Public Health & Primary Care / Research Article
Cogent Medicine (2018), 5: 1430197
Lefebvre et al., Cogent Medicine (2018), 5: 1430197 https://doi.org/10.1080/2331205X.2018.1430197
March 2021
This report presents the key findings of the NFHS-5 survey in Mizoram, followed by detailed tables and an appendix on sampling errors. The 2019-21 National Family Health Survey (NFHS-5), the fifth in the NFHS series, provides information on population, health, and nutrition for India and... each state and union territory.
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Recommended actions at international and national levels
March 2021
This report presents the key findings of the NFHS-5 survey in Tripura, followed by detailed tables and an appendix on sampling errors. The 2019-21 National Family Health Survey (NFHS-5), the fifth in the NFHS series, provides information on population, health, and nutrition for India and... each state and union territory.
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March 2021
This report presents the key findings of the NFHS-5 survey in Kerala, followed by detailed tables and an appendix on sampling errors. The 2019-21 National Family Health Survey (NFHS-5), the fifth in the NFHS series, provides information on population, health, and nutrition for India and ...each state and union territory.
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May 2021
This report presents the key findings of the NFHS-5 survey in Telangana, followed by detailed tables and an appendix on sampling errors. The 2019-21 National Family Health Survey (NFHS-5), the fifth in the NFHS series, provides information on population, health, and nutrition for India and... each state and union territory.
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July 2021
This report presents the key findings of the NFHS-5 survey in Meghalaya, followed by detailed tables and an appendix on sampling errors. The 2019-21 National Family Health Survey (NFHS-5), the fifth in the NFHS series, provides information on population, health, and nutrition for India an...d each state and union territory.
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The classification of digital health interventions (DHIs) categorizes the different ways in which digital and mobile technologies are being used to support health system needs. Historically, the diverse communities working in digital health—including government stakeholders, technologists, clinic...ians, implementers, network operators, researchers, donors— have lacked a mutually understandable language with which to assess and articulate functionality. A shared and standardized vocabulary was recognized as necessary to identify gaps and duplication, evaluate effectiveness, and facilitate alignment across different digital health implementations. Targeted primarily at public health audiences, this Classification framework aims to promote an accessible and bridging language for health program planners to articulate functionalities of digital health implementations.
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