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Abdalla S, Pair E, Mehta K, Ward V, Mahapatra T, Darmstadt GL. Improving the precision of maternal, newborn, and child health impact through geospatial analysis of the association of contextual and programmatic factors with health trends in Bihar, India. J Glob Health 2022; 12:04064. [PMID: 36412069 PMCID: PMC9679706 DOI: 10.7189/jogh.12.04064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Background There is a scarcity of research that comprehensively examines programme impact from a context-specific perspective. We aimed to determine the conditions under which the Bihar Technical Support Programme led to more favourable outcomes for maternal and child health in Bihar. Methods We obtained block-level data on maternal and child health indicators during the state-wide scale-up of the pilot Ananya programme and data on health facility readiness, along with geographical and sociodemographic variables. We examined the associations of these factors with increases in the levels of indicators using multilevel logistic regression, and the associations with rates of change in the indicators using Bayesian Hierarchical modelling. Results Frontline worker (FLW) visits between 2014-2017 were more likely to increase in blocks with better night lighting (odds ratio (OR) = 1.23, 95% confidence interval (CI) = 1.01-1.51). Birth preparedness increased in blocks with increasing FLW visits (OR = 3.43, 95% CI = 1.15-10.21), while dry cord care practice increased in blocks where satisfaction with FLW visits was increasing (OR = 1.52, 95% CI = 1.10-2.11). Age-appropriate frequency of complementary feeding increased in blocks with higher development index (OR = 1.55, 95% CI = 1.16-2.06) and a higher percentage of scheduled caste or tribe (OR = 3.21, 95% CI = 1.13-9.09). An increase in most outcomes was more likely in areas with lower baseline levels. Conclusions Contextual factors (eg, night lighting and development) not targeted by the programme and FLW visits were associated with favourable programme outcomes. Intervention design, including intervention selection for a particular geography, should be modified to fit the local context in the short term. Expanding collaborations beyond the health sector to influence modifiable contextual factors in the long term can result in a higher magnitude and more sustainable impact. Registration ClinicalTrials.gov: NCT02726230.
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Affiliation(s)
- Safa Abdalla
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Emma Pair
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Kala Mehta
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, USA
| | - Victoria Ward
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | | | - Gary L Darmstadt
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
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Ward VC, Raheel H, Weng Y, Mehta KM, Dutt P, Mitra R, Sastry P, Godfrey A, Shannon M, Chamberlain S, Kaimal R, Carmichael SL, Bentley J, Abdalla S, Pepper KT, Mahapatra T, Srikantiah S, Borkum E, Rangarajan A, Sridharan S, Rotz D, Nanda P, Tarigopula UK, Atmavilas Y, Bhattacharya D, Darmstadt GL. Impact of mHealth interventions for reproductive, maternal, newborn and child health and nutrition at scale: BBC Media Action and the Ananya program in Bihar, India. J Glob Health 2020; 10:021005. [PMID: 33425329 PMCID: PMC7758913 DOI: 10.7189/jogh.10.021005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Background Mobile health (mHealth) tools have potential for improving the reach and quality of health information and services through community health workers in low- and middle-income countries. This study evaluates the impact of an mHealth tool implemented at scale as part of the statewide reproductive,maternal, newborn and child health and nutrition (RMNCHN) program in Bihar, India. Methods Three survey-based data sets were analysed to compare the health-related knowledge, attitudes and behaviours amongst childbearing women exposed to the Mobile Kunji and Dr. Anita mHealth tools during their visits with frontline workers compared with those who were unexposed. Results An evaluation by Mathematica (2014) revealed that exposure to Mobile Kunji and Dr. Anita recordings were associated with significantly higher odds of consuming iron-folic acid tablets (odds ratio (OR) = 2.3, 95% confidence interval (CI) = 1.8-3.1) as well as taking a set of three measures for delivery preparedness (OR = 2.8, 95% CI = 1.9-4.2) and appropriate infant complementary feeding (OR = 1.9, 95% CI = 1.0-3.5). CARE India’s Community-based Household Surveys (2012-2017) demonstrated significant improvements in early breastfeeding (OR = 1.64, 95% CI = 1.5-1.78) and exclusive breastfeeding (OR = 1.46, 95% CI = 1.33-1.62) in addition to birth preparedness practices. BBC Media Action’s Usage & Engagement Survey (2014) demonstrated a positive association between exposure to Mobile Kunji and Dr. Anita and exclusive breastfeeding (58% exposed vs 43% unexposed, P < 0.01) as well as maternal respondents’ trust in their frontline worker. Conclusions Significant improvements in RMNCHN-related knowledge and behaviours were observed for Bihari women who were exposed to Mobile Kunji and Dr. Anita. This analysis is unique in its rigorous evaluation across multiple data sets of mHealth interventions implemented at scale. These results can help inform global understanding of how best to use mHealth tools, for whom, and in what contexts. Study registration ClinicalTrials.gov number NCT02726230.
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Affiliation(s)
- Victoria C Ward
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Hina Raheel
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Yingjie Weng
- Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Kala M Mehta
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA.,Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, USA
| | | | | | | | | | | | | | - Rajani Kaimal
- Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Suzan L Carmichael
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA.,Center for Population Health Sciences, Stanford University School of Medicine, Palo Alto, California, USA
| | - Jason Bentley
- Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Safa Abdalla
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Kevin T Pepper
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | | | | | | | | | | | - Dana Rotz
- Mathematica, Princeton, New Jersey, USA
| | - Priya Nanda
- Bill and Melinda Gates Foundation, Delhi, India
| | | | | | | | - Gary L Darmstadt
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA.,Center for Population Health Sciences, Stanford University School of Medicine, Palo Alto, California, USA
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