Geographical variation in perceptions, attitudes and barriers to mental health care-seeking across the UK: a cross-sectional study.

OBJECTIVES: To examine the relative importance of the drivers of mental health care-seeking intention and how these, along with intention itself, are geographically distributed across integrated care systems (ICS) and health boards (HBs) in the UK. Also, to examine the degree of acceptance of virtual modes of care. DESIGN: Community-based cross-sectional survey. PARTICIPANTS AND SETTING: A national online survey of 17 309 adults between August and September 2021 recruited via a research technology company, Lucid. Sample size quotas were set to ensure coverage across the UK and match population distributions for gender, age and ethnicity. After exclusions, 16 835 participants remained (54% female, 89% white). MAIN OUTCOME MEASURES: Care-seeking intention, using a continuous measure of likelihood and a categorical measure of estimated time to seek professional help for a future mental health difficulty. RESULTS: 20.5% (95% CI 19.8% to 21.2%) reported that they would significantly delay or never seek mental healthcare, ranging from 8.3% to 25.7% across ICS/HBs. Multilevel regression analysis showed mental health knowledge was the most predictive of care-seeking intention, followed by attitudes towards others with mental illness and a combination of stigma, negative attitudes to treatment and instrumental barriers to accessing care. The model explained 17% of the variance. There was substantial geographical variation in prevalence of preclinical symptoms of depression and anxiety, attitudes to mental health, and barriers to care, leading to complex ICS/HB profiles. Remote and self-guided therapies did not pose as a major barrier to care with more than half of respondents likely or very likely to use them. CONCLUSIONS: Our locally relevant and actionable findings suggest possible interventions that may improve care-seeking intention and indicate which of these interventions need to be geographically tailored to have maximal effect.

How do psychobehavioural variables shed light on heterogeneity in COVID-19 vaccine acceptance? Evidence from United States general population surveys on a probability panel and social media.

OBJECTIVES: To (1) understand what behaviours, beliefs, demographics and structural factors predict US adults' intention to get a COVID-19 vaccination, (2) identify segments of the population ('personas') who share similar factors predicting vaccination intention, (3) create a 'typing tool' to predict which persona people belong to and (4) track changes in the distribution of personas over time and across the USA. DESIGN: Three surveys: two on a probability-based household panel (NORC's AmeriSpeak) and one on Facebook. SETTING: The first two surveys were conducted in January 2021 and March 2021 when the COVID-19 vaccine had just been made available in the USA. The Facebook survey ran from May 2021 to February 2022. PARTICIPANTS: All participants were aged 18+ and living in the USA. OUTCOME MEASURES: In our predictive model, the outcome variable was self-reported vaccination intention (0-10 scale). In our typing tool model, the outcome variable was the five personas identified by our clustering algorithm. RESULTS: Only 1% of variation in vaccination intention was explained by demographics, with about 70% explained by psychobehavioural factors. We identified five personas with distinct psychobehavioural profiles: COVID Sceptics (believe at least two COVID-19 conspiracy theories), System Distrusters (believe people of their race/ethnicity do not receive fair healthcare treatment), Cost Anxious (concerns about time and finances), Watchful (prefer to wait and see) and Enthusiasts (want to get vaccinated as soon as possible). The distribution of personas varies at the state level. Over time, we saw an increase in the proportion of personas who are less willing to get vaccinated. CONCLUSIONS: Psychobehavioural segmentation allows us to identify why people are unvaccinated, not just who is unvaccinated. It can help practitioners tailor the right intervention to the right person at the right time to optimally influence behaviour.

Discovery of interconnected causal drivers of COVID-19 vaccination intentions in the US using a causal Bayesian network.

Holistic interventions to overcome COVID-19 vaccine hesitancy require a system-level understanding of the interconnected causes and mechanisms that give rise to it. However, conventional correlative analyses do not easily provide such nuanced insights. We used an unsupervised, hypothesis-free causal discovery algorithm to learn the interconnected causal pathways to vaccine intention as a causal Bayesian network (BN), using data from a COVID-19 vaccine hesitancy survey in the US in early 2021. We identified social responsibility, vaccine safety and anticipated regret as prime candidates for interventions and revealed a complex network of variables that mediate their influences. Social responsibility's causal effect greatly exceeded that of other variables. The BN revealed that the causal impact of political affiliations was weak compared with more direct causal factors. This approach provides clearer targets for intervention than regression, suggesting it can be an effective way to explore multiple causal pathways of complex behavioural problems to inform interventions.

Black-White disparities in maternal vulnerability and adverse pregnancy outcomes: an ecological population study in the United States, 2014-2018.

Background: Systematic information on the association between community-level determinants and maternal health outcomes and disparities is needed. We aimed to investigate multi-dimensional place-based contributions to Black-White maternal health disparities in the United States. Methods: We constructed the Maternal Vulnerability Index, a geospatial measure of vulnerability to poor maternal health. The index was linked to 13m live births and maternal deaths to mothers aged 10-44 for 2014-2018 in the United States. We quantified racial disparities in exposure to higher risk environments, and used logistic regression to estimate associations between race, vulnerability, and maternal death (n = 3633), low birthweight (n = 1.1m), and preterm birth (n = 1.3m). Findings: Black mothers lived in disproportionately higher maternal vulnerability counties, when compared to White mothers (median of 55 vs 36/100 points). Giving birth in the highest-quartile MVI counties was associated with an increase in the odds of poor outcomes when compared to the lowest-quartile (aOR 1.43 [95% CI 1.20-1.71] for mortality, 1.39 [1.37-1.41] for low birthweight and 1.41 [1.39-1.43] for preterm birth, adjusted for age, educational attainment level and race/ethnicity). Racial disparities exist in low- and high-vulnerability counties: Black mothers in the least vulnerable counties remain at higher risk of maternal mortality, preterm birth, and low birthweight as White mothers in the most vulnerable. Interpretation: Exposure to community maternal vulnerability is associated with increased odds of adverse outcomes, but the Black-White gap in outcomes remained under all vulnerability levels. Our findings suggest that locally-informed precision health interventions and further research into racism are needed to achieve maternal health equity. Funding: Bill & Melinda Gates Foundation (grant number INV-024583).

Improving Community Health Worker Compensation: A Case Study From India Using Quantitative Projection Modeling and Incentive Design Principles.

INTRODUCTION: Although community health workers (CHWs) are effective at mobilizing important health behaviors, there is limited evidence on how financial incentive systems can best be designed to drive their effectiveness. This study intends to bridge this evidence gap by analyzing the compensation model of India's accredited social health activist (ASHA) program and identifying areas of improvement in the system's design and implementation. METHODS: We analyze the ASHA program in Uttar Pradesh, India. ASHAs receive compensation through a mix of program-linked, performance-based, and routine activity-based incentive structures. Using multiple data sources, including a novel linked household and ASHA survey, we estimate ASHA performance-linked incentive earnings under different scenarios of ASHA actions and household behaviors. Juxtaposing statistical projection models and actual government payments, we identified which incentives promised the highest payments, which were claimed or not, which could be claimed more by increasing ASHA actions, and which were paid despite not meeting payment criteria. We also report findings on ASHA awareness of and experiences with claiming incentives. RESULTS: We find crucial gaps and implementation challenges in the ASHA incentive structure. ASHAs could double their earnings by completing certain tasks within their control. ASHAs may also be paid for partial completion of activities, as incentives are paid in lump sums for a series of activities rather than for each activity. Family planning incentives have the largest gap between potential and actual earnings. Incentivizing ASHAs for achieving certain health outcomes is inefficient, as no clear linkage was found between the achievability of such health outcomes and the claim amounts. CONCLUSION: There are several opportunities for improving CHW compensation, from improving the incentive claims process to shifting focus to achievable outcomes. Optimizing incentive system designs can further enhance CHW effectiveness globally to affect key health behaviors.

Causal Datasheet for Datasets: An Evaluation Guide for Real-World Data Analysis and Data Collection Design Using Bayesian Networks.

Developing data-driven solutions that address real-world problems requires understanding of these problems' causes and how their interaction affects the outcome-often with only observational data. Causal Bayesian Networks (BN) have been proposed as a powerful method for discovering and representing the causal relationships from observational data as a Directed Acyclic Graph (DAG). BNs could be especially useful for research in global health in Lower and Middle Income Countries, where there is an increasing abundance of observational data that could be harnessed for policy making, program evaluation, and intervention design. However, BNs have not been widely adopted by global health professionals, and in real-world applications, confidence in the results of BNs generally remains inadequate. This is partially due to the inability to validate against some ground truth, as the true DAG is not available. This is especially problematic if a learned DAG conflicts with pre-existing domain doctrine. Here we conceptualize and demonstrate an idea of a "Causal Datasheet" that could approximate and document BN performance expectations for a given dataset, aiming to provide confidence and sample size requirements to practitioners. To generate results for such a Causal Datasheet, a tool was developed which can generate synthetic Bayesian networks and their associated synthetic datasets to mimic real-world datasets. The results given by well-known structure learning algorithms and a novel implementation of the OrderMCMC method using the Quotient Normalized Maximum Likelihood score were recorded. These results were used to populate the Causal Datasheet, and recommendations could be made dependent on whether expected performance met user-defined thresholds. We present our experience in the creation of Causal Datasheets to aid analysis decisions at different stages of the research process. First, one was deployed to help determine the appropriate sample size of a planned study of sexual and reproductive health in Madhya Pradesh, India. Second, a datasheet was created to estimate the performance of an existing maternal health survey we conducted in Uttar Pradesh, India. Third, we validated generated performance estimates and investigated current limitations on the well-known ALARM dataset. Our experience demonstrates the utility of the Causal Datasheet, which can help global health practitioners gain more confidence when applying BNs.

Information-Seeking Patterns During the COVID-19 Pandemic Across the United States: Longitudinal Analysis of Google Trends Data.

BACKGROUND: The COVID-19 pandemic has impacted people's lives at unprecedented speed and scale, including how they eat and work, what they are concerned about, how much they move, and how much they can earn. Traditional surveys in the area of public health can be expensive and time-consuming, and they can rapidly become outdated. The analysis of big data sets (such as electronic patient records and surveillance systems) is very complex. Google Trends is an alternative approach that has been used in the past to analyze health behaviors; however, most existing studies on COVID-19 using these data examine a single issue or a limited geographic area. This paper explores Google Trends as a proxy for what people are thinking, needing, and planning in real time across the United States. OBJECTIVE: We aimed to use Google Trends to provide both insights into and potential indicators of important changes in information-seeking patterns during pandemics such as COVID-19. We asked four questions: (1) How has information seeking changed over time? (2) How does information seeking vary between regions and states? (3) Do states have particular and distinct patterns in information seeking? (4) Do search data correlate with-or precede-real-life events? METHODS: We analyzed searches on 38 terms related to COVID-19, falling into six themes: social and travel; care seeking; government programs; health programs; news and influence; and outlook and concerns. We generated data sets at the national level (covering January 1, 2016, to April 15, 2020) and state level (covering January 1 to April 15, 2020). Methods used include trend analysis of US search data; geographic analyses of the differences in search popularity across US states from March 1 to April 15, 2020; and principal component analysis to extract search patterns across states. RESULTS: The data showed high demand for information, corresponding with increasing searches for coronavirus linked to news sources regardless of the ideological leaning of the news source. Changes in information seeking often occurred well in advance of action by the federal government. The popularity of searches for unemployment claims predicted the actual spike in weekly claims. The increase in searches for information on COVID-19 care was paralleled by a decrease in searches related to other health behaviors, such as urgent care, doctor's appointments, health insurance, Medicare, and Medicaid. Finally, concerns varied across the country; some search terms were more popular in some regions than in others. CONCLUSIONS: COVID-19 is unlikely to be the last pandemic faced by the United States. Our research holds important lessons for both state and federal governments in a fast-evolving situation that requires a finger on the pulse of public sentiment. We suggest strategic shifts for policy makers to improve the precision and effectiveness of non-pharmaceutical interventions and recommend the development of a real-time dashboard as a decision-making tool.

Understanding drivers of family planning in rural northern India: An integrated mixed-methods approach.

BACKGROUND: Family planning is a key means to achieving many of the Sustainable Development Goals. Around the world, governments and partners have prioritized investments to increase access to and uptake of family planning methods. In Uttar Pradesh, India, the government and its partners have made significant efforts to increase awareness, supply, and access to modern contraceptives. Despite progress, uptake remains stubbornly low. This calls for systematic research into understanding the 'why'-why people are or aren't using modern methods, what drives their decisions, and who influences them. METHODS: We use a mixed-methods approach, analyzing three existing quantitative data sets to identify trends and geographic variation, gaps and contextual factors associated with family planning uptake and collecting new qualitative data through in-depth immersion interviews, journey mapping, and decision games to understand systemic and individual-level barriers to family planning use, household decision making patterns and community level barriers. RESULTS: We find that reasons for adoption of family planning are complex-while access and awareness are critical, they are not sufficient for increasing uptake of modern methods. Although awareness is necessary for uptake, we found a steep drop-off (59%) between high awareness of modern contraceptive methods and its intention to use, and an additional but smaller drop-off from intention to actual use (9%). While perceived access, age, education and other demographic variables partially predict modern contraceptive intention to use, the qualitative data shows that other behavioral drivers including household decision making dynamics, shame to obtain modern contraceptives, and high-risk perception around side-effects also contribute to low intention to use modern contraceptives. The data also reveals that strong norms and financial considerations by couples are the driving force behind the decision to use and when to use family planning methods. CONCLUSION: The finding stresses the need to shift focus towards building intention, in addition to ensuring access of trained staff, and commodities drugs and equipment, and building capacities of health care providers.

Bringing Greater Precision to Interactions Between Community Health Workers and Households to Improve Maternal and Newborn Health Outcomes in India.

INTRODUCTION: Community health workers (CHWs) play a key role in the health of mothers and newborns in low- and middle-income countries. However, it remains unclear by what actions and messages CHWs enable good outcomes and respectful care. METHODS: We collected a uniquely linked set of questions on behaviors, beliefs, and care pathways from recently delivered women (n=5,469), their husbands (n=3,064), mothers-in-law (n=3,626), and CHWs (accredited social health activists; n=1,052) in Uttar Pradesh, India. We used logistic regression to study associations between CHW actions and household behaviors during antenatal, delivery, and postnatal periods. RESULTS: Pregnant women who were visited earlier in pregnancy and who received multiple visits were more likely to perform recommended health behaviors including attending multiple checkups, consuming iron and folic acid tablets, and delivering in a health facility (ID), compared to women visited later or receiving fewer visits, respectively. Counseling the woman was associated with higher likelihood of attending 3+ checkups and consuming 100+ iron and folic acid tablets, whereas counseling the husband and mother-in-law was associated with higher rates of ID. Certain behavior change messages, such as the danger of complications, were associated with more checkups and ID, but were only used by 50%-80% of CHWs. During delivery, 57% of women had the CHW present, and their presence was associated with respectful care, early initiation of breastfeeding, and exclusive breastfeeding, but not with delayed bathing or clean cord care. The newborn was less likely to receive delayed bathing if the CHW incorrectly believed that newborns could be bathed soon after birth (which is believed by 30% of CHWs). CHW presence was associated with health behaviors more strongly for home than facility deliveries. Home visits after delivery were associated with higher rates of clean cord care and exclusive breastfeeding. Counseling the mother-in-law (but not the husband or woman) was associated with exclusive breastfeeding. CONCLUSION: We identified potential ways in which CHW impact could be improved, specifically by emphasizing the importance of home visits, which household members are targeted during these visits, and what messages are shared. Achieving this change will require training CHWs in counseling and behavior change and providing supervision and modern tools such as apps that can help the CHW keep track of her beneficiaries, suggest behavior change strategies, and direct attention to households that stand to gain the most from support.

Closing the gap on institutional delivery in northern India: a case study of how integrated machine learning approaches can enable precision public health.

INTRODUCTION: Meeting ambitious global health goals with limited resources requires a precision public health (PxPH) approach. Here we describe how integrating data collection optimisation, traditional analytics and causal artificial intelligence/machine learning (ML) can be used in a use case for increasing hospital deliveries of newborns in Uttar Pradesh, India. METHODS: Using a systematic behavioural framework we designed a large-scale survey on perceptual, interpersonal and structural drivers of women's behaviour around childbirth (n=5613). Multivariate logistic regression identified factors associated with institutional delivery (ID). Causal ML determined the cause-and-effect ordering of these factors. Variance decomposition was used to parse sources of variation in delivery location, and a supervised learning algorithm was used to distinguish population subgroups. RESULTS: Among the factors found associated with ID, the causal model showed that having a delivery plan (OR=6.1, 95% CI 6.0 to 6.3), believing the hospital is safer than home (OR=5.4, 95% CI 5.1 to 5.6) and awareness of financial incentives were direct causes of ID (OR=3.4, 95% CI 3.3 to 3.5). Distance to the hospital, borrowing delivery money and the primary decision-maker were not causal. Individual-level factors contributed 69% of variance in delivery location. The segmentation analysis showed four distinct subgroups differentiated by ID risk perception, parity and planning. CONCLUSION: These findings generate a holistic picture of the drivers and barriers to ID in Uttar Pradesh and suggest distinct intervention points for different women. This demonstrates data optimised to identify key behavioural drivers, coupled with traditional and ML analytics, can help design a PxPH approach that maximise the impact of limited resources.

Understanding why at-risk population segments do not seek care for tuberculosis: a precision public health approach in South India.

INTRODUCTION: Delaying 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. METHODS: We 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. RESULTS: Delayed 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. CONCLUSION: Interventions 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.

Facilities are substantially more influential than care providers in the quality of delivery care received: a variance decomposition and clustering analysis in Kenya, Malawi and India.

INTRODUCTION: Improving the quality of care during childbirth is essential for reducing neonatal and maternal mortality. One barrier to improving quality of care is understanding the appropriate level to target interventions. We examine quality of care data during labour and delivery from multiple countries to assess whether quality varies primarily from nurse to nurse within the same facility, or primarily between facilities. METHODS: To assess the relative contributions of nurses and facilities to variance in quality of care, we performed a variance decomposition analysis using a linear mixed effect model on two data sources: (1) the number of vital signs assessed for women in labour from a study of nurse practices in Uttar Pradesh, India; 2) broad-scale indices of respectful and competent care generated from Service Provision Assessments in Kenya and Malawi. We used unsupervised clustering, a data mining technique that groups objects together based on similar characteristics, to identify groups of facilities that displayed distinct patterns of vital signs assessment behaviour. RESULTS: We found 3-10 times more variance in quality of care was explained by the facility where a patient received care than by the nurse who provided it. The unsupervised clustering analysis revealed groups of facilities with highly distinct patterns of vital signs assessment, even when overall rates of vital signs assessments were similar (eg, some facilities consistently test fetal heart rate, but not other vitals, others only blood pressure). CONCLUSION: Facilities within a region can vary substantially in the quality of care they provide to women in labour, but within a facility, nurses tend to provide similar care. This holds true both for care that can be influenced by equipment availability and technical training (eg, vital signs assessment), as well as cultural aspects (eg, respectful care).

Identifying population segments for effective intervention design and targeting using unsupervised machine learning: an end-to-end guide.

One-size-fits-all interventions that aim to change behavior are a missed opportunity to improve human health and well-being, as they do not target the different reasons that drive people's choices and behaviors. Psycho-behavioral segmentation is an approach to uncover such differences and enable the design of targeted interventions, but is rarely implemented at scale in global development. In part, this may be due to the many choices program designers and data scientists face, and the lack of available guidance through the process. Effective segmentation encompasses conceptualization and selection of the dimensions to segment on, which often requires the design of suitable qualitative and quantitative primary research. The choice of algorithm and its parameters also profoundly shape the resulting output and how useful the results are in the field. Analytical outputs are not self-explanatory and need to be subjectively evaluated and described. Finally, segments can be prioritized and targeted with matching interventions via appropriate channels. Here, we provide an end-to-end overview of all the stages from planning, designing field-based research, analyzing, and implementing a psycho-behavioral segmentation solution. We illustrate the choices and critical steps along the way, and discuss a case study of segmentation for voluntary medical male circumcision that implemented the method described here. Though our examples mostly draw on health interventions in the developing world, the principles in this approach can be used in any context where understanding human heterogeneity in driving behavior change is valuable.

CUBES: A practical toolkit to measure enablers and barriers to behavior for effective intervention design.

A pressing goal in global development and other sectors is often to understand what drives people's behaviors, and how to influence them. Yet designing behavior change interventions is often an unsystematic process, hobbled by insufficient understanding of contextual and perceptual behavioral drivers and a narrow focus on limited research methods to assess them. We propose a toolkit (CUBES) of two solutions to help programs arrive at more effective interventions. First, we introduce a novel framework of behavior, which is a practical tool for programs to structure potential drivers and match corresponding interventions. This evidence-based framework was developed through extensive cross-sectoral literature research and refined through application in large-scale global development programs. Second, we propose a set of descriptive, experimental, and simulation approaches that can enhance and expand the methods commonly used in global development. Since not all methods are equally suited to capture the different types of drivers of behavior, we present a decision aid for method selection. We recommend that existing commonly used methods, such as observations and surveys, use CUBES as a scaffold and incorporate validated measures of specific types of drivers in order to comprehensively test all the potential components of a target behavior. We also recommend under-used methods from sectors such as market research, experimental psychology, and decision science, which programs can use to extend their toolkit and test the importance and impact of key enablers and barriers. The CUBES toolkit enables programs across sectors to streamline the process of conceptualizing, designing, and optimizing interventions, and ultimately to change behaviors and achieve targeted outcomes.

Categorizing and assessing comprehensive drivers of provider behavior for optimizing quality of health care.

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.

Achieving scale rapidly in public health: Applying business management principles to scale up an HIV prevention program in India.

Numerous public-health interventions have demonstrated effectiveness in pilots or on a small scale, but have proven challenging to scale up for population-level impact. Avahan, the Bill & Melinda Gates Foundation's HIV prevention program in 6 states of India, confronted the challenge of rapidly scaling up services to reach 300,000 people most at risk of HIV. This meant working in diverse and complex environments with marginalized and largely hidden populations. This case report presents a number of business-management principles that the foundation drew upon to successfully scale up this public-health program: 1) strategy development through market segmentation and complexity analysis, 2) a dynamic and evolving strategy, 3) developing an implementation and management structure to match the strategy, 4) standardization with flexibility, 5) generating demand to balance supply, 6) a customer-centric approach, and 7) data-driven management. Lessons learned from this experience include the crucial role of data in guiding decision-making and strategic and programmatic change; the need for a central body to set strategy; a willingness to change course when experience and data demonstrate the need; and the importance of partnering with program beneficiaries at all stages of program design, operation, evaluation and sustainability. We believe these lessons are applicable to other development programs that seek to foster widespread and sustainable program benefits at scale.

A case study for a psychographic-behavioral segmentation approach for targeted demand generation in voluntary medical male circumcision.

Public health programs are starting to recognize the need to move beyond a one-size-fits-all approach in demand generation, and instead tailor interventions to the heterogeneity underlying human decision making. Currently, however, there is a lack of methods to enable such targeting. We describe a novel hybrid behavioral-psychographic segmentation approach to segment stakeholders on potential barriers to a target behavior. We then apply the method in a case study of demand generation for voluntary medical male circumcision (VMMC) among 15-29 year-old males in Zambia and Zimbabwe. Canonical correlations and hierarchical clustering techniques were applied on representative samples of men in each country who were differentiated by their underlying reasons for their propensity to get circumcised. We characterized six distinct segments of men in Zimbabwe, and seven segments in Zambia, according to their needs, perceptions, attitudes and behaviors towards VMMC, thus highlighting distinct reasons for a failure to engage in the desired behavior.

Attitudes and decision-making about early-infant versus early-adolescent male circumcision: Demand-side insights for sustainable HIV prevention strategies in Zambia and Zimbabwe.

As countries approach their scale-up targets for the voluntary medical male circumcision program for HIV prevention, they are strategizing and planning for the sustainability phase to follow. Global guidance recommends circumcising adolescent (below 14 years) and/or early infant boys (aged 0-60 days), and countries need to consider several factors before prioritizing a cohort for their sustainability phase. We provide community and healthcare provider-side insights on attitudes and decision-making process as a key input for this strategic decision in Zambia and Zimbabwe. We studied expectant parents, parents of infant boys (aged 0-60 days), family members and neo-natal and ante-natal healthcare providers in Zambia and Zimbabwe. Our integrated methodology consisted of in-depth qualitative and quantitative one-on-one interviews, and a simulated-decision-making game, to uncover attitudes towards, and the decision-making process for, early adolescent or early infant medical circumcision (EAMC or EIMC). In both countries, parents viewed early infancy and early adolescence as equally ideal ages for circumcision (38% EIMC vs. 37% EAMC in Zambia; 24% vs. 27% in Zimbabwe). If offered for free, about half of Zambian parents and almost 2 in 5 Zimbabwean parents indicated they would likely circumcise their infant boy; however, half of parents in each country perceived that the community would not accept EIMC. Nurses believed their facilities currently could not absorb EIMC services and that they would have limited ability to influence fathers, who were seen as having the primary decision-making authority. Our analysis suggests that EAMC is more accepted by the community than EIMC and is the path of least resistance for the sustainability phase of VMMC. However, parents or community members do not reject EIMC. Should countries choose to prioritize this cohort for their sustainability phase, a number of barriers around information, decision-making by parents, and supply side will need to be addressed.