnaturalearth" "ggspatial"
[28] "ggmap" "mapdata" "maps"
[31] "scales" "ggplot2" "sf"
[34] "tidycensus" "tidyr" "stringr"
[37] "dplyr" "stats" "graphics"
[40] "grDevices" "utils" "datasets"
[43] "methods" "base"
[[31]]
[1] "automap" "viridis" "viridisLite"
[4] "RColorBrewer" "htmltools" "mapview"
[7] "rgeos" "leafem" "leaflet"
[10] "raster" "rgdal" "sp"
[13] "knitr" "gstat" "ggrepel"
[16] "ggtext" "mapproj" "plotly"
[19] "tigris" "lubridate" "forcats"
[22] "purrr" "readr" "tibble"
[25] "tidyverse" "rnaturalearthdata" "rnaturalearth"
[28] "ggspatial" "ggmap" "mapdata"
[31] "maps" "scales" "ggplot2"
[34] "sf" "tidycensus" "tidyr"
[37] "stringr" "dplyr" "stats"
[40] "graphics" "grDevices" "utils"
[43] "datasets" "methods" "base" Smoke Exposure and COVID-19 in California
Reading layer `ca_smoke_monthly' from data source
`/Users/thomasmchale/Desktop/Wildfire-fungi-project/repository/smoke-infections/shapefiles/ca_smoke_monthly.shp'
using driver `ESRI Shapefile'
Simple feature collection with 580 features and 12 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -124.4096 ymin: 32.5342 xmax: -114.1344 ymax: 42.00952
Geodetic CRS: WGS 84Interactive Exploration of Association between Smoke Exposure and COVID-19 in 2020
Please navigate to the link in order to explore the incidence of COVID-19 cases and deaths for each month of 2020 as well as the smoke exposure for particulate matter (PM) of 2.5 µm in California in 2020.
You can select each month of 2020 for COVID-19 cases and deaths and it will display the incidence per 10,000 persons for each county for that month. Below the COVID-19 map is an interactive map of smoke exposure. This displays the PM 2.5 µm smoke exposure in each county of California in 2020. You can pick each month to display to correspond to the COVID-19 maps.
Since the influence and timing of smoke exposure leading to a respiratory infection is not clear, this lets you interact with different time points between smoke exposure and COVID-19. For example, if there is an incubation period between smoke exposure that lasts several weeks or months you can look at COVID-19 months after the smoke exposure. This might be expected if smoke exposure causes lung injury that pre-disposes individuals to an increased risk of COVID-19
Smoke PM 2.5 Exposure in 2020
COVID-19 Cases in 2020
Smoke PM 2.5 Exposure in 2021
COVID-19 Cases in 2021
Smoke PM 2.5 Exposure in 2022
COVID-19 Cases in 2022
Explore the Associations of Population and Demographic Data
Population Map
According to the American Community Survey (ACS) data, California had an estimated population of over 39 million in 2020, making it the most populous state in the United States. The population is diverse, with individuals from various ethnic and racial backgrounds. About 60% of the population resides in urban areas, with the largest cities being Los Angeles, San Diego, and San Jose. The median household income is $80,440. The education level is diverse, with approximately 31% of the population holding a bachelor’s degree or higher. The follwoing map shows the population of each county on a log base 10 scale. You can hover over each county to view additional demographic data.
Note
Click on a specific county to display population and demographic information. You can zoom in and out with the buttons in the top left corner or scrolling with your mouse.
Demographic Maps
Median Income
<iframe src="~/Desktop/Wildfire-fungi-project/repository/smoke-infections/images/cal_income_map.html" width="100%" height="400"></iframe>Rate of Workers Predominantly Outdoors
Since smoke is likely to affect workers who spend most of their time outdoors, I wanted to look at the rate of outdoor laborers in California. The variables used to determine outdoor laborers compared to all laborers were “B24011_031” and “B24011_034” from the ACS data. These two variables represent the number of workers 16 years and over who worked in farming, fishing, and forestry occupations, specifically those who worked on a farm, ranch, or in an orchard (B24011_031) or those who worked in other farming, fishing, and forestry occupations (B24011_034). These two variables were selected and summed to calculate the total number of outdoor laborers per county. The total number of laborers per county was also calculated by summing all the variables starting with “B24011”. The outdoor laborer rate was then determined by dividing the total number of outdoor laborers by the total number of laborers in each county, and multiplying by 100 to express it as a percentage.
<iframe src="~/Desktop/Wildfire-fungi-project/repository/smoke-infections/images/cal_labor_map.html" width="100%" height="400"></iframe>Mask Use Survey
This data was taken from the New York Times github COVID-19 page. I am displaying the percent of persons who rated “ALWAYS” wearing a mask, since this was the most common survey response. The other responses can be seen in the label when you hover over the county.
Specifically from the READme.md description at https://github.com/nytimes/covid-19-data/tree/master/mask-use:
“This data comes from a large number of interviews conducted online by the global data and survey firm Dynata at the request of The New York Times. The firm asked a question about mask use to obtain 250,000 survey responses between July 2 and July 14, enough data to provide estimates more detailed than the state level. (Several states have imposed new mask requirements since the completion of these interviews.)
Specifically, each participant was asked: How often do you wear a mask in public when you expect to be within six feet of another person?
This survey was conducted a single time, and at this point we have no plans to update the data or conduct the survey again.”
<iframe src="~/Desktop/Wildfire-fungi-project/repository/smoke-infections/images/cal_mask_map.html" width="100%" height="400"></iframe>Environmental Maps
Temperature
<iframe src="~/Desktop/Wildfire-fungi-project/repository/smoke-infections/images/cal_temp_map.html" width="100%" height="400"></iframe>Elevation
<iframe src="~/Desktop/Wildfire-fungi-project/repository/smoke-infections/images/cal_elevation_map.html" width="100%" height="400"></iframe>Statistical Analysis of Smoke Association with COVID-19 Cases Lagged 1, 2, and 3 Months
2020
Univariate Linear Mixed Effects Modelling
Cases
Treating Smoke as Continuous
Linear Mixed Effect Regression Results
================================================================================================================================================================================================
Dependent variable:
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
covid_incidence_1mo covid_incidence_2mo covid_incidence_3mo
Model 1: Case Incidence 1 Month After Smoke Exposure Model 2: Case Incidence 2 Months After Smoke Exposure, Case Incidence 3 Months After Smoke Exposure
(1) (2) (3)
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
smoke 21.667** -10.005 -31.839***
(9.157) (8.538) (7.942)
Constant 2,438.098*** 2,364.625*** 2,217.425***
(300.106) (267.070) (233.356)
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Observations 445 387 329
Log Likelihood -4,236.572 -3,639.831 -3,038.862
Akaike Inf. Crit. 8,481.145 7,287.662 6,085.723
Bayesian Inf. Crit. 8,497.537 7,303.496 6,100.908
================================================================================================================================================================================================
Note: *p<0.1; **p<0.05; ***p<0.01 [1] ""
[2] "Linear Mixed Effect Regression Results"
[3] "================================================================================================================================================================================================"
[4] " Dependent variable: "
[5] " ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------"
[6] " covid_incidence_1mo covid_incidence_2mo covid_incidence_3mo"
[7] " Model 1: Case Incidence 1 Month After Smoke Exposure Model 2: Case Incidence 2 Months After Smoke Exposure, Case Incidence 3 Months After Smoke Exposure "
[8] " (1) (2) (3) "
[9] "------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------"
[10] "smoke 21.667** -10.005 -31.839*** "
[11] " (9.157) (8.538) (7.942) "
[12] " "
[13] "Constant 2,438.098*** 2,364.625*** 2,217.425*** "
[14] " (300.106) (267.070) (233.356) "
[15] " "
[16] "------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------"
[17] "Observations 445 387 329 "
[18] "Log Likelihood -4,236.572 -3,639.831 -3,038.862 "
[19] "Akaike Inf. Crit. 8,481.145 7,287.662 6,085.723 "
[20] "Bayesian Inf. Crit. 8,497.537 7,303.496 6,100.908 "
[21] "================================================================================================================================================================================================"
[22] "Note: *p<0.1; **p<0.05; ***p<0.01"Treating Smoke as a Factor
Linear Mixed Effect Regression Results
================================================================================================================================================================================================
Dependent variable:
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
covid_incidence_1mo covid_incidence_2mo covid_incidence_3mo
Model 1: Case Incidence 1 Month After Smoke Exposure Model 2: Case Incidence 2 Months After Smoke Exposure, Case Incidence 3 Months After Smoke Exposure
(1) (2) (3)
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
smoke_factormedium 4,193.437*** 3,349.888*** 1,930.195***
(324.390) (321.159) (321.996)
smoke_factorhigh 2,014.235*** 776.078** -407.447
(304.788) (302.038) (309.255)
Constant 1,042.642*** 1,012.593*** 1,228.575***
(311.837) (286.178) (269.799)
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Observations 445 387 329
Log Likelihood -4,158.725 -3,581.901 -3,009.133
Akaike Inf. Crit. 8,327.449 7,173.802 6,028.266
Bayesian Inf. Crit. 8,347.940 7,193.594 6,047.246
================================================================================================================================================================================================
Note: *p<0.1; **p<0.05; ***p<0.01 [1] ""
[2] "Linear Mixed Effect Regression Results"
[3] "================================================================================================================================================================================================"
[4] " Dependent variable: "
[5] " ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------"
[6] " covid_incidence_1mo covid_incidence_2mo covid_incidence_3mo"
[7] " Model 1: Case Incidence 1 Month After Smoke Exposure Model 2: Case Incidence 2 Months After Smoke Exposure, Case Incidence 3 Months After Smoke Exposure "
[8] " (1) (2) (3) "
[9] "------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------"
[10] "smoke_factormedium 4,193.437*** 3,349.888*** 1,930.195*** "
[11] " (324.390) (321.159) (321.996) "
[12] " "
[13] "smoke_factorhigh 2,014.235*** 776.078** -407.447 "
[14] " (304.788) (302.038) (309.255) "
[15] " "
[16] "Constant 1,042.642*** 1,012.593*** 1,228.575*** "
[17] " (311.837) (286.178) (269.799) "
[18] " "
[19] "------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------"
[20] "Observations 445 387 329 "
[21] "Log Likelihood -4,158.725 -3,581.901 -3,009.133 "
[22] "Akaike Inf. Crit. 8,327.449 7,173.802 6,028.266 "
[23] "Bayesian Inf. Crit. 8,347.940 7,193.594 6,047.246 "
[24] "================================================================================================================================================================================================"
[25] "Note: *p<0.1; **p<0.05; ***p<0.01"Deaths
Linear Regression Results
=========================================================================
Dependent variable:
-----------------------------------------------------
death_incidence_1mo
Model 1: Smoke as Continuous Model 2: Smoke as Factor
(1) (2)
-------------------------------------------------------------------------
smoke 0.248
(0.166)
smoke_factormedium 59.815***
(6.292)
smoke_factorhigh 26.701***
(5.912)
Constant 36.837*** 16.729***
(6.279) (6.475)
-------------------------------------------------------------------------
Observations 445 445
Log Likelihood -2,466.519 -2,420.209
Akaike Inf. Crit. 4,941.038 4,850.418
Bayesian Inf. Crit. 4,957.430 4,870.908
=========================================================================
Note: *p<0.1; **p<0.05; ***p<0.01 [1] ""
[2] "Linear Regression Results"
[3] "========================================================================="
[4] " Dependent variable: "
[5] " -----------------------------------------------------"
[6] " death_incidence_1mo "
[7] " Model 1: Smoke as Continuous Model 2: Smoke as Factor"
[8] " (1) (2) "
[9] "-------------------------------------------------------------------------"
[10] "smoke 0.248 "
[11] " (0.166) "
[12] " "
[13] "smoke_factormedium 59.815*** "
[14] " (6.292) "
[15] " "
[16] "smoke_factorhigh 26.701*** "
[17] " (5.912) "
[18] " "
[19] "Constant 36.837*** 16.729*** "
[20] " (6.279) (6.475) "
[21] " "
[22] "-------------------------------------------------------------------------"
[23] "Observations 445 445 "
[24] "Log Likelihood -2,466.519 -2,420.209 "
[25] "Akaike Inf. Crit. 4,941.038 4,850.418 "
[26] "Bayesian Inf. Crit. 4,957.430 4,870.908 "
[27] "========================================================================="
[28] "Note: *p<0.1; **p<0.05; ***p<0.01"Multivariate Linear Mixed Effects Modelling
Linear Regression Results
======================================================================
Dependent variable:
-------------------------------------------------
covid_incidence_1mo death_incidence_1mo
Model 1: Covid Incidence Model 2: Death Incidence
(1) (2)
----------------------------------------------------------------------
smoke_factormedium 4,275.531*** 61.458***
(323.096) (6.257)
smoke_factorhigh 2,089.776*** 28.586***
(305.155) (5.906)
median_income -0.033** -0.001***
(0.013) (0.0003)
outdoor_laborer_rate 10.914 -0.324
(68.100) (1.371)
avg_temp 128.480*** 3.747***
(45.904) (0.926)
precip -103.270*** -0.942
(29.079) (0.586)
ALWAYS -2,985.037 83.083
(2,966.741) (59.867)
NEVER -14,877.440* -208.414
(8,964.591) (181.000)
Constant -530.722 -180.690**
(4,322.027) (87.150)
----------------------------------------------------------------------
Observations 445 445
Log Likelihood -4,109.255 -2,391.205
Akaike Inf. Crit. 8,240.511 4,804.410
Bayesian Inf. Crit. 8,285.590 4,849.489
======================================================================
Note: *p<0.1; **p<0.05; ***p<0.01 [1] ""
[2] "Linear Regression Results"
[3] "======================================================================"
[4] " Dependent variable: "
[5] " -------------------------------------------------"
[6] " covid_incidence_1mo death_incidence_1mo "
[7] " Model 1: Covid Incidence Model 2: Death Incidence"
[8] " (1) (2) "
[9] "----------------------------------------------------------------------"
[10] "smoke_factormedium 4,275.531*** 61.458*** "
[11] " (323.096) (6.257) "
[12] " "
[13] "smoke_factorhigh 2,089.776*** 28.586*** "
[14] " (305.155) (5.906) "
[15] " "
[16] "median_income -0.033** -0.001*** "
[17] " (0.013) (0.0003) "
[18] " "
[19] "outdoor_laborer_rate 10.914 -0.324 "
[20] " (68.100) (1.371) "
[21] " "
[22] "avg_temp 128.480*** 3.747*** "
[23] " (45.904) (0.926) "
[24] " "
[25] "precip -103.270*** -0.942 "
[26] " (29.079) (0.586) "
[27] " "
[28] "ALWAYS -2,985.037 83.083 "
[29] " (2,966.741) (59.867) "
[30] " "
[31] "NEVER -14,877.440* -208.414 "
[32] " (8,964.591) (181.000) "
[33] " "
[34] "Constant -530.722 -180.690** "
[35] " (4,322.027) (87.150) "
[36] " "
[37] "----------------------------------------------------------------------"
[38] "Observations 445 445 "
[39] "Log Likelihood -4,109.255 -2,391.205 "
[40] "Akaike Inf. Crit. 8,240.511 4,804.410 "
[41] "Bayesian Inf. Crit. 8,285.590 4,849.489 "
[42] "======================================================================"
[43] "Note: *p<0.1; **p<0.05; ***p<0.01"2021
Univariate Linear Mixed Effects Modelling
Cases
Treating Smoke Exposre as Continuous
Linear Mixed Effect Regression Results
================================================================================================================================================================================================
Dependent variable:
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
covid_incidence_1mo covid_incidence_2mo covid_incidence_3mo
Model 1: Case Incidence 1 Month After Smoke Exposure Model 2: Case Incidence 2 Months After Smoke Exposure, Case Incidence 3 Months After Smoke Exposure
(1) (2) (3)
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
monthly_avg_smoke 1.163 0.761 0.367
(3.917) (4.040) (4.184)
Constant 858.045*** 833.423*** 811.434***
(91.754) (96.104) (101.590)
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Observations 829 778 727
Log Likelihood -7,254.277 -6,829.339 -6,403.257
Akaike Inf. Crit. 14,516.550 13,666.680 12,814.510
Bayesian Inf. Crit. 14,535.430 13,685.300 12,832.870
================================================================================================================================================================================================
Note: *p<0.1; **p<0.05; ***p<0.01 [1] ""
[2] "Linear Mixed Effect Regression Results"
[3] "================================================================================================================================================================================================"
[4] " Dependent variable: "
[5] " ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------"
[6] " covid_incidence_1mo covid_incidence_2mo covid_incidence_3mo"
[7] " Model 1: Case Incidence 1 Month After Smoke Exposure Model 2: Case Incidence 2 Months After Smoke Exposure, Case Incidence 3 Months After Smoke Exposure "
[8] " (1) (2) (3) "
[9] "------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------"
[10] "monthly_avg_smoke 1.163 0.761 0.367 "
[11] " (3.917) (4.040) (4.184) "
[12] " "
[13] "Constant 858.045*** 833.423*** 811.434*** "
[14] " (91.754) (96.104) (101.590) "
[15] " "
[16] "------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------"
[17] "Observations 829 778 727 "
[18] "Log Likelihood -7,254.277 -6,829.339 -6,403.257 "
[19] "Akaike Inf. Crit. 14,516.550 13,666.680 12,814.510 "
[20] "Bayesian Inf. Crit. 14,535.430 13,685.300 12,832.870 "
[21] "================================================================================================================================================================================================"
[22] "Note: *p<0.1; **p<0.05; ***p<0.01"Treating Smoke Exposure as a Factor
Linear Mixed Effect Regression Results
================================================================================================================================================================================================
Dependent variable:
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
covid_incidence_1mo covid_incidence_2mo covid_incidence_3mo
Model 1: Case Incidence 1 Month After Smoke Exposure Model 2: Case Incidence 2 Months After Smoke Exposure, Case Incidence 3 Months After Smoke Exposure
(1) (2) (3)
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
smoke_factormedium 29.503 0.823 -25.654
(143.613) (153.548) (165.941)
smoke_factorhigh 175.466 127.883 83.288
(203.119) (213.432) (224.754)
Constant 828.814*** 824.898*** 821.874***
(140.566) (149.991) (162.014)
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Observations 829 778 727
Log Likelihood -7,244.233 -6,819.359 -6,393.270
Akaike Inf. Crit. 14,498.470 13,648.720 12,796.540
Bayesian Inf. Crit. 14,522.070 13,672.000 12,819.490
================================================================================================================================================================================================
Note: *p<0.1; **p<0.05; ***p<0.01 [1] ""
[2] "Linear Mixed Effect Regression Results"
[3] "================================================================================================================================================================================================"
[4] " Dependent variable: "
[5] " ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------"
[6] " covid_incidence_1mo covid_incidence_2mo covid_incidence_3mo"
[7] " Model 1: Case Incidence 1 Month After Smoke Exposure Model 2: Case Incidence 2 Months After Smoke Exposure, Case Incidence 3 Months After Smoke Exposure "
[8] " (1) (2) (3) "
[9] "------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------"
[10] "smoke_factormedium 29.503 0.823 -25.654 "
[11] " (143.613) (153.548) (165.941) "
[12] " "
[13] "smoke_factorhigh 175.466 127.883 83.288 "
[14] " (203.119) (213.432) (224.754) "
[15] " "
[16] "Constant 828.814*** 824.898*** 821.874*** "
[17] " (140.566) (149.991) (162.014) "
[18] " "
[19] "------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------"
[20] "Observations 829 778 727 "
[21] "Log Likelihood -7,244.233 -6,819.359 -6,393.270 "
[22] "Akaike Inf. Crit. 14,498.470 13,648.720 12,796.540 "
[23] "Bayesian Inf. Crit. 14,522.070 13,672.000 12,819.490 "
[24] "================================================================================================================================================================================================"
[25] "Note: *p<0.1; **p<0.05; ***p<0.01"Deaths
Linear Regression Results
=========================================================================
Dependent variable:
-----------------------------------------------------
death_incidence_1mo
Model 1: Smoke as Continuous Model 2: Smoke as Factor
(1) (2)
-------------------------------------------------------------------------
monthly_avg_smoke 0.021
(0.068)
smoke_factormedium 0.618
(2.496)
smoke_factorhigh 2.124
(3.525)
Constant 11.668*** 11.205***
(1.628) (2.469)
-------------------------------------------------------------------------
Observations 829 829
Log Likelihood -3,900.754 -3,894.999
Akaike Inf. Crit. 7,809.508 7,799.999
Bayesian Inf. Crit. 7,828.389 7,823.600
=========================================================================
Note: *p<0.1; **p<0.05; ***p<0.01 [1] ""
[2] "Linear Regression Results"
[3] "========================================================================="
[4] " Dependent variable: "
[5] " -----------------------------------------------------"
[6] " death_incidence_1mo "
[7] " Model 1: Smoke as Continuous Model 2: Smoke as Factor"
[8] " (1) (2) "
[9] "-------------------------------------------------------------------------"
[10] "monthly_avg_smoke 0.021 "
[11] " (0.068) "
[12] " "
[13] "smoke_factormedium 0.618 "
[14] " (2.496) "
[15] " "
[16] "smoke_factorhigh 2.124 "
[17] " (3.525) "
[18] " "
[19] "Constant 11.668*** 11.205*** "
[20] " (1.628) (2.469) "
[21] " "
[22] "-------------------------------------------------------------------------"
[23] "Observations 829 829 "
[24] "Log Likelihood -3,900.754 -3,894.999 "
[25] "Akaike Inf. Crit. 7,809.508 7,799.999 "
[26] "Bayesian Inf. Crit. 7,828.389 7,823.600 "
[27] "========================================================================="
[28] "Note: *p<0.1; **p<0.05; ***p<0.01"Multivarirate Linear Mixed Regression Modelling
Linear Mixed Effects Regression Results
========================================================================
Dependent variable:
-------------------------------------------------
covid_incidence_1mo death_incidence_1mo
Model 1: Covid Incidence Model 2: Death Incidence
(1) (2)
------------------------------------------------------------------------
smoke_factormedium -28.166 -0.438
(146.803) (2.544)
smoke_factorhigh 75.814 0.566
(205.164) (3.554)
median_income.x -0.007 -0.0001
(0.005) (0.0001)
outdoor_laborer_rate.x 13.022 0.359
(25.539) (0.451)
avg_temp 21.058 0.765*
(23.167) (0.407)
precip -9.158 -0.050
(7.618) (0.134)
Constant -32.344 -34.781
(2,002.354) (35.209)
------------------------------------------------------------------------
Observations 829 829
Log Likelihood -7,233.113 -3,898.968
Akaike Inf. Crit. 14,484.230 7,815.935
Bayesian Inf. Crit. 14,526.710 7,858.417
========================================================================
Note: *p<0.1; **p<0.05; ***p<0.01 [1] ""
[2] "Linear Mixed Effects Regression Results"
[3] "========================================================================"
[4] " Dependent variable: "
[5] " -------------------------------------------------"
[6] " covid_incidence_1mo death_incidence_1mo "
[7] " Model 1: Covid Incidence Model 2: Death Incidence"
[8] " (1) (2) "
[9] "------------------------------------------------------------------------"
[10] "smoke_factormedium -28.166 -0.438 "
[11] " (146.803) (2.544) "
[12] " "
[13] "smoke_factorhigh 75.814 0.566 "
[14] " (205.164) (3.554) "
[15] " "
[16] "median_income.x -0.007 -0.0001 "
[17] " (0.005) (0.0001) "
[18] " "
[19] "outdoor_laborer_rate.x 13.022 0.359 "
[20] " (25.539) (0.451) "
[21] " "
[22] "avg_temp 21.058 0.765* "
[23] " (23.167) (0.407) "
[24] " "
[25] "precip -9.158 -0.050 "
[26] " (7.618) (0.134) "
[27] " "
[28] "Constant -32.344 -34.781 "
[29] " (2,002.354) (35.209) "
[30] " "
[31] "------------------------------------------------------------------------"
[32] "Observations 829 829 "
[33] "Log Likelihood -7,233.113 -3,898.968 "
[34] "Akaike Inf. Crit. 14,484.230 7,815.935 "
[35] "Bayesian Inf. Crit. 14,526.710 7,858.417 "
[36] "========================================================================"
[37] "Note: *p<0.1; **p<0.05; ***p<0.01"2022
Univariate Linear Mixed Effects Modelling
Cases
Treating Smoke Exposure as Continuous
Linear Mixed Effect Regression Results
================================================================================================================================================================================================
Dependent variable:
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
covid_incidence_1mo covid_incidence_2mo covid_incidence_3mo
Model 1: Case Incidence 1 Month After Smoke Exposure Model 2: Case Incidence 2 Months After Smoke Exposure, Case Incidence 3 Months After Smoke Exposure
(1) (2) (3)
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
monthly_avg_smoke 11.587 17.477 15.672
(30.828) (32.757) (34.091)
Constant 2,155.184*** 2,081.134*** 2,063.747***
(300.173) (314.647) (332.164)
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Observations 822 772 722
Log Likelihood -7,875.747 -7,418.487 -6,959.760
Akaike Inf. Crit. 15,759.500 14,844.980 13,927.520
Bayesian Inf. Crit. 15,778.340 14,863.570 13,945.850
================================================================================================================================================================================================
Note: *p<0.1; **p<0.05; ***p<0.01 [1] ""
[2] "Linear Mixed Effect Regression Results"
[3] "================================================================================================================================================================================================"
[4] " Dependent variable: "
[5] " ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------"
[6] " covid_incidence_1mo covid_incidence_2mo covid_incidence_3mo"
[7] " Model 1: Case Incidence 1 Month After Smoke Exposure Model 2: Case Incidence 2 Months After Smoke Exposure, Case Incidence 3 Months After Smoke Exposure "
[8] " (1) (2) (3) "
[9] "------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------"
[10] "monthly_avg_smoke 11.587 17.477 15.672 "
[11] " (30.828) (32.757) (34.091) "
[12] " "
[13] "Constant 2,155.184*** 2,081.134*** 2,063.747*** "
[14] " (300.173) (314.647) (332.164) "
[15] " "
[16] "------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------"
[17] "Observations 822 772 722 "
[18] "Log Likelihood -7,875.747 -7,418.487 -6,959.760 "
[19] "Akaike Inf. Crit. 15,759.500 14,844.980 13,927.520 "
[20] "Bayesian Inf. Crit. 15,778.340 14,863.570 13,945.850 "
[21] "================================================================================================================================================================================================"
[22] "Note: *p<0.1; **p<0.05; ***p<0.01"Treating Smoke Exposure as a Factor
Linear Mixed Effect Regression Results
================================================================================================================================================================================================
Dependent variable:
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
covid_incidence_1mo covid_incidence_2mo covid_incidence_3mo
Model 1: Case Incidence 1 Month After Smoke Exposure Model 2: Case Incidence 2 Months After Smoke Exposure, Case Incidence 3 Months After Smoke Exposure
(1) (2) (3)
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
smoke_factormedium 57.022 44.887 45.266
(312.804) (330.687) (352.249)
smoke_factorhigh 219.669 286.961 240.915
(787.735) (857.508) (889.195)
Constant 2,194.760*** 2,170.068*** 2,141.637***
(297.050) (311.932) (331.031)
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Observations 822 772 722
Log Likelihood -7,865.914 -7,408.629 -6,949.827
Akaike Inf. Crit. 15,741.830 14,827.260 13,909.650
Bayesian Inf. Crit. 15,765.390 14,850.500 13,932.560
================================================================================================================================================================================================
Note: *p<0.1; **p<0.05; ***p<0.01 [1] ""
[2] "Linear Mixed Effect Regression Results"
[3] "================================================================================================================================================================================================"
[4] " Dependent variable: "
[5] " ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------"
[6] " covid_incidence_1mo covid_incidence_2mo covid_incidence_3mo"
[7] " Model 1: Case Incidence 1 Month After Smoke Exposure Model 2: Case Incidence 2 Months After Smoke Exposure, Case Incidence 3 Months After Smoke Exposure "
[8] " (1) (2) (3) "
[9] "------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------"
[10] "smoke_factormedium 57.022 44.887 45.266 "
[11] " (312.804) (330.687) (352.249) "
[12] " "
[13] "smoke_factorhigh 219.669 286.961 240.915 "
[14] " (787.735) (857.508) (889.195) "
[15] " "
[16] "Constant 2,194.760*** 2,170.068*** 2,141.637*** "
[17] " (297.050) (311.932) (331.031) "
[18] " "
[19] "------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------"
[20] "Observations 822 772 722 "
[21] "Log Likelihood -7,865.914 -7,408.629 -6,949.827 "
[22] "Akaike Inf. Crit. 15,741.830 14,827.260 13,909.650 "
[23] "Bayesian Inf. Crit. 15,765.390 14,850.500 13,932.560 "
[24] "================================================================================================================================================================================================"
[25] "Note: *p<0.1; **p<0.05; ***p<0.01"Deaths
Multivariate Linear Mixed Regression Modelling
Linear Regression Results
======================================================================
Dependent variable:
-------------------------------------------------
covid_incidence_1mo death_incidence_1mo
Model 1: Covid Incidence Model 2: Death Incidence
(1) (2)
----------------------------------------------------------------------
smoke_factormedium 13.270 -1.198
(317.380) (3.950)
smoke_factorhigh 75.784 -0.698
(788.890) (9.802)
median_income -0.012 -0.0002
(0.011) (0.0001)
outdoor_laborer_rate 37.530 0.840
(58.450) (0.750)
avg_temp 6.716 0.849
(52.465) (0.668)
precip -31.591 -0.092
(23.170) (0.296)
Constant 2,240.051 -33.084
(4,534.186) (57.859)
----------------------------------------------------------------------
Observations 822 822
Log Likelihood -7,852.854 -4,275.390
Akaike Inf. Crit. 15,723.710 8,568.779
Bayesian Inf. Crit. 15,766.110 8,611.185
======================================================================
Note: *p<0.1; **p<0.05; ***p<0.01 [1] ""
[2] "Linear Regression Results"
[3] "======================================================================"
[4] " Dependent variable: "
[5] " -------------------------------------------------"
[6] " covid_incidence_1mo death_incidence_1mo "
[7] " Model 1: Covid Incidence Model 2: Death Incidence"
[8] " (1) (2) "
[9] "----------------------------------------------------------------------"
[10] "smoke_factormedium 13.270 -1.198 "
[11] " (317.380) (3.950) "
[12] " "
[13] "smoke_factorhigh 75.784 -0.698 "
[14] " (788.890) (9.802) "
[15] " "
[16] "median_income -0.012 -0.0002 "
[17] " (0.011) (0.0001) "
[18] " "
[19] "outdoor_laborer_rate 37.530 0.840 "
[20] " (58.450) (0.750) "
[21] " "
[22] "avg_temp 6.716 0.849 "
[23] " (52.465) (0.668) "
[24] " "
[25] "precip -31.591 -0.092 "
[26] " (23.170) (0.296) "
[27] " "
[28] "Constant 2,240.051 -33.084 "
[29] " (4,534.186) (57.859) "
[30] " "
[31] "----------------------------------------------------------------------"
[32] "Observations 822 822 "
[33] "Log Likelihood -7,852.854 -4,275.390 "
[34] "Akaike Inf. Crit. 15,723.710 8,568.779 "
[35] "Bayesian Inf. Crit. 15,766.110 8,611.185 "
[36] "======================================================================"
[37] "Note: *p<0.1; **p<0.05; ***p<0.01"