BACKGROUND: Alzheimer's disease is the most common type of dementia and is responsible for up to 80% of dementia diagnoses and is the sixth leading cause of death in the United States. An estimated 38,000 American Indian/Alaska Native (AI/AN) people aged ≥65 years were living with Alzheimer's disease and related dementias (ADRD) in 2020, a number expected to double by 2030 and quadruple by 2050. Administrative healthcare data from the Indian Health Service (IHS) were used to estimate ADRD among AI/AN populations. METHODS: Administrative IHS healthcare data from federal fiscal years 2016 to 2020 from the IHS National Data Warehouse were used to calculate the count and rate per 100,000 AI/AN adults aged ≥45 years with at least one ADRD diagnosis code on their medical record. RESULTS: This study identified 12,877 AI/AN adults aged ≥45 years with an ADRD diagnosis code, with an overall rate of 514 per 100,000. Of those, 1856 people were aged 45-64. Females were 1.2 times (95% confidence interval: 1.1-1.2) more likely than males to have a medical visit with an ADRD diagnosis code. CONCLUSIONS: Many AI/AN people with ADRD rely on IHS, tribal, and urban Indian health programs. The high burden of ADRD in AI/AN populations aged 45-64 utilizing IHS health services highlights the need for implementation of ADRD risk reduction strategies and assessment and diagnosis of ADRD in younger AI/AN populations. This study provides a baseline to assess future progress for efforts addressing ADRD in AI/AN communities.

This collection of articles in Preventing Chronic Disease (PCD) brings together scientists and practitioners from the breadth of public health and the social sciences to demonstrate how geospatial perspectives can contribute to understanding and addressing the intersection of chronic disease and COVID-19, a respiratory disease caused by the SARS-CoV-2 virus. The COVID-19 pandemic has affected chronic disease in many complex ways. Early in the pandemic, it became clear that people with chronic conditions and those in older age groups were at the highest risk for COVID-19 hospitalization and death (1–3). Racial and ethnic minority populations experienced disproportionately worse health outcomes (4). Pandemic-related disruptions to the health care system and individuals’ concerns about health care–related exposures affected chronic disease management: in-person visits for people with chronic conditions declined, supply chain disruptions led to shortages of medications, and the number of cancer screenings, treatments, and surgeries declined in the United States (5–7). More recent evidence suggests that COVID-19 may exacerbate existing chronic diseases and increase the risk of developing new chronic conditions, such as diabetes in adults (8,9), type 1 diabetes in children (10), neurological disorders (11), dementia (12), mental illness (13), and cardiovascular disease (14). In addition, an estimated one-half of COVID-19 survivors worldwide continue to have COVID-related health problems 6 months or more after recovery from the acute infection, making “long COVID” our newest and still largely unresearched chronic disease (15). Finally, social and economic inequities underlie disparities in incidence of both chronic diseases and COVID-19, an intersection that has been labeled a syndemic, defined as the “presence of 2 or more disease states that adversely interact with each other, negatively affecting the mutual course of each disease trajectory, enhancing vulnerability, and which are made more deleterious by experienced inequities” (16).

Local-level data on the health of populations are important to inform and drive effective and efficient actions to improve health, but such data are often expensive to collect and thus rare. Population Level Analysis and Community EStimates (PLACES) (www.cdc.gov/places/), a collaboration between the Centers for Disease Control and Prevention (CDC), the Robert Wood Johnson Foundation, and the CDC Foundation, provides model-based estimates for 29 measures among all counties and most incorporated and census-designated places, census tracts, and ZIP Code tabulation areas across the US. PLACES allows local health departments and others to better understand the burden and geographic distribution of chronic disease-related outcomes in their areas regardless of population size and urban-rural status and assists them in planning public health interventions. Online resources allow users to visually explore health estimates geographically, compare estimates, and download data for further use and exploration. By understanding the PLACES overall approach and using the easy-to-use PLACES applications, practitioners, policy makers, and others can enhance their efforts to improve public health, including informing prevention activities, programs, and policies; identifying priority health risk behaviors for action; prioritizing investments to areas with the biggest gaps or inequities; and establishing key health objectives to achieve community health and health equity.

PURPOSE: This report compares COVID-19 incidence and mortality rates in the nonmetropolitan areas of the United States with the metropolitan areas across three 11-week periods from March 1 to October 18, 2020. METHODS: County-level COVID-19 case, death, and population counts were downloaded from USAFacts.org. The 2013 NCHS Urban-Rural Classification Scheme was collapsed into two categories called metropolitan (large central, large fringe, medium, and small metropolitans) and nonmetropolitan (micropolitan/noncore). Daily COVID-19 incidence and mortality rates were computed to show temporal trends for each of these two categories. Maps showing the ratio of nonmetropolitan to metropolitan COVID-19 incidence and mortality rates by state identify states with higher rates in nonmetropolitan areas than in metropolitan areas in each of the three 11-week periods. FINDINGS: In the period between March 1 and October 18, 2020, 13.8% of the 8,085,214 confirmed COVID-19 cases and 10.7% of the 217,510 deaths occurred among people residing in nonmetropolitan counties. The nonmetropolitan incidence and mortality trends steadily increased and surpassed those in metropolitan areas, beginning in early August. CONCLUSIONS: Despite the relatively small size of the US population living in nonmetropolitan areas, these areas have an equal need for testing, health care personnel, and mitigation resources. Having state-specific rural data allow the development of prevention messages that are tailored to the sociocultural context of rural locations.

Introduction Population estimation techniques are often used to provide updated data for a current year. However, estimates for small geographic units, such as census tracts in the United States, are typically not available. Yet there are growing demands from local policy making, program planning and evaluation practitioners for such data because small area population estimates are more useful than those for larger geographic areas. Objectives To estimate the population sizes at the census block level by subgroups (age, sex, and race/ethnicity) so that the population data can be aggregated up to any target small geographic areas. Methods We estimated the population sizes by subgroups at the census block level using an intercensal approach for years between 2000 and 2010 and a postcensal approach for the years following the 2010 decennial census (2011-2017). Then we aggregated the data to the county level (intercensal approach) and incorporated place level (postcensal approach) and compared our estimates to corresponding US Census Bureau (the Census) estimates. Results Overall, our intercensal estimates were close to the Census' population estimates at the county level for the years 2000-2010; yet there were substantive errors in counties where population sizes experienced sudden changes. Our postcensal estimates were also close to the Census' population estimates at the incorporated place level for years closer to the 2010 decennial census. Conclusion The approaches presented here can be used to estimate population sizes for any small geographic areas based on census blocks. The advantages and disadvantages of their application in public health practice should be considered.

The potential for a population at a given location to utilize a health service can be estimated using a newly developed measure called the supply-concentric demand accumulation (SCDA) spatial availability index. Spatial availability is the amount of demand at the given location that can be satisfied by the supply of services at a facility, after discounting the intervening demand among other populations that are located nearer to a facility location than the given population location. This differs from spatial accessibility measures which treat absolute distance or travel time as the factor that impedes utilization. The SCDA is illustrated using pulmonary rehabilitation (PR), which is a treatment for people with chronic obstructive pulmonary disease (COPD). The spatial availability of PR was estimated for each Census block group in Georgia using the 1105 residents who utilized one of 45 PR facilities located in or around Georgia. Data was provided by the Centers for Medicare & Medicaid Services. The geographic patterns of the SCDA spatial availability index and the two-step floating catchment area (2SFCA) spatial accessibility index were compared with the observed PR utilization rate using bivariate local indicators of spatial association. The SCDA index was more associated with PR utilization (Morans I = 0.607, P < 0.001) than was the 2SFCA (Morans I = 0.321, P < 0.001). These results suggest that the measures of spatial availability may be a better way to estimate the health care utilization potential than measures of spatial accessibility.

PURPOSE: Rectal cancer is rarer than colon cancer and is a technically more difficult tumor for surgeons to remove, thus rectal cancer patients may travel longer for specialized treatment compared to colon cancer patients. The purpose of this study was to evaluate whether travel time for surgery was different for colon versus rectal cancer patients. METHODS: A secondary data analysis of colorectal cancer (CRC) incidence data from the Iowa Cancer Registry data was conducted. Travel times along a street network from all residential ZIP Codes to all cancer surgery facilities were calculated using a geographic information system. A new method for analyzing "time-to-place" data using the same type of survival analysis method commonly used to analyze "time-to-event" data is introduced. Cox proportional hazard model was used to analyze travel time differences for colon versus rectal cancer patients. RESULTS: A total of 5,844 CRC patients met inclusion criteria. Median travel time to the nearest surgical facility was 9 minutes, median travel time to the actual cancer surgery facilities was 22 minutes, and the median number of facilities bypassed was 3. Although travel times to the nearest surgery facilities were not significantly different for colon versus rectal cancer patients, rectal cancer patients on average traveled 15 minutes longer to their actual surgery facility and bypassed 2 more facilities to obtain surgery. DISCUSSION: In general, the survival analysis method used to analyze the time-to-place data as described here could be applied to a wide variety of health services and used to compare travel patterns among different groups.

Multiple chronic conditions (MCC) reduce quality of life and are associated with high per capita health care spending. One potential way to reduce Medicare spending for MCC is to identify counties whose populations have high levels of spending compared to level of disease burden. Using a nationally representative sample of Medicare Fee-for-Service beneficiaries, this paper presents a method to measure the collective burden of several chronic conditions in a population, which the authors have termed the concentration of chronic conditions (CCC). The authors observed a significantly positive linear relationship between the CCC measure and county-level per capita Medicare spending. This area-level measure can be operationalized to identify counties that might benefit from targeted efforts designed to optimally manage and prevent chronic illness.

Objectives. To demonstrate a flexible and practical method to obtain near real-time estimates of the number of at-risk community-dwelling adults with a chronic condition in a defined area potentially affected by a public health emergency.Methods. We used small area estimation with survey responses from the 2016 Behavioral Risk Factor Surveillance System together with a geographic information system to predict the number of adults with chronic obstructive pulmonary disease who lived in the forecasted path of Hurricane Florence in North and South Carolina in 2018.Results. We estimated that a range of 32 002 to 676 536 adults with chronic obstructive pulmonary disease resided between 50 and 200 miles of 3 consecutive daily forecasted landfalls. The number of affected counties ranged from 8 to 10 (at 50 miles) to as many as 119 to 127 (at 200 miles).Conclusions. Community preparedness is critical to anticipating, responding to, and ameliorating these health threats. We demonstrated the feasibility of quickly producing detailed estimates of the number of residents with chronic conditions who may face life-threatening situations because of a natural disaster. These methods are applicable to a range of planning and response scenarios.

Introduction: Alzheimer's disease and related dementias (ADRD) cause a high burden of morbidity and mortality in the United States. Age, race, and ethnicity are important risk factors for ADRD. Method(s): We estimated the future US burden of ADRD by age, sex, and race and ethnicity by applying subgroup-specific prevalence among Medicare Fee-for-Service beneficiaries aged >=65 years in 2014 to subgroup-specific population estimates for 2014 and population projection data from the United States Census Bureau for 2015 to 2060. Result(s): The burden of ADRD in 2014 was an estimated 5.0 million adults aged >=65 years or 1.6% of the population, and there are significant disparities in ADRD prevalence among population subgroups defined by race and ethnicity. ADRD burden will double to 3.3% by 2060 when 13.9 million Americans are projected to have the disease. Discussion(s): These estimates can be used to guide planning and interventions related to caring for the ADRD population and supporting caregivers.

A spatially adaptive floating catchment is a circular area that expands outward from a provider location until the estimated demand for services in the nearest population locations exceeds the observed number of health care services performed at the provider location. This new way of creating floating catchments was developed to address the change of spatial support problem (COSP) by upscaling the availability of the service observed at a provider location to the county-level so that its geographic association with utilization could be measured using the same spatial support. Medicare Fee-for-Service claims data were used to identify beneficiaries aged >/=65 years who received outpatient pulmonary rehabilitation (PR) in the Southeastern United States in 2014 (n=8798), the number of PR treatments these beneficiaries received (n=132,508), and the PR providers they chose (n=426). The positive correlation between PR availability and utilization was relatively low, but statistically significant (r=0.619, p<0.001) indicating that most people use the nearest available PR services, but some travel long distances. SAFCs can be created using data from health care systems that collect claim-level utilization data that identifies the locations of providers chosen by beneficiaries of a specific health care procedure.

Chronic obstructive pulmonary disease (COPD) accounts for the majority of deaths from chronic lower respiratory diseases, the third leading cause of death in the United States in 2015 and the fourth leading cause in 2016. Major risk factors include tobacco exposure, occupational and environmental exposures, respiratory infections, and genetics.(dagger) State variations in COPD outcomes (1) suggest that it might be more common in states with large rural areas. To assess urban-rural variations in COPD prevalence, hospitalizations, and mortality; obtain county-level estimates; and update state-level variations in COPD measures, CDC analyzed 2015 data from the Behavioral Risk Factor Surveillance System (BRFSS), Medicare hospital records, and death certificate data from the National Vital Statistics System (NVSS). Overall, 15.5 million adults aged >/=18 years (5.9% age-adjusted prevalence) reported ever receiving a diagnosis of COPD; there were approximately 335,000 Medicare hospitalizations (11.5 per 1,000 Medicare enrollees aged >/=65 years) and 150,350 deaths in which COPD was listed as the underlying cause for persons of all ages (40.3 per 100,000 population). COPD prevalence, Medicare hospitalizations, and deaths were significantly higher among persons living in rural areas than among those living in micropolitan or metropolitan areas. Among seven states in the highest quartile for all three measures, Arkansas, Kentucky, Mississippi, and West Virginia were also in the upper quartile (>/=18%) for rural residents. Overcoming barriers to prevention, early diagnosis, treatment, and management of COPD with primary care provider education, Internet access, physical activity and self-management programs, and improved access to pulmonary rehabilitation and oxygen therapy are needed to improve quality of life and reduce COPD mortality.

INTRODUCTION: Local health authorities need small-area estimates for prevalence of chronic diseases and health behaviors for multiple purposes. We generated city-level and census-tract-level prevalence estimates of 27 measures for the 500 largest US cities. METHODS: To validate the methodology, we constructed multilevel logistic regressions to predict 10 selected health indicators among adults aged 18 years or older by using 2013 Behavioral Risk Factor Surveillance System (BRFSS) data; we applied their predicted probabilities to census population data to generate city-level, neighborhood-level, and zip-code-level estimates for the city of Boston, Massachusetts. RESULTS: By comparing the predicted estimates with their corresponding direct estimates from a locally administered survey (Boston BRFSS 2010 and 2013), we found that our model-based estimates for most of the selected health indicators at the city level were close to the direct estimates from the local survey. We also found strong correlation between the model-based estimates and direct survey estimates at neighborhood and zip code levels for most indicators. CONCLUSION: Findings suggest that our model-based estimates are reliable and valid at the city level for certain health outcomes. Local health authorities can use the neighborhood-level estimates if high quality local health survey data are not otherwise available.

PROBLEM/CONDITION: Persons living in rural areas are recognized as a health disparity population because the prevalence of disease and rate of premature death are higher than for the overall population of the United States. Surveillance data about health-related behaviors are rarely reported by urban-rural status, which makes comparisons difficult among persons living in metropolitan and nonmetropolitan counties. REPORTING PERIOD: 2013. DESCRIPTION OF SYSTEM: The Behavioral Risk Factor Surveillance System (BRFSS) is an ongoing, state-based, random-digit-dialed landline- and cellular-telephone survey of noninstitutionalized adults aged ≥18 years residing in the United States. BRFSS collects data on health-risk behaviors, chronic diseases and conditions, access to health care, and use of preventive health services related to the leading causes of death and disability. BRFSS data were analyzed for 398,208 adults aged ≥18 years to estimate the prevalence of five self-reported health-related behaviors (sufficient sleep, current nonsmoking, nondrinking or moderate drinking, maintaining normal body weight, and meeting aerobic leisure time physical activity recommendations) by urban-rural status. For this report, rural is defined as the noncore counties described in the 2013 National Center for Health Statistics Urban-Rural Classification Scheme for Counties. RESULTS: Approximately one third of U.S. adults practice at least four of these five behaviors. Compared with adults living in the four types of metropolitan counties (large central metropolitan, large fringe metropolitan, medium metropolitan, and small metropolitan), adults living in the two types of nonmetropolitan counties (micropolitan and noncore) did not differ in the prevalence of sufficient sleep; had higher prevalence of nondrinking or moderate drinking; and had lower prevalence of current nonsmoking, maintaining normal body weight, and meeting aerobic leisure time physical activity recommendations. The overall age-adjusted prevalence of reporting at least four of the five health-related behaviors was 30.4%. The prevalence among the estimated 13.3 million adults living in noncore counties was lower (27.0%) than among those in micropolitan counties (28.8%), small metropolitan counties (29.5%), medium metropolitan counties (30.5%), large fringe metropolitan counties (30.2%), and large metropolitan centers (31.7%). INTERPRETATION: This is the first report of the prevalence of these five health-related behaviors for the six urban-rural categories. Nonmetropolitan counties have lower prevalence of three and clustering of at least four health-related behaviors that are associated with the leading chronic disease causes of death. Prevalence of sufficient sleep was consistently low and did not differ by urban-rural status. PUBLIC HEALTH ACTION: Chronic disease prevention efforts focus on improving the communities, schools, worksites, and health systems in which persons live, learn, work, and play. Evidence-based strategies to improve health-related behaviors in the population of the United States can be used to reach the Healthy People 2020 objectives for these five self-reported health-related behaviors (sufficient sleep, current nonsmoking, nondrinking or moderate drinking, maintaining normal body weight, and meeting aerobic leisure time physical activity recommendations). These findings suggest an ongoing need to increase public awareness and public education, particularly in rural counties where prevalence of these health-related behaviors is lowest.

The prevalence of Medicare beneficiaries aged 65 years or older with 6 or more concurrent chronic conditions (MCC6+) varies geographically (1). Preventing chronic disease costs less than treating it. Chronic diseases that are well managed progress slower than those that are untreated (2). Thus, understanding how Medicare spending is distributed across the United States among older adults with the highest burden of multiple chronic conditions can assist with targeting prevention and disease management efforts. The objective of this analysis was to describe the county-level variation in per capita Medicare spending among MCC6+ beneficiaries.

OBJECTIVES: The objectives of this study were to determine whether job strain is more strongly associated with higher ambulatory blood pressure (ABP) among blue-collar workers compared with white-collar workers, to examine whether this pattern generalizes across working and nonworking days and across sex, and to examine whether this pattern is accounted for by psychosocial factors or health behaviors during daily life. METHODS: A total of 480 healthy workers (mean age = 43 years, 53% female) in the Adult Health and Behavior Project-Phase 2 completed ABP monitoring during 3 working days and 1 nonworking day. Job strain was operationalized as high psychological demand (> sample median) combined with low decision latitude (<sample median; Karasek model; Job Content Questionnaire). RESULTS: Covariate-adjusted multilevel random coefficient regressions demonstrated that associations between job strain and systolic and diastolic ABP were stronger among blue-collar workers compared with white-collar workers (b = 6.53 [F(1,464) = 3.89, p = .049] and b = 5.25 [F(1,464) = 6.09, p = .014], respectively). This pattern did not vary by sex, but diastolic ABP findings were stronger when participants were at work. The stronger association between job strain and ABP among blue-collar workers was not accounted for by education, momentary physical activity, or substance use, but was partially accounted for by covariation between higher hostility and blue-collar status. CONCLUSIONS: Job strain is associated with ABP among blue-collar workers. These results extend previous findings to a mixed-sex sample and nonworking days and provide, for the first time, comprehensive exploration of several behavioral and psychosocial explanations for this finding.