scholarly journals Understanding the maternal and child health system response to payment for performance in Tanzania using a causal loop diagram approach

2021 ◽  
pp. 114277
Author(s):  
Rachel Cassidy ◽  
Andrada Tomoaia-Cotisel ◽  
Agnes Rwashana Semwanga ◽  
Peter Binyaruka ◽  
Zaid Chalabi ◽  
...  
Keyword(s):  
Child Health ◽  
Health System ◽  
Maternal And Child Health ◽  
System Response ◽  
Causal Loop Diagram ◽  
Loop Diagram ◽  
Causal Loop ◽  
Diagram Approach ◽  
And Child Health

Health System Impacts on Maternal and Child Health

2009 ◽  
pp. 83-97 ◽  
Author(s):  
Nancy Gerein ◽  
Andrew Green ◽  
Tolib Mirzoev ◽  
Stephen Pearson
Keyword(s):  
Child Health ◽  
Health System ◽  
Maternal And Child Health ◽  
And Child Health

scholarly journals Impact of a health system strengthening intervention on maternal and child health outputs and outcomes in rural Rwanda 2005–2010

2018 ◽  
Vol 3 (2) ◽  
pp. e000674 ◽  
Author(s):  
Dana R Thomson ◽  
Cheryl Amoroso ◽  
Sidney Atwood ◽  
Matthew H Bonds ◽  
Felix Cyamatare Rwabukwisi ◽  
...  
Keyword(s):  
Child Health ◽  
Health Outcomes ◽  
Health System ◽  
Population Health ◽  
Maternal And Child Health ◽  
Rural Areas ◽  
Health System Strengthening ◽  
Live Births ◽  
Intervention Area ◽  
And Child Health

IntroductionAlthough Rwanda’s health system underwent major reforms and improvements after the 1994 Genocide, the health system and population health in the southeast lagged behind other areas. In 2005, Partners In Health and the Rwandan Ministry of Health began a health system strengthening intervention in this region. We evaluate potential impacts of the intervention on maternal and child health indicators.MethodsCombining results from the 2005 and 2010 Demographic and Health Surveys with those from a supplemental 2010 survey, we compared changes in health system output indicators and population health outcomes between 2005 and 2010 as reported by women living in the intervention area with those reported by the pooled population of women from all other rural areas of the country, controlling for potential confounding by economic and demographic variables.ResultsOverall health system coverage improved similarly in the comparison groups between 2005 and 2010, with an indicator of composite coverage of child health interventions increasing from 57.9% to 75.0% in the intervention area and from 58.7% to 73.8% in the other rural areas. Under-five mortality declined by an annual rate of 12.8% in the intervention area, from 229.8 to 83.2 deaths per 1000 live births, and by 8.9% in other rural areas, from 157.7 to 75.8 deaths per 1000 live births. Improvements were most marked among the poorest households.ConclusionWe observed dramatic improvements in population health outcomes including under-five mortality between 2005 and 2010 in rural Rwanda generally and in the intervention area specifically.

scholarly journals Strategic planning for MyRA performance: A causal loop diagram approach

2017 ◽  
Author(s):  
Norhaslinda Zainal Abidin ◽  
Nerda Zura Zaibidi ◽  
Khairah Nazurah Karim
Keyword(s):  
Strategic Planning ◽  
Causal Loop Diagram ◽  
Loop Diagram ◽  
Causal Loop ◽  
Diagram Approach

scholarly journals Explaining the impact of mHealth on maternal and child health care in low- and middle-income countries: A realist synthesis

2020 ◽  
Author(s):  
Eveline Muika Kabongo ◽  
Ferdinand Mukumbang ◽  
Peter N/A Delobelle ◽  
Edward N/A Nicol
Keyword(s):  
Health Care ◽  
Child Health ◽  
Health System ◽  
Health Care Providers ◽  
Maternal And Child Health ◽  
Child Health Care ◽  
Care Providers ◽  
Middle Income ◽  
Middle Income Countries ◽  
And Child Health

Abstract Background: Despite the growing global application of mobile health (mHealth) technology in maternal and child health, contextual factors, and mechanisms by which interventional outcomes are generated, have not been subjected to a systematic examination. In this study, we sought to uncover context, mechanisms, and outcome elements of various mHealth interventions based on implementation and evaluation studies to formulate theories or models explicating how mHealth interventions work (or not) both for health care providers and for pregnant women and mothers.Method: We undertook a realist synthesis. An electronic search of six online databases (Medline, PubMed, Google Scholar, Scopus, Academic Search Premier, and Health Systems Evidence) was performed. Using appropriate Boolean phrases terms and selection procedures, 32 articles were identified. A theory-driven approach, narrative synthesis, was applied to synthesize the data. Thematic content analysis was used to delineate elements of the intervention, including its context, actors, mechanisms, and outcomes. Abduction and retroduction were applied using a realist evaluation heuristic tool to formulate generative theories.Results: We formulated two configurational models illustrating how and why mHealth impacts the implementation and uptake of maternal and child care services. Implementation-related mechanisms include buy-in from health care providers, perceived support of health care providers’ motivation, and perceived ease of use and usefulness. These mechanisms were influenced by adaptive health system conditions including organization, resource availability, policy implementation dynamics, experience with technology, network infrastructure, and connectivity. For pregnant women and mothers, mechanisms that trigger mHealth use and consequently uptake of maternal and child health care include perceived satisfaction, motivation, and positive psychological support. Information overload was identified as a potential negative mechanism impacting the uptake of maternal and child health care. These mechanisms were influenced by health system conditions, socio-cultural characteristics, socio-economic and demographics characteristics, network infrastructure and connectivity, and awareness.Conclusion: Models developed in this study provide a detailed understanding of the implementation and uptake of mHealth interventions and how and why they impact maternal and child health care in low- and middle-income countries. These models provide a foundation for the ‘white box’ of theory-driven evaluation of mHealth interventions and can improve rollout and implementation where required.

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