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Background: Low birth weight (LBW) is one of the major factors affecting child morbidity and mortality worldwide. It also results in substantial costs to the health sector and imposes a significant burden on the society as a whole. This study seeks to investigate the determinants of low birth weight and the incidence of LBW in southern rural Ghana. Methods: Pregnancy, birth, demographic and socioeconomic information of 6777 mothers who gave birth in 2011, 2012, and 2013 and information on their babies were extracted from a database. The database of Dodowa Health and Demographic Surveillance System is a longitudinal follow-up of over 24,000 households. The incidence of LBW was calculated and the univariable and multivariable associations between exposure variables and outcome were explored using logistic regression. STATA 11 was used for the analyses. Result: The results revealed that 40.21% of the infants were not weighed at birth and the incidence of LBW for 2011 to 2013 was 8.72, 7.04 and 7.52% respectively. Women aged 20-24, 25-29, 30-34 years were more than twice more likely to have babies weighing ≥2.5kg compared to those 34years were more than three times more likely to have babies weighed ≥2.5kg (OR: 3.59, 95% CI:2.56-5.04). Mothers who were civil servants were 77% more likely to have babies weighed ≥2.5kg (OR: 1.77, 95% CI: 1.99-2.87) compared to those who were unemployed. After adjusting for other explanation variables, mothers from poorer households were 30% more likely to have babies who weighed ≥2.5kg (OR: 1.30, 95% CI: 1.01-1.66) compared to those from the poorest households. Women with parity2 and parity>3 were 30% and 81% more likely to have babies weighing ≥2.5kg (OR: 1.30, 95% CI: 1.03-1.63, OR: 1.81, 95% CI: 1.38-2.35) compared to those with parity1. Male infants were 52% more likely to weigh ≥2.5kg at birth (OR: 1.52, 95% CI: 1.32-1.76) compared to females. Conclusion: Our study revealed that having infant birth weight≥2.5kg is highly associated with socioeconomic status of women household, the gender of an infant, parity, occupation and maternal age.
Data for this study were extracted from the Dodowa Health and Demographic Surveillance System (DHDSS) site database. The DHDSS is located in the south-eastern part of Ghana and operates within the boundaries of the Shai-Osudoku and Ningo-Prampram districts [37]. The DHDSS site lies between latitude 5° 45′ south and 6° 05′ north and longitude 0° 05′ east and 0° 20′ west with a land area of 1528.9 km2. It is about 41 km from the national capital, Accra [37, 38]. The two districts are made up of a population of 115,754 people in 380 communities. There are 23,647 households living in a total land area of 1442 km2. The inhabitants are predominantly subsistence farmers, fishermen and petty traders [38]. Road networks in the DHDSS are usually inaccessible during the wet seasons, making access to health and other services a challenge. The DHDSS visits every household in the demographic surveillance area twice in a year to collect data on demographic, migration and other health indicators [38]. Health care services in the DHDSS are delivered by hospitals, health centres, CHPS zones, private facilities, clinics, maternity homes, mission clinics and quasi government clinics. The study population is made up of all babies born to resident women in the DHDSS and the study sample comprised 6777 babies born to women who were resident in the DHDSS from 1st January 2011 to 31st December 2013. All babies born to women who were not resident members of the DHDSS and those born outside the study period were excluded. The outcome variable for this study is birth weight which is binary recorded as: 1 “Birth Weigh <2.5 kg” and 0“Birth Weight ≥2.5 kg”. From the available data, eleven exposure variables were selected based on biological plausibility, the available literature and the potential to influence birth weight. These exposure variables include: infant’s gender, maternal age, maternal education, maternal occupation, parity and the intake of Intermittent Preventive Treatment (IPTp) for prevention of malaria in pregnancy. Others include whether this is the mothers first live birth or not, her marital status, antenatal (ANC) attendance, type of cooking fuel, and the wealth index. The wealth index (socioeconomic status) is a proxy measure of a household’s long term standard of living; it is based on social status, asset ownership, and availability of utilities, among others. The index measures were combined into a wealth index, using weights derived through principal component analysis (PCA) [39]. The proxies from the PCA were divided into five quintiles; poorest, very poor, poor, less poor and least poor. Maternal ages at delivery were calculated using the mothers’ and babies’ birthdates. The study used secondary data from the DHDSS. Birth weight variable which was captured from the health record book of the child during the DHDSS data collection period and exposure variables were extracted from the database of DHDSS. The associations between each exposure variable and birth weight were explored at the univariable level and those significant at p < 0.05 were entered together into a multiple logistic regression model. To ensure the assumption of independence of observations, all multiple births were excluded and assessment of clustering at household level was carried out and the assumption of independence was upheld. Collinearity between all variables and models fit with and without adjustment were checked using Pearson’s correlation matrix. All analyses were conducted in Stata version 11 and results were presented in the form of tables and summary statistics in odds ratios (OR), with 95 % confidence intervals (CI) and p-values.
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