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Kok M, Crigler L, Musoke D, Ballard M, Hodgins S, Perry HB. Community health workers at the dawn of a new era: 10. Programme performance and its assessment. Health Res Policy Syst 2021; 19:108. [PMID: 34641901 PMCID: PMC8506096 DOI: 10.1186/s12961-021-00758-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 06/17/2021] [Indexed: 12/02/2022] Open
Abstract
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.
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Affiliation(s)
- Maryse Kok
- Department of Global Health, KIT Royal Tropical Institute, Amsterdam, The Netherlands
| | | | - David Musoke
- Department of Disease Control and Environmental Health, School of Public Health, Makerere University College of Health Sciences, Kampala, Uganda
| | - Madeleine Ballard
- Community Health Impact Coalition, New York, NY, USA
- Department of Global Health and Health Systems Design, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Steve Hodgins
- School of Public Health, University of Alberta, Edmonton, AB, Canada
| | - Henry B Perry
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
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Helfinstein S, Engl E, Thomas BE, Natarajan G, Prakash P, Jain M, Lavanya J, Jagadeesan M, Chang R, Mangono T, Kemp H, Mannan S, Dabas H, Charles GK, Sgaier SK. Understanding why at-risk population segments do not seek care for tuberculosis: a precision public health approach in South India. BMJ Glob Health 2021; 5:bmjgh-2020-002555. [PMID: 32912854 PMCID: PMC7482470 DOI: 10.1136/bmjgh-2020-002555] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 07/15/2020] [Accepted: 07/16/2020] [Indexed: 12/14/2022] Open
Abstract
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.
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Affiliation(s)
| | | | - Beena E Thomas
- Department of Social and Behavioral Research, National Institute for Research in Tuberculosis, Chennai, Tamil Nadu, India
| | | | | | | | - Jayabal Lavanya
- District Tuberculosis Centre, Greater Chennai Corporation, Chennai, India
| | | | - Rebekah Chang
- Clinton Health Access Initiative, Chennai/Delhi/NYC, India/USA
| | | | | | - Shamim Mannan
- Clinton Health Access Initiative, Chennai/Delhi/NYC, India/USA
| | - Harkesh Dabas
- Clinton Health Access Initiative, Chennai/Delhi/NYC, India/USA
| | | | - Sema K Sgaier
- Surgo Foundation, Washington, DC, USA .,Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA.,Department of Global Health, University of Washington, Seattle, WA, United States
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Jain M, Caplan Y, Ramesh BM, Isac S, Anand P, Engl E, Halli S, Kemp H, Blanchard J, Gothalwal V, Namasivayam V, Kumar P, Sgaier SK. Understanding drivers of family planning in rural northern India: An integrated mixed-methods approach. PLoS One 2021; 16:e0243854. [PMID: 33439888 PMCID: PMC7806122 DOI: 10.1371/journal.pone.0243854] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 11/28/2020] [Indexed: 11/19/2022] Open
Abstract
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.
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Affiliation(s)
- Mokshada Jain
- Surgo Foundation, Washington, District of Columbia, United States of America
| | - Yael Caplan
- Surgo Foundation, Washington, District of Columbia, United States of America
| | - B. M. Ramesh
- Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Shajy Isac
- Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
- India Health Action Trust, New Delhi, Delhi, India
| | - Preeti Anand
- Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
- India Health Action Trust, Lucknow, Uttar Pradesh, India
| | - Elisabeth Engl
- Surgo Foundation, Washington, District of Columbia, United States of America
| | - Shiva Halli
- Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Hannah Kemp
- Surgo Foundation, Washington, District of Columbia, United States of America
| | - James Blanchard
- Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Vikas Gothalwal
- Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
- India Health Action Trust, Lucknow, Uttar Pradesh, India
| | - Vasanthakumar Namasivayam
- Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Pankaj Kumar
- National Health Mission, Government of Uttar Pradesh, Lucknow, Uttar Pradesh, India
| | - Sema K. Sgaier
- Surgo Foundation, Washington, District of Columbia, United States of America
- Department of Global Health & Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Global Health, University of Washington, Seattle, Washington, United States of America
- * E-mail:
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Engl E, Smittenaar P, Sgaier SK. Identifying population segments for effective intervention design and targeting using unsupervised machine learning: an end-to-end guide. Gates Open Res 2019; 3:1503. [PMID: 31701090 PMCID: PMC6820452 DOI: 10.12688/gatesopenres.13029.2] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/16/2019] [Indexed: 11/20/2022] Open
Abstract
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.
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Affiliation(s)
| | | | - Sema K Sgaier
- Surgo Foundation, Washington, DC, 20011, USA.,Department of Global Health & Population, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Department of Global Health, University of Washington, Seattle, Washington, USA
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Engl E, Smittenaar P, Sgaier SK. Identifying population segments for effective intervention design and targeting using unsupervised machine learning: an end-to-end guide. Gates Open Res 2019; 3:1503. [DOI: 10.12688/gatesopenres.13029.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/20/2019] [Indexed: 11/20/2022] Open
Abstract
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.
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