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Many sociodemographic factors affect women's ability to meet cancer screening guidelines. Our objective was to examine which sociodemographic characteristics were associated with women meeting US Preventive Services Task Force (USPSTF) guidelines for breast, cervical, and colorectal cancer screening. We used 2018 Behavioral Risk Factor Surveillance System data to examine the association between sociodemographic variables, such as race/ethnicity, rurality, education, and insurance status, and self-reported cancer screening for breast, cervical, and colorectal cancer. We used multivariable log-binomial regression models to estimate adjusted prevalence ratios and 95% CIs. Overall, the proportion of women meeting USPSTF guidelines for breast, cervical, and colorectal cancer screening was more than 70%. The prevalence of meeting screening guidelines was 6% to 10% greater among non-Hispanic Black women than among non-Hispanic White women across all 3 types of cancer screening. Women who lacked health insurance had a 26% to 39% lower screening prevalence across screening types than women with health insurance. Compared with women with $50,000 or more in annual household income, women with less than $50,000 in annual household income had a 3% to 8% lower screening prevalence across all 3 screening types. For colorectal cancer, the prevalence of screening was 7% less among women who lived in rural counties than among women in metropolitan counties. Many women still do not meet current USPSTF guidelines for breast, cervical, and colorectal cancer screening. Screening disparities are persistent among socioeconomically disadvantaged groups, especially women with low incomes and without health insurance. To increase the prevalence of cancer screening and reduce disparities, interventions must focus on reducing economic barriers and improving access to care.

One in every twenty-five persons in America is a racial/ethnic minority who lives in a rural area. Our objective was to summarize how racism and, subsequently, the social determinants of health disproportionately affect rural racial/ethnic minority populations, provide a review of the cancer disparities experienced by rural racial/ethnic minority groups, and recommend policy, research, and intervention approaches to reduce these disparities. We found that rural Black and American Indian/Alaska Native populations experience greater poverty and lack of access to care, which expose them to greater risk of developing cancer and experiencing poorer cancer outcomes in treatment and ultimately survival. There is a critical need for additional research to understand the disparities experienced by all rural racial/ethnic minority populations. We propose that policies aim to increase access to care and healthcare resources for these communities. Further, that observational and interventional research should more effectively address the intersections of rurality and race/ethnicity through reduced structural and interpersonal biases in cancer care, increased data access, more research on newer cancer screening and treatment modalities, and continued intervention and implementation research to understand how evidence-based practices can most effectively reduce disparities among these populations.

PurposeWe determined the proportion of cancer survivors who met each of five health behavior guidelines recommended by the American Cancer Society (ACS), including consuming fruits and vegetables at least five times/day, maintaining a body mass index (BMI) < 30 kg/m2, engaging in 150 min or more of physical activity weekly, not currently smoking, and not excessively drinking alcohol.MethodsUsing data from the 2019 Behavioral Risk Factor Surveillance System (BRFSS), 42,727 survey respondents who reported a previous diagnosis of cancer (excluding skin cancer) were included. Weighted percentages with 95% confidence intervals (95% CI) were estimated for the five health behaviors accounting for BRFSS’ complex survey design.ResultsThe weighted percentage of cancer survivors who met ACS guidelines was 15.1% (95%CI: 14.3%, 15.9%) for fruit and vegetable intake; 66.8% (95%CI: 65.9%, 67.7%) for BMI < 30 kg/m2; 51.1% (95%CI: 50.1%, 52.1%) for physical activity; 84.9% (95%CI: 84.1%, 85.7%) for not currently smoking; and 89.5% (95%CI: 88.8%, 90.3%) for not drinking excessive alcohol. Adherence to ACS guidelines among cancer survivors generally increased with increasing age, income, and education.ConclusionsWhile the majority of cancer survivors met the guidelines for not smoking and limiting alcohol drinking, one-third had elevated BMI, almost half did not meet recommended physical activity levels, and the majority had inadequate fruit and vegetable intake.Implications for Cancer SurvivorsAdherence to guidelines was lowest among younger cancer survivors and those with lower income and education, suggesting these may be populations where resources could be targeted to have the greatest impact.

Background COVID-19 brought enormous challenges to public health surveillance and underscored the importance of developing and maintaining robust systems for accurate surveillance. As public health data collection efforts expand, there is a critical need for infectious disease modeling researchers to continue to develop prospective surveillance metrics and statistical models to accommodate the modeling of large disease counts and variability. This paper evaluated different likelihoods for the disease count model and various spatiotemporal mean models for prospective surveillance. Methods We evaluated Bayesian spatiotemporal models, which are the foundation for model-based infectious disease surveillance metrics. Bayesian spatiotemporal mean models based on the Poisson and the negative binomial likelihoods were evaluated with the different lengths of past data usage. We compared their goodness of fit and short-term prediction performance with both simulated epidemic data and real data from the COVID-19 pandemic. Results The simulation results show that the negative binomial likelihood-based models show better goodness of fit results than Poisson likelihood-based models as deemed by smaller deviance information criteria (DIC) values. However, Poisson models yield smaller mean square error (MSE) and mean absolute one-step prediction error (MAOSPE) results when we use a shorter length of the past data such as 7 and 3 time periods. Real COVID-19 data analysis of New Jersey and South Carolina shows similar results for the goodness of fit and short-term prediction results. Negative binomial-based mean models showed better performance when we used the past data of 52 time periods. Poisson-based mean models showed comparable goodness of fit performance and smaller MSE and MAOSPE results when we used the past data of 7 and 3 time periods. Conclusion We evaluate these models and provide future infectious disease outbreak modeling guidelines for Bayesian spatiotemporal analysis. Our choice of the likelihood and spatiotemporal mean models was influenced by both historical data length and variability. With a longer length of past data usage and more over-dispersed data, the negative binomial likelihood shows a better model fit than the Poisson likelihood. However, as we use a shorter length of the past data for our surveillance analysis, the difference between the Poisson and the negative binomial models becomes smaller. In this case, the Poisson likelihood shows robust posterior mean estimate and short-term prediction results.

Background Health and health disparities vary substantially by geography, including geopolitical boundaries such as United States congressional districts. Every ten years congressional districts for the House of Representatives are redistricted, but occasionally the Courts step in and force states to redistrict gerrymandered congressional maps. Analyses of court mandated redistricting decisions often focus on the distribution of voters by political party and race, but less is known about how health and health disparities are distributed across congressional districts before and after redistricting. In this analysis, we examine how the magnitude of disparities varied between and within congressional districts in Pennsylvania, before and after the state Supreme Court of Pennsylvania’s decision ordering a redistricting in 2018 that produced less politically gerrymandered districts. Methods Using georeferenced vital statistics data from 2013–2015 (before the redistricting), we explore levels of and disparities in infant mortality rates (IMR) and deaths of despair (DoD) using boundaries from before (Congresses 113–115) and after (Congress 116) this redistricting. Results Using consistent mortality data (2013–2015) and boundaries from before and after the 2018 redistricting, we find that after redistricting disparities in infant mortality and deaths of despair between congressional districts were slightly wider for all educational groups except for those with less than a high school degree, and slightly narrower for all racial-ethnic groups other than for Hispanic and non-Hispanic White populations, compared with before redistricting. Conclusions Understanding how disparities vary between and within districts after redistricting can inform our understanding of the relationships between geopolitical boundaries, election processes, and health disparities.

Background The financial burden of cancer treatment can increase the risk of housing insecurity for patients undergoing treatment and survivors. Objective To evaluate the burden of housing and utility insecurity among cancer survivors compared to individuals without a cancer history, examine outcome differences by housing tenure (renters vs. homeowners) and treatment status (active vs. posttreatment), and identify predictors of housing insecurity. Methods We analyzed data from 14 states that completed the Social Determinants and Cancer Survivorship modules of the 2022 and 2023 Behavioral Risk Factor Surveillance System (BRFSS), yielding 5499 respondents with a previous cancer diagnosis (excluding skin cancers) and 61,883 respondents without a cancer diagnosis. We estimated prevalences and fit logistic regressions. Key Results Cancer history was associated with greater odds of housing (AOR 1.43, 95% CI: 1.18–1.74) and utility (AOR 1.36, 95% CI: 1.09–1.69) insecurity, but this varied by treatment timing and housing tenure. Patients currently undergoing treatment were more likely to report housing and utility insecurity (AOR 1.96, 95% CI: 1.28–3.01 and AOR 1.67, 95% CI: 1.06–2.61, respectively) than individuals without a history of cancer. Such insecurity was elevated even after treatment for renters, but not for homeowners. In absolute terms, 34.7% of renters with a cancer history reported housing insecurity, compared to 7.1% of their homeowner counterparts. Conclusions Cancer diagnosis and treatment can contribute to housing and utility insecurity during and after treatment. Addressing this through targeted interventions within both healthcare systems and social policy may mitigate hardship and improve well‐being. Using the latest BRFSS data, our study found that nearly 2 in 5 renters and more than 1 in 9 homeowners currently undergoing cancer treatment cannot pay their housing or utility bills. We also identified subgroups of cancer survivors at elevated risk of housing insecurity and threats of utility shut‐offs, highlighting the urgent need for multilevel interventions that support this vulnerable group of patients and survivors.

Background Diverse environmental factors are associated with physical activity (PA) and healthy eating (HE) among youth. However, no study has created a comprehensive obesogenic environment index for children that can be applied at a large geographic scale. The purpose of this study was to describe the development of a childhood obesogenic environment index (COEI) at the county level across the United States. Methods A comprehensive search of review articles ( n  = 20) and input from experts ( n  = 12) were used to identify community-level variables associated with youth PA, HE, or overweight/obesity for potential inclusion in the index. Based on strength of associations in the literature, expert ratings, expertise of team members, and data source availability, 10 key variables were identified – six related to HE (# per 1000 residents for grocery/superstores, farmers markets, fast food restaurants, full-service restaurants, and convenience stores; as well as percentage of births at baby (breastfeeding)-friendly facilities) and four related to PA (percentage of population living close to exercise opportunities, percentage of population < 1 mile from a school, a composite walkability index, and number of violent crimes per 1000 residents). Data for each variable for all counties in the U.S. ( n  = 3142) were collected from publicly available sources. For each variable, all counties were ranked and assigned percentiles ranging from 0 to 100. Positive environmental variables (e.g., grocery stores, exercise opportunities) were reverse scored such that higher values for all variables indicated a more obesogenic environment. Finally, for each county, a total obesogenic environment index score was generated by calculating the average percentile for all 10 variables. Results The average COEI percentile ranged from 24.5–81.0 (M = 50.02,s.d. = 9.01) across US counties and was depicted spatially on a choropleth map. Obesogenic counties were more prevalent (F = 130.43, p  < .0001) in the South region of the U.S. (M = 53.0,s.d. = 8.3) compared to the Northeast (M = 43.2,s.d. = 6.9), Midwest (M = 48.1,s.d. = 8.5), and West (M = 48.4,s.d. = 9.8). When examined by rurality, there were also significant differences (F = 175.86, p  < .0001) between metropolitan (M = 46.5,s.d. = 8.4), micropolitan (M = 50.3,s.d. = 8.1), and rural counties (M = 52.9,s.d. = 8.8) across the U.S. Conclusion The COEI can be applied to benchmark obesogenic environments and identify geographic disparities and intervention targets. Future research can examine associations with obesity and other health outcomes.

We examined the geographic distribution and sociodemographic and economic characteristics of chronic disease prevalence in the US. Understanding disease prevalence and its impact on communities is crucial for effective public health interventions. Data came from the American Community Survey, the American Hospital Association Survey, and the Centers for Disease Control and Prevention's PLACES. We used quartile thresholds for 10 chronic diseases to assess chronic disease prevalence by Zip Code Tabulation Areas (ZCTAs). ZCTAs were scored from 0 to 20 based on their chronic disease prevalence quartile. Three prevalence categories were established: least prevalent (score ≤6), moderately prevalent (score 7-13), and highest prevalence (score ≥14). Community characteristics were compared across categories and spatial analyses to identify clusters of ZCTAs with high disease prevalence. Our study showed a high prevalence of chronic disease in the southeastern region of the US. Populations in ZCTAs with the highest prevalence showed significantly greater socioeconomic disadvantages (ie, lower household income, lower home value, lower educational attainment, and higher uninsured rates) and barriers to health care access (lower percentage of car ownership and longer travel distances to hospital-based intensive care units, emergency departments, federally qualified health centers, and pharmacies) compared with ZCTAs with the lowest prevalence. Socioeconomic disparities and health care access should be addressed in communities with high chronic disease prevalence. Carefully directed resource allocation and interventions are necessary to reduce the effects of chronic disease on these communities. Policy makers and clinicians should prioritize efforts to reduce chronic disease prevalence and improve the overall health and well-being of affected communities throughout the US.

Fine particulate matter 2.5 (PM2.5) is a widely studied pollutant with substantial health impacts, yet little is known about the urban‐rural differences across the United States. Trends of PM2.5 in urban and rural census tracts between 2010 and 2019 were assessed alongside sociodemographic characteristics including race/ethnicity, poverty, and age. For 2010, we identified 13,474 rural tracts and 59,065 urban tracts. In 2019, 13,462 were rural and 59,055 urban. Urban tracts had significantly higher PM2.5 concentrations than rural tracts during this period. Levels of PM2.5 were lower in rural tracts compared to urban and fell more rapidly in rural than urban. Rural tract annual means for 2010 and 2019 were 8.51 [2.24] μg/m3 and 6.41 [1.29] μg/m3, respectively. Urban tract annual means for 2010 and 2019 were 9.56 [2.04] μg/m3 and 7.51 [1.40] μg/m3, respectively. Rural and urban majority Black communities had significantly higher PM2.5 pollution levels (10.19 [1.64] μg/m3 and 9.79 [1.10] μg/m3 respectively), in 2010. In 2019, they were: 7.75 [1.1] μg/m3 and 7.09 [0.78] μg/m3, respectively. Majority Hispanic communities had higher PM2.5 levels and were the highest urban concentration among all races/ethnicities (8.01 [1.73] μg/m3), however they were not the highest rural concentration among all races/ethnicities (6.22 [1.60] μg/m3) in 2019. Associations with higher levels of PM2.5 were found with communities in the poorest quartile and with higher proportions of residents age<15 years old. These findings suggest greater protections for those disproportionately exposed to PM2.5 are needed, such as, increasing the availability of low‐cost air quality monitors. Plain Language Summary PM2.5 is a well‐known air pollutant that impacts human health. However, little is known about how it differs between urban and rural areas in the United States (U.S). This study investigated these differences between 2010 and 2019 at a level that had not been assessed before across the United States. Rural areas generally had lower PM2.5 levels compared to urban areas and the pollution decreased faster in rural areas during this time. Both rural and urban areas with higher proportions of residents that are Black, Hispanic, and in poverty had higher PM2.5 levels. There were no consistent patterns between the age distribution of urban or rural census tracts and PM2.5 levels. Key Points Between 2010 and 2019, PM2.5 levels were consistently lower in rural communities than in urban communities across the United States High percentage Black communities had significantly higher PM2.5 pollution levels in both rural and urban census tracts Greater protection from air pollution for socially disadvantaged communities in both rural and urban settings is warranted

Background:The 2009 Health Information Technology for Economic and Clinical Health Act sets three stages of Meaningful Use requirements for the electronic health records incentive program. Health information exchange (HIE) technologies are critical in the meaningful use of electronic health records to support patient care coordination. However, HIE use trends and barriers remain unclear across hospitals in South Carolina (SC), a state with the earliest HIE implementation.Objective:This study aims to explore changes in the proportion of HIE participation and factors associated with HIE participation, and barriers to exchange and interoperability across SC hospitals.Methods:This study derived data from a longitudinal data set of the 2014-2020 American Hospital Association Information Technology Supplement for 69 SC hospitals. The primary outcome was whether a hospital participated in HIE in a year. A cross-sectional multivariable logistic regression model, clustered at the hospital level and weighted by bed size, was used to identify factors associated with HIE participation. The second outcome was barriers to sending, receiving, or finding patient health information to or from other organizations or hospital systems. The frequency of hospitals reporting each barrier related to exchange and interoperability were then calculated.Results:Hospitals in SC have been increasingly participating in HIE, improving from 43% (24/56) in 2014 to 82% (54/66) in 2020. After controlling for other hospital factors, teaching hospitals (adjusted odds ratio [AOR] 3.7, 95% CI 1.0-13.3), system-affiliated hospitals (AOR 6.6, 95% CI 3.2-13.7), and rural referral hospitals (AOR 8.0, 95% CI 1.2-53.4) had higher odds to participate in HIE than their counterparts, whereas critical access hospitals (AOR 0.1, 95% CI 0.02-0.6) were less likely to participate in HIE than their counterparts reimbursed by the prospective payment system. Hospitals with greater ratios of Medicare or Medicaid inpatient days to total inpatient days also reported higher odds of HIE participation. Despite the majority of hospitals reporting HIE participation in 2020, barriers to exchange and interoperability remained, including lack of provider contacts (27/40, 68%), difficulty in finding patient health information (27/40, 68%), adapting different vendor platforms (26/40, 65%), difficulty matching or identifying same patients between systems (23/40, 58%), and providers that do not typically exchange patient data (23/40, 58%).Conclusions:HIE participation has been widely adopted in SC hospitals. Our findings highlight the need to incentivize optimization of HIE and seamless information exchange by facilitating and implementing standardization of health information across various HIE systems and by addressing other technical issues, including providing providers’ addresses and training HIE stakeholders to find relevant information. Policies and efforts should include more collaboration with vendors to reduce platform compatibility issues and more user engagement and technical training and support to facilitate effective, accurate, and efficient exchange of provider contacts and patient health information.