Determinants of Low Birth Weight in Ghana: Does Quality of Antenatal Care Matter?

Banchani, Emmanuel; Tenkorang, Eric Y.

doi:10.1007/s10995-020-02895-6

Cited by 22 publications

(18 citation statements)

References 33 publications

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“…The prevalence of low birth weight and preterm birth in our study is higher than that reported in other regions of Ghana [42,43], the current 2017 Ghana Maternal Health Survey [44], in Brazil [8,22], and Ethiopia [45]. Low birth weight is a signi cant underlying cause of neonatal and infant mortality in low-and-middle-income countries such as Ghana [44], which implies that participants with low birth weight are at increased risk of neonatal and infant mortality. A multiple regression analysis showed that maternal anthropometry and gestation age were co-effective in determining birth weight.…”

Section: Discussioncontrasting

confidence: 62%

Nutritional status and birth outcomes among pregnant teenagers in Ghana

Annan

Apprey

Asamoah-Boakye

et al. 2020

Preprint

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Background: Inadequate nutritional status during pregnancy can lead to adverse birth outcomes but this is more so for pregnant adolescents as they require nutrients to meet their needs for growth and that of the foetus. The study examined the relationship between nutritional status and birth outcomes among Ghanaian teenagers in rural and urban districts of the Ashanti Region, Ghana.Methods In this prospective cohort study, 416 pregnant teenagers recruited at hospitals/health centres during antenatal care were followed until delivery. Data on weight, height (for body mass index), mid-upper arm circumference (MUAC), and nutrients intake using a repeated 24-hour dietary recall were determined. Blood samples were also taken and analysed for haemoglobin (Hb), serum levels of ferritin, prealbumin, vitamin A, total antioxidant capacity (TAC), C-reactive protein (CRP), and zinc protoporphyrin (ZPP). Birth weight, gestational age at birth, type of delivery, and outcome of delivery were collected as each teenager gave birth. Data were analysed using Chi-square (Fisher’s exact) analysis, t-test, two-way ANOVA and multiple linear regression. Results: 15.9% had low birth weight, 12.5% had preterm birth, 11.5% had a postpartum haemorrhage, and 3.1% neonatal deaths. Majority of the participants had inadequate intakes of energy (81.4%), protein (77.2%), vitamins A (97.1%) and E (97.7%), calcium (99.8%), iron (97.6%), folate (93.0%), zinc (83.7%), riboflavin (88.4%), thiamin (74.4%), and fiber (74.4%) intakes. Anaemia and wasting prevalence were 57.1% and 27.8%. Between-subject effects determined using Generalized Linear Modelling indicated MUAC (p=0.058), and gestational age at delivery (p=0.023) significantly affected birth weight. A multiple linear regression analysis indicated MUAC (β=0.283, p=0.002), BMI (β=0.221, p=0.015), gestational age (β=0.285, p<0.001), and ZPP (β=0.131, p=0.057) were significantly associated with birth weight (F(23, 223), P=0.008, R2=1.941).Conclusions: Inadequate nutrients intake, anaemia, and LBW were found in these pregnant teenagers. Maternal MUAC, gestational age, and serum ZPP were associated with birth weight. Interventions for improving the nutritional status of teenagers before and during pregnancy are urgently needed to reduce the risk of adverse birth outcomes.

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Section: Discussioncontrasting

confidence: 62%

Nutritional status and birth outcomes among pregnant teenagers in Ghana

Annan

Apprey

Asamoah-Boakye

et al. 2020

Preprint

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“…during pregnancy for 8 times. It was informed that mothers who make ANC visits as recommended by the government (at least 8 visits) are significantly less likely to have LBW babies than mothers who make fewer visits 41 . Similar information was also found in the Indian study.…”

Section: Discussionmentioning

confidence: 99%

Socioeconomic Disparities of Low Birth Weight in Rural Indonesia

Laksono

Wulandari

Matahari

2020

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Background: Poverty is estimated to increase a mother's chances of giving birth to a low birth weight baby (LBW). Objective: This study aimed to analyze the socioeconomic disparities of LBW in rural Indonesia.Methods: Mother aged 15-49 years old who had given birth in the last 5 years in rural Indonesia was used as the unit of analysis. With stratification and multistage random sampling found 19,283 mothers were sampled. The variables analyzed included LBW, socioeconomic, age, marital, education, employment, and antenatal care (ANC). The binary logistic regression test was used at the final stage to determine the existence of disparities.Results: The results show that the mother with the poorer wealth status was 0.686 times more likely than the poorest mother to give birth to an LBW baby. Mothers with middle wealth status were 0.688 times more likely than the poorest mothers to give birth to LBW babies. Mothers with richer wealth status were 0.621 times more likely than the poorest mothers to give birth to LBW babies. Finally, the richest mothers were 0.452 times more likely than the poorest mothers to give birth to LBW babies. The results of this analysis inform that the better the socioeconomic of mothers, the lower the likelihood of giving birth to LBW babies. Apart from socioeconomics, the analysis also found two other predictors of LBW in rural Indonesia, namely education level and ANC visits.Conclusion: It was concluded that there were socioeconomic disparities of LBW in rural Indonesia.

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“…All men who are usual residents of selected households or visitors who slept in the households on the night before the survey were interviewed. However, for this study, only young men (15)(16)(17)(18)(19)(20)(21)(22)(23)(24) for whom there was information on tobacco use were included (N = 48,885). Details of the DHS methodology has been extensively described elsewhere [18,19] Data is available freely at https://dhsprogram.com/data/available-datasets.cfm.…”

Section: Materials and Methods Data Source And Sampling Of Participantsmentioning

confidence: 99%

“…The explanatory variables which showed statistical significance with tobacco use (p < 0.05) from the chi-square test, were moved to the regression analysis stage to estimate the association between explanatory variables and the dependent variable. Specifically, the complementary log-log (Clog-log) link function was used because the outcome variable was unevenly distributed [20]. The results were presented as crude odds ratios (CORs) and adjusted odds ratios (AORs) with their CIs.…”

Section: Analytical Proceduresmentioning

confidence: 99%

Prevalence and Drivers of Tobacco use Among Young Men in Sub-Saharan Africa: Evidence from 21 Nationally Representative Surveys

Darteh¹,

Yaya²,

Dickson³

et al. 2020

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Background Tobacco consumption – including smoking, chewing tobacco and snuff has been recognized as a global public health issue and a major cause of premature mortality and morbidity. An annual estimated figure of more than 7 million people dies due to tobacco consumption with more than 6 million of those deaths attributed to direct tobacco use and about 890, 000 resulting from exposure to second-hand smoke. This paper seeks to examine the prevalence and drivers of tobacco use among young men in sub-Saharan Africa.Methods Using pooled data from the most recent Demographic and Health Survey (DHS) conducted between January 1, 2010, and December 3, 2018, in 21 countries that had data on tobacco use in SSA this paper examines tobacco use among young men in sub-Saharan Africa. A chi-square was conducted at the bivariate level to test for the significance of tobacco use across the socio-demographic characteristics. A binary logistic regression test was conducted at the multi-variate level to estimate the determinants of tobacco use among young people in SSA. The confidence interval for both the bivariate and multivariate tests was 95%.Results The overall prevalence of tobacco use was 9.34% (95% CI: 7.52–11.16) with the lowest prevalence in Ghana (1.46%) and the highest in Lesotho (39.19%). It was also found that those aged 20–24 [AOR = 3.080, CI = 2.840–3.341], engaged in unskilled labour [AOR = 2.651, CI = 2.333–3.012], who are widowed/divorced/separated [AOR = 2.747, CI= [2.301–3.279] and with no religion [AOR = 1.460, CI = 1.273–1.676] had higher odds of using tobacco. On the other hand, young, men with secondary [AOR = 0.726, CI = 0.645–0.818] and higher [AOR = 0.563, CI= [0.446–0.710] level of education, those in the richest wealth quintile[AOR = 0.758, CI = 0.660–0.871], those in rural areas [AOR = 0.869, CI = 0.795–0.950], and those who read newspaper or magazine at least once a week [AOR = 0.898, CI = 0.808–0.998] had lower odds of tobacco use.Conclusion Age, level of education, wealth status, working status, marital status, religion and place of residence were statistically significant association with tobacco use among young men in SSA. These socioeconomic factors should be considered when trying to tackle tobacco use in SSA. Specific public health interventions should take into account these socio-economic factors to reduce tobacco use among young people in SSA

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Determinants of Low Birth Weight in Ghana: Does Quality of Antenatal Care Matter?

Cited by 22 publications

References 33 publications

Nutritional status and birth outcomes among pregnant teenagers in Ghana

Nutritional status and birth outcomes among pregnant teenagers in Ghana

Socioeconomic Disparities of Low Birth Weight in Rural Indonesia

Prevalence and Drivers of Tobacco use Among Young Men in Sub-Saharan Africa: Evidence from 21 Nationally Representative Surveys

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