Disparities in Unmet Health Care Needs Among US Children During the COVID-19 Pandemic.

PURPOSE: The COVID-19 pandemic disrupted pediatric health care in the United States, and this disruption layered on existing barriers to health care. We sought to characterize disparities in unmet pediatric health care needs during this period. METHODS: We analyzed data from Wave 1 (October through November 2020) and Wave 2 (March through May 2021) of the COVID Experiences Survey, a national longitudinal survey delivered online or via telephone to parents of children aged 5 through 12 years using a probability-based sample representative of the US household population. We examined 3 indicators of unmet pediatric health care needs as outcomes: forgone care and forgone well-child visits during fall 2020 through spring 2021, and no well-child visit in the past year as of spring 2021. Multivariate models examined relationships of child-, parent-, household-, and county-level characteristics with these indicators, adjusting for child's age, sex, and race/ethnicity. RESULTS: On the basis of parent report, 16.3% of children aged 5 through 12 years had forgone care, 10.9% had forgone well-child visits, and 30.1% had no well-child visit in the past year. Adjusted analyses identified disparities in indicators of pediatric health care access by characteristics at the level of the child (eg, race/ethnicity, existing health conditions, mode of school instruction), parent (eg, childcare challenges), household (eg, income), and county (eg, urban-rural classification, availability of primary care physicians). Both child and parent experiences of racism were also associated with specific indicators of unmet health care needs. CONCLUSIONS: Our findings highlight the need for continued research examining unmet health care needs and for continued efforts to optimize the clinical experience to be culturally inclusive.

How Socio-economic Inequalities Cluster People with Diabetes in Malaysia: Geographic Evaluation of Area Disparities Using a Non-parameterized Unsupervised Learning Method.

Accurate assessments of epidemiological associations between health outcomes and routinely observed proximal and distal determinants of health are fundamental for the execution of effective public health interventions and policies. Methods to couple big public health data with modern statistical techniques offer greater granularity for describing and understanding data quality, disease distributions, and potential predictive connections between population-level indicators with areal-based health outcomes. This study applied clustering techniques to explore patterns of diabetes burden correlated with local socio-economic inequalities in Malaysia, with a goal of better understanding the factors influencing the collation of these clusters. Through multi-modal secondary data sources, district-wise diabetes crude rates from 271,553 individuals with diabetes sampled from 914 primary care clinics throughout Malaysia were computed. Unsupervised machine learning methods using hierarchical clustering to a set of 144 administrative districts was applied. Differences in characteristics of the areas were evaluated using multivariate non-parametric test statistics. Five statistically significant clusters were identified, each reflecting different levels of diabetes burden at the local level, each with contrasting patterns observed under the influence of population-level characteristics. The hierarchical clustering analysis that grouped local diabetes areas with varying socio-economic, demographic, and geographic characteristics offer opportunities to local public health to implement targeted interventions in an attempt to control the local diabetes burden.

Statistical implications of endogeneity induced by residential segregation in small-area modelling of health inequities.

Health inequities are assessed by health departments to identify social groups disproportionately burdened by disease and by academic researchers to understand how social, economic, and environmental inequities manifest as health inequities. To characterize inequities, group-specific small-area health data are often modeled using log-linear generalized linear models (GLM) or generalized linear mixed models (GLMM) with a random intercept. These approaches estimate the same marginal rate ratio comparing disease rates across groups under standard assumptions. Here we explore how residential segregation combined with social group differences in disease risk can lead to contradictory findings from the GLM and GLMM. We show that this occurs because small-area disease rate data collected under these conditions induce endogeneity in the GLMM due to correlation between the model's offset and random effect. This results in GLMM estimates that represent conditional rather than marginal associations. We refer to endogeneity arising from the offset, which to our knowledge has not been noted previously, as "offset endogeneity". We illustrate this phenomenon in simulated data and real premature mortality data, and we propose alternative modeling approaches to address it. We also introduce to a statistical audience the social epidemiologic terminology for framing health inequities, which enables responsible interpretation of results.

Telehealth Services for Substance Use Disorders During the COVID-19 Pandemic: Longitudinal Assessment of Intensive Outpatient Programming and Data Collection Practices.

BACKGROUND: The onset of the COVID-19 pandemic necessitated the rapid transition of many types of substance use disorder (SUD) treatments to telehealth formats, despite limited information about what makes treatment effective in this novel format. OBJECTIVE: This study aims to examine the feasibility and effectiveness of virtual intensive outpatient programming (IOP) treatment for SUD in the context of a global pandemic, while considering the unique challenges posed to data collection during an unprecedented public health crisis. METHODS: The study is based on a longitudinal study with a baseline sample of 3642 patients who enrolled in intensive outpatient addiction treatment (in-person, hybrid, or virtual care) from January 2020 to March 2021 at a large substance use treatment center in the United States. The analytical sample consisted of patients who completed the 3-month postdischarge outcome survey as part of routine outcome monitoring (n=1060, 29.1% response rate). RESULTS: No significant differences were detected by delivery format in continuous abstinence (χ22=0.4, P=.81), overall quality of life (F2,826=2.06, P=.13), financial well-being (F2,767=2.30, P=.10), psychological well-being (F2,918=0.72, P=.49), and confidence in one's ability to stay sober (F2,941=0.21, P=.81). Individuals in hybrid programming were more likely to report a higher level of general health than those in virtual IOP (F2,917=4.19, P=.01). CONCLUSIONS: Virtual outpatient care for the treatment of SUD is a feasible alternative to in-person-only programming, leading to similar self-reported outcomes at 3 months postdischarge. Given the many obstacles presented throughout data collection during a pandemic, further research is needed to better understand under what conditions telehealth is an acceptable alternative to in-person care.

Risk Factors for Septicemia Deaths and Disparities in a Longitudinal US Cohort.

BACKGROUND: There are few longitudinal data on the risk factors and mediators of racial disparities in sepsis among community- dwelling US adults. METHODS: This is a longitudinal study of adult participants in the 1999-2005 National Health Interview Survey with data linked to the 1999-2011 National Death Index. We utilized National Vital Statistics System's ICD-10 schema to define septicemia deaths (A40-A41), utilizing influenza and pneumonia deaths (J09-J11) and other causes of death as descriptive comparators. All statistics utilized survey design variables to approximate the US adult population. RESULTS: Of 206 691 adult survey participants, 1523 experienced a septicemia death. Factors associated with a >2-fold larger hazard of septicemia death included need for help with activities of daily living; self-reported "poor" and "fair" general health; lower education; lower poverty index ratio; self-reported emphysema, liver condition, stroke, and weak or failing kidneys; numerous measures of disability; general health worse than the year prior; >1 pack per day cigarette use; and higher utilization of health care. Blacks had age- and sex-adjusted hazards that were higher for septicemia deaths (hazard ratio [HR], 1.92; 95% confidence interval [CI], 1.65-2.23) than for other causes of death (HR, 1.32; 95% CI, 1.25-1.38). The strongest mediators of the septicemia disparity included self-reported general health condition, family income-poverty ratio, and highest education level achieved. CONCLUSIONS: In this cohort, the major risk factors for septicemia death were similar to those for other causes of death, there was approximately a 2-fold black-white disparity in septicemia deaths, and the strongest mediators of this disparity were across domains of socioeconomic status.

Longitudinal impacts of two causal drivers of alcohol demand on outlet concentrations within community settings: Population size and income effects.

We analyzed counts of licensed bars, restaurants and off-premise alcohol outlets within 53 California cities from 2000-2013. Poisson models were used to assess overall space-time associations between outlet numbers and population size and median household income in local and spatially adjacent block groups. We then separated covariate effects into distinct spatial and temporal components ("decomposed" models). Overall models showed that densities of all outlet types were generally greatest within block groups that had lower income, were adjacent to block groups with lower income, had greater populations, and were adjacent to block groups that had greater populations. Decomposed models demonstrate that over time greater income was associated with increased counts of bars, and greater population was associated with greater numbers of restaurants and off-premise outlets. Acknowledging the many negative consequences for populations living in areas of high outlet density, these effects are a predictable and powerful social determinant of health.

Exploring spatial patterns in the associations between local AIDS incidence and socioeconomic and demographic variables in the state of Rio de Janeiro, Brazil.

Access to antiretroviral therapy (ART), universally provided in Brazil since 1996, resulted in a reduction in overall morbidity and mortality due to AIDS or AIDS-related complications, but in some municipalities of Rio de Janeiro, AIDS incidence remains high. Public health surveillance remains an invaluable tool for understanding current AIDS epidemiologic patterns and local socioeconomic and demographic factors associated with increased incidence. Geographically Weighted Poisson Regression (GWPR) explores spatial varying impacts of these factors across the study area focusing attention on local variations in ecological associations. The set of sociodemographic variables under consideration revealed significant associations with local AIDS incidence and these associations varied geographically across the study area. We find the effects of predictors on AIDS incidence are not constant across the state, contrary to assumptions in the global models. We observe and quantify different local factors driving AIDS incidence in different parts of the state.

Assessment of critical exposure and outcome windows in time-to-event analysis with application to air pollution and preterm birth study.

In reproductive epidemiology, there is a growing interest to examine associations between air pollution exposure during pregnancy and the risk of preterm birth (PTB). One important research objective is to identify critical periods of exposure and estimate the associated effects at different stages of pregnancy. However, population studies have reported inconsistent findings. This may be due to limitations from the standard analytic approach of treating PTB as a binary outcome without considering time-varying exposures together over the course of pregnancy. To address this research gap, we present a Bayesian hierarchical model for conducting a comprehensive examination of gestational air pollution exposure by estimating the joint effects of weekly exposures during different vulnerable periods. Our model also treats PTB as a time-to-event outcome to address the challenge of different exposure lengths among ongoing pregnancies. The proposed model is applied to a dataset of geocoded birth records in the Atlanta metropolitan area between 1999-2005 to examine the risk of PTB associated with gestational exposure to ambient fine particulate matter [Formula: see text]m in aerodynamic diameter (PM[Formula: see text]). We find positive associations between PM[Formula: see text] exposure during early and mid-pregnancy, and evidence that associations are stronger for PTBs occurring around week 30.

Second-order spatial analysis of epidermal nerve fibers.

Breakthroughs in imaging of skin tissue reveal new details on the distribution of nerve fibers in the epidermis. Preliminary neurologic studies indicate qualitative differences in the spatial patterns of nerve fibers based on pathophysiologic conditions in the subjects. Of particular interest is the evolution of spatial patterns observed in the progression of diabetic neuropathy. It appears that the spatial distribution of nerve fibers becomes more 'clustered' as neuropathy advances, suggesting the possibility of diagnostic prediction based on patterns observed in skin biopsies. We consider two approaches to establish statistical inference relating to this observation. First, we view the set of locations where the nerves enter the epidermis from the dermis as a realization of a spatial point process. Secondly, we treat the set of fibers as a realization of a planar fiber process. In both cases, we use estimated second-order properties of the observed data patterns to describe the degree and scale of clustering observed in the microscope images of blister biopsies. We illustrate the methods using confocal microscopy blister images taken from the thigh of one normal (disease-free) individual and two images each taken from the thighs of subjects with mild, moderate, and severe diabetes and report measurable differences in the spatial patterns of nerve entry points/fibers associated with disease status.

Metropolitan isolation segregation and Black-White disparities in very preterm birth: a test of mediating pathways and variance explained.

Residential isolation segregation (a measure of residential inter-racial exposure) has been associated with rates of preterm birth (<37 weeks gestation) experienced by Black women. Epidemiologic differences between very preterm (<32 weeks gestation) and moderately preterm births (32-36 weeks) raise questions about whether this association is similar across gestational ages, and through what pathways it might be mediated. Hierarchical Bayesian models were fit to answer three questions: is the isolation-prematurity association similar for very and moderately preterm birth; is this association mediated by maternal chronic disease, socioeconomic status, or metropolitan area crime and poverty rates; and how much of the geographic variation in Black-White very preterm birth disparities is explained by isolation segregation? Singleton births to Black and White women in 231 U.S. metropolitan statistical areas in 2000-2002 were analyzed and isolation segregation was calculated for each. We found that among Black women, isolation is associated with very preterm birth and moderately preterm birth. The association may be partially mediated by individual level socioeconomic characteristics and metropolitan level violent crime rates. There is no association between segregation and prematurity among White women. Isolation segregation explains 28% of the geographic variation in Black-White very preterm birth disparities. Our findings highlight the importance of isolation segregation for the high-burden outcome of very preterm birth, but unexplained excess risk for prematurity among Black women is substantial.

Do measures matter? Comparing surface-density-derived and census-tract-derived measures of racial residential segregation.

BACKGROUND: Racial residential segregation is hypothesized to affect population health by systematically patterning health-relevant exposures and opportunities according to individuals' race or income. Growing interest into the association between residential segregation and health disparities demands more rigorous appraisal of commonly used measures of segregation. Most current studies rely on census tracts as approximations of the local residential environment when calculating segregation indices of either neighborhoods or metropolitan areas. Because census tracts are arbitrary in size and shape, reliance on this geographic scale limits understanding of place-health associations. More flexible, explicitly spatial derivations of traditional segregation indices have been proposed but have not been compared with tract-derived measures in the context of health disparities studies common to social epidemiology, health demography, or medical geography. We compared segregation measured with tract-derived as well as GIS surface-density-derived indices. Measures were compared by region and population size, and segregation measures were linked to birth record to estimate the difference in association between segregation and very preterm birth. Separate analyses focus on metropolitan segregation and on neighborhood segregation. RESULTS: Across 231 metropolitan areas, tract-derived and surface-density-derived segregation measures are highly correlated. However overall correlation obscures important differences by region and metropolitan size. In general the discrepancy between measure types is greatest for small metropolitan areas, declining with increasing population size. Discrepancies in measures are greatest in the South, and smallest in Western metropolitan areas. Choice of segregation index changed the magnitude of the measured association between segregation and very preterm birth. For example among black women, the risk ratio for very preterm birth in metropolitan areas changed from 2.12 to 1.68 for the effect of high versus low segregation when using surface-density-derived versus tract-derived segregation indices. Variation in effect size was smaller but still present in analyses of neighborhood racial composition and very preterm birth in Atlanta neighborhoods. CONCLUSION: Census tract-derived measures of segregation are highly correlated with recently introduced spatial segregation measures, but the residual differences among measures are not uniform for all areas. Use of surface-density-derived measures provides researchers with tools to further explore the spatial relationships between segregation and health disparities.

Spatial patterns in discordant diagnostic test results for Chagas disease: links to transmission hotspots.

Diagnosis of Chagas disease is hindered by discordance between screening and confirmatory test results for Trypanosoma cruzi infection. In periurban Arequipa, Peru, spatial analysis revealed that individuals with discordant test results are spatially clustered in hotspots of T. cruzi transmission, suggesting that discordant results likely represent true infections in this setting.

Small-area racial disparity in stroke mortality: an application of bayesian spatial hierarchical modeling.

BACKGROUND: In the United States, excess burden of stroke mortality has persisted among African Americans compared with whites despite declines in stroke mortality for both groups. New insights may be gleaned by examining local, small-area patterns in racial disparities in stroke. METHODS: The study population includes all non-Hispanic African Americans and non-Hispanic whites aged 35 to 64 in the southeastern United States during 1999 to 2002. We assessed county-level numbers of stroke deaths and population estimates in a Bayesian spatial hierarchical modeling framework allowing for inclusion of potential covariates (poverty and rurality), and generating county-specific model-based estimates of both absolute and relative racial disparity. The resulting estimates of race-specific stroke death rates, relative racial disparity, and absolute racial disparity were expressed in maps. RESULTS: After adjustment for age, poverty, and rurality, county-level estimates of relative racial disparity ranged from 2.3 to 3.3 and estimates of absolute racial disparity ranged from 19 to 45 excess deaths per 100,000. For both racial groups, stroke death rates were higher in rural areas and with increasing poverty. High relative racial disparity was concentrated primarily in the eastern portion of the region and large absolute racial disparity was concentrated primarily in the western portion. CONCLUSIONS: The results highlight the pervasiveness and magnitude of substantial local racial disparities in stroke mortality in the southeast.

Modeling epilepsy disparities among ethnic groups in Philadelphia, PA.

The Centers for Disease Control and Prevention defined epilepsy as an emerging public health issue in a recent report and emphasized the importance of epilepsy studies in minorities and people of low socioeconomic status. Previous research has suggested that the incidence rate for epilepsy is positively associated with various measures of social and economic disadvantage. In response, we utilize hierarchical Bayesian models to analyze health disparities in epilepsy and seizure risks among multiple ethnicities in the city of Philadelphia, Pennsylvania. The goals of the analysis are to highlight any overall significant disparities in epilepsy risks between the populations of Caucasians, African Americans, and Hispanics in the study area during the years 2002--2004 and to visualize the spatial pattern of epilepsy risks by ethnicity to indicate where certain ethnic populations were most adversely affected by epilepsy within the study area. Results of the Bayesian model indicate that Hispanics have the highest epilepsy risk overall, followed by African Americans, and then Caucasians. There are significant increases in relative risk for both African Americans and Hispanics when compared with Caucasians, as indicated by the posterior mean estimates of 2.09 with a 95 per cent credible interval of (1.67, 2.62) for African Americans and 2.97 with a 95 per cent credible interval of (2.37, 3.71) for Hispanics. Results also demonstrate that using a Bayesian analysis in combination with geographic information system (GIS) technology can reveal spatial patterns in patient data and highlight areas of disparity in epilepsy risk among subgroups of the population.

Relationship of diabetes with cardiovascular disease-related hospitalization rates, length of stay, and charges: analysis by race/ethnicity, age, and sex.

OBJECTIVE: Determine relationship of diabetes with risk of cardiovascular disease hospitalizations and the effect on hospital length of stay and charges. DESIGN: A cross-sectional analysis of Georgia hospital discharge data for 1998 through 2001. PATIENTS: Patients hospitalized principally with one of six cardiovascular disease (CVD) conditions (myocardial infarction, ischemic heart disease, cardiac dysrhythmia, heart failure, cerebrovascular events, peripheral vascular disease) were identified in the hospital discharge data. MAIN OUTCOME MEASURES: Aggregated CVD-related hospitalization rates, length of stay, and charges were compared by presence of diabetes. Analyses were adjusted for age, sex, and race/ethnicity. RESULTS: A total of 3,900,337 discharges were recorded between 1998 to 2001. Of these, 468,957 discharges (12%) had one of the six selected CVD diagnoses (average age 67 years, average length of stay 4.7 days, average total charge $15,702, 48% women, 76% non-Hispanic Whites, 22% non-Hispanic Blacks, and 1% Hispanics). Diabetes was a concurrent diagnosis in 30% of these CVD-related discharges. CVD hospitalization rates were significantly higher and length of stay and total charges were significantly greater among non-Hispanic Whites and Blacks-but not in Hispanics-with diabetes compared to persons without diabetes. Diabetes had a similar effect on CVD hospitalizations among men and women, but the effect of diabetes was lessened with increasing age. CONCLUSION: These data suggests that aggressive outpatient modification of metabolic abnormalities in diabetes patients should be attempted to decrease risk of CVD-related hospitalization and lower the economic impact of these combined conditions.

Disparities in diabetes-related hospitalizations: relationship of age, sex, and race/ethnicity with hospital discharges, lengths of stay, and direct inpatient charges.

OBJECTIVE: To identify any differences in hospitalization rates of diabetes patients by age, sex, or race/ethnicity. DESIGN: A cross-sectional study of Georgia hospital discharge data between 1998 and 2001. PATIENTS/PARTICIPANTS: Patients with a principal discharge diagnosis of diabetes. MAIN OUTCOME MEASURES: Adjusted hospitalization data (discharge rates, length of stay, direct charges) reported as standardized rates per 10,000 person-years, standardized rate differences, and standardized rate ratios, compared by age, sex, and race/ethnicity. RESULTS: Diabetes was the principal diagnosis in 50,301 discharges (average age, 51 years; length of stay, 5.1 days; median total charge, $5893). Persons > or = 60 years old had higher discharge rates, longer stays, and higher charges than persons 18-29 years old. Women had fewer hospitalizations, shorter stays, and lower charges than men. Non-Hispanic Blacks had more than three times as many hospitalizations, markedly longer stays, and higher charges than non-Hispanic Whites. Hispanics with diabetes had lower hospitalization rates, shorter stays, and lower charges than Whites. CONCLUSIONS: Differences by age, sex, and race/ethnicity in hospital discharge rates, lengths of stay, and charges exist for diabetes inpatients. Further study should examine potential causes (severity of disease, comorbidity, and differential access to preventive care) of these disparities.