Investigating Longitudinal Associations Between Racial Microaggressions, Coping, Racial/Ethnic Identity, and Mental Health in Black Girls and Women

Racial microaggressions pose significant risk to health and well-being among Black adolescents and adults. Yet, protective factors (i.e., coping, racial/ethnic identity) can moderate the impact of racial microaggressions over time. Unfortunately, few studies have evaluated the role of these protective factors longitudinally or specifically among Black girls and women. In the current study, we focused on the experiences of Black girls and women and investigated the longitudinal links between racial microaggressions and mental health symptoms over 1 year. We then explored the role of two key protective factors as moderators-coping with racial discrimination and racial/ethnic identity-for mental health. Participants included 199 Black adolescent girls (Mage  = 16.02) and 199 Black women (Mage  = 42.82) who completed measures on two types of racial microaggressions, three types of coping strategies, racial/ethnic identity, and mental health symptomology. Girls and women completed measures at three time points over 1 year. Results indicated both types of microaggressions predicted increased mental health symptoms in Black women. Among Black girls, assumptions of criminality predicted increased externalizing symptoms only when protective factors were included in the model. Analysis of the protective factors indicated a potential direct benefit rather than a moderating role of coping with racial discrimination through positive thinking for mental health in both Black girls and women. Evidence suggests that coping may have had a direct rather than an indirect effect on Black girls' mental health over time. We conclude with future directions for research and considerations for practice

Total cases and incidence for the Global Burden of Disease regions and subregions made up of low- and middle-income countries.

<p>Total cases are shown in millions and incidence is per 100,000 person-years.</p

Observed versus predicted age-specific incidence rates.

<p>Sites are labeled by location and year, and plots are ordered by decreasing overall model-predicted incidence. The red line and regions represent the model fits—median and 95% credible interval of the expected incidence estimated by the joint posterior distribution of model parameter (excluding study specific random effects and the impact of the observation process). The black symbols are the observed incidence with the 95% credible intervals after adjusting for the observation process: surveillance type (active/augmented passive versus passive surveillance), the participation rate, and blood culture sensitivity. Only studies that reported age-specific incidence are featured here.</p

Map of the location of studies in our dataset.

<p>Studies used in the estimation sample are depicted in red and the studies used in the validation sample are depicted in blue. The studies in the validation sample come from the Typhoid Fever Surveillance in Africa Program (TSAP).</p

Observed versus model-predicted incidence.

<p>(A) Posterior predictions from the null model, which only adjusts for age and the observation process. (B) Posterior predictions from the model using fixed effects for the predictors. (C) Leave-3-out validation results. The gray markers represent the density of model-predicted posterior distributions of incidence, while the red dots represent the median posterior predicted incidence. The size of the red circular markers is proportional to the number of person-years of observation in each study. All predictions are of the mean incidence and were generated using only the fixed-effect terms of the model, and hence do not account for unmeasured location-specific differences, e.g. in healthcare-seeking behavior.</p

Probability that each location falls into one of four incidence categories: <10, 10-<100, 100-<500, and ≥500 cases per 100,000 person-years, designated as low, medium, high, and very high incidence, respectively.

<p>Probability that each location falls into one of four incidence categories: <10, 10-<100, 100-<500, and ≥500 cases per 100,000 person-years, designated as low, medium, high, and very high incidence, respectively.</p

Uncertainty in incidence estimates for the Global Burden of Disease regions and subregions made up of low- and middle-income countries.

<p>Uncertainty in incidence estimates for the Global Burden of Disease regions and subregions made up of low- and middle-income countries.</p

Model summary.

<p>A) The posterior marginal probability that each variable was excluded from the model (black) or included as a predictor of the intercept (dark grey) or intercept and slope (light grey) is shown for two chains. Our stochastic search variable selection algorithm could include variables either as a predictor of the intercept (the incidence in 5–14 year olds) or as a predictor of the intercept as well as the slopes (the incidence rate ratios between the other age groups and the referent age group of 5–14 year olds). B) Distribution of the average number of covariates in the model. Chain 1 was initiated using a model that included all the covariates as predictors of the main effect, while chain 2 was initiated as the null model. The null model was never sampled, implying that the models including at least one predictor better described the data than the null model. C) Posterior distributions of age-specific incidence rate ratios between the referent age group (5–14 years of age) and other age groups: <2 years, 2–4 years, ≥15 years old.</p

Out-of-sample validation.

<p>The observed versus predicted incidence of typhoid fever is plotted for studies in the Typhoid Fever Surveillance in Africa Program (TSAP) using a model estimated from previously published data identified in our literature review. The numbers represent the median posterior predicted incidence for each TSAP site: 1- Nioko II, Burkina Faso. 2 –Polesgo, Burkina Faso. 3 –Ashanti Akim North, Ghana. 4 –Bandim, Guinea Bissau. 5 –Kibera, Kenya. 6 –Antananarivo, Madagascar. 7 –Imerintsiatosika, Madagascar. 8 –Moshi rural, Tanzania. 9 –Moshi urban, Tanzania. The gray markers represent the density of model-predicted posterior distributions of incidence. The gray horizontal lines represent 95% confidence intervals for the observed incidence.</p

Model-predicted age-specific incidence per 100,000 person-years.

<p>The median posterior predicted incidence per 100,000 person-years in each of the age groups (<2 years, 2–4 years, 5–14 years, and ≥15 years) is mapped for all low- and middle-income countries (LMICs) with a resolution of 0.1 degrees.</p