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Abstract Background This study reports the outcomes of Communities for Healthy Living (CHL), a cluster randomized obesity prevention trial implemented in partnership with Head Start, a federally-funded preschool program for low-income families. Methods Using a stepped wedge design, Head Start programs (n = 16; Boston, MA, USA) were randomly assigned to one of three intervention start times. CHL involved a media campaign and enhanced nutrition support. Parents were invited to join Parents Connect for Healthy Living (PConnect), a 10-week wellness program. At the beginning and end of each school year (2017-2019), data were collected on the primary outcome of child Body Mass Index z-score (BMIz) and modified BMIz, and secondary outcomes of child weight-related behaviors (diet, physical activity, sleep, media use) and parents’ weight-related parenting practices and empowerment. Data from 2 years, rather than three, were utilized to evaluate CHL due to the COVID-19 pandemic. We used mixed effects linear regression to compare relative differences during intervention vs. control periods (n = 1274 vs. 2476 children) in (1) mean change in child BMIz and modified BMIz, (2) the odds of meeting child health behavior recommendations, (3) mean change in parenting practices, and (4) mean change in parent empowerment. We also compared outcomes among parents who chose post-randomization to participate in PConnect vs. not (n = 55 vs. 443). Results During intervention periods (vs. control), children experienced greater increases in BMIz and modified BMIz (b = 0.06, 95% CI = 0.02,0.10; b = 0.07, 95% CI = 0.03, 0.12), yet were more likely to meet recommendations related to three of eight measured behaviors: sugar-sweetened beverage consumption (i.e., rarely consume; Odds Ratio (OR) = 1.5, 95% CI = 1.2,2.3), water consumption (i.e., multiple times per day; OR = 1.6, 95% CI = 1.2,2.3), and screen time (i.e., ≤1 hour/day; OR = 1.4, 95% CI = 1.0,1.8). No statistically significant differences for intervention (vs. control) periods were observed in parent empowerment or parenting practices. However, parents who enrolled in PConnect (vs. not) demonstrated greater increases in empowerment (b = 0.17, 95% CI = 0.04,0.31). Conclusions Interventions that emphasize parent engagement may increase parental empowerment. Intervention exposure was associated with statistically, but not clinically, significant increases in BMIz and increased odds of meeting recommendations for three child behaviors; premature trial suspension may explain mixed results. Trial registration ClinicalTrials.gov, NCT03334669 , Registered October 2017.

Abstract Background Maternal and neonatal mortality remain elevated in low and middle income countries, and progress is slower than needed to achieve the Sustainable Development Goals. Existing strategies appear to be insufficient. One proposed alternative strategy, Service Delivery Redesign for Maternal and Neonatal Health (SDR), centers on strengthening higher level health facilities to provide rapid, definitive care in case of delivery and post-natal complications, and then promoting delivery in these hospitals, rather than in primary care facilities. However to date, SDR has not been piloted or evaluated. Methods We will use a prospective, non-randomized stepped-wedge design to evaluate the effectiveness and implementation of Service Delivery Redesign for Maternal and Neonatal Health in Kakamega County, Kenya. Discussion This protocol describes a hybrid effectiveness/implementation evaluation study with an adaptive design. The impact evaluation (“effectiveness”) study focuses on maternal and newborn health outcomes, and will be accompanied by an implementation evaluation focused on program reach, adoption, and fidelity.

Abstract Background Preconception pregnancy risk profiles—characterizing the likelihood that a pregnancy attempt results in a full-term birth, preterm birth, clinical pregnancy loss, or failure to conceive—can provide critical information during the early stages of a pregnancy attempt, when obstetricians are best positioned to intervene to improve the chances of successful conception and full-term live birth. Yet the task of constructing and validating risk assessment tools for this earlier intervention window is complicated by several statistical features: the final outcome of the pregnancy attempt is multinomial in nature, and it summarizes the results of two intermediate stages, conception and gestation, whose outcomes are subject to competing risks, measured on different time scales, and governed by different biological processes. In light of this complexity, existing pregnancy risk assessment tools largely focus on predicting a single adverse pregnancy outcome, and make these predictions at some later, post-conception time point. Methods We reframe the individual pregnancy attempt as a multistate model comprised of two nested multinomial prediction tasks: one corresponding to conception and the other to the subsequent outcome of that pregnancy. We discuss the estimation of this model in the presence of multiple stages of outcome missingness and then introduce an inverse-probability-weighted Hypervolume Under the Manifold statistic to validate the resulting multivariate risk scores. Finally, we use data from the Effects of Aspirin in Gestation and Reproduction (EAGeR) trial to illustrate how this multistate competing risks framework might be utilized in practice to construct and validate a preconception pregnancy risk assessment tool. Results In the EAGeR study population, the resulting risk profiles are able to meaningfully discriminate between the four pregnancy attempt outcomes of interest and represent a significant improvement over classification by random chance. Conclusions As illustrated in our analysis of the EAGeR data, our proposed prediction framework expands the pregnancy risk assessment task in two key ways—by considering a broader array of pregnancy outcomes and by providing the predictions at an earlier, preconception intervention window—providing obstetricians and their patients with more information and opportunities to successfully guide pregnancy attempts.

Abstract Background A large multi-center survey was conducted to understand patients’ perspectives on biobank study participation with particular focus on racial and ethnic minorities. In order to enrich the study sample with racial and ethnic minorities, disproportionate stratified sampling was implemented with strata defined by electronic health records (EHR) that are known to be inaccurate. We investigate the effect of sampling strata misclassification in complex survey design. Methods Under non-differential and differential misclassification in the sampling strata, we compare the validity and precision of three simple and common analysis approaches for settings in which the primary exposure is used to define the sampling strata. We also compare the precision gains/losses observed from using a disproportionate stratified sampling scheme compared to using a simple random sample under varying degrees of strata misclassification. Results Disproportionate stratified sampling can result in more efficient parameter estimates of the rare subgroups (race/ethnic minorities) in the sampling strata compared to simple random sampling. When sampling strata misclassification is non-differential with respect to the outcome, a design-agnostic analysis was preferred over model-based and design-based analyses. All methods yielded unbiased parameter estimates but standard error estimates were lowest from the design-agnostic analysis. However, when misclassification is differential, only the design-based method produced valid parameter estimates of the variables included in the sampling strata. Conclusions In complex survey design, when the interest is in making inference on rare subgroups, we recommend implementing disproportionate stratified sampling over simple random sampling even if the sampling strata are misclassified. If the misclassification is non-differential, we recommend a design-agnostic analysis. However, if the misclassification is differential, we recommend using design-based analyses.

Abstract Background Semi-competing risks arise when interest lies in the time-to-event for some non-terminal event, the observation of which is subject to some terminal event. One approach to assessing the impact of covariates on semi-competing risks data is through the illness-death model with shared frailty, where hazard regression models are used to model the effect of covariates on the endpoints. The shared frailty term, which can be viewed as an individual-specific random effect, acknowledges dependence between the events that is not accounted for by covariates. Although methods exist for fitting such a model to right-censored semi-competing risks data, there is currently a gap in the literature for fitting such models when a flexible baseline hazard specification is desired and the data are left-truncated, for example when time is on the age scale. We provide a modeling framework and openly available code for implementation. Methods We specified the model and the likelihood function that accounts for left-truncated data, and provided an approach to estimation and inference via maximum likelihood. Our model was fully parametric, specifying baseline hazards via Weibull or B-splines. Using simulated data we examined the operating characteristics of the implementation in terms of bias and coverage. We applied our methods to a dataset of 33,117 Kaiser Permanente Northern California members aged 65 or older examining the relationship between educational level (categorized as: high school or less; trade school, some college or college graduate; post-graduate) and incident dementia and death. Results A simulation study showed that our implementation provided regression parameter estimates with negligible bias and good coverage. In our data application, we found higher levels of education are associated with a lower risk of incident dementia, after adjusting for sex and race/ethnicity. Conclusions As illustrated by our analysis of Kaiser data, our proposed modeling framework allows the analyst to assess the impact of covariates on semi-competing risks data, such as incident dementia and death, while accounting for dependence between the outcomes when data are left-truncated, as is common in studies of aging and dementia.

Abstract Background Peer leadership can be an effective strategy for implementing health programs, benefiting both program participants and peer leaders. To realize such benefits, the peer leader role must be appropriate for the community context. Also, peer leaders must find their role acceptable (i.e., satisfactory) to ensure their successful recruitment and retention. To date, parent peer leaders have seldom been part of early childhood obesity prevention efforts. Moreover, parents at Head Start preschools have rarely been engaged as peer leaders. The aim of this study is to evaluate the appropriateness and acceptability of an innovative model for engaging parents as peer leaders for this novel content area (early childhood obesity prevention) and setting (Head Start). Methods Parents Connect for Healthy Living (PConnect) is a 10-session parent program being implemented in Head Start preschools as part of the Communities for Healthy Living early childhood obesity prevention trial. PConnect is co-led by a parent peer facilitator who is paired with a Head Start staff facilitator. In the spring of 2019, 10 PConnect facilitators participated in a semi-structured interview about their experience. Interview transcripts were analyzed by two coders using an inductive-deductive hybrid analysis. Themes were identified and member-checked with two interviewees. Results Themes identified applied equally to parent and staff facilitators. Acceptability was high because PConnect facilitators were able to learn and teach, establish meaningful relationships, and positively impact the parents participating in their groups, although facilitators did express frustration when low attendance limited their reach. Appropriateness was also high, as PConnect provided adequate structure and support without being overly rigid, and facilitators were able to overcome most challenges they encountered. Conclusions The PConnect co-facilitation model was highly acceptable and appropriate for both the parent facilitators (peer leaders) and the staff facilitators. Including parents as peer leaders aligns to Head Start’s emphasis on parent engagement, making it a strong candidate for sustained implementation in Head Start. The insights gained about the drivers of peer leadership appropriateness and acceptability in this particular context may be used to inform the design and implementation of peer-led health programs elsewhere. Trial registration clinicaltrials.gov, NCT03334669 (7–11-17).