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Article

Mediating Effect of the Stay-at-Home Order on the Association between Mobility, Weather, and COVID-19 Infection and Mortality in Indiana and Kentucky: March to May 2020

by
Shaminul H. Shakib
1,*,
Bert B. Little
1,2,*,
Seyed Karimi
1,2,
William Paul McKinney
3,
Michael Goldsby
1 and
Maiying Kong
2
1
Department of Health Management and Systems Sciences, School of Public Health and Information Sciences, University of Louisville, Louisville, KY 40202, USA
2
Department of Bioinformatics and Biostatistics, School of Public Health and Information Sciences, University of Louisville, Louisville, KY 40202, USA
3
Department of Health Promotion and Behavioral Sciences, School of Public Health and Information Sciences, University of Louisville, Louisville, KY 40202, USA
*
Authors to whom correspondence should be addressed.
Atmosphere 2024, 15(9), 1100; https://doi.org/10.3390/atmos15091100
Submission received: 31 July 2024 / Revised: 5 September 2024 / Accepted: 6 September 2024 / Published: 10 September 2024
(This article belongs to the Section Biometeorology and Bioclimatology)
Review Reports Versions Notes

Abstract

:
(1) Background: The association of COVID-19 infection and mortality with mobility and weather in Indiana and Kentucky was compared for the period from 1 March to 15 May 2020. (2) Methods: The risk of COVID-19 infection and mortality was evaluated using Cox regressions with the following covariates: mobility (retail/recreation, grocery/pharmacy, and workplace), weather (precipitation, minimum and maximum temperature, ultraviolet [UV] index), and metropolitan status. (3) Results: A higher maximum temperature (adjusted hazard ratioinfection (aHRi) = 1.01; adjusted hazard ratiodeath (aHRd) = 1.001), metropolitan status (aHRi = 1.12; aHRd = 2.05), and a higher minimum temperature (aHRi = 1.01) were associated with increased risks of COVID-19 infection and/or mortality. Protection against COVID-19 infection and/or mortality was associated with retail/recreation (aHRi = 0.97; aHRd = 0.937), grocery/pharmacy (aHRi = 0.991; aHRd = 0.992), workplace (aHRi = 0.99; aHRd = 0.965), precipitation (aHRi = 0.999; aHRd = 0.9978), UV index (aHRi = 0.37; aHRd = 0.748), and a higher minimum temperature (aHRd = 0.994). COVID-19 infection (aHRi = 1.18) and mortality (aHRd = 1.59) risks were higher in Indiana compared to Kentucky. (4) Conclusions: COVID-19 infection and mortality were 18% and 59% more likely among Indiana residents compared to Kentucky residents, respectively. This may be attributed to variations in stay-at-home order compliance and enforcement between Indiana and Kentucky.

1. Introduction

Several United States (U.S.) states made social distancing and face masks or coverings mandatory in public places by the end of April 2020 in response to the COVID-19 pandemic [1]. Social distancing and mask-wearing were then known to be effective in the prevention of COVID-19 infection being spread in public settings [2,3,4]. Aerosol transmission came to be known as posing an increased risk of COVID-19 virus infection, especially in densely populated public settings with high personal contact and increased mobility [5].
COVID-19 is highly infectious and spreads rapidly, with a reproduction rate (R0) of over 4.0 [6]. Over 1.1 million COVID-19-related deaths were reported in the U.S. as of 26 April 2023 [7]. COVID-19 is caused by the SARS-CoV-2 virus and its subvariants. Severe acute respiratory syndrome (SARS) is a viral respiratory disease (viral pneumonia).
COVID-19 infects people of different ethnicities, ages, and genders, and some sub-groups have varying symptoms [8,9]. Infected people suffer from a wide range of symptoms, which can be classified as mild, moderate, and severe. Common mild symptoms of a COVID-19 infection include fever, body aches, dry cough, fatigue, chills, headache, sore throat, loss of appetite, and loss of smell and taste. Moderate symptoms include a high fever and a severe cough. Severe COVID-19 symptoms include viral pneumonia, shortness of breath, neurological symptoms, gastrointestinal (GI) symptoms, and hypoxia [10].
COVID-19 transmission has been associated with local meteorological conditions, such as temperature and humidity, in several studies [11,12]. A negative association between temperature and COVID-19 cases was reported in one study [13]. However, other studies found that rising temperatures in some cities resulted in an increased daily incidence of COVID-19 cases [12,13,14]. The impact of non-pharmaceutical interventions (i.e., social distancing and mask-wearing) on COVID-19 transmission patterns was not analyzed by either investigation.
Multiple studies have investigated the association between mobility and COVID-19 incidence [15,16,17]. The geographical locations analyzed in these studies had high demographic, socioeconomic, weather, and air pollution heterogeneity [15,16,17]. Such heterogeneity can mask the effects of COVID-19-related policies intended to curb the infection rate.
Indiana and Kentucky are neighboring states and declared a pandemic state of emergency on 6 March 2020, after confirming the first positive COVID-19 case [18]. Kentucky declared a stay-at-home order from 26 March 2020 to 11 May 2020 (46 days). Similarly, Indiana declared a stay-at-home order from 24 March 2020 to 1 May 2020 (38 days) [19]. Compliance and enforcement measures differed between Indiana and Kentucky, although both states implemented stay-at-home orders during the same period. Kentucky had a lower rate of incidence of COVID-19 infection compared to Indiana. Indiana reported 256 COVID-19-positive cases on 23 March 2020. An additional 365 COVID-19-positive cases were reported the following day, which translates to a 42% increase in 24 h. In the same 24-h period, only 39 new cases were reported in Kentucky.
In the first three weeks of March 2020, infection rates were substantially different in both states; these differences were apparently associated with human mobility. Kentucky limited visits to nursing homes and prisons and recommended social distancing. Schools, daycares, restaurants, and bars were closed. In Kentucky, businesses that involved face-to-face contact (e.g., gyms, salons, and movie theaters) halted their operations to enforce social distancing. In Indiana, initiatives to restrict human mobility were less rigorously enforced compared to Kentucky. In Indiana, restaurants, bars, and nightclubs were closed to control foot traffic.
In the present study, COVID-19-related infection incidence and mortality rates in Indiana and Kentucky were analyzed. The two states are geographically contiguous and similar in terms of demography, socioeconomics, weather variation, and air pollution. These effects were controlled as covariates, using the state as the main effect, and analyses were conducted at the county-day level. Kentucky implemented a strict stay-at-home order and mask mandate, while Indiana did not have similarly strict COVID-19 mandates [20]. Therefore, in the present analysis of COVID-19 transmission, infection and mortality were analyzed over time between the states.

2. Materials and Methods

County-level daily data from several sources were integrated for analysis in this study: (1) COVID-19 infection and mortality rates from the New York Times database; (2) human mobility data from the Google database; (3) 2023 Rural-Urban Continuum Codes from the USDA’s Economic Research Service; (4) weather data from the National Oceanic and Atmospheric Administration’s (NOAA) Global Historical Climatology Network daily (GHCNd) database; (5) ultraviolet (UV) light data from https://openweathermap.org/ (accessed on 12 December 2020). The duration of this analysis was from 1 March 2020 to 15 May 2020 and included 16,112 data points (county–day pairings).
The New York Times has released data files with cumulative counts of COVID-19 cases and deaths at the state and county levels in the U.S. over time. The data were collected from state and local governments and health departments. COVID-19 case and death counts included both laboratory-confirmed and probable cases, using criteria that were developed by states and by the federal government [21]. Cumulative COVID-19 infection and mortality county-day rates were computed from the New York Times for Indiana and Kentucky.
Google’s community mobility reports (CMR) provide movement trends over time, grouped by geography across different categories of places such as retail, recreation, groceries, pharmacies, parks, public transit stations, workplaces, and residential areas. For each geographical category, CMR contains the percentage change in activity from baseline days prior to the introduction of COVID-19. Daily activity variations are contrasted with the matching baseline figure and day; for instance, Monday’s data would be contrasted with Monday’s baseline data for the same date prior to the COVID-19 pandemic. For each day of the week and for each county, Google calculated the baseline day median values. Google’s CMR had missing data for categories in counties where anonymity could not be ensured, due to insufficient mobility among the residents [22]. Daily human mobility data in the analytical models with respect to essential commuting needs during the stay-at-home order were included for: (1) retail/recreation; (2) grocery/pharmacy; and (3) workplace.
The 2023 Rural-Urban Continuum Codes classify metropolitan (metro) and non-metropolitan (non-metro) counties based on their level of urbanization and proximity to a metro area [23]. Three metro and six non-metro categories were present in the original dataset. They were recoded to a binary form (metro vs. non-metro).
NOAA’s GHCNd database includes information from over 80,000 weather stations in 180 nations and territories. It provides the official archive for daily weather data such as maximum and minimum temperatures, precipitation, snowfall, and snow depth for the U.S. (Menne et al., 2012). The weather parameters included in the models were: (1) precipitation (24-h sum in millimeters (mm)); (2) minimum temperature (Fahrenheit (°F)); and (3) maximum temperature (°F). In addition, daily UV light data were obtained from OpenWeatherMap, an online service that contains historical, current, and weather data forecasts [24].
A multiple imputation technique based on linear regression was used to replace missing values for county-day human mobility and weather data. Single-value imputation techniques such as mean imputation, last observation carried forward, and random imputation methods produce results biased to a central tendency rather than county-day fluctuations in mobility and data. Multiple imputation can improve missing data estimation [25]. Little’s test of missing completely at random (MCAR) data was conducted to test whether or not significant differences existed between the means of different missing-value patterns [26]. Only 13.24% of overall data were missing, although Little’s MCAR test indicated that human mobility and weather data were not missing completely at random (p-value < 0.05).
Furthermore, a “missing value patterns” chart indicates that the patterns of random value distribution were identical for precipitation, maximum and minimum temperatures, and UV index. Missing value patterns were not identical for mobility data regarding the workplace, retail/recreation, and grocery/pharmacy (see Supplementary Figure S1). These blocks of patterns violate MCAR but appear to be a consequence of the different databases joined for the analysis. The frequency of missing values before and after the application of multiple imputations is reported in Supplementary Table S1.
A Mann–Whitney Wilcoxon (Wilcoxon’s rank sum) test was used to evaluate the differences between states for mobility and weather indicators. A chi-square test of independence was performed to determine the association between metropolitan status and states with outcome variables. Two Cox regression models were computed, with (1) the daily cumulative reported COVID-19 infection counts and (2) the daily cumulative reported COVID-19-related mortality counts as the dependent variables. The independent variables included human mobility, weather parameters, and metro/non-metro county status. County-day was the unit of analysis. Daily cumulative reported COVID-19 infection counts were used as weights in the Cox regression analysis for COVID-19-related mortality to fully account for effect magnitude differences across time within counties. Unweighted Cox regression results for COVID-19-related mortality are reported in Supplementary Table S2.
A second set of Cox regression models was created with counties bordering the Ohio River (separating Indiana to the north from Kentucky in the south). In the sensitivity analysis (Supplementary Tables S4 and S5), the bordering counties (two counties deep) in each state were analyzed (Supplementary Table S3). The adjusted hazard ratios (aHR) with 95% confidence intervals (CIs) were reported for all Cox regression models. The minimum significance level (p-value) was set at ≤ 0.05. Analyses were conducted using KNIME v5.1, STATA 18, and SPSS v28 (IBM SPSS, Chicago, IL, USA).

3. Results

3.1. Descriptive

All counties from Kentucky (n = 120) and Indiana (n = 90) were included in the study, covering the period from 1 March 2020 to 15 May 2020 (76 days), with 16,112 county-day pairings for the final analysis. Indiana reported 406 COVID-19 infections per 100,000 people and 26 COVID-19-related mortalities per 100,000 people during the study period. The equivalent rates in Kentucky were lower, with 175 COVID-19 infections and 8 COVID-19-related deaths per 100,000 people (Table 1).
Average human mobility by state for the study period in Kentucky and Indiana decreased in terms of mobility for retail/recreation (−15.3% and −18.8%, respectively) and the workplace (−26.8% and −28.1%, respectively), compared to mobility observed for the respective categories during the baseline period in 2019 (Table 2). Compared to Kentucky, Indiana mobility averages indicate reduced mobility to places of retail/recreation and work by 3.5 and 1.3 percent compared to the baseline, respectively. Average mobility to the grocery/pharmacy in Kentucky and Indiana reduced by 4.4 and 2.6 percent, respectively, compared to the baseline period. The difference of 1.8 percent compared to baseline among the states implies greater foot traffic on average in grocery stores and pharmacies in Kentucky compared to Indiana during the study period, but this is a very small change.
Weather parameter averages in Kentucky were higher compared to Indiana: 24-h sum precipitation (46.6 mm and 29.3 mm, respectively), minimum temperature (41.1 °F and 37.4 °F, respectively), maximum temperature (63.6 °F and 58.3 °F, respectively), and UV index (6.6 and 5.9, respectively). The Mann–Whitney Wilcoxon (Wilcoxon’s rank sum) tests indicated a significant difference in each of the human mobility and weather-related categories between Kentucky and Indiana (p < 0.001), with the higher rates appearing in Kentucky.
Out of 120 counties in Kentucky, 39 were metropolitan (32.5%), with the remaining 81 being non-metropolitan counties (67.5%). Indiana had a more balanced distribution of metropolitan and non-metropolitan counties. Of the 92 counties in Indiana, 44 were metropolitan (47.8%) and 48 were non-metropolitan (52.2%). The chi-square test of independence indicated that there was a significant association between metropolitan status and the state (p < 0.023).

3.1.1. Cox Regression: COVID-19 Infection

Adjusted hazard ratios (aHR) from the Cox regression analysis of COVID-19 cases of infection included daily human mobility, daily weather, and metro/non-metropolitan status in Kentucky and Indiana (Table 3). The unadjusted hazard ratio (uHR) for COVID-19 infection indicated that Indiana residents had 2.45 times (uHR = 2.45, 95% CI = 2.30–2.60) higher COVID-19 infection risk than Kentucky residents. The adjusted Cox regression model of COVID-19 infection indicated that Indiana residents had an 18% (aHR = 1.18, 95% CI = 1.10–1.26) increased risk of infection compared to Kentucky residents, controlling for daily human mobility (retail/recreation, grocery/pharmacy, and workplace), daily weather (precipitation, minimum temperature, maximum temperature, and UV index), and metropolitan status.
Protective factors against COVID-19 infection included the human mobility indicators: retail/recreation (aHR = 0.97, 95% CI = 0.96–0.97), grocery/pharmacy (aHR = 0.991, 95% CI = 0.989–0.994), and workplace (aHR = 0.99, 95% CI = 0.98–0.99). Conversely, daily weather indicators that increased the risk of infection included a higher minimum temperature (aHR = 1.01, 95% CI = 1.01–1.02) and a higher maximum temperature (aHR = 1.01, 95% CI = 1.01–1.02). Precipitation (aHR = 0.999, 95% CI = 0.998–0.999) had a statistically significant but marginal effect against COVID-19 infection. A unit increase in the UV index was found to be highly protective against COVID-19 infection by 63% (aHR = 0.37, 95% CI = 0.36–0.39). Lastly, residents from metropolitan counties were at a significantly higher risk of contracting COVID-19 infection of 12%, compared to residents from non-metropolitan counties (aHR = 1.12, 95% CI = 1.05–1.19).

3.1.2. Cox Regression: COVID-19 Mortality

Adjusted hazard ratios (aHR) from the Cox regression analysis of COVID-19-related mortality included daily human mobility, daily weather, and metro/non-metropolitan status in Kentucky and Indiana (Table 4). uHR modeling for COVID-19 deaths indicated that Indiana residents had a significant risk of COVID-19-related mortality compared to Kentucky residents by 2.39 times (uHR = 2.39, 95% CI = 2.37–2.41). The adjusted Cox regression model of COVID-19 deaths indicated Indiana residents had a significant risk of COVID-19-related mortality compared to Kentucky residents by 1.59 times (aHR = 1.59, 95% CI = 1.57–1.60), controlling for daily human mobility (retail/recreation, grocery/pharmacy, workplace), daily weather (precipitation, minimum temperature, maximum temperature, UV index), and metropolitan status.
Daily human mobility indicators indicated protection against COVID-19-related mortality for retail/recreation (aHR = 0.937, 95% CI = 0.937–0.938), grocery/pharmacy (aHR = 0.992, 95% CI = 0.992–0.993), and workplace (aHR = 0.965, 95% CI = 0.965–0.966). Mobility to places of retail/recreation and work provided slightly better protection against COVID-19-related mortality (6% vs. 3%, respectively) compared to COVID-19 infection (3% vs. 1%, respectively). A statistically significant but minimal protective effect against COVID-19-related mortality was found for precipitation (aHR = 0.9978, 95% CI = 0.9977–0.9978) and a higher minimum temperature (aHR = 0.994, 95% CI = 0.993–0.994). A higher maximum temperature (aHR = 1.001, 95% CI = 1.001–1.002) had a significant minimal effect on the risk of COVID-19-related mortality. The UV index had a moderate protective effect against COVID-19-related mortality by 25% (aHR = 0.748, 95% CI = 0.746–0.751). The increased risk of COVID-19-related mortality for non-metropolitan residents compared to residents from non-metropolitan counties (aHR = 2.05, 95% CI = 2.02–2.07) was more than doubled (2.1%).

3.1.3. Cox Regression: Sensitivity Analysis

A sensitivity analysis of Cox regression models for only two county units that share a border with Indiana and Kentucky by the Ohio River was conducted to compare the results with the original model that incorporated data from all counties in Indiana and Kentucky. The Cox regression analysis of COVID-19 infections was limited to neighboring counties by the Ohio River bordering Indiana and Kentucky and used the same covariates from the state-level comparisons. Significant predictors of infection included, daily: human mobility (retail/recreation), weather (minimum temperature, maximum temperature, UV index), metropolitan status, and state (Supplementary Table S4). In the original model, all the predictors were significantly related to COVID-19 infection. Minimal differences in the hazard ratios were found for the predictors in the state vs. bordering county models. Metropolitan and state status remained important. In contrast to the findings on metropolitan counties from the original model (aHR = 1.12, 95% CI = 1.05–1.19), residents from metropolitan counties by the Ohio River bordering Indiana and Kentucky had an additional 31% significant risk of contracting COVID-19 infection compared to residents from non-metropolitan counties (aHR = 1.43, 95% CI = 1.19–1.71). Compared to the observed COVID-19 infection risk derived from the original state comparison model (aHR = 1.18, 95% CI = 1.10–1.26), Indiana residents (2 counties deep) by the Ohio River had an additional 7% significant risk of COVID-19 infection compared to the neighboring Kentucky residents (aHR = 1.25, 95% CI = 1.04–1.51), controlling for daily human mobility and other covariates.
All predictors in the Cox regression model for COVID-19-related mortality, limited to neighboring counties by the Ohio River bordering Indiana and Kentucky, were significant (Supplementary Table S5), similar to the state-level model. Key differences between the state-level comparison and the Ohio River bordering counties can be observed for UV index, metropolitan status, and state. The UV index had a higher protective effect that differed by 44% (aHR = 0.31, 95% CI = 0.31–0.32) compared to an original effect of 25% (aHR = 0.748, 95% CI = 0.746–0.751) against COVID-19-related mortality. The risk of COVID-19-related mortality was significantly reduced by 13% (aHR = 1.92, 95% CI = 1.82–2.02) among residents in the metropolitan counties bordering the Ohio River between Indiana and Kentucky, compared to the findings in the original model (aHR = 2.05, 95% CI = 2.02–2.07). Compared to the COVID-19-related mortality risk observed in the state-level comparisons in the original model (aHR = 1.59, 95% CI = 1.57–1.60), Indiana residents (within two counties of the state border) by the Ohio River had an additional 31% significant risk of COVID-19-related mortality compared to the neighboring Kentucky residents (aHR = 1.90, 95% CI = 1.85–1.96), controlling for daily human mobility and other covariates.
Sensitivity analysis added validity to the analysis comparing the neighboring states. Two counties deep bordering the Ohio River for Indiana and Kentucky were considered when replicating the models, and the hazard ratio for the states increased significantly, supporting the key research questions in the present study: Indiana had a less stringent COVID-19-related stay-at-home order and mask mandate compared to Kentucky.

4. Discussion

In the present investigation, the risk of COVID-19 infection and COVID-19-related mortality was evaluated among Indiana residents compared to Kentucky residents after controlling for significant covariates: human mobility regarding retail, recreation, grocery, and work, daily weather parameters (precipitation, minimum and maximum temperature, and UV index), and county metropolitan status during the stay-at-home order period in 2020.
The stay-at-home order, social distancing, and mask mandate practices were the primary preventive non-pharmaceutical public health COVID-19 infection and mortality measures during the pandemic. Kentucky imposed a strict stay-at-home order (26 March 2020 to 26 May 2020), social distancing, and a mask/face covering mandate. Indiana did not mandate the rigorous COVID-19 restrictions (stay-at-home order: 24 March 2020 to 1 May 2020) [20]. The present study found that Indiana residents were at a higher risk of contracting COVID-19 infection by 18% and, if infected, were 59% more likely to have a fatal outcome compared to Kentucky residents after controlling for human mobility, weather, and metropolitan status.
Human mobility to retail, recreation, grocery, pharmacy, and work was found to have a marginal protective effect against both COVID-19 infection and mortality in the current study. Restricting mobility to places classified by Google’s Community Mobility Reports (CMR) as retail/recreation sites significantly reduced the risk of COVID-19 infection and mortality by 3% (aHR = 0.97, 95% CI = 0.96–0.97) and 6% (aHR = 0.937, 95% CI = 0.937–0.938), respectively. Furthermore, restricting mobility to workplace locations decreased the risk of COVID-19-related mortality by 3% (aHR = 0.965, 95% CI = 0.965–0.966). The findings from the current study are contrary to the limited existing evidence on human mobility and COVID-19-related health outcomes. A positive association between human mobility and COVID-19 infection and/or mortality was observed nationally using an ordinary least squares (OLS) regression (coefficientinfection = 22.99, coefficientmortality = 19.81) and across multiple counties in different states in the U.S. using a generalized linear model (GLM) [27,28]. No county from Kentucky or Indiana was included in the GLM model [27]. The differences in findings between prior studies and the current study may be due to the limitations of human mobility data. Cellphone human mobility data were not able to capture an individual’s protective behaviors, such as mask usage and social distancing. Behavioral compliance may have varied significantly across sub-populations and fluctuated dramatically over the course of the pandemic.
Weather can affect virus transmission in two ways: (1) epidemiological and (2) behavioral health. An epidemiological perspective suggests that a virus’s ability to survive and spread is influenced by temperature. A behavioral health perspective suggests that weather affects mobility levels, social distancing, the location of social gatherings, and viral spread [29]. An association between precipitation, maximum and minimum temperatures, and UV index regarding COVID-19 infection and mortality is observed in the current study. Precipitation (24-h sum) had a marginal protective effect against COVID-19 infection and mortality, probably because it limited outdoor contact. A higher minimum temperature (°F) increased the risk of COVID-19 infection by a small magnitude but provided a marginally significant protective effect against COVID-19 mortality. A higher maximum temperature (°F) nominally increased the risk of COVID-19 infection but had no effect on mortality. The findings for the UV index on COVID-19 infection and mortality were remarkable. The UV index significantly reduced the risk of COVID-19 infection by 63% while decreasing the risk of COVID-19 mortality by 25%.
There is limited evidence on the association between precipitation and COVID-19 infection and mortality, and it is not consistent. The association between COVID-19 infection, controlling for precipitation and other weather indicators, was analyzed in New York, U.S., New South Wales, Australia, and Jakarta, Indonesia, respectively [30,31,32]. No significant association between precipitation and COVID-19 infection was found. In contrast, a study from Oslo, Norway, reported a negative association between COVID-19 infection and precipitation, paralleling the current study [33]. An analysis of socio-ecological and economic factors and weather on COVID-19 mortality risk across 178 countries and regions worldwide reported that the risk was reduced with increasing precipitation [34], which is consistent with the present study.
The relationship between temperature and COVID-19 infection and mortality is not consistent across studies [35,36,37]. A meta-analysis including 110 country-level data sets analyzed the association between weather parameters (temperature and precipitation) and COVID-19 infection and/or mortality and found a significant positive association with temperature for COVID-19 infection (r = 0.22, 95% CI = 0.16–0.28) and for mortality (r = 0.11, 95% CI = 0.01–0.22) [38]. The results of the present study align with those from this meta-analysis of 110 countries.
Ultraviolet B (UVB) radiation provides additional protection against viral transmission, through (1) the direct anti-viral activity of UVB against the virus and (2) increased vitamin D skin synthesis. UVB radiation boosts vitamin D synthesis, possibly lowering the risk of vitamin D deficiencies. Previous research has shown that vitamin D deficiency increases the risk of upper respiratory infections [39,40,41]. Thus, vitamin D may reduce the risk of COVID-19 infection and mortality [42,43]. A meta-regression of data from 33 large U.S. cities assessed the relationship between meteorological conditions (e.g., UV index) and COVID-19 infection [44] and found that the UV index is negatively associated with cases of COVID-19 infection (coefficient = −0.45, 95% CI = −0.59–−0.31), which aligns with expectations based on the effect of UVB on pathogens. An inverse relationship between COVID-19 mortality and UV radiation was also found in a global analysis of 183 countries. The analysis found that for each unit increase in the UV index, there was a 1.2 percent decline in daily COVID-19 mortality rates [45]. The results from two large multinational studies concur with the findings in the present study: UV is protective against COVID-19 infection and mortality [44,45]. Far-UVC radiation (207–222 nautical miles (nm)) effectively destroys the SARS-CoV-2 virus without causing damage to human tissues [46]. UV disinfection technologies can be used to disinfect bio-contaminated air and surfaces to curb the transmission of COVID-19 infection [47]. Considering the protective nature of UVC radiation against COVID-19 infection, UV disinfection technologies could be implemented in public settings, i.e., healthcare facilities, shopping malls, and airports, for disinfecting frequently touched surfaces and circulating air streams.
The U.S. Department of Agriculture Economic Research Service (USDA ERS) reported that 62% of U.S. counties (n = 1958) out of 3144 counties are non-metropolitan [23]. Non-metropolitan residents were found to be at a higher risk of COVID-19-related hospitalizations and mortality compared to their metropolitan counterparts. The residents of non-metropolitan counties tend to be older, uninsured, have more co-morbidities, and live farther away from medical care facilities [48,49]. An analysis of differences in the practice of COVID-19-related health behaviors between metropolitan and non-metropolitan communities in the U.S. found that non-metropolitan residents were less likely to use face masks in public, disinfect their residence or office, avoid restaurants, or work from home [50]. Higher COVID-19 infection incidence and mortality rates were reported in several studies in non-metropolitan counties compared to metropolitan counties [51,52,53,54]. In the current study, metropolitan residents were at a higher risk of COVID-19 infection and mortality compared to non-metropolitan residents, which was possibly due to higher population density. An analysis using an in-house-developed COVID-19 susceptibility scale at the county level (n = 3079) assessed the health and socioeconomic resilience of susceptible areas across the rural–urban continuum and reported that approximately 6% of metropolitan counties across the U.S. were at risk of COVID-19 infection because of the community spread from dense populations [55]. Hierarchical generalized linear models (HGLM) analyses of county-level health factors related to COVID-19 mortality across 3141 U.S. counties showed that highly urbanized areas were more likely to have higher COVID-19 mortality than rural areas [56]. The present investigation’s findings are similar to these previously published studies.
A key finding from the current study is that Indiana residents were at higher risk of COVID-19 infection (aHR = 1.18, 95% CI = 1.10–1.26) and mortality (aHR = 1.59, 95% CI = 1.57–1.60) compared to Kentucky residents. In addition to their state of residence, other influences included human mobility, weather, and metropolitan status during the period of the stay-at-home order. Precipitation and UV index had a protective effect against COVID-19 infection and mortality, similar to other findings [33,34,44,45]. Limited evidence suggests that metropolitan areas are at increased risk of COVID-19 infection and mortality, which is probably attributable to population density [55,56]. Average precipitation and UV index were higher in Kentucky compared to Indiana during the study period, by 59% and 11%, respectively. Indiana has 12% more metropolitan counties compared to Kentucky, and the number of non-metropolitan counties is lower in Indiana by 51%. Precipitation, UV index, and metropolitanization may contribute to the state-associated increased risk of COVID-19 infection and mortality among Indiana residents compared to Kentucky residents.
During the study period, average mobility to places of retail/recreation and the workplace decreased in both Indiana and Kentucky. This drop was slightly greater in Indiana compared to Kentucky residents for retail/recreation (3%) and the workplace (1.3%). However, average mobility to grocery stores and pharmacies was slightly increased by 2% among Kentucky residents compared to Indiana residents. Increased human mobility can proliferate the risk associated with COVID-19 infection and mortality [27,28]. Although findings on the state-associated risk of COVID-19 infection and mortality in relation to human mobility are conflicting, it is important to consider that Indiana had a less stringent stay-at-home order compared to Kentucky [20]. Indiana residents were allowed to leave home for certain employment types, for outdoor activities, and to take care of others from 24 March to 1 May 2020. However, travel was restricted, and only life-sustaining businesses (retail, gas station, etc.) could remain open in Kentucky from 25 March to 29 April 2020. Furthermore, social distancing and hygiene were required, and the use of facial masks was promoted in public settings. The use of face masks in community settings is known to reduce the transmission risk of COVID-19 infection [57]. The difference in compliance and the enforcement of stay-at-home orders between Indiana and Kentucky may help explain some of the variability in state-level differences in COVID-19 infection and mortality observed in the current study.

5. Limitations

This study has several limitations, notably, that such an observational study cannot establish causality. The findings cannot be generalized to other U.S. states or regions. A key limitation is the use of multiple imputations to replace missing values. This particular method can cause biased results. However, the missing value patterns chart shows no consistent patterns in the missing data for the variables except for grocery/pharmacy. A multiple imputation using a linear regression technique was implemented; therefore, inconsistent patterns in the missing data strengthened the validity of the imputed data. The lack of information on other seasons (summer, fall, winter), non-pharmaceutical interventions (NPIs) (use of facial masks, social distancing compliance), and socio-economic factors (i.e., income and occupation) are underlying limitations to this study’s analytical models. Other limitations include possible discrepancies in the reported COVID-19 infection and mortality data and a lack of information on state-level healthcare capacity, public health infrastructure, and public adherence to guidelines. Future studies should consider studying data representing a longer duration, segmenting analyses by different seasons for comparison, and the inclusion of socio-economic information to fully capture the long-term effects of social distancing and mask mandates on COVID-19 transmission and mortality.

6. Conclusions

Indiana and Kentucky are neighboring states with similar demographics, socioeconomic variation, weather variation, and air pollution. Enforcement of and adherence to stay-at-home orders between Indiana and Kentucky varied during the spring 2020 COVID-19 pandemic. Indiana residents were at a higher risk of COVID-19 infection and mortality compared to Kentucky residents when holding human mobility, weather parameters, and metropolitanization as constants. The findings from the current study suggest that UV index, precipitation, and human mobility were protective against COVID-19 infection and mortality. Differences in these parameters contributed to the observed state-associated risk. Metropolitan county residents were at increased risk of COVID-19 infection and mortality compared to non-metropolitan county residents, likely due to the high population density. The adherence to and enforcement of stay-at-home orders between Indiana and Kentucky were different. This key difference may explain the findings on human mobility and the use of facial coverings, and the state-wide associated risk of COVID-19 mortality in Indiana compared to Kentucky.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/atmos15091100/s1, Figure S1: Missing Value Patterns; Table S1: Pre- and post-multiple imputation frequencies; Table S2: Cox Regression Analysis of COVID-19 Deaths among Indiana and Kentucky (Unweighted); Table S3: List of Counties neighboring to Indiana and Kentucky Ohio river border (2 units deep); Table S4: Cox Regression Analysis of COVID-19 Infection among neighboring counties in Indiana and Kentucky; Table S5: Cox Regression Analysis of COVID-19 Mortality among neighboring counties in Indiana and Kentucky.

Author Contributions

S.H.S.: Conceptualization, methodology, software, validation, formal analysis, investigation, data curation, writing—original draft, writing—review and editing, and visualization. B.B.L.: Conceptualization, methodology, software, validation, formal analysis, investigation, data curation, writing—original draft, writing—review and editing, and visualization. S.K.: Conceptualization, methodology, software, validation, formal analysis, investigation, data curation, writing—review and editing, and visualization. W.P.M.: Conceptualization, formal analysis, visualization, and writing—review and editing. M.G.: Software, formal analysis, visualization, and writing—review and editing. M.K.: Methodology, validation, and writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study did not require ethical approval.

Informed Consent Statement

Not applicable.

Data Availability Statement

All public data used for this study can be accessed from the websites of the sources mentioned in Section 2: Materials and Methods.

Acknowledgments

This study is a by-product of a PhD dissertation. I, Shaminul Shakib, was granted the University of Louisville (UofL) Summer 2024 Doctoral Dissertation Completion Award, which greatly assisted me in conducting this study. I would like to acknowledge the Graduate School and Beth Boehm from UofL, for providing the award. Additionally, I would like to thank all co-authors for their invaluable contributions, especially Bert Little.

Conflicts of Interest

The authors declare no conflicts of interest.

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Table 1. COVID-19 infection and mortality rates per 100,000 people for 1 March 2020–15 May 2020.
Table 1. COVID-19 infection and mortality rates per 100,000 people for 1 March 2020–15 May 2020.
KentuckyIndiana
Total Number of COVID-19 Infections782927,433
Total Number of COVID-19 Mortality3581739
Population Based on the 2020 Census4,477,0006,755,000
COVID-19 Infection Rate per 100,000175406
COVID-19 Mortality Rate per 100,000826
Table 2. Social mobility and weather effects, according to state, for 1 March 2020–15 May 2020.
Table 2. Social mobility and weather effects, according to state, for 1 March 2020–15 May 2020.
Total
MeanSDp-Value 3
Kentucky 1
Mobility
Retail/Recreation (%)−15.318.7<0.001
Grocery/Pharmacy (%)4.414.6<0.001
Workplace (%)−26.815.6<0.001
Weather
Precipitation (mm, 24 h sum)46.693.0<0.001
Temperature minimum (°F)41.19.2<0.001
Temperature maximum (°F)63.610.3<0.001
UV Index6.61.4<0.001
Indiana 2
Mobility
Retail/Recreation (%)−18.821.0
Grocery/Pharmacy (%)2.616.5
Workplace (%)−28.117.5
Weather
Precipitation (mm, 24 h sum)29.373.1
Temperature minimum (°F)37.48.9
Temperature maximum (°F)58.311.0
UV Index5.91.3
1 Kentucky: 9120 observations. 2 Indiana: 6992 observations. 3 Mann–Whitney Wilcoxon test, by state.
Table 3. Cox regression analysis of COVID-19 infection rates for Indiana and Kentucky.
Table 3. Cox regression analysis of COVID-19 infection rates for Indiana and Kentucky.
HR95% CIp-Value
LowerUpper
Mobility
Retail/Recreation0.970.960.97<0.001
Grocery/Pharmacy0.9910.9890.994<0.001
Workplace0.990.980.99<0.001
Weather
Precipitation (24 h sum)0.9990.9980.999<0.001
Temperature minimum1.011.011.02<0.001
Temperature maximum1.011.011.02<0.001
UV Index0.370.360.39<0.001
Geographics
Metropolitan Status (ref: non-metro)1.121.051.19<0.001
State (ref: Kentucky)1.181.101.26<0.001
Note: Unadjusted hazard ratio (infection) for the state (ref: Kentucky): 2.45 (95% CI 2.30–2.60), p-value < 0.001.
Table 4. Cox regression analysis of COVID-19 mortality for Indiana and Kentucky.
Table 4. Cox regression analysis of COVID-19 mortality for Indiana and Kentucky.
HR95% CIp-Value
LowerUpper
Mobility
Retail/Recreation0.9370.9370.938<0.001
Grocery/Pharmacy0.9920.9920.993<0.001
Workplace0.9650.9650.966<0.001
Weather
Precipitation (24 h sum)0.99780.99770.9978<0.001
Temperature minimum0.9940.9930.994<0.001
Temperature maximum1.0011.0011.002<0.001
UV Index0.7480.7460.751<0.001
Geographics
Metropolitan Status (ref: non-metro)2.052.022.07<0.001
State (ref: Kentucky)1.591.571.60<0.001
Note: Unadjusted hazard ratio (mortality) for the state (ref: Kentucky): 2.39 (95% CI 2.37–2.41), p-value < 0.001.
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Shakib, S.H.; Little, B.B.; Karimi, S.; McKinney, W.P.; Goldsby, M.; Kong, M. Mediating Effect of the Stay-at-Home Order on the Association between Mobility, Weather, and COVID-19 Infection and Mortality in Indiana and Kentucky: March to May 2020. Atmosphere 2024, 15, 1100. https://doi.org/10.3390/atmos15091100

AMA Style

Shakib SH, Little BB, Karimi S, McKinney WP, Goldsby M, Kong M. Mediating Effect of the Stay-at-Home Order on the Association between Mobility, Weather, and COVID-19 Infection and Mortality in Indiana and Kentucky: March to May 2020. Atmosphere. 2024; 15(9):1100. https://doi.org/10.3390/atmos15091100

Chicago/Turabian Style

Shakib, Shaminul H., Bert B. Little, Seyed Karimi, William Paul McKinney, Michael Goldsby, and Maiying Kong. 2024. "Mediating Effect of the Stay-at-Home Order on the Association between Mobility, Weather, and COVID-19 Infection and Mortality in Indiana and Kentucky: March to May 2020" Atmosphere 15, no. 9: 1100. https://doi.org/10.3390/atmos15091100

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