DOI: 10.5061/dryad.j3tx95xtt
title: Modeling respiratory related mortality in California from air pollution and social deprivation data

description: In the application study of our submitted manuscript, we demonstrate our
 proposed methodology by modeling respiratory related deaths at the county
 and monthly level across California from 2015-2019. This data is
 downloaded from the California Department of Public Health's
 California Vital Data (Cal-ViDa) query tool. The model leverages spatial
 patterns in social deprivation index (SDI) from the Society of Actuaries
 (SoA) and air pollutant measurements from the US Environmental Protection
 Agency (EPA) to estimate the spatiotemporal dependence structure of the
 response. All of the data used for this application study is publicly
 available and thus will all be provided in this
 repository.  
description: # Modeling respiratory related mortality in California from air pollution
 and social deprivation data Dataset DOI:
 [10.5061/dryad.j3tx95xtt](https://doi.org/10.5061/dryad.j3tx95xtt) ##
 Description of the data and file structure This folder contains all the
 datasets and geographic files needed to run the analysis code in
 INLA-7.2-Final.Rmd. These datasets were downloaded from various publicly
 available sources.  ### Files and variables * CA_census_pops1019.xlsx:
 census population data for counties of California for each year 2010-2019
 * REGION: US census region code * DIVISION: US census division code *
 STATE: US census state code * COUNTY: US census county code * STNAME:
 state name (all California)  * CTYNAME: county name (all Californian
 counties)  * POPESTIMATE2010: census population estimate for given county
 in 2010 * POPESTIMATE201: census population estimate for given county in
 2011 * POPESTIMATE2012: census population estimate for given county in
 2012 * POPESTIMATE2013: census population estimate for given county in
 2013 * POPESTIMATE2014: census population estimate for given county in
 2014 * POPESTIMATE2015: census population estimate for given county in
 2015 * POPESTIMATE2016: census population estimate for given county in
 2016 * POPESTIMATE2017: census population estimate for given county in
 2017 * POPESTIMATE2018: census population estimate for given county in
 2018 * POPESTIMATE2019: census population estimate for given county in
 2019 * CA_shapefiles.zip: shape files needed to perform spatial analysis
 and plot maps of California. Shapefiles are a common format for
 vector-based geographic information system (GIS) data. They can be opened
 and used in any GIS software and in R or Python. A shapefile consists of
 multiple file types beyond the .shp (specifically, .cpg, .dbf, .prj, .sbn,
 and .sbx). The user only interacts directly with the .shp file but the
 other files need to be in the same directory. - Cal-ViDa_Death1423.xlsx *
 Year_of_Death: year of death * Month_of_Death: month of death *
 County_of_Death: county of death * Age: age group (categorical) *
 Cause_of_Death: reason for death (filter to influenza and pneumonia and
 chronic lower respiratory diseases) * Total_Deaths: death counts (small
 counts censored) - final_EPA_data.csv * County: county * Year-Month: month
 of observation in date form * Value: air pollutant measurement value (unit
 varies by pollutant) * Lead (micrograms/m^3^) * CO (parts per million) *
 SO2 (parts per billion) * NO2 (parts per billion) * O3 (parts per million)
 * PM10 (micrograms/m^3^) * PM2.5 (micrograms/m^3^) * AQI: air quality
 index (separate variable from air pollutants) * Pollutant: air pollutant
 variable code * 14129: Lead * 42101: CO * 42401: SO2 * 42602: NO2 * 44201:
 O3 * 81102: PM10 * 88101: PM2.5 - Population_Categories.xlsx: annual
 estimates of the resident population for California state for selected age
 groups and by sex for the years 2020-2022 - SoA.data.1019.xlsx *
 county_fips: unique identifier for each county * county_name: county name
 * State_USPS: state abbreviation (all CA) * Year: year * Source: where the
 data came from (American Community Survey) * Score: social deprivation
 index score (unitless) calculated from subindices * Decile: score decile
 among counties * Quintile: score quintile among counties * Total_Pop:
 population of county * EDUC_Lessthan9: % of the population aged 25 and
 over with less than 9 years of education * EDUC_college: % of the
 population aged 25 and over with at least 4 years of college education *
 White_Collar: % of the population aged 16 and over employed in a white
 collar occupation * Unemployment_Rate: unemployment rate (%) for the
 population 16 years and over * Adj_HH_income: median household income
 adjusted for local housing costs (dollars) * Income_Disparity: ratio of
 the average household income in the lowest quintile to the average
 household income in the highest quintile * Individuals_Below_Poverty: % of
 the population below the federal poverty threshold * Median_Home_Value:
 median home value for owner occupied units (dollars) * Median_Gross_Rent:
 median gross rent for rental units (dollars) * Housing_No_Telephone: % of
 housing without a telephone * Housing_Incomplete_Plumbing: % of housing
 without complete plumbing To obtain all the cleaned, aggregated, assembled
 data at once, one can use INLA-7.2-Premodeling.RData file. 
 See [https://github.com/jeffwu25/KGR-SKATER/tree/main](https://github.com/jeffwu25/KGR-SKATER/tree/main) to see main analysis examples and supplementary analysis. ## Code/software All analysis was run in RStudio using R 4.5.1. The RMD file INLA-7.2-Final contains an example of the full analysis workflow.\ \ The following packages are used in that code file:  * dplyr * tidyverse * lubridate * stringr * zoo * ggplot2 * urbnmapr * devtools * readxl * spdep * sp * huge * INLA * HMMpa * invgamma * brinla * reshape2 * patchwork * jsonlite * geosphere * RAQSAPI * con2aqi * pscl * corrplot * superheat * shapes * scales ## Access information Data was derived from the following sources: * Mortality data: [https://cal-vida.cdph.ca.gov/VSQWeb](https://cal-vida.cdph.ca.gov/VSQWeb) * EPA air pollutant data: [https://aqs.epa.gov/aqsweb/documents/data_api.html](https://aqs.epa.gov/aqsweb/documents/data_api.html) * Social deprivation index data: [https://www.soa.org/resources/research-reports/2020/us-mort-rate-socioeconomic/](https://www.soa.org/resources/research-reports/2020/us-mort-rate-socioeconomic/) ## Human subjects data The mortality data is from Cal-ViDa which already de-identifies the data. Furthermore, small count cells are censored and must be imputed. The data is aggregated to the county level for the application study.