U.S. congressional district cancer death rates

Gerrymandering and the Packing and Cracking of Medical Uninsurance Rates in the United States

CONTEXT

Technological innovation and access to big data have allowed partisan gerrymandering to increase dramatically in recent redistricting cycles.

OBJECTIVE

To understand whether and how partisan gerrymandering, including "packing" and "cracking" (ie, respectively concentrating within or dividing specified social groups across political boundaries), distorts understanding of public health need when health statistics are calculated for congressional districts (CDs).

DESIGN

Cross-sectional study using 2020 CDs and nonpartisan simulated districts.

SETTING

United States, 2017-2021.

PARTICIPANTS

United States residents.

MAIN OUTCOME MEASURE

Percent with no medical insurance (uninsured), within-district variance of percent uninsured, and between-district variance of percent uninsured.

RESULTS

At the state level, states where partisan redistricting plans showed greater evidence of partisan gerrymandering were more likely to contain CDs with more extreme values of uninsurance rates than districts in states with less evidence for gerrymandering (association between z-scores for gerrymandering and between-district variation in uninsurance = 0.25 (-0.04, 0.53), P = .10). Comparing variation in uninsurance rates for observed CDs vs nonpartisan simulated districts across all states with more than 1 CD, in analyses stratified by state gerrymander status (no gerrymander, Democratic gerrymander, and Republican gerrymander), we found evidence of particularly extreme distortion of rates in Republican gerrymandered states, whereby Republican-leaning districts tended to have lower uninsurance rates (the percentage of Republican-leaning districts that were significantly lower than nonpartisan simulated districts was 5.1 times that of Democratic-leaning districts) and Democrat-leaning districts had higher uninsurance rates (the percentage of Democrat-leaning districts that were significantly higher than nonpartisan simulated districts was 3.0 times that of Republican-leaning districts).

CONCLUSIONS

Partisan gerrymandering can affect determination of CD-level uninsurance rates and distort understanding of public health burdens.

Measuring variation in infant mortality and deaths of despair by US congressional district in Pennsylvania: a methodological case study

Many ecological studies examine health outcomes and disparities using administrative boundaries such as census tracts, counties, or states. These boundaries help us to understand the patterning of health by place, along with impacts of policies implemented at these levels. However, additional geopolitical units (units with both geographic and political meaning), such as congressional districts (CDs), present further opportunities to connect research with public policy. Here we provide a step-by-step guide on how to conduct disparities-focused analysis at the CD level. As an applied case study, we use geocoded vital statistics data from 2010-2015 to examine levels of and disparities in infant mortality and deaths of despair in the 19 US CDs of Pennsylvania for the 111th-112th (2009-2012) Congresses and 18 CDs for the 113th-114th (2013-2016) Congresses. We also provide recommendations for extending CD-level analysis to other outcomes, states, and geopolitical boundaries, such as state legislative districts. Increased surveillance of health outcomes at the CD level can help prompt policy action and advocacy and, hopefully, reduce rates of and disparities in adverse health outcomes.

Relationship of political ideology of US federal and state elected officials and key COVID pandemic outcomes following vaccine rollout to adults: April 2021–March 2022

Background

Scant research, including in the United States, has quantified relationships between the political ideologies of elected representatives and COVID-19 outcomes among their constituents.

Methods

We analyzed observational cross-sectional data on COVID-19 mortality rates (age-standardized) and stress on hospital intensive care unit (ICU) capacity for all 435 US Congressional Districts (CDs) in a period of adult vaccine availability (April 2021–March 2022). Political metrics comprised: (1) ideological scores based on each US Representative's and Senator's concurrent overall voting record and their specific COVID-19 votes, and (2) state trifectas (Governor, State House, and State Senate under the same political party control). Analyses controlled for CD social metrics, population density, vaccination rates, the prevalence of diabetes and obesity, and voter political lean.

Findings

During the study period, the higher the exposure to conservatism across several political metrics, the higher the COVID-19 age-standardized mortality rates, even after taking into account the CD's social characteristics; similar patterns occurred for stress on hospital ICU capacity for Republican trifectas and US Senator political ideology scores. For example, in models mutually adjusting for CD political and social metrics and vaccination rates, Republican trifecta and conservative voter political lean independently remained significantly associated with an 11%–26% higher COVID-19 mortality rate.

Interpretation

Associations between the political ideologies of US federal elected officials and state concentrations of political party power with population health warrant greater consideration in public health analyses and monitoring dashboards.

Funding

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Life expectancies across congressional districts in the United States

Examining data on the congressional district level may better align with the interests of Members of Congress who have the power to implement federal health policies. Despite this importance, measurement of health indicators at the congressional district level remains widely understudied. In this study, we estimated overall life expectancy and variation within each congressional district by computing standard deviations of census tract-level age-specific life expectancies for 2010-2015. We found smaller standard deviations in congressional districts with higher overall life expectancy at younger ages, but the pattern was reversed at older ages.

Visualizing Cancer Incidence and Mortality Estimates by Congressional Districts, United States 2012-2016

BACKGROUND

Cancer incidence and death rates in the United States are often published at the county or statelevels; examining cancer statistics at the congressional district (CD) level allows decision makers to better understand how cancer is impacting the specific populations they represent.

METHODS

Cancer incidence data were obtained from the Centers for Disease Control and Prevention's National Program of Cancer Registries and the National Cancer Institute's Surveillance, Epidemiology, and End Results Program. Mortality data were obtained from the National Center for Health Statistics. CD rates were estimated by assigning the county-level age-adjusted rates to the census block and weighting those by the block population proportion of the CD. Those weighted rates were then aggregated over the blocks within the CD to estimate the district rate. Incidence rate estimates for 406 CDs and death rate estimates for 436 CDs were reported according to the boundaries for the 115th Congress of the United States. Maps showing rate estimates for all cancers combined, lung/bronchus, colorectal, female breast, cervical, and prostate cancer are presented by sex and race/ethnicity.

RESULTS

The distribution of cancer incidence and death rates by CDs show similar patterns to those that have been observed at the county and state levels, with the highest cancer incidence and death rates observed in CDs in the South and Eastern regions.

CONCLUSION

This examination of cancer rates at the CD-level provides data that can be used to inform cancer control strategies at the local and national levels. Displaying the data with the Data Visualizations tool makes it easily accessible to the public and decision makers.

Mortality-to-incidence ratios by US Congressional District: Implications for epidemiologic, dissemination and implementation research, and public health policy

The mortality-to-incidence ratio (MIR) can be computed from readily accessible, public-use data on cancer incidence and mortality, and a high MIR value is an indicator of poor survival relative to incidence. Newly available data on congressional district-specific cancer incidence and mortality from the U.S. Cancer Statistics (USCS) database from 2011 to 2015 were used to compute MIR values for overall (all types combined), breast, cervix, colorectal, esophagus, lung, oral, pancreas, and prostate cancer. Congressional districts in the South and Midwest, including MS, AL, and KY, had higher (worse) MIR values for all cancer types combined than for the U.S. as a whole. For all cancers combined, there was a positive correlation between each district's percent of rural residents and the MIR (r = 0.47; p < .001). The MIR for all cancer types combined was lower in districts within states that expanded Medicaid vs. those states that did not expand Medicaid (0.36 vs. 0.38; p < .001). A positive correlation was seen between the proportion of non-Hispanic Black residents and MIR (r = 0.15; p < .01 for all cancers). Lower MIRs were observed in districts in New England and in states that expanded Medicaid. However, there also were some interesting departures from this rule (e.g., Wyoming, South Dakota, parts of Wisconsin and Florida). Rural congressional districts have generally higher MIRs than more urban districts. There is some concern that poorer, more rural states that did not expand Medicaid may experience greater disparities in MIRs relative to Medicaid expansion states in the future.

Opioid Prescribing Rates by Congressional Districts, United States, 2016

OBJECTIVES

To determine the extent to which opioid prescribing rates vary across US congressional districts.

METHODS

In an observational cross-sectional framework using secondary data, we constructed 2016 congressional district-level opioid prescribing rate estimates using a population-weighted methodology.

RESULTS

High prescribing rate districts were concentrated in the South, Appalachia, and the rural West. Low-rate districts were concentrated in urban centers.

CONCLUSIONS

In the midst of an opioid overdose crisis, we identified congressional districts of particular concern for opioid prescription saturation. Public Health Implications. The congressional district geography represents a policy-relevant boundary and a politically important level at which to monitor the crisis and determine program funding. Furthermore, in the context of the opioid crisis, knowing how congressional districts rank across the country and in states is useful in the creation of policies targeted to areas in need.

Transforming geographic scale: a comparison of combined population and areal weighting to other interpolation methods

Background

Transforming spatial data from one scale to another is a challenge in geographic analysis. As part of a larger, primary study to determine a possible association between travel barriers to pediatric cancer facilities and adolescent cancer mortality across the United States, we examined methods to estimate mortality within zones at varying distances from these facilities: (1) geographic centroid assignment, (2) population-weighted centroid assignment, (3) simple areal weighting, (4) combined population and areal weighting, and (5) geostatistical areal interpolation. For the primary study, we used county mortality counts from the National Center for Health Statistics (NCHS) and population data by census tract for the United States to estimate zone mortality. In this paper, to evaluate the five mortality estimation methods, we employed address-level mortality data from the state of Georgia in conjunction with census data. Our objective here is to identify the simplest method that returns accurate mortality estimates.

Results

The distribution of Georgia county adolescent cancer mortality counts mirrors the Poisson distribution of the NCHS counts for the U.S. Likewise, zone value patterns, along with the error measures of hierarchy and fit, are similar for the state and the nation. Therefore, Georgia data are suitable for methods testing. The mean absolute value arithmetic differences between the observed counts for Georgia and the five methods were 5.50, 5.00, 4.17, 2.74, and 3.43, respectively. Comparing the methods through paired t-tests of absolute value arithmetic differences showed no statistical difference among the methods. However, we found a strong positive correlation (r = 0.63) between estimated Georgia mortality rates and combined weighting rates at zone level. Most importantly, Bland–Altman plots indicated acceptable agreement between paired arithmetic differences of Georgia rates and combined population and areal weighting rates.

Conclusions

This research contributes to the literature on areal interpolation, demonstrating that combined population and areal weighting, compared to other tested methods, returns the most accurate estimates of mortality in transforming small counts by county to aggregated counts for large, non-standard study zones. This conceptually simple cartographic method should be of interest to public health practitioners and researchers limited to analysis of data for relatively large enumeration units.

Racial and Ethnic Variations in Lung Cancer Incidence and Mortality: Results From the Women's Health Initiative

PURPOSE

This study aimed to evaluate racial/ethnic differences in lung cancer incidence and mortality in the Women's Health Initiative Study, a longitudinal prospective cohort evaluation of postmenopausal women recruited from 40 clinical centers.

METHODS

Lung cancer diagnoses were centrally adjudicated by pathology review. Baseline survey questionnaires collected sociodemographic and health information. Logistic regression models estimated incidence and mortality odds by race/ethnicity adjusted for age, education, calcium/vitamin D, body mass index, smoking (status, age at start, duration, and pack-years), alcohol, family history, oral contraceptive, hormones, physical activity, and diet.

RESULTS

The cohort included 129,951 women--108,487 (83%) non-Hispanic white (NHW); 10,892 (8%) non-Hispanic black (NHB); 4,882 (4%) Hispanic; 3,696 (3%) Asian/Pacific Islander (API); 534 (< 1%) American Indian/Alaskan Native; and 1,994 (1%) other. In unadjusted models, Hispanics had 66% lower odds of lung cancer compared with NHW (odds ratio [OR], 0.34; 95% CI, 0.2 to 0.5), followed by API (OR, 0.45; 95% CI, 0.27 to 0.75) and NHB (OR, 0.75; 95% CI, 0.59 to 0.95). In fully adjusted multivariable models, the decreased lung cancer risk for Hispanic compared with NHW women attenuated to the null (OR, 0.59; 95% CI, 0.35 to 0.99). In unadjusted models Hispanic and API women had decreased risk of death compared with NHW women (OR, 0.30 [95% CI, 0.15 to 0.62] and 0.34 [95% CI, 0.16 to 0.75, respectively); however, no racial/ethnic differences were found in risk of lung cancer death in fully adjusted models.

CONCLUSION

Differences in lung cancer incidence and mortality are associated with sociodemographic, clinical, and behavioral factors. These findings suggest modifiable exposures and behaviors may contribute to differences in incidence of and mortality by race/ethnicity for postmenopausal women. Interventions focused on these factors may reduce racial/ethnic differences in lung cancer incidence and mortality.

Cancer death rates in US congressional districts

Knowledge of the cancer burden is important for informing and advocating cancer prevention and control. Mortality data are readily available for states and counties, but not for congressional districts, from which representatives are elected and which may be more influential in compelling legislation and policy. The authors calculated average annual cancer death rates during 2002 to 2011 for each of the 435 congressional districts using mortality data from the National Center for Health Statistics and population estimates from the US Census Bureau. Age-standardized death rates were mapped for all sites combined and separately for cancers of the lung and bronchus, colorectum, breast, and prostate by race/ethnicity and sex. Overall cancer death rates vary by almost 2-fold and are generally lowest in Mountain states and highest in Appalachia and areas of the South. The distribution is similar for lung and colorectal cancers, with the lowest rates consistently noted in districts in Utah. However, for breast and prostate cancers, while the highest rates are again scattered throughout the South, the geographic pattern is less clear and the lowest rates are in Hawaii and southern Texas and Florida. Within-state heterogeneity is limited, particularly for men, with the exceptions of Texas, Georgia, and Florida. Patterns also vary by race/ethnicity. For example, the highest prostate cancer death rates are in the West and north central United States among non-Hispanic whites, but in the deep South among African Americans. Hispanics have the lowest rates except for colorectal cancer in Wyoming, eastern Colorado, and northern New Mexico. These data can facilitate cancer control and stimulate conversation about the relationship between cancer and policies that influence access to health care and the prevalence of behavioral and environmental risk factors.

A nonparametric approach for determining significance of county cancer rates compared to the overall state rate: illustrated with Minnesota data

BACKGROUND

The study of the geographical distribution of disease has expanded greatly with GIS technology and its application to increasingly available public health data. The emergence of this technology has increased the challenges for public health practitioners to provide meaningful interpretations for county-based state cancer maps.

METHODS

One of these challenges-spurious inferences about the significance of differences between county and overall state cancer rates-can be addressed through a nonparametric statistical method. The Wilcoxon's signed rank test (WSRT) has a practical application for determining the significance of county cancer rates compared to the statewide rate. This extension of the WSRT, developed by John Tukey, forms the basis for constructing a single confidence interval for all differences in county and state directly age-adjusted cancer rates. Empirical evaluation of this WSRT application was conducted using Minnesota cancer incidence data.

RESULTS

The WSRT procedure reduced the impact of statistical artifacts that are frequently encountered with standard normal significance testing of the difference between directly age-adjusted county and the overall state cancer rates.

CONCLUSION

Although further assessment of its performance is required, the WSRT procedure appears to be a useful complement for mapping directly age-adjusted state cancer rates by county.

An eight-year snapshot of geospatial cancer research (2002-2009): clinico-epidemiological and methodological findings and trends

Geographic information systems (GIS) offer a very rich toolbox of methods and technologies, and powerful research tools that extend far beyond the mere production of maps, making it possible to cross-link and study the complex interaction of disease data and factors originating from a wide range of disparate sources. Despite their potential indispensable role in cancer prevention and control programmes, GIS are underrepresented in specialised oncology literature. The latter has provided an impetus for the current review. The review provides an eight-year snapshot of geospatial cancer research in peer-reviewed literature (2002-2009), presenting the clinico-epidemiological and methodological findings and trends in the covered corpus (93 papers). The authors concluded that understanding the relationship between location and cancer/cancer care services can play a crucial role in disease control and prevention, and in better service planning, and appropriate resource utilisation. Nevertheless, there are still barriers that hinder the wide-scale adoption of GIS and related technologies in everyday oncology practice.

Geographic variation in U.S. thyroid cancer incidence and a cluster near nuclear reactors in New Jersey, New York, and Pennsylvania

In the United States, thyroid cancer incidence (along with liver cancer) is increasing more rapidly than any other malignancy, rising nearly threefold from 1980 to 2006. Improved diagnosis has been proposed by some as the major reason for this change, while others contend that additional factors also account for the increase. Among U.S. states, 2001-2005 age-adjusted thyroid cancer incidence rates vary from 5.4 to 12.8 per 100,000. County-specific incidence data, available for the first time, document that most U.S. counties with the highest thyroid cancer incidence are in a contiguous area of eastern Pennsylvania, New Jersey, and southern New York State. Exposures to radioactive iodine emissions from 16 nuclear power reactors within a 90-mile radius in this area indicate that these emissions are a likely etiological factor in rising thyroid cancer incidence rates.

Choropleth map design for cancer incidence, part 1

Choropleth maps are commonly used in cancer reports and community discussions about cancer rates. Cancer registries increasingly use geographic information system techniques. The Centers for Disease Control and Prevention's Division of Cancer Prevention and Control convened a Map Work Group to help guide application of geographic information systems mapping techniques and to promote choropleth mapping of data from central cancer registries supported by the National Program of Cancer Registries, especially for planning and evaluation of comprehensive cancer control programs. In this 2-part series in this issue of Preventing Chronic Disease, we answer frequently asked questions about choropleth map design to display cancer incidence data. We recommend that future initiatives consider more advanced mapping, spatial analysis, and spatial statistics techniques, and include usability testing with representatives of state and local programs and other cancer prevention partners.

Racial disparities in lung cancer mortality in U.S. congressional districts, 1990-2001

The objective of this study was to detect statistically significant racial disparities in lung cancer mortality at the U.S. congressional district level. We applied absolute disparity statistics to mortality data from the National Center for Health Statistics (NCHS) for 1990-2001, mapped significant lung cancer mortality disparities by race and gender within U.S. congressional districts, and uncovered previously unreported disparities. The disparity statistics comparing black and white females revealed higher mortality rates for black females in the Midwestern U.S., and higher mortality rates for white females in the South-eastern U.S. Our methodology provides a spatial tool for guiding public health cancer control practices to monitor, target and reduce disparities.

Visualization and interpretation of birth defects data using linked micromap plots

BACKGROUND

Many states have implemented birth defects surveillance systems to monitor and disseminate information regarding birth defects. However, many of these states rely on tabular methods to disseminate statistical birth defects summaries. An innovative presentation technique for birth defect data that portrays the information in a joint geographical and statistical context is the linked micromap (LM) plot.

METHODS

LM plots were generated for oral cleft data at two geographical resolutions-USA states and counties of Utah. The LM plots also included demographic and behavioral risk data.

RESULTS

A LM plot for the USA reveals spatial patterns indicating higher oral cleft occurrence in the southwest and the midwest and lower occurrence in the east. The LM plot also indicates relationships between oral cleft occurrence and maternal smoking rates and the proportion of American Indians and Alaskan Natives. In particular, the five states with the highest oral cleft occurrence had a higher proportion of American Indians and Alaskan Natives. Among the 15 states with the highest oral cleft occurrence, nine had a smoking rate of 16% or higher while among the 15 states with the lowest oral cleft occurrence only one state had a smoking rate greater than 16%. The LM plot for the state of Utah shows no clear geographic pattern, due perhaps to a relatively small number of cases in a limited geographic area.

CONCLUSIONS

LM plots are effective in representing complex and large volume birth defects data. Integration to birth defects surveillance systems will improve both presentation and interpretation.

Exploring the role of GIS during community health assessment problem solving: experiences of public health professionals

BACKGROUND

A Community health assessment (CHA) involves the use of Geographic Information Systems (GIS) in conjunction with other software to analyze health and population data and perform numerical-spatial problem solving. There has been little research on identifying how public health professionals integrate this software during typical problem solving scenarios. A better understanding of this is needed to answer the "What" and the "How". The "What" identifies the specific software being used and the "How" explains the way they are integrated together during problem solving steps. This level of understanding will highlight the role of GIS utilization during problem solving and suggest to developers how GIS can be enhanced to better support data analysis during community health assessment.

RESULTS

An online survey was developed to identify the information technology used during CHA analysis. The tasks were broken down into steps and for our analysis these steps were categorized by action: Data Management/Access, Data Navigation, Geographic Comparison, Detection of Spatial Boundaries, Spatial Modelling, and Ranking Analysis. 27 CHA professionals completed the survey, with the majority of participants (14) being from health departments. Statistical software (e.g. SPSS) was the most popular software for all but one of the types of steps. For this step (detection of spatial boundaries), GIS was identified as the most popular technology.

CONCLUSION

Most CHA professionals indicated they use statistical software in conjunction with GIS. The statistical software appears to drive the analysis, while GIS is used primarily for simple spatial display (and not complex spatial analysis). This purpose of this survey was to thoroughly examine into the process of problem solving during community health assessment data analysis and to gauge how GIS is integrated with other software for this purpose. These findings suggest that GIS is used more for spatial display while other software such as statistical packages (the "What") are used to drive data management, data navigation, and data calculation (the "How"). Focusing at the level of how public health problems are solved, can shed light on how GIS technology can be enhanced to encompass a stronger role during community health assessment problem solving.