Chronic Disease Prevalence in the US: Sociodemographic and Geographic Variations by Zip Code Tabulation Area.

Introduction: 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. Methods: 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. Results: 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. Conclusion: 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.

Dialysis More Available Than Patient Education in Counties With High Diabetes Prevalence.

Introduction: Poorly controlled diabetes is a principal cause of end stage renal disease (ESRD), generating an estimated 44% of new cases. Diabetes self-management education and support (DSMES) has been documented to reduce adverse outcomes such as ESRD. Helping patients better manage their condition could ultimately reduce ESRD prevalence. Methods: We compared the county-level availability of DSMES and dialysis as of November 2022 sorted by the estimated prevalence of diabetes among residents aged 18 years or older. The locations of DSMES programs and ESRD dialysis facilities were obtained from 2 professional organizations and the Centers for Medicare & Medicade Services. Estimated diabetes prevalence was obtained from the Centers for Disease Control and Prevention's PLACES data set. Counties were considered to have high diabetes prevalence if they fell into the top quartile for diabetes prevalence in 2019 (≥14.4% of adults). Analyses were conducted in 2023. Results: DSMES was available in 41.0% of counties but in only 20.7% of counties with high diabetes prevalence versus 47.9% of low prevalence counties. Dialysis facilities were present in 59.2% of all counties, in 52.8% of all high diabetes prevalence counties, and in 61.4% of other counties. DSMES availability was linked to the presence of a hospital in the county, with only 6.3% of counties without a hospital offering the service. Implications: DSMES could play a role in reducing the prevalence of ESRD. Public health professionals need to be aware of the differing levels of local availability of this service and work to develop partnerships to provide DSMES in high-prevalence areas not currently served.

Eliminating Medicaid dental benefits and early-stage oral cancer diagnoses.

BACKGROUND: Despite the importance of regular dental visits for detecting oral cancer, millions of low-income adults lack access to dental services. In July 2009, California eliminated adult Medicaid dental benefits. We tested if this impacted oral cancer detection for Medicaid enrollees. METHODS: We analyzed Surveillance, Epidemiology, and End Results-Medicaid data, which contains verified Medicaid enrollment status, to estimate a difference-in-differences model. Our design compares the change in early-stage (Stages 0-II) diagnoses before and after dropping dental benefits in California with the change in early-stage diagnoses among eight states that did not change Medicaid dental benefits. Patients were grouped by oropharyngeal cancer (OPC) and non-OPC (oral cavity cancer), type, and the length of Medicaid enrollment. We also assessed if the effect of dropping dental benefits varied by the number of dentists per capita. RESULTS: Dropping Medicaid dental benefits was associated with a 6.5%-point decline in early-stage diagnoses of non-OPC (95% CI = -14.5, -3.2, p = 0.008). This represented a 20% relative reduction from baseline rates. The effect was highest among beneficiaries with 3 months of continuous Medicaid enrollment prior to diagnosis who resided in counties with more dentists per capita. Specifically, dropping dental coverage was associated with a 1.25%-point decline in the probability of early-stage non-OPC diagnoses for every additional dentist per 5000 population (p = 0.006). CONCLUSIONS: Eliminating Medicaid dental benefits negatively impacted early detection of cancers of the oral cavity. Continued volatility of Medicaid dental coverage and provider shortages may be further delaying oral cancer diagnoses. Alternative approaches are needed to prevent advanced stage OPC.

Rural-Urban Cancer Incidence and Trends in the United States, 2000 to 2019.

BACKGROUND: Despite consistent improvements in cancer prevention and care, rural and urban disparities in cancer incidence persist in the United States. Our objective was to further examine rural-urban differences in cancer incidence and trends. METHODS: We used the North American Association of Central Cancer Registries dataset to investigate rural-urban differences in 5-year age-adjusted cancer incidence (2015-2019) and trends (2000-2019), also examining differences by region, sex, race/ethnicity, and tumor site. Age-adjusted rates were calculated using SEER∗Stat 8.4.1, and trend analysis was done using Joinpoint, reporting annual percent changes (APC). RESULTS: We observed higher all cancer combined 5-year incidence rates in rural areas (457.6 per 100,000) compared with urban areas (447.9), with the largest rural-urban difference in the South (464.4 vs. 449.3). Rural populations also exhibited higher rates of tobacco-associated, human papillomavirus-associated, and colorectal cancers, including early-onset cancers. Tobacco-associated cancer incidence trends widened between rural and urban from 2000 to 2019, with significant, but varying, decreases in urban areas throughout the study period, whereas significant rural decreases only occurred between 2016 and 2019 (APC = -0.96). Human papillomavirus-associated cancer rates increased in both populations until recently with urban rates plateauing whereas rural rates continued to increase (e.g., APC = 1.56, 2002-2019). CONCLUSIONS: Rural populations had higher overall cancer incidence rates and higher rates of cancers with preventive opportunities compared with urban populations. Improvements in these rates were typically slower in rural populations. IMPACT: Our findings underscore the complex nature of rural-urban disparities, emphasizing the need for targeted interventions and policies to reduce disparities and achieve equitable health outcomes.

Health care access barriers among metropolitan and nonmetropolitan populations of eight geographically diverse states, 2018.

INTRODUCTION: Nonmetropolitan populations face frequent health care access barriers compared to their metropolitan counterparts, but differences in the number of these barriers across groups are not known. Our objective was to examine the differences in health care access barriers across metropolitan, micropolitan, and noncore populations. METHODS: We used Behavioral Risk Factor Surveillance System data from the optional "Health Care Access" module to perform a cross-sectional analysis examining access barriers across levels of rurality using bivariate analyses and Poisson models. Access barriers were operationalized as a count ranging from 0 to 5, reflective of the number of financial barriers and nonfinancial barriers. RESULTS: Micropolitan and noncore respondents had lower educational attainment, were older, and were less racially/ethnically diverse than metropolitan respondents. They also reported more barriers, including lacking health insurance, medical debt, and foregoing care or medication due to cost. These barriers were most pronounced in non-Hispanic Black, Hispanic, and American Indian/Alaska Native nonmetropolitan populations, compared to their White counterparts. In adjusted analysis, micropolitan respondents reported more barriers compared to metropolitan (prevalence rate ratio = 1.06; 95% confidence interval: 1.02-1.10) as did women, racial/ethnic minority populations, and those with less education. CONCLUSIONS: Micropolitan populations experience more barriers to health care, and nonmetropolitan respondents report more cost-related barriers than their metropolitan counterparts, raising concerns on health care disparities and financial burdens for these underserved populations. This underscores the need to mitigate these barriers, particularly among those in micropolitan areas and minorized populations.