Doctor of Philosophy / Human Nutrition / Richard R. Rosenkranz / Child stunting refers to a condition where the child is relatively shorter in height, in comparison to their age group. Child stunting is a public health nutrition problem that hinders the development of future generations, not only physiologically but also potentially deprives their cognitive function and productivity. The demographic transition, conjoined with the epidemiological and nutrition transitions, has resulted in the coexistence of an over- and under-nutrition problem known as double burden of malnutrition, and child stunting has been a persistent part of the problem. In 2014, the World Health Organization (WHO) reported that one-fourth of the children in the developing countries have been suffering from child stunting. The objective of this research was to apply the behavioral epidemiology approach to tackle child stunting in households with double burden of malnutrition. It was hypothesized that unlike any other households with problem of child stunting, households with double burden of malnutrition possess some degree of capacity that, with proper support and direction, might enable them to help themselves reduce or prevent this nutrition-related debacle. Results from a secondary data analysis revealed that child stunting was associated with lower dietary diversity as an indication of poor food choice in the household, related to children’s nutrient requirements. Another cross-sectional study in this dissertation was conducted in an urban setting in Indonesia, and found that households with child stunting alone was associated with extreme food insecurity, while households with double burden of malnutrition ─ in the form of stunted child and overweight/obese mother (SCOWT) ─ was associated with even a mild degree of food insecurity. These results support our hypothesis that households with double burden of malnutrition lack the capacity to direct their resources properly to prevent child stunting. Most notably, we expected that the role of the mothers to manage healthy food choices through indirect measure of dietary diversity, availability and distribution within the household was lacking. In order to equip mothers with necessary components to be able to overcome these problems, we conducted a behaviorally based intervention that targeted mothers in the households experiencing the problem of double burden of malnutrition. The intervention provided the potential to achieve participant self-administered goal setting to improve diet, as well as child feeding behavior, by means of improved self-efficacy, nutrition literacy and dietary diversity. Maternal self-efficacy may be potentially enhanced by vicarious experience and active mastery experience gained during 6 sessions of behavioral intervention and verbal motivation by community health workers during 6 additional home visits. These studies, collectively comprising the present dissertation, present a message for policy makers in developing countries: nutrition literacy and behaviors for choosing healthy foods are lacking in mothers that affect both maternal and child food intake, but efforts such as improving vicarious and mastery experience on child feeding practices and healthy food choices can boost mother’s self-efficacy to engage in appropriate behaviors and improve their child’s nutrition.

Parameter estimation of respiratory infection surveillance dynamics commonly utilize data aggregated over space and time. However, estimates derived from aggregated data may fail to account for biologically meaningful spatiotemporal heterogeneity of effects or to identify where and when transmissions occur. This dissertation shows that high-resolution temporal and spatial data can improve our understanding of heterogeneity while producing more valid and precise estimates of transmission parameters (e.g., contagiousness), behavioral trends (e.g., face mask utilization), and intervention effects (e.g., at-home test distribution). In three projects, we evaluate spatiotemporal heterogeneity in the context of two major respiratory pathogens: Tuberculosis and SARSCoV-2. First, in project one, we identify disease transmission hotspots from a tuberculosis case surveillance system in Greater Vitória, Brazil. Utilizing a human mobility model and recently developed method to quantify disease transmission, we overcome multiple methodological constraints that often obscure spatially and temporally accurate transmission measurements. We estimate that two cities in Greater Vitória, Vila Velha (reproductive number = 1.05, 95%CI: 1.03–1.07) and Vitória (reproductive number = 1.04, 95%CI: 1.02–1.06), help sustain tuberculosis transmission in the entire region and may be effective targets for intervention, while Cariacica (reproductive number = 0.95, 95%CI: 0.94–0.97) fell below the critical threshold of 1 required to sustain transmission alone. Next, in project two, we utilize interrupted time series methods to estimate the effect of mask mandates on mask adherence using a nationally representative digital health survey on masking and a comprehensive database of pandemic-related government policies. The analysis focuses on improving previous attempts at measuring the effectiveness of mask mandates at the state level, by utilizing county-level exposure and outcome data. We find that mask mandates were associated with a large heterogeneity of effects, ranging from increasing masking approximately 8% in counties with low levels of prior masking to 1% or lower change in masking in places like the Northeast U.S. where masking levels were already high. Last, in project three, we leverage the same nationally representative digital health survey to understand at-home testing patterns in the United States. We utilize two different economic measures of resource allocation and a regression model with autoregressive integrated moving average errors to examine if the Covidtests.gov government program reduced at-home testing inequities. We show that Covidtest.gov did increase at-home testing across all demographics; however, income-, geographic- and race-based disparities in at-home test utilization were heightened during periods when the program was active. Specifically, the regression results estimate that Theil’s T, an economic metric used here to measure at-home testing disparities, was 53% (95%CI: 6%–121%) higher for household income, 214% (95%CI: 86%–429%) higher for race, and 90% (95%CI: 23%–193%) higher for geography during Covidtest.gov dissemination periods. Disparities were not elevated for age. Together, these three projects demonstrate the substantial role that high-resolution data can play in improving our understanding of respiratory infection surveillance and informing effective public health interventions.