Preventable Premature Deaths from the Five Leading Causes of Death in Nonmetropolitan and Metropolitan Counties, United States, 2010-2022.

Problem/Condition: A 2019 report quantified the higher percentage of potentially excess (preventable) deaths in U.S. nonmetropolitan areas compared with metropolitan areas during 2010-2017. In that report, CDC compared national, regional, and state estimates of preventable premature deaths from the five leading causes of death in nonmetropolitan and metropolitan counties during 2010-2017. This report provides estimates of preventable premature deaths for additional years (2010-2022). Period Covered: 2010-2022. Description of System: Mortality data for U.S. residents from the National Vital Statistics System were used to calculate preventable premature deaths from the five leading causes of death among persons aged <80 years. CDC's National Center for Health Statistics urban-rural classification scheme for counties was used to categorize the deaths according to the urban-rural county classification level of the decedent's county of residence (1: large central metropolitan [most urban], 2: large fringe metropolitan, 3: medium metropolitan, 4: small metropolitan, 5: micropolitan, and 6: noncore [most rural]). Preventable premature deaths were defined as deaths among persons aged <80 years that exceeded the number expected if the death rates for each cause in all states were equivalent to those in the benchmark states (i.e., the three states with the lowest rates). Preventable premature deaths were calculated separately for the six urban-rural county categories nationally, the 10 U.S. Department of Health and Human Services public health regions, and the 50 states and the District of Columbia. Results: During 2010-2022, the percentage of preventable premature deaths among persons aged <80 years in the United States increased for unintentional injury (e.g., unintentional poisoning including drug overdose, unintentional motor vehicle traffic crash, unintentional drowning, and unintentional fall) and stroke, decreased for cancer and chronic lower respiratory disease (CLRD), and remained stable for heart disease. The percentages of preventable premature deaths from the five leading causes of death were higher in rural counties in all years during 2010-2022. When assessed by the six urban-rural county classifications, percentages of preventable premature deaths in the most rural counties (noncore) were consistently higher than in the most urban counties (large central metropolitan and fringe metropolitan) for the five leading causes of death during the study period.During 2010-2022, preventable premature deaths from heart disease increased most in noncore (+9.5%) and micropolitan counties (+9.1%) and decreased most in large central metropolitan counties (-10.2%). Preventable premature deaths from cancer decreased in all county categories, with the largest decreases in large central metropolitan and large fringe metropolitan counties (-100.0%; benchmark achieved in both county categories in 2019). In all county categories, preventable premature deaths from unintentional injury increased, with the largest increases occurring in large central metropolitan (+147.5%) and large fringe metropolitan (+97.5%) counties. Preventable premature deaths from CLRD decreased most in large central metropolitan counties where the benchmark was achieved in 2019 and increased slightly in noncore counties (+0.8%). In all county categories, preventable premature deaths from stroke decreased from 2010 to 2013, remained constant from 2013 to 2019, and then increased in 2020 at the start of the COVID-19 pandemic. Percentages of preventable premature deaths varied across states by urban-rural county classification during 2010-2022. Interpretation: During 2010-2022, nonmetropolitan counties had higher percentages of preventable premature deaths from the five leading causes of death than did metropolitan counties nationwide, across public health regions, and in most states. The gap between the most rural and most urban counties for preventable premature deaths increased during 2010-2022 for four causes of death (cancer, heart disease, CLRD, and stroke) and decreased for unintentional injury. Urban and suburban counties (large central metropolitan, large fringe metropolitan, medium metropolitan, and small metropolitan) experienced increases in preventable premature deaths from unintentional injury during 2010-2022, leading to a narrower gap between the already high (approximately 69% in 2022) percentage of preventable premature deaths in noncore and micropolitan counties. Sharp increases in preventable premature deaths from unintentional injury, heart disease, and stroke were observed in 2020, whereas preventable premature deaths from CLRD and cancer continued to decline. CLRD deaths decreased during 2017-2020 but increased in 2022. An increase in the percentage of preventable premature deaths for multiple leading causes of death was observed in 2020 and was likely associated with COVID-19-related conditions that contributed to increased mortality from heart disease and stroke. Public Health Action: Routine tracking of preventable premature deaths based on urban-rural county classification might enable public health departments to identify and monitor geographic disparities in health outcomes. These disparities might be related to different levels of access to health care, social determinants of health, and other risk factors. Identifying areas with a high prevalence of potentially preventable mortality might be informative for interventions.

Differences in Travel Time to Cancer Surgery for Colon versus Rectal Cancer in a Rural State: A New Method for Analyzing Time-to-Place Data Using Survival Analysis.

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.

Urban-Rural County and State Differences in Chronic Obstructive Pulmonary Disease - United States, 2015.

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.† 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.

New spatially continuous indices of redlining and racial bias in mortgage lending: links to survival after breast cancer diagnosis and implications for health disparities research.

Racial health disparities continue to be a serious problem in the United States and have been linked to contextual factors, including racial segregation. In some cases, including breast cancer survival, racial disparities appear to be worsening. Using the Home Mortgage Disclosure Act (HMDA) database, we extend current spatial analysis methodology to derive new, spatially continuous indices of (1) racial bias in mortgage lending and (2) redlining. We then examine spatial patterns of these indices and the association between these new measures and breast cancer survival among Black/African American women in the Milwaukee, Wisconsin metropolitan area. These new measures can be used to examine relationships between mortgage discrimination and patterns of disease throughout the United States.

Breast and Colorectal Cancer Survival Disparities in Southeastern Wisconsin.

BACKGROUND: Cancer health disparities by race, ethnicity, socioeconomic status, and geography are a top public health priority. Breast and colorectal cancer, in particular, have been shown to exhibit significant disparities and contribute a large proportion of morbidity and mortality from cancer. In addition, breast and colorectal cancer offer targets for prevention and control, including nutrition, physical activity, screening, and effective treatments to prolong and enhance the quality of survival. However, despite the investment of significant time and resources over many years, breast and colorectal cancer disparities persist, and in some cases, may be growing. METHODS: This paper examines breast and colorectal cancer survival disparities in an 8-county region in southeastern Wisconsin, including the City of Milwaukee. Cox proportional hazards models were used to examine survival trends, and a new adaptation of adaptive spatial filtering--a disease mapping method--was used to examine spatial patterns of survival. RESULTS: Disparities by race and ethnicity are revealed, and spatial analyses identify specific areas within the study region that have lower than expected survival rates. CONCLUSIONS: Cancer control efforts in southeastern Wisconsin should focus on black/African American and Hispanic/Latina women to reduce breast cancer survival disparities, and black/African American populations to reduce colorectal cancer disparities. Evidence indicates that targeted interventions may be needed to serve populations in the Milwaukee and Kenosha metropolitan areas, as well as areas of Walworth, Ozaukee, and Waukesha counties.

Is Travel Time to Colonoscopy Associated With Late-Stage Colorectal Cancer Among Medicare Beneficiaries in Iowa?

BACKGROUND: Colorectal cancer (CRC) screening has been shown to decrease the incidence of late-stage colorectal cancer, yet a substantial proportion of Americans do not receive screening. Those in rural areas may face barriers to colonoscopy services based on travel time, and previous studies have demonstrated lower screening among rural residents. Our purpose was to assess factors associated with late-stage CRC, and specifically to determine if longer travel time to colonoscopy was associated with late-stage CRC among an insured population in Iowa. METHODS: SEER-Medicare data were used to identify individuals ages 65 to 84 years old diagnosed with CRC in Iowa from 2002 to 2009. The distance between the centroid of the ZIP code of residence and the ZIP code of colonoscopy was computed for each individual who had continuous Medicare fee-for-service coverage for a 3- to 4-month period prior to diagnosis, and a professional claim for colonoscopy within that time frame. Demographic characteristics and travel times were compared between those diagnosed with early- versus late-stage CRC. Also, demographic differences between those who had colonoscopy claims identified within 3-4 months prior to diagnosis (81%) were compared to patients with no colonoscopy claims identified (19%). RESULTS: A total of 5,792 subjects met inclusion criteria; 31% were diagnosed with early-stage versus 69% with late-stage CRC. Those divorced or widowed (vs married) were more likely to be diagnosed with late-stage CRC (OR: 1.20, 95% CI: 1.06-1.37). Travel time was not associated with diagnosis of late-stage CRC. DISCUSSION: Among a Medicare-insured population, there was no relationship between travel time to colonoscopy and disease stage at diagnosis. It is likely that factors other than distance to colonoscopy present more pertinent barriers to screening in this insured population. Additional research should be done to determine reasons for nonadherence to screening among those with access to CRC screening services, given that over two-thirds of these insured individuals were diagnosed with late-stage CRC.

Where do patients with cancer in Iowa receive radiation therapy?

PURPOSE: Multiple studies have shown survival benefits in patients with cancer treated with radiation therapy, but access to treatment facilities has been found to limit its use. This study was undertaken to examine access issues in Iowa and determine a methodology for conducting a similar national analysis. PATIENTS AND METHODS: All Iowa residents who received radiation therapy regardless of where they were diagnosed or treated were identified through the Iowa Cancer Registry (ICR). Radiation oncologists were identified through the Iowa Physician Information System (IPIS). Radiation facilities were identified through IPIS and classified using the Commission on Cancer accreditation standard. RESULTS: Between 2004 and 2010, 113,885 invasive cancers in 106,603 patients, 28.5% of whom received radiation treatment, were entered in ICR. Mean and median travel times were 25.8 and 20.1 minutes, respectively, to the nearest facility but 42.4 and 29.1 minutes, respectively, to the patient's chosen treatment facility. Multivariable analysis predicting travel time showed significant relationships for disease site, age, residence location, and facility category. Residents of small and isolated rural towns traveled nearly 3× longer than urban residents to receive radiation therapy, as did patients using certain categories of facilities. CONCLUSION: Half of Iowa patients could reach their nearest facility in 20 minutes, but instead, they traveled 30 minutes on average to receive treatment. The findings identified certain groups of patients with cancer who chose more distant facilities. However, other groups of patients with cancer, namely those residing in rural areas, had less choice, and some had to travel considerably farther to radiation facilities than urban patients.

Access to chemotherapy services by availability of local and visiting oncologists.

PURPOSE: Geographic disparities have raised important questions about factors related to treatment choice and travel time, which can affect access to cancer care. PATIENTS AND METHODS: Iowa residents who received chemotherapy regardless of where they were diagnosed or treated were identified through the Iowa Cancer Registry (ICR), a member of the SEER program. Oncologists and their practice locations, including visiting consulting clinics (VCCs), were tracked through the Iowa Physician Information System. Oncologists, VCCs, and patients were mapped to hospital service areas (HSAs). RESULTS: Between 2004 and 2010, 113,885 newly diagnosed invasive cancers were entered into ICR; among patients in whom these cancers were diagnosed, 31.6% received chemotherapy as a first course of treatment. During this period, 106 Iowa oncologists practiced in 14 cities, and 82 engaged in outreach to 85 VCCs in 77 rural communities. Of patients receiving chemotherapy, 63.0% resided in an HSA that had a local oncologist and traveled 21 minutes for treatment on average. In contrast, 29.3% of patients receiving chemotherapy resided in an HSA with a VCC, and 7.7% resided in an HSA with no oncology provider. These latter two groups of patients traveled 58 minutes on average to receive chemotherapy. Availability of oncologists and VCCs affected where patients received chemotherapy. The establishment of VCCs increased access to oncologists in rural communities and increased the rate that chemotherapy was administered in rural communities from 10% to 24%, a notable increase in local access. CONCLUSION: Access to cancer care is dependent on the absolute number of providers, but it is also dependent on their geographic distribution.

Who does not receive treatment for cancer?

PURPOSE: Little has been published on nontreatment of cancer, yet the National Cancer Data Base (NCDB) indicates that 9.2% of patients receive no first course of treatment. Because the NCDB is limited to accredited cancer programs, there is potential for the actual rate to differ. We sought to understand the rate and characteristics of patients with cancer who receive no first course of treatment in a more population-representative data source. MATERIALS AND METHODS: The Iowa Cancer Registry (ICR) strives to capture 100% of newly diagnosed cancer cases among Iowa residents, regardless of where they are diagnosed or treated. RESULTS: In the ICR from 2004 to 2010, 12.3% of newly diagnosed patients with cancer did not receive a first course of treatment, which is 48% higher than the NCDB data for the state of Iowa (8.3%) during the same time period. Logistic regression indicated that nontreatment was more common in certain cancers (ie, small-cell and non-small-cell lung/bronchial cancers and low-grade non-Hodgkin lymphoma), advanced stages, older patients, those receiving treatment recommendations at nonaccredited cancer programs, and patients who never consulted an oncologist, radiation therapist, or surgeon. Distance to treatment facilities was not related to nontreatment. CONCLUSION: The rate of nontreatment varies by cancer type and stage and is higher in patients receiving initial treatment recommendations in nonaccredited cancer programs than in accredited cancer programs. This pattern seems to be correlated with patient characteristics but also may be related to provider and facility characteristics available to people locally that influence both patient and provider decision making.

Geographic bias related to geocoding in epidemiologic studies.

BACKGROUND: This article describes geographic bias in GIS analyses with unrepresentative data owing to missing geocodes, using as an example a spatial analysis of prostate cancer incidence among whites and African Americans in Virginia, 1990-1999. Statistical tests for clustering were performed and such clusters mapped. The patterns of missing census tract identifiers for the cases were examined by generalized linear regression models. RESULTS: The county of residency for all cases was known, and 26,338 (74%) of these cases were geocoded successfully to census tracts. Cluster maps showed patterns that appeared markedly different, depending upon whether one used all cases or those geocoded to the census tract. Multivariate regression analysis showed that, in the most rural counties (where the missing data were concentrated), the percent of a county's population over age 64 and with less than a high school education were both independently associated with a higher percent of missing geocodes. CONCLUSION: We found statistically significant pattern differences resulting from spatially non-random differences in geocoding completeness across Virginia. Appropriate interpretation of maps, therefore, requires an understanding of this phenomenon, which we call "cartographic confounding."