scholarly journals Benchmarking health system performance across states in Nigeria: a systematic analysis of levels and trends in key maternal and child health interventions and outcomes, 2000–2013

BMC Medicine ◽  
2015 ◽  
Vol 13 (1) ◽  
Author(s):  
Alexandra Wollum ◽  
Roy Burstein ◽  
Nancy Fullman ◽  
Laura Dwyer-Lindgren ◽  
Emmanuela Gakidou
2009 ◽  
pp. 83-97 ◽  
Author(s):  
Nancy Gerein ◽  
Andrew Green ◽  
Tolib Mirzoev ◽  
Stephen Pearson

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Elizabeth M. Simmons ◽  
Kavita Singh ◽  
Jamiru Mpiima ◽  
Manish Kumar ◽  
William Weiss

Abstract Background Nationally representative household surveys are the gold standard for tracking progress in coverage of life-saving maternal and child interventions, but often do not provide timely information on coverage at the local and health facility level. Electronic routine health information system (RHIS) data could help provide this information, but there are currently concerns about data quality. This analysis seeks to improve the usability of and confidence in electronic RHIS data by using adjustments to calculate more accurate numerators and denominators for essential interventions. Methods Data from three sources (Ugandan Demographic and Health (UDHS) survey, electronic RHIS, and census) were used to provide estimates of essential maternal (> 4 antenatal care visits (ANC), skilled delivery, and postnatal care visit (PNC)) and child health interventions (diphtheria, pertussis, tetanus, and hepatitis B and Haemophilus influenzae type b and polio vaccination series, measles vaccination, and vitamin A). Electronic RHIS data was checked for quality and both numerators and denominators were adjusted to improve accuracy. Estimates were compared between the three sources. Results Estimates of maternal health interventions from adjusted electronic RHIS data were lower than those of the UDHS, while child intervention estimates were typically higher. Adjustment of electronic RHIS data generally improved accuracy compared with no adjustment. There was considerable agreement between estimates from adjusted, electronic RHIS data, and UDHS for skilled delivery and first dose of childhood vaccination series, but lesser agreement for ANC visits and second and third doses of childhood vaccinations. Conclusions Nationally representative household surveys will likely continue being the gold standard of coverage estimates of maternal and child health interventions, but this analysis shows that current approaches to adjusting health facility estimate works better for some indications than others. Further efforts to improve accuracy of estimates from RHIS sources are needed.


2015 ◽  
Vol 19 (8) ◽  
pp. 1813-1824 ◽  
Author(s):  
Oluwaseun Ireti Obasola ◽  
Iyabo Mabawonku ◽  
Ikeoluwa Lagunju

2018 ◽  
Vol 3 (2) ◽  
pp. e000674 ◽  
Author(s):  
Dana R Thomson ◽  
Cheryl Amoroso ◽  
Sidney Atwood ◽  
Matthew H Bonds ◽  
Felix Cyamatare Rwabukwisi ◽  
...  

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.


2016 ◽  
Vol 16 (S2) ◽  
Author(s):  
Nadia Akseer ◽  
Zaid Bhatti ◽  
Arjumand Rizvi ◽  
Ahmad S. Salehi ◽  
Taufiq Mashal ◽  
...  

2020 ◽  
Author(s):  
Eveline Muika Kabongo ◽  
Ferdinand Mukumbang ◽  
Peter N/A Delobelle ◽  
Edward N/A Nicol

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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