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Item A protocol for monitoring fidelity of a preconception-life course intervention in a middle-income setting: the healthy life trajectories initiative (Helti), South Africa(Trials, 2022-09-06) Soepnel, Larske M.; Draper, Catherine E.; Mabetha, Khuthala; Dennis, Cindy-Lee; Prioreschi, Alessandra; Lye, Stephen; Norris, Shane A.Introduction: Despite the importance of intervention fidelity in interpreting the outcomes of complex public health interventions, there is a lack of both reporting fidelity trial protocols and uniformity. In evaluating complex, adaptable/pragmatic interventions in resource-strapped settings with systemic issues, unique challenges to intervention adherence and monitoring are introduced, increasing the importance of a fidelity protocol. We aim to describe the intervention fidelity and monitoring protocol for the Healthy Life Trajectories Initiative (HeLTI) South Africa, a complex four-phase intervention set in urban Soweto, starting preconceptionally and continuing through to pregnancy, infancy, and early childhood to improve the health of young women and reduce the intergenerational risk of obesity. Methods: The HeLTI SA fidelity protocol was based on the NIH Behaviour Change Consortium (NIH BCC) Treatment Fidelity Framework, outlining the following components of intervention fidelity: study design, provider training, intervention delivery, intervention receipt, and intervention enactment. Context-specific fidelity challenges were identified. The intervention fidelity components and associated monitoring strategies were developed to align with HeLTI SA. Strategies for fidelity monitoring include, amongst others, qualitative process evaluation methods, reviewing observed and recorded intervention sessions, monitoring of activity logs, standardized training, and intervention session checklists. Possible challenges to fidelity and fidelity monitoring include high provider turnover, lack of qualification amongst providers, difficulty tracing participants for follow-up sessions, participant health literacy levels, and the need to prioritize participants’ non-health-related challenges. Solutions proposed include adapting intervention delivery methods, recruitment methods, and provider training methods. Discussion: The NIH BCC Treatment Fidelity Framework provided a solid foundation for reporting intervention fidelity across settings to improve intervention validity, ability to assess intervention effectiveness, and transparency. However, context-specific challenges to fidelity (monitoring) were identified, and transparency around such challenges and possible solutions in low- and middle-income settings could help foster solutions to improve adherence, reporting, and monitoring of intervention fidelity in this setting.Item Disparities in preconception health indicators — behavioral risk factor surveillance system, 2013–2015, and pregnancy risk assessment monitoring system, 2013–2014(Surveillance Summaries (Washington, D.C. : 2002), 2018-01-19) Robbins, Cheryl; Boulet, Sheree L.; Morgan, Isabel; D’Angelo, Denise V.; Zapata, Lauren B.; Morrow, Brian; Sharma, Andrea; Kroelinger, Charlan D.Problem/Condition: Preconception health is a broad term that encompasses the overall health of nonpregnant women during their reproductive years (defined here as aged 18–44 years). Improvement of both birth outcomes and the woman’s health occurs when preconception health is optimized. Improving preconception health before and between pregnancies is critical for reducing maternal and infant mortality and pregnancy-related complications. The National Preconception Health and Health Care Initiative’s Surveillance and Research work group suggests ten prioritized indicators that states can use to monitor programs or activities for improving the preconception health status of women of reproductive age. This report includes overall and stratified estimates for nine of these preconception health indicators. Reporting Period: 2013–2015. Description of Systems: Survey data from two surveillance systems are included in this report. The Behavioral Risk Factor Surveillance System (BRFSS) is an ongoing state-based, landline and cellular telephone survey of noninstitutionalized adults in the United States aged ≥18 years that is conducted by state and territorial health departments. BRFSS is the main source of self-reported data for states on health risk behaviors, chronic health conditions, and preventive health services primarily related to chronic disease in the United States. The Pregnancy Risk Assessment Monitoring System (PRAMS) is an ongoing U.S. state- and population-based surveillance system administered collaboratively by CDC and state health departments. PRAMS is designed to monitor selected maternal behaviors, conditions, and experiences that occur before, during, and shortly after pregnancy that are self-reported by women who recently delivered a live-born infant. This report summarizes BRFSS and PRAMS data on nine of 10 prioritized preconception health indicators (i.e., depression, diabetes, hypertension, current cigarette smoking, normal weight, recommended physical activity, recent unwanted pregnancy, prepregnancy multivitamin use, and postpartum use of a most or moderately effective contraceptive method) for which the most recent data are available. BRFSS data from all 50 states and the District of Columbia were used for six preconception health indicators: depression, diabetes (excluded if occurring only during pregnancy or if limited to borderline/prediabetes conditions), hypertension (excluded if occurring only during pregnancy or if limited to borderline/prehypertension conditions), current cigarette smoking, normal weight, and recommended physical activity. PRAMS data from 30 states, the District of Columbia, and New York City were used for three preconception health indicators: recent unwanted pregnancy, prepregnancy multivitamin use, and postpartum use of a most or moderately effective contraceptive method by women or their husbands or partners (i.e., male or female sterilization, hormonal implant, intrauterine device, injectable contraceptive, oral contraceptive, hormonal patch, or vaginal ring). Heavy alcohol use during the 3 months before pregnancy also was included in the prioritized set of 10 indicators, but PRAMS data for each reporting area are not available until 2016 for that indicator. Therefore, estimates for heavy alcohol use are not included in this report. All BRFSS preconception health estimates are based on 2014–2015 data except two (hypertension and recommended physical activity are based on 2013 and 2015 data). All PRAMS preconception health estimates rely on 2013–2014 data. Prevalence estimates of indicators are reported for women aged 18–44 years overall, by age group, race-ethnicity, health insurance status, and reporting area. Chi-square tests were conducted to assess differences in indicators by age group, race/ethnicity, and insurance status. Results: During 2013–2015, prevalence estimates of indicators representing risk factors were generally highest and prevalence estimates of health-promoting indicators were generally lowest among older women (35–44 years), non-Hispanic black women, uninsured women, and those residing in southern states. For example, prevalence of ever having been told by a health care provider that they had a depressive disorder was highest among women aged 35–44 years (23.1%) and lowest among women aged 18–24 years (19.2%). Prevalence of postpartum use of a most or moderately effective method of contraception was lowest among women aged 35–44 years (50.6%) and highest among younger women aged 18–24 years (64.9%). Self-reported prepregnancy multivitamin use and getting recommended levels of physical activity were lowest among non-Hispanic black women (21.6% and 42.8%, respectively) and highest among non-Hispanic white women (37.8% and 53.8%, respectively). Recent unwanted pregnancy was lowest among non-Hispanic white women and highest among non-Hispanic black women (5.0% and 11.6%, respectively). All but three indicators (diabetes, hypertension, and use of a most or moderately effective contraceptive method) varied by insurance status; for instance, prevalence of current cigarette smoking was higher among uninsured women (21.0%) compared with insured women (16.1%), and prevalence of normal weight was lower among women who were uninsured (38.6%), compared with women who were insured (46.1%). By reporting area, the range of women reporting ever having been told by a health care provider that they had diabetes was 5.0% (Alabama) to 1.9% (Utah), and women reporting ever having been told by a health care provider that they had hypertension ranged from 19.2% (Mississippi) to 7.0% (Minnesota). Interpretation: Preconception health risk factors and health-promoting indicators varied by age group, race/ethnicity, insurance status, and reporting area. These disparities highlight subpopulations that might benefit most from interventions that improve preconception health. Public Health Action: Eliminating disparities in preconception health can potentially reduce disparities in two of the leading causes of death in early and middle adulthood (i.e., heart disease and diabetes). Public health officials can use this information to provide a baseline against which to evaluate state efforts to improve preconception health.Item Questioning the indicators of need for obstetric care(WHO, 2002) Ronsmans, Carine; Campbell, Oona Meave Renee; McDermott, Jeanne & Koblinsky, MargeItem Monitoring emergency obstetric care : a handbook(WHO, 2009) WHO;UNFPA;Mailman School of Public Health. Averting Maternal Death and Disability;UNICEF