Article type
Year
Abstract
Background: It has been well established that interventions may have variations in safety and efficacy across various populations. Response can differ based on multiple factors, including age, sex, gender, and race. Genetic differences in the expression of metabolizing enzymes or therapy targets drive some of these altered effects. However, research does not adequately explore these potential differences and resulting guidelines are unclear about how to proceed in these cases. Trial authors should be encouraged to make individual patient data (IPD) available by key demographics, which would allow systematic review teams to run a more flexible and sophisticated meta-analysis to analyze potential differences.
An example: A retrospective cohort study reporting labor outcomes after induction noted that non-white race was independently associated with increased odds of delivering by cesarean, hemorrhage, transfusion, and peripartum infection (Singh 2018). Another study analyzing labor outcomes by race after specifically using vaginal prostaglandins for induction noted that black mothers were more likely than any other group to undergo cesarean sections and have these performed due to non-reassuring fetal heart rate tracings. Hispanic mothers in this study were more likely to have postpartum hemorrhage than other groups (Stephenson 2015). Considering these populations have much higher rates of maternal mortality, it may be ideal to use particular induction methods over others to minimize these risks. Unfortunately, limited data exist to establish efficacy on a racial basis.
Driving factors: Some of the inequities arise from a fear of perceived exploitation of minorities, given a history of unethical research practices. Minority researchers are more likely to focus on disparities but are less likely to get federal funding. One review of inequalities in the research noted that black investigators were half as likely to receive the National Institute of Health (NIH) grants as white investigators even after controlling for education, training, and experience. Furthermore, only 10.9% of NIH grant reviewers, who are chosen from the already diluted pool of successful grant winners, are underrepresented minorities (Konkel 2015).
Conclusions: While healthcare equity is driven by many other factors such as implicit bias, mistrust and systemic barriers, we must start analyzing interventions in a multifactorial manner to explore the intersection of demographical differences in both efficacy as well as the magnitude of response. When planning a systematic review, authors should consider if the intervention has potentially different responses amongst different populations and, if so, run an IPD meta-analysis, which is widely considered to be the “gold standard” approach. The results should then be disseminated in a culturally competent evidence package, so consumers and providers can weigh the risks and benefits of each intervention on a more individualized basis.
An example: A retrospective cohort study reporting labor outcomes after induction noted that non-white race was independently associated with increased odds of delivering by cesarean, hemorrhage, transfusion, and peripartum infection (Singh 2018). Another study analyzing labor outcomes by race after specifically using vaginal prostaglandins for induction noted that black mothers were more likely than any other group to undergo cesarean sections and have these performed due to non-reassuring fetal heart rate tracings. Hispanic mothers in this study were more likely to have postpartum hemorrhage than other groups (Stephenson 2015). Considering these populations have much higher rates of maternal mortality, it may be ideal to use particular induction methods over others to minimize these risks. Unfortunately, limited data exist to establish efficacy on a racial basis.
Driving factors: Some of the inequities arise from a fear of perceived exploitation of minorities, given a history of unethical research practices. Minority researchers are more likely to focus on disparities but are less likely to get federal funding. One review of inequalities in the research noted that black investigators were half as likely to receive the National Institute of Health (NIH) grants as white investigators even after controlling for education, training, and experience. Furthermore, only 10.9% of NIH grant reviewers, who are chosen from the already diluted pool of successful grant winners, are underrepresented minorities (Konkel 2015).
Conclusions: While healthcare equity is driven by many other factors such as implicit bias, mistrust and systemic barriers, we must start analyzing interventions in a multifactorial manner to explore the intersection of demographical differences in both efficacy as well as the magnitude of response. When planning a systematic review, authors should consider if the intervention has potentially different responses amongst different populations and, if so, run an IPD meta-analysis, which is widely considered to be the “gold standard” approach. The results should then be disseminated in a culturally competent evidence package, so consumers and providers can weigh the risks and benefits of each intervention on a more individualized basis.