Contextual factors and spatial trends of childhood malnutrition in Zambia

Million Phiri; David Mulemena; Chester Kalinda; Julius Nyerere Odhiambo

doi:10.1371/journal.pone.0277015

. 2022 Nov 3;17(11):e0277015. doi: 10.1371/journal.pone.0277015

Contextual factors and spatial trends of childhood malnutrition in Zambia

Million Phiri ^1,^2,^#, David Mulemena ^3,^#, Chester Kalinda ^4,^5,^#, Julius Nyerere Odhiambo ^6,^*,^#

Editor: Inés González-Casanova⁷

PMCID: PMC9632925 PMID: 36327254

Abstract

Background

Understanding the national burden and epidemiological profile of childhood malnutrition is central to achieving both national and global health priorities. However, national estimates of malnutrition often conceal large geographical disparities. This study examined the prevalence of childhood malnutrition across provinces in Zambia, changes over time, and identified factors associated with the changes.

Methods

We analyzed data from the 2013/4 and 2018 Zambia demographic and health surveys (ZDHS) to examine the spatial heterogeneity and mesoscale correlates of the dual burden of malnutrition in children in Zambia. Maps illustrating the provincial variation of childhood malnutrition were constructed. Socio-demographic and clinical factors associated with childhood malnutrition in 2013 and 2018 were assessed independently using a multivariate logistic model.

Results

Between 2013/4 and 2018, the average prevalence of stunting decreased from 40.1% (95% CI: 39.2–40.9) to 34.6% (95% CI:33.6–35.5), wasting decreased from 6.0% (95% CI: 5.6–6.5) to 4.2% (95% CI: 3.8–4.7), underweight decreased from 14.8% (95% CI: 14.1–15.4) to 11.8% (95% CI: 11.2–12.5) and overweight decreased from 5.7% (95% CI: 5.3–6.2) to 5.2% (95% CI: 4.8–5.7). High variability in the prevalence of childhood malnutrition across the provinces were observed. Specifically, stunting and underweight in Northern and Luapula provinces were observed in 2013/14, whereas Lusaka province had a higher degree of variability over the two survey periods.

Conclusion

The study points to key sub-populations at greater risk and provinces where malnutrition was prevalent in Zambia. Overall, these results have important implications for nutrition policy and program efforts to reduce the double burden of malnutrition in Zambia.

Introduction

Malnutrition refers to excesses, deficiencies, or imbalances in a person’s intake of nutrients and energy. In children, it remains a persistent global public health problem that accounts for half of the global childhood mortality and impairs childhood physical and cognitive development [1, 2]. It endangers a child’s success as an adult by reducing their productivity and making them more susceptible to premature death. Studies suggest that reversing the negative effects of malnutrition on cognitive development sorely through nutrition-based interventions is ineffective unless integrated with medical treatment or social enrichment [2, 3]. To reduce the detrimental effects of malnutrition on childhood development, the United Nations Decade of Action on Nutrition (2016–2025) has been developed to provide a unique and time-bound opportunity to address the burden of malnutrition worldwide [4]. Halfway through the implementation period, ending malnutrition appears insufficient and limited, especially in sub-Saharan Africa where infectious diseases, health inequalities, and food insecurity remain unresolved [5]. Furthermore, the dual burden of poverty and malnutrition in this region increases the difficulties associated with obtaining data on the geographical distribution and hotspots of childhood overnutrition [6, 7].

Various intervention strategies and programs have been launched to reduce malnutrition [8, 9]. The intervention strategies have been developed on the premise of achieving one of the Sustainable Development Goals (SDGs) Target 2.2 which focuses on ending all forms of malnutrition. The success of these activities may depend on scaling up access to safe and effective health interventions, quantifying the nutritional status by mapping changes, and identifying hot spots of malnutrition to give insights into the spread of the problem. In Zambia, nutrition intervention programs to address different forms of malnutrition using integrated approaches have been implemented [10, 11]. Yet, malnutrition persists and the emerging dual burden of malnutrition [12] introduces additional obstacles to the realization of the Zambia National Health Strategic Plan (ZNHSP) of 201–21 whose overarching nutrition objective is to “reduce under and over nutrition and improve clinical nutrition by 2021” [13]. Therefore, determining the spatial epidemiology of childhood under and overnutrition and exploring associated characteristics would be vital in developing and focusing the public health policy on redesigning effective malnutrition intervention and prevention approaches.

A vital component of spatial epidemiology lies in its ability to provide an articulate visual summary of the spatial phenomena that may be challenging to achieve using conventional tabular representations. Previous studies quantifying childhood malnutrition in Zambia have used hospital-based data [14, 15]. However, the burden may vary geographically thus, its detection in time and space allows focused and tailored intervention strategies. Here, the study also provides the basis for mounting meaningful suites of nutrition-based interventions at the provincial level and gives a glimpse of the temporal progress and the contribution of socio-economic determinants, communicable and non-communicable diseases on the epidemiological transition of childhood malnutrition.

Methods

Study design

This study is based on a secondary analysis of the existing data from the International Demographic and Health Survey (DHS) programme. A detailed description of the methods used in these surveys is included in the survey reports for Zambia [16, 17]. The current study utilized data from the 2013–14 and 2018 Zambia Demographic and Health Surveys (ZDHS). The nature of DHS data allowed for comparisons between and over periods. This allows the monitoring of changes in key indicators of variables of interest in different geographical areas. The data analyzed in this paper relate under and over-nutrition among children aged under five in households systematically selected from a household listing of all households in the enumeration area. To determine the anthropometric measures, children between 0–59 months from households who consented, were enrolled in the study. The women respondents who participated in the study were aged between 15–49 years. The 2013–14 and 2018 DHS captured samples of 12,328 and 9,689 children aged between 0–59 months, respectively.

Outcome measures

The outcome variable of interest in this study was childhood malnutrition (stunting, wasting, underweight, and overweight). The WHO standards were used to define the anthropometric indicators of nutritional status [18]. Relative to WHO standards, children with a Z score less than minus 2 (-2) were classified as undernourished while those with a Z score greater than 2(+2). Weight-for-age was used to define underweight; height-for-age for stunting; and weight-for-height for wasting. Although each indicator shows different nutritional statuses, deviations below—2 standard deviations (SD) show moderate or severe undernourishment among children. As previously defined, we considered malnutrition to include both under and over-nutrition [7, 19]. Using the published WHO conceptual framework on the determinant of childhood malnutrition [20] and 2018 WHO global nutrition report [21], we identified factors that could be potentially associated with malnutrition among children aged under five. The two datasets, DHS reference materials, and data collection forms were used to identify key variables. These variables were classified at three levels: individual level, household level, and maternal level. The datasets of interest were limited to community-level information hence all analysis was based on the provincial level for which the data had been aggregated.

Statistical analysis

Data analyses were performed using Stata version 15 (Stata Corp 2015, College Station, TX) taking into consideration survey design, cluster effect, and post-stratification weights. Key socio-economic and demographic factors were described and expressed in frequency and percentage. The significant differences were assessed between the two survey periods using the Chi-square test. Exploratory bivariate analysis was carried out separately for two datasets to assess the association between the prevalence of childhood malnutrition and selected variables (child’s age in months, sex of child, residence, mother’s education level, household wealth index, child’s birth weight, birth interval, mother employment status, water source and presence of diarrhea).

To assess the progressive health in the various groups, age was categorized into classes. Variables that were observed to have a statistically significant association with our indicators were included in the multivariate analysis. Four multivariate logistic regression models were then used to assess the independent factors associated with childhood malnutrition outcomes. Odds ratios with their corresponding 95% confidence intervals after adjusting for all our independent variables were reported. The initial logistic models included all significantly associated factors from the bivariate analyses as well as factors that had a p-value of <0.20. Also, interested covariates were included in the multivariate analyses regardless of their significant levels. The prevalence of stunting, wasting, underweight and overweight and its distribution across 10 provinces was estimated. The results were then used to construct maps highlighting the provincial variation in prevalence and trends between the two survey periods using ArcMap 10.7.1 (ESRI Inc., Redlands, CA, USA).

Ethical considerations

The 2013–14 and 2018 Zambia Demographic and Health Survey protocols for survey methodology and biomarker measurements were approved by both the Inner City Fund (ICF) institutional review boards (IRBs) and the Tropical Diseases Research Centre (TDRC) in Zambia. Both IRBs approved the protocols before the commencement of data collection activities. Study protocols were carried out following relevant guidelines and regulations on confidentiality, benevolence, non-maleficence, and informed consent. The study participants gave written informed consent before participation and all information was collected confidentially. Permission to use DHS data for this study was sought from ICF international [27].

Results

Bivariate analysis of childhood malnutrition prevalence in 2013 and 2018 by background characteristics

Overall, 20, 588 anthropometric indices (12,327 and 8,261 for 2013/14 and 2018, respectively) for children aged between 0–59 months were available in the two DHS waves under study. Approximately 66.1% and 65.6% of the children were from rural populations in 2013 and 2018, respectively. In both periods, the prevalence of malnutrition across all indicators, was substantially higher in children born in poor households, although this association was only significant for children who had stunted growth and were underweight (Table 1). The prevalence of stunting, and underweight among children born to mothers with no schooling, was significantly higher when compared to children born to mothers with primary, secondary, and tertiary education. However, the prevalence of overweight was higher for children born to women with tertiary education (8.8%). The presence of diarrhea was significantly associated with stunting, wasting, and underweight in children. There was no significant association between diarrhea and overweight reported in both 2013/14 and 2018.

Table 1. Bivariate analysis of childhood malnutrition indicators with background characteristics (2013/14 DHS data).

Background Characteristics	2013–14 DHS
	Stunted	No-stunted	p-value	Wasted	No-wasted	p-value	Underweight	No-underweight	p-value	Overweight	No -overweight	p-value
	N = 12,328		p-value	N = 12,328		p-value	N = 12,328		p-value	N = 12,328		p-value
Age in months
< 6 months	13.6	86.4	0.000	8	92	0.000	5.8	94.2	0.000	15.4	84.6	0.000
6–8	25.1	74.9		9	91		11.4	88.6		11.6	88.4
9–11	38.5	61.5		10.1	89.9		17.4	82.6		10.4	89.6
12–17	43.1	56.9		7.6	92.4		13.5	86.5		6.1	93.9
18–23	54	46		6.1	93.9		17.8	82.2		5.7	94.3
24–35	51	49		5.2	94.8		17.1	82.9		4	96
36–47	41.6	58.4		5.1	94.9		15.2	84.8		3.4	96.6
48–59	34.6	65.4		4.5	95.5		14.9	85.1		3.3	96.7
Sex of Child
Male	42.4	57.6	0.000	6.2	93.8	0.440	16	84	0.001	6.1	93.9	0.167
Female	37.6	62.4	0.000	5.8	94.2	0.440	13.5	86.5	0.001	5.4	94.6	0.167
Residence					100						100
Urban	36	64	0.000	6.4	93.6	0.399	12.9	87.1	0.004	6.5	93.5	0.066
Rural	42.1	57.9	0.000	5.9	94.1	0.399	15.7	84.3	0.004	5.4	94.6	0.066
Size at birth
Very small	62.1	37.9	0.000	9.3	90.7	0.001	32.4	67.6	0.000	2.7	97.3	0.127
Small	51.5	48.5		9.2	90.8		25.8	74.2		7.1	92.9
Average or larger	38.3	61.7		5.7	94.3		13.2	86.8		8.3	91.7
Birth Interval
First birth	40	60	0.000	6	94	0.068	14.5	85.5	0.000	5.8	94.2	0.403
Less than 24 months	46.1	53.9		5.8	94.2		19.6	80.4		5.7	94.3
24–47 months	40.5	59.5		5.6	94.4		14.7	85.3		5.6	94.4
48+ months	34.4	65.6		7.5	92.5		12.2	87.8		6.6	93.4
Wealth index
Poorest	47.3	52.7	0.000	6.8	93.2	0.387	20.1	79.9	0.000	5.3	94.7	0.237
Poor	41.7	58.3		5.5	94.5		15.7	84.3		4.9	95.1
Middle	40.7	59.3		4.9	95.1		13.7	86.3		6.2	93.8
Richer	37.6	62.4		4.4	95.6		12.7	87.3		6.1	93.9
Richest	28.4	71.6		4	96		8.9	91.1		6.7	93.3
Mother’s Education
None	44.4	55.6	0.000	7.1	92.9	0.504	20	80	0.000	6	94	0.113
Primary	41.9	58.1		5.8	94.2		15.5	84.5		5.6	94.4
Secondary	36.9	63.1		6.1	93.9		12	88		5.9	94.1
Tertiary	17.9	82.1		5.4	94.6		4.8	95.2		8.8	91.2
Mother’s occupation status
Not employed	39.5	60.5	0.5696	5.8	94.2	0.411	13.7	86.3	0.086	6	94	0.808
Employed	40.2	59.8	0.5696	6.2	93.8	0.411	15.3	84.7	0.086	5.8	94.2	0.808
Region
Central	42.5	57.5	0.000	4.6	95.4	0.000	15.3	84.7	0.000	6.7	93.3	0.002
Copperbelt	36.2	63.8		5.8	94.2		14.1	85.9		5.2	94.8
Eastern	43.3	56.7		5.1	94.9		12.8	87.2		6	94
Luapula	43	57		13.1	86.9		21.2	78.8		4.9	95.1
Lusaka	35.7	64.3		7	93		11	89		8	92
Muchinga	43.6	56.4		4.1	95.9		15.6	84.4		5.2	94.8
Northern	58.5	41.5		3.7	96.3		19	81		5.3	94.7
North western	36.9	63.1		8.2	91.8		13.8	86.2		7.4	92.6
Southern	37.2	62.8		4.2	95.8		13.1	86.9		4.6	95.4
Western	36.2	63.8		6.5	93.5		16.2	83.8		3.1	96.9
Source of water
Improved	37.4	62.6	0.000	5.9	94.1	0.433	13.1	86.9	0.000	6	94	0.195
Non improved	43.8	56.2	0.000	6.3	93.7	0.433	17.1	82.9	0.000	5.3	94.7	0.195
Presence of Diarrhea
No	38.1	61.9	0.001	5.8	94.2	0.036	14	86	0.001	6	94	0.186
Yes, in last 2 weeks	43.8	56.2	0.001	7.3	92.7	0.036	17.5	82.5	0.001	5.2	94.8	0.186
Total	40.1	59.9		6	94		14.8	85.2		5.7	94.3

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Between 2013 and 2018, the average prevalence of stunting decreased from 40.1% (95% CI: 39.2–40.9) to 34.6% (95% CI:33.6–35.5), wasting decreased from 6.0% (95% CI: 5.6–6.5) to 4.2% (95% CI: 3.8–4.7), underweight decreased from 14.8% (95% CI: 14.1–15.4) to 11.8% (95% CI: 11.2–12.5) and overweight decreased from 5.7% (95% CI: 5.3–6.2) to 5.2% (95% CI: 4.8–5.7). Fig 1 shows the prevalence rates of malnutrition indicators across all age groups, wealth index, and maternal education. Stunting was strongly associated with age (p < 0.01) in both DHS waves. Both in 2014 and 2018, stunting and underweight were most prevalent in children aged 18–23 months. Furthermore, these two indicators were also prevalent in children coming from the poorest households and those whose mothers had no formal education. In both waves, an increase in the prevalence of overweight children was observed in both waves among those from the richest households and those whose mothers had attained tertiary education.

Fig 1 — Trends of malnutrition Indicators by (A) Age in 2013/14 (B) Age in 2018 (C) Wealth Index 2013/14 (D) Wealth Index 2018 (E) Maternal education level in 2013/14 (F) Maternal education level in 2018. (Source: author generated map).

Analysis of malnutrition prevalence by province

The results in Fig 2 show the standard deviation maps of malnutrition in Zambia. The maps show higher uncertainties in the prevalence of childhood malnutrition across different provinces in Zambia. Over the two study periods, stunting in Northern province and Luapula provinces was highly uncertain when compared to other provinces. In 2013/14 wasting was highly uncertain in Luapula. For underweight, Luapula and Northern provinces had the highest degree of uncertainty in 2013/14. This trend was consistent in 2018 with Luapula, Northern, Muchinga, and Western provinces having the highest levels of uncertainty. For overweight, Lusaka province had a higher degree of uncertainty over the two survey periods. However, in 2018, the Northern province also exhibited a higher degree of uncertainty.

Multivariable analysis results and the association with socioeconomic determinants

Stunting

Results of the multivariable logistic regression of malnutrition indicators and its associated explanatory variables showed that compared to those who were not stunted, the unadjusted odds of stunting in 2014/13 and 2018 for children in the age group 18–23 were 7.81 (95%CI: 6.02–10.12) and 3.99 (95% CI: 3.06–5.19) times respectively, higher than those aged less than 6 months. Furthermore, an association between gender and stunting was observed with female children when compared to their male counterparts were 22% (0.78; 95% CI: 0.72–0.87) less likely to be stunted in 2013/4 and 32% (0.68; 95% CI: 0.61–0.76) less likely to be stunted compared in 2018. In 2013/4 and 2018, children from mothers with tertiary education were 57% (0.43; 95% CI: 0.27–6.02) and 55% (0.45; 95%CI: 0.28–0.73) less likely to be stunted, respectively. Both in 2013/14 and 2018, children from mothers who had a parity interval of more than 48 months were 16% (0.84; 95%CI: 0.71–0.99) and 17% (0.83; 95% CI: 0.71–0.97) less likely to be stunted when compared to children whose mother had the first birth (Table 2).

Table 2. Bivariate analysis of childhood malnutrition indicators with background characteristics (2018 DHS data).

Background Characteristics	2018 DHS
	Stunted	No-stunted	p-value	Wasted	No-wasted	p-value	Underweight	No-underweight	p-value	Overweight	No -overweight	p-value
	N = 12,328		p-value	N = 12,328		p-value	N = 12,328		p-value	N = 12,328		p-value
Age in months						0.021			0.001
< 6 months	18.7	81.3	0.000	5.1	94.9		7.4	92.6		15	85	0.000
6–8	22.5	77.5		3.7	96.3		10.2	89.8		5.9	94.1
9–11	28.5	71.5		6.6	93.4		12.6	87.4		5.6	94.4
12–17	36.2	63.8		6	94		13	87		5.2	94.8
18–23	46.3	53.7		4.6	95.4		15.9	84.1		5.2	94.8
24–35	42.7	57.3		4.2	95.8		11.7	88.3		4.3	95.7
36–47	38.0	62.0		2.8	97.2		12.2	87.8		3.7	96.3
48–59	28.5	71.5		3.7	96.3		10.5	89.5		2.5	97.5
Sex of Child
Male	38.3	61.7	0.000	4.8	95.2	0.046	13.5	86.5	0.000	5.4	94.6	0.447
Female	31.0	69.0	0.000	3.7	96.3	0.046	10.2	89.8	0.000	5	95	0.447
Residence
Urban	32.1	67.9	0.006	5	95	0.091	10.8	89.2	0.129	5.7	94.3	0.292
Rural	35.9	64.1	0.006	3.8	96.2	0.091	12.4	87.6	0.129	5	95	0.292
Size at birth
Very small	49.9	50.1	0.000	6.1	93.9	0.023	24.1	75.9	0.000	3.6	96.4	0.202
Small	46.5	53.5		6	94		21.5	78.5		3.7	96.3
Average or larger	33.0	67.0		3.9	96.1		10.3	89.7		5.5	94.5
Birth Interval
First birth	35.0	65.0	0.000	4.4	95.6	0.076	12.5	87.5	0.000	4.9	95.1	0.092
Less than 24 months	42.1	57.9		4.1	95.9		17	83		4.9	95.1
24–47 months	35.1	64.9		3.5	96.5		10.7	89.3		4.8	95.2
48+ months	30.5	69.5		5.3	94.7		10.5	89.5		6.5	93.5
Wealth index
Poorest	40.1	59.9	0.000	4.3	95.7	0.090	15	85	0.000	5.8	94.2	0.207
Poor	36.6	63.4		3.9	96.1		13.2	86.8		4.4	95.6
Middle	33.9	66.1		3	97		9.3	90.7		4.5	95.5
Richer	35.3	64.7		4.8	95.2		10.8	89.2		5.6	94.4
Richest	23.9	76.1		5.6	94.4		9.1	90.9		6.1	93.9
Mother’s Education
None	37.8	62.2	0.000	4.8	95.2	0.183	15.7	84.3	0.004	5.3	94.7	0.715
Primary	37.6	62.4		3.6	96.4		12.5	87.5		5.2	94.8
Secondary	31.5	68.5		4.9	95.1		10.3	89.7		5.3	94.7
Tertiary	15.4	84.6		5	95		8.6	91.4		6.9	93.1
Mother’s occupation status
Not employed	34.5	65.5	0.991	4.7	95.3	0.203	12.9	87.1	0.020	6	94	0.029
Employed	34.5	65.5	0.991	3.8	96.2	0.203	10.8	89.2	0.020	4.6	95.4	0.029
Region
Central	33.4	66.6	0.000	4.6	95.4	0.000	11.4	88.6	0.010	3.9	96.1	0.000
Copperbelt	29.7	70.3		5.4	94.6		12.1	87.9		5	95
Eastern	34.2	65.8		2.2	97.8		9.2	90.8		5	95
Luapula	44.9	55.1		6.2	93.8		15.2	84.8		5.2	94.8
Lusaka	35.6	64.4		5.5	94.5		10.6	89.4		8.1	91.9
Muchinga	32.1	67.9		8.2	91.8		15.3	84.7		3.5	96.5
Northern	45.8	54.2		3.1	96.9		14.1	85.9		8.3	91.7
North western	31.9	68.1		2.4	97.6		10.4	89.6		3.3	96.7
Southern	29.4	70.6		2.3	97.7		9.7	90.3		3.8	96.2
Western	29.0	71.0		3	97		14.1	85.9		3	97
Source of water
Improved	32.9	67.1	0.000	4.4	95.6	0.369	11	89	0.003	5.5	94.5	0.287
Non improved	38.0	62.0	0.000	3.9	96.1	0.369	13.5	86.5	0.003	4.8	95.2	0.287
Presence of Diarrhea
No	34.5	65.5	0.627	4	96	0.114	11.1	88.9	0.000	5.4	94.6	0.100
Yes, in last 2 weeks	35.3	64.7	0.627	5.3	94.7	0.114	15.3	84.7	0.000	4.2	95.8	0.100
Total	34.6	65.4		4.2	95.8		11.8	88.2		5.2	94.8

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Wasting

Our results show that the odds of wasting reduced with age. In 2013/14, children aged between 48–59 months old were 44% (0.56; 95% CI: 0.39–0.79) less likely to be wasted compared to those aged less than 6 months. Our results from the model further indicate that maternal characteristics such as the mother’s education level, parity, and mother’s employment status had no significant association with wasting. However, in 2013/14 and 2018, children from middle-income households were 32% (0.68 95% CI: 0.50–0.94) and 41% (0.59, 95% CI: 0.38–0.94) less likely to be wasted when compared to those from poorest households (Table 3).

Table 3. Multivariate analysis of malnutrition indicators (stunting and wasting) for 2013/14 and 2018 DHS waves.

	Stunting			Wasting			Stunting			Wasting
	AOR	p-values	[95% CI]	AOR	p-values	[95% CI]	AOR	p-values	[95% CI]	AOR	p-values	[95% CI]
Child’s Age in months
Less than 6 months	1			1			1			1
6–8	2.100	0.000	(1.528 2.887)	1.153	0.549	(0.724 1.834)	1.340	0.080	(0.965 1.861)	0.716	0.443	(0.304 1.695)
9–11	4.128	0.000	(3.012 5.659)	1.203	0.403	(0.781 1.850)	1.704	0.001	(1.229 2.362)	1.374	0.349	(0.706 2.672)
12–17	5.094	0.000	(3.946 6.576)	0.897	0.557	(0.623 1.291)	2.836	0.000	(2.140 3.758)	1.226	0.498	(0.679 2.213)
18–23	7.805	0.000	(6.022 10.116)	0.724	0.08	(0.505 1.039)	3.988	0.000	(3.066 5.185)	0.909	0.765	(0.486 1.701)
24–35	7.045	0.000	(5.534 8.912)	0.615	0.006	(0.434 0.871)	3.447	0.000	(2.753 4.315)	0.816	0.408	(0.503 1.323)
36–47	4.699	0.000	(3.684 5.993)	0.605	0.003	(0.432 0.846)	2.919	0.000	(2.321 3.671)	0.610	0.080	(0.351 1.061)
48–59	3.387	0.000	(2.684 4.273)	0.561	0.001	(0.397 0.793)	1.802	0.000	(1.409 2.306)	0.803	0.356	(0.503 1.281)
Sex of Child
Male	1			1			1			1
Female	0.789	0.000	(0.719 0.865)	0.907	0.289	(0.756 1.087)	0.678	0.000	(0.608 0.755)	0.782	0.090	(0.589 1.039)
Residence
Urban	1			1			1			1
Rural	0.795	0.004	(0.680 0.928)	0.743	0.07	(0.538 1.025)	0.770	0.009	(0.633 0.937)	0.891	0.564	(0.601 1.320)
Education level of mother
No education	1			1			1			1
Primary	0.930	0.405	(0.784 1.103)	0.842	0.232	(0.635 1.116)	1.062	0.535	(0.878 1.284)	0.838	0.459	(0.525 1.339)
Secondary	0.903	0.321	(0.740 1.101)	0.904	0.563	(0.642 1.273)	0.882	0.315	(0.691 1.126)	1.074	0.786	(0.641 1.799)
Tertiary	0.430	0.000	(0.272 0.682)	0.871	0.681	(0.451 1.682)	0.449	0.001	(0.276 0.730)	1.071	0.853	(0.517 2.219)
Household Wealth index
Poorest	1			1			1			1
Poorer	0.802	0.003	(0.694 0.927)	0.851	0.202	(0.664 1.090)	0.8730	0.0780	(0.750 1.015)	0.8630	0.4330	(0.597 1.247)
Middle	0.729	0.000	(0.624 0.852)	0.689	0.020	(0.504 0.943)	0.7250	0.0000	(0.606 0.867)	0.5950	0.0250	(0.378 0.937)
Richer	0.646	0.000	(0.531 0.785)	0.770	1.177	(0.526 1.126)	0.7550	0.0120	(0.606 0.940)	1.0040	0.9860	(0.632 1.595)
Richest	0.438	0.000	(0.334 0.575)	0.591	1.041	(0.357 0.9780)	0.5120	0.0000	(0.381 0.688)	0.9360	0,830	(0.508 1.722)
Child’s birth weight
Very small	1			1			1			1
Small	0.606	0.010	(0.413 0.888)	1.017	0.961	(0.512 2.020)	0.808	0.237	(0.568 1.151)	0.934	0.845	(0.469 1.861)
Average or large	0.360	0.000	(0.253 0.511)	0.613	0.128	(0.326 1.151)	0.426	0.000	(0.309 0.587)	0.585	0.094	(0.312 1.096)
Birth interval
First birth	1			1			1			1
Less than 24 months	1.178	0.060	(0.993 1.396)	1.032	0.840	(0.756 1.410)	1.224	0.054	(0.997 1.504)	1.012	0.958	(0.647 1.584)
25–47 months	0.975	0.701	(0.855 1.111)	0.947	0.674	(0.734 1.221)	0.905	0.250	(0.764 1.072)	0.896	0.481	(0.659 1.217)
48+ months	0.840	0.041	(0.710 0.993)	1.289	0.113	(0.942 1.763)	0.833	0.025	(0.711 0.977)	1.316	0.172	(0.887 1.952)
Mothers Employment Status
Employed	1			1			1			1
Not employed	0.992	0.885	(0.884 1.112)	1.115	0.249	(0.926 1.342)	1.001	0.979	(0.882 1.138)	0.831	0.230	(0.614 1.125)
Water source
Improves	1			1			1			1
Non-improved	1.100	0.089	(0.985 1.229)	1.045	0.698	(0.838 1.303)	1.114	0.127	(0.970 1.280)	0.928	0.668	(0.658 1.309)
Presence of diarrhea
No	1			1			1			1
Yes, in the last 2 weeks	1.046	0.480	(0.923 1.184)	1.110	0.393	(0.873 1.411)	0.906	0.217	(0.774 1.060)	1.143	0.510	(0.767 1.704)

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Underweight

In 2013/14 and 2018, children aged 18–23 months were 3.32 (95% CI: 2.35–4.70) and 2.17 (95% CI: 1.49–3.17) times more likely to be underweight when compared to those aged less than 6 months. Children from mothers with tertiary education were 64% (0.36, 95% CI: 0.19–0.67) and 15% (0.85, 95% CI: 0.45–1.60) less likely to be underweight in 2013/4 and 2018 respectively. Children from the richest families were 67% (0.33, 95% CI: 0.23–0.46) and 44% (0.56, 95% CI: 0.35–0.88) less likely to be underweight compared to those from poor households in 2013/14 and 2018 respectively. Furthermore, children who at birth were classified as average or large were 66% (0.34, 95% CI: 0.23–0.50) less likely to be underweight compared to those who were classified as small. In 2018, children with diarrhea were 1.24 (95% CI: 1.02–1.50) times more likely to be underweight compared to those who did have diarrhea (Table 4).

Table 4. Multivariate analysis of malnutrition indicators (Underweight and Overweight) for 2013/14 and 2018 DHS waves.

	2013–14 DHS						2018 DHS
Background Characteristics	Underweight			Overweight			Underweight			Overweight
Background Characteristics	AOR	p-values	[95% CI]	AOR	p-values	[95% CI]	AOR	p-values	[95% CI]	AOR	p-values	[95% CI]
Child’s Age in months
Less than 6 months	1			1			1			1
6–8	2.080	0.001	(1.377 3.142)	0.740	0.096	(0.519 1.054)	1.350	0.205	(0.849 2.147)	0.366	0.000	(0.232 0.579)
9–11	3.030	0.000	(2.001 4.587)	0.699	0.078	(0.469 1.041)	1.626	0.032	(1.044 2.532)	0.322	0.000	(0.190 0.546)
12–17	2.342	0.000	(1.633 3.358)	0.382	0.000	(0.270 0.539)	2.001	0.000	(1.389 2.882)	0.317	0.000	(0.205 0.492)
18–23	3.324	0.000	(2.351 4.699)	0.368	0.000	(0.258 0.526)	2.173	0.000	(1.488 3.173)	0.330	0.000	(0.223 0.488)
24–35	3.257	0.000	(2.377 4.61)	0.217	0.000	(0.156 0.302)	1.765	0.000	(1.291 2.413)	0.240	0.000	(0.163 0.352)
36–47	2.803	0.000	(2.057 3.820)	0.207	0.000	(0.149 0.288)	1.933	0.000	(1.369 2.729)	0.209	0.000	(0.142 0.309)
48–59	2.736	0.000	(2.003 3.737)	0.166	0.000	(0.117 0.234)	1.512	0.015	(1.085 2.106)	0.136	0.000	(0.089 0.208)
Sex of Child
Male	1			1			1			1
Female	0.804	0.001	(0.710 0.910)	0.925	0.412	(0.767 1.115)	0.684	0.000	(0.584 0.801)	0.922	0.515	(0.722 1.178)
Residence
Urban	1			1			1			1
Rural	0.642	0.000	(0.520 0.793)	0.880	0.364	(0.667 1.160)	0.732	0.048	(0.537 0.997)	0.911	0.662	(0.598 1.387)
Education level of mother
No education	1			1			1			1
Primary	0.773	0.008	(0.639 0.934)	0.857	0.334	(0.626 1.173)	0.908	0.426	(0.717 1.151)	0.911	0.644	(0.612 1.355)
Secondary	0.719	0.007	(0.566 0.914)	0.793	0.227	(0.543 1.156)	0.776	0.077	(0.585 1.028)	0.865	0.558	(0.532 1.406)
Tertiary	0.360	0.001	(0.193 0.670)	1.023	0.948	(0.521 2.010)	0.847	0.607	(0.448 1.599)	1.061	0.892	(0.452 2.488)
Household Wealth index
Poorest	1			1			1			1
Poorer	0.749	0.000	(0.638 0.879)	0.908	0.502	(0.686 1.202)	0.866	0.150	(0.711 1.054)	0.775	0.119	(0.561 1.068)
Middles	0.563	0.000	(0.467 0.679)	1.233	0.153	(0.925 1.645)	0.592	0.000	(0.451 0.778)	0.792	0.250	(0.532 1.179)
Richer	0.444	0.000	(0.338 0.582)	1.053	0.787	(0.723 1.535)	0.646	0.007	(0.470 0.888)	0.921	0.766	(0.536 1.584)
Richest	0.325	0.000	(0.231 0.457)	1.215	0.462	(0.723 2.039)	0.557	0.012	(0.352 0.879)	0.828	0.570	(0.432 1.587)
Child’s birth weight
Very small	1			1			1			1
Small	0.754	0.186	(0.496 1.146)	1.835	0.192	(0.737 4.571)	0.840	0.416	(0.553 1.278)	1.143	0.803	(0.400 3.264)
Average or large	0.338	0.000	(0.227 0.503)	2.321	0.047	(1.012 5.325)	0.344	0.000	(0.233 0.510)	1.706	0.247	(0.690 4.222)
Birth interval
First birth	1			1			1			1
Less than 24 months	1.352	0.01	(1.076 1.699)	1.115	0.556	(0.776 1.603)	1.319	0.025	(1.035 1.680)	1.069	0.767	(0.686 1.667)
25–47 months	1.001	0.991	(0.831 1.205)	0.998	0.986	(0.771 1.290)	0.791	0.020	(0.649 0.964)	0.974	0.864	(0.722 1.314)
48+ months	0.935	0.572	(0.740 1.181)	1.032	0.837	(0.763 1.396)	0.863	0.197	(0.692 1.079)	1.261	0.134	(0.931 1.707)
Mothers Employment Status
Employed	1			1			1			1
Not employed	1.041	0.599	(0.896 1.209)	1.060	0.592	(0.856 1.312)	0.804	0.015	(0.675 0.978)	0.810	0.103	(0.629 1.043)
Water source
Improves	1			1			1			1
Nonimproved	1.067	0.339	(0.934 1.220)	0.987	0.906	(0.790 1.233)	1.124	0.210	(0.936 1.350)	0.924	0.638	(0.666 1.283)
Presence of diarrhea
No	1			1			1			1
Yes, in the last 2 weeks	1.224	0.020	(1.033 1.450)	0.777	0.047	(0.606 0.996)	1.242	0.026	(1.026 1.504)	0.755	0.097	(0.542 1.052)

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Overweight

The results showed that the odds of being overweight in 2013/14 and 2018 reduced with age. Children aged 48–59 months were 83% (0.17, 95% CI: 0.12–0.23) and 86% (0.14; 95% CI: 0.09–0.21) less likely to be overweight in 2013/4 and 2018 respectively, when compared to those below 6 months. Furthermore, in 2013/4, children with average or large birth weight were 2.32 (95% CI: 1.01–5.32) times more likely to be overweight compared to those classified as small at birth. None of the household characteristics and maternal characteristics were observed to have a significant relationship with overweight in both 2013/14 and 2018 (Table 4).

Discussion

The study describes variations in the prevalence of childhood malnutrition (stunting, wasting, underweight, overweight) in Zambia using selected individual, household, and maternal characteristics from the DHS data of 2013/4 and 2018. Although the prevalence of malnutrition decreased considerably between the survey years, the unequal burden of childhood malnutrition across the 2 survey periods was observed within provinces, with high prevalence reported in Luapula, Muchinga, and Northern provinces across most outcomes. Our results agree with results obtained from the Rural Agricultural Livelihoods Surveys (RALS) of 2012 and 2015 which concluded that Northern, Luapula, and Muchinga provinces had a high incidence of chronic poverty [22] thus increasing the risks of undernutrition. Our findings corroborate with studies done in LMIC within sub-Saharan Africa which found similar differences in the prevalence of malnutrition [1, 23, 24]. The information generated from this study showing the unequal burden of malnutrition is essential as it would help policymakers, clinicians, and nutrition fieldworkers plan regional tailored programs in responding to the different forms of malnutrition and address the underlying determinants of childhood malnutrition.

Our study has shown geographical disparities in the prevalence of stunting, wasting, underweight, and overweight suggesting the need for deliberate policies to reduce malnutrition. With the current population of 18 million, recent estimates project an annual population growth rate of 3% for Zambia [16]. Much of this population resides in rural areas (65% in 2010, 66% in 2013–14, and 57% in 2018) which are characterized by limited access to health and poor socio-economic conditions [16]. In addition, poverty remains high in rural areas in Zambia, with recent estimates indicating that 54% of Zambians are living on less than $1.90 a day [25]. Furthermore, changes in rainfall patterns and distribution due to climate change coupled with the rise in population, food price volatility, and cost of living increase the risks of food insecurity among rural families which may in turn influence the nutritional status of children in rural areas [26, 27].

Consistent with previous studies, the mother’s educational status continues to be associated with childhood malnutrition, with lower odds across all indicators being observed among children whose mothers had tertiary education [28, 29]. Although education has been suggested to be linked to social status, our results suggest that maternal education can influence pathways such as income generation which may contribute to alleviating the burden caused by malnutrition. Furthermore, Iftikhar, Bari [30] suggested that educated mothers are better placed to adopt health-promoting behaviors, childhood feeding practices, and equitable sharing of resources within households thus significantly lowering childhood malnutrition. Furthermore, the acquisition of education plays a role in increasing responsiveness to health practices such as sanitation practices which are essential in reducing childhood risks to diarrheal [31, 32] and helminthic infections [33] which can lead to anaemia. Our study showed that sanitation in terms of having access to improved water sources did not influence malnutrition. The results obtained in our study are in agreement with earlier studies by Ukuwuani and Suchindran [34] and Van de Poel, Hosseinpoor [35]. Despite this outcome, improved sanitation is vital in reducing the risks of diarrheal which was observed to increase the likelihood of being underweight; a result that has been reported in an earlier study conducted among children with malnutrition in Zambia [36].

Our results show that household wealth was significantly associated with stunting and underweight in children. We observed that children from poor households are more likely to experience poor outcomes compared to those from privileged households. Several studies have suggested a strong association between childhood malnutrition and the household’s economic condition [37, 38]. Although our study like several other studies has observed that malnutrition is higher in perceived rural regions than urban regions due to household wealth differences, earlier studies showed that urban regions and cities have isolated clusters of communities living in severe poverty and deprivation compared to rural areas leading to risks of malnutrition among children from these households [39, 40]. On the other hand, high-income households tend to spend more resources on nutrition and tend to have better access to healthcare, which in turn has a diverse impact on the health and nutritional status of the child [1, 41]. Primary school feeding programs have been implemented to reduce childhood malnutrition [42]. However, in areas where the rates of preschool and primary school dropouts are high, the impact of these programs may not be significant. Although these programs show gains in poverty reduction, there are possibilities of their implementations addressing symptoms of child malnutrition and not the determinants of childhood malnutrition. For instance, Roothaert, Mpogole [43] observed that school-based programs may lack food diversity and nutritive value and may not be enough for all students without contributions from parents.

While the prevalence of stunting and underweight appear to be more pronounced, our results suggest emerging patterns of child overweight in certain regions such as Lusaka and Northern provinces. Amidst the rising cases of overweight/obesity in children, more studies in LMICs tend to focus more on the contributory role of poverty and malnutrition, without considering other important contextual factors [23]. The development of policies and programs that address food security, maternal education, and socio-economic inequalities will be essential in mitigating the long-term impacts of childhood malnutrition on a child’s intellectual development. Our findings suggest the need for policies that will address the proximate determinants of childhood malnutrition. There is a need to strength policies, and programs targeting specific populations such as children from poor families and communities to achieve pro- parity health outcomes. With the growing population of Zambia, is a pressing need for policies to alleviate childhood malnutrition; designed through community involvement and consultation to enhance participation, especially in the northern regions of the country.

Study strengths and study limitations

A major strength of our study is the utility of nationally representative survey data that allowed us to examine the prevalence of childhood malnutrition in 2013/4 and 2018. The study was limited to the explanatory factors for which data was collected and reported in DHS 2013 and DHS 2018. Thus, data on many potential correlates/other risk factors such as access to the health facility, adolescent pregnancy, comorbidities, and NCDs were not included in the analysis. The available data was measured during the survey period and may have changed.

Conclusions

Health equity is a critical component for achieving Sustainable Development Goals (SDG 3). Furthermore, the WHO has identified geographical disparities and inequalities in inequitable implementation of intervention programs. Our findings contribute to the understanding of the etiology and epidemiology of child malnutrition in a bid to reduce persistent inequalities in childhood malnutrition. In particular, the findings highlight regional variations and key factors driving the trends in childhood malnutrition between the two survey periods. Our study suggests that reducing malnutrition in Zambia will require specific regional tailored interventions in the identified provinces. Furthermore, in-depth studies on other risk factors related to childhood malnutrition should also be conducted. The country should develop a core set of nutritional indicators to be monitored rapidly/frequently across provinces and on all the nutritional programs.

Acknowledgments

Thanks go to the Demographic and Health Survey Program for the approval to use the 2013–14 and 2018 Zambia Demographic and Health Survey datasets for analysis.

Abbreviations

AOR: Adjusted odds ratio
OR: Odds ratio
CI: Confidence Interval
CPH: Census of Population and Housing
DHS: Demographic and Health Survey
HIV: Human Immuno-deficiency virus
ZDHS: Zambia Demographic and Health Survey
ZMPR: Zambia Population Recode File
WHO: World Health Organization
UNICEF: United Nations Children’s Fund

Data Availability

The data underlying the results presented in the study are available from the Demographic Health Survey website at https://dhsprogram.com/data/.

Funding Statement

The author(s) received no specific funding for this work.

References

1.Akombi B, Agho K, Merom D, Renzaho A, Hall J. Child malnutrition in sub-Saharan Africa: A meta-analysis of demographic and health surveys (2006–2016). PLoS ONE. 2017;12(5):e0177338. doi: 10.1371/journal.pone.0177338 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Isanaka S, Barnhart DA, McDonald CM, Ackatia-Armah RS, Kupka R, Doumbia S, et al. Cost-effectiveness of community-based screening and treatment of moderate acute malnutrition in Mali. BMJ global health. 2019;4(2):e001227. doi: 10.1136/bmjgh-2018-001227 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Development Initiatives. Global Nutrition Report: Shining a light to spur action on nutrition. Bristol, UK. 2018;7. [Google Scholar]
4.WHO. Reducing stunting in children: equity considerations for achieving the global nutrition targets 2025. World Health Organization, 2018. 9241513640. [Google Scholar]
5.Pelletier DL, Frongillo EA. Changes in child survival are strongly associated with changes in malnutrition in developing countries. The Journal of nutrition. 2003;133(1):107–19. doi: 10.1093/jn/133.1.107 [DOI] [PubMed] [Google Scholar]
6.Victora CG, Christian P, Vidaletti LP, Gatica-Domínguez G, Menon P, Black RE. Revisiting maternal and child undernutrition in low-income and middle-income countries: variable progress towards an unfinished agenda. The Lancet. 2021;397(10282):1388–99. doi: 10.1016/S0140-6736(21)00394-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Wells JC, Sawaya AL, Wibaek R, Mwangome M, Poullas MS, Yajnik CS, et al. The double burden of malnutrition: aetiological pathways and consequences for health. The Lancet. 2020;395(10217):75–88. doi: 10.1016/S0140-6736(19)32472-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Gillespie S, Haddad L, Mannar V, Menon P, Nisbett N, Maternal, et al. The politics of reducing malnutrition: building commitment and accelerating progress. The lancet. 2013;382(9891):552–69. doi: 10.1016/S0140-6736(13)60842-9 [DOI] [PubMed] [Google Scholar]
9.Pearson R, Killedar M, Petravic J, Kakietek JJ, Scott N, Grantham KL, et al. Optima Nutrition: an allocative efficiency tool to reduce childhood stunting by better targeting of nutrition-related interventions. BMC Public Health. 2018;18(1):1–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Moramarco S, Amerio G, Gozza Maradini G, Garuti E. The Rainbow Project: A Model to Fight Child Malnutrition in Zambia. 2014. [Google Scholar]
11.Seco Grütz S, Sadlier M, Brunet D. Reflections on the Role of Donors in Scaling Up Nutrition in Zambia from 2010 to 2013: Successes, Challenges and Lessons Learnt. 2014. [Google Scholar]
12.van den Assum SJ, Lambert SR, de Bresser I, Schilpzand RA. Analysis of implementation of global nutrition policies to tackle the double burden of malnutrition in Nigeria, Zambia and Indonesia. Proceedings of the Nutrition Society. 2020;79(OCE2). [Google Scholar]
13.MoH. The 2017–2021 National Health Sector Strategic Plan (NHSP). 2016. [Google Scholar]
14.Munthali T, Chabala C, Chama E, Mugode R, Kapata N, Musonda P, et al. Tuberculosis caseload in children with severe acute malnutrition related with high hospital based mortality in Lusaka, Zambia. BMC research notes. 2017;10(1):1–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Munthali T, Jacobs C, Sitali L, Dambe R, Michelo C. Mortality and morbidity patterns in under-five children with severe acute malnutrition (SAM) in Zambia: a five-year retrospective review of hospital-based records (2009–2013). Archives of Public Health. 2015;73(1):1–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Central Statistical Office (CSO) [Zambia], Ministry of Health (MOH) [Zambia], International aI. Zambia Demographic and Health Survey 2013–14. Rockville, Maryland, USA: Central Statistical Office, Ministry of Health, and ICF International; 2014. [Google Scholar]
17.Zambia Statistics Agency, Ministry of Health (MOH) Zambia, ICF a. Zambia Demographic and Health Survey 2018. Lusaka, Zambia and Rockville, Maryland, USA: Zambia Statistics Agency, Ministry of Health, and ICF; 2019. [Google Scholar]
18.WHO. WHO child growth standards: length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age: methods and development. WHO, Geneva; 2006. [Google Scholar]
19.Nugent R, Levin C, Hale J, Hutchinson B. Economic effects of the double burden of malnutrition. The Lancet. 2020;395(10218):156–64. doi: 10.1016/S0140-6736(19)32473-0 [DOI] [PubMed] [Google Scholar]
20.Black RE, Laxminarayan R, Temmerman M, Walker N. Reproductive, Maternal, newborn, and child health: disease control priorities, (volume 2). 2016. [PubMed] [Google Scholar]
21.Fanzo J, Hawkes C, Udomkesmalee E, Afshin A, Allemandi L, Assery O, et al. 2018 Global Nutrition Report: Shining a light to spur action on nutrition. 2018. [Google Scholar]
22.Ngoma H, Mulenga BP, Snyder J, Banda A, Chapoto A. Poverty and Weather Shocks: A Panel Data Analysis of Structural and Stochastic Poverty in Zambia. 2019. [Google Scholar]
23.Akombi BJ, Chitekwe S, Sahle BW, Renzaho A. Estimating the double burden of malnutrition among 595,975 children in 65 low-and middle-income countries: a meta-analysis of demographic and health surveys. International journal of environmental research and public health. 2019;16(16):2886. doi: 10.3390/ijerph16162886 [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Yourkavitch J, Burgert-Brucker C, Assaf S, Delgado S. Using geographical analysis to identify child health inequality in sub-Saharan Africa. PLoS One. 2018;13(8):e0201870. doi: 10.1371/journal.pone.0201870 [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Schmidt-Traub G, Kroll C, Teksoz K, Durand-Delacre D, Sachs JD. National baselines for the Sustainable Development Goals assessed in the SDG Index and Dashboards. Nature geoscience. 2017;10(8):547–55. [Google Scholar]
26.Gillespie S, van den Bold M. Agriculture, food systems, and nutrition: meeting the challenge. Global Challenges. 2017;1(3):1600002. doi: 10.1002/gch2.201600002 [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Wossen T, Berger T, Haile MG, Troost C. Impacts of climate variability and food price volatility on household income and food security of farm households in East and West Africa. Agricultural systems. 2018;163:7–15. [Google Scholar]
28.Khan GN, Turab A, Khan MI, Rizvi A, Shaheen F, Ullah A, et al. Prevalence and associated factors of malnutrition among children under-five years in Sindh, Pakistan: a cross-sectional study. BMC nutrition. 2016;2(1):1–7. [Google Scholar]
29.Obasohan PE, Walters SJ, Jacques R, Khatab K. Risk Factors Associated with Malnutrition among Children Under-Five Years in Sub-Saharan African Countries: A Scoping Review. International Journal of Environmental Research and Public Health. 2020;17(23):8782. [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Iftikhar A, Bari A, Iqbal Bano QM. Impact of maternal education, employment and family size on nutritional status of children. Pakistan journal of medical sciences. 2017;33(6):1401. doi: 10.12669/pjms.336.13689 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Yasmin G, Kustiyah L, Dwiriani CM. Risk factors of stunting among school-aged children from eight provinces in Indonesia. Pakistan Journal of Nutrition. 2014;13(10):557. [Google Scholar]
32.Modjadji P, Madiba S. Childhood undernutrition and its predictors in a rural health and demographic surveillance system site in South Africa. International journal of environmental research and public health. 2019;16(17):3021. [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Sacolo H, Chimbari M, Kalinda C. Knowledge, attitudes and practices on Schistosomiasis in sub-Saharan Africa: a systematic review. BMC infectious diseases. 2018;18(1):46. doi: 10.1186/s12879-017-2923-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Ukuwuani F, Suchindran C. Implications of women’s work for child nutritional status in sub-Saharan Africa: a case study of Nigeria. Social Science and Medicine. 2003;56:2109–21. doi: 10.1016/s0277-9536(02)00205-8 [DOI] [PubMed] [Google Scholar]
35.Van de Poel E, Hosseinpoor AR, Jehu-Appiah C, Vega J, Speybroeck N. Malnutrition and the disproportional burden on the poor: the case of Ghana. International journal for equity in health. 2007;6(1):1–12. doi: 10.1186/1475-9276-6-21 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Irena AH, Mwambazi M, Mulenga V. Diarrhea is a major killer of children with severe acute malnutrition admitted to inpatient set-up in Lusaka, Zambia. Nutrition journal. 2011;10(1):1–6. doi: 10.1186/1475-2891-10-110 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Talukder A. Factors associated with malnutrition among under-five children: illustration using Bangladesh demographic and health survey, 2014 data. Children. 2017;4(10):88. doi: 10.3390/children4100088 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Habyarimana F. Key determinants of malnutrition of children under five years of age in Rwanda: Simultaneous measurement of three anthropometric indices. African Population Studies. 2016;30(2). [Google Scholar]
39.Menon P, Ruel MT, Morris SS. Socio-economic differentials in child stunting are consistently larger in urban than in rural areas. Food and Nutrition Bulletin. 2000;21(3):282–9. [Google Scholar]
40.Zere E, McIntyre D. Inequities in under-five child malnutrition in South Africa. International journal for equity in health. 2003;2(1):1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Tette EM, Sifah EK, Nartey ET, Nuro-Ameyaw P, Tete-Donkor P, Biritwum RB. Maternal profiles and social determinants of malnutrition and the MDGs: What have we learnt? BMC public health. 2016;16(1):1–11. doi: 10.1186/s12889-016-2853-z [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Wei D, Brigell R, Khadka A, Perales N, Fink G. Comprehensive school-based health programs to improve child and adolescent health: Evidence from Zambia. PloS one. 2019;14(5):e0217893. doi: 10.1371/journal.pone.0217893 [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Roothaert R, Mpogole H, Hunter D, Ochieng J, Kejo D. Policies, Multi-Stakeholder Approaches and Home-Grown School Feeding Programs for Improving Quality, Equity and Sustainability of School Meals in Northern Tanzania. Frontiers in Sustainable Food Systems. 2021;5:43. [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0277015.r001

Decision Letter 0

Elisa Panada

2 Mar 2022

PONE-D-21-18211Contextual Factors and Spatial Trends of Childhood Malnutrition in Zambia

PLOS ONE

Dear Dr. Odhiambo,

The manuscript has been evaluated by two reviewers, and their comments are available below. The reviewers have raised a number of concerns. They request improvements to the reporting of methodological aspects of the study, for example, regarding the exclusion criteria and more information on how the data collection was completed. The reviewers also note concerns about the statistical analyses presented. Could you please carefully revise the manuscript to address all comments raised?

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PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

**********

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: 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 manuscript is written well but with unknown reason I did not access the tables. However, the presented information is enough to forward my concerns on the paper.

1. As the abstract is the stand alone summary of the whole work; information like the total number of participants involved in the analysis must be included. It might be editorial problem use multivariate vs multivariable appropriately; in the paper it is used interchangeably; which is not right.

2. Though the introduction section address key points; it needs further modification; especially the figurative expressions of each malnutrition from the global to local context must be incorporated. If possible also add the global and the national trend from the previous reports. In addition, as mentioned in the title, "contextual factors", factors associated with each malnutrition type must be addressed in the introduction section.

3. In the result section, particularly the bivariate and the multivariable analysis section; most of the measure of effects are below 1 (null value for odds ratio); this is due to the selection of the reference category; why you did that? In addition, though the measure of effect is put with its 95% CI; its interpretation is a bit confusing. Just an example let me raise this one: "In 2014, children aged 48-59 months were 0.17(95% CI: 0.12–0.23) times less likely to be overweight". First, better to change the reference category with the smallest percentage or you have to interpret as In 2014, children aged 48-59 months were 83% (AOR: 0.17; 95% CI: 0.12–0.23) less likely to be overweight".

4. In the discussion; section from lines 240-243, is it the justification given for the similarities/discrepancies of your findings and the previous findings or recommendation? I think this is recommendation and take it this to the appropriate place and put the justification for the similarities/discrepancies of the findings.

Reviewer #2: Overall – a well written manuscript reporting on an analysis from the DHS that looks at geographical differences in child malnutrition and sociodemographic predictors within Zambia; however, the manuscript could be strengthened by increasing the focus on these geographic variations and highlighting the importance of the sociodemographic characteristics of provinces within Zambia in relation with the outcomes.

Specific recommendations follow:

Abstract –

The justification for the study is that there are within country variations that are important beyond the national estimates and the methods describe assessment of within province variations, however the results only provide changes in national estimates from 2013 to 2018. The results need to be updated to support the rest of the abstract, including the conclusions.

I do not think that there was a decrease in overweight based on the estimates provided. I would say, it remained about the same.

Introduction –

37- It would be good to clarify what is meant by malnutrition, which is normally used for both under and over nutrition. While undernutrition does account for childhood mortality and impairs development, the role of overweight in childhood is not as clear. You could also mention the long-lasting consequences for chronic diseases.

68 – Please clarify what is meant by “a relative shift in the contribution of socio-economic determinants, communicable and non-communicable diseases”. Also provide a citation for this statement.

Methods –

84 – there seems to be a typo “of women aged of selected households”.

84- please clarify the concept of biological children, was this asked during a screening? Were adopted children excluded?

86- Was child weight and height measured or reported by the mothers? Since the study is in children, I do not understand what is meant by women participants. Please clarify this in the study design or sample selection. Also, please distinguish between the inclusion criteria for the DHS and the inclusion criteria for this analysis.

99- The statement about malnutrition and this study including over- and under- nutrition is unclear. Please rephrase to clarify.

99-101 Perhaps this information is better suited in the introduction and/or discussion rather than in the methods.

Statistical analysis – is the survey designed to be representative at the province level? If so, please state it in the study description.

Please add a section describing the covariates for the models. The case for studying these associations should be made in the introduction.

Results

123-125 – it sounds like the variables were included in the model based on their known importance as predictors. Perhaps that is a simpler way to state this (and in general a better one than basing it on significance). (There is a typo – “interested variables” should be variables of interest).

169 – what is meant by uncertainty? This is not defined before in the methods.

The analysis of malnutrition by province needs to be expanded to describe highest and lowest prevalence of under- and over- nutrition, as well as greatest changes over time.

Additional models correlating geographic or province sociodemographic characteristics to malnutrition outcomes would greatly increase the value of this paper. Please consider including these additional analysis which can be performed either by multilevel modeling with a province level or simple spatial modeling using the ARCGis

Conclusion

Even though the introduction emphasizes the relevance of geographic differences, the conclusion just presents these as an afterthought and concentrates on the associations between sociodemographic characteristics and malnutrition. There is a missed opportunity to integrate these two in a discussion about the importance of geographical variation and changes over time, and what the role of the characteristics of the different provinces means. I recommend re-writing the results and discussion, and potentially adding more analysis to fully address the geographic differences, rather than associations that are in general expected for child malnutrition and do not inform policies or interventions.

**********

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2022 Nov 3;17(11):e0277015. doi: 10.1371/journal.pone.0277015.r002

Author response to Decision Letter 0

20 Apr 2022

We have attached our responses.

Attachment

Submitted filename: Authors_Rebuttal.pdf

Click here for additional data file.^{(189.6KB, pdf)}

PLoS One. doi: 10.1371/journal.pone.0277015.r003

Decision Letter 1

Inés González-Casanova

12 Jul 2022

PONE-D-21-18211R1

Contextual factors and spatial trends of childhood malnutrition in Zambia

PLOS ONE

Dear Dr. Odhiambo,

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. As context, I served as reviewer 2 for the initial submission of the manuscript and then agreed to serve as guest editor for this resubmission. This led me to the decision to invite an additional reviewer to account for any potential bias. While previous comments have been addressed, the new reviewer found that major changes needed to be done before the manuscript is suitable for publication. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Aug 26 2022 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,

Ines Gonzalez Casanova

Guest Editor

PLOS ONE

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

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 #3: All comments have been addressed

**********

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

Reviewer #1: Yes

Reviewer #3: No

**********

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

Reviewer #1: Yes

Reviewer #3: I Don't Know

**********

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

Reviewer #1: Yes

Reviewer #3: No

**********

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

Reviewer #1: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Reviewer #1: (No Response)

Reviewer #3: I was excited to be considered to review the manuscript titled:

Contextual factors and spatial trends of childhood malnutrition in Zambia

The manuscript attempts to write about malnutrition in Zambia using DHA data from two different surveys.

Mi biggest comment is that the manuscript has an flaw that needs to be addressed: there is not a clear research question, or questions, or hypotheses.

The main outcome is malnutrition, but relative to what? Is it changes? Or is it identifying factors? Or identifying factors that changed over time? ?

ABSTRACT:

The abstract has the following conclusion: “The study points to key sub-populations at greater risk and provinces where malnutrition was prevalent in Zambia”. The results included in the abstract explain reductions in the odds ratios of stunting, wasting, underweight and overweight. The conclusion is not related to the results included in the abstract. I suggest the authors modify the abstract in such way that the most important results from the manuscript are listed, and the conclusion is based on the most important findings of the study.

OVERALL: undernutrition and malnutrition are two different concepts. The authors should consider revising the manuscript and use the appropriate term when needed: https://www.who.int/news-room/fact-sheets/detail/malnutrition

In the introduction, the authors consistently are using “malnutrition” when referring to “undernutrition”.

What are the associated issues with overweight and obesity during childhood? And double burden?

I suggest to work a bit more on working more on the “ problem” that under and over nutrition has, especially within the context of LMIC, Africa and Zambia.

Introduction:

The Lancet global series has new references on unde- and over- rnutrition in LMIC. Consider including some more updated references on worldwide undernutrition:

See here: https://www.thelancet.com/series/maternal-child-undernutrition-progress

Line 46 needs a reference.

Consider rewording 76-78

METHODS:

Table 1 should be supplementary material.

Undernourishment,. Consider changing to undernutrition

Line 148 is malnutrition? Or undernutrition? (is it also overnutrition?)

The tables are very large. Consider including the most important variales.

Where is table 3?

Outcome measures: theauthors refer that the outcome is childgood malnutrition), but relative to what?

Is it time changes? Is it factors?

Based on the main research question of this manuscript, then the authors should consuider using generalized linear mixed models, as there are two potential clsuters of data that should be accounted for as random effects. (Although I am not a biostatistician, so I would defer to other reviewers with more knowledge on this topic).

1) Years

2) Provinces.

**********

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 #3: No

**********

PLoS One. 2022 Nov 3;17(11):e0277015. doi: 10.1371/journal.pone.0277015.r004

Author response to Decision Letter 1

16 Aug 2022

The manuscript attempts to write about malnutrition in Zambia using DHA data from two different surveys.

Mi biggest comment is that the manuscript has an flaw that needs to be addressed: there is not a clear research question, or questions, or hypotheses.

The main outcome is malnutrition, but relative to what? Is it changes? Or is it identifying factors? Or identifying factors that changed over time? ?

In this paper we intended to look at the individual socioeconomic, demographic and contextual risk factors associated with malnutrition between two DHS survey periods.

As defined by the WHO, childhood malnutrition, in all its forms, includes undernutrition (wasting, stunting, underweight), inadequate vitamins or minerals, overweight, obesity, and resulting diet-related noncommunicable diseases. More emphasis has been added in the manuscript. Please see lines 43 -45 and 97 - 104

Based on the hypothesis/research questions, the authors have an amazing opportunity to use the scientific method to respond to the main questions they are asking. Right now, it reads as a report of the two DHA surveys, which I don’t think it is suitable for publication. Our study sought to highlights the geographical disparity of childhood malnutrition using the 2013 and 2018 ZDHS data.

Our hypothesis is that childhood malnutrition patterns change in time and space.

ABSTRACT:

Our study highlights the overall trends across all the provinces and the heterogeneity between the two periods.

We have reworded our abstract to enhance its clarity.

In the introduction, the authors consistently are using “malnutrition” when referring to “undernutrition”.

What are the associated issues with overweight and obesity during childhood? And double burden?

I suggest to work a bit more on working more on the “ problem” that under and over nutrition has, especially within the context of LMIC, Africa and Zambia. According to the World Health Organization, malnutrition refers to “deficiencies, excesses or imbalances in a person’s intake of energy and/or nutrients.”

This can result in either undernutrition or overweight.

The introduction has been refined and additional references included.

Line 76 -78 has also been reworded. Please see lines 75 - 79

Introduction:

The Lancet global series has new references on unde- and over- rnutrition in LMIC. Consider including some more updated references on worldwide undernutrition:

See here: https://www.thelancet.com/series/maternal-child-undernutrition-progress

Line 46 needs a reference.

Consider rewording 76-78

METHODS:

Table 1 should be supplementary material.

Undernourishment,.

Consider changing to undernutrition Agreed.

Table 1 has been removed.

Line 148 is malnutrition? Or undernutrition? (is it also overnutrition?) According to the World Health Organization, malnutrition refers to “deficiencies, excesses or imbalances in a person’s intake of energy and/or nutrients.”

This can result in either undernutrition or overweight.

The tables are very large. Consider including the most important variales.

Where is table 3?

Table 3 is clearly annotated.

The p-values highlights if the association was significant.

1) Years

2) Provinces. We used a logistic regression model since our dependent variable (malnutrion) was binary.

Attachment

Submitted filename: Rebuttal_v3.pdf

Click here for additional data file.^{(136.1KB, pdf)}

PLoS One. doi: 10.1371/journal.pone.0277015.r005

Decision Letter 2

Inés González-Casanova

21 Sep 2022

PONE-D-21-18211R2Contextual factors and spatial trends of childhood malnutrition in ZambiaPLOS ONE

Dear Dr. Odhiambo,

ACADEMIC EDITOR: While the manuscript is almost ready for publication, the latest version still did not fully address the reviewer's comments. Minor edits are necessary so that the manuscript can be accepted. Please address the following comments:

Abstract: The results still do not reflect the regional differences that are described in the background, methods, and conclusion. You need to describe at least some evidence of heterogeneity in the results.

To address concerns related to the use of different terminology to describe undernutrition or malnutrition, I suggest that you add a footnote explaining how you are using each term in each context and that you are consistent with the use across.

Please limit the tables to key variables that illustrate this study and the results. This will address the reviewer's comment that states that this looks more like a report than an analysis.

Add this information to table legends (include comparison groups, dependent variable, independent variable, co-variates, how variables were analyzed, etc).:

Tables 2 are showing stratified ages, however only a p-value is shown when comparing the age+ stunting, wasting a the age used as a contibnuous variable? It is not clear what the P-values are comparing? Example: is it male vs female in stunted? Is it stunting for males and females? How did the authors analize the age variable?The association between categorical variables were analysed the chi-square test. The p-values highlights if the association was significant. Based on the main research question of this manuscript, then the authors should consuider using generalized linear mixed models, as there are two potential clsuters of data that should be accounted for as random effects. (Although I am not a biostatistician, so I would defer to other reviewers with more knowledge on this topic). 1) Years 2) Provinces.We used a logistic regression model since our dependent variable (malnutrion) was binary.

Make sure all tables and figures have descriptive legends and can be interpreted on their own.

Please submit your revised manuscript by Nov 05 2022 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. You should upload this letter as a separate file labeled 'Response to Reviewers'.
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Additional Editor Comments:

Please limit the tables to key variables that illustrate this study and the results. This will address the reviewer comment that states that this looks more like a report than an analysis.

Add this information to table legends (include comparison groups, dependent variable, independent variable, co-variates, how variables were analyzed, etc).:

PLoS One. 2022 Nov 3;17(11):e0277015. doi: 10.1371/journal.pone.0277015.r006

Author response to Decision Letter 2

4 Oct 2022

AR1:Agreed.

We have added more information on the regional disparities. Please see lines 35 - 38

AR2: Agreed

The WHO and Zambian standards have been used to define the anthropometric indicators of nutritional status i.e. stunting, wasting, underweight and overweight. Please see lines 98 - 106

Please limit the tables to key variables that illustrate this study and the results. This will address the reviewer's comment that states that this looks more like a report than an analysis.

AR3: Agreed.

Please see Table 1 and 2.

Add this information to table legends (include comparison groups, dependent variable, independent variable, co-variates, how variables were analyzed, etc).

AR4: Agreed. Please see Table 1 and 2.

AR5:

This seems to be a verbatim repetition of the initial reviewer comments and our response.

Attachment

Submitted filename: Response to reviewers_Oct.pdf

Click here for additional data file.^{(69.9KB, pdf)}

PLoS One. doi: 10.1371/journal.pone.0277015.r007

Decision Letter 3

Inés González-Casanova

19 Oct 2022

Contextual factors and spatial trends of childhood malnutrition in Zambia

PONE-D-21-18211R3

Dear Dr. Odhiambo,

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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Inés González-Casanova

Guest Editor

PLOS ONE

Additional Editor Comments (optional):

Please explain what is meant by uncertainty in the abstract. Perhaps a better term is variability within the province? That result is not clear and should be better explained.

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0277015.r008

Acceptance letter

Inés González-Casanova

25 Oct 2022

PONE-D-21-18211R3

Contextual factors and spatial trends of childhood malnutrition in Zambia

Dear Dr. Odhiambo:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Inés González-Casanova

Guest Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Attachment

Submitted filename: Authors_Rebuttal.pdf

Click here for additional data file.^{(189.6KB, pdf)}

Attachment

Submitted filename: Rebuttal_v3.pdf

Click here for additional data file.^{(136.1KB, pdf)}

Attachment

Submitted filename: Response to reviewers_Oct.pdf

Click here for additional data file.^{(69.9KB, pdf)}

Data Availability Statement

The data underlying the results presented in the study are available from the Demographic Health Survey website at https://dhsprogram.com/data/.

[pone.0277015.ref001] 1.Akombi B, Agho K, Merom D, Renzaho A, Hall J. Child malnutrition in sub-Saharan Africa: A meta-analysis of demographic and health surveys (2006–2016). PLoS ONE. 2017;12(5):e0177338. doi: 10.1371/journal.pone.0177338 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0277015.ref002] 2.Isanaka S, Barnhart DA, McDonald CM, Ackatia-Armah RS, Kupka R, Doumbia S, et al. Cost-effectiveness of community-based screening and treatment of moderate acute malnutrition in Mali. BMJ global health. 2019;4(2):e001227. doi: 10.1136/bmjgh-2018-001227 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0277015.ref003] 3.Development Initiatives. Global Nutrition Report: Shining a light to spur action on nutrition. Bristol, UK. 2018;7. [Google Scholar]

[pone.0277015.ref004] 4.WHO. Reducing stunting in children: equity considerations for achieving the global nutrition targets 2025. World Health Organization, 2018. 9241513640. [Google Scholar]

[pone.0277015.ref005] 5.Pelletier DL, Frongillo EA. Changes in child survival are strongly associated with changes in malnutrition in developing countries. The Journal of nutrition. 2003;133(1):107–19. doi: 10.1093/jn/133.1.107 [DOI] [PubMed] [Google Scholar]

[pone.0277015.ref006] 6.Victora CG, Christian P, Vidaletti LP, Gatica-Domínguez G, Menon P, Black RE. Revisiting maternal and child undernutrition in low-income and middle-income countries: variable progress towards an unfinished agenda. The Lancet. 2021;397(10282):1388–99. doi: 10.1016/S0140-6736(21)00394-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0277015.ref007] 7.Wells JC, Sawaya AL, Wibaek R, Mwangome M, Poullas MS, Yajnik CS, et al. The double burden of malnutrition: aetiological pathways and consequences for health. The Lancet. 2020;395(10217):75–88. doi: 10.1016/S0140-6736(19)32472-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0277015.ref008] 8.Gillespie S, Haddad L, Mannar V, Menon P, Nisbett N, Maternal, et al. The politics of reducing malnutrition: building commitment and accelerating progress. The lancet. 2013;382(9891):552–69. doi: 10.1016/S0140-6736(13)60842-9 [DOI] [PubMed] [Google Scholar]

[pone.0277015.ref009] 9.Pearson R, Killedar M, Petravic J, Kakietek JJ, Scott N, Grantham KL, et al. Optima Nutrition: an allocative efficiency tool to reduce childhood stunting by better targeting of nutrition-related interventions. BMC Public Health. 2018;18(1):1–12. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0277015.ref010] 10.Moramarco S, Amerio G, Gozza Maradini G, Garuti E. The Rainbow Project: A Model to Fight Child Malnutrition in Zambia. 2014. [Google Scholar]

[pone.0277015.ref011] 11.Seco Grütz S, Sadlier M, Brunet D. Reflections on the Role of Donors in Scaling Up Nutrition in Zambia from 2010 to 2013: Successes, Challenges and Lessons Learnt. 2014. [Google Scholar]

[pone.0277015.ref012] 12.van den Assum SJ, Lambert SR, de Bresser I, Schilpzand RA. Analysis of implementation of global nutrition policies to tackle the double burden of malnutrition in Nigeria, Zambia and Indonesia. Proceedings of the Nutrition Society. 2020;79(OCE2). [Google Scholar]

[pone.0277015.ref013] 13.MoH. The 2017–2021 National Health Sector Strategic Plan (NHSP). 2016. [Google Scholar]

[pone.0277015.ref014] 14.Munthali T, Chabala C, Chama E, Mugode R, Kapata N, Musonda P, et al. Tuberculosis caseload in children with severe acute malnutrition related with high hospital based mortality in Lusaka, Zambia. BMC research notes. 2017;10(1):1–6. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0277015.ref015] 15.Munthali T, Jacobs C, Sitali L, Dambe R, Michelo C. Mortality and morbidity patterns in under-five children with severe acute malnutrition (SAM) in Zambia: a five-year retrospective review of hospital-based records (2009–2013). Archives of Public Health. 2015;73(1):1–9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0277015.ref016] 16.Central Statistical Office (CSO) [Zambia], Ministry of Health (MOH) [Zambia], International aI. Zambia Demographic and Health Survey 2013–14. Rockville, Maryland, USA: Central Statistical Office, Ministry of Health, and ICF International; 2014. [Google Scholar]

[pone.0277015.ref017] 17.Zambia Statistics Agency, Ministry of Health (MOH) Zambia, ICF a. Zambia Demographic and Health Survey 2018. Lusaka, Zambia and Rockville, Maryland, USA: Zambia Statistics Agency, Ministry of Health, and ICF; 2019. [Google Scholar]

[pone.0277015.ref018] 18.WHO. WHO child growth standards: length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age: methods and development. WHO, Geneva; 2006. [Google Scholar]

[pone.0277015.ref019] 19.Nugent R, Levin C, Hale J, Hutchinson B. Economic effects of the double burden of malnutrition. The Lancet. 2020;395(10218):156–64. doi: 10.1016/S0140-6736(19)32473-0 [DOI] [PubMed] [Google Scholar]

[pone.0277015.ref020] 20.Black RE, Laxminarayan R, Temmerman M, Walker N. Reproductive, Maternal, newborn, and child health: disease control priorities, (volume 2). 2016. [PubMed] [Google Scholar]

[pone.0277015.ref021] 21.Fanzo J, Hawkes C, Udomkesmalee E, Afshin A, Allemandi L, Assery O, et al. 2018 Global Nutrition Report: Shining a light to spur action on nutrition. 2018. [Google Scholar]

[pone.0277015.ref022] 22.Ngoma H, Mulenga BP, Snyder J, Banda A, Chapoto A. Poverty and Weather Shocks: A Panel Data Analysis of Structural and Stochastic Poverty in Zambia. 2019. [Google Scholar]

[pone.0277015.ref023] 23.Akombi BJ, Chitekwe S, Sahle BW, Renzaho A. Estimating the double burden of malnutrition among 595,975 children in 65 low-and middle-income countries: a meta-analysis of demographic and health surveys. International journal of environmental research and public health. 2019;16(16):2886. doi: 10.3390/ijerph16162886 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0277015.ref024] 24.Yourkavitch J, Burgert-Brucker C, Assaf S, Delgado S. Using geographical analysis to identify child health inequality in sub-Saharan Africa. PLoS One. 2018;13(8):e0201870. doi: 10.1371/journal.pone.0201870 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0277015.ref025] 25.Schmidt-Traub G, Kroll C, Teksoz K, Durand-Delacre D, Sachs JD. National baselines for the Sustainable Development Goals assessed in the SDG Index and Dashboards. Nature geoscience. 2017;10(8):547–55. [Google Scholar]

[pone.0277015.ref026] 26.Gillespie S, van den Bold M. Agriculture, food systems, and nutrition: meeting the challenge. Global Challenges. 2017;1(3):1600002. doi: 10.1002/gch2.201600002 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0277015.ref027] 27.Wossen T, Berger T, Haile MG, Troost C. Impacts of climate variability and food price volatility on household income and food security of farm households in East and West Africa. Agricultural systems. 2018;163:7–15. [Google Scholar]

[pone.0277015.ref028] 28.Khan GN, Turab A, Khan MI, Rizvi A, Shaheen F, Ullah A, et al. Prevalence and associated factors of malnutrition among children under-five years in Sindh, Pakistan: a cross-sectional study. BMC nutrition. 2016;2(1):1–7. [Google Scholar]

[pone.0277015.ref029] 29.Obasohan PE, Walters SJ, Jacques R, Khatab K. Risk Factors Associated with Malnutrition among Children Under-Five Years in Sub-Saharan African Countries: A Scoping Review. International Journal of Environmental Research and Public Health. 2020;17(23):8782. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0277015.ref030] 30.Iftikhar A, Bari A, Iqbal Bano QM. Impact of maternal education, employment and family size on nutritional status of children. Pakistan journal of medical sciences. 2017;33(6):1401. doi: 10.12669/pjms.336.13689 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0277015.ref031] 31.Yasmin G, Kustiyah L, Dwiriani CM. Risk factors of stunting among school-aged children from eight provinces in Indonesia. Pakistan Journal of Nutrition. 2014;13(10):557. [Google Scholar]

[pone.0277015.ref032] 32.Modjadji P, Madiba S. Childhood undernutrition and its predictors in a rural health and demographic surveillance system site in South Africa. International journal of environmental research and public health. 2019;16(17):3021. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0277015.ref033] 33.Sacolo H, Chimbari M, Kalinda C. Knowledge, attitudes and practices on Schistosomiasis in sub-Saharan Africa: a systematic review. BMC infectious diseases. 2018;18(1):46. doi: 10.1186/s12879-017-2923-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0277015.ref034] 34.Ukuwuani F, Suchindran C. Implications of women’s work for child nutritional status in sub-Saharan Africa: a case study of Nigeria. Social Science and Medicine. 2003;56:2109–21. doi: 10.1016/s0277-9536(02)00205-8 [DOI] [PubMed] [Google Scholar]

[pone.0277015.ref035] 35.Van de Poel E, Hosseinpoor AR, Jehu-Appiah C, Vega J, Speybroeck N. Malnutrition and the disproportional burden on the poor: the case of Ghana. International journal for equity in health. 2007;6(1):1–12. doi: 10.1186/1475-9276-6-21 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0277015.ref036] 36.Irena AH, Mwambazi M, Mulenga V. Diarrhea is a major killer of children with severe acute malnutrition admitted to inpatient set-up in Lusaka, Zambia. Nutrition journal. 2011;10(1):1–6. doi: 10.1186/1475-2891-10-110 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0277015.ref037] 37.Talukder A. Factors associated with malnutrition among under-five children: illustration using Bangladesh demographic and health survey, 2014 data. Children. 2017;4(10):88. doi: 10.3390/children4100088 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0277015.ref038] 38.Habyarimana F. Key determinants of malnutrition of children under five years of age in Rwanda: Simultaneous measurement of three anthropometric indices. African Population Studies. 2016;30(2). [Google Scholar]

[pone.0277015.ref039] 39.Menon P, Ruel MT, Morris SS. Socio-economic differentials in child stunting are consistently larger in urban than in rural areas. Food and Nutrition Bulletin. 2000;21(3):282–9. [Google Scholar]

[pone.0277015.ref040] 40.Zere E, McIntyre D. Inequities in under-five child malnutrition in South Africa. International journal for equity in health. 2003;2(1):1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0277015.ref041] 41.Tette EM, Sifah EK, Nartey ET, Nuro-Ameyaw P, Tete-Donkor P, Biritwum RB. Maternal profiles and social determinants of malnutrition and the MDGs: What have we learnt? BMC public health. 2016;16(1):1–11. doi: 10.1186/s12889-016-2853-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0277015.ref042] 42.Wei D, Brigell R, Khadka A, Perales N, Fink G. Comprehensive school-based health programs to improve child and adolescent health: Evidence from Zambia. PloS one. 2019;14(5):e0217893. doi: 10.1371/journal.pone.0217893 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0277015.ref043] 43.Roothaert R, Mpogole H, Hunter D, Ochieng J, Kejo D. Policies, Multi-Stakeholder Approaches and Home-Grown School Feeding Programs for Improving Quality, Equity and Sustainability of School Meals in Northern Tanzania. Frontiers in Sustainable Food Systems. 2021;5:43. [Google Scholar]

PERMALINK

Contextual factors and spatial trends of childhood malnutrition in Zambia

Million Phiri

David Mulemena

Chester Kalinda

Julius Nyerere Odhiambo

Roles

Abstract

Background

Methods

Results

Conclusion

Introduction

Methods

Study design

Outcome measures

Statistical analysis

Ethical considerations

Results

Bivariate analysis of childhood malnutrition prevalence in 2013 and 2018 by background characteristics

Table 1. Bivariate analysis of childhood malnutrition indicators with background characteristics (2013/14 DHS data).

Fig 1.

Analysis of malnutrition prevalence by province

Fig 2. Spatial variation of stunting prevalence, wasting prevalence, and underweight prevalence by provinces in Zambia.

Multivariable analysis results and the association with socioeconomic determinants

Stunting

Table 2. Bivariate analysis of childhood malnutrition indicators with background characteristics (2018 DHS data).

Wasting

Table 3. Multivariate analysis of malnutrition indicators (stunting and wasting) for 2013/14 and 2018 DHS waves.

Underweight

Table 4. Multivariate analysis of malnutrition indicators (Underweight and Overweight) for 2013/14 and 2018 DHS waves.

Overweight

Discussion

Study strengths and study limitations

Conclusions

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Elisa Panada

Roles

Author response to Decision Letter 0

Decision Letter 1

Inés González-Casanova

Roles

Author response to Decision Letter 1

Decision Letter 2

Inés González-Casanova

Roles

Author response to Decision Letter 2

Decision Letter 3

Inés González-Casanova

Roles

Acceptance letter

Inés González-Casanova

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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