Association of US county-level social vulnerability index with breast, colorectal, and lung cancer screening, incidence, and mortality rates across US counties
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ABSTRACT:
SUBMITTER: Mehta A
PROVIDER: S-EPMC11335618 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:ImportanceArea-level factors have been identified as important social determinants of health (SDoH) that impact many health-related outcomes. Less is known about how the social vulnerability index (SVI), as a scalable composite score, can multidimensionally explain the population-based cancer screening program uptake at a county level.ObjectiveTo examine the geographic variation of US Preventive Services Task Force (USPSTF)-recommended breast, cervical, and colorectal cancer screening rates and the association between county-level SVI and the 3 screening rates.Design, setting, and participantsThis population-based cross-sectional study used county-level information from the Centers for Disease Control and Prevention's PLACES and SVI data sets from 2018 for 3141 US counties. Analyses were conducted from October 2021 to February 2022.ExposuresSocial vulnerability index score categorized in quintiles.Main outcomes and measuresThe main outcome was county-level rates of USPSTF guideline-concordant, up-to-date breast, cervical, and colorectal screenings. Odds ratios were calculated for each cancer screening by SVI quintile as unadjusted (only accounting for eligible population per county) or adjusted for urban-rural status, percentage of uninsured adults, and primary care physician rate per 100 000 residents.ResultsAcross 3141 counties, county-level cancer screening rates showed regional disparities ranging from 54.0% to 81.8% for breast cancer screening, from 69.9% to 89.7% for cervical cancer screening, and from 39.8% to 74.4% for colorectal cancer screening. The multivariable regression model showed that a higher SVI was significantly associated with lower odds of cancer screening, with the lowest odds in the highest SVI quintile. When comparing the highest quintile of SVI (SVI-Q5) with the lowest quintile of SVI (SVI-Q1), the unadjusted odds ratio was 0.86 (95% posterior credible interval [CrI], 0.84-0.87) for breast cancer screening, 0.80 (95% CrI, 0.79-0.81) for cervical cancer screening, and 0.72 (95% CrI, 0.71-0.73) for colorectal cancer screening. When fully adjusted, the odds ratio was 0.92 (95% CrI, 0.90-0.93) for breast cancer screening, 0.87 (95% CrI, 0.86-0.88) for cervical cancer screening, and 0.86 (95% CrI, 0.85-0.88) for colorectal cancer screening, showing slightly attenuated associations.Conclusions and relevanceIn this cross-sectional study, regional disparities were found in cancer screening rates at a county level. Quantifying how SVI associates with each cancer screening rate could provide insight into the design and focus of future interventions targeting cancer prevention disparities.
| S-EPMC9516325 | biostudies-literature
Project description:PurposeInvestigating CRC screening rates and rurality at the county-level may explain disparities in CRC survival in Georgia. Although a few studies examined the relationship of CRC screening rates, rurality, and/or CRC outcomes, they either used an ecological study design or focused on the larger population.MethodsWe conducted a retrospective analysis utilizing data from the 2004-2010 Surveillance, Epidemiology, and End Results Program. The 2013 United States Department of Agriculture rural-urban continuum codes and 2004-2010 National Cancer Institute small-area estimates for screening behaviors were used to identify county-level rurality and CRC screening rates. Kaplan-Meier method and Cox proportional hazard regression were performed.ResultsAmong 22,160 CRC patients, 5-year CRC survival rates were lower among CRC patients living in low screening areas in comparison with intermediate/high areas (69.1% vs. 71.6% /71.3%; p-value = 0.030). Patients living in rural high-screening areas also had lower survival rates compared to non-rural areas (68.2% vs. 71.8%; p-value = 0.009). Our multivariable analysis demonstrated that patients living in intermediate (HR, 0.91; 95% CI, 0.85-0.98) and high-screening (HR, 0.92; 95% CI, 0.85-0.99) areas were at 8%-9% reduced risk of CRC death. Further, non-rural CRC patients living in intermediate and high CRC screening areas were 9% (HR, 0.91; 95% CI, 0.83-0.99) and 10% (HR, 0.90; 95% CI, 0.82-0.99) less likely to die from CRC.ConclusionsLower 5-year survival rates were observed in low screening and rural high-screening areas. Living in intermediate/high CRC screening areas was negatively associated with the risk of CRC death. Particularly, non-rural patients living in intermediate/high-screening areas were 8%-9% less likely to die from CRC. Targeted CRC screening resources should be prioritized for low screening and rural communities.
| S-EPMC10807605 | biostudies-literature
Project description:BackgroundSocial vulnerability indices were created to measure resiliency to environmental disasters based on socioeconomic and population characteristics of discrete geographic regions. They are composed of multiple validated constructs that can also potentially identify geographically vulnerable populations after injury. Our objective was to determine if these indices correlate with injury fatality rates in the US.MethodsWe evaluated three social vulnerability indices: The Hazards & Vulnerability Research Institute's Social Vulnerability Index (SoVI), the Center for Disease Control's Social Vulnerability Index (SVI), and the Economic Innovation Group's Distressed Community Index (DCI). We analyzed SVI subindices and common individual census variables as indicators of socioeconomic status. Outcomes included age-adjusted county-level overall, firearm, and motor vehicle collision deaths per 100,000 population. Linear regression determined the association of injury fatality rates with the SoVI, SVI, and DCI. Bivariate choropleth mapping identified geographic variation and spatial autocorrelation of overall fatality, SoVI, and DCI.ResultsA total of 3,137 US counties were included. Only 24.6% of counties fell into the same vulnerability quintile for all three indices. Despite this, all indices were associated with increasing fatality rates for overall, firearm, and motor vehicle collision fatality. The DCI performed best by model fit, explanation of variance, and diagnostic performance on overall injury fatality. There is significant geographic variation in SoVI, DCI, and injury fatality rates at the county level across the United States, with moderate spatial autocorrelation of SoVI (Moran's I, 0.35; p < 0.01) and high autocorrelation of injury fatality rates (Moran's I, 0.77; p < 0.01) and DCI (Moran's I, 0.53; p < 0.01).ConclusionWhile the indices contribute unique information, higher social vulnerability is associated with higher injury fatality across all indices. These indices may be useful in the epidemiologic and geographic assessment of injury-related fatality rates. Further study is warranted to determine if these indices outperform traditional measures of socioeconomic status and related constructs used in trauma research.Level of evidenceEpidemiological, level IV.
| S-EPMC8375410 | biostudies-literature
Project description:Background Although cardiovascular mortality (CVM) rates in the United States have been declining overall, our study evaluated whether this holds true for areas with increased social deprivation. Methods and Results We used county-level cross-sectional age-adjusted CVM rates (aa-CVM) (2000-2019) linked to the Centers for Disease Control and Prevention Social Vulnerability Index (SVI-2010). We grouped counties as per SVI (Groups I 0-0.2, II 0.21-0.4, III 0.41-0.6, IV 0.61-0.8, and V 0.81-1) and calculated the relative change in the aa-CVM between 2000 to 2003 and 2016 to 2019. We used adjusted linear regression analyses to explore the association between a higher SVI and temporal aa-CVM improvement; we studied this temporal change in aa-CVM across subgroups of race, sex, and location. The median aa-CVM rate (per 100 000) was 272.6 (interquartile range [IQR]: 237.5-311.7). The aa-CVM was higher in men (315.6 [IQR: 273.4-363.9]) than women (221.3 [IQR: 189.6-256.7]), and in Black residents (347.2 [IQR: 301.1-391.1]; P<0.001) than White residents (258.9 [IQR: 226-299.1]; P<0.001). The aa-CVM for SVI I (233.6 [IQR: 214.8-257.0]) was significantly lower than that of group V (323.6 [IQR: 277.2-359.2]; P<0.001). The relative reduction in CVM was significantly higher for SVI group I (32.2% [IQR: 24.2-38.4]) than group V (27.2% [IQR: 19-34.1]) counties. After multivariable adjustment, a higher SVI index was associated with lower relative improvement in the age-adjusted CVM (model coefficient -3.11 [95% CI, -5.66 to -1.22]; P<0.001). Conclusions Socially deprived counties in the United States had higher aa-CVM rates, and the improvement in aa-CVM over the past 20 years was lower in these counties.
| S-EPMC10757513 | biostudies-literature
Project description:We used the COVID-19 Community Vulnerability Index and 7 theme scores to assess associations between vulnerability and county-level COVID-19 vaccination (n = 2415 counties) through May 25th, 2021. When comparing vaccination rates among quintiles of CCVI scores, Theme 3 (housing type, transportation, household composition, and disability) was associated with the largest disparity, with the least vulnerable counties (Q1) having 33% higher rates of vaccination among individuals aged 18+ (53.5% vs 40.2%) compared to counties with the highest vulnerability (Q5). Using generalized linear models with binomial distributions and log links, we found that a 10-point increase in the CCVI index, socioeconomic vulnerability, housing type and composition, and epidemiological factors were associated with at least a 1.0 percentage point decline in county-level vaccination. The association between community vulnerability and lower vaccination rates suggests the need for continued efforts for equitable COVID-19 vaccination across marginalized communities.
| S-EPMC8215509 | biostudies-literature
Project description:Many countries, including the USA, publish predicted numbers of cancer incidence and death in current and future years for the whole country. These predictions provide important information on the cancer burden for cancer control planners, policymakers and the general public. Based on evidence from several empirical studies, the joinpoint (segmented-line linear regression) model (JPM) has been adopted by the American Cancer Society to estimate the number of new cancer cases in the USA and in individual states since 2007. Recently, cancer incidence in smaller geographic regions such as counties, and local policy makers are increasingly interested with Federal Information Processing Standard code regions. The natural extension is to directly apply the JPM to county-level cancer incidence data. The direct application has several drawbacks and its performance has not been evaluated. To address the concerns, we developed a spatial random-effects JPM for county-level cancer incidence data. The proposed model was used to predict both cancer incidence rates and counts at the county level. The standard JPM and the proposed method were compared through a validation study. The proposed method outperformed the standard JPM for almost all cancer sites, especially for moderate or rare cancer sites and for counties with small population sizes. As an application, we predicted county-level prostate cancer incidence rates and counts for the year 2011 in Connecticut.
| S-EPMC5933533 | biostudies-literature
Project description:Cardiometabolic diseases are a group of interrelated diseases that pose greater burden among socially vulnerable communities. The social vulnerability index (SVI) identifies communities vulnerable to emergencies and may also help determine communities at risk of adverse chronic health outcomes. However, no studies have examined the relationship between the SVI and cardiometabolic health outcomes in Colorado or focused on rural settings. The aim of this ecological study was to determine whether the county-level SVI is associated with county-level cardiometabolic health indicators with a particular focus on rurality and racial/ethnic diversity. We obtained 2014 SVI scores from the Centers for Disease Control and Prevention (scored 0-1; higher = more vulnerable) and 2013-2015 cardiometabolic health estimates from the Colorado Department of Public Health and Environment. The distribution of social determinants of health was spatially evaluated. Bivariate relationships between the SVI and cardiometabolic indicators were estimated using simple linear regression models. The highest SVI scores were observed in rural areas, including the San Luis Valley (mean: 0.78, median: 0.91), Southeast (mean: 0.72, median: 0.73), and Northeast (mean: 0.66, median: 0.76) regions. Across Colorado, the SVI accounted for 41% of the variability in overweight and obesity prevalence (p < 0.001), 17% of the variability in diabetes prevalence (p = 0.001), and 58% of the age-adjusted myocardial infarction hospitalization rate (p < 0.001). SVI values may be useful in determining a community's burden of cardiometabolic diseases.
| S-EPMC8872484 | biostudies-literature
Project description: Not available
| S-EPMC10576214 | biostudies-literature
Project description:Neighborhoods have been identified as important determinants of health-related outcomes, but limited research has assessed the influence of neighborhood context along the cancer continuum. This study used census tract-level data from the United States Census Bureau and Centers for Disease Control and Prevention to characterize Miami-Dade County census tracts (n = 492) into social vulnerability clusters and assess their associated breast, cervical, and colorectal cancer screening participation rates. We identified disparities by social vulnerability cluster in cancer screening participation rates. Further investigation of geographic disparities in social vulnerability and cancer screening participation could inform equity-focused cancer control efforts.
| S-EPMC10465939 | biostudies-literature
Project description:While the COVID-19 pandemic presents a global challenge, the U.S. response places substantial responsibility for both decision-making and communication on local health authorities, necessitating tools to support decision-making at the community level. We created a Pandemic Vulnerability Index (PVI) to support counties and municipalities by integrating baseline data on relevant community vulnerabilities with dynamic data on local infection rates and interventions. The PVI visually synthesizes county-level vulnerability indicators, enabling their comparison in regional, state, and nationwide contexts. We describe the data streams used and how these are combined to calculate the PVI, detail the supporting epidemiological modeling and machine-learning forecasts, and outline the deployment of an interactive web Dashboard. Finally, we describe the practical application of the PVI for real-world decision-making. Considering an outlook horizon from 1 to 28 days, the overall PVI scores are significantly associated with key vulnerability-related outcome metrics of cumulative deaths, population adjusted cumulative deaths, and the proportion of deaths from cases. The modeling results indicate the most significant predictors of case counts are population size, proportion of black residents, and mean PM2.5. The machine learning forecast results were strongly predictive of observed cases and deaths up to 14 days ahead. The modeling reinforces an integrated concept of vulnerability that accounts for both dynamic and static data streams and highlights the drivers of inequities in COVID-19 cases and deaths. These results also indicate that local areas with a highly ranked PVI should take near-term action to mitigate vulnerability. The COVID-19 PVI Dashboard monitors multiple data streams to communicate county-level trends and vulnerabilities and facilitates decision-making and communication among government officials, scientists, community leaders, and the public to enable effective and coordinated action to combat the pandemic.
| S-EPMC7430608 | biostudies-literature