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

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.

Implementation practice models for development in low- and middle-income countries: systematic review of peer-reviewed literature

Introduction

This study operationally defines a relatively small, but growing field of study on implementation practice models for health behavior change in the context of international development. We define ‘implementation practice models’ as theoretical models that take a practical and practitioner-focused approach to behavior change, and we illustrate how these models have been developed and applied. The paper examines the continuum of behavioral theories and their application in the context of development programs and research in low- and middle-income countries (LMICs). We describe implementation practice models, examine how they have been used to design and evaluate theory-based interventions in LMIC, and describe the state of evidence in this field of study.

Methods

The authors conducted a systematic search of the published, peer-reviewed literature following the widely accepted PRISMA methods for systematic reviews. We aimed to identify all relevant manuscripts published in the English language in health, social science, and business literature that apply implementation practice models, located in an LMIC, with a behavior change objective. We located 1,078 articles through database searching and 106 through other means. Ultimately, we identified 25 relevant articles for inclusion.

Results

We found that the peer-reviewed literature on implementation practice models for development has been growing in recent years, with 80% of reviewed papers published since 2015. There was a wide range of different models revealed by this review but none demonstrated clear-cut evidence of being most effective. However, the models found in this review share common characteristics of focusing on the three central tenets of Opportunity, Ability, and Motivation (OAM).

Conclusions

This review found that implementation practice models for development are a promising and growing approach to behavior change in LMICs. Intervention practice models research should be expanded and applied in new domains, such as vaccination.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13530-0.

Community health workers at the dawn of a new era: 10. Programme performance and its assessment

BACKGROUND

While the evidence supporting the effectiveness of community health worker (CHW) programmes is substantial, there is also considerable evidence that many of these programmes have notable weaknesses that need to be addressed in order for them to reach their full potential. Thus, considerations about CHW programme performance and its assessment must be taken into account as the importance of these programmes is becoming more widely appreciated. In this paper, the tenth in our 11-paper series, "Community health workers at the dawn of a new era", we address CHW programme performance and how it is assessed from a systems perspective.

METHODS

The paper builds on the 2014 CHW Reference Guide, a compendium of case studies of 29 national CHW programmes, the 2018 WHO guideline on health policy and system support to optimize CHW programmes, and scientific studies on CHW programme performance published in the past 5 years.

RESULTS

The paper provides an overview of existing frameworks that are useful for assessing the performance of CHW programmes, with a specific focus on how individual CHW performance and community-level outcomes can be measured. The paper also reviews approaches that have been taken to assess CHW programme performance, from programme monitoring using the routine health information system to national assessments using quantitative and/or qualitative study designs and assessment checklists. The paper also discusses contextual factors that influence CHW programme performance, and reflects upon gaps and needs for the future with regard to assessment of CHW programme performance.

CONCLUSION

Assessments of CHW programme performance can have various approaches and foci according to the programme and its context. Given the fact that CHW programmes are complex entities and part of health systems, their assessment ideally needs to be based on data derived from a mix of reliable sources. Assessments should be focused not only on effectiveness (what works) but also on contextual factors and enablers (how, for whom, under what circumstances). Investment in performance assessment is instrumental for continually innovating, upgrading, and improving CHW programmes at scale. Now is the time for new efforts in implementation research for strengthening CHW programming.

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.

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 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.

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.