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dc.contributor.authorKebaabetswe, Poloko
dc.contributor.authorPrice, Natalie
dc.contributor.authorEl-halabi, Shenaaz
dc.contributor.authorMlaudzi, Naledi
dc.contributor.authorKeapoletswe, Koona
dc.contributor.authorLebelonyane, Kefeletswe
dc.contributor.authorFetogang, Ernest Benny
dc.contributor.authorChebani, Tony
dc.contributor.authorMasupe, Tiny
dc.contributor.authorGabaake, Keba
dc.contributor.authorAuld, Andrew F.
dc.contributor.authorNkomazana, Oathokwa
dc.contributor.authorMarlink, Richard
dc.date.accessioned2017-05-11T07:56:15Z
dc.date.available2017-05-11T07:56:15Z
dc.date.issued2016-08-04
dc.identifier.citationKebaabetswe, P. et al (2016) Impact of health system inputs on health outcome: a multilevel longitudinal analysis of Botswana National Antiretroviral Program (2002-2013). Plos one, Vol. 11, No. 8, pp. 1-14en_US
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/10311/1659
dc.description.abstractObjective To measure the association between the number of doctors, nurses and hospital beds per 10,000 people and individual HIV-infected patient outcomes in Botswana. Design Analysis of routinely collected longitudinal data from 97,627 patients who received ART through the Botswana National HIV/AIDS Treatment Program across all 24 health districts from 2002 to 2013. Doctors, nurses, and hospital bed density data at district-level were collected from various sources. Methods A multilevel, longitudinal analysis method was used to analyze the data at both patient- and district-level simultaneously to measure the impact of the health system input at districtlevel on probability of death or loss-to-follow-up (LTFU) at the individual level. A marginal structural model was used to account for LTFU over time. Results Increasing doctor density from one doctor to two doctors per 10,000 population decreased the predicted probability of death for each patient by 27%. Nurse density changes from 20 nurses to 25 nurses decreased the predicted probability of death by 28%. Nine percent decrease was noted in predicted mortality of an individual in the Masa program for every five hospital bed density increase. Conclusion Considerable variation was observed in doctors, nurses, and hospital bed density across health districts. Predictive margins of mortality and LTFU were inversely correlated with doctor, nurse and hospital bed density. The doctor density had much greater impact than nurse or bed density on mortality or LTFU of individual patients. While long-term investment in training more healthcare professionals should be made, redistribution of available doctors and nurses can be a feasible solution in the short term.en_US
dc.language.isoenen_US
dc.publisherPublic Library Science, https://www.plos.org/en_US
dc.subjectHealth systemen_US
dc.subjecthealth outcomeen_US
dc.subjectmultilevel longitudinal analysien_US
dc.subjectnational antiretroviral programen_US
dc.subjectBotswanaen_US
dc.titleImpact of health system inputs on health outcome: a multilevel longitudinal analysis of Botswana National Antiretroviral Program (2002-2013)en_US
dc.typePublished Articleen_US
dc.linkhttps://www.ncbi.nlm.nih.gov/pubmed/27490477en_US


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