Prevalence of low birth weight and its associated factor at birth in Sub-Saharan Africa: A generalized linear mixed model

Zemenu Tadesse Tessema; Koku Sisay Tamirat; Achamyeleh Birhanu Teshale; Getayeneh Antehunegn Tesema

doi:10.1371/journal.pone.0248417

. 2021 Mar 11;16(3):e0248417. doi: 10.1371/journal.pone.0248417

Prevalence of low birth weight and its associated factor at birth in Sub-Saharan Africa: A generalized linear mixed model

Zemenu Tadesse Tessema ^1,^*, Koku Sisay Tamirat ¹, Achamyeleh Birhanu Teshale ¹, Getayeneh Antehunegn Tesema ¹

Editor: Clive J Petry²

PMCID: PMC7951905 PMID: 33705473

Abstract

Background

Low birth weight (LBW) is one of the major determinants of perinatal survival, infant morbidity, and mortality, as well as the risk of developmental disabilities and illnesses in future lives. Though studies were conducted to assess the magnitude and associated factors of low birth weight, most of the studies were at a single center and little information on the regional level. Hence, this study assessed the prevalence and associated factors of low birth weight in Sub-Saharan countries.

Method

This study was based on secondary data sources from 35 Sub-Saharan countries’ Demography and Health Survey (DHS). For this study, we used the Kids Record (KR file) data set. In the KR file, all under-five children who were born in the last five years preceding the survey in the selected enumeration area who had birth weight data were included for the study. To identify determinants of low birth weight multivariable mixed-effect logistic regression model fitted. Adjusted Odds Ratios (AOR) with a 95% Confidence Interval (CI) and p-value ≤0.05 in the multivariable model were used to declare significant factors associated with low birth weight at birth.

Result

The pooled prevalence of newborn babies’ low birth weight measured at birth in Sub-Saharan Africa was 9.76% with (95% CI: 9.63% to 9.89%). Female child, women not participated in healthcare decision making, and wider birth intervals, divorced/ separated women, and twin pregnancies associated with increased occurrences of low birth weight, while some level of woman and husband education, antenatal care visits, older maternal age, and multiparity associated with reduced occurrence low birth weight.

Conclusion

This study revealed that the magnitude of low birth weight was high in sub-Saharan Africa countries. Therefore, the finding suggests that more emphasis is important for women with a lack of support, multiples, and healthcare decision-making problems.

Introduction

Low birth weight(LBW) is one of the major determinants of perinatal survival, infant morbidity, mortality, and the risk of developmental disabilities and illnesses in future lives [1,2]. Globally, WHO estimates that about 30 million low birth weight babies are born annually (23.4% of all births), and they often face short and long-term health consequences [3,4]. Half of all low birth weight babies are born in South-central Asia, where 27 percent are below 2500g at birth, while LBW levels in sub-Saharan Africa are estimated at 15 percent [4,5].

LBW is determined by two major processes, which are the duration of gestation and intrauterine growth rate. Several studies indicated that a baby‘s low weight at birth is either the result of preterm birth (before 37 weeks of gestation) or restricted fetal (intrauterine) growth [5–8]. There is a strong consensus that birth weight plays an important role in infant mortality, morbidity, development, and future health of the child [9]. Particularly, low birth weight is the most significant risk factor for adverse health outcomes, including common childhood diseases [9]. The association between LBW and a greatly elevated risk of infant mortality and other physical and neurological impairments are well-established facts [10]. The risk of neonatal mortality for very LBW infants is 25 to 30 times greater than for infants with a birth weight exceeding 2500g, and it increases sharply as birth weight decreases [10].

Demographic risk factors include young maternal age, primiparity, and low education level, and poor maternal nutritional status–both before and during pregnancy—which are well-recognized determinants of birth outcomes [2,5–7,10–13]. Empirical studies from developed and developing countries show that maternal undernutrition, chronic illnesses like HIV/AIDS, and cardiovascular diseases are associated with low birth weight [14,15]. Focused antenatal care, nutritional counseling during the prenatal and perinatal period, and institutional deliveries are some of the interventions to avert low birth weight. To achieve the targets of reducing child mortality of the Sustainable Development Goals (SDGs)-2030, it is necessary to generate adequate evidence on the magnitude of low birth weight and associated factors to contribute to the development of timely interventions. Studies were conducted on LBW at birth among mothers who gave birth in different countries [4,16]. However, the results of the studies were inconsistent; factors that had an association in some studies may be had no association in other studies and vice-versa. Moreover, to the best of our knowledge, there is no pooled data on LBW magnitudes and associate factors at the Sub-Saharan African level.

Hence, this study assessed the pooled prevalence and associated factors of low birth weight in Sub-Saharan countries. This study could give additional knowledge about the risk factors of low birth weight in low-income countries and for evidence-based interventions. Moreover, this study’s result could support policymakers, clinicians, and programmers to design interventions on preventing LBW.

Method

Data source

Secondary data analysis was done based on the most recent Demographic and Health Surveys (DHS) conducted in the 35 Sub-African (SSA) countries. Southern Region of Africa (Lesotho, Namibia and South Africa), Central Region of Africa(Angola, DR Congo, Congo, Cameroon, Gabon, Sao Tome & Principe, and Chad), Eastern Region of Arica (Burundi, Ethiopia, Kenya, Comoros, Madagascar, Malawi, Mozambique, Rwanda, Tanzania, Uganda, Zambia, and Zimbabwe), Western Region of Africa (Burkina-Faso, Benin, Cote d’Ivoire, Ghana, Gambia, Guinea, Liberia, Mali, Nigeria, Niger, Sierra Leone, Senegal, and Togo). Each country’s sampling procedure was the same or homogeneity across countries [17] (Table 1). These datasets were appended together to investigate mothers’ perceived birth size for the prediction of low birth weight babies in SSA. The DHS is a nationally representative survey that collects data on basic health indicators like mortality, morbidity, family planning service utilization, fertility, maternal and child health. The data were derived from the measure DHS program. The DHS has different datasets (men, women, children, birth, and household datasets). For this study, we used the Kids Record (KR file) data set. In the KR file, all under-five children who were born in the last five years preceding the survey in the selected enumeration area who had birth weight data were included for the study. The DHS used two stages of stratified sampling technique to select the study participants. We pooled the DHS surveys conducted in the 35 Sub-African countries, and a total weighted sample of 202,878 under-five children was included in the study (Fig 1).

Table 1. The study country, DHS year and number of study participants in SSA.

Country	DHS year	Low Birth weight	Sample size (202,878)
Angola	2014	778	7326
Benin	2013	985	13882
Burkina Faso	2016	740	7372
Burundi	2015/16	527	6897
Cameron	2013/14	647	4585
Chad	2011/12	123	1484
Comoros	2011	157	2221
Congo	2012	1092	10921
Cote de vaire	2008/09	198	1499
Dr Congo	2014/15	461	6123
Ethiopia	2010	349	2145
Gabon	2016	643	5036
Gambia	2014	1789	14557
Ghana	2012	841	5980
Guinea	2008/09	465	7376
Kenya	2015/16	439	6369
Lesotho	2011	985	10254
Liberia	2014/15	672	7882
Madagascar	2015/16	498	5268
Malawi	2011	363	2517
Mali	2018	531	4071
Mozambique	2013/14	174	2268
Namibia	2010	450	3123
Niger	2017	1351	9765
Nigeria	2011	949	8182
Rwanda	2014	634	4460
Saotome and Principe	2013	321	3416
Senegal	2018	550	4685
Sierra Leone	2013	396	3841
South Africa	2018	146	1509
Tanzania	2018	589	8048
Togo	2012	365	3082
Uganda	2010/11	410	5786
Zambia	2010/11	1092	6936
Zimbabwe	2013/14	379	3997

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Variables of the study

Dependent variable

The main outcome variable of this study was birth weight. Data on children’s birth weight were collected from mothers who gave birth within five years before the survey of each Sub-Saharan Africa country either by accessing birth weight through record review or by the mother’s report by recalling the measured weight of the child at birth. The births without recorded birth weight were excluded from the study. Finally, LWB was defined as a birth weight <2.5kg, and those 2.5 kgs were considered normal and above normal birthweights.

Independent variables

Potential risk factors for LBW were included based on the literature review [4,11,12,16,18,19], we included two types of variables in the analysis. Level one variable (individual-level variables) that is maternal and husband education (has no education, primary education and secondary and above, no education means respondents cannot read and write or had no any formal education), maternal age, mother marital status, household wealth index, maternal occupation status, women health care decision making autonomy, media exposure, number of antenatal care (ANC) visit, preceding birth interval, parity(recoded as 1–2,3–5 and 6+), sex of the child, type of birth and iron supplementation. Level two variables (community-level variables) included in this study were region (recoded as West Africa, East Africa, Central Africa, and South Africa), residence, and country.

Operational definition

Household wealth index

The wealth variable was generated from the wealth index for the households. In the dataset, the index has five quintiles, such as; the lowest quintile (poorest), second quintile (poorer), third quintile (middle), four quintiles (wealthier), and the fifth quintile (wealthiest). In this study for ease of analysis, this variable was categorized as ‘poorest’ and ‘poorer’ were coded as (1) ‘poor,’ the middle was coded as (2) ‘middle,’ and ‘wealthier’ and ‘wealthiest’ were coded as (3) ‘rich”.

Women health care decision-making autonomy

Defines as women health care decision-making capacity for a woman to achieve well-being and decision making a role.

Media exposure

A respondent said to be media exposed if they listen/read at least one media in the week (Radio or TV or Newspapers)

Data management and analysis

We pooled the data from the 35 Sub-Saharan African countries together after extracting the variables based on literature. Before any statistical analysis, the data were weighted using sampling weight, primary sampling unit, and strata to restore the survey’s representativeness and take sampling design when calculating standard errors and reliable estimates. Cross tabulations and summary statistics were done using STATA version 14 software. A meta-analysis was done using the “meta-prop” Stata command. A fixed-effect meta-analysis was done to estimate the pooled prevalence of LBW in SSA. Pooled analysis was done for both SSA regions and sub-level regions. The pooled prevalence of low birth weight at birth with the 95% Confidence Interval (CI) was reported using a forest plot. For the determinants factors, the DHS data had a hierarchical structure; this violates the independence of observations and equal variance assumption of the traditional logistic regression model. Hence, children are nested within a cluster, and we expect that children within the same cluster may be more similar to each other than women in the rest of the country. This implies that there is a need to take into account the between cluster variability by using advanced models. Therefore, a mixed effect logistic regression model (both fixed and random effect) was fitted. Since the outcome variable was binary, standard logistic regression and Generalized Linear Mixed Models (GLMM) were fitted. Model comparison and fitness were made based on the Intra-class Correlation Coefficient (ICC), Likelihood Ratio (LR) test, Median Odds Ratio (MOR), and deviance (-2LLR) values since the models were nested. The model with the lowest deviance was chosen. Accordingly, the mixed-effect logistic regression model was the best-fitted model. Variables with a p-value <0.2 in the bi-variable analysis were considered in the multivariable mixed-effect logistic regression model. Adjusted Odds Ratios (AOR) with a 95% Confidence Interval (CI) and p-value ≤0.05 in the multivariable model were used to declare significant factors associated with low birth weight at birth.

Ethical consideration

Permission for data access was obtained from a major demographic and health survey through an online request from http://www.dhsprogram.com. The data used for this study were publicly available with no personal identifier.

Result

Socio-demographic and economic characteristics

A total of 202,878 mothers were included; of these, 83,512 (41.0%) were in West Africa. The majority (58.3%) of the mothers were rural residents, and 103,763 (51.0%) were aged 20–29 years. About 76,879 (37.8%) of mothers had attained primary level of education, and 52.7% of their husbands attained secondary education and above (Table 3).

Table 3. Multivariable multilevel logistic regression analysis to assess factors associated with low birth weight in Sub-Saharan Africa.

Variables	Newborn birth weight (n = 202,878)		Column (%)	Odds ratio
Variables	Normal and above	Low birth weight	Column (%)	COR (95%CI)	AOR (95%CI)
Residence
Urban	75834	8798	84632(41.7)	1
Rural	106038	12208	118246(58.3)	1.00(0.97,1.03)	0.98(0.97,1.02)
Maternal age
15–19	10688	1753	12441(6.13)	1	1
20–29	92530	10955	103485(51.01)	0.71(0.68,0.75)	0.75(0.71,0.80)^*
30–39	64427	6686	71113(35.05)	0.62(0.59,0.65)	0.76(0.71,0.82)^*
40–49	14226	1612	15838(7.81)	0.66(0.62,0.71)	0.86(0.78,0.95)^*
Mothers education status
No	46045	6096	52141(25.70)	1	1
Primary	68894	7811	76705(37.81)	0.87(0.84,0.90)	0.87(0.83,0.92)^*
Secondary and above	66935	7097	74052(36.49)	0.81(0.78,0.90)	0.79(0.75,0.84)^*
Husband education status
No	36156	4795	40951(23.52)	1	1
Primary	49617	5389	55006(31.60)	0.82(0.79,0.86)	0.89(0.84,0.95)^*
Secondary and above	70862	7276	78138(44.88)	0.87(0.83,0.90)	0.89(0.84,0.94)^*
Marital status
Married	153994	17115	171109(84.34)	1	1
Divorced/widowed/not in union	27877	3890	27787(15.66)	1.27(1.23,1.32)	1.22(1.16,1.28)^*
Wealth status
Poor	61288	7600	68888(33.96)	1	1
Middle	36022	4119	40141(19.79)	0.91(0.87,0.95)	0.91(0.86,0.95)^*
Rich	84563	9285	93848(46.26)	0.83(0.81,0.86)	0.82(0.78,0.86)^*
Women had occupation
No	61442	7859	69303(34.16)	1	1
Yes	120429	13145	133574(65.84)	0.84(0.81,0.86)	0.88(0.84,0.91)^*
Women participating in making their own health care decisions
Yes	93000	9685	102685(50.61)	1	1
No	88873	11320	100193(49.39)	1.24(1.20,1.28)	1.25(1.21,1.29)^*
Media exposure
No	46390	5834	52224(25.74)	1	1
Yes	135482	15172	105654(74.26)	0.91(0.88,0.94)	0.97(0.93,1.01)
Number of ANC visit
No visit	52323	7194	59518(29.34)	1	1
1–3 visits	42443	5263	47706(23.51)	0.90(0.87,0.94)	0.91(0.83,0.99)^*
≥ 4 visits	87105	8547	95653(47.15)	0.71(0.69,0.74)	0.72(0.66,0.79)^*
Preceding birth interval (in months)
< 24	22107	2320	24427(12.04)	1	1
≥ 24	159765	18685	178450(87.96)	1.09(1.04,1.14)	1.09(1.04,1.14)^*
Parity
1–2	83996	10803	94799(46.73)		1
3–5	69056	7098	76154(37.54)	0.76(0.72,0.79)	0.74(0.70,0.77)^*
≥6	28819	3104	31923(15.74)	0.69(0.65,0.74)	0.68(0.63,0.73)^*
Sex of child
Male	93333	9494	102828(50.68)	1	1
Female	88539	11510	100050(49.32)	1.35(1.25,1.37)	1.30(1.25,1.35)^*
Type of births
Single	177599	17731	195330(96.28)	1	1
Twin	4273	3274	7547(3.72)	8.45(7.42,9.75)	9.03(8.37,9.75)^*
Iron supplementation
No	17715	1972	19687(13.28)	1	1
Yes	116435	1248	128924(86.75)	0.10(0.92,1.05)	0.99(0.94,1.04)

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* P-value ≤0.05; COR = Crude Odds Ratio; AOR = Adjusted Odds Ratio; ANC = Antenatal care.

The pooled prevalence of low birth weight measured at birth in Sub-Saharan Africa

The pooled prevalence of newborn babies’ low birth weight measured at birth in Sub-Saharan Africa was 9.76%, with a 95% confidence interval from 9.63% to 9.89%. The highest LBW was recorded in Ethiopia, 16.21%, with its 95% confidence interval of 14.71 to 17.83. The lowest LBW was recorded in Guinea, 6.30%, with a 95% confidence interval of 5.75 to 6.85 (Fig 2).

Model comparison

Log-likelihood and deviance were checked, and the mixed effect logistic regression model was chosen because of the smallest value of deviance since the model (Table 2). Furthermore, MOR 1.08 indicates if we randomly select two women from different enumeration areas, women from enumeration areas with low birth weight were increased by 8% than women from clusters with lower birth weight. Besides, the likelihood ratio test was (LR test vs. Logistic model: chibar2 (01) = 5.56 Prob > = chibar2 = 0.0092), which informed that the mixed-effect logistic regression model (GLMM) is the better model over the basic model (Table 3).

Table 2. Model comparison and random effect results.

Parameter	Standard logistic regression	Mixed-effect logistic regression analysis (GLMM)
LLR	-45366	-45363
Deviance	90732	90726
LR-test	LR test vs. logistic model: chibar2(01) = 5.56 Prob > = chibar2 = 0.0092
MOR	1.08 [1.05, 1.13]
Cluster variance	0.0069 [0.0025, 0.0169]

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*LLR; log-likelihood ratio, ICC; Intra-class Correlation Coefficient, MOR; Median Odds Ratio, LR-test; Likelihood Ratio test.

Determinants of low birth weight in Sub-Saharan Africa

In the multivariable mixed-effect logistic regression analysis; maternal age, maternal education, husband education, marital status, wealth status, maternal occupation, women decision making autonomy, number of ANC visit, birth interval, parity, sex of the child, and types of birth were significant predictors of LBW babies in Sub-Saharan Africa.

The odds of LBW among mothers age 20–29, 30–39 and 40–49 were decrease by 25% (AOR = 0.0.75, 95% CI: 0.71, 0.80), 24% (AOR = 0.076, 95% CI: 0.71, 0.82), and 14% (AOR = 0.86, 95% CI: 0.78, 0.95) than mothers aged 15–19 years, respectively. Mothers who attained primary education (AOR = 0.87, 95% CI: 0.83, 0.92) and secondary education and above (AOR = 0.79, 95% CI: 0.75, 0.84) were less likely to LBW at birth than mother who didn’t attain formal education. Mothers whose husbands attained primary education (AOR = 0.89, 95% CI: 0.84, 0.94) and secondary education and above (AOR = 0.89, 95% CI: 0.84, 0.95) were less likely to LBW at birth than mother whose husband didn’t attain formal education. Mothers who are Divorced/widowed/not living together were 1.22 times more likely LBW at birth than married mothers (AOR = 1.22, 95% CI: 1.16, 1.28). The odds of LBW among mothers of middle were decrease by 9% (AOR = 0.91, 95% CI: 0.86, 0.95) and 18% (AOR = 0.82, 95% CI: 0.78, 0.86) respectively. Mothers who had occupation decreased the odds of LBW at birth by 12% (AOR = 0.88, 95% CI: 0.84, 0.91) compared to mothers who had no occupation. Mothers who have no participation in making their own health care decisions were 1.25 times more likely to LBW at birth than mothers who have no participation in making their own health care decisions (AOR = 1.25, 95% CI: 1.21, 1.29). Mothers who had 1–3 and four and above ANC visits were decreasing the odds of LBW at birth by 9% (AOR = 0.91, 95% CI: 0.83, 0.99) and 28% (AOR = 0.72, 95% CI: 0.66, 0.79) respectively. The odds of LBW of babies at birth among women who had 3–5 births and ≥ six births were decreased by 26% (AOR = 0.74, 95% CI: 0.70, 0.77) and 32% (AO = 0.68, 95% CI: 0.63, 0.73) than mothers who had 1–2 births respectively. Being a female baby increases the odds of LBW at birth by 30% as compared to males (AOR = 1.30, 95% CI: 1.25, 1.35). Being twin birth was 9.03 times more likely to LBW at birth than single birth (AOR = 9.03, 95% CI: 8.37, 9.75) (Table 3).

Discussion

The pooled analysis from 35 SSA countries showed that the overall prevalence of LBW was 9.47%, with variations among African regions ranged from 8.9% in eastern Africa to 10.3% in Southern Africa. In addition, variations also observed between countries ranged from 6.3% in Guinea to 16.21% in Ethiopia, as shown in Fig 1. This was consistent with the results of systematic and meta-analysis findings in Iran (9%) [20], Nepal (9.4%) [19], and Ghana (9.69%) [21]. However, this work finding was lower than those studies in Kenya (12.3%) [12], selected African countries such as Burkina Faso, Ghana, Malawi, Senegal, and Uganda was, respectively, 13.4%, 10.2%, 12.1%, 15.7% and 10% [22], and systematic and meta-analysis results in Ethiopia (18%) [16]. This could be attributed to differences in study population and design. Demography and health surveys were conducted in the community-based study, whereas those studies included in the systematic and meta-analysis were single-center and health facility-based study that could overestimate the magnitude of LBW. In addition, the finding of this study was lower than global and sub-Saharan countries estimate of 14.6% and 16.4%, according to the national, regional, and global systematic analysis [4]. This could be because interventions and efforts made to curb maternal and neonatal mortality in low-income countries included in SSA [23]. Moreover, the overall prevalence of LBW in this study was higher than in a study elsewhere (7.6%) [24]. This could be due to socio-demographic, economic, health care system differences.

Different individual and contextual factors attributed to the occurrence of LBW, as the age of the mother increased the odds of low birth weight were decreased. This finding was consistent with those of studies conducted in Nepal and Iran [24–26]. This could be attributed to problems of child marriage and malnutrition in adolescent girls in Africa [27]. Similarly, some women and husbands’ education levels are associated with decreased odds of LBW than uneducated women and men. This finding was consistent with those of studies in Ethiopia, Kenya, and Nepal [12,24–26,28]. Literacy is often associated with good knowledge about the better nutritional practice and health-seeking behaviors during pregnancies, which could impact birth outcomes [7].

Furthermore, middle, rich wealth status, those who had occupation associated with a reduced probability of LBW compared to the poor and those who had no occupation, respectively. This could be due to the fact women who had good economic status and occupation had better nutritional status during pregnancies [6,29]. In addition, better wealth conditions and occupation are more likely to increase women’s health-seeking behaviors, which could be helpful for early detection of intrauterine growth retardations. This finding implies that improving women’s socio-economic conditions might help reduce unfavorable birth outcomes like LBW. The odds of LBW for multiparous were reduced than primiparous women. This finding was consistent with those of studies [6,12]. Most of the pregnancy-related symptoms diminished as the parity and gravidity increased, which could impact the nutritional status of women [19]. In addition, strengthening of focused ANC follow-up for primiparous mothers, counseling, and nutritional help.

In addition, one or more antenatal care (ANC) visits were associated with reduced odds of LBW compared to those who had no follow-ups. This could be due to the fact that ANC has packages like nutritional counseling and iron supplementations for maternal and fetal well-being. Conversely, divorced and separated women are associated with an increased risk of LBW compared to married women. This could be due to the fact that women divorced and had no partner support make them more vulnerable to malnutrition [7,11]. In addition, divorced and separated women have poor psychological and social support, which could impact adverse birth outcomes. Moreover, a mother who has poor marital relationships may experience intimate partner violence that leads to premature rupture of membrane and onset of preterm labor [30]. Addressing the social, psychological constraints, and financial hardship might have valuable importance to reducing unfavorable birth outcomes like LBW.

This study also showed that twin pregnancy is associated with higher odds of LBW than a singleton. This finding was consistent with those of studies in Lusaka, Zambia, and Nigeria [31,32]. This could be due to the fact that twin pregnancies are associated with the increased demand for nutrients and oxygenated blood [24]. Women who didn’t participate in healthcare-associated decision-making with increased LBW occurrence to their newborns compared to those who didn’t participate in decision-making. Empowerment and women’s autonomy would be helpful to make the right choices to access quality health care during pregnancy. Increasing women empowerment and autonomy to increase utilization of maternal health services. This finding was supported by previous results in Ghana [33] and Bangladesh [18]. Likewise, female newborns associated higher odds of LBW than males. This finding was consistent with those of a previous study [34]. Furthermore, wider birth intervals (above 24 months) are associated with an increased occurrence of LBW. This finding was contrary to a previous study in Ethiopia [34]. This could be due to the sample size differences and socio-demographic characteristics in SSA.

This study showed that iron supplementation did not create a statistically significant association with LBW during the prenatal era. However, the coefficient showed that the correlation between low birth weight and increased iron supplementation during the pregnancy period was negative. This is of clinical significance for both pregnant mothers and newborns and highlights the effects of improving the coverage of iron supplementation in pregnant women [35]. In addition to clinical evaluations, provision of therapy, and recognition of danger signs, iron supplementation is also an effective intervention during follow-up of antenatal treatment for pregnant mothers.

Strength

This study was based on large observation (sample size) from SSA countries and helpful for an overall assessment of LBW. In addition, pooled estimates of LBW will be helpful for policymakers and stakeholders for the situation of the problems. This result would be important for employing combined efforts to identified modifiable risk factors.

Limitation

The data’s cross-sectional nature may not show a true temporal relationship between the outcome and independent variables. Much of the birth weight was taken from the mother’s self-report, and this may result in recall bias. The health section and calendar age/date boundaries, using data for last-born children or non-last-born children instead of all children born in the five years preceding the survey, will probably result in biased estimates [36]. Missing important maternal clinical parameters (chronic illnesses status, malaria infection, nutritional status) were the limitations of the study. The problem is overfitting may affect our result. Climate changes such as heatwave exposure and air temperature rise are very prevalent in low-income countries; the impact on adverse birth outcomes such as low birth weight has not been assessed. Combined low birth weight projections can be useful for problem situations for policymakers and stakeholders.

Conclusion

In this study, the magnitude of low birth weight was high with significant variations among countries. Female child, women not participated in healthcare decision making, and wider birth intervals, divorced/ separated women, and twin pregnancies associated with increased occurrences of low birth weight, whilst some level of woman and husband education, antenatal care visits, older maternal age, and multiparity associated with reduced occurrence low birth weight. This finding suggests that more emphasis is important for women with a lack of support, multiples, and addressing healthcare decision-making problems.

Abbreviations

ANC: Antenatal Care
AOR: Adjusted Odds Ratio
Area: Under the Curve
CI: Confidence Interval
DHS: Demographic and Health Survey
EA: Enumeration Area
LBW: Low Birth Weight
LLR: Likelihood Ratio
NPV: Negative Predictive Value
PPV: Positive Predictive Value
ROC: Reciever Operating Curve
SD: Standard Deviation
SGD: Sustainable Development Goal
SSA: Sub-Saharan Africa
USA: United States of America

Data Availability

All relevant data are within the manuscript.

Funding Statement

We didn’t receive external funds for this research.

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PLoS One. doi: 10.1371/journal.pone.0248417.r001

Decision Letter 0

Clive J Petry

2 Dec 2020

PONE-D-20-27726

Determinants of low birth weight at birth in Sub-Saharan Africa: A Generalized Linear Mixed Model

PLOS ONE

Dear Dr. Tadesse,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

All the reviewers recognise some merit in this manuscript but also have some concerns about it. Each of them have highlighted changes that need to be made in any revision.

I would add the following points:

1. The standard of English used in the manuscript needs to be improved (maybe via checking by a native English speaker).

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I look forward to seeing a revised version of the manuscript.

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We look forward to receiving your revised manuscript.

Kind regards,

Clive J Petry, PhD

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PLOS ONE

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5. We noticed you have some minor occurrence of overlapping text with the following previous publications, which needs to be addressed:

- https://worldwidescience.org/topicpages/l/lbw+births+lbw.html

- http://etd.aau.edu.et/bitstream/handle/123456789/8229/29.Andenet%20Gebrekidan.pdf?sequence=1

In your revision ensure you cite all your sources (including your own works), and quote or rephrase any duplicated text outside the methods section. Further consideration is dependent on these concerns being addressed.

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Reviewers' comments:

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Reviewer #1: Partly

Reviewer #2: No

Reviewer #3: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: No

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors were wise to take into account the between cluster variability by using the appropriate models. Therefore, a mixed effect logistic regression model (both fixed and random effect) was fitted. Since the outcome variable was binary, standard logistic regression and Generalized Linear Mixed Models (GLMM) were fitted, as expected.

Assuming the model inputs are appropriate this is a straight forward application of the models. The timing of the information is seen in Table 1 as noted in the ‘Data Source’ information. Also assurances should be given of the homogeneity of the sample across the countries.

Reviewer #2: The authors present an important analysis assessing risk factors for low birthweight in Sub-Saharan Africa. The analysis pools recent (2008-2018) DHS surveys from 35 different to represent the sub-continent and specific regions, Central Africa, East Africa, West, Africa, South Africa. The authors find risk factors and protective factors for low birth weight similar to those established in the literature. While there may be some advantage to assessing risk factors by region as the authors aim, there are some concerns with the overall methods used in the manuscript. Further the manuscript would benefit from English language editing. I have provided some suggestions below. See the attachment for more details.

Reviewer #3: The topic of determinants of low birth weight at birth is of high interest, particularly in sub-Saharan Africa. The manuscript is clearly written and presented.

However, there are some aspects that the authors should consider.

Major comments

1. The principal issue of this work is the use of DHS data.

DHS is known as a formidable source of data and give a general overview of sanitary situation of each country (trend of key indicators). However, there are many methodological concerns about accuracy of data collection, population sample and high risk of potential biases: Selection biases (due to different household sampling techniques), information or classification biases (due to self-report data or declared data, medical records, standardization of data collection …). In addition, the methodology can differ between countries, especially concerning the under-five children data. Indeed, two main different methods can be used to collect these data: Most-Recently-Born Children and Non-Most-Recently-Born Children.

I invite the authors to read this critical analysis of the use of DHS data for children (pdf attached), whose conclusion I quote: “When researchers use the DHS surveys for the analysis of children born in the five years preceding the survey, they must be very careful to consider and avoid inherent biases that derive from both the data structure and the nature of the analysis. Beyond the usual considerations of omission of births and transference of births across questionnaire age boundaries, particularly the health section and calendar age/date boundaries, using data for last-born children or non-last-born children instead of all children born in the five years preceding the survey will probably result in biased research findings.”

2. Analysis (statistical issue)

Multiple pregnancies (such as twin pregnancy) were included in the same models with single pregnancy. Multiple pregnancies (MP) are known as a mediator factor of LBW (MP is on the pathway of LBW). Therefore, this can lead to a sur-adjustment. I will be more useful to perform the main analysis after excluding MP and to perform a sensitivity analysis with MP.

Other comments

The use of “Determinants” in the title by the authors is misleading to me. The notion of determinant implies a causal role while the notion of risk factor or associated factor is broader and refers to a higher probability of disease in exposed subjects. In this work, it is rather “associated factors” that have been highlighted.

Abstract

Lines 47-48: In conclusion of abstract, the authors declared “this study… with significant variations among countries” but any variations or range have been presented in the results paragraph of abstract. Please, correct it.

Introduction

Lines 71-72: 25-30 times seems very high today (substantial progress has been made, even in countries with limited resources). I suggest that the authors further clarify this information, either by adding that this very high risk is for very low birth weight infants, or that the information is several years old.

Lines 80-82: There are many studies such as systematic reviews, multicenter pooled analyses (some of which have even been cited by the authors). The rationale for this study therefore needs to be better specified. For the moment, we do not really see the added value or the new knowledge brought.

Lines 85-86: I am not convinced.

Methods

See my major comment about the use of DHS data. I suggest that the authors briefly present the methods used to collect data on children in the DHS to give us an idea of the robustness or otherwise of the data and potential biases.

Also, what about children who were not included in this analysis. Do the authors check any potential selection biases?

It will be useful to have a country included in each region.

How infant weight were measured at birth? Standardization? Declared (or self-report data?) Medical record?

Variable definition paragraph is missing in this work, particularly for the clarification of the definition of variable such as “household wealth index”, “women health care decision making autonomy”.

What about other important independent variables such as malaria infection or BMI?

ANC: please, define the acronym the first time you use it.

The parameters of mixed logistic regression need to be more detail. Indeed, the authors used multilevel models to take into account the hierarchical structure of data. However, it is important to specify what represents the 1st level (individual?), the 2nd level (regions or countries?). Please, clarify.

Results

Table 2: West Africa weighted frequency is 12170, but Benin (which is West African country) only provide 13909 participants. Please, clarify.

Table 4: It will be useful to have also percentage in normal and LBW columns.

Lines 162-163: “Women from a cluster … lower birth weight”. This sentence is a bit confusing. Please, clarify.

Discussion

Line 197: “8.9% to 10.3%”, please include this result in results session also.

In general, the discussion section need to be more focus on comparison of this study with other multicentric pooled analysis and meta-analysis studies in sub-Saharan Africa, instead of comparison of single center or no-pooled analysis studies.

Lines 199-207: For me, these comparisons are not relevant (single center studies vs. pooled multicenter study).

Lines 214-224: All this paragraph is a speculation. What about nutritional factors in this study. Iron supplementation was not associated with LBW in this study. Why? A discussion of this negative finding will be useful. Why didn't the authors consider other indicators of nutritional status such as BMI? If nutritional status plays an important role in the occurrence of LBW, all of the above should be discussed.

See in major comment my comment about multi pregnancies.

Limitations paragraph need to be more detail, including discussion about potential methodological issues, need to take into account nutritional data and the robustness of the findings.

**********

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Reviewer #1: No

Reviewer #2: Yes: Emily Deichsel

Reviewer #3: No

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PLoS One. 2021 Mar 11;16(3):e0248417. doi: 10.1371/journal.pone.0248417.r002

Author response to Decision Letter 0

29 Dec 2020

PLOS ONE

Point by point response for editors/reviewers comments

The manuscript title “Prevalence of low birth weight and its associated factor at birth in Sub-Saharan Africa: A Generalized Linear Mixed Model”

Manuscript ID: PONE-D-20-27726

Dear editor/reviewer.

Dear all,

I would like to thank you for these fair, constructive, building, and improvable comments on this manuscript that would improve the substance and content of the manuscript. We considered each comment and clarification questions of reviewers on the manuscript thoroughly. My point-by-point responses for each comment and question are described in detail on the following pages. Further, the details of changes were shown by track changes in the supplementary document attached.

Response to Reviewers comments

Reviewer #1

1. The timing of the information is seen in Table 1 as noted in the ‘Data Source’ information. Also assurances should be given of the homogeneity of the sample across the countries

Author response:- Thank you very much for your fair and contractive comments. We accept your comment and corrected it accordingly. We included your concern in the revised manuscript which describe the homogeneity of sampling procedure across sub-Saharan Africa countries line 94-95(see the revised manuscript)

Reviewer #2

1. There may be some advantage to assessing risk factors by region as the authors aim,

Author’s response:- Thank you very much for your concern. There are different research articles done on assessing risk factors by regions(Eastern Africa, Southern Africa, Western Africa, and Central Africa). Our primary objective was assessing factors that represent and can be generalizable at sub-Saharan Africa which consists of the entire four WHO sub-Saharan Africa region(Eastern Africa, Southern Africa, Western Africa, and Central Africa). The 35 sub-Saharan Africa country datasets were appended together for the prediction of low birth weight babies in SSA. The DHS data is a nationally representative survey that collects data on basic health indicators like mortality, morbidity, family planning service utilization, fertility, maternal and child health. The data were derived from the measure DHS program. The DHS has different datasets (men, women, children, birth, and household datasets). For this study, we used the children dataset(KR dataset.)

2. There are some concerns with the overall methods used in the manuscript.

Authors response:- Thank you very much for your concern. We would be very happy if the comment was clear and indicate the specific methodological problem. The given comment is crude and blind. We would be very happy if you tell us the methodological problems you observed. The method session is adopted from the procedure of DHS program from across all sub-Saharan Africa countries. We try to use the advanced statistical technique that considers the hierarchical nature of the DHS data i.e Generalized Linear Mixed Effect Model(GLMM). We are open to correct the methodological problems you observed if you indicate us clearly for the improvement of the manuscript.

3. The manuscript would benefit from English language editing. I have provided some suggestions below. See the attachment for more details

Authors response:- Thank you very much for your contractive and fair comments for the improvement of the manuscript. We accept your comment and corrected it accordingly. In addition, we gave our manuscript to an English editor and we corrected grammatical and punctuation errors accordingly (see the revised manuscript).

Reviewer #3

1. The principal issue of this work is the use of DHS data.

Authors response:- Thank you very much for the concerned reviewer. For this study, we used KR dataset. This dataset records children with the most recent one rather than all children in the household. We read a pdf you attached us. We included this issue as a limitation after we read the pdf. For this study, we used the most recent birth within five years preceding the survey for each country. Therefore, not to miss leading the reader we included it in the limitation session(see the revised manuscript).

Analysis (statistical issue)

2. Multiple pregnancies (such as twin pregnancies) were included in the same models as a single pregnancy. Multiple pregnancies (MP) are known as a mediator factor of LBW (MP is on the pathway of LBW). Therefore, this can lead to a sur-adjustment. I will be more useful to perform the main analysis after excluding MP and to perform a sensitivity analysis with MP.

Authors response:- We conducted the sensitivity analysis according to your recommendation(with and without the multiple pregnancies the result presented as below).

The sensitivity analysis without MP(multiple pregnancies)

Sensitivity analysis with MP (multiple pregnancies ) included

Model comparison With MP(multiple pregnancies) Without multiple pregnancies Decision

LL -45356 -46696 Largest bette

Deviance 90717 93392 Smallest better

Thank you very much for your concern. We conducted a sensitivity analysis with and without including multiple pregnancies in the model. The Area under the curve (ROC) curves with multiple pregnancies in the model was 62.19% and without Multiple pregnancies in the model was 58.18%. Therefore, including MP in the model is better than excluding it. The likelihood (Largest better)and Deviance(smallest better) also support model with MP included was better than the model without MP. The works of literature also support that multiple pregnancies are a predictor of low birth weight.

3. The use of “Determinants” in the title by the authors is misleading to me. The notion of determinant implies a causal role while the notion of the risk factor or associated factor is broader and refers to a higher probability of disease in exposed subjects. In this work, it is rather “associated factors” that have been highlighted.

Authors response:- Thank you very much for your comment. We accept your comment and corrected it accordingly. We change the title to “Prevalence of low birth weight and its associated factor at birth in Sub-Saharan Africa: A Generalized Linear Mixed Model”(see the revised manuscript).

Abstract

4. Lines 47-48: In conclusion of abstract, the authors declared “this study… with significant variations among countries” but any variations or range have been presented in the results paragraph of abstract. Please, correct it.

Authors response:- Thank you very much for your contractive comment. We accept your comment and corrected it accordingly line number 48-49 (see the revised manuscript).

Introduction

5. Lines 71-72: 25-30 times seems very high today (substantial progress has been made, even in countries with limited resources). I suggest that the authors further clarify this information, either by adding that this very high risk is for very low birth weight infants, or that the information is several years old.

Author’s response:- Thank you very much for your contractive comment. We accept your comment and corrected it accordingly line number 71 (see the revised manuscript).

6. Lines 80-82: There are many studies such as systematic reviews, multicenter pooled analyses (some of which have even been cited by the authors). The rationale for this study therefore needs to be better specified. For the moment, we do not really see the added value or the new knowledge brought.

Author’s response:- Thank you very much for your comment. We accept your comment and corrected it accordingly line number 80-87 (see the revised manuscript)

7. Lines 85-86: I am not convinced.

Authors response:- Thank you very much for your comment. The intention for this point was the finding of this study is better than systematic review and meta-analysis. The reason was DHS program used the same study design(cross-sectional nature), the same manual for data collection, the variable coding, the same sampling procedure etc… As we know in the systematic review and meta-analysis there may be different study design, data collection tool, different coding of variables, publication bias, etc…..because of this difference we believe that this study finding is better than a systematic review and meta-analysis. We remove this idea from the revised manuscript if it misses leads readers and we substitute other word line number 90-92(see the revised manuscript).

Methods

8. See my major comment about the use of DHS data. I suggest that the authors briefly present the methods used to collect data on children in the DHS to give us an idea of the robustness or otherwise of the data and potential biases.

Authors response:- Thank you very much for your concern. For this study, we used KR(kids record dataset). This dataset records the last or the recent child in the family. We read your materials attached us with the comment regarding data analysis, especially for under-five children data. We agree with your concern. There may be a bias result if we did not include all children’s in the family. According to our scenario, we used the most recent child five years preceding the survey for each 35 sub-Saharan Africa countries. So we boldly mentioned this point in the limitation session line (see the revised manuscript)

9. What about children who were not included in this analysis. Do the authors check any potential selection biases?

Authors response:- Thank you very much for your constructive comment. The data used for this study was secondary data obtained from DHS program after project proposal was submitted to the DHS program office and permission was obtained from data acrchivest. The dataset records for those children born from women within five years preceding the survey and the most recent one. Within one family there may be more than one child but the KR dataset recorded the most recent one. According to the above scenario, there is no selection bias for this study because the most recent bias was obtained.

10. It will be useful to have a country included in each region.

Authors response:- Thank you very much for your contractive comment. We accept your comment and corrected it accordingly see Table 1 (see the revised manuscript ).

11. How infant weight were measured at birth? Standardization? Declared (or self-report data?) Medical record?

Authors response:- Thank you very much for your comment. The birth size was measured using self-report from mother recall, medical records from a nearby health facility, and weights measured at birth. In SSA a small number of children were recorded their weight birth size. Most infant weights were obtained from their mother's self-report. Because of this, we include this point as a limitation because this may result recall bias.

12. Variable definition paragraph is missing in this work, particularly for the clarification of the definition of variables such as “household wealth index”, “women health care decision making autonomy”.

Authors response:- Thank you very much for your contractive comment. We accept your comment and corrected it accordingly line 130-137(see the revised manuscript ).

13. What about other important independent variables such as malaria infection or BMI?

Authors response:- Thank you very much for your concern. Yes, you are right these variables are important independent variables but malaria infection was not collected in the DHS dataset and BMI has had a high missing value because of this we excluded it from the analysis.

14. ANC: please, define the acronym the first time you use it.

Authors response:- Authors response:- Thank you very much for your contractive comment. We accept your comment and corrected it accordingly(see the revised manuscript ).

15. The parameters of mixed logistic regression need to be more detail. Indeed, the authors used multilevel models to take into account the hierarchical structure of data. However, it is important to specify what represents the 1st level (individual?), the 2nd level (regions or countries?). Please, clarify.

Authors response:- Thank you very much for your comment. There are two types of variable in the analysis. Level one varianles include (maternal education, husband education, maternal age, mother marital status, household wealth index, maternal occupation status, women health care decision making autonomy, media exposure, number of ANC visit, preceding birth interval, parity, sex of the child, type of birth and iron supplementation) and level two variables (residence , region and country)

Results

16. Table 2: West Africa weighted frequency is 12170, but Benin (which is West African country) only provide 13909 participants. Please, clarify.

Authors response:- Thank you very much for your comment. We corrected it accordingly(see the revised manuscript).

17. Table 4: It will be useful to have also percentage in normal and LBW columns.

Authors response:- Thank you very much for your comment. We corrected it accordingly(see the revised manuscript)

18. Lines 162-163: “Women from a cluster … lower birth weight”. This sentence is a bit confusing. Please, clarify.

Authors response:- Thank you very much for your comment. We accept your comment and corrected it accordingly(see the revised manuscript)

Discussion

19. Line 197: “8.9% to 10.3%”, please include this result in results session also.

Authors response:- Thank you very much for your comment. We corrected it accordingly(see the revised manuscript)

20. In general, the discussion section need to be more focus on comparison of this study with other multicentric pooled analysis and meta-analysis studies in sub-Saharan Africa, instead of comparison of single center or no-pooled analysis studies.

Authors response:-Thank you very much for your comment. We accept your comment and corrected it accordingly(see the revised manuscript).

21. Lines 199-207: For me, these comparisons are not relevant (single center studies vs. pooled multicenter study).

Authors response:- Thank you very much for your comment. We accept your comment and corrected it accordingly(see the revised manuscript).

22. Lines 214-224: All this paragraph is a speculation. What about nutritional factors in this study. Iron supplementation was not associated with LBW in this study. Why? A discussion of this negative finding will be useful. Why didn't the authors consider other indicators of nutritional status such as BMI? If nutritional status plays an important role in the occurrence of LBW, all of the above should be discussed.

Authors response:- Thank you very much for your comment. We accept your comment and corrected it accordingly(see the revised manuscript).

23. See in major comment my comment about multi pregnancies.

Authors response:- Thank you very much for your comment. We try to address this issue in number two of your question.

24. Limitations paragraph need to be more detailed, including discussion about potential methodological issues, need to take into account nutritional data and the robustness of the findings.

Author response:- Thank you very much for your comment. We revised the limitation section of this manuscript after your suggestion including missed variables in the dataset and methodological issues that readers should take into account(see the revised manuscript).

Attachment

Submitted filename: Point by point response.docx

Click here for additional data file.^{(74KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0248417.r003

Decision Letter 1

Clive J Petry

19 Jan 2021

PONE-D-20-27726R1

Prevalence of low birth weight and its associated factor at birth in Sub-Saharan Africa: A Generalized Linear Mixed Model

PLOS ONE

Dear Dr. Tadesse,

==============================

Although the revised manuscript is an improvement over the original version, reviewer 2 is concerned that you did not seem to get their comments - certainly the manuscript was not revised accordingly. Therefore we are giving you another chance to deal with them. Each point needs to be dealt with carefully in another revision (especially those marked as "major"). I look forward to seeing the next version.

==============================

Please submit your revised manuscript by Mar 05 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Clive J Petry, PhD

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: (No Response)

Reviewer #2: No

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: (No Response)

Reviewer #2: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: (No Response)

Reviewer #2: PLOS ONE

The authors present an important analysis assessing risk factors for low birthweight in Sub-Saharan Africa. The analysis pools recent (2008-2018) DHS surveys from 35 different to represent the sub-continent and specific regions, Central Africa, East Africa, West, Africa, South Africa. The authors find risk factors and protective factors for low birth weight similar to those established in the literature. While there may be some advantage to assessing risk factors by region as the authors aim, there are some concerns with the overall methods used in the manuscript. Further the manuscript would benefit from further English language editing. It appears the authors did not receive the detailed comments and suggestions below after the first review.

Major:

Methods

1) A pooled risk factor analysis may be inappropriate with these data. Pooling multiple surveys from 35 countries over a 10-year period results in a poorly defined population. An analysis such as LBW is likely to be overpowered with such a large dataset and so may detect statistical differences that are not clinically meaningful. For example, all of the included risk factors had a pvalue<0.2 and were included in the multivariable analysis. For low birthweight, you likely to have enough power in each individual survey and so don’t benefit from this type of pooling. It would be appropriate however to use weighted data from these studies to estimate relative burden of LBW and the risk factors in the larger region or sub-regions. The authors could consider focusing on just one region and stratifying by country and assess for differences by country.

2) It would be helpful for the authors to provide a flow chart of the data. It would be helpful to know what proportion of respondents were excluded based on missing birthweight and know if the risk factors were similar to those who were included in the study. Please include number of live births, and what proportion had LBW measured.

3) Line 101-102: Please provide DHS or other citations (or an appendix) referencing the sampling procedure in each country is the same. It is very likely that the sampling strategies were different in different countries.

4) Line 125: Please provide more details about the meta analysis shown in the forest plot. I assume this was fixed effects meta analysis? Is this how the pooled prevalence was calculated? Please clarify. The forest plot is only a visualization of the analysis.

5) It seems more levels of the hierarchical model may be needed. Consider clustering on the woman, household, cluster and country. (region might not be necessary).

6) It is not clear how missing data is handled. Was the multivariable model a complete case analysis?

Results:

7) Line: 166: the text says 202878 mothers were included, yet the variables in table 2 sub to 203,466. Is this due to the weighting? Other numbers in this paragraph also don’t match the tables.

8) Line 166: I think you mean West Africa and not East Africa?

9) Table 2 shows the same number of women and the same number of children. If all children born to women in the last 5 years are included in the analysis, I would expect there to be more children included in the analysis than women included. Please clarify the discrepancy and add the number of women and number of children included in the analysis separately in the text (the flow chart in suggestion 2 would also help this point).

10) Sample sizes from table 1 don’t match those listed in figure1

11) The variable of husband’s education is misrepresented. 31904 women who are not married should have husband’s education as missing.

12) Please clarify the meaning of “No” for education status. Do you mean less than primary education?

13) Similar to 6: Please check variable for preceding birth interval. How is this defined for a woman’s first birth? If they are in the ≥24 months, this could be the reason for the unexpected results.

14) Table 4: Please present column % instead of row % for comparison between normal and low birthweight.

Discussion:

15) Overall the discussion could be improved to put the results in context for the reader. Consider restricting the discussion to focus on some key results. The conclusion discusses modifiable risk factors. One approach could be to discuss the modifiable risk factors and the context of interventions for these risk factors. Brining in the discussion of preterm and fetal growth restriction may also bring context to these results.

16) All statements of explanation for these findings should be based on the literature and so need to be sited (line 227, 233, 237, 240, 249, 252, 254. 258).

17) Line 236: LBW associated with multiples is well established and it is an oversight here not to mention that multiples are almost always born early, and so increased risk for LBW for that reason.

Minor:

In response to reviewer 3’s suggestions, remove the word determinants in in line 38, 184

Introduction:

1) Line 56: Please define LBW abbreviation – the first sentence could read, Low birth weight (LBW) is one of the….

2) Line 61-63: The last sentence in the first paragraph doesn’t seem relevant to this overall summary that is at the SSA level. I suggest deleting or replacing high and low estimates for the region.

3) Line 71 “well facts” should be reworded. Could say are well established.

4) Line 78 Nutritional shouldn’t be capitalized.

5) Please provide citations for other LBW studies, meta analyses systematic reviews that referenced in line 83.

6) 89-92. I am not convinced region level estimates are very informative for interventions and policy decisions.

Methods:

7) Line 96-101: Please include rational for why these countries were selected. The list does not include the WHO African region or UN Africa region.

8) Line 111: the specific coded values is not necessary to include. This line could just say “LWB was defined as a birth weight <2.5kg, and those ≥2.5kg were considered normal and above normal birthweights.” – this suggestion could be applied throughout the manuscript.

9) Line 122: “Based on kinds of literature” needs to be edited. It could be revised to say, potential risk factors for LBW were included based on a review of the literature. (Please include a citation).

10) Line 156: If the P-value is used to base the decision on inclusion into the multivariable model, please include P-values with the bi-variate results. Because of the study is over powered for these detections, consider lowering the threshold of what is included in the multivariable analysis. If there were variables considered that did not meet this threshold, they should also be included in the manuscript.

Results:

1) Line 172: Please reword the description of highest low birth weight and lowest birthweight to make it easier to read. Consider saying highest LBW prevalence, or lowest rate of LBW births.

2) Line 193: Careful saying mothers were less likely to low birthweight. Consider revising to say “were less likely to deliver LBW infants than mothers without formal education:

3) Line 201: type on the confidence interval. Should be 0.91 instead of 0.81 (according to the table)

4) Line 210: Typo Bing should be being.

5) Table 2: please define media exposure.

6) Table 4 CORs are missing on the last page for parity, sex of child, type of births, iron supplementation.

Discussion

7) Line 229: The statement about Brazil seems irrelevant for a paper focused on SSA- consider removing. The authors could consider comparing to other regions instead.

8) consider the term multiples throughout instead of multiple pregnancies.

Considerations

1) It would be helpful to re-orient the risk factors so they are all risk factors or protective factors to help readability.

2) Consider looking for trends in birthweight by year, may want to control for the year of the DHS survey.

3) Consider combining Table 2 and 4.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Emily L Deichsel

Attachment

Submitted filename: PLOS ONE v3.docx

Click here for additional data file.^{(18.8KB, docx)}

PLoS One. 2021 Mar 11;16(3):e0248417. doi: 10.1371/journal.pone.0248417.r004

Author response to Decision Letter 1

15 Feb 2021

PLOS ONE

Point by point response for editors/reviewers comments

The manuscript title “Prevalence of low birth weight and its associated factor at birth in Sub-Saharan Africa: A Generalized Linear Mixed Model”

Manuscript ID: PONE-D-20-27726R1

Dear editor/reviewer.

Dear all,

Response to Reviewers comments

Reviewer #1

Major:

Methods

Author’s Response:- Thank you very much for your comment. Our objective was to know the pooed magnitude of LBW and factors related to Low Birth Weight (LBW) at the SSA level, not a single region (East, West, Central, and South). Thank you very for your concern related to overfitting. Actually having an adequate sample size gives an efficient estimate and robust standard error. But the problem is overfitting. After your comment, we included this issue in our limitation session(line 300) as it may affect our result. If you know any solution for such types of problems (overfitting) we are ready to learn from you. For this study, we used weighted data for analysis(table 2 weighted frequency done using weighting variable v005).

2) It would be helpful for the authors to provide a flow chart of the data. It would be helpful to know what proportion of respondents were excluded based on missing birthweight and know if the risk factors were similar to those who were included in the study. Please include the number of live births, and what proportion had LBW measured.

Author’s response:-Thank you very much for your nice and constructive comments. We accept your comment and included a flow chart of the data. A total of 365,818 live births were recorded in the 35 SSA countries. From this only 202, 878(55%) births were their birth weight measure at birth. 162,940 of them were excluded since their weight was not measured at birth and missingness.

Author’s response:-Thank you very much for your comment. We accept your comment and we included the citation in the main text. The DHS program uses the same variable code, data collection manual, and sampling procedure though out the world that’s why we appended each country by giving code for each and able to work this analysis. You can read more on (https://www.dhsprogram.com/pubs/pdf/DHSG1/Guide_to_DHS_Statistics_DHS-7_v2.pdf ).

Author’s response:-Thank you very much for your constructive comments. We accept your comments and included the meta-analysis part in the method session of data management and analysis section line 156-158 (see the revised manuscript)

5) It seems more levels of the hierarchical model may be needed. Consider clustering on the woman, household, cluster and country (region might not be necessary).

Author’s Response:- Thank you very much for your comment. Regarding the levels of a variable actually, some literature uses more than two levels. For our study, we included two levels(individual and community indicated in the independent variable ). We included region (SSA) as a community level variable because we believe that there is a variation on LBW in each sub-regions(South, East, West, and Central Africa). The forest plot result supports this.

6) It is not clear how missing data is handled. Was the multivariable model a complete case analysis?

Author’s Response:- Thank you very much for your comment. The missing data concerning the outcome(Birth weight(normal vs LBW)) variable was the drop. Not only missing data but also those births with which birth weight not measured at birth were also drop before any statistical analysis (see the revised manuscript in the flow chart of the data).

Results:

7) Line: 166: the text says 202878 mothers were included, yet the variables in table 2 sub to 203,466. Is this due to the weighting? Other numbers in this paragraph also don’t match the tables.

Author’s Response:- Thank you very much for your constructive comments. Due to tabulating errors in Stata, some variables were presented without weighing it. We accept your comment and corrected the table figures accordingly after weighing the figure 202,878 is the weighted total sample size figure. (see the revised manuscript).

8) Line 166: I think you mean West Africa and not East Africa?

Author’s Response:- Yes that was it was a writing error. Thank you very much for the correction. We accept your comment and corrected it accordingly(see the revised manuscript).

Author’s Response:- Thank you very much for your concern related to the number of children and number of women included in this study. If a woman had more than one child five years preceding the survey, the most recent birth was taken because the number of children and women included in this study were the same.

10) Sample sizes from table 1 don’t match those listed in figure1

Author’s Response:- Thank you very much for your comment. We accept your comment and corrected it accordingly(see the revised manuscript).

11) The variable of the husband’s education is misrepresented. 31904 women who are not married should have a husband’s education as missing.

Author’s Response:- Thank you very much for your comment. We accept your comment and corrected it accordingly( see the revised manuscript)

12) Please clarify the meaning of “No” for education status. Do you mean less than primary education?

Author’s response:-No education means respondents who had no formal education (can not read and write)

Author’s response:- This variable very important variable for LBW. Before we fit the model, we exclude women who had one child. as we know stata uses complete case analysis during model fitting. Therefore this issue was resolved before any statistical analysis.

14) Table 4: Please present column % instead of row % for comparison between normal and low birthweight.

Author’s response:-Thank you very much for your comment. We accept your comment and corrected it accordingly(see the revised manuscript).

Discussion

Author’s response:-Thank you very much for your comment. We accept your comment and corrected it accordingly(see the revised manuscript).

16) All statements of explanation for these findings should be based on the literature and so need to be cited (line 227, 233, 237, 240, 249, 252, 254. 258).

Author’s Response:- Thank you very much for your comment. We accept your comment and corrected it accordingly(see the revised manuscript).

17) Line 236: LBW associated with multiples is well established and it is an oversight here not to mention that multiples are almost always born early, and so increased risk for LBW for that reason.

Author’s Response:- Thank you very much for your comment. We accept your comment and corrected it accordingly(see the revised manuscript).

Minor:

In response to reviewer 3’s suggestions, remove the word determinants in line 38, 184

Introduction:

Author’s response:-

1) Line 56: Please define LBW abbreviation – the first sentence could read, Low birth weight (LBW) is one of the….

Author’s response:- Author’s response:- Thank you very much for your comment. We accept your comment and corrected it accordingly(see the revised manuscript).

3) Line 71 “well facts” should be reworded. Could say are well established.

4) Line 78 Nutritional shouldn’t be capitalized.

5) Please provide citations for other LBW studies, meta-analyses systematic reviews referenced in line 83.

6) 89-92. I am not convinced region-level estimates are very informative for interventions and policy decisions.

Author’s Response:- Thank you very much for your comment. We accept your comment(3-6) and corrected it accordingly(see the revised manuscript).

Methods:

7) Line 96-101: Please include the rationale for why these countries were selected. The list does not include the WHO African region or UN Africa region.

Author’s response:-WHO had five Africa regions that are (North, West, East, Central, and South). We used the four African regions that represent sub-Saharan Africa (West, East, Central, and South). I hope the data flow char may answer this question(see the revised manuscript).

Author’s response:-Thank you very much for your comment. We accept your comment(3-6) and corrected it accordingly(see the revised manuscript).

Author’s response:-Thank you very much for your comment. We accept your comment and corrected it accordingly(see the revised manuscript).

10) Line 156: If the P-value is used to base the decision on inclusion into the multivariable model, please include P-values with the bi-variate results. Because the study is overpowered for these detections, consider lowering the threshold of what is included in the multivariable analysis. If there were variables considered that did not meet this threshold, they should also be included in the manuscript.

Author’s response:-Thank you very much for your comment. As we know the sample size is very large and we try to select variables based on literature. As commented previously, this analysis may had a problem of overfitting. We included this as a limitation. We believe that the confidence interval is better than the p-value since it measures precision.

Results:

1) Line 172: Please reword the description of highest low birth weight and lowest birthweight to make it easier to read. Consider saying highest LBW prevalence, or the lowest rate of LBW births.

Author’s response:-Thank you very much for your comment. We accept your comment and corrected it accordingly(see the revised manuscript).

2) Line 193: Careful saying mothers were less likely to low birthweight. Consider revising to say “were less likely to deliver LBW infants than mothers without formal education:

Author’s response:-Thank you very much for your comment. We accept your comment and corrected it accordingly(see the revised manuscript).

3) Line 201: Type on the confidence interval. Should be 0.91 instead of 0.81 (according to the table)

4) Line 210: Typo Bing should be being.

5) Table 2: please define media exposure.

6) Table 4 CORs are missing on the last page for parity, sex of the child, type of births, iron supplementation.

Authors’ response:-Thank you very much for your comment. We accept your comment from 3-6 and corrected it accordingly(see the revised manuscript).

Discussion

7) Line 229: The statement about Brazil seems irrelevant for a paper focused on SSA- consider removing. The authors could consider comparing to other regions instead.

Authors’ response:-Thank you very much for your comment. We accept your comment from 3-6 and corrected it accordingly(see the revised manuscript).

8) consider the term multiples throughout instead of multiple pregnancies.

Authors’ response:-Thank you very much for your comment. We accept your comment from 3-6 and corrected it accordingly(see the revised manuscript).

Considerations

1) It would be helpful to re-orient the risk factors so they are all risk factors or protective factors to help readability.

Author’s response:-Thank you very much for your comment. There is only one/two variables change that is a risk almost all are in the protective size. We put the original presentation as it is.

2) Consider looking for trends in birth weight by year, may want to control for the year of the DHS survey.

Author’s response:-Thank you very much for your comment. We only take a single survey for each country the recent one. There to do trend in needs more than one survey for each country that’s not our objective.

3) Consider combining Table 2 and 4.

Authors’ response:-Thank you very much for your comment. We accept your comment from 3-6 and corrected it accordingly(see the revised manuscript).

Attachment

Submitted filename: Point by point response.docx

Click here for additional data file.^{(33.3KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0248417.r005

Decision Letter 2

Clive J Petry

24 Feb 2021

PONE-D-20-27726R2

Prevalence of low birth weight and its associated factor at birth in Sub-Saharan Africa: A Generalized Linear Mixed Model

PLOS ONE

Dear Dr. Tadesse,

==============================

I believe that the revisions made so far have improved the manuscript. Sorry, once again, to ask for further revision but the reviewer feels that this is important and I agree with them. The required revisions should not be too onerous and I look forward to seeing the newly revised version of the manuscript.

==============================

Please submit your revised manuscript by Apr 10 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Clive J Petry, PhD

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #2: Thank you to the authors for addressing the previous comments in such detail.

Critical

13) thank you for the explanation about the preceding birth interval variable. The sum of the birth interval variable equals the total sample size (<24=24,427, ≥24=178,450). If you dropped all primigravida women, that means the entire sample size is among multiparous women and this should be clarified in the methods, results, and discussion. If that is not correct, as I suspect the primigravida women are included in one of birth interval categories by mistake. Stata treats a missing variable as a very large number, so depending on how this categorical variable was created those with only one child may be included in the ≥24 category. You may want to include these women in their own category so they can be included in the analysis. Please clarify if this population is among multiparous women or correct the birth interval variable to include primigravida women in their own category.

Minor:

#12) Thank you for the clarification of No for education. I think it would be helpful to include this in the manuscript for readers that may have the same question.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: Yes: Emily Deichsel

PLoS One. 2021 Mar 11;16(3):e0248417. doi: 10.1371/journal.pone.0248417.r006

Author response to Decision Letter 2

25 Feb 2021

PLOS ONE

Point by point response for editors/reviewers comments

The manuscript title “Prevalence of low birth weight and its associated factor at birth in Sub-Saharan Africa: A Generalized Linear Mixed Model”

Manuscript ID: PONE-D-20-27726R3

Dear editor/reviewer.

Dear all,

Response to Reviewers comments

Reviewer #1

Critical

Author’s Response:- Thank you very much for your condtractive and critical issues you raised. We accept you comments corrected accordingly. The reality is that this variables are included in the analysis and we try to explore our data and analysis and we included in the analysis at this stage as separate category. we included the primigravida in the analysis and we corrected in the table (table 3). See the revised manuscript. From a total of 202,878 samples 52,207 of them were primigravida and we include them in the analysis and we recoded this this variable 1-2, 3-5, and 6+ ( see revised table 3 and this number of sample size was edual to birth interval aand method session line 126) (<24=24,427, ≥24=178,450). The following stata output is the sample analysis when explore our data again. Many thanks your careful review of our manuscript. Therefore those population was part of our analysis and we included in the revised session.

Minor:

#12) Thank you for the clarification of No for education. I think it would be helpful to include this in the manuscript for readers that may have the same question.

Author’s Response:- Thank you very much for comment and we accept your comment and included this concern in the revised session of our manuscript(see the revised manuscript line number 122-123 ).

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PLoS One. doi: 10.1371/journal.pone.0248417.r007

Decision Letter 3

Clive J Petry

26 Feb 2021

Prevalence of low birth weight and its associated factor at birth in Sub-Saharan Africa: A Generalized Linear Mixed Model

PONE-D-20-27726R3

Dear Dr. Tadesse,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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PLoS One. doi: 10.1371/journal.pone.0248417.r008

Acceptance letter

Clive J Petry

2 Mar 2021

PONE-D-20-27726R3

Prevalence of low birth weight and its associated factor at birth in Sub-Saharan Africa: A Generalized Linear Mixed Model

Dear Dr. Tessema:

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Data Availability Statement

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PERMALINK

Prevalence of low birth weight and its associated factor at birth in Sub-Saharan Africa: A generalized linear mixed model

Zemenu Tadesse Tessema

Koku Sisay Tamirat

Achamyeleh Birhanu Teshale

Getayeneh Antehunegn Tesema

Roles

Abstract

Background

Method

Result

Conclusion

Introduction

Method

Data source

Table 1. The study country, DHS year and number of study participants in SSA.

Fig 1. Flow chart of data for low birth weight in SSA countries using recent Demographic and Health Surveys.

Variables of the study

Dependent variable

Independent variables

Operational definition

Household wealth index

Women health care decision-making autonomy

Media exposure

Data management and analysis

Ethical consideration

Result

Socio-demographic and economic characteristics

Table 3. Multivariable multilevel logistic regression analysis to assess factors associated with low birth weight in Sub-Saharan Africa.

The pooled prevalence of low birth weight measured at birth in Sub-Saharan Africa

Fig 2. Forest plot of pooled prevalence of low birth weight of new babies measured at birth in SSA.

Model comparison

Table 2. Model comparison and random effect results.

Determinants of low birth weight in Sub-Saharan Africa

Discussion

Strength

Limitation

Conclusion

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Clive J Petry

Roles

Author response to Decision Letter 0

Decision Letter 1

Clive J Petry

Roles

Author response to Decision Letter 1

Decision Letter 2

Clive J Petry

Roles

Author response to Decision Letter 2

Decision Letter 3

Clive J Petry

Roles

Acceptance letter

Clive J Petry

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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