BACKGROUND: In 2015, the Massachusetts Department of Public Health (MDPH) adopted a Title V maternal and child health priority to "promote health and racial equity by addressing racial justice and reducing disparities." A survey assessing staff capacity to support this priority identified data collection and use as opportunities for improvement. In response, MDPH initiated a quality improvement project to improve use of data for action to promote racial equity. METHODS: MDPH conducted value stream mapping to understand existing processes for using data to inform racial equity work. Key informant interviews and a survey of program directors identified challenges to using data to promote racial equity. MDPH used a cause-and-effect diagram to identify and organize challenges to using data to inform racial equity work and better understand opportunities for improvement and potential solutions. RESULTS: Key informants highlighted the need to consider structural factors and historical and community contexts when interpreting data. Program directors noted limited staff time, lack of performance metrics, competing priorities, low data quality, and unclear expectations as challenges. To address the identified challenges, the team identified potential solutions and prioritized development and piloting of the MDPH Racial Equity Data Road Map (Road Map). CONCLUSIONS: The Road Map framework provides strategies for data collection and use that support the direction of actionable data-driven resources to racial inequities. The Road Map is a resource to support programs to authentically engage communities; frame data in the broader contexts that impact health; and design solutions that address root causes. With this starting point, public health systems can work toward creating data-driven programs and policies to improve racial equity.

The science behind the combined effect of (and possible interaction between) physical activity and air pollution exposure on health endpoints is not well established, despite the fact that independent effects of physical activity and air pollution on health are well known. The objective of this review is to systematically assess the available literature pertaining to exposure to air pollution while being physically active, in order to assess statistical interaction. Articles published during 2000-2020 were identified by searching PubMed, Science Direct, and ProQuest Agricultural & Environmental Science Database for terms encompassing air pollution and exercise/physical activity. Articles were included if they examined the following four scenarios: at rest in clean air, physical activity in clean air, at rest in polluted air, and physical activity in polluted air. Risk of bias assessment was performed on all included articles. We identified 25 articles for inclusion and determined risk of bias was low to moderate. Nine articles identified evidence of statistical interaction between air pollution exposure and physical activity, while 16 identified no such interaction. However, pollutant levels, exercise intensity, and the population studied appeared to influence statistical interaction. Even in low levels of air pollution, low-intensity activities (i.e., walking), may intensify the negative impacts of air pollution, particularly among those with pre-existing conditions. However, among healthy adults, the review suggests that exercise is generally beneficial even in high air pollution environments. Particularly, the review indicates that moderate to high-intensity exercise may neutralize any short-term negative effects of air pollution.

Purpose Describe how Ohio and Massachusetts explored severe maternal morbidity (SMM) data, and used these data for increasing awareness and driving practice changes to reduce maternal morbidity and mortality. Description For 2008-2013, Ohio used de-identified hospital discharge records and International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes to identify delivery hospitalizations. Massachusetts used existing linked data system infrastructure to identify delivery hospitalizations from birth certificates linked to hospital discharge records. To identify delivery hospitalizations complicated by one or more of 25 SMMs, both states applied an algorithm of ICD-9-CM diagnosis and procedure codes. Ohio calculated a 2013 SMM rate of 144 per 10,000 delivery hospitalizations; Massachusetts calculated a rate of 162. Ohio observed no increase in the SMM rate from 2008 to 2013; Massachusetts observed a 33% increase. Both identified disparities in SMM rates by maternal race, age, and insurance type. Assessment Ohio and Massachusetts engaged stakeholders, including perinatal quality collaboratives and maternal mortality review committees, to share results and raise awareness about the SMM rates and identified high-risk populations. Both states are applying findings to inform strategies for improving perinatal outcomes, such as simulation training for obstetrical emergencies, licensure rules for maternity units, and a focus on health equity. Conclusion Despite data access differences, examination of SMM data informed public health practice in both states. Ohio and Massachusetts maximized available state data for SMM investigation, which other states might similarly use to understand trends, identify high risk populations, and suggest clinical or population level interventions to improve maternal morbidity and mortality.

BACKGROUND: Zika virus has recently emerged as a novel cause of microcephaly. CDC has asked states to rapidly ascertain and report cases of Zika-linked birth defects, including microcephaly. Massachusetts added head circumference to its birth certificate (BC) in 2011. The accuracy of head circumference measurements from state vital records data has not been reported. METHODS: We sought to assess the accuracy of Massachusetts BC head circumference measurements by comparing them to measurements for 2,217 infants born during 2012-2013 captured in the Massachusetts Birth Defects Monitoring Program (BDMP) data system. BDMP contains information abstracted directly from infant medical records and served as the true head circumference value (i.e., gold standard) for analysis. We calculated the proportion of head circumference measurements in agreement between the BC and BDMP data. We assigned growth chart head circumference percentile categories to each BC and BDMP measurement, and calculated the sensitivity and specificity of BC-based categories to predict BDMP-based categories. RESULTS: No difference was found in head circumference measurements between the two sources in 77.9% (n = 1,727) of study infants. The sensitivity of BC-based head circumference percentile categories ranged from 85.6% (<3rd percentile) to 92.7% (≥90th percentile) and the specificity ranged from 97.6% (≥90th percentile) to 99.3% (<3rd percentile). CONCLUSIONS: BC head circumference measurements agreed with those abstracted from the medical chart the majority of the time. Head circumference measurements on the BC were more specific than sensitive across all standardized growth chart percentile categories.