Smoke Exposure and COVID-19 in California
Interactive 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
Please Click on the links below to explore the assoications between Smoke Exposure and COVID-19
Smoke Exposure and COVID-19 in 2020
Smoke Exposure and COVID-19 in 2021
Smoke Exposure and COVID-19 in 2021
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 following 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
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.
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.”
Environmental Maps
Temperature
Elevation
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
<table class="kable_wrapper">
<caption>Linear Mixed Effect Regression Results</caption>
<tbody>
<tr>
<td>
|effect |group |term | estimate| std.error| statistic| df| p.value|
|:--------|:--------|:---------------|---------:|---------:|---------:|---------:|-------:|
|fixed |NA |(Intercept) | 511.77808| 76.373689| 6.700974| 82.68627| 0|
|fixed |NA |smoke | 20.87656| 2.335372| 8.939286| 419.97811| 0|
|ran_pars |NAME |sd__(Intercept) | 435.20633| NA| NA| NA| NA|
|ran_pars |Residual |sd__Observation | 798.01809| NA| NA| NA| NA|
</td>
<td>
|effect |group |term | estimate| std.error| statistic| df| p.value|
|:--------|:--------|:---------------|----------:|---------:|---------:|---------:|---------:|
|fixed |NA |(Intercept) | 596.025613| 80.924074| 7.365245| 94.24321| 0.0000000|
|fixed |NA |smoke | 9.665711| 2.612266| 3.700125| 370.46066| 0.0002482|
|ran_pars |NAME |sd__(Intercept) | 412.932835| NA| NA| NA| NA|
|ran_pars |Residual |sd__Observation | 861.926921| NA| NA| NA| NA|
</td>
<td>
|effect |group |term | estimate| std.error| statistic| df| p.value|
|:--------|:--------|:---------------|----------:|---------:|---------:|--------:|---------:|
|fixed |NA |(Intercept) | 863.554507| 86.41320| 9.993317| 113.2336| 0.0000000|
|fixed |NA |smoke | -8.799764| 3.08198| -2.855231| 319.4480| 0.0045819|
|ran_pars |NAME |sd__(Intercept) | 358.541053| NA| NA| NA| NA|
|ran_pars |Residual |sd__Observation | 938.578005| NA| NA| NA| NA|
</td>
</tr>
</tbody>
</table>Treating Smoke as a Factor
<table class="kable_wrapper">
<caption>Linear Mixed Effect Regression Results</caption>
<tbody>
<tr>
<td>
|effect |group |term | estimate| std.error| statistic| df| p.value|
|:--------|:--------|:------------------|--------:|---------:|---------:|--------:|---------:|
|fixed |NA |(Intercept) | 294.8912| 79.51803| 3.708483| 101.1368| 0.0003408|
|fixed |NA |smoke_factormedium | 795.4004| 91.31157| 8.710839| 407.8537| 0.0000000|
|fixed |NA |smoke_factorhigh | 995.2168| 85.80768| 11.598225| 406.9107| 0.0000000|
|ran_pars |NAME |sd__(Intercept) | 440.3845| NA| NA| NA| NA|
|ran_pars |Residual |sd__Observation | 741.3178| NA| NA| NA| NA|
</td>
<td>
|effect |group |term | estimate| std.error| statistic| df| p.value|
|:--------|:--------|:------------------|---------:|---------:|---------:|--------:|---------:|
|fixed |NA |(Intercept) | 200.7387| 86.66217| 2.316336| 116.1621| 0.0222933|
|fixed |NA |smoke_factormedium | 1030.6770| 98.78296| 10.433753| 351.6583| 0.0000000|
|fixed |NA |smoke_factorhigh | 757.0009| 92.91201| 8.147503| 348.9732| 0.0000000|
|ran_pars |NAME |sd__(Intercept) | 452.9763| NA| NA| NA| NA|
|ran_pars |Residual |sd__Observation | 748.4800| NA| NA| NA| NA|
</td>
<td>
|effect |group |term | estimate| std.error| statistic| df| p.value|
|:--------|:--------|:------------------|--------:|---------:|---------:|--------:|---------:|
|fixed |NA |(Intercept) | 397.1945| 100.9917| 3.9329416| 163.5408| 0.0001237|
|fixed |NA |smoke_factormedium | 894.0903| 123.1187| 7.2620162| 307.2222| 0.0000000|
|fixed |NA |smoke_factorhigh | 103.3302| 118.3077| 0.8734022| 302.5614| 0.3831366|
|ran_pars |NAME |sd__(Intercept) | 406.4523| NA| NA| NA| NA|
|ran_pars |Residual |sd__Observation | 857.4175| NA| NA| NA| NA|
</td>
</tr>
</tbody>
</table>Deaths
<table class="kable_wrapper">
<caption>Linear Regression Results</caption>
<tbody>
<tr>
<td>
|effect |group |term | estimate| std.error| statistic| df| p.value|
|:--------|:--------|:------------------|---------:|---------:|---------:|---------:|---------:|
|fixed |NA |(Intercept) | 3.836041| 1.773054| 2.163521| 94.24158| 0.0330301|
|fixed |NA |smoke_factormedium | 12.425741| 1.947515| 6.380305| 404.60686| 0.0000000|
|fixed |NA |smoke_factorhigh | 13.175109| 1.829978| 7.199601| 403.77266| 0.0000000|
|ran_pars |NAME |sd__(Intercept) | 10.180926| NA| NA| NA| NA|
|ran_pars |Residual |sd__Observation | 15.782912| NA| NA| NA| NA|
</td>
<td>
|effect |group |term | estimate| std.error| statistic| df| p.value|
|:--------|:--------|:------------------|---------:|---------:|---------:|--------:|---------:|
|fixed |NA |(Intercept) | 4.095229| 1.944844| 2.105685| 108.6988| 0.0375346|
|fixed |NA |smoke_factormedium | 17.426904| 2.144011| 8.128179| 348.7862| 0.0000000|
|fixed |NA |smoke_factorhigh | 6.606018| 2.016107| 3.276621| 346.1374| 0.0011569|
|ran_pars |NAME |sd__(Intercept) | 10.522973| NA| NA| NA| NA|
|ran_pars |Residual |sd__Observation | 16.215616| NA| NA| NA| NA|
</td>
<td>
|effect |group |term | estimate| std.error| statistic| df| p.value|
|:--------|:--------|:------------------|---------:|---------:|----------:|--------:|---------:|
|fixed |NA |(Intercept) | 7.478680| 2.101534| 3.5586772| 158.3024| 0.0004922|
|fixed |NA |smoke_factormedium | 9.783055| 2.492436| 3.9250978| 305.1225| 0.0001072|
|fixed |NA |smoke_factorhigh | -1.596426| 2.393469| -0.6669927| 300.5630| 0.5052885|
|ran_pars |NAME |sd__(Intercept) | 9.015876| NA| NA| NA| NA|
|ran_pars |Residual |sd__Observation | 17.289115| NA| NA| NA| NA|
</td>
</tr>
</tbody>
</table>Multivariate Linear Mixed Effects Modelling
<table class="kable_wrapper">
<caption>Linear Mixed Effect Regression Results</caption>
<tbody>
<tr>
<td>
|effect |group |term | estimate| std.error| statistic| df| p.value|
|:--------|:--------|:--------------------|------------:|------------:|----------:|---------:|---------:|
|fixed |NA |(Intercept) | -487.5799772| 1015.6283473| -0.4800772| 63.69875| 0.6328170|
|fixed |NA |smoke_factormedium | 816.2873879| 90.5845050| 9.0113357| 416.86927| 0.0000000|
|fixed |NA |smoke_factorhigh | 1017.2658123| 85.9157213| 11.8402755| 404.33211| 0.0000000|
|fixed |NA |median_income | -0.0068204| 0.0033273| -2.0498088| 60.93169| 0.0446937|
|fixed |NA |outdoor_laborer_rate | -5.8155759| 16.8999813| -0.3441173| 66.00000| 0.7318521|
|fixed |NA |avg_temp | 36.1684859| 11.3189597| 3.1953896| 60.60931| 0.0022180|
|fixed |NA |precip | -24.9197030| 7.1654259| -3.4777700| 62.28502| 0.0009278|
|fixed |NA |ALWAYS | -555.9181879| 668.6615405| -0.8313895| 57.50139| 0.4091928|
|ran_pars |NAME |sd__(Intercept) | 268.1790147| NA| NA| NA| NA|
|ran_pars |Residual |sd__Observation | 741.0810806| NA| NA| NA| NA|
</td>
<td>
|effect |group |term | estimate| std.error| statistic| df| p.value|
|:--------|:--------|:--------------------|-----------:|----------:|----------:|---------:|---------:|
|fixed |NA |(Intercept) | -59.3302010| 21.7388504| -2.7292244| 64.23480| 0.0081812|
|fixed |NA |smoke_factormedium | 12.8750867| 1.9267312| 6.6823472| 416.75524| 0.0000000|
|fixed |NA |smoke_factorhigh | 13.8102500| 1.8271477| 7.5583654| 404.49450| 0.0000000|
|fixed |NA |median_income | -0.0002021| 0.0000712| -2.8370347| 61.49865| 0.0061585|
|fixed |NA |outdoor_laborer_rate | -0.1855464| 0.3616901| -0.5129983| 66.52457| 0.6096519|
|fixed |NA |avg_temp | 1.1165703| 0.2423158| 4.6079144| 61.14883| 0.0000212|
|fixed |NA |precip | -0.1953537| 0.1533828| -1.2736351| 62.82252| 0.2074850|
|fixed |NA |ALWAYS | 25.6022825| 14.3172843| 1.7882080| 58.05840| 0.0789608|
|ran_pars |NAME |sd__(Intercept) | 5.7852300| NA| NA| NA| NA|
|ran_pars |Residual |sd__Observation | 15.7575164| NA| NA| NA| NA|
</td>
</tr>
</tbody>
</table>2021
Univariate Linear Mixed Effects Modelling
Cases
Treating Smoke Exposre as Continuous
<table class="kable_wrapper">
<caption>Linear Mixed Effect Regression Results</caption>
<tbody>
<tr>
<td>
|effect |group |term | estimate| std.error| statistic| df| p.value|
|:--------|:--------|:-----------------|------------:|---------:|----------:|---:|---------:|
|fixed |NA |(Intercept) | 3.5781086| 62.055129| 0.0576602| 827| 0.9540333|
|fixed |NA |monthly_avg_smoke | -0.1239279| 3.666895| -0.0337964| 827| 0.9730477|
|ran_pars |NAME |sd__(Intercept) | 0.0000000| NA| NA| NA| NA|
|ran_pars |Residual |sd__Observation | 1464.7421484| NA| NA| NA| NA|
</td>
<td>
|effect |group |term | estimate| std.error| statistic| df| p.value|
|:--------|:--------|:-----------------|------------:|---------:|----------:|---:|---------:|
|fixed |NA |(Intercept) | -0.3340054| 65.80980| -0.0050753| 776| 0.9959518|
|fixed |NA |monthly_avg_smoke | 0.0102252| 3.79326| 0.0026956| 776| 0.9978499|
|ran_pars |NAME |sd__(Intercept) | 0.0000000| NA| NA| NA| NA|
|ran_pars |Residual |sd__Observation | 1511.6004249| NA| NA| NA| NA|
</td>
<td>
|effect |group |term | estimate| std.error| statistic| df| p.value|
|:--------|:--------|:-----------------|------------:|---------:|----------:|---:|--------:|
|fixed |NA |(Intercept) | 4.4722624| 70.301526| 0.0636154| 725| 0.949294|
|fixed |NA |monthly_avg_smoke | -0.0142235| 3.933658| -0.0036158| 725| 0.997116|
|ran_pars |NAME |sd__(Intercept) | 0.0000000| NA| NA| NA| NA|
|ran_pars |Residual |sd__Observation | 1562.6552416| NA| NA| NA| NA|
</td>
</tr>
</tbody>
</table>Treating Smoke Exposure as a Factor
<table class="kable_wrapper">
<caption>Linear Mixed Effect Regression Results</caption>
<tbody>
<tr>
<td>
|effect |group |term | estimate| std.error| statistic| df| p.value|
|:--------|:--------|:------------------|------------:|---------:|----------:|---:|---------:|
|fixed |NA |(Intercept) | 0.5286544| 104.1577| 0.0050755| 826| 0.9959516|
|fixed |NA |smoke_factormedium | 2.9862215| 121.7954| 0.0245184| 826| 0.9804451|
|fixed |NA |smoke_factorhigh | -0.8391302| 184.9254| -0.0045377| 826| 0.9963806|
|ran_pars |NAME |sd__(Intercept) | 0.0000000| NA| NA| NA| NA|
|ran_pars |Residual |sd__Observation | 1465.6286981| NA| NA| NA| NA|
</td>
<td>
|effect |group |term | estimate| std.error| statistic| df| p.value|
|:--------|:--------|:------------------|------------:|---------:|----------:|---:|---------:|
|fixed |NA |(Intercept) | 2.1238842| 111.5084| 0.0190468| 775| 0.9848086|
|fixed |NA |smoke_factormedium | -3.8107486| 130.2827| -0.0292498| 775| 0.9766729|
|fixed |NA |smoke_factorhigh | 0.9631167| 194.5637| 0.0049501| 775| 0.9960517|
|ran_pars |NAME |sd__(Intercept) | 0.0000000| NA| NA| NA| NA|
|ran_pars |Residual |sd__Observation | 1512.5740302| NA| NA| NA| NA|
</td>
<td>
|effect |group |term | estimate| std.error| statistic| df| p.value|
|:--------|:--------|:------------------|-----------:|---------:|----------:|---:|---------:|
|fixed |NA |(Intercept) | -6.076432| 120.2863| -0.0505164| 724| 0.9597248|
|fixed |NA |smoke_factormedium | 14.725792| 140.3340| 0.1049339| 724| 0.9164573|
|fixed |NA |smoke_factorhigh | 7.473632| 204.0538| 0.0366258| 724| 0.9707935|
|ran_pars |NAME |sd__(Intercept) | 0.000000| NA| NA| NA| NA|
|ran_pars |Residual |sd__Observation | 1563.721784| NA| NA| NA| NA|
</td>
</tr>
</tbody>
</table>Deaths
<table class="kable_wrapper">
<caption>Linear Regression Results</caption>
<tbody>
<tr>
<td>
|effect |group |term | estimate| std.error| statistic| df| p.value|
|:--------|:--------|:------------------|----------:|---------:|----------:|---:|---------:|
|fixed |NA |(Intercept) | -0.0339885| 1.843408| -0.0184379| 826| 0.9852940|
|fixed |NA |smoke_factormedium | 0.1045274| 2.155564| 0.0484919| 826| 0.9613359|
|fixed |NA |smoke_factorhigh | -0.0263469| 3.272854| -0.0080501| 826| 0.9935789|
|ran_pars |NAME |sd__(Intercept) | 0.0000000| NA| NA| NA| NA|
|ran_pars |Residual |sd__Observation | 25.9390541| NA| NA| NA| NA|
</td>
<td>
|effect |group |term | estimate| std.error| statistic| df| p.value|
|:--------|:--------|:------------------|----------:|---------:|----------:|---:|---------:|
|fixed |NA |(Intercept) | 0.0195996| 1.971760| 0.0099401| 775| 0.9920716|
|fixed |NA |smoke_factormedium | -0.0723687| 2.303737| -0.0314136| 775| 0.9749478|
|fixed |NA |smoke_factorhigh | -0.0112498| 3.440393| -0.0032699| 775| 0.9973918|
|ran_pars |NAME |sd__(Intercept) | 0.0000000| NA| NA| NA| NA|
|ran_pars |Residual |sd__Observation | 26.7462492| NA| NA| NA| NA|
</td>
<td>
|effect |group |term | estimate| std.error| statistic| df| p.value|
|:--------|:--------|:------------------|----------:|---------:|----------:|---:|---------:|
|fixed |NA |(Intercept) | -0.1153178| 2.125619| -0.0542514| 724| 0.9567498|
|fixed |NA |smoke_factormedium | 0.2445318| 2.479889| 0.0986060| 724| 0.9214784|
|fixed |NA |smoke_factorhigh | 0.0850162| 3.605903| 0.0235770| 724| 0.9811966|
|ran_pars |NAME |sd__(Intercept) | 0.0000000| NA| NA| NA| NA|
|ran_pars |Residual |sd__Observation | 27.6330452| NA| NA| NA| NA|
</td>
</tr>
</tbody>
</table>Multivarirate Linear Mixed Regression Modelling
county_code NAME year.x month
Min. : 1.00 Length:880 Min. :2021 Min. : 1.000
1st Qu.: 33.00 Class :character 1st Qu.:2021 1st Qu.: 3.000
Median : 53.00 Mode :character Median :2021 Median : 6.000
Mean : 53.45 Mean :2021 Mean : 6.324
3rd Qu.: 77.00 3rd Qu.:2021 3rd Qu.: 9.000
Max. :113.00 Max. :2021 Max. :12.000
monthly_avg_smoke daily_aqi_value date.x Month
Min. : 1.207 Min. : 1.00 Min. :2021-01-01 Min. : 1.000
1st Qu.: 5.223 1st Qu.: 17.00 1st Qu.:2021-03-05 1st Qu.: 3.000
Median : 7.456 Median : 29.00 Median :2021-06-03 Median : 6.000
Mean : 9.687 Mean : 33.91 Mean :2021-06-12 Mean : 6.324
3rd Qu.: 10.291 3rd Qu.: 45.00 3rd Qu.:2021-09-01 3rd Qu.: 9.000
Max. :248.030 Max. :179.00 Max. :2021-12-12 Max. :12.000
fips year.y mo_avg_casesp10k mo_avg_deathsp10k
Min. : 4023 Min. :2021 Min. :-10482.099 Min. :-190.10943
1st Qu.: 6039 1st Qu.:2021 1st Qu.: 0.383 1st Qu.: 0.00124
Median : 6081 Median :2021 Median : 1.706 Median : 0.02392
Mean :13798 Mean :2021 Mean : 2.375 Mean : 0.03101
3rd Qu.:17097 3rd Qu.:2021 3rd Qu.: 17.057 3rd Qu.: 0.20008
Max. :51137 Max. :2021 Max. : 8352.164 Max. : 187.89038
NA's :50 NA's :50 NA's :50 NA's :50
GEOID cases date.y covid_incidence_1mo
Length:880 Min. : 162 Min. :2021-01-01 Min. :-10482.099
Class :character 1st Qu.: 1736 1st Qu.:2021-03-01 1st Qu.: 0.387
Mode :character Median : 11770 Median :2021-06-01 Median : 1.709
Mean : 56333 Mean :2021-05-31 Mean : 2.377
3rd Qu.: 47103 3rd Qu.:2021-09-01 3rd Qu.: 17.092
Max. :1494823 Max. :2021-11-01 Max. : 8352.164
NA's :50 NA's :50 NA's :51
covid_incidence_2mo covid_incidence_3mo smoke_factor death_incidence_1mo
Min. :-10482.099 Min. :-10482.099 low :204 Min. :-190.10943
1st Qu.: 0.320 1st Qu.: 0.283 medium:576 1st Qu.: 0.00124
Median : 1.656 Median : 1.648 high :100 Median : 0.02401
Mean : -0.233 Mean : 4.328 Mean : 0.03105
3rd Qu.: 20.107 3rd Qu.: 28.110 3rd Qu.: 0.20258
Max. : 8352.164 Max. : 8352.164 Max. : 187.89038
NA's :102 NA's :153 NA's :51
death_incidence_2mo death_incidence_3mo avg_temp precip
Min. :-190.10943 Min. :-190.10943 Min. :46.40 Min. : 2.49
1st Qu.: 0.00000 1st Qu.: 0.00000 1st Qu.:57.20 1st Qu.:14.22
Median : 0.02312 Median : 0.02359 Median :59.30 Median :24.66
Mean : -0.02858 Mean : 0.05262 Mean :59.99 Mean :26.05
3rd Qu.: 0.22408 3rd Qu.: 0.34394 3rd Qu.:63.30 3rd Qu.:36.57
Max. : 187.89038 Max. : 187.89038 Max. :75.70 Max. :75.66
NA's :102 NA's :153
population median_income outdoor_laborer_rate ALWAYS
Min. : 12541 Min. : 41780 Min. :19.06 Min. :0.5450
1st Qu.: 64276 1st Qu.: 49254 1st Qu.:24.17 1st Qu.:0.6820
Median : 218774 Median : 63188 Median :25.98 Median :0.7540
Mean : 832557 Mean : 69770 Mean :26.80 Mean :0.7463
3rd Qu.: 874784 3rd Qu.: 86173 3rd Qu.:29.68 3rd Qu.:0.8060
Max. :10040682 Max. :130890 Max. :45.93 Max. :0.8890
NEVER
Min. :0.00100
1st Qu.:0.01000
Median :0.02100
Mean :0.02487
3rd Qu.:0.02700
Max. :0.13200
<table class="kable_wrapper">
<caption>Linear Mixed Effects Regression Results</caption>
<tbody>
<tr>
<td>
|effect |group |term | estimate| std.error| statistic| df| p.value|
|:--------|:--------|:--------------------|------------:|------------:|----------:|---:|---------:|
|fixed |NA |(Intercept) | 11.4928354| 1437.8959339| 0.0079928| 822| 0.9936247|
|fixed |NA |smoke_factormedium | 3.1398432| 132.2464377| 0.0237424| 822| 0.9810639|
|fixed |NA |smoke_factorhigh | -1.4588163| 190.9306360| -0.0076406| 822| 0.9939056|
|fixed |NA |median_income | -0.0000281| 0.0034804| -0.0080856| 822| 0.9935506|
|fixed |NA |outdoor_laborer_rate | 0.1196812| 16.6512171| 0.0071875| 822| 0.9942670|
|fixed |NA |avg_temp | -0.1637528| 16.8049182| -0.0097443| 822| 0.9922276|
|fixed |NA |precip | -0.0924572| 5.3587690| -0.0172534| 822| 0.9862386|
|ran_pars |NAME |sd__(Intercept) | 0.0000000| NA| NA| NA| NA|
|ran_pars |Residual |sd__Observation | 1469.1899342| NA| NA| NA| NA|
</td>
<td>
|effect |group |term | estimate| std.error| statistic| df| p.value|
|:--------|:--------|:--------------------|----------:|----------:|----------:|---:|---------:|
|fixed |NA |(Intercept) | 0.0354693| 25.4482277| 0.0013938| 822| 0.9988883|
|fixed |NA |smoke_factormedium | 0.1192380| 2.3405292| 0.0509449| 822| 0.9593818|
|fixed |NA |smoke_factorhigh | -0.0302011| 3.3791363| -0.0089375| 822| 0.9928711|
|fixed |NA |median_income | -0.0000006| 0.0000616| -0.0104607| 822| 0.9916563|
|fixed |NA |outdoor_laborer_rate | 0.0037518| 0.2946972| 0.0127310| 822| 0.9898455|
|fixed |NA |avg_temp | -0.0018365| 0.2974175| -0.0061749| 822| 0.9950746|
|fixed |NA |precip | -0.0009223| 0.0948408| -0.0097244| 822| 0.9922435|
|ran_pars |NAME |sd__(Intercept) | 0.0000000| NA| NA| NA| NA|
|ran_pars |Residual |sd__Observation | 26.0020765| NA| NA| NA| NA|
</td>
</tr>
</tbody>
</table>2022
Univariate Linear Mixed Effects Modelling
Cases
Treating Smoke Exposure as Continuous
<table class="kable_wrapper">
<caption>Linear Mixed Effect Regression Results</caption>
<tbody>
<tr>
<td>
|effect |term | estimate| std.error| statistic| df| p.value|
|:------|:-----------------|----------:|---------:|---------:|--------:|---------:|
|fixed |(Intercept) | 2155.18413| 300.17265| 7.179815| 336.1730| 0.0000000|
|fixed |monthly_avg_smoke | 11.58729| 30.82784| 0.375871| 792.2126| 0.7071134|
</td>
<td>
|effect |term | estimate| std.error| statistic| df| p.value|
|:------|:-----------------|----------:|---------:|---------:|--------:|---------:|
|fixed |(Intercept) | 2081.13413| 314.64698| 6.6141876| 337.8140| 0.0000000|
|fixed |monthly_avg_smoke | 17.47657| 32.75653| 0.5335293| 749.5916| 0.5938254|
</td>
<td>
|effect |term | estimate| std.error| statistic| df| p.value|
|:------|:-----------------|---------:|---------:|---------:|--------:|---------:|
|fixed |(Intercept) | 2063.7475| 332.16362| 6.2130449| 332.7099| 0.0000000|
|fixed |monthly_avg_smoke | 15.6723| 34.09126| 0.4597162| 702.1698| 0.6458622|
</td>
</tr>
</tbody>
</table>Treating Smoke Exposure as a Factor
Table: Linear Mixed Effect Regression Results
|effect |term | estimate| std.error| statistic| df| p.value|
|:------|:--------------------|------------:|------------:|----------:|--------:|---------:|
|fixed |(Intercept) | 11.4928354| 1437.8959339| 0.0079928| 822.0000| 0.9936247|
|fixed |smoke_factormedium | 3.1398432| 132.2464377| 0.0237424| 822.0000| 0.9810639|
|fixed |smoke_factorhigh | -1.4588163| 190.9306360| -0.0076406| 822.0000| 0.9939056|
|fixed |median_income | -0.0000281| 0.0034804| -0.0080856| 822.0000| 0.9935506|
|fixed |outdoor_laborer_rate | 0.1196812| 16.6512171| 0.0071875| 822.0000| 0.9942670|
|fixed |avg_temp | -0.1637528| 16.8049182| -0.0097443| 822.0000| 0.9922276|
|fixed |precip | -0.0924572| 5.3587690| -0.0172534| 822.0000| 0.9862386|
|fixed |(Intercept) | 2170.0680839| 311.9321976| 6.9568583| 274.9220| 0.0000000|
|fixed |smoke_factormedium | 44.8871352| 330.6869495| 0.1357391| 670.4550| 0.8920683|
|fixed |smoke_factorhigh | 286.9613481| 857.5079028| 0.3346457| 762.1902| 0.7379844|
|fixed |(Intercept) | 2141.6370818| 331.0305847| 6.4696049| 273.1221| 0.0000000|
|fixed |smoke_factormedium | 45.2658111| 352.2494306| 0.1285050| 632.9440| 0.8977902|
|fixed |smoke_factorhigh | 240.9154190| 889.1945240| 0.2709367| 710.0224| 0.7865185|Deaths
<table class="kable_wrapper">
<caption>Linear Regression Results</caption>
<tbody>
<tr>
<td>
|effect |group |term | estimate| std.error| statistic| df| p.value|
|:--------|:--------|:------------------|----------:|---------:|----------:|--------:|---------:|
|fixed |NA |(Intercept) | 21.6208235| 3.825858| 5.6512346| 253.1570| 0.0000000|
|fixed |NA |smoke_factormedium | -0.8107082| 3.927550| -0.2064158| 741.6586| 0.8365228|
|fixed |NA |smoke_factorhigh | 1.2406919| 9.838440| 0.1261066| 808.8056| 0.8996789|
|ran_pars |NAME |sd__(Intercept) | 12.8539632| NA| NA| NA| NA|
|ran_pars |Residual |sd__Observation | 43.2520108| NA| NA| NA| NA|
</td>
<td>
|effect |group |term | estimate| std.error| statistic| df| p.value|
|:--------|:--------|:------------------|---------:|---------:|----------:|--------:|---------:|
|fixed |NA |(Intercept) | 21.617019| 4.006266| 5.3958017| 254.7776| 0.0000002|
|fixed |NA |smoke_factormedium | -1.020235| 4.151177| -0.2457700| 694.1373| 0.8059330|
|fixed |NA |smoke_factorhigh | 1.283373| 10.699279| 0.1199495| 764.9403| 0.9045546|
|ran_pars |NAME |sd__(Intercept) | 13.029688| NA| NA| NA| NA|
|ran_pars |Residual |sd__Observation | 44.560247| NA| NA| NA| NA|
</td>
<td>
|effect |group |term | estimate| std.error| statistic| df| p.value|
|:--------|:--------|:------------------|----------:|---------:|----------:|--------:|---------:|
|fixed |NA |(Intercept) | 21.5191812| 4.244659| 5.0697081| 257.7810| 0.0000008|
|fixed |NA |smoke_factormedium | -0.8954068| 4.421070| -0.2025317| 654.9263| 0.8395640|
|fixed |NA |smoke_factorhigh | 1.1358130| 11.101560| 0.1023111| 712.8800| 0.9185385|
|ran_pars |NAME |sd__(Intercept) | 13.2870757| NA| NA| NA| NA|
|ran_pars |Residual |sd__Observation | 46.0003557| NA| NA| NA| NA|
</td>
</tr>
</tbody>
</table>Multivariate Linear Mixed Regression Modelling
Table: Linear Mixed Effect Regression Results
|effect |term | estimate| std.error| statistic| df| p.value|
|:------|:--------------------|------------:|------------:|----------:|---------:|---------:|
|fixed |(Intercept) | 2240.0506891| 4534.1862744| 0.4940359| 107.69524| 0.6222870|
|fixed |smoke_factormedium | 13.2701106| 317.3801255| 0.0418114| 741.71042| 0.9666603|
|fixed |smoke_factorhigh | 75.7841673| 788.8897971| 0.0960643| 798.73892| 0.9234936|
|fixed |median_income | -0.0118658| 0.0114816| -1.0334596| 96.20813| 0.3039797|
|fixed |outdoor_laborer_rate | 37.5303258| 58.4496008| 0.6420972| 78.24223| 0.5226870|
|fixed |avg_temp | 6.7159771| 52.4650609| 0.1280086| 118.89493| 0.8983585|
|fixed |precip | -31.5909698| 23.1695933| -1.3634667| 103.70842| 0.1756880|
|fixed |(Intercept) | -33.0843340| 57.8588175| -0.5718114| 99.00851| 0.5687457|
|fixed |smoke_factormedium | -1.1980719| 3.9503893| -0.3032794| 751.47189| 0.7617609|
|fixed |smoke_factorhigh | -0.6984168| 9.8018693| -0.0712534| 800.98547| 0.9432138|
|fixed |median_income | -0.0002307| 0.0001468| -1.5716690| 89.47094| 0.1195572|
|fixed |outdoor_laborer_rate | 0.8396342| 0.7498430| 1.1197467| 73.35855| 0.2664745|
|fixed |avg_temp | 0.8490282| 0.6684597| 1.2701262| 108.89011| 0.2067470|
|fixed |precip | -0.0923947| 0.2958286| -0.3123251| 95.73077| 0.7554728|