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

Smittenaar, Peter; Ramesh, Banadakoppa M; Jain, Mokshada; Blanchard, James; Kemp, Hannah; Engl, Elisabeth; Isac, Shajy; Anthony, John; Prakash, Ravi; Gothalwal, Vikas; Namasivayam, Vasanthakumar; Kumar, Pankaj; Sgaier, Sema K.

doi:10.9745/ghsp-d-20-00027

Cited by 25 publications

(37 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Smittenaar et al (2020) conducted large-scale surveys including recently delivered women, their husbands, mothers-in-law, and accredited social health activists (ASHAs) in Uttar Pradesh, India. Household visitation timing and rate, ASHAs’ knowledge and beliefs, and the content and target of their advice were found to influence maternal and newborn health-promoting and health-seeking behaviour [ 54 ].…”

Section: Resultsmentioning

confidence: 99%

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

Kok

Crigler²,

Musoke

et al. 2021

Health Res Policy Sys

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

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

Kok

Crigler²,

Musoke

et al. 2021

Health Res Policy Sys

View full text Add to dashboard Cite

show abstract

“…As the second example, we generated a Causal Datasheet for a global development dataset we administered in Uttar Pradesh, India in 2016 (Smittenaar et al, 2020) (Supplementary Material B). For simplicity, we refer to this dataset as Surgo Household survey or SHH.…”

Section: Causal Datasheet For Existing Datasets Example: Analysis Of An Existing Global Health Survey (Surgo Household Dataset)mentioning

confidence: 99%

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

Butcher

Huang²,

Robinson

et al. 2021

Front. Artif. Intell.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…The frontline service providers (AWWs and ASHAs) have inadequate skills and tools to counsel and demonstrate the ECD activities to parents [5][6][7][8]. The ASHAs/AWWs are likely to have similar challenges for ECD targeted service delivery and counselling.…”

Section: S S S S S P P P P P E E E E E C C C C C I I I I I a A A A A L A L A L A L A L A R R R R R T T T T T I I I I I C C C C C L L L L mentioning

confidence: 99%

Promoting Family Integrated Early Childhood Development During the First 1000 Days in Urban Slums of Delhi: Feasibility and Early Implementation Experience

et al. 2021

View full text Add to dashboard Cite

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

Cited by 25 publications

References 30 publications

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

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

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

Promoting Family Integrated Early Childhood Development During the First 1000 Days in Urban Slums of Delhi: Feasibility and Early Implementation Experience

Contact Info

Product

Resources

About