Background Intermittent presumptive treatment in pregnancy (IPTp) of malaria using sulfadoxine-pyrimethamine (SP) was introduced in Nigeria in 2005 to reduce the burden of malaria in pregnancy. By 2013, 23% of reproductive aged women surveyed received SP for malaria prevention in their last pregnancy of the past 5 years. This paper highlights geographic and socio-economic variations and inequities in accessing and using SP for malaria prophylaxis in pregnancy in Nigeria, as well as client-related and service delivery determinants. Methods Secondary data from 2013 Nigeria demographic and health survey (DHS) was used. Sample of 38,948 eligible women were selected for interview using stratified three-stage cluster design. Data obtained from the individual recode dataset was used for descriptive and logistic regression analysis of factors associated with SP use in pregnancy was performed. Independent variables were age, media exposure, region, place of residence, wealth index, place of antenatal care (ANC) attendance and number of visits. Results Women in the upper three wealth quintiles were 1.33-1.80 times more likely to receive SP than the poorest (CI: 1.15-1.56; 1.41-1.97; 1.49-2.17). Women who received ANC from public health facilities were twice as likely (inverse of OR 0.68) to use SP in pregnancy than those who used private facilities (CI: 0.60-0.76). Those who attended at least 4 ANC visits were 1.46 times more likely to get SP prophylaxis (CI: 1.31-1.63). Using the unadjusted odds ratio, women residing in rural areas were 0.86 times less likely to use SP compared to those in urban areas. Conclusions Inequities in access to and use of SP for malaria prophylaxis in pregnancy exist across sub-population groups in Nigeria. Targeted interventions on the least covered are needed to reduce existing inequities and scale-up IPTp of malaria.
This study used secondary data from the 2013 Nigeria Demographic Health Survey (NDHS). The NDHS is a national sample survey which is conducted at five year intervals by the National Population Commission, within the months of June and October of the reporting year, to provide up to date information on demographic characteristics and health status of households in Nigeria. Nigeria has an annual population growth rate of 3.2 percent and ranks seventh among highly populated countries in the world13. The constitution of Nigeria provides for the operation of three tiers of government – the Federal, 36 semi-autonomous States (and the Federal Capital Territory) and 774 local government areas grouped into six geopolitical zones. In the last national census of 2006, each locality in Nigeria was subdivided into census enumeration areas determined by average number of households13. Primary health care is recognized nationally as the framework for achieving universal health care, including provision of maternal and child health (MCH) care at primary health centers12. Utilization of services in the primary health facilities is limited and varies across socioeconomic and geopolitical differences. Ante-natal care attendance ranges from 31% north-east to 87% in the south-west whereas health facility delivery ranges from 8.4% in the north-east to 73% in the south-west17. On the other hand, majority of PHCs in the country do not run 24-hour services, thereby denying a lot of patients the opportunity to patronize such centres when ill or for deliveries. In order to address these and other challenges in MCH service delivery, Nigeria introduced the Midwives Service Scheme (MSS) from 2009–2011, the Subsidy Reinvestment and Empowerment Program (SURE-P MCH) in 2012–2015, and the most recent PHC revitalization in 2017, to strengthen coordination and improve quality of service delivery12. The 2013 NDHS sample was selected using a stratified three-stage cluster design consisting of 904 clusters, 372 in urban areas and 532 in rural areas. The list of census enumeration areas of 2006 population census formed the DHS sampling frame and primary sampling unit. The sample design allowed for specific indicators to be calculated at zonal and state levels. Mapping of households was done between December 2012 and January 2013 by trained enumerators using Global Positioning System (GPS) receivers. An updated list of households in each CEA was produced and this formed the sampling frame for households12. A fixed sample of 45 households was selected per cluster giving a total of 40,680 households. In each selected household, all reproductive aged women who were resident de facto were surveyed12. Two modified NDHS model questionnaires (Households and Women’s) were used to collect information on maternal and child health including antenatal care, malaria preventive strategies as well as other relevant health issues. Relevant data from this study were obtained from the individual recode dataset for women. This dataset is generated using relevant information from the household and women’s questionnaires and contains data on intermittent preventive treatment for malaria during pregnancy, antenatal visits, type of place of residence (urban or rural), geopolitical region, asset ownership and wealth index. The women’s questionnaire was administered to all reproductive aged women in every second household in the 2013 NDHS sample. Questions concerning IPTp access and use were asked, as well as questions on frequency of antenatal visits and antenatal care service provider. Principal component analysis based on household ownership of goods, characteristics of dwelling place, source of drinking water, sanitary/toilet facilities and level of education of head of household, was used to rank households into socioeconomic quintiles namely: Q1 – poorest, Q2 – poorer, Q3 – middle, Q4 – richer, Q5 – richest. DHS dataset is self-weighted by the selection of clusters with probability-proportionate-to-size (PPS)12. To create the individual women recode dataset, data from household questionnaire and women’s questionnaires were merged. Individual responses were matched to household identification numbers. A total of 38,948 women were surveyed. A new SPSS file was created with relevant variables for analysis. Relevant variables were identified and their data extracted from the individual women recode dataset into a new SPSS file. Descriptive statistics were performed to determine the respondent characteristics, place and number of ANC visits, and malaria prophylaxis in pregnancy. Use of SP for malaria prophylaxis in pregnancy was cross-tabulated with respondent characteristics, place and number of ANC visits, to check for statistical significance. Regression analysis was done to identify the determinants of use of SP for malaria prophylaxis in pregnancy. The independent variables included age category, media exposure, geopolitical region, place of residence (urban vs rural), wealth index, place of ANC attendance and number of ANC visits.
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