M-Health for Improving Screening Accuracy of Acute Malnutrition in a Community-Based Management of Acute Malnutrition Program in Mumbai Informal Settlements

Chanani, Sheila; Wacksman, Jeremy; Deshmukh, Devika; Pantvaidya, Shanti; Fernandez, Armida; Jayaraman, Anuja

doi:10.1177/0379572116657241

Cited by 17 publications

(13 citation statements)

References 14 publications

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“…Most of the available evidence supports the ability of CHWs to reliably measure MUAC to the standard necessary for SAM screening. It is worth noting that although MUAC is widely considered a simple indicator for measurement and interpretation, there are examples where the metric was not accepted by CHWs and/or not accurately measured by community workers in the context of growth monitoring and basic anthropometric training 30 – 32 (not included in this review as they did not meet inclusion criteria).…”

Section: Discussionmentioning

confidence: 99%

Use of Mid-Upper Arm Circumference by Novel Community Platforms to Detect, Diagnose, and Treat Severe Acute Malnutrition in Children: A Systematic Review

Bliss

Lelijveld

Briend

et al. 2018

Glob Health Sci Pract

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Section: Discussionmentioning

confidence: 99%

Use of Mid-Upper Arm Circumference by Novel Community Platforms to Detect, Diagnose, and Treat Severe Acute Malnutrition in Children: A Systematic Review

Bliss

Lelijveld

Briend

et al. 2018

Glob Health Sci Pract

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“…Mobile health (mHealth) technologies have been variously employed either as interventions in their own right or as aids to CHWs delivering interventions . The incorporation of mHealth components into existing interventions, however appealing, runs the risk of perpetuating the use of technology for technology's sake, rather than adding to intervention effectiveness .…”

Section: Implementation Lessons From Philanimentioning

confidence: 99%

Diffusing and scaling evidence‐based interventions: eight lessons for early child development from the implementation of perinatal home visiting in South Africa

Tomlinson

Hunt

2018

Annals of the New York Academy of Sciences

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Most low- and middle-income countries lack resources with which to implement public health programs. As such, there is a necessity to facilitate programing that judiciously makes use of what resources there are. However, despite evidence for the efficacy of many interventions, translating these into real-world effectiveness, and then into scalability, is complex and has often been neglected. We draw on a case study of Philani+ (a maternal and child health intervention implemented in South Africa) to distil eight features of health programing that aid intervention effectiveness. We argue that implementation science should turn its attention to the human resource "process" features of interventions. We describe the importance of staff selection (rigorous selection and hiring procedures); training (developing a set of common core pragmatic problem-solving skills); monitoring (feedback about quality); community and institutional support (rapport with intervention communities); the importance of stable leadership (consistent leadership focusing on how to optimize the potential of staff); the importance of implementing with sustainable, long-term change in mind; and, finally, we describe how cultivating consistency within an organization requires disciplined action and disciplined focus on the organization's vision.

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“…In the medical evaluation of children with symptoms such as diarrhea, vomiting, and fever concerning for infection, health workers may concurrently evaluate for malnutrition and the need for nutritional supplementation particularly during times of acute illness [30]. Community-based malnutrition programs have started to explore the use of novel mobile technology devices to improve the accuracy of identifying children at risk for malnutrition [15]. …”

Section: Discussionmentioning

confidence: 99%

“…A systematic review of 29 efficacy and 13 effectiveness trials of complementary feeding interventions during early childhood in developing countries have shown a mean effect size of 0.26 (range -0.02 to 0.57) for weight for age z-score (WAZ) [14]. In addition, mobile technology may be a novel platform to improve screening accuracy for child malnutrition in community-based models [15]. …”

Section: Introductionmentioning

confidence: 99%

Prevalence and Predictors of Malnutrition among Guatemalan Children at 2 Years of Age

et al. 2016

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ObjectiveTo identify the prevalence and predictors of malnutrition among 2-year old children in the Western Highlands of Guatemala.MethodsProspective cohort of 852 Guatemalan children in San Lucas Toliman, Guatemala followed from birth to age 2 from May 2008 to December 2013. Socio-demographic, anthropometric, and health data of children was collected at 2 month intervals.ResultsAmong the 402 males and 450 females in the cohort, mean weight-for-age Z-score (WAZ) declined from -0.67 ± 1.01 at 1 year to -1.07 ± 0.87 at 2 years, while mean height-for-age Z-score (HAZ) declined from -1.88 ± 1.19 at 1 year to -2.37 ± 0.99 at 2 years. Using multiple linear regression modeling, number of children <5 years old, vomiting in the past week, fever in the past week, and WAZ at 1 year were significant predictors of WAZ at 2 years. Significant predictors of HAZ at 2 years included household size, number of children <5 years old, diarrhea in the past week, WAZ at 1 year, and HAZ at 1 year. Vomiting in the past week and WAZ at 1 year were significant predictors of weight-for-height z-score (WHZ) at 2 years.ConclusionsNumber of children <5 years old, symptoms such as vomiting or diarrhea in the previous week, and prior nutritional status were the most significant predictors of malnutrition in this cohort. Future research may focus on the application of models to develop predictive algorithms for mobile device technology, as well as the identification of other predictors of malnutrition that are not well characterized such as the interaction of environmental exposures with protein consumption and epigenetics.

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M-Health for Improving Screening Accuracy of Acute Malnutrition in a Community-Based Management of Acute Malnutrition Program in Mumbai Informal Settlements

Abstract: The mobile calculator significantly reduces an important component of human error in using the WHO tables to assess acute malnutrition at the community level.

Cited by 17 publications

References 14 publications

Use of Mid-Upper Arm Circumference by Novel Community Platforms to Detect, Diagnose, and Treat Severe Acute Malnutrition in Children: A Systematic Review

Use of Mid-Upper Arm Circumference by Novel Community Platforms to Detect, Diagnose, and Treat Severe Acute Malnutrition in Children: A Systematic Review

Diffusing and scaling evidence‐based interventions: eight lessons for early child development from the implementation of perinatal home visiting in South Africa

Prevalence and Predictors of Malnutrition among Guatemalan Children at 2 Years of Age

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