Impacts of a social and behavior change communication program implemented at scale on infant and young feeding practices in Nigeria: Results of a cluster-randomized evaluation.

BACKGROUND: Infant and young child feeding (IYCF) practices are important for child survival and healthy growth, but IYCF practices remain suboptimal in Nigeria. The objective of this study was to measure the impact of Alive & Thrive's IYCF social and behavior change communication intervention on early initiation of breastfeeding, exclusive breastfeeding, and minimum dietary diversity in Kaduna and Lagos States. METHODS: Local government areas were randomly allocated to intervention or comparison. Cross-sectional surveys of households with children aged 0-23 months were conducted [N = 6,266 baseline (2017), N = 7,320 endline (2020)]. Logistic regression was used to calculate difference-in-differences estimates (DDEs) of impact on IYCF practices and to assess within group changes from baseline to endline. Associations between intervention exposures and IYCF practices were tested in both study groups combined. RESULTS: In Kaduna, a positive differential effect of the intervention was found for exclusive breastfeeding (adjusted DDE 8.9 pp, P<0.099). Increases in both study groups from baseline to endline were observed in Kaduna for early initiation of breastfeeding (intervention 12.2 pp, P = 0.010; comparison 6.4 pp, P = 0.118) and minimum dietary diversity (intervention 20.0 pp, P<0.001; comparison 19.7 pp, P<0.001), which eliminated differential effects. In Lagos, no differential intervention impacts were found on IYCF practices because changes in early initiation of breastfeeding from baseline to endline were small in both study groups and increases in both study groups from baseline to endline were observed for exclusive breastfeeding (intervention 8.9 pp, P = 0.05; comparison 6.6 pp, P<0.001) and minimum dietary diversity (intervention 18.9 pp, P<0.001; comparison 24.3 pp, P<0.001). Odds of all three IYCF practices increased with exposure to facility-based interpersonal communication in both states and with community mobilization or mass media exposure in Kaduna. CONCLUSIONS: This evaluation found weak impacts of the Alive & Thrive intervention on IYCF practices in the difference-in-differences analysis because of suspected intervention spillover to the comparison group. Substantial within group increases in IYCF practices from baseline to endline are likely attributable to the intervention, which was the major IYCF promotion activity in both states. This is supported by the association between intervention exposures and IYCF practices. TRIAL REGISTRATION: The study was registered with clinicaltrials.gov (NCT02975063).

Transitioning the Healthy Chicago Survey From a Telephone Mode to Self-administered by Mail Mode.

CONTEXT: As response rates to health surveys conducted by telephone continue to decline and costs continue to increase, practitioners are increasingly considering a transition to self-administered mail contact modes. OBJECTIVE: To compare empirical differences observed across adjacent administrations of the Healthy Chicago Survey (HCS) conducted by telephone versus self-administered via mail contact. DESIGN: Data from the 2016, 2018, and 2020 administrations of the HCS are contrasted, and demographic distributions are benchmarked against the American Community Survey to investigate differences that may be linked to the HCS' transition from a telephone to self-administered mail mode between 2018 and 2020. SETTING: All survey data were collected from adult residents of Chicago, Illinois, between 2016 and 2020. MAIN OUTCOME MEASURES: Costs, response rates, key health statistics, demographic distributions, and measures of precision generated from the HCS. RESULTS: The mail mode led to a response rate increase of 6.8% to 38.2% at half the cost per complete. Mail respondents are more likely to be nonminority, female, and hold a college degree. Key health statistic differences are mixed, but design effects are larger in the mail mode, which we attribute to more detailed geographic stratification and weighting employed in 2020. CONCLUSIONS: The mail mode is a less costly data collection strategy for the HCS, but it comes with trade-offs. The quasi-random selection of an individual in the household exacerbates sociodemographic distribution disparities.

Enhancing the analytic utility of clinical trial data to inform health disparities research.

Clinical trials are often conducted among younger, healthier, and less racially diverse patient populations than the population at large. Health disparities for individuals with cancer are most apparent when there are notable differences in the occurrence, frequency, burden of cancer and mortality rates among specific population groups. Enhancing the diversity of participants in clinical trials to reflect the characteristics of cancer survivors in the U.S. population is of growing interest to better insure the safety and efficacy of resultant treatments. The Project Data Sphere ® (PDS) cancer research platform is a first-of-its kind research environment that provides the research community with broad access to both de-identified patient-level clinical trial data and advanced analytic tools to enable big data-driven research. To address these analytic constraints, the data profiles in selected PDS patient-level cancer phase III clinical datasets have been augmented by linking the social, economic, and health-related characteristics of like cancer survivors from nationally representative health and health care-related survey data from the Medical Expenditure Panel Survey (MEPS). Our article shines a spotlight on this ongoing initiative to improve access to clinical trial data in support of health care disparities research initiatives.

Data Integration Innovations to Enhance Analytic Utility of Clinical Trial Content to Inform Health Disparities Research.

Project Data Sphere (PDS) is a research platform that provides the research community with broad access to both de-identified patient-level data from oncology clinical trials and related analytic tools. While these data are rich in measures that characterize the clinical trials under study, data providers are required to de-identify patient-level data by removing key demographic data. To address these analytic constraints, the data profiles in selected PDS patient-level cancer phase III clinical datasets have been augmented by linking the social, economic, and health-related characteristics of like cancer survivors from nationally representative health and health care-related survey data. Using statistical linkage and model-based techniques, patient-level records in selected PDS datasets have been linked to those of comparable cancer survivors, and are thereby augmented with survey content on social, economic, and health-related characteristics. These new analytically enhanced PDS data resources enable more targeted analyses designed to examine questions such as how disparities in cancer patients' access to health care and income impact patient outcomes in specific phase III clinical trials, and what variations in patient outcomes are associated with specific demographic, socioeconomic, and health-related factors. This study provides an overview of the methodologies used to connect patient-level clinical trial data with nationally representative health-related data on cancer survivors from the national Medical Expenditure Panel Survey (MEPS). MEPS was designed to provide national population-based health care use, expenditure, and source of payment estimates in addition to measures of health status, demographic characteristics, employment, health insurance coverage, and access to health care. Study findings include probabilistic assessments of the representation of the patients in the respective clinical trials relative to the characteristics of cancer survivors in the general population. The study also demonstrates how the augmented datasets serve to enable researchers to assess the impact of socioeconomic factors added through data integration on cancer survival and related outcomes of interest.

Residential scene classification for gridded population sampling in developing countries using deep convolutional neural networks on satellite imagery.

BACKGROUND: Conducting surveys in low- and middle-income countries is often challenging because many areas lack a complete sampling frame, have outdated census information, or have limited data available for designing and selecting a representative sample. Geosampling is a probability-based, gridded population sampling method that addresses some of these issues by using geographic information system (GIS) tools to create logistically manageable area units for sampling. GIS grid cells are overlaid to partition a country's existing administrative boundaries into area units that vary in size from 50 m × 50 m to 150 m × 150 m. To avoid sending interviewers to unoccupied areas, researchers manually classify grid cells as "residential" or "nonresidential" through visual inspection of aerial images. "Nonresidential" units are then excluded from sampling and data collection. This process of manually classifying sampling units has drawbacks since it is labor intensive, prone to human error, and creates the need for simplifying assumptions during calculation of design-based sampling weights. In this paper, we discuss the development of a deep learning classification model to predict whether aerial images are residential or nonresidential, thus reducing manual labor and eliminating the need for simplifying assumptions. RESULTS: On our test sets, the model performs comparable to a human-level baseline in both Nigeria (94.5% accuracy) and Guatemala (96.4% accuracy), and outperforms baseline machine learning models trained on crowdsourced or remote-sensed geospatial features. Additionally, our findings suggest that this approach can work well in new areas with relatively modest amounts of training data. CONCLUSIONS: Gridded population sampling methods like geosampling are becoming increasingly popular in countries with outdated or inaccurate census data because of their timeliness, flexibility, and cost. Using deep learning models directly on satellite images, we provide a novel method for sample frame construction that identifies residential gridded aerial units. In cases where manual classification of satellite images is used to (1) correct for errors in gridded population data sets or (2) classify grids where population estimates are unavailable, this methodology can help reduce annotation burden with comparable quality to human analysts.