Abstract
Global evidence suggests that high out-of-pocket (OOP) expenditure negatively affects health service utilization and creates an economic burden on households during pregnancy. This study aimed to estimate the magnitude and associated factors of OOP expenditure for antenatal care (ANC) in a rural Sri Lankan setting by following up with a large pregnancy cohort (The Rajarata Pregnancy Cohort [RaPCo]) in Anuradhapura District, Sri Lanka. Data were collected from July 2019 to May 2020. An interviewer-administered questionnaire was used to collect socioeconomic data and OOP expenditures in the first trimester. Self-administered questionnaires were used monthly to collect OOP expenditures in the second and third trimesters. In-depth financial information of 1,558 pregnant women was analyzed using descriptive statistics, nonparametric statistics, and a multiple linear regression model. The majority of participants used both government and private health facilities for ANC. The mean (standard deviation [SD]) OOP expenditure per ANC visit was US$4.18 (US$4.19), and the mean (SD) OOP expenditure for total ANC was US$57.74 (US$80.96). Pregnant women who used only free government health services also spent 28% and 14% of OOP expenditure on medicines and laboratory investigations. Household income (P<.001), household expenditure (P<.1), used health care mode (P<.05), maternal morbidities (P<.05), and the number of previous pregnancies (P<.1) were the statistically significant independent predictors of OOP expenditure. OOP expenditure per visit for ANC equals half of the daily household expenditure. Despite having freely available government health facilities, most pregnant women tend to use both government and private health facilities and incur higher OOP expenditure. Free government health care users also incur a direct medical OOP expenditure for medicines and laboratory investigations. Monthly household income, expenditure, used health care mode, maternal morbidities, and the number of previous pregnancies are independent predictors of OOP expenditure.
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