Impact of 2009 American Recovery and Reinvestment Act (ARRA) health center investments on disadvantaged neighborhoods after recession.

BACKGROUND: Federally qualified health centers (FQHCs) are integral to the U.S. healthcare safety net and uniquely situated in disadvantaged neighborhoods. The 2009 American Recovery and Reinvestment Act (ARRA) invested $2 billion in FQHC stimulus during the Great Recession; but it remains unknown whether this investment was associated with extended benefits for disadvantaged neighborhoods. METHODS: We used a propensity-score matched longitudinal design (2008-2012) to examine whether the 2009 ARRA FQHC investment was associated with local jobs and establishments recovery in FQHC neighborhoods. Job change data were obtained from the Longitudinal Employer-Household Dynamics (LEHD) survey and calculated as an annual rate per 1,000 population. Establishment change data were obtained from the National Neighborhood Data Archive (NaNDA) and calculated as an annual rate per 10,000 population. Establishment data included 4 establishment types: healthcare services, eating/drinking places, retail establishments, and grocery stores. Fixed effects were used to compare annual rates of jobs and establishments recovery between ARRA-funded FQHC census tracts and a matched control group. RESULTS: Of 50,381 tracts, 2,223 contained ≥ 1 FQHC that received ARRA funding. A higher proportion of FQHC tracts had an extreme poverty designation (11.6% vs. 5.4%), high unemployment rate (45.4% vs. 30.3%), and > 50% minority racial/ethnic composition (48.1% vs. 36.3%). On average, jobs grew at an annual rate of 3.84 jobs per 1,000 population (95% CI: 3.62,4.06). In propensity-score weighted models, jobs in ARRA-funded tracts grew at a higher annual rate of 4.34 per 1,000 (95% CI: 2.56,6.12) relative to those with similar social vulnerability. We observed persistent decline in non-healthcare establishments (-1.35 per 10,000; 95% CI: -1.68,-1.02); but did not observe decline in healthcare establishments. CONCLUSIONS: Direct funding to HCs may be an effective strategy to support healthcare establishments and some jobs recovery in disadvantaged neighborhoods during recession, reinforcing the important multidimensional roles HCs play in these communities. However, HCs may benefit from additional investments that target upstream determinants of health to mitigate uneven recovery and neighborhood decline.

Quantifying climatic and socioeconomic drivers of urban malaria in Surat, India: a statistical spatiotemporal modelling study.

BACKGROUND: Cities are becoming increasingly important habitats for mosquito vectors of disease. The pronounced heterogeneity of urban landscapes challenges our understanding of the effects of climate and socioeconomic factors on mosquito-borne disease dynamics at different spatiotemporal scales. Here, we quantify the impact of climatic and socioeconomic factors on urban malaria risk, using an extensive dataset in both space and time for reported Plasmodium falciparum cases in the city of Surat, northwest India. METHODS: We analysed 10 years of monthly P falciparum cases resolved at three nested spatial resolutions (seven zones, 32 units, and 478 worker units) with a Bayesian hierarchical mixed model that incorporates the effects of population density, poverty, relative humidity, and temperature, in addition to random effects (structured and unstructured). To reduce dimensionality and avoid correlation of covariates, socioeconomic variables from survey data were summarised into main axes of variation using principal component analysis. With model selection, we identified the main drivers of spatiotemporal variation in malaria incidence rates at each of the three spatial resolutions. We also compared observations to model-fitted cases by quantifying the percentage of predictions within five discrete levels of malaria risk. FINDINGS: The spatial variation of urban malaria cases was stationary over time, whereby locations with high and low yearly cases remained largely consistent across years. Local socioeconomic variation could be summarised with three principal components accounting for approximately 80% of the variance. The model that incorporated local temperature and relative humidity together with two of these principal components, largely representing population density and poverty, best explained monthly malaria patterns in models formulated at the three different spatial scales. As model resolution increased, the effect size of humidity decreased, whereas those of temperature and the principal component associated with population density increased. Model predictions accurately captured aggregated total monthly cases for the city; in space-time, they more closely matched observations at the intermediate scale, with around 57% of units estimated to fall in the observed category on average across years. The mean absolute error was lower at the intermediate level, showing that this is the best aggregation level to predict the space-time dynamics of malaria incidence rates across the city with the selected model. INTERPRETATION: This statistical modelling framework provides a basis for development of a climate-driven early warning system for urban malaria for the units of Surat, including spatially explicit prediction of malaria risk several weeks to months in advance. Results indicate environmental and socioeconomic covariates for which further measurement at high resolution should lead to model improvement. Advanced warning combined with local surveillance and knowledge of disease hotspots within the city could inform targeted intervention as part of urban malaria elimination efforts. FUNDING: US National Institutes of Health.

Spatial inequities in access to medications for treatment of opioid use disorder highlight scarcity of methadone providers under counterfactual scenarios.

Access to treatment and medication for opioid use disorder (MOUD) is essential in reducing opioid use and associated behavioral risks, such as syringe sharing among persons who inject drugs (PWID). Syringe sharing among PWID carries high risk of transmission of serious infections such as hepatitis C and HIV. MOUD resources, such as methadone provider clinics, however, are often unavailable to PWID due to barriers like long travel distance to the nearest methadone provider and the required frequency of clinic visits. The goal of this study is to examine the uncertainty in the effects of travel distance in initiating and continuing methadone treatment and how these interact with different spatial distributions of methadone providers to impact co-injection (syringe sharing) risks. A baseline scenario of spatial access was established using the existing locations of methadone providers in a geographical area of metropolitan Chicago, Illinois, USA. Next, different counterfactual scenarios redistributed the locations of methadone providers in this geographic area according to the densities of both the general adult population and according to the PWID population per zip code. We define different reasonable methadone access assumptions as the combinations of short, medium, and long travel distance preferences combined with three urban/suburban travel distance preference. Our modeling results show that when there is a low travel distance preference for accessing methadone providers, distributing providers near areas that have the greatest need (defined by density of PWID) is best at reducing syringe sharing behaviors. However, this strategy also decreases access across suburban locales, posing even greater difficulty in regions with fewer transit options and providers. As such, without an adequate number of providers to give equitable coverage across the region, spatial distribution cannot be optimized to provide equitable access to all PWID. Our study has important implications for increasing interest in methadone as a resurgent treatment for MOUD in the United States and for guiding policy toward improving access to MOUD among PWID.

Individual, interpersonal, and neighborhood measures associated with opioid use stigma: Evidence from a nationally representative survey.

Despite growing awareness of opioid use disorder (OUD), fatal overdoses and downstream health conditions (e.g., hepatitis C and HIV) continue to rise in some populations. Various interrelated structural forces, together with social and economic determinants, contribute to this ongoing crisis; among these, access to medications for opioid use disorder (MOUD) and stigma towards people with OUD remain understudied. We combined data on methadone, buprenorphine, and naltrexone providers from SAMHSA's 2019 directory, additional naltrexone providers from Vivitrol's location finder service, with a nationally representative survey called "The AmeriSpeak survey on stigma toward people with OUD." Integrating the social-ecological framework, we focus on individual characteristics, personal and family members' experience with OUD, and spatial access to MOUD at the community level. We use nationally representative survey data from 3008 respondents who completed their survey in 2020. Recognizing that stigma is a multifaceted construct, we also examine how the process varies for different types of stigma, specifically perceived dangerousness and untrustworthiness, as well as social distancing measures under different scenarios. We found a significant association between stigma and spatial access to MOUD - more resources are related to weaker stigma. Respondents had a stronger stigma towards people experiencing current OUD (versus past OUD), and they were more concerned about OUD if the person would marry into their family (versus being their coworkers). Additionally, respondents' age, sex, education, and personal experience with OUD were also associated with their stigma, and the association can vary depending on the specific type of stigma. Overall, stigma towards people with OUD was associated with both personal experiences and environmental measures.

ChicagO Multiethnic Prevention and Surveillance Study (COMPASS): Increased Response Rates Among African American Residents in Low Socioeconomic Status Neighborhoods.

African American (AA) populations experience persistent health disparities in the USA. Low representation in bio-specimen research precludes stratified analyses and creates challenges in studying health outcomes among AA populations. Previous studies examining determinants of bio-specimen research participation among minority participants have focused on individual-level barriers and facilitators. Neighborhood-level contextual factors may also inform bio-specimen research participation, possibly through social norms and the influence of social views and behaviors on neighbor's perspectives. We conducted an epidemiological study of residents in 5108 Chicago addresses to examine determinants of bio-specimen research participation among predominantly AA participants solicited for participation in the first 6 years of ChicagO Multiethnic Prevention and Surveillance Study (COMPASS). We used a door-to-door recruitment strategy by interviewers of predominantly minority race and ethnicity. Participants were compensated with a $50 gift card. We achieved response rates of 30.4% for non-AA addresses and 58.0% for AA addresses, with as high as 80.3% response among AA addresses in low socioeconomic status (SES) neighborhoods. After multivariable adjustment, we found approximately 3 times the odds of study participation among predominantly AA addresses in low vs. average SES neighborhoods (odds ratio (OR) = 3.06; 95% confidence interval (CI) = 2.20-4.24). Conversely, for non-AA addresses, we observed no difference in the odds of study participation in low vs. average SES neighborhoods (OR = 0.89; 95% CI = 0.69-1.14) after multivariable adjustment. Our findings suggest that AA participants in low SES neighborhoods may be recruited for bio-specimen research through door-to-door approaches with compensation. Future studies may elucidate best practices to improve bio-specimen research participation among minority populations.

Operational Local Join Count Statistics for Cluster Detection.

This paper operationalizes the idea of a local indicator of spatial association (LISA) for the situation where the variables of interest are binary. This yields a conditional version of a local join count statistic. The statistic is extended to a bivariate and multivariate context, with an explicit treatment of co-location. The approach provides an alternative to point pattern based statistics for situations where all potential locations of an event are available (e.g., all parcels in a city). The statistics are implemented in the open source GeoDa software and yield maps of local clusters of binary variables, as well as co-location clusters of two (or more) binary variables. Empirical illustrations investigate local clusters of house sales in Detroit in 2013 and 2014, and urban design characteristics of Chicago census blocks in 2017.

Spatial Heterogeneity in Cancer Control Planning and Cancer Screening Behavior.

Each state is autonomous in its comprehensive cancer control (CCC) program, and considerable heterogeneity exists in the program plans. However, researchers often focus on the concept of nationally representative data and pool observations across states using regression analysis to come up with average effects when interpreting results. Due to considerable state autonomy and heterogeneity in various dimensions-including culture, politics, historical precedent, regulatory environment, and CCC efforts-it is important to examine states separately and to use geographic analysis to translate findings in place and time. We used 100 percent population data for Medicare-insured persons aged 65 or older and examined predictors of breast cancer (BC) and colorectal cancer (CRC) screening from 2001-2005. Examining BC and CRC screening behavior separately in each state, we performed 100 multilevel regressions. We summarize the state-specific findings of racial disparities in screening for either cancer in a single bivariate map of the 50 states, producing a separate map for African American and for Hispanic disparities in each state relative to whites. The maps serve to spatially translate the voluminous regression findings regarding statistically significant disparities between whites and minorities in cancer screening within states. Qualitative comparisons can be made of the states' disparity environments or for a state against a national benchmark using the bivariate maps. We find that African Americans in Michigan and Hispanics in New Jersey are significantly more likely than whites to utilize CRC screening and that Hispanics in 6 states are significantly and persistently more likely to utilize mammography than whites. We stress the importance of spatial translation research for informing and evaluating CCC activities within states and over time.

Managed care and the diffusion of endoscopy in fee-for-service Medicare.

OBJECTIVE: To determine whether Medicare managed care penetration impacted the diffusion of endoscopy services (sigmoidoscopy, colonoscopy) among the fee-for-service (FFS) Medicare population during 2001-2006. METHODS: We model utilization rates for colonoscopy or sigmoidoscopy as impacted by both market supply and demand factors. We use spatial regression to perform ecological analysis of county-area utilization rates over two time intervals (2001-2003, 2004-2006) following Medicare benefits expansion in 2001 to cover colonoscopy for persons of average risk. We examine each technology in separate cross-sectional regressions estimated over early and later periods to assess differential effects on diffusion over time. We discuss selection factors in managed care markets and how failure to control perfectly for market selection might impact our managed care spillover estimates. RESULTS: Areas with worse socioeconomic conditions have lower utilization rates, especially for colonoscopy. Holding constant statistically the socioeconomic factors, we find that managed care spillover effects onto FFS Medicare utilization rates are negative for colonoscopy and positive for sigmoidoscopy. The spatial lag estimates are conservative and interpreted as a lower bound on true effects. Our findings suggest that managed care presence fostered persistence of the older technology during a time when it was rapidly being replaced by the newer technology.

Geographic disparities in late-stage breast cancer diagnosis in California.

Using cancer registry data for the population of California women aged 67+ with breast cancers, we estimated random intercept logistic models to examine how two socio-ecological predictors (residential isolation and poverty) were associated with probability of late-stage diagnosis for breast cancer. Using the multilevel modeling results, we calculated fully adjusted predicted probabilities associated with women in each Medical Service Study Area (MSSA) in California and classified the areas into two distinct groups: MSSAs with predicted rates below the 25th percentile (presumably the better outcome areas) and MSSAs with predicted rates above the 75th percentile (presumably the worse outcome areas) for two minority groups. Some areas had better outcomes for one group but worse outcomes for the other, suggesting that interventions to improve outcomes need different strategies for different groups in the same areas. Using information from geographic risk factors and multilevel modeling, this study informs interventions designed to reduce disparities in breast cancer outcomes.

Predictors of endoscopic colorectal cancer screening over time in 11 states.

OBJECTIVES: We study a cohort of Medicare-insured men and women aged 65+ in the year 2000, who lived in 11 states covered by Surveillance, Epidemiology, and End Results (SEER) cancer registries, to better understand various predictors of endoscopic colorectal cancer (CRC) screening. METHODS: We use multilevel probit regression on two cross-sectional periods (2000-2002, 2003-2005) and include people diagnosed with breast cancer, CRC, or inflammatory bowel disease (IBD) and a reference sample without cancer. RESULTS: Men are not universally more likely to be screened than women, and African Americans, Native Americans, and Hispanics are not universally less likely to be screened than whites. Disparities decrease over time, suggesting that whites were first to take advantage of an expansion in Medicare benefits to cover endoscopic screening for CRC. Higher-risk persons had much higher utilization, while older persons and beneficiaries receiving financial assistance for Part B coverage had lower utilization and the gap widened over time. CONCLUSIONS: Screening for CRC in our Medicare-insured sample was less than optimal, and reasons varied considerably across states. Negative managed care spillovers were observed, demonstrating that policy interventions to improve screening rates should reflect local market conditions as well as population diversity.

Heterogeneity in mammography use across the nation: separating evidence of disparities from the disproportionate effects of geography.

BACKGROUND: Mammography is essential for early detection of breast cancer and both reduced morbidity and increased survival among breast cancer victims. Utilization is lower than national guidelines, and evidence of a recent decline in mammography use has sparked concern. We demonstrate that regression models estimated over pooled samples of heterogeneous states may provide misleading information regarding predictors of health care utilization and that comprehensive cancer control efforts should focus on understanding these differences and underlying causal factors. Our study population includes all women over age 64 with breast cancer in the Surveillance Epidemiology and End Results (SEER) cancer registries, linked to a nationally representative 5% reference sample of Medicare-eligible women located in 11 states that span all census regions and are heterogeneous in racial and ethnic mix. Combining women with and without cancer in the sample allows assessment of previous cancer diagnosis on propensity to use mammography. Our conceptual model recognizes the interplay between individual, social, cultural, and physical environments along the pathways to health care utilization, while delineating local and more distant levels of influence among contextual variables. In regression modeling, we assess individual-level effects, direct effects of contextual factors, and interaction effects between individual and contextual factors. RESULTS: Pooling all women across states leads to quite different conclusions than state-specific models. Commuter intensity, community acculturation, and community elderly impoverishment have significant direct impacts on mammography use which vary across states. Minorities living in isolated enclaves with others of the same race/ethnicity may be either advantaged or disadvantaged, depending upon the place studied. CONCLUSION: Careful analysis of place-specific context is essential for understanding differences across communities stemming from different causal factors. Optimal policy interventions to change behavior (improve screening rates) will be as heterogeneous as local community characteristics, so no "one size fits all" policy can improve population health. Probability modeling with correction for clustering of individuals within multilevel contexts can reveal important differences from place to place and identify key factors to inform targeting of specific communities for further study.

Visualizing seismic risk and uncertainty: a review of related research.

Government agencies and other authorities often communicate earthquake risks using maps derived from geographic information systems. Yet, little is known about the effects of these maps on risk perceptions. While mental models research and other approaches are available to inform risk communication text design, similar empirically derived guidance is lacking for visual risk communications, such as maps, which are likely to trump text in their impact and appeal. This paper reviews the empirical research that might inform such guidance. Research on graphs, spatial and visual perception, and map design suggests that graphics increase risk avoidance over numerical risk representations, and countable visuals, like dots, can increase the accuracy of perceived risks, but not always. Cartographic design features, such as color, animation, interactivity, and depth cues, are all candidates to represent risk and uncertainty and to influence risk perception. While there are robust known effects of color (e.g., red = danger), with some cultural variability, animation can increase the salience of otherwise obscure features but is not uniformly effective. Depth cues, dimensionality, and the extent to which a representation depicts versus symbolizes a scene will influence the viewer's perspective and perception, depending on the viewer's familiarity with the scene; their effects on risk perception remain unclear. The translation and representation of technical information about risk and uncertainty is critical to risk communication effectiveness. Our review suggests a handful of candidate criteria for evaluating the effects of risk visualizations, short of changes in behavior: accuracy, accessibility, retention, and perceived risk and usefulness.

Spatial analysis of elderly access to primary care services.

BACKGROUND: Admissions for Ambulatory Care Sensitive Conditions (ACSCs) are considered preventable admissions, because they are unlikely to occur when good preventive health care is received. Thus, high rates of admissions for ACSCs among the elderly (persons aged 65 or above who qualify for Medicare health insurance) are signals of poor preventive care utilization. The relevant geographic market to use in studying these admission rates is the primary care physician market. Our conceptual model assumes that local market conditions serving as interventions along the pathways to preventive care services utilization can impact ACSC admission rates. RESULTS: We examine the relationships between market-level supply and demand factors on market-level rates of ACSC admissions among the elderly residing in the U.S. in the late 1990s. Using 6,475 natural markets in the mainland U.S. defined by The Health Resources and Services Administration's Primary Care Service Area Project, spatial regression is used to estimate the model, controlling for disease severity using detailed information from Medicare claims files. Our evidence suggests that elderly living in impoverished rural areas or in sprawling suburban places are about equally more likely to be admitted for ACSCs. Greater availability of physicians does not seem to matter, but greater prevalence of non-physician clinicians and international medical graduates, relative to U.S. medical graduates, does seem to reduce ACSC admissions, especially in poor rural areas. CONCLUSION: The relative importance of non-physician clinicians and international medical graduates in providing primary care to the elderly in geographic areas of greatest need can inform the ongoing debate regarding whether there is an impending shortage of physicians in the United States. These findings support other authors who claim that the existing supply of physicians is perhaps adequate, however the distribution of them across the landscape may not be optimal. The finding that elderly who reside in sprawling urban areas have access impediments about equal to residents of poor rural communities is new, and demonstrates the value of conceptualizing and modelling impedance based on place and local context.