Comparison of small-area deprivation measures as predictors of chronic disease burden in a low-income population

Racial disparities between measures of area deprivation and financial toxicity, and uterine volume in myomectomy patients

BACKGROUND

At time of myomectomy, a surgical procedure to remove uterine fibroids, Black women tend to have larger uteri than White women. This makes Black patients less likely to undergo a minimally invasive myomectomy which has been shown to have less postoperative pain, less frequent postoperative fever and shorter length of stay compared to abdominal myomectomies. The associations between individual financial toxicity and community area deprivation and uterine volume at the time of myomectomy have not been investigated.

METHODS

We conducted a secondary data analysis of patients with fibroids scheduled for myomectomy using data from a fibroid treatment registry in [location]. We used validated measures of individual-level Financial Toxicity (higher scores = better financial status) and community-level Area Deprivation (ADI, high scores = worse deprivation). To examine associations with log transformed uterine volume, we used linear regression clustered on race (Black vs. White).

RESULTS

Black participants had worse financial toxicity, greater deprivation and larger uterine volumes compared with White participants. A greater Financial Toxicity score (better financial status) was associated with lower uterine volume. For every 10 unit increase in Financial Toxicity, the mean total uterine volume decreased by 9.95% (Confidence Interval [CI]: -9.95%, -3.99%). ADI was also associated with uterine volume. A single unit increase in ADI (worse deprivation) was associated with a 5.13% (CI: 2.02%, 7.25%) increase in mean uterine volume.

CONCLUSION

Disproportionately worse Financial Toxicity and ADI among Black patients is likely due to structural racism - which now must be considered in gynecologic research and practice.

TRIAL REGISTRATION

Not applicable.

Measuring disadvantage: A systematic comparison of United States small-area disadvantage indices

Extensive evidence demonstrates the effects of area-based disadvantage on a variety of life outcomes, such as increased mortality and low economic mobility. Despite these well-established patterns, disadvantage, often measured using composite indices, is inconsistently operationalized across studies. To address this issue, we systematically compared 5 U.S. disadvantage indices at the county-level on their relationships to 24 diverse life outcomes related to mortality, physical health, mental health, subjective well-being, and social capital from heterogeneous data sources. We further examined which domains of disadvantage are most important when creating these indices. Of the five indices examined, the Area Deprivation Index (ADI) and Child Opportunity Index 2.0 (COI) were most related to a diverse set of life outcomes, particularly physical health. Within each index, variables from the domains of education and employment were most important in relationships with life outcomes. Disadvantage indices are being used in real-world policy and resource allocation decisions; an index's generalizability across diverse life outcomes, and the domains of disadvantage which constitute the index, should be considered when guiding such decisions.

Use Of Area-Based Socioeconomic Deprivation Indices: A Scoping Review And Qualitative Analysis

There is considerable interest among researchers, clinicians, and policy makers in understanding the impact of place on health. In this scoping review and qualitative analysis, we sought to assess area-level socioeconomic deprivation indices used in public health and health outcomes research in the US. We conducted a systematic scoping review to identify area-level socioeconomic deprivation indices commonly used in the US since 2015. We then qualitatively compared the indices based on the input-variable domains, data sources, index creation characteristics, index accessibility, the geography over which the index is applied, and the nature of the output measure or measures. We identified fifteen commonly used indices of area-level socioeconomic deprivation. There were notable differences in the characteristics of each index, particularly in how they define socioeconomic deprivation based on input-variable domains, the geography over which they are applied, and their output measures. These characteristics can help guide future index selection and application in clinical care, research, and policy decisions.

Classification of Deprivation Indices That Applied to Detect Health Inequality: A Scoping Review

INTRODUCTION

Many studies around the world are undertaken to establish the association between deprivation and public health indicators. Both separate indicators (e.g., income, education, occupation, public security and social support) and complex models (indices) include several indicators. Deprivation indices are actively used in public health since the mid 1980s. There is currently no clear classification of indices.

METHODS

In the current review, data related to deprivation indices are combined and analyzed in order to create a taxonomy of indices based on the results obtained. The search was carried out using two bibliographic databases. After conducting a full-text review of the articles and searching and adding relevant articles from the bibliography, and articles that were already known to the authors, sixty studies describing the use of sixty deprivation indices in seventeen countries were included in the narrative synthesis, resulting in development of a taxonomy of indices. When creating the taxonomy, an integrative approach was used that allows integrating new classes and sub-classes in the event that new information appears.

RESULTS

In the review, 68% (41/60) of indices were classified as socio-economic, 7% (4/60) of indices as material deprivation, 5% (3/60) of indices as environmental deprivation and 20% (12/60) as multidimensional indices.

CONCLUSIONS

The data stimulates the use of a competent approach, and will help researchers and public health specialist in resolving conflicts or inconsistencies that arise during the construction and use of indices.

Estimating uncertainty in a socioeconomic index derived from the American community survey

Socioeconomic indexes are widely used in public health to facilitate neighborhood-scale analyses. Although they are calculated with high levels of precision, they are rarely reported with accompanying measures of uncertainty (e.g., 90% confidence intervals). Here we use the variance replicate tables that accompany the United States Census Bureau's American Community Survey to report confidence intervals around the Yost Index, a socioeconomic index comprising seven variables that is frequently used in cancer surveillance. The Yost Index is reported as a percentile score from 1 (most affluent) to 100 (most deprived). We find that the average uncertainty for a census tract in the United States is plus or minus 8 percentiles, with the uncertainty a function of the value of the index itself. Scores at the extremes of the distribution are more precise and scores near the center are less precise. Less-affluent tracts have greater uncertainty than corresponding more-affluent tracts. Fewer than 50 census tracts of 72,793 nationally have unusual distributions of socioeconomic conditions that render the index uninformative. We demonstrate that the uncertainty in a census-based socioeconomic index is calculable and can be incorporated into any analysis using such an index.

Gendering Neighbourhood Marginalization Metrics in Mental Health Services Research: A Cross-Sectional Exploration of a Rural and Small Urban Population

Background: Little is known about the extent to which socioenvironmental characteristics may influence mental health outcomes in smaller population centres or differently among women and men. This study used a gender-based analysis approach to explore individual- and neighbourhood-level sex differences in mental health service use in a context of uniquely smaller urban and rural settlements. Methods: This cross-sectional analysis leveraged multiple person-based administrative health datasets linked with geospatial datasets among the population aged 1 and over in the province of New Brunswick, Canada. We used multinomial logistic regression to examine associations between neighbourhood characteristics with risk of service contacts for mood and anxiety disorders in 2015/2016, characterizing the areal measures among all residents (gender neutral) and by males and females separately (gender specific), and controlling for age group. Results: Among the province’s 707,575 eligible residents, 10.7% (females: 14.0%; males: 7.3%) used mental health services in the year of observation. In models adjusted for gender-neutral neighbourhood characteristics, service contacts were significantly more likely among persons residing in the most materially deprived areas compared with the least (OR = 1.09 [95% CI: 1.05–1.12]); when stratified by individuals’ sex, the risk pattern held for females (OR = 1.13 [95% CI: 1.09–1.17]) but not males (OR = 1.00 [95% CI: 0.96–1.05]). Residence in the most female-specific materially deprived neighbourhoods was independently associated with higher risk of mental health service use among individual females (OR = 1.08 [95% CI: 1.02–1.14]) but not among males (OR = 1.02 [95% CI: 0.95–1.10]). Conclusion: These findings emphasize that research needs to better integrate sex and gender in contextual measures aiming to inform community interventions and neighbourhood designs, notably in small urban and rural settings, to reduce socioeconomic inequalities in the burden of mental disorders.

Acute chest syndrome in pediatric sickle cell disease: Associations with racial composition and neighborhood deprivation

BACKGROUND

Acute chest syndrome (ACS) is the leading cause of death for children with sickle cell disease (SCD). Recurrent ACS has detrimental effects on pulmonary health and health care costs. Neighborhood characteristics affect the outcomes of many pediatric chronic diseases, but their role in SCD is not well studied. In this study, we investigated the effects of area-level socioeconomic deprivation and racial composition on the recurrence of ACS.

STUDY DESIGN

We performed a retrospective cross-sectional analysis of clinical data from a large pediatric SCD center. Patients' residential addresses were geocoded and linked to a composite area deprivation index (ADI) and percent African American population at the level of Census block groups. The association of recurrent ACS with neighborhood characteristics was evaluated using logistic regression analysis.

RESULTS

The sample included 709 children with SCD. Residence in a socioeconomically deprived neighborhood was associated with 27% less risk of recurrent ACS, and residence in a predominantly African American neighborhood was associated with 41% less risk of ACS recurrence. The racial composition explained the protective effect of living in a high-deprivation area after adjusting for sociodemographic and clinical covariates. Demographic and clinical factors associated with recurrent ACS included older age, male gender, asthma, hydroxyurea use, and chronic transfusion therapy.

CONCLUSIONS

This is the first study to report a protective effect of residing in a predominantly African American community for ACS recurrence. Further prospective studies are needed to confirm the association and to understand the mechanisms of such relationship.

How does area-level deprivation depress an individual's self-rated health and life satisfaction? Evidence from a nationwide population-based survey in Japan

BACKGROUND

Area-level deprivation is well known to have an adverse impact on mortality, morbidity, or other specific health outcomes. This study examined how area-level deprivation may affect self-rated health (SRH) and life satisfaction (LS), an issue that is largely understudied.

METHODS

We used individual-level data obtained from a nationwide population-based internet survey conducted between 2019 and 2020, as well as municipality-level data obtained from a Japanese government database (N = 12,461 living in 366 municipalities). We developed multilevel regression models to explain an individual's SRH and LS scores using four alternative measures of municipality-level deprivation, controlling for individual-level deprivation and covariates. We also examined how health behavior and interactions with others mediated the impact of area-level deprivation on SRH and LS.

RESULTS

Participants in highly deprived municipalities tended to report poorer SRH and lower LS. For example, when living in municipalities falling in the highest tertile of municipality-level deprivation as measured by the z-scoring method, SRH and LS scores worsened by a standard deviation of 0.05 (p < 0.05) when compared with those living in municipalities falling in the lowest tertile of deprivation. In addition, health behavior mediated between 17.6 and 33.1% of the impact of municipality-level deprivation on SRH and LS, depending on model specifications.

CONCLUSION

Results showed that area-level deprivation modestly decreased an individual's general health conditions and subjective well-being, underscoring the need for public health policies to improve area-level socioeconomic conditions.

Association Between County-Level Change in Economic Prosperity and Change in Cardiovascular Mortality Among Middle-aged US Adults

IMPORTANCE

After a decline in cardiovascular mortality for nonelderly US adults, recent stagnation has occurred alongside rising income inequality. Whether this is associated with underlying economic trends is unclear.

OBJECTIVE

To assess the association between changes in economic prosperity and trends in cardiovascular mortality in middle-aged US adults.

DESIGN, SETTING, AND PARTICIPANTS

Retrospective analysis of the association between change in 7 markers of economic prosperity in 3123 US counties and county-level cardiovascular mortality among 40- to 64-year-old adults (102 660 852 individuals in 2010).

EXPOSURES

Mean rank for change in 7 markers of economic prosperity between 2 time periods (baseline: 2007-2011 and follow-up: 2012-2016). A higher mean rank indicates a greater relative increase or lower relative decrease in prosperity (range, 5 to 92; mean [SD], 50 [14]).

MAIN OUTCOMES AND MEASURES

Mean annual percentage change (APC) in age-adjusted cardiovascular mortality rates. Generalized linear mixed-effects models were used to estimate the additional APC associated with a change in prosperity.

RESULTS

Among 102 660 852 residents aged 40 to 64 years living in these counties in 2010 (51% women), 979 228 cardiovascular deaths occurred between 2010 and 2017. Age-adjusted cardiovascular mortality rates did not change significantly between 2010 and 2017 in counties in the lowest tertile for change in economic prosperity (mean [SD], 114.1 [47.9] to 116.1 [52.7] deaths per 100 000 individuals; APC, 0.2% [95% CI, -0.3% to 0.7%]). Mortality decreased significantly in the intermediate tertile (mean [SD], 104.7 [38.8] to 101.9 [41.5] deaths per 100 000 individuals; APC, -0.4% [95% CI, -0.8% to -0.1%]) and highest tertile for change in prosperity (100.0 [37.9] to 95.1 [39.1] deaths per 100 000 individuals; APC, -0.5% [95% CI, -0.9% to -0.1%]). After accounting for baseline prosperity and demographic and health care-related variables, a 10-point higher mean rank for change in economic prosperity was associated with 0.4% (95% CI, 0.2% to 0.6%) additional decrease in mortality per year.

CONCLUSIONS AND RELEVANCE

In this retrospective study of US county-level mortality data from 2010 to 2017, a relative increase in county-level economic prosperity was significantly associated with a small relative decrease in cardiovascular mortality among middle-aged adults. Individual-level inferences are limited by the ecological nature of the study.

Impact of Individual versus Geographic-Area Measures of Socioeconomic Status on Health Associations Observed in the Behavioral Risk Factor Surveillance System

Efforts to enhance Electronic Health Record (EHR) data for the study of conditions in which social and economic variables play a prominent role include linking clinical data to sources of external information via patient-specific geocodes. This approach is convenient, but whether geographic-area-level information from secondary sources is adequate as a surrogate of individual-level information is not fully understood. We used Behavioral Risk Factor Surveillance System (BRFSS) epidemiologic data to compare associations of individual income, median aggregate income, and Area Deprivation Index (ADI)-a validated score of U.S. socioeconomic deprivation-with various health outcomes. Median income and ADI assigned according to respondent area of residence were significantly associated with various health outcomes, but with substantially lower effect sizes than those of individual income. Our results show the limited ability of median income and ADI at the level of metropolitan/micropolitan statistical areas versus individual income for use as measures of socioeconomic status.

The Association Between Area Deprivation Index and Patient-Reported Outcomes in Patients with Advanced Cancer

Background: This analysis describes associations between area deprivation and patient-reported outcomes among patients with advanced cancer. Methods: This is a cross-sectional analysis of baseline data from a multisite primary palliative care intervention trial. Participants were adult patients with advanced cancer. Patient-level area deprivation scores were calculated using the Area Deprivation Index (ADI). Quality of life and symptom burden were measured. Uni- and multivariate regressions estimated associations between area deprivation and outcomes of interest. Results: Among 672 patients, ∼0.5 (54%) were women and most (94%) were Caucasian. Mean age was 69.3±10.2 years. Lung (36%), breast (13%), and colon (10%) were the most common malignancies. Mean ADI was 64.0, scale of 1 (low)-100 (high). In unadjusted univariate analysis, Functional Assessment of Cancer Therapy-Palliative (p=0.002), Edmonton Symptom Assessment Scale (p=0.025) and the Hospital Anxiety and Depression Scale anxiety (p=0.003) and depression (p=0.029) scores were significantly associated with residence in more deprived areas (p=0.003). In multivariate analysis, controlling for patient-level factors, living in more deprived areas was associated with more anxiety (p=0.019). Conclusion: Higher ADI was associated with higher levels of anxiety among patients with advanced cancer. Geographic information could assist clinicians with providing geographically influenced social support strategies.

Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments

BACKGROUND

Risk adjustment models are employed to prevent adverse selection, anticipate budgetary reserve needs, and offer care management services to high-risk individuals. We aimed to address two unknowns about risk adjustment: whether machine learning (ML) and inclusion of social determinants of health (SDH) indicators improve prospective risk adjustment for health plan payments.

METHODS

We employed a 2-by-2 factorial design comparing: (i) linear regression versus ML (gradient boosting) and (ii) demographics and diagnostic codes alone, versus additional ZIP code-level SDH indicators. Healthcare claims from privately-insured US adults (2016-2017), and Census data were used for analysis. Data from 1.02 million adults were used for derivation, and data from 0.26 million to assess performance. Model performance was measured using coefficient of determination (R²), discrimination (C-statistic), and mean absolute error (MAE) for the overall population, and predictive ratio and net compensation for vulnerable subgroups. We provide 95% confidence intervals (CI) around each performance measure.

RESULTS

Linear regression without SDH indicators achieved moderate determination (R² 0.327, 95% CI: 0.300, 0.353), error ($6992; 95% CI: $6889, $7094), and discrimination (C-statistic 0.703; 95% CI: 0.701, 0.705). ML without SDH indicators improved all metrics (R² 0.388; 95% CI: 0.357, 0.420; error $6637; 95% CI: $6539, $6735; C-statistic 0.717; 95% CI: 0.715, 0.718), reducing misestimation of cost by $3.5 M per 10,000 members. Among people living in areas with high poverty, high wealth inequality, or high prevalence of uninsured, SDH indicators reduced underestimation of cost, improving the predictive ratio by 3% (~$200/person/year).

CONCLUSIONS

ML improved risk adjustment models and the incorporation of SDH indicators reduced underpayment in several vulnerable populations.

Acculturation and selected birth defects among non-Hispanic Blacks in a population-based case-control study

BACKGROUND

There are noted birth defects prevalence differences between race/ethnicity groups. For instance, non-Hispanic (NH) Black mothers are more likely to have an infant with encephalocele, although less likely to have an infant with anotia/microtia compared to NH Whites. When stratifying by nativity and years lived within the United States, additional variations become apparent.

METHODS

Data from the National Birth Defects Prevention Study were used to calculate descriptive statistics and estimate crude/adjusted odds ratios (aORs) and 95% confidence intervals (95%CIs) among NH Blacks with one of 30 major defects and non-malformed controls. Total case/controls were as follows: U.S.- (2,773/1101); Foreign- (343/151); African-born (161/64). Study participants were also examined by number of years lived in the U.S. (≤5 vs. 6+ years).

RESULTS

Compared to U.S.-born, foreign-born NH Black controls tended to be older, had more years of education, and were more likely to have a higher household income. They also had fewer previous livebirths and were less likely to be obese. In the adjusted analyses, two defect groups were significantly attenuated: limb deficiencies, aORs/95%CIs = (0.44 [0.20-0.97]) and septal defects (0.69 [0.48-0.99]). After stratifying by years lived in the United States, the risk for hydrocephaly (2.43 [1.03-5.74]) became apparent among those having lived 6+ years in the United States. When restricting to African-born mothers, none of the findings were statistically significant.

CONCLUSIONS

Foreign-born NH Blacks were at a reduced risk for a few selected defects. Results were consistent after restricting to African-born mothers and did not change considerably when stratifying by years lived in the United States.

Assessing the potential utility of commercial 'big data' for health research: Enhancing small-area deprivation measures with Experian™ Mosaic groups

In contrast to area-based deprivation measures, commercial datasets remain infrequently used in health research and policy. Experian collates numerous commercial and administrative data sources to produce Mosaic groups which stratify households into 15 groups for marketing purposes. We assessed the potential utility of Mosaic groups for health research purposes by investigating their relationships with Indices of Multiple Deprivation (IMD) for the British population. Mosaic groups showed significant associations with IMD quintiles. Correspondence Analysis revealed variations in patterns of association, with Mosaic groups either showing increasing, decreasing, or some mixed trends with deprivation quintiles. These results suggest that Experian's Mosaics additionally measure other aspects of socioeconomic circumstances to those captured by deprivation measures. These commercial data may provide new insights into the social determinants of health at a small area level.

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Mosaic groups showed a significant association with IMD quintiles.

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Trend patterns varied between different Mosaic groups across IMD quintiles.

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Mosaic groups have potential to enhance routinely used socioeconomic measures in research.

Comparing Classic and Interval Analytical Hierarchy Process Methodologies for Measuring Area-Level Deprivation to Analyze Health Inequalities

Deprivation indices are useful measures to study health inequalities. Different techniques are commonly applied to construct deprivation indices, including multi-criteria decision methods such as the analytical hierarchy process (AHP). The multi-criteria deprivation index for the city of Quito is an index in which indicators are weighted by applying the AHP. In this research, a variation of this index is introduced that is calculated using interval AHP methodology. Both indices are compared by applying logistic generalized linear models and multilevel models, considering self-reported health as the dependent variable and deprivation and self-reported quality of life as the independent variables. The obtained results show that the multi-criteria deprivation index for the city of Quito is a meaningful measure to assess neighborhood effects on self-reported health and that the alternative deprivation index using the interval AHP methodology more thoroughly represents the local knowledge of experts and stakeholders. These differences could support decision makers in improving health planning and in tackling health inequalities in more deprived areas.