Measuring Coverage in MNCH: New Findings, New Strategies, and Recommendations for Action

Bryce, Jennifer; Arnold, Fred; Blanc, Ann K.; Hancioglu, Attila; Newby, Holly; Requejo, Jennifer; Wardlaw, Tessa

doi:10.1371/journal.pmed.1001423

Cited by 132 publications

(167 citation statements)

References 35 publications

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“…In order to address fragmentation and non-existent private sector linkages, key services and mortality reporting can also be institutionalized at the facility as well as the community levels. We also agree to strategies proposed by Bryce and colleagues, which include linking household surveys to other sources of information on service provision [30]. …”

Section: Resultssupporting

confidence: 59%

Production and use of estimates for monitoring progress in the health sector: the case of Bangladesh

Ahsan

Tahsina

Iqbal

et al. 2017

Global Health Action

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Background: In order to support the progress towards the post-2015 development agenda for the health sector, the importance of high-quality and timely estimates has become evident both globally and at the country level. Objective and Methods: Based on desk review, key informant interviews and expert panel discussions, the paper critically reviews health estimates from both the local (i.e. nationally generated information by the government and other agencies) and the global sources (which are mostly modeled or interpolated estimates developed by international organizations based on different sources of information), and assesses the country capacity and monitoring strategies to meet the increasing data demand in the coming years. Primarily, this paper provides a situation analysis of Bangladesh in terms of production and use of health estimates for monitoring progress towards the post-2015 development goals for the health sector. Results: The analysis reveals that Bangladesh is data rich, particularly from household surveys and health facility assessments. Practices of data utilization also exist, with wide acceptability of survey results for informing policy, programme review and course corrections. Despite high data availability from multiple sources, the country capacity for providing regular updates of major global health estimates/indicators remains low. Major challenges also include limited human resources, capacity to generate quality data and multiplicity of data sources, where discrepancy and lack of linkages among different data sources (local sources and between local and global estimates) present emerging challenges for interpretation of the resulting estimates. Conclusion: To fulfill the increased data requirement for the post-2015 era, Bangladesh needs to invest more in electronic data capture and routine health information systems. Streamlining of data sources, integration of parallel information systems into a common platform, and capacity building for data generation and analysis are recommended as priority actions for Bangladesh in the coming years. In addition to automation of routine health information systems, establishing an Indicator Reference Group for Bangladesh to analyze data; building country capacity in data quality assessment and triangulation; and feeding into global, inter-agency estimates for better reporting would address a number of mentioned challenges in the short- and long-run.

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Section: Resultssupporting

confidence: 59%

Production and use of estimates for monitoring progress in the health sector: the case of Bangladesh

Ahsan

Tahsina

Iqbal

et al. 2017

Global Health Action

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“…Other interventions may also change. For example, oral rehydration therapy -defined as increased fluids plus continued feeding -is being replaced with treatment with oral rehydration solution 4 plus zinc. 5 In 2015, oral rehydration solution plus zinc was available for 37 of the 75 Countdown countries, with a median coverage of only 1%.…”

Section: Researchmentioning

confidence: 99%

“…3 Monitoring the coverage of interventions in maternal and child health continues to be central to assessing progress towards development goals. 4 Our experience with the Countdown to 2015 initiative (which tracks progress in interventions in 75 countries) is directly relevant to monitoring the four SDGs mentioned above. 4,5 Starting with 35 coverage indicators monitored in 2005, 6 the Countdown list grew to 73 indicators by 2015.…”

Section: Introductionmentioning

confidence: 99%

“…4 Our experience with the Countdown to 2015 initiative (which tracks progress in interventions in 75 countries) is directly relevant to monitoring the four SDGs mentioned above. 4,5 Starting with 35 coverage indicators monitored in 2005, 6 the Countdown list grew to 73 indicators by 2015. 5 Reporting separately on each indicator proved to be useful at the country and global level for tracking progress, evaluating programmes and planning future actions.…”

Section: Introductionmentioning

confidence: 99%

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Summary indices for monitoring universal coverage in maternal and child health care

Wehrmeister

Restrepo‐Méndez

França

et al. 2016

Bull World Health Organ

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“…Different models of services such as Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) [9] in addition to deployment models such as private, community, public and hybrid also need attention in terms of scalability of the system. In regards to data collection surveys based on cloud computing using smart technologies can play a vital role by reducing the time and cost associated with data collection [10,11]. Furthermore, with the reduction in prices of mobile phones and almost universal coverage, it can make healthcare data capture more feasible at low cost by taking geographical accessibility out of equation [12,13], thus providing a fast reliable method of capturing accurate data from rural populations.…”

Section: Introductionmentioning

confidence: 99%

Cloud enabled data analytics and visualization framework for health-shocks prediction

Mahmud

Iqbal

Doctor

2016

Future Generation Computer Systems

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In this paper, we present a data analytics and visualization framework for health-shocks prediction based on large-scale health informatics dataset. The framework is developed using cloud computing services based on Amazon web services (AWS) integrated with geographical information systems (GIS) to facilitate big data capture, storage, index and visualization of data through smart devices for different stakeholders. In order to develop a predictive model for health-shocks, we have collected a unique data from 1000 households, in rural and remotely accessible regions of Pakistan, focusing on factors like health, social, economic, environment and accessibility to healthcare facilities. We have used the collected data to generate a predictive model of health-shock using a fuzzy rule summarization technique, which can provide stakeholders with interpretable linguistic rules to explain the causal factors affecting healthshocks. The evaluation of the proposed system in terms of the interpret-ability and accuracy of the generated data models for classifying health-shock shows promising results. The prediction accuracy of the fuzzy model based on a k-fold cross-validation of the data samples shows above 89% performance in predicting health-shocks based on the given factors.

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Measuring Coverage in MNCH: New Findings, New Strategies, and Recommendations for Action

Cited by 132 publications

References 35 publications

Production and use of estimates for monitoring progress in the health sector: the case of Bangladesh

Production and use of estimates for monitoring progress in the health sector: the case of Bangladesh

Summary indices for monitoring universal coverage in maternal and child health care

Cloud enabled data analytics and visualization framework for health-shocks prediction

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