Finite mixture models in neighbourhoods-to-health research: A systematic review

A tale of many neighborhoods: Latent profile analysis to derive a national neighborhood typology for the US

INTRODUCTION

Neighborhoods are complex and multi-faceted. Analytic strategies used to model neighborhoods should reflect this complexity, with the potential to better understand how neighborhood characteristics together impact health. We used latent profile analysis (LPA) to derive a residential neighborhood typology applicable for census tracts across the US.

METHODS

From tract-level 2015-2019 American Community Survey (ACS) five-year estimates, we selected five indicators that represent four neighborhood domains: demographic composition, commuting, socioeconomic composition, and built environment. We compared model fit statistics for up to eight profiles to identify the optimal number of latent profiles of the selected neighborhood indicators for the entire US. We then examined differences in national tract-level 2019 prevalence estimates of physical and mental health derived from CDC's PLACES dataset between derived profiles using one-way analysis of variance (ANOVA).

RESULTS

The 6-profile LPA model was the optimal categorization of neighborhood profiles based on model fit statistics and interpretability. Neighborhood types were distinguished most by demographic composition, followed by commuting and built environment domains. Neighborhood profiles were associated with meaningful differences in the prevalence of health outcomes. Specifically, tracts characterized as "Less educated non-immigrant racial and ethnic minority active transiters" (n = 3,132, 4%) had the highest poor health prevalence (Mean poor physical health: 18.6 %, SD: 4.30; Mean poor mental health: 19.6 %, SD: 3.85), whereas tracts characterized as "More educated metro/micropolitans" (n = 15, 250, 21%) had the lowest prevalence of poor mental and physical health (Mean poor physical health: 10.6 %, SD: 2.41; Mean poor mental health: 12.4 %, SD: 2.67; p < 0.001).

CONCLUSION

LPA can be used to derive meaningful and standardized profiles of tracts sensitive to the spatial patterning of social and built conditions, with observed differences in mental and physical health by neighborhood type in the US.

The relationship between neighborhood typologies and self-rated health in Maryland: A latent class analysis

Despite widespread evidence that neighborhood conditions impact health, few studies apply theory to clarify the physical and social factors in communities that drive health outcomes. Latent class analysis (LCA) addresses such gaps by identifying distinct neighborhood typologies and the joint influence that neighborhood-level factors play in health promotion. In the current study, we conducted a theory-driven investigation to describe Maryland neighborhood typologies and examined differences in area-level self-rated poor mental and physical health across typologies. We conducted an LCA of Maryland census tracts (n = 1384) using 21 indicators of physical and social characteristics. We estimated differences in tract-level self-rated physical and mental health across neighborhood typologies using global Wald tests and pairwise comparisons. Five neighborhood classes emerged: Suburban Resourced (n = 410, 29.6%), Rural Resourced (n = 313, 22.6%), Urban Underserved (n = 283, 20.4%), Urban Transient (n = 226, 16.3%), Rural Health Shortage (n = 152, 11.0%). Prevalence of self-rated poor physical and mental health varied significantly (p < 0.0001) by neighborhood typology, with the Suburban Resourced neighborhood class demonstrating the lowest prevalence of poor health and the Urban Underserved neighborhoods demonstrating the poorest health. Our results highlight the complexity of defining "healthy" neighborhoods and areas of focus to mitigate community-level health disparities to achieve health equity.

Longitudinal profiles of neighborhood socioeconomic vulnerability influence blood pressure changes across the female midlife period

PURPOSE

To examine whether longitudinal exposure to neighborhood socioeconomic vulnerability influences blood pressure changes throughout midlife in a racially, ethnically, and geographically-diverse cohort of women transitioning through menopause.

METHODS

We used longitudinal data on 2738 women (age 42-52 at baseline) living in six United States cities from The Study of Women's Health Across the Nation. Residential histories, systolic blood pressures (SBP), and diastolic blood pressures (DBP) were collected annually for ten years. We used longitudinal latent profile analysis to identify patterns of neighborhood socioeconomic vulnerability occurring from 1996 to 2007 in participant neighborhoods. We used linear mixed-effect models to determine if a woman's neighborhood profile throughout midlife was associated with blood pressure changes.

RESULTS

We identified four unique profiles of neighborhood socioeconomic vulnerability - differentiated by residential socioeconomic status, population density, and vacant housing conditions - which remained stable across time. Women residing in the most socioeconomically vulnerable neighborhoods experienced the steepest increase in annual SBP growth by 0.93 mmHg/year (95% CI: 0.65-1.21) across ten-year follow-up.

CONCLUSIONS

Neighborhood socioeconomic vulnerability was significantly associated with accelerated SBP increases throughout midlife among women.

Associations of four indexes of social determinants of health and two community typologies with new onset type 2 diabetes across a diverse geography in Pennsylvania

Evaluation of geographic disparities in type 2 diabetes (T2D) onset requires multidimensional approaches at a relevant spatial scale to characterize community types and features that could influence this health outcome. Using Geisinger electronic health records (2008–2016), we conducted a nested case-control study of new onset T2D in a 37-county area of Pennsylvania. The study included 15,888 incident T2D cases and 79,435 controls without diabetes, frequency-matched 1:5 on age, sex, and year of diagnosis or encounter. We characterized patients’ residential census tracts by four dimensions of social determinants of health (SDOH) and into a 7-category SDOH census tract typology previously generated for the entire United States by dimension reduction techniques. Finally, because the SDOH census tract typology classified 83% of the study region’s census tracts into two heterogeneous categories, termed rural affordable-like and suburban affluent-like, to further delineate geographies relevant to T2D, we subdivided these two typology categories by administrative community types (U.S. Census Bureau minor civil divisions of township, borough, city). We used generalized estimating equations to examine associations of 1) four SDOH indexes, 2) SDOH census tract typology, and 3) modified typology, with odds of new onset T2D, controlling for individual-level confounding variables. Two SDOH dimensions, higher socioeconomic advantage and higher mobility (tracts with fewer seniors and disabled adults) were independently associated with lower odds of T2D. Compared to rural affordable-like as the reference group, residence in tracts categorized as extreme poverty (odds ratio [95% confidence interval] = 1.11 [1.02, 1.21]) or multilingual working (1.07 [1.03, 1.23]) were associated with higher odds of new onset T2D. Suburban affluent-like was associated with lower odds of T2D (0.92 [0.87, 0.97]). With the modified typology, the strongest association (1.37 [1.15, 1.63]) was observed in cities in the suburban affluent-like category (vs. rural affordable-like–township), followed by cities in the rural affordable-like category (1.20 [1.05, 1.36]). We conclude that in evaluating geographic disparities in T2D onset, it is beneficial to conduct simultaneous evaluation of SDOH in multiple dimensions. Associations with the modified typology showed the importance of incorporating governmentally, behaviorally, and experientially relevant community definitions when evaluating geographic health disparities.

Pathways linking census tract typologies with subjective neighborhood disorder and depressive symptoms in the Black Women's Experiences Living with Lupus (BeWELL) Study

Depression is a common comorbidity among Black women with systemic lupus erythematosus (SLE), an understudied autoimmune disease characterized by major racial and gender inequities. Research is needed that examines how area-level factors influence risk of depression in this population. Latent profile analysis revealed four neighborhood typologies among metropolitan Atlanta, Georgia census tracts that participants (n=438) in the Black Women's Experiences Living with Lupus (BeWELL) Study were living in: Integrated/High-SES, Moderately Segregated/Mid-SES, Highly Segregated/Mid-SES, and Highly Segregated/Low-SES. Structural equation models indicated that highly segregated census tracts were associated with the greatest levels of depression via increased subjective assessments of neighborhood disorder. Policies that invest in segregated areas and address physical and social aspects of the environment that contribute to neighborhood disorder may promote mental health among Black women with SLE.

The Diabetes Location, Environmental Attributes, and Disparities Network: Protocol for Nested Case Control and Cohort Studies, Rationale, and Baseline Characteristics

BACKGROUND

Diabetes prevalence and incidence vary by neighborhood socioeconomic environment (NSEE) and geographic region in the United States. Identifying modifiable community factors driving type 2 diabetes disparities is essential to inform policy interventions that reduce the risk of type 2 diabetes.

OBJECTIVE

This paper aims to describe the Diabetes Location, Environmental Attributes, and Disparities (LEAD) Network, a group funded by the Centers for Disease Control and Prevention to apply harmonized epidemiologic approaches across unique and geographically expansive data to identify community factors that contribute to type 2 diabetes risk.

METHODS

The Diabetes LEAD Network is a collaboration of 3 study sites and a data coordinating center (Drexel University). The Geisinger and Johns Hopkins University study population includes 578,485 individuals receiving primary care at Geisinger, a health system serving a population representative of 37 counties in Pennsylvania. The New York University School of Medicine study population is a baseline cohort of 6,082,146 veterans who do not have diabetes and are receiving primary care through Veterans Affairs from every US county. The University of Alabama at Birmingham study population includes 11,199 participants who did not have diabetes at baseline from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study, a cohort study with oversampling of participants from the Stroke Belt region.

RESULTS

The Network has established a shared set of aims: evaluate mediation of the association of the NSEE with type 2 diabetes onset, evaluate effect modification of the association of NSEE with type 2 diabetes onset, assess the differential item functioning of community measures by geographic region and community type, and evaluate the impact of the spatial scale used to measure community factors. The Network has developed standardized approaches for measurement.

CONCLUSIONS

The Network will provide insight into the community factors driving geographical disparities in type 2 diabetes risk and disseminate findings to stakeholders, providing guidance on policies to ameliorate geographic disparities in type 2 diabetes in the United States.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/21377.