Disaster Medicine and Public Health Preparedness

BackgroundWisconsin (WI) held a primary election in the midst of the COVID-19 pandemic. Live voting at polls was allowed despite concern over increasing the spread of COVID-19. In addition to 1.1 million absentee ballots cast, 453,222 persons voted live. The purpose of our study was to determine if an increase in COVID-19 activity was associated with the election. MethodsUsing the voting age population for the United States (US), WI, and its 3 largest counties, and daily new COVID-19 case reports from various COVID-19 web-based dashboards, daily new case rates were calculated. With election day April 7, the incubation period included April 12-21. The new case activity in the rest of the US was compared with the Wisconsin activity during the incubation period. ResultsWI daily new case rates were lower than those of the rest of the US for the 10-day period before the election and remained lower during the post exposure incubation period. The ratio of Wisconsin new case rates to US new case rates was 0.34 WI: 1 US for the 10 days leading up to the election and declined to 0.28 WI: 1 US for the 10-day post-incubation period after the election. Similar analysis for Milwaukee county showed a pre-election ratio of 1.02 Milwaukee: 1 US and after the election the ratio was 0.63 Milwaukee: 1 US. Dane county had a pre-election ratio of 0.21 Dane: 1 US case, and it fell to 0.13 Dane: 1 US after the election. Waukesha county had a pre-election ratio of 0.27 Waukesha: 1 US case and that fell to 0.19 Waukesha: 1 US after the election. ConclusionsThere was no increase in COVID-19 new case daily rates observed for Wisconsin or its 3 largest counties following the election on April 7, 2020, as compared to the US, during the post-incubation interval period.

BackgroundThe first shelter-in-place (SIP) order in the United States was issued across six counties in the San Francisco Bay Area to reduce the impact of COVID-19 on critical care resources. We sought to assess the impact of this large-scale intervention on emergency departments (ED) in Marin County, California. MethodsWe conducted a retrospective descriptive and trend analysis of all ED visits in Marin County, California from January 1, 2018 to May 4, 2020 to quantify the temporal dynamics of ED utilization before and after the March 17, 2020 SIP order. ResultsThe average number of ED visits per day decreased by 52.3% following the SIP order compared to corresponding time periods in 2018 and 2019. Both respiratory and non-respiratory visits declined, but this negative trend was most pronounced for non-respiratory admissions. ConclusionsThe first SIP order to be issued in the United States in response to COVID-19 was associated with a significant reduction in ED utilization in Marin County.

The United States (US) public health interventions were rigorous and rapid, yet failed to arrest the spread of the Coronavirus Disease 2019 (COVID-19) pandemic as infections spread throughout the US. Many factors have contributed to the spread of COVID-19, and the success of public health interventions depends on the level of community adherence to preventative measures. Public health professionals must also understand regional demographic variation in health disparities and determinants to target interventions more effectively. In this study, a systematic evaluation of three significant interventions employed in the US, and their effectiveness in slowing the early spread of COVID-19 was conducted. Next, community-level compliance with a state-level stay at home orders was assessed to determine COVID-19 spread behavior. Finally, health disparities that may have contributed to the disproportionate acceleration of early COVID-19 spread between certain counties were characterized. The contribution of these factors for the disproportionate spread of the disease was analyzed using both univariate and multivariate statistical analyses. Results of this investigation show that delayed implementation of public health interventions, a low level of compliance with the stay at home orders, in conjunction with health disparities, significantly contributed to the early spread of the COVID-19 pandemic.

Medicaid expansion is a federally-funded program to expand health care access and coverage to economically challenged populations by increasing eligibility to Medicaid enrollment and investing in public health preventive services in the individual states. Yet, when the COVID-19 epidemic plagued the country, fourteen states were practicing their chosen decision not to enact the Medicaid expansion policy. We examined the consequences of this nationwide split in Medicaid design on the spread of the COVID-19 epidemic between the expansion and non-expansion states. Our study shows that, on average, the expansion states had 217.56 fewer confirmed COVID-19 cases per 100,000 residents than the non-expansion states [-210.41; 95%CI (-411.131) - (-2.05); P<0.05]. Also, the doubling time of COVID-19 cases in Medicaid expansion states was longer than that of non-expansion states by an average of 1.68 days [1.6826; 95%CI 0.4035-2.9617; P<0.05]. These findings suggest that proactive investment in public health preparedness was an effective protective policy measure in this crisis, unsurpassed by the benefits of COVID-19 emergency plans and funds. The study findings could be relevant to policymakers and healthcare strategists in non-expansion states considering their states preparations for such public health crises.

The US public health system is organized in 3 levels: national, state-level, and county-level. Public health messaging both within and across these scales may not always be consistent, and for transmissible public health threats where cases in one spatial location may impact other areas, this lack of consistency could create problems. Here, we collected and analyzed data on influenza vaccination recommendations across public health administration levels. We assess spatial heterogeneity at the county level, and analyze consistency in recommendations across spatial scales. We also compare information accessibility with influenza vaccine affordability and availability to identify factors that may be most related to vaccine uptake. We find that influenza vaccine recommendations are highly variable in both their priority group specificity and in their ease of access, and there is poor agreement across spatial scales. This lack of consistency results in a lack of clear relationship between vaccination information and vaccine uptake. This work highlights the need for greater consistency in specific, easily accessed public health information from trusted sources.

COVID-19, caused by the SARS-CoV-2 virus, has quickly spread throughout the world, necessitating assessment of the most effective containment methods. Very little research exists on the effects of social distancing measures on this pandemic. The purpose of this study was to examine the effects of government implemented social distancing measures on the cumulative incidence rates of COVID-19 in the United States on a state level, and in the 25 most populated cities, while adjusting for socio-demographic risk factors. The social distancing variables assessed in this study were: days to closing of non-essential business; days to stay home orders; days to restrictions on gathering, days to restaurant closings and days to school closing. Using negative binomial regression, adjusted rate ratios and 95% confidence intervals were calculated comparing two levels of a binary variable: "above median value," and "median value and below" for days to implementing a social distancing measure. For city level data, the effects of these social distancing variables were also assessed in high (above median value) vs low (median value and below) population density cities. For the state level analysis, days to school closing was associated with cumulative incidence, with an adjusted rate ratio of 1.59 (95% CI:1.03,2.44), p=0.04 at 35 days. Some results were counterintuitive, including inverse associations between cumulative incidence and days to closure of non-essential business and restrictions on gatherings. This finding is likely due to reverse causality, where locations with slower growth rates initially chose not to implement measures, and later implemented measures when they absolutely needed to respond to increasing rates of infection. Effects of social distancing measures seemed to vary by population density in cities. Our results suggest that the effect of social distancing measures may differ between states and cities and between locations with different population densities. States and cities need individual approaches to containment of an epidemic, with an awareness of their own structure in terms of crowding and socio-economic variables. In an effort to reduce infection rates, cities may want to implement social distancing in advance of state mandates.

The first purpose of this study is to describe a project focused on comparing the numbers of COVID-19 cases and deaths in the United States reported by four different online trackers, namely, those maintained by USAFacts, the New York Times, Johns Hopkins University, and the COVID Tracking Project. The second purpose of this study is to present results from the first five months of 2020 (January 22-May 31, 2020). This project is ongoing, so it will be updated regularly as new data from each of these trackers become available. Based on the time period included, the NYT has reported more cases than any of the other three trackers since late March/early April, and COVID Tracking Project has reported fewer deaths than any of the other three trackers since mid-March. It is hoped that the discrepancies identified by this project will provide avenues for research on their causes.

ObjectiveIndoor mass gatherings in counties with high COVID-19 incidence have been linked to infections. We examined if outdoor mass gatherings in counties with low COVID-19 incidence are also followed by infections. MethodsWe retrospectively examined COVID-19 incidence in 20 counties that held mass gathering rallies (19 outdoor and 1 indoor) in the United States in August-September 2020. They were compared to the rest of the United States counties. We utilized a 7-day moving average and compared the change on the gathering date and 15 days later, based on the 95% confidence interval. For control counties we used the median of the gathering dates. SettingThe United States Population8.4 million in the counties holding mass gatherings, and 324 Million in the rest of the counties in the United States. Main Outcome MeasureChange in COVID-19 incidence rate per 100,000 capita during the two weeks following mass gatherings. ResultsIn the two weeks following the gatherings, the COVID-19 incidence increased significantly in 14 of 20 counties. The county with the highest incidence increase (3.8-fold) had the 2nd lowest incidence before the gathering. The county with the highest decrease (0.4-fold) had the 3rd highest incidence before the gathering. At the gathering date, the average incidence of counties with gatherings was lower than the rest of the United States, and after the gathering, it increased 1.5-fold, while the rest of the United States increased 1.02-fold. ConclusionThese results suggest that even outdoor gatherings in areas with low COVID-19 incidence are followed by increased infections, and that further precautions should be taken at such gatherings. What is already known on the topicMass gatherings have been linked to COVID-19 infections, but it is less clear how much it happens outdoors, and in areas with low incidence. What this study addsCOVID-19 infections increased significantly in 14 of 20 counties that held mass gathering rallies in the United States, 19 of which were outdoors. The county with the highest incidence increase (3.8-fold) was outdoors and had a low incidence before the gathering. The average incidence of all 20 counties with gatherings was lower at the gathering day compared with the rest of the United State, and it increased 1.5-fold following the gatherings. Our findings suggest a need for precautions in mass gatherings, even when outdoors and in areas with a low incidence of COVID-19.

BackgroundThere have been outbreaks of SARS-CoV-2 in long term care facilities and recent reports of disproportionate death rates among the vulnerable population. The goal of this study was to better understand the impact of SARS-CoV-2 infection on the non-institutionalized disabled population in the United States using data from the most affected states as of April 9th, 2020. MethodsThis was an ecological study of county-level factors associated with the infection and mortality rate of SARS-CoV-2 in the non-institutionalized disabled population. We analyzed data from 369 counties from the most affected states (Michigan, New York, New Jersey, Pennsylvania, California, Louisiana, Massachusetts) in the United States using data available by April 9th, 2020. The variables include changes in mobility reported by Google, race/ethnicity, median income, education level, health insurance, and disability information from the United States Census Bureau. Bivariate regression analysis adjusted for state and median income was used to analyze the association between death rate and infection rate. ResultsThe independent sample t-test of two groups (group 1: Death rate[&ge;]3.4% [median] and group 2: Death rate < 3.4%) indicates that counties with a higher total population, a lower percentage of Black males and females, higher median income, higher education, and lower percentage of disabled population have a lower rate (< 3.4%) of SARS-CoV-2 related mortality (all p-values<4.3E-02). The results of the bivariate regression when controlled for median income and state show counties with a higher White disabled population (est: 0.19, 95% CI: 0.01-0.37; p-value:3.7E-02), and higher population with independent living difficulty (est: 0.15, 95% CI: -0.01-0.30; p-value: 6.0E-02) have a higher rate of SARS-CoV-2 related mortality. Also, the regression analysis indicates that counties with higher White disabled population (est: - 0.22, 95% CI: -0.43-(-0.02); p-value: 3.3E-02), higher population with hearing disability (est: -0.26, 95% CI: - 0.42- (-0.11); p-value:1.2E-03), and higher population with disability in the 18-34 years age group (est: -0.25, 95% CI: -0.41-(-0.09); p-value:2.4E-03) show a lower rate of SARS-CoV-2 infection. ConclusionOur results indicate that while counties with a higher percentage of non-institutionalized disabled population, especially White disabled population, show a lower infection rate, they have a higher rate of SARS-CoV-2 related mortality.

With the number of confirmed COVID-19 cases rapidly growing in the U.S., many states are experiencing a shortage of hospital--especially ICU--beds. In addition to discharging non-critical patients, expanding local hospitals capacity as well as re-opening closed healthcare facilities, these states are actively building or converting public venues into field hospitals to fill the gap1. By studying these makeshift hospitals, we found that the states most severely impacted by the pandemic are fast at responding with the first wave of hospitals opening around the date of peak demand and the majority ready to use by the end of April. However, depending on the types of patients the field hospitals accept (COVID-19 vs. non-COVID-19) and how they are incorporated to local healthcare system, these field hospitals have utilization rate ranging from 100% to 0%. The field hospitals acting as alternative site to treat non-COVID-19 patients typically had low utilization rate and often faced the risk of COVID-19 outbreak in the facility. As overflow facilities, the field hospitals providing intensive care were highly relied on by local healthcare systems whereas the field hospitals dedicated to patients with mild symptoms often found it hard to fill the beds due to a combination of factors such as strict regulation on transferring patients from local hospitals, complication of health insurance discouraging health-seeking behavior, and effective public health measure to "flatten the curve" so that the additional beds were no longer needed.

This study analyzed spatiotemporal spread patterns of COVID-19 confirmed cases at the zip code level in the County of San Diego and compared them to neighborhood social and economic factors. We used correlation analysis, regression models, and geographic weighted regression to identify important factors and spatial patterns. We broke down the temporal confirmed case patterns into four stages from 1 April 2020 to 31 December 2020. The COVID-19 outbreak hotspots in San Diego County are South Bay, El Cajon, Escondido, and rural areas. The spatial patterns among different stages may represent fundamental health disparity issues in neighborhoods. We also identified important variables with strong positive or negative correlations in these categories: ethnic groups, languages, economics, and education. The highest association variables were Pop5andOlderSpanish (Spanish-speaking) in Stage 4 (0.79) and Pop25OlderLess9grade (Less than 9th grade education) in Stage 4 (0.79). We also observed a clear pattern that regions with more well-educated people have negative associations with COVID-19. Additionally, our OLS regression models suggested that more affluent populations have a negative relationship with COVID-19 cases. Therefore, the COVID-19 outbreak is not only a medical disease but a social inequality and health disparity problem.

ObjectiveTo examine data on COVID-19 disease associated with a 10 percent increase in U.S. road deaths from 2020 to 2021 that raises the question of the potential effect of pandemic stress and neurological damage from COVID-19 disease. MethodsPoisson regression was used to estimate the association of recent COVID-19 cases, accumulated cases, maximum temperatures, truck registrations, and gasoline prices with road deaths monthly among U.S. states in 2021. Using the regression coefficients, changes in each risk factor from 2020 to 2021 were used to calculate expected deaths in 2021 if each factor had remained the same as in 2020. ResultsCorrected for the other risk factors, road deaths were associated with accumulated COVID-19 cases but not cases in the previous month. More than 20,700 road deaths were associated with the changes in accumulated COVID-19 cases but were substantially offset by about 19,100 less-than-expected deaths associated with increased gasoline prices. ConclusionsWhile more research is needed, the data are sufficient to warn people with "long COVID" to minimize road use. What is already known about this topicPrevious short-term fluctuations in road deaths are related to changes in temperature, fuel prices, and truck registrations. What this study addsCorrected for other risk factors, the monthly changes in road deaths from 2020 to 2021 in U.S. states were associated with cumulative COVID-19 cases. How this study might affect research, practice, or policyStudies are needed to distinguish the potential relative effects of neurological damage as well as the stress of coping with the pandemic on driving, walking, and bicyclist behavior. Warning people with "long covid" about road risk is warranted.

ObjectivesTo describe and understand the funding, personnel, expertise, community resources, and issues in rural settings by local public health departments post COVID-19. MethodsRural county health departments in ten states were sent a survey via web link in the Spring of 2021. 552 responses were collected with a 63% completion rate for all counties surveyed. ResultsMost counties utilized public health nurses, administrators, or community health professionals. Of these, 25% had formal education in public health and 10% had public health experience. 65% of respondents disagreed with having adequate funding, staff, and resources. 83% of counties reported working with nonprofits and 43% utilized volunteers. The top two issues in rural public health identified were mental health and substance use. ConclusionsRural county public health departments do not have the support needed to sustain or advance public health in their specific population. Policy implicationsThis report gives insight into the needs of rural health in 2021 that can be used to guide policy and funding to support rural healths specific needs.

To optimize epidemiologic interventions, predictors of mortality should be identified. The US COVID-19 epidemic data -reported up to 3-31-2020- were analyzed using kernel regularized least squares regression. Six potential predictors of mortality were investigated: (i) the number of diagnostic tests performed in testing week I; (ii) the proportion of all tests conducted during week I of testing; (iii) the cumulative number of (test-positive) cases through 3-31-2020, (iv) the number of tests performed/million citizens; (v) the cumulative number of citizens tested; and (vi) the apparent prevalence rate, defined as the number of cases/million citizens. Two metrics estimated mortality: the number of deaths and the number of deaths/million citizens. While both expressions of mortality were predicted by the case count and the apparent prevalence rate, the number of deaths/million citizens was {approx}3.5 times better predicted by the apparent prevalence rate than the number of cases. In eighteen states, early testing/million citizens/population density was inversely associated with the cumulative mortality reported by 31 March, 2020. Findings support the hypothesis that early and massive testing saves lives. Other factors -e.g., population density-may also influence outcomes. To optimize national and local policies, the creation and dissemination of high-resolution geo-referenced, epidemic data is recommended.

In our recent paper Why do per capita COVID-19 Case Rates Differ Between U.S. States? we established that U.S. states with a Democratic governor and a Democratic legislature have lower COVID-19 per capita case rates than states with a Republican governor and a Republican legislature, and case rates of states with a mixed government fall between the two. This difference remained after accounting for differences between states in several demographic and socio-economic variables. In a recent working paper The Changing Political Geographies of COVID-19 in the U.S. it was found that that early in the pandemic U.S. counties at higher levels of percentage Democratic vote in the 2016 presidential election had higher weekly per capita COVID-19 rates, but that the situation was in the opposite direction by August 2020. We show here that counties with a higher percentage of Democratic vote in the 2016 presidential election have a lower mean cumulative per capita rate of COVID-19 cases and of COVID-19 deaths, adjusted for county demographic and socio-economic characteristics, but only for counties in states that currently have a Democratic governor and both chambers of the legislature Democratic or in states that have a mixed government, but not for states that currently have a Republican governor and both chambers in the legislature Republican. One possible contributor to this difference is that some state Republican governments have restricted local action to fight the spread of COVID-19.

IntroductionOptimal pandemic monitoring and management requires unbiased and regionally specific estimates of disease incidence and epidemic growth. MethodsI estimated growth rates and doubling times across a 22-week period of the SARS-COV-2 pandemic using hospital admissions incidence data collected through the US CDC COVID-NET surveillance program which operates in 98 U.S. counties located in 13 states. I cross validated the growth measures using mortality incidence data for the same regions and time periods. ResultsBetween March 1 and August 8, 2020, two distinct waves of epidemic activity occurred. During the first wave in the COVID-NET monitoring regions, the harmonic mean of the maximum weekly growth rate was 534% (Median: 575; Range: 250 to 2250) and this maximum occurred in the second or third week of March in different regions. The harmonic mean of the minimum doubling time occurred with maximum growth rate and was 0.35 weeks (Median 0.36 weeks; Range: 0.22 to 0.55 weeks). The harmonic mean of the maximum incidence rate during the first wave of the epidemic was 8.5 hospital admissions per 100,000 people per week (Median: 9.2, Range: 4 to 40.5) and the peak of epidemic infection transmission associated with this maximum occurred on or before March 27, 2020 in eight of the 13 regions. Dividing the 22-week observed period into four intervals, the harmonic mean of the weekly hospitalization incidence rate was highest during the second interval (4.6 hospitalizations per week per 100,000), then fell during the third and fourth intervals. Growth rates declined from 101 percent per week in the first interval to 2.5 percent per week in the last. Doubling time have lengthened from 3/5th of a week in the first interval to 12.5 weeks in the last. Period by period, the cumulative incidence has grown primarily in a linear mode. The mean cumulative incidence of hospitalizations on Aug 8th, 2020 in the COVID-NET regions is 96 hospitalizations per 100,000. Regions which experienced the highest maximum weekly incidence rates or greatest cumulative incidence rates in the first wave, generally, but not uniformly, observed the lower incidence rates in the second wave. Growth measures calculated based on mortality incidence data corroborate these findings. ConclusionsDeclining epidemic growth rates of SARS-COV-2 infection appeared in early March in the first observations of nationwide hospital admissions surveillance program in multiple U.S. regions. A sizable fraction of the U.S. population may have been infected in a cryptic February epidemic acceleration phase. To more accurately monitor epidemic trends and inform pandemic mitigation planning going forward, the US CDC needs measures of epidemic disease incidence that better reflect clinical disease and account for large variations in case ascertainment strategies over time.

BackgroundWith the pandemic of COVID-19 and the release of related policies, discussions about the COVID-19 are widespread online. Social media becomes a reliable source for understanding public opinions toward this virus outbreak. ObjectiveThis study aims to explore public opinions toward COVID-19 on social media by comparing the differences in sentiment changes and discussed topics between California and New York in the United States. MethodsA dataset with COVID-19-related Twitter posts was collected from March 5, 2020 to April 2, 2020 using Twitter streaming API. After removing any posts unrelated to COVID-19, as well as posts that contain promotion and commercial information, two individual datasets were created based on the geolocation tags with tweets, one containing tweets from California state and the other from New York state. Sentiment analysis was conducted to obtain the sentiment score for each COVID-19 tweet. Topic modeling was applied to identify top topics related to COVID-19. ResultsWhile the number of COVID-19 cases increased more rapidly in New York than in California in March 2020, the number of tweets posted has a similar trend over time in both states. COVID-19 tweets from California had more negative sentiment scores than New York. There were some fluctuations in sentiment scores in both states over time, which might correlate with the policy changes and the severity of COVID-19 pandemic. The topic modeling results showed that the popular topics in both California and New York states are similar, with "protective measures" as the most prevalent topic associated with COVID-19 in both states. ConclusionsTwitter users from California had more negative sentiment scores towards COVID-19 than Twitter users from New York. The prevalent topics about COVID-19 discussed in both states were similar with some slight differences.

This study examines the implementation of naloxone distribution initiatives in Pennsylvania public libraries following a nationwide program offering free Narcan. We conducted a cross-sectional telephone survey with a random sample of Pennsylvania public libraries (n=352). Overall, nearly one-quarter of respondents reported stocking naloxone (23.9%), and over one in ten libraries reported a previous on-site overdose (11.9%). Nearly 30% of respondents had received naloxone training. Significant predictors of on-site overdoses included the librarys county urbanization status and higher county-level overdose mortality rates. Among libraries that stocked naloxone, 73% obtained the medication from local health departments or community-based organizations. This study underscores the role of public libraries in opioid overdose crisis response and the need for tailored strategies to enhance naloxone accessibility, especially in high-risk urban areas. Collaboration between libraries, public health entities, and pharmaceutical companies is crucial to amplify naloxone distribution efforts and address the escalating opioid overdose crisis.

This study aims to map significant cold spots of postacute rehabilitation therapy delivery rates for Original Medicare beneficiaries in the U.S. and determine the prevalence of those cold spots in rural areas. Statistical spatial clustering of postacute therapy delivery rates was conducted in ArcGIS Pro using hot and cold spot analyses (Getis-Ord Gi*). County-level therapy delivery volume was defined as the total minutes of physical, occupational, and speech therapy provided by skilled nursing facilities (SNFs), home health agencies (HHAs), and inpatient rehabilitation facilities (IRFs). Therapy delivery rates were then computed as minutes per Original Medicare beneficiary at the county level and adjusted using a county-level Hierarchical Condition Category risk score. Spatial clustering identified cold spots (statistically significant clusters of low rates) and hot spots (clusters of high rates). We also computed the proportion of cold spots in rural counties and the relative percentage difference compared to the national rural county baseline, using two rural classification systems. Identified coldspots varied by provider type. For SNFs, they were notably identified in the Mountain and West North Central US divisions. For HHAs, cold spots appeared across more U.S. Census Divisions, including areas (e.g., Kentucky, Indiana, southern Illinois) where SNFs showed hot spots. Cold spots were more prevalent in rural--and especially in small rural--counties across all provider types. In rural counties, cold spot rates were 42.8% to 71.5% higher than the rural county baseline. In small rural counties, differences were larger, at 69.1% to 95.5% higher. Concluding, cold spots of postacute therapy delivery varied across the continental U.S. by provider type but were more prevalent in rural and especially in rural counties with smaller population size -- across provider types. Identifying these cold-spot locations may support geographically targeted policy responses and the development of alternative service?delivery models in underserved areas.

The role of religion and politics in the responses to the coronavirus pandemic raises the question of their influence on the risk of other diseases. This study focuses on age-adjusted death rates of cancer, heart disease, and infant mortality per 1000 live births before the pandemic (2018-2019) and COVID-19 in 2020-2021. Eight hypothesized predictors of health effects were considered by examining their correlation to age-adjusted death rates and indicators of health behavior among U.S. states, percentage who pray once or more daily, Republican attitudes and influence on state health policies as indicated by the percentage vote for Trump in 2016, percent of household incomes below poverty, median family income divided by a cost-of-living index, the Gini income inequality index, urban concentration of the population, physicians per capita, and public health expenditures per capita. Since prayer for divine intervention is common to otherwise diverse religious beliefs and practices, the percentage of people claiming to pray daily in each state was used to indicate potential religious influence. Based on collinearity, inequality was chosen for inclusion over poverty, and the prayer and political variables were analyzed separately. All of the death rates were higher in states where more people claimed to pray daily. Only cancer and COVID-19 were correlated significantly with Trumps percentage of the vote. Lower death rates from cancer and heart disease are associated with more public health expenditures, but not for infant mortality or COVID-19 deaths. COVID-19 death rates were lower in states with more physicians per capita, but that variable was not significantly associated with the other death rates. Heart disease, infant mortality, and COVID-19 death rates were higher in states with more income inequality. All rates except infant mortality were lower in states where a greater percentage of the population resides in urban areas. The correlation between daily prayer and smoking cigarettes, as well as the neglect of public health recommendations for fruit and vegetable consumption and COVID-19 vaccination, suggests that reliance on prayer may be a factor in neglect of preventive practices.