The economic burden of malaria in pregnancy: a cross-sectional study.
Vivian Uchenna OnyiaMaduka Donatus UghasoroObinna Emmanuel OnwujekwePublished in: The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians (2018)
Background: Malaria in pregnancy carries a proven huge health burden; however, the economic challenges have not been properly evaluated in Nigeria.Methodology: The study was a descriptive cross-sectional hospital-based approach. A structured questionnaire was used to collect microeconomic data from pregnant women, on the medical and nonmedical cost of malaria to them.Results: A total of 371 questionnaires were analyzed (93%; 371/400), of 400 respondents interviewed. The average direct medical cost was N3581.78 naira (N) (US$11.86) with SD of N177.9 and mean direct nonmedical cost of N5741.5 (US$18.97). Of the patients, 86.8% received artemisinin-based combination therapy (ACTs) for the treatment of malaria. Nigeria has an estimated population of women of child-bearing age of 40 million and, the fertility rate of 124 per 1000. On the basis of estimation of 56.5% of pregnant women receiving at least one intermittent preventive therapy (IPT), will approximate to 22.8 billion naira (US$75.5 million) national annual expenditure for malaria in pregnancy. This approximates to 0.016% of the Nigerian gross domestic product of 481 billion USD of 2015. The major mechanism that was used to pay for treatment was out-of-pocket (OOP).Conclusions: Malaria carries high-economic burden both on individual and national levels, especially in Nigeria where OOPs is the major payment mechanism. Scaling up malaria control measures will not only improve the lives of pregnant women but will also improve the economy of the nation.
Keyphrases
- plasmodium falciparum
- pregnant women
- pregnancy outcomes
- combination therapy
- cross sectional
- healthcare
- end stage renal disease
- mental health
- newly diagnosed
- ejection fraction
- physical activity
- emergency department
- chronic kidney disease
- machine learning
- risk factors
- polycystic ovary syndrome
- adipose tissue
- mesenchymal stem cells
- stem cells
- patient reported
- electronic health record
- high intensity
- young adults
- deep learning
- climate change
- health promotion