Levels and Determinants of Low Birth Weight in Infants Delivered Under the National Health Insurance Scheme in Northern Ghana

Abstract

This research determined the levels and odds ratios for low birth weight (LBW) infants delivered under the National Health Insurance Scheme (NHIS) compared to LBW infants delivered under the previous "Cash and Carry" system in Northern Ghana. Birth records of infants delivered before and after implementation of the NHIS in Northern Ghana were examined. Records of each day's births during the identified periods were abstracted. Days with fewer or no births were accommodated by oversampling from days before or after. Chi squared tests of independence were used to examine the bivariate association between categorical independent variables and LBW. Multiple logistic regression models were used to examine the relationships among selected variables for mothers and infants and the odds ratios for LBW. Infants delivered under NHIS had lower rates of LBW (16.8 %) compared to infants born under Cash and Carry (23.3 %). Mothers who delivered under NHIS were significantly less likely to have infants at LBW (unadjusted OR 0.65; 95 % CI 0.49, 0.86). The rate of LBW among infants delivered under NHIS is significantly lower than among infants delivered under Cash and Carry. The rate of LBW under Cash and Carry in 2000 fell by 27 % in relation to the NHIS in 2010. These findings confirm that the NHIS, which gives pregnant women in Northern Ghana four antenatal visits and access to skilled health professionals for delivery at no cost to the mother, significantly improved birth weight outcomes.

Complications associated with adolescent childbearing in Sub-Saharan Africa: A systematic literature review and meta-analysis

“…Three studies had data on adolescents aged less than 15 years [ 16 , 18 , 19 ], four studies had an adolescent group aged 15 years or less [ 22 – 25 ], five studies included adolescents aged 16 years or less [ 20 , 21 , 26 – 28 ], while six included adolescents up to 17 years old [ 17 , 29 – 34 ]. There were three studies from Cameroon [ 23 , 27 , 28 ], two each from Nigeria[ 25 , 29 ], Tanzania [ 24 , 32 ], South Africa [ 18 , 34 ] and Zambia [ 16 , 17 ], one study each from Namibia [ 31 ], Gabon [ 20 ], Ghana [ 33 ], Ethiopia [ 19 ] and Sudan [ 26 ], and two multi-country studies: Ganchimeg et al with data from Algeria, Angola, Democratic Republic of Congo, Niger, Nigeria, Kenya and Uganda [ 22 ], and Mombo-Ngoma et al which included data from Benin, Gabon and Mozambique [ 21 ]. See Table 2 for study characteristics, and Tables 3 and 4 for results for the individual studies.…”

“…This was due to lack of adjustment for potential confounding factors and various other limitations in the individual studies, including the risk of selection bias when conducting hospital-based studies. Five of the studies [ 17 , 20 , 21 , 33 , 34 ] had adjusted for sociodemographic characteristics. Ganchimeg et al [ 22 ] was a multi-country study, and adjusted for sociodemographic factors in the pooled analysis, but did not adjust the separate analysis of data from African countries.…”

“…Assessment of gestational age was performed with varying methods in the different studies. None of them had access to ultrasound for all their estimations, and several studies listed lack of information on gestational age [ 33 ] or inaccurate estimations [ 34 ] as a limitation. This could lead to misclassification of premature and full-term babies.…”

Health Insurance in Sub-Saharan Africa: A Scoping Review of the Methods Used to Evaluate its Impact

“…Another major limitation frequently discussed by authors (n = 13 studies) concerned recall bias [13,21,25,26,30,31,33,35,38,39,48,73,76], as information on insurance membership (exposure) and health status and/or health-service use (outcomes of interest) was often collected at different time points, not always allowing a perfect match between the two. Authors of studies relying on secondary data often discussed limitations due to the nature of the secondary data [15,19,21,27,32,33,36,37,39,49,55,56,58,64,67,77], including lack of information on specific covariates to refine model estimation; impossibility of checking the quality and/or accuracy of the data; and for one study relying on medical charts, with large amounts of missing data [65]. Finally, only eight authors (four from quasi-experimental studies [22,61,62,73] and four from observational studies [37,38,59,76]), acknowledged the impossibility of establishing a causal link between insurance and outcomes of interest due to the crosssectional nature of the data being used.…”

Adverse obstetric and neonatal outcomes of adolescent pregnancies in Africa: a scoping review

“…We found an increased risk of adverse perinatal and neonatal outcomes for adolescent mothers (Table 3 ). Adolescent mothers are at higher risk of LBW and preterm births and younger adolescents are at higher risk of LBW with preterm births than their older counterparts [ 7 , 17 , 19 , 20 , 22 – 24 , 26 , 28 , 29 , 32 – 35 , 38 , 42 , 44 – 48 , 50 – 63 , 65 ]. The review also showed that the two extreme age groups in pregnant women (below age 15 and above age 40) have a higher incidence of LBW compared with older adolescents [ 41 , 50 ].…”