IntroductionDelaying care-seeking for tuberculosis (TB) symptoms is a major contributor to mortality, leading to worse outcomes and spread. To reduce delays, it is essential to identify barriers to care-seeking and target populations most at risk of delaying. Previous work identifies barriers only in people within the health system, often long after initial care-seeking.MethodsWe conducted a community-based survey of 84 625 households in Chennai, India, to identify 1667 people with TB-indicative symptoms in 2018–2019. Cases were followed prospectively to observe care-seeking behaviour. We used a comprehensive survey to identify care-seeking drivers, then performed multivariate analyses to identify care-seeking predictors. To identify profiles of individuals most at risk to delay care-seeking, we segmented the sample using unsupervised clustering. We then estimated the per cent of the TB-diagnosed population in Chennai in each segment.ResultsDelayed care-seeking characteristics include smoking, drinking, being employed, preferring different facilities than the community, believing to be at lower risk of TB and believing TB is common. Respondents who reported fever or unintended weight loss were more likely to seek care. Clustering analysis revealed seven population segments differing in care-seeking, from a retired/unemployed/disabled cluster, where 70% promptly sought care, to a cluster of employed men who problem-drink and smoke, where only 42% did so. Modelling showed 54% of TB-diagnosed people who delay care-seeking might belong to the latter segment, which is most likely to acquire TB and least likely to promptly seek care.ConclusionInterventions to increase care-seeking should move from building general awareness to addressing treatment barriers such as lack of time and low-risk perception. Care-seeking interventions should address specific beliefs through a mix of educational, risk perception-targeting and social norms-based campaigns. Employed men who problem-drink and smoke are a prime target for interventions. Reducing delays in this group could dramatically reduce TB spread.

Community health worker (CHW) presence, number and timing of visits, behavior change messaging strategies, and focus on specific household members for different behaviors associates with maternal and newborn care practices. n Local sociocultural factors such as the decision dynamics of households and common false beliefs about neonatal care should inform how the CHW communicates.

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Inadequate quality of care in healthcare facilities is one of the primary causes of patient mortality in low- and middle-income countries, and understanding the behavior of healthcare providers is key to addressing it. Much of the existing research concentrates on improving resource-focused issues, such as staffing or training, but these interventions do not fully close the gaps in quality of care. By contrast, there is a lack of knowledge regarding the full contextual and internal drivers–such as social norms, beliefs, and emotions–that influence the clinical behaviors of healthcare providers. We aimed to provide two conceptual frameworks to identify such drivers, and investigate them in a facility setting where inadequate quality of care is pronounced. Using immersion interviews and a novel decision-making game incorporating concepts from behavioral science, we systematically and qualitatively identified an extensive set of contextual and internal behavioral drivers in staff nurses working in reproductive, maternal, newborn, and child health (RMNCH) in government public health facilities in Uttar Pradesh, India. We found that the nurses operate in an environment of stress, blame, and lack of control, which appears to influence their perception of their role as often significantly different from the RMNCH program’s perspective. That context influences their perceptions of risk for themselves and for their patients, as well as self-efficacy beliefs, which could lead to avoidance of responsibility, or incorrect care. A limitation of the study is its use of only qualitative methods, which provide depth, rather than prevalence estimates of findings. This exploratory study identified previously under-researched contextual and internal drivers influencing the care-related behavior of staff nurses in public facilities in Uttar Pradesh. We recommend four types of interventions to close the gap between actual and target behaviors: structural improvements, systemic changes, community-level shifts, and interventions within healthcare facilities.

Social distancing emerged as one of the early critical nonpharmaceutical interventions to fight the spread of COVID-19. However, in the United States, this behavior was not universally adopted. Understanding COVID-relevant behaviors is crucial to helping policymakers develop targeted, actionable interventions that meet the urgent needs of a global pandemic in a precision public health approach-that is, getting the right intervention to the right person, at the right time and place. In this article, we demonstrate how using a toolbox that includes a comprehensive data collection design framework, machine learning tools to do causal discovery (i.e., structural learning of a causal Bayesian network), and clustering analysis can help disentangle the intertwined hierarchy of drivers of social distancing to design targeted, actionable interventions in a precision public health application. We integrated several machine learning techniques to generate insights from a nationally representative social distancing survey of 2,500 U.S. respondents, conducted in March 2020. This approach goes beyond measuring the correlates of social distancing intentions and behavior and narrows in on the potential causal drivers of social distancing. Our approach identifies the factors that social distancing directly and conditionally depends upon: higher financial security, higher information-seeking, and higher worry about the coronavirus, as well as other upstream factors. We also identify four population segments to help target interventions: "Worriers," "Rule-followers," "Financially constrained," and "Skeptics." Two subgroups in particular, the "Skeptics" and the "Financially constrained," had low uptake of social distancing, but would require different targeted messages to increase social distancing behavior. Taken together, these results demonstrate how machine learning techniques can help prioritize messages most effective for matched population targets, increasing desirable outcomes while potentially saving resources.