The application of intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to examine birthweight inequities in New York City

ElsevierVolume 81, May 2023, 103029Health & PlaceAuthor links open overlay panel, , , , Abstract

Exploring the intersection of dimensions of social identity is critical for understanding drivers of health inequities. We used multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to examine the intersection of age, race/ethnicity, education, and nativity status on infant birthweight among singleton births in New York City from 2012 to 2018 (N = 725,875). We found evidence of intersectional effects of various systems of oppression on birthweight inequities and identified U.S.-born Black women as having infants of lower-than-expected birthweights. The MAIHDA approach should be used to identify intersectional causes of health inequities and individuals affected most to develop policies and interventions redressing inequities.

Section snippetsBackground

Health inequities, caused by unjust systems, practices, and policies, continue to be pervasive despite growing interest in addressing them. Much research has explored the effects of social conditions on health inequities, like socio-economic status, housing, and employment, among others (Belle and Doucet, 2003; Braveman et al., 2010; Dooley et al., 1996; Freudenberg and Ruglis, 2007; Friedland and Price, 2003; Kasl and Cobb, 1970; Krueger et al., 2015; Kushel et al., 2006; Schanzer et al., 2007

Data sources

We used birth certificate data from 2012 to 2018 from the NYC Department of Health and Mental Hygiene (DOHMH) Bureau of Vital Statistics to ascertain the exposure, outcome, and covariates. Birth certificates contained data on maternal socio-demographics, including age, race/ethnicity, education, and nativity status, as well as data related to labor and delivery, including infant birthweight. Data for birth records in NYC are collected according to the U.S. Centers for Disease Control and

Results

The characteristics for the study population are presented in Table 1. Overall, most participants were between 20 and 39 years old (91.0%), almost a third were white (31.4%) or Latino (30.4%), more than 40% had attained a college degree or more, and more than half were foreign-born (53.1%). Most women had either Medicaid/Family Health Plus (61.5%) or private insurance (35.9%), were married (59.5%), had at least 1 previous birth (55.8%), did not have gestational diabetes (93.0%) or hypertension

Discussion

Our findings suggest that maternal social identity may be a significant factor in the patterning of observed inequities in birthweight., Moreover, the findings highlight the application of intersectionality theory to the MAIHDA approach to understand and address inequities in infant birthweight by considering inequality, intersectionality, and variance.

These findings underscore the advantages of using the MAIHDA approach to describe and better understand the patterns of inequities in

Conclusions

Multilevel analysis of individual heterogeneity and discriminatory accuracy, or MAIHDA, is an approach that can be used to better understand health inequities. The application of an intersectional framework to health inequities is essential because the social determinants of health inequities are interlocking. By transforming individual-level characteristics into contextual social strata to represent the interlocking systems of oppression operating to cause inequities, this approach is more

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