Friberg I.K., Bhutta Z.A., Darmstadt G.L., Bang A., Cousens S., Baqui A.H., Kumar V., Walker N., Lawn J.E.
Johns Hopkins Bloomberg School of Public Health, Department of International Health, Baltimore, MD, United States; Divsion of Women and Child Health, The Aga Khan University, Karachi, Pakistan; SEARCH (Society for Education, Action and Research in Community Health), Gadchiroli, India; London School of Tropical Medicine and Hygiene, London, United Kingdom; Saving Newborn Livesm, Save the Children, South Africa; Family Health Division, Global Health Program, Bill and Melinda Gates Foundation, Seattle, WA, United States
Friberg, I.K., Johns Hopkins Bloomberg School of Public Health, Department of International Health, Baltimore, MD, United States; Bhutta, Z.A., Divsion of Women and Child Health, The Aga Khan University, Karachi, Pakistan; Darmstadt, G.L., Johns Hopkins Bloomberg School of Public Health, Department of International Health, Baltimore, MD, United States, Family Health Division, Global Health Program, Bill and Melinda Gates Foundation, Seattle, WA, United States; Bang, A., SEARCH (Society for Education, Action and Research in Community Health), Gadchiroli, India; Cousens, S., London School of Tropical Medicine and Hygiene, London, United Kingdom; Baqui, A.H., Johns Hopkins Bloomberg School of Public Health, Department of International Health, Baltimore, MD, United States; Kumar, V., Johns Hopkins Bloomberg School of Public Health, Department of International Health, Baltimore, MD, United States; Walker, N., Johns Hopkins Bloomberg School of Public Health, Department of International Health, Baltimore, MD, United States; Lawn, J.E., Saving Newborn Livesm, Save the Children, South Africa
Background: There is an increasing body of evidence from trials suggesting that major reductions in neonatal mortality are possible through community-based interventions. Since these trials involve packages of varying content, determining how much of the observed mortality reduction is due to specific interventions is problematic. The Lives Saved Tool (LiST) is designed to facilitate programmatic prioritization by modelling mortality reductions related to increasing coverage of specific interventions which may be combined into packages. Methods: To assess the validity of LiST outputs, we compared predictions generated by LiST with observed neonatal mortality reductions in trials of packages which met inclusion criteria but were not used as evidence inputs for LiST. Results: Four trials, all from South Asia, met the inclusion criteria. The neonatal mortality rate (NMR) predicted by LiST matched the observed rate very closely in two effectiveness-type trials. LiST predicted NMR reduction was close (absolute difference <5/1000 live births) in a third study. The NMR at the end of the fourth study (Shivgarh, India) was overestimated by 39% or 16/1000 live births. Conclusions: These results suggest that LiST is a reasonably reliable tool for use by policymakers to prioritize interventions to reduce neonatal deaths, at least in South Asia and where empirical data are unavailable. Reasons for the underestimated reduction in one trial likely include the inability of LiST to model all effective interventions. © The Author 2010; all rights reserved.
child mortality; comparative study; data set; empirical analysis; health policy; neonate; numerical model; policy making; prediction; prioritization; survival; antibiotic therapy; article; cause of death; clinical effectiveness; community care; comparative study; death; disease surveillance; emergency care; home care; human; immunization; live birth; meningitis; newborn care; newborn infection; newborn mortality; newborn sepsis; newborn tetanus; obstetric procedure; overall survival; perinatal asphyxia; perinatal period; pneumonia; predictive value; premature labor; priority journal; resuscitation; South Asia; statistical model; The Lives Saved Tool; Asia; developing country; female; health care delivery; health care quality; infant mortality; male; newborn; newborn disease; prediction and forecasting; statistics; theoretical model; treatment outcome; South Asia; Asia; Community Health Services; Delivery of Health Care; Developing Countries; Female; Humans; Infant Mortality; Infant, Newborn; Infant, Newborn, Diseases; Male; Models, Theoretical; Outcome and Process Assessment (Health Care); Predictive Value of Tests; Program Evaluation
