Data, Social Determinants, and Better Decision-making for Health: the 3-D Commission

Biermann, Olivia; Mwoka, Meggie; Ettman, Catherine K.; Abdalla, Salma M; Shawky, Sherine; Ambuko, Jane; Pearson, Mark; Zeinali, Zahra; Galea, Sandro; Mberu, Blessing; Valladares, Laura Magaña

doi:10.1007/s11524-021-00556-9

Cited by 14 publications

(13 citation statements)

References 29 publications

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“…Our results are also consistent with recent studies that call for greater clarity in conceptualizing and more specificity in measuring SDoH to support improved clinical and shared decision making and patient self‐care. 10 , 73 , 74 Moreover, our findings regarding the inconsistent definitions and lack of construct validity in SDoH measures help to explain, in part, the differences in results across studies that investigate the effect of SDoH on HF mortality and readmissions. 75 , 76 The use of standardized SDoH measures and covariates across studies could help to produce more consistent results and enable researchers to conduct robust meta‐analyses.…”

Section: Discussionmentioning

confidence: 86%

Influence of Social Determinants of Health on Heart Failure Outcomes: A Systematic Review

Enard

Coleman

Yakubu

et al. 2023

JAHA

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Background Prior research suggests an association between clinical outcomes in heart failure (HF) and social determinants of health (SDoH). Because providers should identify and address SDoH in care delivery, we evaluated how SDoH have been defined, measured, and evaluated in studies that examine HF outcomes. Methods and Results Following Preferred Reporting Items for Systematic Reviews and Meta‐Analysis guidelines, databases were searched for observational or interventional studies published between 2009 and 2021 that assessed the influence of SDoH on outcomes. Selected articles were assessed for quality using a validated rating scheme. We identified 1373 unique articles for screening; 104 were selected for full‐text review, and 59 met the inclusion criteria, including retrospective and prospective cohort, cross‐sectional, and intervention studies. The majority examined readmissions and hospitalizations (k=33), mortality or survival (k=29), and success of medical devices and transplantation (k=8). SDoH examined most commonly included race, ethnicity, age, sex, socioeconomic status, and education or health literacy. Studies used a range of 1 to 9 SDoH as primary independent variables and 0 to 7 SDoH as controls. Multiple data sources were employed and frequently were electronic medical records linked with national surveys and disease registries. The effects of SDoH on HF outcomes were inconsistent because of the heterogeneity of data sources and SDoH constructs. Conclusions Our systematic review reveals shortcomings in measurement and deployment of SDoH variables in HF care. Validated measures need to be prospectively and intentionally collected to facilitate appropriate analysis, reporting, and replication of data across studies and inform the design of appropriate, evidence‐based interventions that can ameliorate significant HF morbidity and societal costs.

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Section: Discussionmentioning

confidence: 86%

Influence of Social Determinants of Health on Heart Failure Outcomes: A Systematic Review

Enard

Coleman

Yakubu

et al. 2023

JAHA

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“…The explanatory variables used in the study were selected based on existing literature on factors that influence health outcomes for U5y as well as informed by the framework developed by the Commission for Social Determinants of Health (CSDH) [13,32]. The explanatory variables were categorised into four groups: U5y demographic characteristics (e.g., age, sex), mothers' sociodemographic characteristics (e.g., age, ever attended school, employment, and marital status), head of households' sociodemographic characteristics (e.g., age, sex, marital status), and households' social structural characteristics (e.g., wealth index, housing condition, having separate kitchen, type of cooking fuel used, migration status, garbage disposal method, ownership of dwelling, and ownership of land) (S1 Table ).…”

Section: Plos Global Public Healthmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The poor health outcomes experienced by U5y living in Bangladesh slums are often shaped by social determinants of health (SDoH) [13][14][15]. SDoH are described as the conditions in which people live, grow, learn, work, play, and age [13,16]. For instance, unhygienic conditions, overcrowding, lack of formal health facilities, the transmission of vaccine-preventable diseases, low immunization coverage coupled with low socioeconomic status of parents make U5y more vulnerable in slums [17,18].…”

Section: Introductionmentioning

confidence: 99%

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The effects of social determinants on children’s health outcomes in Bangladesh slums through an intersectionality lens: An application of multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)

Barua

Kibuchi²,

Aktar³

et al. 2023

PLOS Glob Public Health

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Empirical evidence suggests that the health outcomes of children living in slums are poorer than those living in non-slums and other urban areas. Improving health especially among children under five years old (U5y) living in slums, requires a better understanding of the social determinants of health (SDoH) that drive their health outcomes. Therefore, we aim to investigate how SDoH collectively affects health outcomes of U5y living in Bangladesh slums through an intersectionality lens. We used data from the most recent national Urban Health Survey (UHS) 2013 covering urban populations in Dhaka, Chittagong, Khulna, Rajshahi, Barisal, Sylhet, and Rangpur divisions. We applied multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to estimate the Discriminatory Accuracy (DA) of the intersectional effects estimates using Variance Partition Coefficient (VPC) and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). We also assessed the Proportional Change in Variance (PCV) to calculate intersectional effects. We considered three health outcomes: cough, fever, and acute respiratory infections (ARI) in U5y.We found a low DA for cough (VPC = 0.77%, AUC-ROC = 61.90%), fever (VPC = 0.87%, AUC-ROC = 61.89%) and ARI (VPC = 1.32%, AUC-ROC = 66.36%) of intersectional strata suggesting that SDoH considered do not collectively differentiate U5y with a health outcome from those with and without a health outcome. The PCV for cough (85.90%), fever (78.42%) and ARI (69.77%) indicates the existence of moderate intersectional effects. We also found that SDoH factors such as slum location, mother’s employment, age of household head, and household’s garbage disposal system are associated with U5y health outcomes. The variables used in this analysis have low ability to distinguish between those with and without health outcomes. However, the existence of moderate intersectional effect estimates indicates that U5y in some social groups have worse health outcomes compared to others. Therefore, policymakers need to consider different social groups when designing intervention policies aimed to improve U5y health outcomes in Bangladesh slums.

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“…Concerning medical assistance, Wang Chaoqun and Nelson demonstrated that medical assistance and life assistance could improve the poor individuals' or families' ability to purchase medical services or daily necessities by increasing their income, thus improving their quality of life. Bierman et al ( 6 ) and Liu Jiankun et al ( 7 ) proved that supplementing the income of residents and improving their quality of life is beneficial to increase the health level of residents. HyunSoo et al ( 8 ) carried out an empirical study based on several countries and several aged people during COVID-19 and concluded that relatively high income was positively related to good subjective health.…”

Section: Theoretical Basis and Research Hypothesesmentioning

confidence: 99%

Effects of social assistance on self-rated health

Shao

Che

Zhou

2022

Front. Public Health

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Based on the China Health and Retirement Longitudinal Study (CHARLS) data in 2018, medical assistance and life assistance have significant negative influences on self-rated health, found via an empirical analysis based on the Oprobit model. Such negative influences are robust based on the substitution of explained variables and propensity score matching. It can be found from a heterogeneity analysis that the negative influences of medical assistance on self-rated health are more significant in urban residents and residents in Central China and East China. Meanwhile, negative influences of life assistance on self-rated health are more significant in urban residents, and residents in Central China, East China, and Northeast China. This study provides empirical evidence to improve the health of residents by using medical assistance and life assistance accurately and offers important policy enlightenments to formulate appropriate social assistance policies.

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Data, Social Determinants, and Better Decision-making for Health: the 3-D Commission

Cited by 14 publications

References 29 publications

Influence of Social Determinants of Health on Heart Failure Outcomes: A Systematic Review

Influence of Social Determinants of Health on Heart Failure Outcomes: A Systematic Review

The effects of social determinants on children’s health outcomes in Bangladesh slums through an intersectionality lens: An application of multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)

Effects of social assistance on self-rated health

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