Patterns of Cancer-Related Healthcare Access across Pennsylvania: Analysis of Novel Census Tract-Level Indicators of Persistent Poverty.

BACKGROUND: Persistent poverty census tracts have had ≥20% of the population living below the federal poverty line for 30+ years. We assessed the relationship between persistent poverty and cancer-related healthcare access across census tracts in Pennsylvania. METHODS: We gathered publicly available census tract-level data on persistent poverty, rurality, and sociodemographic variables, as well as potential access to healthcare (i.e., prevalence of health insurance, last-year check-up), realized access to healthcare (i.e., prevalence of screening for cervical, breast, and colorectal cancers), and self-reported cancer diagnosis. We used multivariable spatial regression models to assess the relationships between persistent poverty and each healthcare access indicator. RESULTS: Among Pennsylvania's census tracts, 2,789 (89.8%) were classified as non-persistent poverty, and 316 (10.2%) were classified as persistent poverty (113 did not have valid data on persistent poverty). Persistent poverty tracts had lower prevalence of health insurance [estimate = -1.70, standard error (SE) = 0.10], screening for cervical cancer (estimate = -4.00, SE = 0.17) and colorectal cancer (estimate = -3.13, SE = 0.20), and cancer diagnosis (estimate = -0.34, SE = 0.05), compared with non-persistent poverty tracts (all P < 0.001). However, persistent poverty tracts had higher prevalence of last-year check-up (estimate = 0.22, SE = 0.08) and screening for breast cancer (estimate = 0.56, SE = 0.15; both P < 0.01). CONCLUSIONS: Relationships between persistent poverty and cancer-related healthcare access outcomes differed in direction and magnitude. Health promotion interventions should leverage data at fine-grained geographic units (e.g., census tracts) to motivate focus on communities or outcomes. IMPACT: Future studies should extend these analyses to other states and outcomes to inform public health research and interventions to reduce geographic disparities.

Exploring Smoking Disparities and Sociodemographic Factors in a Peri-urban Landscape: A Census Tract-Level Analysis in McLennan County, Texas.

Cigarette smoking significantly contributes to preventable illness, death, and economic costs. Despite overall reduction in national smoking rates, disparities persist between demographic groups and geographic regions. While some studies have explored urban-rural differences in smoking prevalence, gaps exist in understanding localized patterns. This study focuses on examining smoking rates and related factors at the census tract level in McLennan County, Texas, a county that contains a mixture of urban, peri-urban, and rural areas. This study uses census tract level aggregate sociodemographic, smoking, and health-related data from the American Community Survey and the PLACES Project City Health Dashboard. Geospatial analyses mapped co-occurrence of high prevalence of smoking, mental and physical distress, and co-occurrence of lower routine medical check-ups, household income, and education. Multiple linear regression modeled associations between smoking and sociodemographic, and health-related factors. Geospatial analyses identified census tracts with co-occurring high prevalence of smoking, mental and physical distress, and co-occurrence of lower routine medical check-ups, household income, and education level in McLennan County. Regression analyses identified that smoking rates were positively correlated with frequent physical distress (p < 0.0001) and negatively correlated with the proportion of routine medical check-ups (p < 0.0001) and the proportion living in poverty (p = 0.0002). This study found significant variations in smoking rates, physical and mental distress, medical check-ups, and sociodemographic factors between neighboring census tracts which geospatial analyses examining larger geographic units may have overlooked. Future research should focus on obtaining individual-level and community-level data to develop more targeted interventions sensitive to specific community contexts.

Drive Time to Addiction Treatment Facilities Providing Contingency Management across Rural and Urban Census Tracts in 6 US States.

OBJECTIVE: We examined drive times to outpatient substance use disorder treatment providers that provide contingency management (CM) and those that integrate CM with medication for opioid use disorder (MOUD) services in 6 US states. METHODS: We completed cross-sectional geospatial analysis among census tracts in Delaware, Louisiana, Massachusetts, North Carolina, New York, and West Virginia. We excluded census tracts with a population of zero. Using data from the 2020 Shatterproof substance use treatment facility survey, our outcome was the minimum drive time in minutes from the census tract mean center of population to the nearest outpatient CM provider, outpatient CM provider with MOUD services, and federally qualified health centers (FQHC). We stratified census tracts by 2010 Rural-Urban Commuting Area codes and by state. RESULTS: The population was greater than zero in 11,719 of 11,899 census tracts. The median drive time to the nearest CM provider was 12.2 [interquartile range (IQR), 7.0-23.5) minutes and the median drive time to the nearest CM provider increased from 9.7 (IQR, 6.0-15.0) minutes in urban census tracts to 38.8 (IQR, 25.4-53.0) minutes in rural ( H = 3683, P < 0.001). The median drive time increased to the nearest CM provider with MOUD services [14.2 (IQR, 7.9-29.5) minutes, W = 18,877, P < 0.001] and decreased to the nearest FQHC [7.9 (IQR, 4.3-13.6) minutes, W = 11,555,894, P < 0.001]. CONCLUSIONS: These results suggest limited availability of CM, particularly within rural communities and for patients needing concurrent CM and MOUD treatment. Our results suggest greater adoption of CM within FQHCs could reduce urban-rural disparities in CM availability.

Assessing performance of ZCTA-level and Census Tract-level social and environmental risk factors in a model predicting hospital events.

Predictive analytics are used in primary care to efficiently direct health care resources to high-risk patients to prevent unnecessary health care utilization and improve health. Social determinants of health (SDOH) are important features in these models, but they are poorly measured in administrative claims data. Area-level SDOH can be proxies for unavailable individual-level indicators, but the extent to which the granularity of risk factors impacts predictive models is unclear. We examined whether increasing the granularity of area-based SDOH features from ZIP code tabulation area (ZCTA) to Census Tract strengthened an existing clinical prediction model for avoidable hospitalizations (AH events) in Maryland Medicare fee-for-service beneficiaries. We created a person-month dataset for 465,749 beneficiaries (59.4% female; 69.8% White; 22.7% Black) with 144 features indexing medical history and demographics using Medicare claims (September 2018 through July 2021). Claims data were linked with 37 SDOH features associated with AH events from 11 publicly-available sources (e.g., American Community Survey) based on the beneficiaries' ZCTA and Census Tract of residence. Individual AH risk was estimated using six discrete time survival models with different combinations of demographic, condition/utilization, and SDOH features. Each model used stepwise variable selection to retain only meaningful predictors. We compared model fit, predictive performance, and interpretation across models. Results showed that increasing the granularity of area-based risk factors did not dramatically improve model fit or predictive performance. However, it did affect model interpretation by altering which SDOH features were retained during variable selection. Further, the inclusion of SDOH at either granularity level meaningfully reduced the risk that was attributed to demographic predictors (e.g., race, dual-eligibility for Medicaid). Differences in interpretation are critical given that this model is used by primary care staff to inform the allocation of care management resources, including those available to address drivers of health beyond the bounds of traditional health care.

Income and rural-urban status moderate the association between income inequality and life expectancy in US census tracts.

BACKGROUND: A preponderance of evidence suggests that higher income inequality is associated with poorer population health, yet recent research suggests that this association may vary based on other social determinants, such as socioeconomic status (SES) and other geographic factors, such as rural-urban status. The objective of this empirical study was to assess the potential for SES and rural-urban status to moderate the association between income inequality and life expectancy (LE) at the census-tract level. METHODS: Census-tract LE values for 2010-2015 were abstracted from the US Small-area Life Expectancy Estimates Project and linked by census tract to Gini index, a summary measure of income inequality, median household income, and population density for all US census tracts with non-zero populations (n = 66,857). Partial correlation and multivariable linear regression modeling was used to examine the association between Gini index and LE using stratification by median household income and interaction terms to assess statistical significance. RESULTS: In the four lowest quintiles of income in the four most rural quintiles of census tracts, the associations between LE and Gini index were significant and negative (p between < 0.001 and 0.021). In contrast, the associations between LE and Gini index were significant and positive for the census tracts in the highest income quintiles, regardless of rural-urban status. CONCLUSION: The magnitude and direction of the association between income inequality and population health depend upon area-level income and, to a lesser extent, on rural-urban status. The rationale behind these unexpected findings remains unclear. Further research is needed to understand the mechanisms driving these patterns.

Mapeando as disparidades socioeconômicas de saúde urbana: um estudo comparativo entre seis capitais brasileiras / Mapping socioeconomic disparities in urban health: a comparative study of six Brazilian capitals / Mapeando las disparidades socioeconómicas de salud urbana: un estudio comparativo entre seis capitales brasileñas

Resumo A cidade é um modo de viver, pensar e sentir. O modo de vida urbano é capaz de produzir ideias, comportamentos, valores e conhecimentos, mas também pode acirrar disparidades socioeconômicas e de saúde da população que ali reside. Este artigo examina as disparidades em saúde urbana em seis capitais brasileiras: São Paulo, Rio de Janeiro, Salvador, Fortaleza, Belo Horizonte e Manaus. Para quantificar e mapear as disparidades intraurbanas nesses espaços, foram utilizados os dados do Censo Demográfico de 2010 para a aplicação do índice de saúde urbana (ISU), uma métrica que sintetiza oito diferentes variáveis socioeconômicas e de saneamento desagregadas por setores censitários. Os resultados são discutidos à luz de três vertentes teóricas: a diferenciação centro-periferia; abordagem econômica da saúde; e epidemiologia social. As descobertas desse estudo revelam que os setores censitários que abrangem populações com maior status socioeconômico e melhores condições de saneamento apresentaram índices de saúde urbana mais elevados do que os da periferia da cidade. Há indícios de melhores indicadores de saúde urbana para o Rio de Janeiro e São Paulo, em comparação com as demais capitais analisadas. No entanto, há importantes nuances em cada uma das seis cidades estudadas, especialmente quando se atribuem diferentes pesos às variáveis que compõem o ISU, apesar da marcada segregação espacial comum a todas elas. Considerar as distinções dentro do espaço urbano é uma estratégia fundamental para a compreensão desses aspectos sociais e econômicos e seus potenciais desdobramentos nas condições de saúde da população.

Abstract A city is a way of living, thinking, and feeling. The urban lifestyle can produce ideas, behaviors, values, and knowledge. Still, it can also intensify socioeconomic and health disparities in the population. This article examines urban health disparities in six Brazilian capitals: São Paulo, Rio de Janeiro, Salvador, Fortaleza, Belo Horizonte, and Manaus. To quantify and map intra-urban disparities in these spaces, data from the 2010 Demographic Census are used to apply the Urban Health Index, a metric that synthesizes eight different socio-economic and sanitation variables disaggregated by census tracts. The results are discussed in light of three theoretical perspectives: center-periphery differentiation, the economic approach to health, and social epidemiology. The findings of this study reveal that census tracts covering populations with higher socio-economic status and better sanitation conditions exhibited higher urban health index scores than those in the city's periphery. Results indicate better urban health indicators for Rio de Janeiro and São Paulo, compared to the other capitals analyzed. However, there are important nuances in each of the six cities, especially when assigning different weights to the variables that compose the Urban Health Index, despite the marked spatial segregation common to all. Considering distinctions within urban space is a fundamental strategy to understand these social and economic aspects and their potential implications for population health conditions.

Resumen La ciudad es una forma de vivir, pensar y sentir. El modo de vida urbano es capaz de producir ideas, comportamientos, valores y conocimientos, pero también lo es de intensificar las disparidades socioeconómicas y de salud de la población que reside en ella. Este artículo examina las disparidades en salud urbana en seis capitales brasileñas: São Paulo, Río de Janeiro, Salvador, Fortaleza, Belo Horizonte y Manaus. Para cuantificar y mapear las disparidades intraurbanas en estos espacios, se utilizan datos del censo demográfico de 2010 para aplicar el índice de salud urbana, una métrica que sintetiza ocho diferentes variables socioeconómicas y de saneamiento desagregadas por sectores censales. Los resultados se discuten a la luz de tres perspectivas teóricas: la diferenciación centro-periferia, el enfoque económico de la salud y la epidemiología social. Los hallazgos de este estudio revelan que los sectores censales que abarcan poblaciones con un mayor estatus socioeconómico y mejores condiciones de saneamiento presentaron puntajes más altos en el índice de salud urbana que los de la periferia de la ciudad. Hay indicios de mejores indicadores de salud urbana para Río de Janeiro y São Paulo, en comparación con las demás capitales analizadas. Sin embargo, se observan matices importantes en cada una de las seis ciudades analizadas, especialmente al asignar diferentes pesos a las variables que componen el pindice de salud urbana, a pesar de la marcada segregación espacial común a todas ellas. Considerar las distinciones dentro del espacio urbano es una estrategia fundamental para comprender estos aspectos sociales y económicos y sus posibles implicaciones en las condiciones de salud de la población.

A Framework for Interfacing and Partnering with Environmental Justice Communities as a Prelude to Human Health and Hazard Identification in the Vulnerable Census Tracts of Columbus, Ohio.

Columbus, Ohio is one of the more prosperous, well-educated, and progressive cities in the United States. However, it ranks as the second worst life expectancy at birth, has a census tract wealth gap (27-year disparity), and one of the higher infant mortality rates in the country. These data suggest that there are likely several high-risk, vulnerable neighborhoods in Columbus with residents experiencing disparate and adverse outcomes. Illustrative of this fact are studies that have examined the social processes and mechanisms through which neighborhood contexts are at the forefront, including exposures to chemical stressors such as particulate matter (PM2.5) as well as non-chemical stressors including violence, social determinants of health, zoning, and land use policies. It is documented that disparate and adverse outcomes are magnified in the vulnerable neighborhoods on the Near East Side as compared to Columbus city proper, Franklin County and/or the state of Ohio. As such, we developed a nuanced community engagement framework to identify potential environmental hazards associated with adverse pregnancy outcomes in those census tracts. The refined framework uses a blended version of traditional community-based participatory research (CBPR) models and is referred to as E6, Enhancing Environmental Endeavors via e-Equity, Education, and Empowerment.

Using Social Media to Predict Food Deserts in the United States: Infodemiology Study of Tweets.

BACKGROUND: The issue of food insecurity is becoming increasingly important to public health practitioners because of the adverse health outcomes and underlying racial disparities associated with insufficient access to healthy foods. Prior research has used data sources such as surveys, geographic information systems, and food store assessments to identify regions classified as food deserts but perhaps the individuals in these regions unknowingly provide their own accounts of food consumption and food insecurity through social media. Social media data have proved useful in answering questions related to public health; therefore, these data are a rich source for identifying food deserts in the United States. OBJECTIVE: The aim of this study was to develop, from geotagged Twitter data, a predictive model for the identification of food deserts in the United States using the linguistic constructs found in food-related tweets. METHODS: Twitter's streaming application programming interface was used to collect a random 1% sample of public geolocated tweets across 25 major cities from March 2020 to December 2020. A total of 60,174 geolocated food-related tweets were collected across the 25 cities. Each geolocated tweet was mapped to its respective census tract using point-to-polygon mapping, which allowed us to develop census tract-level features derived from the linguistic constructs found in food-related tweets, such as tweet sentiment and average nutritional value of foods mentioned in the tweets. These features were then used to examine the associations between food desert status and the food ingestion language and sentiment of tweets in a census tract and to determine whether food-related tweets can be used to infer census tract-level food desert status. RESULTS: We found associations between a census tract being classified as a food desert and an increase in the number of tweets in a census tract that mentioned unhealthy foods (P=.03), including foods high in cholesterol (P=.02) or low in key nutrients such as potassium (P=.01). We also found an association between a census tract being classified as a food desert and an increase in the proportion of tweets that mentioned healthy foods (P=.03) and fast-food restaurants (P=.01) with positive sentiment. In addition, we found that including food ingestion language derived from tweets in classification models that predict food desert status improves model performance compared with baseline models that only include socioeconomic characteristics. CONCLUSIONS: Social media data have been increasingly used to answer questions related to health and well-being. Using Twitter data, we found that food-related tweets can be used to develop models for predicting census tract food desert status with high accuracy and improve over baseline models. Food ingestion language found in tweets, such as census tract-level measures of food sentiment and healthiness, are associated with census tract-level food desert status.

Census Tract Demographics Associated with Libraries' Social, Economic, and Health-Related Programming.

Background: Public libraries can contribute to reducing economic, social, and health inequities through their programming and practices. However, the extent to which libraries regularly provide programming that improve the social determinants of health (SDH) in underserved communities is unclear. Objective: This study explored the relationship between census tract demographic characteristics and library programming implicated in the SDH for underserved groups at risk for health disparities. Method: A stratified random sample of libraries (n = 235) who completed the 2017 Public Libraries Survey were recruited. Librarians completed surveys about their libraries' economic, social, and health-related programming. Libraries' census tract demographic characteristics were taken from the 2013-2017 American Community Survey. Linear regressions were estimated to determine the relationship between relevant census tract demographic characteristics and programming offered at libraries in the census tracts. Results: Higher proportions of racial and ethnic minorities were associated with more frequent economic and social programs, but results were mixed for health-related programs. Lower proportions of populations with no more than a high school diploma or GED were related to more frequent economic, social, and health-related programs. Conclusions: The inequitable distribution of SDH-related library programming highlights gaps in libraries' responsiveness to community needs. Libraries' programming likely perpetuate systemic inequities.

Telemedicine catches on: changes in the utilization of telemedicine services during the COVID-19 pandemic.

OBJECTIVES: To determine the degree of telemedicine expansion overall and across patient subpopulations and diagnoses. We hypothesized that telemedicine visits would increase substantially due to the need for continuity of care despite the disruptive effects of COVID-19. STUDY DESIGN: A retrospective study of health insurance claims for telemedicine visits from January 1, 2018, through March 10, 2020 (prepandemic period), and March 11, 2020, through October 31, 2020 (pandemic period). METHODS: We analyzed claims from 1,589,777 telemedicine visits that were submitted to Independence Blue Cross (Independence) from telemedicine-only providers and providers who traditionally deliver care in person. The primary exposure was the combination of individual behavior changes, state stay-at-home orders, and the Independence expansion of billing policies for telemedicine. The comparison population consisted of telemedicine visits in the prepandemic period. RESULTS: Telemedicine increased rapidly from a mean (SD) of 773 (155) weekly visits in prepandemic 2020 to 45,632 (19,937) weekly visits in the pandemic period. During the pandemic period, a greater proportion of telemedicine users were older, had Medicare Advantage insurance plans, had existing chronic conditions, or resided in predominantly non-Hispanic Black or African American Census tracts compared with during the prepandemic period. A significant increase in telemedicine claims containing a mental health-related diagnosis was observed. CONCLUSIONS: Telemedicine expanded rapidly during the COVID-19 pandemic across a broad range of clinical conditions and demographics. Although levels declined later in 2020, telemedicine utilization remained markedly higher than 2019 and 2018 levels. Trends suggest that telemedicine will likely play a key role in postpandemic care delivery.

Census-Tract-Level Median Household Income and Median Family Income Estimates: A Unidimensional Measure of Neighborhood Socioeconomic Status?

Previous studies suggested either census-tract-level median household income (MHI) or median family income (MFI) estimates may be used as a unidimensional measure of neighborhood socioeconomic status (SES) in the United States (US). To better understand its general use, the purpose of this study was to assess the usefulness of MHI and MFI in a wide range of geographic areas. Area-based socioeconomic data at the census tract level were obtained from the 2000 Census as well as the 2005-2009, 2010-2014, and 2015-2019 American Community Survey. MHI and MFI were used as two simple measures of neighborhood SES. Based on the five area-based indexes developed in the US, several census-tract-level socioeconomic indicators were used to derive five composite measures of neighborhood SES. Then, a series of correlation analyses was conducted to assess the relationships between these seven measures in the State of California and its seven Metropolitan Statistical Areas. Two simple measures were very strongly and positively correlated with one another, and were also strongly or very strongly correlated, either positively or negatively, with five composite measures. Hence, the results of this study support an analytical thinking that simple measures and composite measures may capture the same dimension of neighborhood SES in different geographic areas.

When more is less: Urban disparities in access to surgical care by transportation means.

BACKGROUND: Structural factors limiting access to surgical care require elucidation. We hypothesize transportation time to hospitals with surgical capacity disproportionately burdens minority populations. METHODS: We identified hospitals with surgical capacity within a 20-mile radius of our city center. Using geocoding, we estimated travel times from each census tract to the nearest facility by car or public bus. RESULTS: For 143 tracts within the county, drive time was 13 ± 4 min and bus time was 33 ± 15 min. Only 41.2% of the population had a facility within 30 min by bus; access was further diminished for those with minority race/ethnicity and/or no insurance. Bus time was associated with percent minority population in a census tract: for each 10% increase in minority population there was a 4.3-min increase in bus time (p < 0.001) when controlling for socioeconomic status and other characteristics. CONCLUSIONS: Geographic information systems analysis has potential to identify communities with disproportionate burden to access surgical services.

Social disparities in neighborhood heat in the Northeast United States.

Upward trends in ground-level warming are expected to intensify, affecting the health of human populations. Specific to the United States, the Northeast (NE) region is one of the most vulnerable to these warming trends. Previous research has found social disparities in the distribution of heat, while recent studies have examined associations between metropolitan racial/ethnic segregation and heat exposures. We advance upon previous research by including a novel measure of neighborhood-level racial/ethnic diversity in our examination of social inequalities in heat for NE neighborhoods (census tracts). We paired data derived from the United States Geological Survey on mean land surface temperature (LST) for the summer months of 2013-2017 with sociodemographic data from the American Community Survey (5-year estimates, 2013-2017). We use multivariable generalized estimating equations (GEEs) that adjust for geographic clustering. Findings reveal heat exposure disparities across NE neighborhoods. Neighborhoods with higher proportions of racial/ethnic minorities, people of lower socioeconomic status, households without access to an automobile, and greater diversity experience higher temperatures. Diversity was more strongly related to increased heat in neighborhoods with lower Latinx and lower Black composition suggesting that neighborhood homogeneity confers a differentially greater cooling effect based on higher White composition. The social groups that carry the unequal thermal burdens are also those who are most vulnerable. Interventions to reduce heat risks in the NE should therefore prioritize reducing the burden on historically disadvantaged communities.

Deindustrialisation, demographic decline, aging, economic crisis and social involution in a metropolitan area analysed by applying Socio-Economic and Health Deprivation Indices.

AIMS: Genoa is a city hit by a strong economic, demographic and social involution. The changes in the demographic and socio-economic (SE) situation were analysed and the capacity of two Socio-Economic and Health Deprivation Indices (SEHDI) in describing the evolutions of the recent period were verified. MATERIAL AND METHODS: The data about the evolution of demographic and SE situation in Genoa came from publications of Statistics Offices of Genoa Municipality and Liguria Region and from published analyses of Bank of Italy. The two SEHDIs, referring to 2001 and 2011 population, were computed at census tract level by linear regression, factor and clusters analyses and had been already validated and published. RESULTS: Wide transformations in aging and population composition by age groups and gender occurred in Genoa between 1951 and 2016. Internal (from other Italian regions) and external (from other countries) migrations concurred to change the profile of Genoese population. These changes followed the industrial history of city and its deindustrialization occurred since 2001. A progressive SE involution, worsened by the Italian and international crises, carried out the recent impoverishment of the city. Between 2001 and 2011 the population at medium-high deprivation increased and the SEHDIs 2001 and 2011 contributed to describe the population distribution by deprivation groups, either geographically, and by groups of citizenships (Italians and Foreigners). The first identified in 2001 some aspects of a well-off society regarding education, labour market and characteristics of the family and housing structure. The second depicted in 2011 an impoverished society in aging, lack of family support and of property of the main house, diminishing of educational level. DISCUSSION: Genoa city demonstrated an its own specific decline. Starting from the deindustrialization, a worsening of welfare, independently from the national and international economic troubles, was evident. The aging and the changed equilibria among age groups testified the growing difficulties of society in keeping up with the deep social and economic changes. The results demonstrated that specific deprivation indices aid to better define the populations under analysis, because they identify the subpopulations that could have the maximum benefit from investments of resources targeted to the correction of inequalities.

Application of Socio-Economic and Health Deprivation Indices to study the relationships between socio-economic status and disease onset and outcome in a metropolitan area subjected to aging, demographic fall and socio-economic crisis.

AIMS: Genoa is a city affected by a deep economic, demographic and social involution. The association between disease onset and outcome and socioeconomic status (SES) was assessed in the mortality by cause in two periods, using indices referred to the distribution of deprivation in the population defined in a ten-years span (2001 to 2011). MATERIAL AND METHODS: Two Socio-Economic and Health Deprivation Indices (SEHDIs), computed at census tract level (2001 and 2011 Censuses), were applied to analyse the SMRs by cause, age (0-64 and 65+ years) and gender of the five normalised groups of deprivation individuated in the two population distribution. The associations between SES and onset of disease was described in the mortality 2008-11 using the index referred to 2001 population. The second index, referred to 2011 population, described the associations between SES and disease outcomes in the mortality 2009-13. Two ANOVAs evaluated the statistical significance (p < 0.05) of differences in death distribution among groups. RESULTS: The population at medium-high deprivation increased in Genoa between 2001 and 2011. The mortality by age and gender showed different trends. Not significant trends (NS) in both periods regarded only the younger (respiratory diseases in both sexes, prostate cancer, diabetes in women). Linearly positives (L↑) trends in both periods were observed only in men (all cancers and lung cancers, overall mortality and cardiovascular diseases in younger, diabetes in older). Not linear trends (NL) in both periods interested both sexes for flu and pneumonia, women for lung cancer, old women for overall mortality and respiratory diseases, old men for colorectal cancers. Instead, L↑ trends in the final phases of disease interest all cancers in the elderly (NS trend at the disease onset), all cancers and breast cancer in young women, diabetes and colorectal cancers in young men (NL trends at the disease onset). On the contrary, L↑ trends at the disease onset and NL trends in the final phases regarded cardiovascular diseases in elderly, overall mortality, respiratory diseases and prostate cancer in old men, diabetes and colorectal cancers in old women. Finally, NL trends at the disease onset regarded colorectal cancers in young women (NS trend in the final phases) and breast cancer in the older (linearly negative trend, L↓, in the final phases). DISCUSSION: Deprivation trends confirmed the literature about populations shifting towards poverty. Aging-linked social risks were revealed, reflecting the weakening of social-health care, which worsened in elderly if alone. Serious problems in younger singles or in the single-parent families arose. Cardiovascular diseases, all cancers and colorectal cancers trends confirmed the advantage of less deprived when diseases are preventable and curable. Prostate and breast cancers trends reflected the rising incidence and increasing problems in care. The need of corrective interventions in social and health policies was emerging, aimed to support in a targeted way a population in an alarming condition of socio-economic deterioration.

Association of Residence in High-Police Contact Neighborhoods With Preterm Birth Among Black and White Individuals in Minneapolis.

Importance: Police contact may have negative psychological effects on pregnant people, and psychological stress has been linked to preterm birth (ie, birth at <37 weeks' gestation). Existing knowledge of racial disparities in policing patterns and their associations with health suggest redesigning public safety policies could contribute to racial health equity. Objective: To examine the association between community-level police contact and the risk of preterm birth among White pregnant people, US-born Black pregnant people, and Black pregnant people who were born outside the US. Design, Setting, and Participants: This cross-sectional study used medical record data of 745 White individuals, 121 US-born Black individuals, and 193 Black individuals born outside the US who were Minneapolis residents and gave birth to a live singleton at a large health system between January 1 and December 31, 2016. Data were analyzed from March 2019 to October 2020. Exposures: Police contact was measured at the level of the census tract where the pregnant people lived. Police incidents per capita (ie, the number of police incidents divided by the census tract population estimate) were dichotomized into high if the value was in the fourth quartile and low for the remaining three quartiles. Main Outcomes and Measures: Preterm birth status was based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision, Clinical Modification (ICD-10-CM) code. Preterm infants were those with ICD-10-CM codes P07.2 and P07.3 documented in their charts. Results: Of 1059 pregnant people (745 [70.3%] White, 121 [11.4%] US-born Black, 193 [18.2%] Black born outside the US) in the sample, 336 White individuals (45.1%) and 62 Black individuals who were born outside the US (32.1%) gave birth between the ages of 30 and 34 years, while US-born Black individuals gave birth at younger ages, with 49 (40.5%) aged 25 years or younger. The incidence of preterm birth was 6.7% for White individuals (50 pregnant people), 14.0% for US-born Black individuals (17 pregnant people), and 5.7% for Black individuals born outside the US (11 pregnant people). In areas with high police contact vs low police contact, the odds of preterm birth were 90% higher for White individuals (odds ratio [OR], 1.9; 95% CI, 1.9-2.0), 100% higher for US-born Black individuals (OR, 2.0; 95% CI, 1.8-2.2), and 10% higher for Black individuals born outside the US (OR, 1.1; 95% CI, 1.0-1.2). Secondary geospatial analysis further revealed that the proportion of Black residents in Minneapolis census tracts was correlated with the number of police incidents reported between 2012 and 2016 (P = .001). Conclusions and Relevance: In this study, police contact was associated with preterm birth for both Black and White pregnant people. Predominantly Black neighborhoods had greater police contact than predominantly White neighborhoods, indicating that Black pregnant people were more likely to be exposed to police than White pregnant people. These findings suggest that racialized police patterns borne from a history of racism in the United States may contribute to racial disparity in preterm birth.

How Phantom Networks, Provider Qualities, and Poverty Sway Medicaid Dental Care Access: A Geospatial Analysis of Manhattan.

Access to general dental care is essential for preventing and treating oral diseases. To ensure adequate spatial accessibility for the most vulnerable populations, New York State mandates a ratio of one general dentist to 2000 Medicaid recipients within 30 min of public transportation. This study employed geospatial methods to determine whether the requirement is met in Manhattan by verifying the online directories of ten New York managed care organizations (MCOs), which collectively presented 868 available dentists from 259 facilities. Our survey of 118 dental facilities representing 509 dentists revealed that significantly fewer dentists are available to treat Medicaid recipients compared to MCO directories. The average dentist-to-patient ratio derived from the MCO listings by the Two-Step Floating Catchment Area (2SFCA) method was 1:315, while the average verified ratio was only 1:1927. "Phantom networks", or inaccurate provider listings, substantially overstated Medicaid dental accessibility. Surprisingly, our study also discovered additional Medicaid providers unlisted in any MCO directory, which we coined "hidden networks". However, their inclusion was inconsequential to the overall dental supply. We further scrutinized dental care access by uniquely applying six "patient-centered characteristics", and these criteria vastly reduced accessibility to an average ratio of merely 1:4587. Our novel evaluation of the spatial association between poverty, dental care access, and phantom networks suggests that Medicaid dental providers wish to be located in wealthier census tracts that are in proximity to impoverished areas for maximum profitability. Additionally, we discovered that poverty and phantom networks were positively correlated, and phantom providers masked a lack of dental care access for Medicaid recipients.

The spatiotemporal distribution of pre-exposure prophylaxis accessibility in the United States, 2016-2020.

PURPOSE: Residing in areas with little spatial accessibility to HIV pre-exposure prophylaxis (PrEP) providers, or PrEP deserts, contributes to low PrEP uptake. This study examines and characterizes the spatial distribution of PrEP accessibility in the United States over time. METHODS: We conducted spatial network analyses and geographic mapping to explore the spatiotemporal distribution of persistent PrEP deserts (census tracts with suboptimal accessibility in 2016 and 2020), new PrEP deserts (tracts with suboptimal accessibility in 2020 but not 2016), new PrEP oases (tracts with suboptimal accessibility in 2016 but not 2020), and persistent PrEP oases (tracts with optimal accessibility in 2016 and 2020). We used polytomous logistic regression to determine area-level factors associated with these four spatiotemporal PrEP accessibility types. RESULTS: There was a reduction of 52.8% in the prevalence of 30-minute PrEP deserts from 2016 (28,055 tracts) to 2020 (13,240 tracts) and an increase of 33.5% in 30-minute PrEP oases from 2016 (44,259 tracts) to 2020 (59,074 tracts). Of all tracts, 12,487 (17.3%) were persistent PrEP deserts, 753 (1.0%) were new PrEP deserts, 15,568 (21.5%) were new PrEP oases, and 43,506 (60.1%) were persistent PrEP oases. Overall, persistent PrEP oases were more likely to be of higher socioeconomic status, racially/ethnically diverse, located in urban areas, and located in the Northeast compared with other spatiotemporal PrEP accessibility types, with variation by urbanicity and U.S. Census region. CONCLUSIONS: Efforts to improve PrEP accessibility should be especially focused in disadvantaged communities in nonurban areas and the South, Midwest, and West. Monitoring changes in the spatial accessibility of PrEP over time and determining the factors associated with such changes can help to evaluate progress made towards improving PrEP accessibility.

Out-of-Pocket Spending for Asthma-Related Care Among Commercially Insured Patients, 2004-2016.

BACKGROUND: Out-of-pocket (OOP) health care costs can cause financial burden and deferred care for many Americans. Little is known about OOP spending for asthma-related care among the commercially insured. OBJECTIVES: To analyze OOP spending for asthma-related care overall, across types of care, and by income. METHODS: Using enrollment, claims, and geocoded census tract data on income from a large US commercial health plan from 2004 to 2016, we measured inflation-adjusted OOP spending for individuals with asthma ages 4 to 64 years (n = 1,986,769). We estimated annual asthma-related OOP spending over time, and average total, asthma-related, asthma type of care, and asthma medication spending by income. We measured trends in median OOP cost per medication. Linear regression models were adjusted for patient covariates and deductible level. RESULTS: Asthma-related OOP spending decreased over time both for patients enrolled in high-deductible health plans and for those in traditional plans. High-deductible plan enrollment increased from 7% to 54%. Compared with patients living in high-income areas, patients in the lowest-income areas had similar annual total and asthma-related OOP spending, but spent 30% less on controller medications and a higher proportion of their asthma-related OOP spending on inpatient and emergency care (10% vs 3%; P < .001). Asthma-related OOP spending represented a higher proportion of household income for patients in lower-income areas. CONCLUSIONS: Patients with asthma living in the lowest-income areas have greater cost burden, lower spending on controller medications, and greater spending on high-acuity care than higher-income counterparts.

Income-driven socioeconomic status and presenting illness severity in children with acute respiratory failure.

Children living in low socioeconomic communities are vulnerable to poor health outcomes, especially when critically ill. The purpose of this study was to investigate the association between socioeconomic status (SES) and illness severity upon pediatric intensive care unit (PICU) admission in children with acute respiratory failure. This secondary analysis of the multicenter Randomized Evaluation of Sedation Titration for Respiratory Failure clinical trial includes children, 2 weeks to 17 years old, mechanically ventilated for acute respiratory failure; specifically, subjects who had parental consent for follow-up and residential addresses that could be matched with census tracts (n = 2006). Subjects were categorized into quartiles based on income, with a median income of $54,036 for the census tracts represented in the sample. Subjects in the highest income quartile were more likely to be older, non-Hispanic White, and hospitalized for pneumonia. Subjects in the lowest income quartile were more likely to be Black, younger, and hospitalized for asthma or bronchiolitis, to have age-appropriate baseline functional status, and history of prematurity and asthma. After controlling for age group, gender, race, and primary diagnosis, there were no associations between income quartile and either Pediatric Risk of Mortality scores or pediatric acute respiratory distress syndrome. As measured, income-based SES was not associated with illness severity upon PICU admission in this cohort of patients. More robust and reliable methods for measuring SES may help to better explain the mechanisms by which socioeconomic affect critical illness.