Abstract
ABSTRACTBACKGROUNDSubstandard and falsified (SF) oxytocin threatens the health of pregnant patients, resulting in prolonged illness and severe avertable disease outcomes. Additionally, SF oxytocin leads to an economic burden on the healthcare system and society due to increased treatment costs and productivity losses from sickness and premature death. While oxytocin is widely accessible, there are concerns about its quality and the burden of SF oxytocin remains understudied.OBJECTIVETo develop an impact model to estimate the health and economic burden of SF oxytocin in Kenya. This paper presents the methodology and the findings of assessing SF oxytocin in Kenya.METHODSA decision tree model was developed to compare health outcomes and costs with and without SF oxytocin from a healthcare sector and societal perspective. This model incorporates healthcare seeking behavior, epidemiological parameters, medicine quality, health outcomes and costs. The main assumption of the model is that lower active pharmaceutical ingredient (API) percentage results in lower medicine efficacy. Sensitivity analyses were performed to evaluate parameter uncertainty.FINDINGSFor 1.1 million pregnant patients delivering in a healthcare facility in Kenya and a 7% prevalence of oxytocin with 75%-90% API, the model estimates that the presence of SF oxytocin in Kenya is associated with 1,484 additional cases of mild PPH, 583 additional cases of severe PPH, 15 hysterectomies, 32 deaths, 633 DALYs accrued, 560 QALYs lost, and 594 years of life lost yearly. The economic burden of SF oxytocin was $1,970,013 USD from a societal perspective, including $1,219,895 from the healthcare sector perspective. Productivity losses included $12,069 due to missed days of work and $725,979 due to premature death.CONCLUSIONSBy providing local estimates on the burden of SF medicines, the model can inform key policy makers on the magnitude of their impact and support initiatives that facilitate greater access to quality medicines.
Publisher
Cold Spring Harbor Laboratory
Reference75 articles.
1. World Health Organization. Trends in maternal mortality 2000 to 2017: estimates by WHO, UNICEF, UNFPA, World Bank Group and the United Nations Population Division: executive summary [Internet]. 2019 [cited 2023 Jan 15]. Available from: https://apps.who.int/iris/handle/10665/327596
2. Kenya National Bureau of Statistics (KNBS) and ICF Macro. Kenya Demographic and Health Survey 2008-09 [Internet]. [cited 2022 Oct 7]. Available from: https://dhsprogram.com/methodology/survey/survey-display-566.cfm
3. World Health Organization. Maternal mortality [Internet]. 2019 [cited 2023 Jan 15]. Available from: https://www.who.int/news-room/fact-sheets/detail/maternal-mortality
4. Say L , Chou D , Gemmill A , Tunçalp Ö , Moller AB , Daniels J , et al. Global causes of maternal death: A WHO systematic analysis. Lancet Glob Heal [Internet]. 2014 [cited 2021 Feb 11];2(6). Available from: https://pubmed.ncbi.nlm.nih.gov/25103301/
5. World Health Organization. HRP Project Brief. WHO postpartum haemorrhage (PPH) summit [Internet]. 2022 Sep [cited 2023 Jan 27]. Available from: https://www.who.int/publications/m/item/who-postpartum-haemorrhage-(pph)-summit