A Double Burden of Malnutrition Among Women in the Reproductive Age (15-49 years) in Sierra Leone: A Secondary Data Analysis of the Demographic Health Survey of 2019 (SLDH-2019)

doi:10.21203/rs.3.rs-2570369/v1

Background: A double burden of malnutrition is rising globally but particularly in sub-Saharan Africa. Recently in Sierra Leone, the incidence of overweight, obesity, and overnutrition among women have sharply increased. This study aimed to determine the prevalence of different malnutrition categories (underweight, overweight, obesity, and overnutrition) and associated factors among women of reproductive age in Sierra Leone using Demographic Health Survey of 2019.

Methods: We conducted data analysis of 7,514 women aged 15 to 49 years. Data was collected using validated questionnaires. Participants were selected using a multistage stratified sampling approach. A multivariable regression analysis was used to determine factors associated with different malnutrition categories.

Results: The prevalence of underweight was, 6.7%(95%CI:4.5%-8.9%); overweight, 19.7%(95%CI:17.7%-21.7%); obesity, 7.4% (95% CI:5.2%-9.6%); and overnutrition, 27.1%(95%CI:25.2%-29.0%). Underweight was more likely in 25-34 years (AOR=1.670, 95%CI:1.254-2.224;p<0.001) than 15-24 years; unlikely among single women (AOR=0.594,95%CI:0.467-0.755;p<0.001) than married. Unlikely in the north (AOR=0.734,95%CI:0.559-0.963;p=0.026) compared to east, and unlikely among those who did not listen to radios (AOR=0.673;95%CI:0.549-0.826;p<0.001) compared to those who did.

Overweight was unlikely among 25-34 years (AOR=0.609,95%CI:0.514-0.722;p<0.001) and 35-49 years (AOR=0.480,95%CI:0.403-0.571;p<0.001) than 15-24 years; likely among single women (AOR=1.470,95%CI:1.249-1.730;p<0.001) compared to married; unlikely among working-class (AOR=0.840,95%CI:0.720-0.980;p=0.026) compared to not working-class; most likely in the north (AOR=1.325,95%CI:1.096-1.602;p=0.004), and unlikely in the south (AOR=0.755,95%CI:0.631-0.903;p=0.002) compared to east; unlikely among middle-wealth-index (AOR=0.656,95%CI:0.535-0.804;p<0.001), richer-wealth-index (AOR=0.400,95%CI:0.309-0.517;p<0.001), and richest-wealth-index (AOR=0.317,95%CI:0.234-0.431;p<0.001) compared to poorest-wealth-index respectively, and likely among women who did not listen to radios (AOR=1.149; 95%CI:1.002-1.317;p=0.047) compared to those who did. Determinants of obesity and overnutrition were the same with overweight, except they were unlikely in female-headed-households (AOR=0.717,95%CI:0.578-0.889;p<0.001).

Conclusion: The prevalence of different types of malnutrition among women 15-49 years in Sierra Leone were high; affirming a double burden of malnutrition. It was likely to be underweight among 25-34 years. The predictors of overweight, obesity, and overnutrition were single women, from the north and not listening to radios. There is an urgent need for policies on healthy lifestyles and the dangers of malnutrition.

Malnutrition

double burden

women in reproductive age

Sierra Leone

DHS of 2019

It was estimated in 2015 that approximately nine percent of the world's adult population were underweight, and 30-40% were overweight or obese [1,2,3]. Globally, it is reported that women have a slightly higher prevalence of overweight and obesity (OWOB) compared to males [1,2]. Even though studies show that there is a marginal decline in the prevalence of underweight, the rise in the proportion of overweight or obese people is considered by many as a global epidemic [1,2,3].

Of great significance is that being overweight or underweight affects the physiological and anatomical functioning of our organs, thus increasing risks of mortality [4,5]. In addition, overweight and obesity are linked to many harmful health outcomes, for example, cardiovascular diseases, type 2 diabetes mellitus, musculoskeletal and mental health disorders, and complications of pregnancy [4,6,7,8]. On the other hand, undernutrition increases risks of infections in women [9], pre-term births, and low birth weights of offspring [8,10].

Globally, malnutrition affects all countries [11] and many face a double burden of malnutrition, where undernutrition and OWOB exist in the same population, communities, households, and individuals [12]. In what is considered a contrasting pattern, OWOB is usually more prevalent among populations with low socioeconomic status in high-income countries, and the reverse in low-income countries [3,13,14].

A recent analysis of 126 low and middle-income countries (LMIC) showed that increase in global DBM was propelled by countries with low gross domestic products (GDP) as they tend to have a greater increase in OWOB and a slight decrease in the prevalence of undernutrition [3,13,14].

Most undernourished people in Africa are in sub-Saharan region where hunger has been rising since 2014 [15]. As of 2019, a significant rise in undernutrition has been in Africa's eastern and middle subregions, reaching 27% and 29% of the total population, respectively [16]. Undernutrition and micronutrient deficiencies are however, not the only nutritional concerns globally [17]. The state of malnutrition in sub-Saharan Africa characterizes DBM, with a high prevalence of undernutrition and increasing overweight and obesity [17].

It is increasingly becoming more unlikely that sub-Saharan Africa will achieve Sustainable Development Goals (SDGs) of ending hunger and all forms of malnutrition by 2030 if these rates are maintained [18]. For now, overweight and obesity are increasing in all age groups, with girls and women being affected more than boys and men [17].

Interestingly, drivers of DBM are known to originate from factors outside the health sector and operate across national and regional boundaries [17].

The increasing incidence and prevalence of DBM in many African countries encouraged this research team to investigate and determine the prevalence and factors associated with different category of malnutrition among women in the reproductive age of 15-49 years in Sierra Leone using secondary data analysis of the demographic health survey of 2019 (SLDHS-2019).

Study design: We conducted a secondary data analysis of the 2019 Sierra Leone Demographic Health Survey (2019 SLDHS) dataset.

Data collection: This data was collected from May 14, 2019, to August 31, 2019 [19]. It was a nationally representative survey carried out by the Bureau of Statistics Sierra Leone as part of the international MEASURE Demographic Health Surveys (DHS) with the support of Inner-City Fund (ICF) International and the United States Agency for International Development (USAID) [19]. SLDHS is a periodical survey conducted every five years in Sierra Leone, and this was the third survey, with the second being conducted in 2014 and the first in 2010 [19]. Five validated questionnaires based on the DHS Program's standard Demographic and Health Survey (DHS-7) T4 adapted to reflect the population and health issues relevant to Sierra Leone were used for the 2019 SLDHS [19]. These questionnaires include the Household Questionnaire, the Woman's Questionnaire, Man's Questionnaire, the Biomarker Questionnaire, and the Fieldworker Questionnaire [19]. The Sierra Leone Ethics and Scientific Review Committee (SLESRC) and the ICF Institutional Review Board reviewed and approved the survey protocol [19]. All questionnaires were in English, and the 2019 SLDHS used computer-assisted personal interviewing (CAPI) for data collection [19]. Household questionnaires collected data on household environment, assets, and basic demographic information of household members, while women's questionnaires collected data about women's background characteristics, reproductive health, domestic violence, and nutrition [19]. Regarding anthropometry, weight was recorded in kilograms (kg) to the nearest one decimal point and was measured using an electronic scale (SECA 878) [19]. The height of participants was measured using a stadiometer in centimeters (cm) to one decimal point [19].

Study setting: As of July 2019, Sierra Leone had a population of 8,200,00 million people, with 23.8% of the population residing in urban areas with a total land area of 78,000 km² [20]. Sierra Leone's health system has six levels ranging from the highest level at national referral hospitals to the lowest level at community level [21]. Agriculture contributes about 24% of gross domestic product (GDP), providing half of the export earnings, and a primary source of income for 84% of Sierra Leones living in rural areas [22].

Study sampling and participants: The 2015 population and housing census of the Republic of Sierra Leone conducted by Statistics Sierra Leone (Stats SL) provided the ready-made sampling frame for the 2019 SLDHS [23]. Sierra Leone is administratively divided into provinces, which are further subdivided into districts. Each district is subdivided into chiefdoms/census wards, and each chiefdom/census ward is subdivided into sections [20,21,22]. The 2015 Population and housing census subdivided each locality into convenient census Enumeration Areas (EAs) [22,23]. The 2015 census EAs were used as the primary sampling units (PSUs) and clusters for the 2019 SLDHS [19,20,21,22,23]. The list of EAs from the 2015 population and housing census formed the basis for estimating the number of households and distinguishing EAs (clusters) as urban or rural for the 2019 SLDHS sample frame [19,20,22].

The 2019 SLDHS employed a two-stage stratified sample design [19]. Stratification was achieved by separating each district into urban and rural areas [19]. In total, 31 sampling strata were created and samples were selected independently in every stratum via a two-stage selection process [19]. Implicit stratifications were achieved at each lower administrative level by sorting the sampling frame before sample selection according to administrative order and using a probability proportional-to-size selection during the first sampling stage [19].

In the first stage, 578 EAs were selected with a probability proportional to EA size [19]. Enumeration Area (EA) size was the number of households residing in the enumeration area [19]. A household listing operation was carried out in all selected enumeration areas [19]. The resulting lists of households served as a sampling frame for selecting households in the second stage [19].

In the second stage's selection, a fixed number of 24 households was selected in every cluster through equal probability systematic sampling, resulting in a total sample size of approximately 13,872 selected households, distributed in a total of 578 clusters [19]. The household listing was done using tablets, and households were selected randomly through computer programming [19]. The survey interviewers questioned only the pre-selected households in the clusters [19]. No replacements or changes of the pre-selected households were allowed in the implementing stages to prevent selection bias in the study [19].

Due to the non-proportional allocation of the sample to the different districts and the possible differences in response rates, sampling weights were calculated, added to the data file, and applied so that the results would be represented at the national level and the domain level [19]. Because the 2019 SLDHS sample was a two-stage stratified cluster sample selected from the sampling frame, sampling weights were calculated separately for each sampling stage and cluster based on sampling probabilities [19]. The 2019 SLDHS included all women aged 15-49 in the sample households [19]. Permanent residents of the selected households and visitors who stayed in the households over the night prior to the survey were eligible for interviews in the selected households [19]. The men's survey was conducted in one-half of the sample households, and all men aged 15-59 in these households were included [19]. One eligible woman in each household was randomly selected to question incidences of domestic violence in this population subsample [19].

Similarly, biomarker information was collected only in those households selected for the men's survey [19]. The biomarkers included in this survey were weight for women aged 15-49, hemoglobin testing for women aged 15-49, height, and testing for HIV for women 15-49 years [19].

Operational definitions.

Body Mass Index (BMI): Weight in kilograms divided by height in meters squared.

Underweight: BMI <18.5 kg/m²

Overweight: BMI > = 25.00 kg/m² and < = 29.99 kg/m²

Obese: BMI > = 30.00 kg/m² and < = 50.00 kg/m²

Overnutrition (Overweight and obese): BMI > = 25.00 kg/m² and < = 50.00 kg/m².

Enumeration Area (Clusters): An EA is a geographic area consisting of a convenient number of dwelling units that serve as a counting unit for the census.

Statistical analysis: The SPSS analytic software version 24.0 Complex samples package was used for this analysis. The use of complex samples package accounted for the complex survey sampling, while the use of sample weighted data accounted for the unequal probability sampling in different strata. Descriptive statistics and multivariable logistic regressions were used for data analysis. Frequency tables and proportions/percentages to describe categorical variables were conducted, while means and standard deviations were for continuous variables. Initially, each exposure was assessed separately for its association with the outcome variables (underweight, overweight, Obesity, and overnutrition) using bivariate logistic regression, and we presented crude odds ratio (COR), 95% confidence interval (CI), and p-values. Independent variables found significant at a p-value less than 0.2 were added to the multivariable model [24-26]. The multivariable logistic regression models included the non-significant variables associated with being underweight, overweight, Obesity, and overnutrition, as observed in previous studies. In the multivariable logistic regression analysis, we constructed two models based on categorizing the independent variables into women's individual, household, and community factors. We first performed a logistic regression model which included only individual characteristics (age, education level, working status, and marital status). After that, we constructed a final model which included individual characteristics adjusted for household and community characteristics (wealth index, residence, region, household size, and sex of household head). Adjusted odds ratios (AOR) at 95% Confidence Intervals (CI) and p-values were calculated, with a statistical significance level set at p-value < 0.05. Sensitivity analysis was conducted with underweight women and those with normal BMI after excluding those with BMI above 25.0.

The socioeconomic characteristics of women in the reproductive age of 15-49 years (N=7,514) were described from the Sierra Leone Demographic Health Survey of 2019 (SLDHS-2019) (Table 1).

The prevalence of Malnutrition: The total female population aged 15-49 years who participated in the 2019 demographic health survey (2019 SLDHS) was15,574. The female population aged 15-49 years with BMI results were 48% (7,514/15,574). Among women 15-49 years with a BMI measurement, 66.2%(95% CI, 64.9%-67.5%; 4,974/7,514) had normal BMI. The prevalence of different categories of Malnutrition among the women was; underweight, 6.7%(95%CI, 4.5%-8.9%; 502/7,514); overweight, 19.7% (95%CI, 17.7%-21.7%; 1,479/7,514); obesity, 7.44% (95%CI, 5.2%-9.6%; 559/7,514); and overnutrition 27.12%, (95%CI, 25.2%-29.0%; 2,038/7,514).

Sociodemographic characteristics of women in Sierra Leone: Most Sierra Leone women with BMI results were in 15-24 year age group, 2,916/7,514(38.8%), parity of up to four 3,892/7,514(51.8%), of rural residence 4,422/7,514(58.9%), male headed households 5,356/7,514(71.3%), household size of six and above 4,519/7,514(60.1%), working class 5,234/7,514(69.7%), married 4,795/7,514(63.8%), from the South 1,831/7,514(24.4%), had no formal education 3,571/7,514(47.5%)_’, richer wealth index 1,634/7,514(21.7%), did not watch TV 5,625/7,514(74.9%), did not listen to radios 4,372/7,514(58.2%), did not read magazines 7,025/7,514(93.5%), did not smoke cigarettes 7,290/7,514(97.0%), and did not use alcohol 3,081/7,514(41.0%) (Table 1).

Weight classification of women in Sierra Leone. The weight classifications were as follows: underweight was commonest among the age group of 15-24 years, 289/7,514(3.85%); normal weight among 15-24 years, 2,137/7,514(28.44%); overweight among 35-49 years, 565/7,514 (7.52%); obesity among 35-49 years, 302/5,514 (4.02%), and overnutrition among the 35–49-year age group, 867/7,514(11.54%) (Table 2).

BMI classification of women in Sierra Leone: Most women who were moderately or severely thin (BMI <17kg/m²) were in 20-29 year age-group, 69/7,514 (0.918%); mildly thin (BMI=17.0-18.4kg/m²) in the 40-49 year age-group, 72/7,514(0.958%); normal weight (BMI=18.5-24.9kg/m²) in the 20-29 year age-group, 1173/7,514(15.61%); overweight (BMI=25.0-29.9kg/m²) in the 30–39-year age-group, 334/7,514(4.445%), in obese (BMI>30kg/m²) 188/7,514(2.502%) (Table 3).

In Table 4, the independent predictors of underweight among Sierra Leone women aged 15-49 years were likely among age-group of 25-34 years (AOR 1.670; 95%CI 1.254-2.224;p<0.001), unlikely among not married/single (AOR 0.594; 95%CI 0.467-0.755;p<0.001), unlikely among women from the north (AOR 0.734; 95%CI 0.559-0.963; p=0.026), and unlikely among women who did not listen to radios (AOR 0.673; 95%CI 0.549-0.826;p<0.001).

Independent predictors of overweight: Predictors of overweight among Sierra Leone women in the reproductive age were unlikely among the age-group of 25-34 years (AOR 0.609; 95%CI 0.514-0.722;p<0.001), unlikely among the 35-49 year age-group (AOR 0.480; 95%CI 0.403-0.571;p<0.001), likely among the not married/single women (AOR 1.470; 95%CI 1.249-1.730;p<0.001), unlikely among working-class women (AOR 0.840; 95%CI 0.720-0.980;p<0.026), likely among women from the north (AOR 1.325; 95%CI 1.096-1.602;p=0.004), unlikely among women from the South (AOR 0.755; 95%CI 0.631-0.903;p=0.002), unlikely among women of middle wealth index (AOR 0.656; 95%CI 0.535-0.804;p<0.001), unlikely among richer wealth index (AOR 0.400; 95%CI 0.309-0.517;p<0.001), unlikely among the richest wealth index (AOR 0.317; 95%CI 0.234-0.431;p<0.001), and likely in women who did not listen to radios (AOR 1.149; 95%CI 1.002-1.317;p<0.047).

Independent predictors of obesity: The predictors of obesity among Sierra Leone women in the reproductive age were unlikely among the 25-34 year age-group (AOR 0.609; 95%CI 0.514-0.722;p<0.001), unlikely among 35-49 year age-group (AOR 0.480; 95%CI 0.403-0.571;p<0.001), unlikely among female-headed household (AOR 0.717; 95%CI 0.578-0.889;p<0.001), unlikely among working-class women (AOR 0.840; 95%CI 0.720-0.980;p<0.026), likely among women from the north (AOR 1.447; 95%CI 1.054-1.985;p=0.022), unlikely among women from the South (AOR 0.740; 95%CI 0.552-0.991;p=0.043), unlikely among the middle wealth index (AOR 0.418; 95%CI 0.283-0.618;p<0.001), unlikely among richer wealth index (AOR 0.156; 95%CI 0.101-0.242;p<0.001), unlikely among the richest wealth index (AOR 0.095; 95%CI 0.058-0.155;p<0.001), and likely among women who did not listen to radios (AOR 1.370; 95%CI 1.105-1.699;p<0.004).

Independent predictors of overnutrition: Predictors of overnutrition among women in Sierra Leone were unlikely among the 25-34 year age-group (AOR 0.512; 95%CI 0.438-0.599;p<0.001), unlikely among the 35-49 year age-group (AOR 0.350; 95%CI 0.298-0.41;p<0.001), unlikely among female-headed households (AOR 0.836; 95%CI 0.736-0.950;p<0.006), unlikely among working-class women (AOR 0.840; 95%CI 0.720-0.980;p<0.026), likely among the not married/single woman (AOR 1.432; 95%CI 1.235-1.660;p<0.001), likely among women from the north (AOR 1.359; 95%CI 1.143-1.660;p=0.001), unlikely among women from the South (AOR 0.750; 95%CI 0.637-0.884;p=0.001), unlikely among middle wealth index (AOR 0.603; 95%CI 0.499-0.728;p<0.001), unlikely among the richer wealth index (AOR 0.333; 95%CI 0.264-0.42;p<0.001), unlikely among the richest wealth index (AOR 0.248; 95%CI 0.188-0.326;p<0.001), and likely among women who did not listen to radios (AOR 1.201; 95%CI 1.061-1.359;p<0.004).

The frequency of underweight, overweight, and obesity by age-groups among women in Sierra Leone.

Underweight was commonest among women aged 15-24 years, overweight in the 35–49-year-old, obesity in the 35–49-year age group, and over nourished in the 35–49-year age group (Figure 1).

Table 1: The sociodemographic characteristics of women of reproductive age in Sierra Leone (15-49 years).

Variable	Frequency (N=7,514)	Percent (%)
Ages (years)
15-24	2,916	38.8
25-34	2,176	29.0
35-49	2,422	32.2
Parity
Never gave birth	1,895	25.2
Up to four	3,892	51.8
Five and above	1,727	23
Residence
Urban	3,092	41.1
Rural	4,422	58.9
Sex of household head
Male	5,356	71.3
Female	2,158	28.7
Household size
Less than six	2,995	39.9
Six and above	4,519	60.1
Work status
Not working	2,280	30.3
Working	5,234	69.7
Marital status
Married	4,795	63.8
Not married	2,719	36.2
Regions of Sierra Leone
East	1,579	21.0
North	1,822	24.2
Northwest	1,026	13.7
South	1,831	24.4
Western	1,256	16.7
Level of education attained
No formal educated	3,571	47.5
Primary	1,017	13.5
Secondary	2,641	35.2
Higher	285	3.8
Wealth Index
Poorest	1,533	20.4
Poorer	1,428	19.0
Middle	1,531	20.4
Richer	1,634	21.7
Richest	1,388	18.5
Watching TV
Yes	1,889	25.1
No	5,625	74.9
Listening to radios
Yes	3,142	41.8
No	4,372	58.2
Reading magazine
Yes	489	6.5
No	7.025	93.5
Smoking
Yes	224	3.0
No	7,290	97.0
Alcohol use
Yes	667	8.9
No	3,081	41.0

The data source is SLDHS-2019.

Table 1 shows that most Sierra Leone women of reproductive age were in the 15-24 year age-group 2,916/7,514(38.8%), parity of up to four 3,892/7,514(51.8%), of rural residence 4,422/7,514(58.9%), male headed households 5,356/7,514(71.3%), household size of six and above 4,519/7,514(60.1%), working class 5,234/7,514(69.7%), married 4,795/7,514(63.8%), from the South 1,831/7,514(24.4%), had no formal education 3,571/7,514(47.5%); richer wealth index 1,634/7,514(21.7%); did not watch TV 5,625/7,514(74.9%); did not listen to radios 4,372/7,514(58.2%), did not read magazines 7,025/7,514(93.5%), did not smoke cigarettes 7,290/7,514(97.0%), and did not use alcohol 3,081/7,514(41.0%).

Table 2: Descriptive statistics for the weight categories of participants in the 2019 SLDHS.

	Underweight		Normal weight		Over nourished
Variables	Freq (N=7,514)	(Percent) %	Freq (N=7,514)	(Percent) %	Freq (N=7,514)	(Percent) %
Ages (years)
15-24	289	3.85	2,137	28.44	490	6.52
25-34	84	1.12	1,411	18.78	681	9.06
35-49	129	1.72	1,426	18.98	867	11.54
Parity
Never gave birth	225	2.99	1,330	17.70	340	4.52
Up to four	182	2.42	2,537	33.76	1,173	15.61
Five and above	95	1.26	1,107	14.73	525	6.99
Residence		0.00		0.00		0.00
Urban	162	2.16	1,818	24.19	1112	14.80
Rural	340	4.52	3,156	42.00	926	12.32
Sex of the household head
Male	343	4.56	3,621	48.19	1392	18.53
Female	159	2.12	1,353	18.01	646	8.60
Household size		0.00		0.00		0.00
Less than six	181	2.41	1,976	26.30	838	11.15
Six and above	321	4.27	2,998	39.90	1,200	15.97
Work status
Not working	191	2.54	1,529	20.35	560	7.45
Working	311	4.14	3,445	45.85	1,478	19.67
Marital status		0.00		0.00		0.00
Married	232	3.09	3,102	41.28	1,461	19.44
Not Married	270	3.59	1,872	24.91	270	3.59
Region
East	96	1.28	1,082	14.40	402	5.35
North	153	2.04	1,305	17.37	364	4.84
Northwest	73	0.97	724	9.64	229	3.05
South	134	1.78	1,173	15.61	524	6.97
Western	10	0.13	690	9.18	520	6.92
Level of education
No formal education	211	2.81	2,399	31.93	961	12.79
Primary	96	1.28	686	9.13	235	3.13
Secondary	185	2.46	1,755	23.36	701	9.33
Higher	10	0.13	134	1.78	141	1.88
Wealth index
Poorest	104	1.38	1,156	15.38	273	3.63
Poorer	120	1.60	1,053	14.01	255	3.39
Middle	121	1.61	1,050	13.97	360	4.79
Richer	97	1.29	974	12.96	563	7.49
Richest	60	0.80	741	9.86	587	7.81
Watching TV
Yes	98	1.30	1,123	14.95	688	9.16
No	404	5.38	3,851	51.25	1370	18.23
Listening to radios
Yes	152	2.02	1,967	26.18	1023	13.61
No	350	4.66	3,007	40.02	1015	13.51
Reading of magazines
Yes	29	0.39	276	3.67	184	2.45
No	473	6.29	4,698	62.52	1854	24.67
Smoking cigarettes
Yes	18	0.24	139	1.85	67	0.89
No	484	6.44	4,835	64.35	1971	26.23
Alcohol use
Yes	35	0.47	429	5.71	203	2.70
No	140	1.86	2,005	26.68	936	12.46

The data source is SLDHS-2019.

Table 2 shows the weight classifications of women of reproductive age as per SLDHS of 2019. Underweight was commonest among age-group of 15-24 years 289/7,514(3.85%); normal weight among 15-24 years 2,137/7,514(28.44%) and over nourished among 35–49-year age-group 867/7,514(11.54%).

Table 3: The BMI classification among women of reproductive age in Sierra Leone (SLDHS-2019).

Variables	Moderately and severely thin (BMI <17) (N=7,514)	Mildly thin (BMI 17.0-18.4) (N=7,514)	Normal weight (BMI=18.5-24.9) (N=7,514)	Overweight (BMI=25.0-29.9) (N=7,514)	Obese (BMI>30) (N=7,514)
Ages (years)
15-19	4(0.053)	18(0.240)	207(2.75)	26(0.346)	8(0.106)
20-29	69(0.918)	0(0.00)	1,173(15.61)	334(4.445)	92(1.224)
30-39	20(0.266)	69(0.918)	1,099(14.63)	411(5.47)	188(2.502)
40-49	18(0.240)	72(0.958)	738(9.82)	290(3.86)	169(2.249)
Parity
Never gave birth	41(0.546)	169(2.249)	1,330 (17.70)	271 (3.607)	69 (0.918)
Up to four	34(0.452)	134(1.783)	2537 (33.76)	844 (11.232)	329 (4.378)
Five and above	18(0.240)	71(0.945)	1,107 (14.73)	364 (4.844)	161 (2.143)
Residence
Urban	10(0.133)	51(0.679)	960(12.78)	519(6.907)	282(3.753)
Rural	40(0.532)	160(2.129)	2,257(30.04)	542(7.213)	175(2.329)
The sex of the household head
Male	35(0.466)	149(1.983)	2,422(32.23)	734(9.768)	288(3.833)
Female	15(0.200)	62(0.825)	581(7.73)	327(4.352)	169(2.249)
Household size
Less than six	25(0.333)	80(1.065)	1,368(18.21)	467(6.215)	179(2.382)
Six and above	25(0.333)	131(1.743)	1,849(24.61)	594(7.905)	278(3.700)
Work status
Not working	7(0.093)	39(0.519)	583(7.76)	206(2.742)	103(1.371)
Working	43(0.572)	172(2.289)	2,634(35.05)	855(11.379)	354(4.711)
Marital status
Married	41(0.546)	169(2.249)	2,566(34.15)	858(11.419)	356(4.738)
Not married	9(0.120)	42(0.559)	651(8.66)	203(2.702)	101(1.344)
Regions of Sierra Leone
East	10(0.133)	50(0.665)	702(9.34)	220(2.928)	88(1.171)
North	12(0.160)	48(0.639)	873(11.62)	203(2.702)	67(0.892)
Northwest	6(0.080)	37(0.492)	480(6.39)	122(1.624)	49(0.652)
South	16(0.213)	63(0.838)	782(10.41)	290(3.859)	115(1.530)
Western	6(0.080)	13(0.173)	380(5.06)	226(3.008)	138(1.837)
Level of education
No formal education	33(0.439)	129(1.717)	1,983(26.39)	538(7.160)	235(3.127)
Primary	7(0.093)	25(0.333)	412(5.48)	136(1.810)	47(0.625)
Secondary	9(0.120)	52(0.692)	740(9.85)	288(3.833)	125(1.664)
Higher	2(0.027)	5(0.067)	82(1.09)	54(0.719)	50(0.665)
Wealth index
Poorest	8(0.106)	59(0.785)	839(11.17)	180(2.396)	39(0.519)
Poorer	16(0.123)	64(0.852)	767(10.21)	43(0.572)	43(0.519)
Middle	17(0.226)	38(0.506)	720(9.58)	68(0.905)	68(0.905)
Richer	30(0.399)	30(0.399)	528(7.03)	141(1.876)	141(1.876)
Richest	1(0.013)	20(0.266)	363(4.83)	166(2.209)	166(2.209)
Watching TV
Yes	5(0.067)	31(0.413)	603(8.03)	304(4.046)	171(2.276)
No	45(0.599)	180(2.396)	2,614(34.79)	757(10.075)	2863.806)
Listening to radio
Yes	12(0.160)	70(0.932)	1,210(16.10)	501(6.668)	256(3.407)
No	38(0.506)	141(1.876)	2,007(26.71)	560(7.453)	201(2.675)
Reading Magazine
Yes	3(0.040)	7(0.093)	132(1.76)	67(0.892)	46(0.612)
No	47(0.625)	204(2.715)	3,085(41.06)	994(13.229)	411(5.470)
Smoking
Yes	2(0.027)	14(0.186)	121(1.61)	39(0.519)	16(0.213)
No	48(0.639)	197(2.622)	3,096(41.20)	1.022(13.601)	441(5.869)
Alcohol use
Yes	3(0.040)	30(0.399)	370(4.92)	124(1.650)	44(0.586)
No	30(0.399)	99(1.318)	1,639(21.81)	556(7.400)	229(3.048)

The data source is SLDHS-2019.

Table 3 shows the majority who were moderately and severely thin (BMI <17kg/m²) were in the age-group of 20-29 years 69/7,514(0.918%); mildly thin (BMI=17.0-18.4kg/m²) in 40-49 years 72/7,514(0.958%); normal weight (BMI=18.5-24.9kg/m²) in 20-29 years 1173/7,514(15.61%); overweight (BMI=25.0-29.9kg/m²) in 30–39-year age-group 334/7,514(4.445%), in obese (BMI>30kg/m²) 188/7,514(2.502%).

Table 4: Prevalence and predictors of underweight (BMI<18.5kg/m²) among participants.

Variables	Underweight (N=502) (n, %)	Normal weight N= (4,974) n (%)	Unadjusted COR	95% CI	p value	Adjusted POR	95% CI	p value
Ages (years)
15-24	289(11.9)	2,137(88.1)	Reference			Reference
25-34	84(5.6)	1,411(94.4)	2.272	(1.765-2.923)	<.001	1.670	(1.254-2.224)	<.001
35-49	129(8.3)	1,426(91.7)	1.495	(1.202-1.859)	<.001	1.136	(0.870-1.483)	.350
Parity
Up to four	407(9.5)	3,867(90.5)	Reference
Five and above	95(7.9)	1,107(92.1)	1.226	(0.971-1.548)	.086
Residence
Urban	162(8.2)	1,818(91.8)	Reference
Rural	340(9.7)	3,156(90.3)	0.827	(0.680-1.006)	.057
Sex household head
Male	343(8.7)	3,621(91.3)	Reference			Reference
Female	159(10.5)	1,353(89.5)	0.806	(0.661-0.983)	.033	0.925	(0.750-1.141)	.469
Household size
Less than six	181(8.4)	1,976(91.6)	Reference
Six and above	321(9.7)	2,998(90.3)	0.855	(0.707-1.035)	.109
Work status
Not working	191(11.1)	1,529(88.90	Reference
Working	311(8.3)	3,445(91.7)	1.384	(1.144-1.673)	.001	1.082	(0.868-1.349)	.481
Marital status
Married	232(7.0)	3,102(93.0)	Reference
Not married	270(12.6)	1,872(87.4)	0.519	(0.431-0.624)	<.001	0.594	(0.467-0.755)	<.001
Regions of Sierra Leone
East	96(8.1)	1,082(91.9)	Reference			Reference
North	153(10.5)	1,305(89.5)	0.757	(0.579-0.989)	.041	0.734	(0.559-0.963)	.026
Northwest	73(9.2)	724(90.8)	0.88	(0.640-1.210)	.431	0.840	(0.609-1.160)	.290
South	134(10.3)	1,173(89.7)	0.777	(0.590-1.022)	.071	0.776	(0.588-1.204)	.073
Western	46(6.2)	690(93.8)	1.331	(0.925-1.916)	.777	1.385	(0.954-2.011)	.087
Level of education
No formal education	211(8.1)	2,399(91.9)	Reference
Primary	96(12.3)	686(87.7)	0.628	(0.487-0.812)	<.001
Secondary	185(9.5)	1,755(90.5)	0.834	(0.679-1.026)	.086
Higher	10(6.9)	134(93.1)	1.179	(0.611-2.275)	.624
Wealth Index
Poorest	104(8.3)	1,156(91.7)	Reference
Poorer	120(10.2)	1,053(89.8)	.789	(0.599-1.040)	.093
Middle	121(10.3)	1,050(89.7)	.781	(0.593-1.028)	.078
Richer	97(9.1)	974(90.9)	.903	(0.676-1.207)	.491
Richest	60(7.5)	741(92.5)	1.111	(0.798-1.547)	.533
Watching TV
Yes	98(8.0)	1,123(92.0)	Reference
No	404(9.5)	3,851(90.5)	0.832	(0.661-1.047)	.117
Listens to radio
Yes	152(7.2)	1,967(92.8)	Reference
No	350(10.4)	3,007(89.6)	0.664	(0.544-0.810)	<.001	0.673	(0.549-0.826)	<.001
Reading of magazines
Yes	29(9.5)	276(90.5)	Reference
No	473(9.1)	4,698(90.9)	1.044	(0.704-1.548)	.832
Smokes cigarettes
Yes	18(11.5)	139(88.5)	Reference
No	484(9.1)	4,835(90.9)	1.294	(0.785-2.132)	.313
Alcohol use
Yes	35(7.5)	429(92.5)	Reference
No	140(6.7)	2,005(93.3)	1.168	(0.795-1.717)	.428

The data source is SLDHS-2019.

AOR: Adjusted Odds Ratio; CI: Confidence Interval; COR: Crude Odds Ratio; SLDHS: Sierra Leone Demographic Health Survey.

In Table 4, independent predictors of underweight among Sierra Leone women were likely among the age-group of 25-34 years (AOR 1.670; 95%CI 1.254-2.224;p<0.001), unlikely among married/single women (AOR 0.594; 95%CI 0.467-0.755;p<0.001), unlikely among women from the north (AOR 0.734; 95%CI 0.559-0.963;p=0.026), and unlikely among women who did not listen to radios (AOR 0.673; 95%CI 0.549-0.826;p<0.001).

Table 5: The prevalence and predictors of overweight (BMI=25-29.99kg/m²) among women in Sierra Leone.

Variables	Overweight (N=1,479) (n, %)	Normal weight N= (4,974) n (%)	Unadjusted COR	95% CI	p value	Adjusted OR	95% CI	p value
Ages (years)
15-24	411(16.1)	2,137(83.9)	Reference			Reference
25-34	503(26.3)	1,411(73.7)	0.540	(0.466-0.625)	<.001	0.609	(0.514-0.722)	<.001
35-49	565(28.4)	1,426(71.6)	0.485	(0.420-0.560)	<.001	0.480	(0.403-0.571)	<.001
Parity
Up to four	1,115(22.4)	3,867(77.6)	Reference
Five and above	364(24.7)	1,107(75.3)	0.765	(0.756-1.005)	.058
Residence
Urban	753(29.3)	1,818(70.7)	Reference			Reference
Rural	726(18.7)	3,156(81.3)	1.801	(1.601-2.024)	<.001	1.049	(0.851-1.292)	0.653
Sex of the household head
Male	1,041(22.2)	3,621(77.7)	Reference			Reference
Female	438(24.5)	1,353(75.5)	0.806	(0.661-0.983)	.033	0.893	(0.776-1.029)	.119
Household size
Less than six	615(23.7)	1,976(76.3)	Reference
Six and above	864(22.4)	2,998(77.6)	1.080	(0.960-1.215)	.201
Work status
Not working	400(20.7)	1,529(79.3)	Reference			Reference
Working	1,079(23.9)	3,445(76.1)	0.835	(0.734-0.951)	.006	0.840	(0.720-0.980)	.026
Marital status
Married	1,058(25.4)	3,102(74.6)	Reference			Reference
Not Married	421(18.4)	1,872(81.6)	1.517	(1.336-1.721)	<.001	1.470	(1.249-1.730)	<.001
Region
East	297(21.5)	1,082(78.5)	Reference			Reference
North	278(17.6)	1,305(82.4)	1.289	(1.074-1.546)	.006	1.325	(1.096-1.602)	.004
Northwest	169(18.9)	724(81.1)	1.176	(0.952-1.452)	.132	1.164	(0.936-1.449)	.171
South	394(25.1)	1,173(74.9)	0.817	(0.688-0.970)	.021	0.755	(0.631-0.903)	.002
Western	341(33.1)	690(66.9)	0.555	(0.463-0.667)	<.001	0.922	(0.741-1.147)	.465
Level of education
No education	699(8.1)	2,399(77.4)	Reference
Primary	180(12.3)	686(79.2)	1.110	(0.923-1.335)	.266
Secondary	521(9.5)	1,755(77.1)	0.981	(0.863-1.117)	.777
Higher	79(6.9)	134(62.9)	0.494	(0.370-0.661)	<.001
Wealth Index
Poorest	229(16.5)	1,156(83.5)	Reference			Reference
Poorer	204(16.2)	1,053(83.8)	1.023	(0.832-1.257)	0.832	0.943	(0.763-1.165)	.587
Middle	279(21.0)	1,050(79.0)	0.746	(0.614-0.905)	.003	0.656	(0.535-0.804)	<.001
Richer	390(28.6)	974(71.4)	0.495	(0.412-0.595)	<.001	0.400	(0.309-0.517)	<.001
Richest	377(33.7)	741(66.3)	0.389	(0.323-0.470)	<.001	0.317	(0.234-0.431)	<.001
Watching TV
Yes	454(28.8)	1,123(71.2)	Reference			Reference
No	1,025(21.0)	3,851(79.0)	1.519	(1.335-1.728)	<.001	0.979	(0.821-1.168)	.817
Listens to radios
Yes	708(26.5)	1,967(73.5)	Reference			Reference
No	771(20.4)	3,007(79.6)	1.404	(1.249-1.578)	<.001	1.149	(1.002-1.317)	.047
Reading of magazines
Yes	120(30.3)	276(69.7)	Reference			Reference
No	1,359(22.4)	4,698(77.6)	1.503	(1.203-1.878)	<.001	1.188	(0.926-1.525)	.176
Smokes cigarettes
Yes	46(24.9)	139(75.1)	Reference
No	1,433(22.9)	4,835(77.1)	1.117	(0.796-1.566)	.523
Alcohol use
Yes	149(25.8)	429(74.2)	Reference
No	686(25.5)	2,005(74.5)	1.015	(0.827-1.247)	.886

In Table 5, independent predictors of overweight among Sierra Leone women were unlikely among age-group of 25-34 years (AOR 0.609; 95%CI 0.514-0.722;p<0.001), unlikely among 35-49 years (AOR 0.480; 95%CI 0.403-0.57;p<0.001), likely among not married/single women (AOR 1.470; 95%CI 1.249-1.730;p<0.001), unlikely among working-class women (AOR 0.840; 95%CI 0.720-0.980;p<0.026), likely among women from the North (AOR 1.325; 95%CI 1.096-1.602;p=0.004), unlikely among women from the South (AOR 0.755; 95%CI 0.631-0.903; p=0.002), unlikely among the middle wealth index (AOR 0.656; 95%CI 0.535-0.804;p<0.001), unlikely among richer wealth index (AOR 0.400; 95%CI 0.309-0.517;p<0.001), unlikely among the richest wealth index (AOR 0.317; 95%CI 0.234-0.431;p<0.001), and likely among those who did not listen to radios (AOR 1.149; 95%CI 1.002-1.317;p<0.047).

Table 6: The prevalence and predictors of obesity (BMI =>30kg/m²) among women in Sierra Leone.

Variables	Obese (N=559) (n, %)	Normal weight N= (4,974) (%)	Unadjusted POR	95% CI	p value	Adjusted POR	95% CI	p value
Ages (years)
15-24	79(3.6)	2,137(96.4)	Reference			Reference
25-34	178(11.2)	1,411(88.8)	0.293	(0.223-0.385)	<.001	0.265	(0.196-0.359)	<.001
35-49	302(17.5)	1,426(82.5)	0.175	(0.135-0.226)	<.001	0.122	(0.090-0.164)	<.001
Parity
Up to four	398(9.3)	3,867(90.7)	Reference
Five and above	161(12.7)	1,107(87.3)	0.708	(0.582-0.860)	.001	1.243	(0.976-1.583)	.078
Residence
Urban	359(16.5)	1,818(83.5)	Reference			Reference
Rural	200(6.0)	3,156(94.0)	3.116	(2.597-3.739)	<.001	0.977	(0.706-1.353)	.889
Sex of the household head
Male	351(351)	3,621(91.2)	Reference			Reference
Female	208(208)	1,353(86.7)	0.631	(0.525-0.757)	<.001	0.717	(0.578-0.889)	.002
Household size
Less than six	223(10.1)	1,976(89.9)	Reference
Six and above	336(10.1)	2,998(89.9)	1.007	(0.842-1.204)	0.939
Work status
Not working	160(9.5)	1,529(90.5)	Reference
Working	399(10.4)	3,445(89.6)	0.904	(0.745-1.096)	.303
Marital status
Married	403(11.5)	3,102(88.5)	Reference			Reference
Not Married	156(7.7)	1,872(92.3)	1.559	(1.285-1.892)	<.001	1.293	(1.001-1.669)	<.049
Region
East	104(8.8)	1,082(91.2)	Reference			Reference
North	86(6.2)	1,305(93.8)	1.459	(1.084-1.963)	.013	1.447	(1.054-1.985)	.022
Northwest	60(7.7)	724(92.3)	1.160	(0.833-1.616)	.381	1.107	(0.777-1.577)	.573
South	130(10.0)	1,173(90.0)	0.867	(0.662-1.137)	.303	0.740	(0.552-0.991)	.043
Western	179(20.6)	690(79.4)	0.371	(0.286-0.480)	<.001	0.857	(0.628-1.169)	.331
Level of education
No formal education	262(9.8)	2,399(90.2)	Reference
Primary	55(7.4)	686(92.6)	0.628	(0.487-0.812)	<.001
Secondary	180(9.3)	1,755(99.7)	0.834	(0.679-1.026)	.086
Higher	62(31.6)	134(88.4)	1.179	(0.611-2.275)	.624
Wealth Index
Poorest	44(3.7)	1,156(96.3)	Reference			Reference
Poorer	51(4.6)	1,053(95.4)	0.786	(0.521-1.186)	.251	0.705	(0.463-1.074)	.104
Middle	81(7.2)	1,050(92.8)	0.493	(0.339-7.19)	<.001	0.418	(0.283-0.618)	<.001
Richer	173(15.1)	974(84.9)	0.214	(0.152-0.302)	<.001	0.156	(0.101-0.242)	<.001
Richest	210(22.1)	741(77.9)	0.134	(0.096-0.188)	<.001	0.095	(0.058-0.155)	<.001
Watching TV
Yes	214(16.0)	1,123(84.0)	Reference			Reference
No	345(8.2)	3,851(91.8)	2.127	(1.771-2.554)	<.001	0.880	(0.675-1.148)	.347
Listens to radio
Yes	315(13.8)	1,967(86.2)	Reference			Reference
No	244(7.5)	3,007(92.5)	1.974	(1.654-2.355)	<.001	1.370	(1.105-1.699)	.004
Reading of magazines
Yes	64(18.8)	276(81.2)	Reference			Reference
No	495(9.5)	4,698(90.5)	2.201	(1.651-2.933)	<.001	1.224	(0.869-1.723)	.248
Smokes cigarettes
Yes	21(13.1)	139(86.9)	Reference
No	538(10.0)	4,835(90.0)	1.358	(0.851-2.167)	.200
Alcohol use
Yes	54(11.2)	429(88.8)	Reference
No	250(11.1)	2,005(88.9)	1.010	(0.739-1.379)	.953

The data source is SLDHS-2019

In Table 6, independent predictors of obesity among Sierra Leone women were unlikely in the age-group of 25-34 years (AOR 0.609; 95%CI 0.514-0.722;p<0.001), unlikely among 35-49 years (AOR 0.480; 95%CI 0.403-0.57;p<0.001), unlikely among female headed households (AOR 0.717; 95%CI 0.578-0.889;p<0.001), unlikely among working-class women (AOR 0.840; 95%CI 0.720-0.980;p<0.026), likely among women from the north (AOR 1.447; 95%CI 1.054-1.985;p=0.022), unlikely among women from the South (AOR 0.740; 95%CI 0.552-0.991;p=0.043), unlikely among middle wealth index (AOR 0.418; 95%CI 0.283-0.618;p<0.001), unlikely among richer wealth index (AOR 0.156; 95%CI 0.101-0.242;p<0.001), unlikely among the richest wealth Index (AOR 0.095; 95%CI 0.058-0.155;p<0.001), and likely among women who did not listen to radios (AOR 1.370; 95%CI 1.105-1.699;p<0.004).

Table 7: Prevalence and predictors of overnutrition (BMI =>25kg/m²) among women in the SLDHS of 2019.

Variable	Overnutrition (N=2,038) (n, %)	Normal weight N= (4,974) n%	Unadjusted COR	95% CI	p value	Adjusted OR	95% CI	p value
Ages (years)
15-24	490(18.7)	2,137 (81.3)	Reference			Reference
25-34	681(32.6)	1,411 (67.4)	0.475	(0.415-0.543)	<.001	0.512	(0.438-0.599)	<.001
35-49	867(37.8)	1,426 (62.2)	0.377	(0.331-0.429)	<.001	0.350	(0.298-0.411)	<.001
Parity
Up to four	1,513(28.1)	3,867 (71.9)	Reference
Five and above	525(32.2)	1,107 (67.8)	0.825	(0.732-0.930)	.002	1.151	(0.989-1.339)	.070
Residence
Urban	1,112(38.0)	1,818 (62.0)	Reference			Reference
Rural	926(22.7)	3,156 (77.3)	2.085	(1.878-2.314)	<.001	1.050	(0.870-1.267)	.611
Sex of household head
Male	1,392(27.8)	3,621 (72.2)	Reference			Reference
Female	646(32.3)	1,353 (67.7)	0.805	(0.720-0.901)	<.001	0.836	(0.736-0.950)	.006
Household size
Less than six	838(8.4)	1,976 (70.2)	Reference
Six and above	1,200(28.6)	2,998 (71.4)	1.060	(0.954-1.177)	0.280
Work status
Not working	560(26.8)	1,529 (73.2)	Reference			Reference
Working	1,478(30.0)	3,445 (70.0)	0.854	(0.761-0.957	.007	0.886	(0.771-1.018)	.088
Marital status
Married	1,461(732)	3,102 (68.0)	Reference			Reference
Not married	270(12.6)	1,872 (76.4)	1.528	(1.366-1.709)	<.001	1.432	(1.235-1.660)	<.001
Region
East	401(27.0)	1,082 (73.0)	Reference			Reference
North	364(21.8)	1,305 (78.2)	1.329	(1.129-1.564)	.001	1.359	(1.143-1.616)	.001
Northwest	229(24.0)	724 (76.0)	1.172	(0.971-1.414)	.098	1.151	(0.944-1.403)	.164
South	524(30.9)	1,173 (69.1)	.830	(0.711-0.968)	.017	0.750	(0.637-0.884)	.001
Western	520(43.0)	690 (57.0)	.492	(0.418-0.578)	<.001	0.891	(0.733-1.083)	.247
Level of education
No formal education	961(28.6)	2,399 (71.4)	Reference
Primary	235(25.5)	686 (74.5)	1.169	(0.991-1.380)	.965
Secondary	701(28.5)	1,755 (71.5)	1.003	(0.894-1.125)	.961
Higher	141(651.3)	134 (48.7	0.381	(0.297-0.880)	<.001
Wealth Index
Poorest	273(19.1)	1,156 (80.9)	Reference			Reference
Poorer	255(19.5)	1,053 (80.5)	0.975	(0.806-1.179)	0.796	0.892	(0.733-1.086)	.255
Middle	360(25.5)	1,050 (74.5)	0.689	(0.576-0.823)	<.001	0.603	(0.499-0.728)	<.001
Richer	563(36.6)	974 (63.4)	0.409	(0.345-4.83)	<.001	0.333	(0.264-0.421)	<.001
Richest	587(44.2)	741 (55.8)	0.298	(0.251-0.354)	<.001	0.248	(0.188-0.326)	<.001
Watching TV
Yes	688(37.3)	1,123 (62.7)	Reference			Reference
No	1,370(26.2)	3,851 (73.8)	1.672	(1.492-1.874)	<.001	0.954	(813-1.118)	.560
Listens to radio
Yes	1,023(34.2)	1,967 (65.8)	Reference			Reference
No	1,015(25.2)	3,007 (74.8)	1.541	(1.389-1.709)	<.001	1.201	(1.061-1.359)	.004
Reading magazine
Yes	184(40.0)	276 (60.0)	Reference			Reference
No	1,854(28.3)	4,698 (71.7)	1.689	(1.391-2.051)	<.001	1.217	(0.974-1.521)	.085
Smokes cigarettes
Yes	67(32.5)	139 (67.5)	Reference
No	1,971(29.0)	4,835 (71.0)	1.182	(0.879-1.590)	.267
Alcohol use
Yes	203(32.1)	429 (67.9)	Reference
No	936(31.8)	2,005 (68.2)	1.014	(0.843-1.219)	.885

In Table 7, independent predictors of overnutrition were unlikely among ag-group of 25-34 years (AOR 0.512; 95%CI 0.438-0.599;p<0.001), unlikely among 35-49 years (AOR 0.350; 95%CI 0.298-0.411;p<0.001), unlikely among female-headed of households (AOR 0.836; 95%CI 0.736-0.950;p<0.006), unlikely among working-class women (AOR 0.840; 95%CI 0.720-0.980;p<0.026), likely among not married/single woman (AOR 1.432; 95%CI 1.235-1.660;p<0.001), likely among women from the North (AOR 1.359; 95%CI 1.143-1.660;p=0.001), unlikely among women from the South (AOR 0.750; 95%CI 0.637-0.884;p=0.001), unlikely among middle wealth index (AOR 0.603; 95%CI 0.499-0.728;p<0.001), unlikely among richer wealth index (AOR 0.333; 95%CI 0.264-0.421;p<0.001), unlikely among women in the richest wealth index (AOR 0.248; 95% CI 0.188-0.326; p<0.001), and likely among women who did not listen to radios (AOR 1.201; 95%CI 1.061-1.359;p<0.004).

In figure 1, most underweight women were in the age-group 15-24 years, overweight in 35-49 years, obese in 35–49-year age-group, and over nourished in 35–49-year age-group.

The most substantial finding from the study was that the prevalence of underweight among women of reproductive age in Sierra Leone was lower compared to many countries in sub-Saharan Africa [27-29], except Uganda [30]. Also, it was lower compared to many Asian countries for example, Indonesia and Bangladesh at 11.2% and 30.4%, respectively [31]. The proportion of underweight was highest among 15–24-year-olds, 289/502(57.5%) and lowest among women of 25-34 years, 84/502(16.7%) (Table 1, Table 2, Table 3, Table 4, and Figure 1), like a study where younger adolescent mothers (<20 years) were found to be more underweight than older mothers (20-49 years) attributing this to increased mother-to-child nutritional demands [32].

In contrast to previous findings, parity, residency, female-headed households, household size, work status, level of education, wealth indices, reading magazines, smoking cigarettes, and alcohol use were not associated with underweight in this study population [33,34,35]. This exclusive study finding on the covariates of underweight in Sierra Leone compared to other countries are points of great interest for further studies.

Findings of a double burden of malnutrition that is underweight (6.7%) and overnutrition (27.12%, BMI≥25kg/m²) in Sierra Leone, a low-income country (Table 7), are not unique but are worrisome as policymakers will have to design comprehensive public health programs that address the extremes of malnutrition. The co-existence of undernutrition and overnutrition has been in many low-income countries in Asian Pacific region [36] and sub-Saharan Africa [2,3,4,5,6,7]. The evidence of underweight, overweight, obesity and overnutrition are in this study in Sierra Leone (Table 4, Table 5, Table 6, Table 7). Studies suggest that a rapid dietary and lifestyle transition is the leading path of dual burden, with increase in overnutrition and diet related noncommunicable diseases (NCDs) [36,37]. We, the authors, suggest increased efforts on policy initiatives and lifestyle changes in Sierra Leone to combat the double burden of malnutrition which is prevalent in the country.

In addition, the independent predictors of overweight (Table 5), obesity (Table 6), and overnutrition (Table 7) among the study population included age groups of 25-34 years and 35-49 years, not married, working-class, women from the north and south of Sierra Leone, middle, richer and richest wealth index; and not listening to radios (Table 5). The independent predictors of obesity and overnutrition were like overweight, with one exception of female-headed households (AOR 0.717; 95%CI 0.578-0.889;p<0.001) being unlikely in obesity and overnutrition (Table 6, Table 7).

The current study finding that overweight, obesity, and overnutrition (Figure I) increased with age is consistent with previous studies [38,39,40]. However, contrary to previous studies [38,39,41], associations between higher economic status (e.g., less household crowding), educational level attained, and residing in urban areas with being overweight or obese were not statistically significant in this current study (Table 5, Table 6, Table 7). Of concern was that 490(18.7%) of young girls and women aged 15–24 years were over nourished, indicating a large proportion of overweight and obesity in early adulthood (Table 5, Table 6, Table 7). This finding requires that overweight and obesity investigations following the lifecycle of women should be prioritized in Sierra Leone, as in many low to middle-income countries [41].

In terms of dietary behaviors, previous research found the independent predictors of obesity were due to inadequate fruit and vegetable intakes [42,43], eating occasions away from home [44], high salt intakes [45], consumption of ultra-processed foods, and saturated fats [46]. However, our current study is a secondary data analysis, and we could not assess associations of dietary behaviors such as the frequency of snacking, skipping breakfast, high intake of sugary beverages, overweight or obesity [43,47], and over nutrition among the study population.

Unlike previous research [48,49,50], this study showed no associations between tobacco and alcohol use and the prevalence of overweight, obesity, and overnutrition among the study population (Table 5, Table 6, Table 7).

Working-class women, overweight, obesity, and overnutrition.

Our study found that working-class women were unlikely of being overweight and no association with obesity, and overnutrition among the study population (Table 5, Table 6, Table 7). Previous studies from Addis Ababa, Ethiopia, reported higher figures for overweight and obesity, ranging from (26.7 to 38%) among workers in Wonji Shewa sugar factory [51-54]. The availability of more energy-dense fast foods and exposure to sedentary life in Addis Ababa and its surroundings (compared to other urban settings) explains the high figures for overweight and obesity in Ethiopia [51-54]. The observed overweight and obesity among sugar factory workers in Ethiopia is likely due to unfettered access to cheap and free sugar from their workplaces.

This finding in Sierra Leone on overnutrition at (27.12%) is lower than most studies in Ethiopia [51-54]. This Sierra Leone finding is also lower than other studies based on DHS data from Nigeria (26.7 and 36.4 %) [55,56] and seven African countries (average prevalence of 31%) [57]. It is also lower than other studies conducted in Benin (41.3%), South Africa (56.6%), Iran (61.3%), and India (75.33%) [58–61]. The current study findings may be diverse due to disparity in dietary patterns, lifestyles, level of urbanization, and economic development in Sierra Leone.

Of special interest in this Sierra Leone study is that it was unlikely to have overweight women among the working-class contrary to findings from other African countries. Is there something we can learn from findings among these Sierra Leonean women that can be replicated elsewhere in Africa? Is there dietary discipline and good dietary habits among working-class women in Sierra Leone? This can only be established by conducting a comprehensive study on working-class women in Sierra Leone.

In the current study, age of women, marital status, wealth indices, working-class women, female-headed households, and residence in the South of Sierra Leone were unlikely associated with overweight, obesity, and overnutrition of women in the reproductive age (Table 5, Table 6, Table 7). Consistent with other studies, this study's results demonstrate that malnutrition's prevalence is higher among older women [62,63,64,65,66]. A decrease in levels of physical activities and higher intakes of energy-dense foods as the age of women advances is suggested as a possible explanation [67]. In contrast, being overweight, obese, and over nourished was unlikely among women in the middle, more prosperous, and richest quintile in Sierra Leone women, inconsistent with studies from Addis Ababa and Wonji Shewa sugar factory [49,62,63] and studies from elsewhere [59,60,61,64,65]. In developing countries, wealthier women are more likely to consume more energy-dense foods and follow a sedentary lifestyle; hence they are more likely to be overweight, obese, and over nourished [59,60,61,64,65]. However, this was not the case in Sierra Leone where overweight, obesity and over nutrition were unlikely among working-class women in the reproductive age. There is a need to explore this finding in future studies among women in Sierra Leone.

Also, previous studies showed that the prevalence of overweight, obesity, and overnutrition was significantly higher among working women who attained higher educational levels [65,66]. However, a higher educational level was not statistically significant in our study population except for the crude odds ratios for primary level of education in participants with obesity (Table 6). Our current study finding on the level of education attained and association with obesity contrasts with other studies where higher school level attained is associated with obesity [65,66,67]. This finding on obesity in other settings may be a result of changes in lifestyles as disposal income rises; these classes of women tend to go for processed carb-diets, more sugary drinks, including drinking tea three to four times a day with bread with a shift from manual labor to more sedentary occupations and the related decline in physical activities.

In this, we the authors note that sedentary life alone may not be the only reason for high rates of overweight and obesity as it is becoming clear that physical exercise alone does not contribute to weight loss, much as exercise is a healthy lifestyle.

In contrast to other studies, not-married women in the current study were an independent predictor of overweight, obesity, and overnutrition [68,69,70]. Previously, married women were more likely to have higher parity, resulting in adopting a more sedentary lifestyle and eating high-energy foods, usually offered to women during the postpartum period, and thus becoming overweight and/or obese [68,69,70]. On the contrary, we found in the current study that not-married Sierra Leone women were more likely to be overweight, obese, and over nourished (Table 5, Table 6, Table 7). On this finding, we the authors suggest that perhaps many not-married women in reproductive age in Sierra Leone lead a more sedentary lifestyle, have higher energy-rich diet, and are from northern region, and this may in part explain the associations between not married women with overweight, obesity, and overnutrition. These authors recommend further studies to determine why not-women were more likely to be overweight, obese, and over nourished in Sierra Leone in contrast to other countries in sub-Saharan Africa.

Female-headed households, wealth indices, overweight, obesity, and overnutrition.

Our current study found that better wealth indices and female-headed households were unlikely associated with overweight, obesity and overnutrition in women of reproductive age in Sierra Leone (Table 5, Table 6, and Table 7).

Although there are cross-country differences and shares for both populations living in female-headed households and households headed by women have been rising over the years in Africa, current data show that the probability of a woman aged fifteen or older headed household, controlling for her age, has been increasing since the early 1990s in all regions and across the entire age distribution in Africa [71].

Using complete series of DHSs fielded in Africa over the last 25 years and covering 89% of Africa's population, recent research has investigated Africa-wide changes in the prevalence of female-headed households [71]. The results suggest that recent economic growth in Africa brings more female headship, presumably due to lower work-related migration by men, but associated with a growing local economy [71]. The seeming paradox that female household headship is rising during a period of growth is that other factors are also changing across the African continent [71]. Changes in the demographic and population characteristics, social norms, education of women, and the nature of the family appear to be encouraging female household headships in the African continent [71].

Current reports show that an extra year of schooling produces a three-percentage increase in shares of the population living in female-headed households [71]. In addition, a one-year rise in women's age at first marriage produces a 2.5%-point increase in the share of the population living in female-headed households, an effect almost as strong as that of an extra year of schooling [71]. Life expectancy's positive effect equals a 0.5%-point boost per extra year among women, presumably reflecting the natural survival advantage of women with higher overall life expectancy and resulting incidence of widow-headed households [71].

Furthermore, conflicts, wars, and HIV and AIDs in many communities in the African continent have raised many countries' share of the population in female-headed households [71]. Thus, female-headed households' prevalence has been rising while poverty has been falling in the African continent [71].

Past literatures have generally been suggestive that female-headed households tend to be poorer, but the critical question is whether this occurrence implies that recent improvements in living standards have left behind female-headed households [71]. On the contrary, female-headed households are a diverse group of people and some, such as married women with non-resident husbands (polygynous or migrant) or educated women who may choose, and can socially and economically afford not to marry or remarry can be expected to be relatively well-off [71]. Others, such as wars or AIDS widows, separated or abandoned women, and single mothers who have not chosen headship but have no options, are frequently found to head disadvantaged households [71].

So, the finding in our current study that it was unlikely to have overweight, obese and over nourished women among better wealth quintiles and female headed households attract interest since previous studies appear to inform that better wealth quintiles are associated with obesity, overweight and over nutrition.

Interestingly, poverty has been declining in both household groups over the years but in most countries in Africa, it fell faster for female-headed households (FHHs) than MHHs [71]. This finding is also suggested as factual when one allows for diversity among FHHs, for example, comparing households with a widow and non-widowed heads, married heads with and without a male adult household member, and the same for non-married heads. The finding that poverty is falling faster for FHHs is robust for testing sensitivity in allowing for a generally smaller size of FHHs and economies of scale in consumption, which does not alter these key findings [71].

In addition, living standards of various types of FHHs followed different paths across countries and periods, with no one type consistently outperforming others, yet at least one type usually outperforming male-headed households [71]. Furthermore, there is little discernible pattern across countries in the African continent where one category of FHH may do well in one country or a period, while another does best elsewhere [71].

In all, poverty has fallen more rapidly in FHHs in the African continent; a decomposition in changes in poverty indicates that, rather than put a break on poverty reduction, FHHs are contributing appreciably to the decline of poverty despite their smaller share in the population [71].

Nevertheless, the big question is, why has poverty fallen faster in FHHs? There are different explanations, but perhaps poor FHHs faced a relatively high economic return to the new opportunities unleashed by economic growth, or perhaps they have benefited disproportionately from the expansion of social protection in the region, or perhaps the group of people living in FHHs has fundamentally changed over time [71]. This finding in our current study means that a superficial examination of FHHs and better wealth indices observed from this study may not support any of these explanations, but this new stylized fact about poverty in Africa warrants a closer look in the future.

A Double Burden of Malnutrition (DBM) in Africa.

We, the authors, argue that among drivers of DBM, poverty-related factors, such as food insecurity and infectious diseases, persistent droughts, floods, gender prejudices, and protracted humanitarian crises, continue to mark the face of Africa [72]. For overweight and obesity, cultural expectations and early onset of puberty predispose girls to high adiposity and lifestyles [73]. Cultural perceptions of female body size also drive DBM as being overweight is considered a sign of wealth, achievement, and marital harmony [73]. This cultural aspect and reduced physical activity could explain why obesity is consistently higher in women compared to men in the African continent [73]. Meanwhile, consumption of processed foods is increasing at the expense of fresh and minimally processed foods among the African population [73,74]. Commercialization of food production, processing, and distribution is correlated with decreasing smallholder farming, dietary diversity, and increasing household dependence on purchased foods, resulting in diets of low nutritional quality, energy-dense, high in sugars, salt, and fats [74]. The underlying causes of DBM may vary by subregion, but the increasing consumption of cheap, processed foods [75] and reduced physical activity are among key drivers of DBM.

In summary, the prevalence of overweight and obesity exceeds underweight in most women of reproductive age and are risk factors for cardiovascular diseases in developing countries [76,77,78]. Findings from Sierra Leone show that it is not exception to the growing prevalence of DBM among women of reproductive age. Of special interest in the Sierra Leone case, the number of adolescents with underweight, overweight, obesity and overnutrition are relatively high. If this current high prevalence of the different categories of malnutrition among young women are not arrested soon in Sierra Leone, we the authors believe that in the coming years, we shall begin to observe higher incidences and prevalence of NCDs, poor obstetric outcomes and disadvantaged off springs.

Strengths and Limitations of this study.

The use of a nationally representative data and considerations of complex sampling methods are the strengths of this study. However, three limitations should be considered while interpreting the results of this study. First, the study's cross-sectional nature does not allow for establishing causality of associations. Second, significant predictors of the outcome variable, such as physical activity and total energy intake (nutritional history), food availability, and types consumed, are absent due to secondary nature of the data. Likewise, there was no data on central obesity since the survey did not collect data on abdominal and waist-to-hip circumferences. Finally, apart from physical and biomedical measures of self-reported questionnaire, these data may have suffered from social desirability biases.

Generalizability of results: Results of this study can be generalized to low-resource settings, particularly in low to middle-income countries.

Conclusion: The prevalence of different categories of malnutrition among women of reproductive age in Sierra Leone were high, affirming a double burden of malnutrition in this study population. It was more likely to be underweight among the 25–34-year age-group compared to 15–24-year. The independent predictors of overweight, obesity, and overnutrition were not-married women, women from the north and not listening to radios. There is an urgent need for policymakers in Sierra Leone to design comprehensive educational campaigns to sensitize and mobilize women in reproductive age on healthy lifestyles and the dangers of being underweight or over-nourished.

Ethics approval and consent to participate: All experimental protocols were approved by Sierra Leone Ethics and Scientific Review Committee (SLESRC) and the ICF Institutional Review Board. Informed consent was obtained from all participants and their legal representatives. This study on women of reproductive age in Sierra Leone followed relevant institutional guidelines and regulations.

Consent to publish: Not applicable.

Availability of data and material: All datasets supporting this article's conclusion are within the article and are accessible by a reasonable request to the corresponding author.

Competing interests: All authors declare no conflict of interest.

Funding: All funds for this study were contributions from individual research members.

Authors' contributions: DLK, ENI, JA, and FWDO designed the study, JA, ENI, FWDO, and DLK supervised data management, ENI, JA, MAT, LN, and DLK analyzed and interpreted data, ENI, FWDO, JA, JBM, MAT, LN, KK, AN, RK, and DLK wrote and revised the manuscript. All Authors approved the manuscript.

Authors' Information: Dr. Eric Nzirakaindi Ikoona (ENI) is a Technical Director at ICAP at the University of Columbia, Sierra Leone; Dr. Freddy Wathum Drinkwater Oyat (FWDO) is a senior physician, a public health specialist, and a member of Uganda Medical Association, UMA-Acholi branch, Gulu City, Uganda; Dr. Mame Awa Toure (MAT) is at ICAP at the University of Columbia, Freetown, Sierra Leone; Dr. Lucy Namulemo (LN) is at Foothills Community Based interventions, Monticello, Kentucky, USA; Dr. Kassim Kamara (KK) is at the Directorate of health security and emergencies, Ministry of Health and Sanitation, Sierra Leone; Dr. Amon Njenga (AN) is at ICAP at the University of Columbia, Freetown, Sierra Leone; Dr. Ronald Kaluya (RK) is at Uganda Counseling and Support Services, Kampala, Uganda; Dr. John Bosco Matovu is at ICAP in Nairobi, Kenya, Dr. Judith Aloyo (JA) is a Technical Director at the Rhites-N, Acholi, Gulu City, Uganda; Prof. David Lagoro Kitara (DLK) is a Takemi fellow of Harvard University and a Professor at Gulu University, Faculty of Medicine, Department of Surgery, Gulu City, Uganda

Acknowledgment: We acknowledge with many thanks for the assistance from 2019 SLDHS for the information obtained.

Catherine Schwinger RK, Chandyo M, Ulak M, Hysing M, Shrestha S, Ranjitkar, et al. Prevalence of Underweight, Overweight, and Obesity in Adults in Bhaktapur, Nepal in 2015–2017. Front Nutr. 2020;7:567164. 10.3389/fnut.2020.567164.
NCD risk factor collaboration. Trends in the adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19.2 million participants. Lancet. 2016;387(16):1377–96. 10.1016/S0140-6736.
Chooi YC, Ding C, Magkos F. The epidemiology of Obesity. Metabolism. 2019;92:6–10. 10.1016/j.metabol.2018.09.005.
Black RE, Victora CG, Walker SP, Bhutta ZA, Christian P, de Onis M, et al. Maternal and child undernutrition and overweight in low-income and middle-income countries. Lancet. 2013;382:427–51. 10.1016/S0140-6736(13)60937-X.
The Global BMI Mortality Collaboration. Body-mass index and all-cause mortality: an individual-participant-data meta-analysis of 239 prospective studies in four continents. Lancet. 2016;388:776–86. 10.1016/S0140-6736(16)30175-1.
GBD Risk Factors Collaborators. Global, regional, and national comparative risk assessment of 79 behavioral, environmental, occupational, and metabolic risks or clusters of risks in 188 countries, 1990–2013: a systematic analysis for the global burden of disease study 2013. Lancet. 2015;386(22):87–323. 10.1016/S0140-6736(15)00128-2.
The GBD 2015 Obesity Collaborators. Health effects of overweight and Obesity in 195 Countries over 25 Years. N Engl J Med. 2017;377:13–27. 10.1056/NEJMoa1614362.
Patel A, Prakash AA, Das PK, Gupta S, Pusdekar YV, Hibberd PL. Maternal anemia and underweight as determinants of pregnancy outcomes: a cohort study in eastern rural Maharashtra, India. BMJ open. 2018;8:e021623. 10.1136/bmjopen-2018-021623.
Harpsoe MC, Nielsen NM, Friis-Moller N, Andersson M, Wohlfahrt J, Linneberg A, et al. Body mass index and risk of infections among women in the Danish National Birth Cohort. Am J Epidemiol. 2016;183:1008–17. 10.1093/aje/kwv300.
Han Z, Mulla S, Beyene J, Liao G, McDonald SD, Knowledge Synthesis G. Maternal underweight and the risk of pre-term birth and low birth weight: a systematic review and meta-analysis. Int J Epidemiol. 2011;40:65–101. 10.1093/ije/dyq195.
Development Initiatives. 2018 Global Nutrition Report: Shining a light to spur action on nutrition. Bristol, UK: Development Initiatives; 2018.
Popkin BM, Corvalan C, Grummer-Strawn LM. Dynamics of the double burden of Malnutrition and the changing nutrition reality. Lancet. 2020;395:65–74. 10.1016/S0140-6736(19)32497-3.
Mamun AA, Finlay JE. Shifting of undernutrition to overnutrition and its determinants among women of reproductive ages in the 36 low to medium income countries. Obes Res Clin Pract. 2015;9:75–86. 10.1016/j.orcp.2014.03.001.
Young MF, Nguyen P, Tran LM, Avula R, Menon P. A Double-edged sword? Improvements in economic conditions over a decade in India led to declines in undernutrition as well as increases in overweight among adolescents and women. J Nutr. 2020;150:364–72. 10.1093/jn/nxz251.
Giancola A, Hauerslev M, Ndinda C. Addressing the double-burden of Malnutrition among children and adolescents in sub-Saharan Africa. J Global Health Rep. 2022;6:e2022033. 10.29392/001c.36904.
UNICEF, WFP, WHO. The State of Food Security and Nutrition in the World (SOFI). Food and Agriculture Organization of the United Nations; 2020. 10.4060/ca9692en.
Onyango AW, Jean-Baptiste J, Samburu B, Mahlangu TLM. Regional Overview on the Double Burden of Malnutrition and Examples of Program and Policy Responses: African Region. Ann Nutr Metab. 2019;75(2):127–30. 10.1159/000503671.
Prudence Atukunda, Wenche Barth Eide, Kristin R Kardel, Per Ole Iversen, Ane C Westerberg.. Unlocking the potential for achievement of the UN Sustainable Development Goal 2 - 'Zero Hunger' - in Africa: targets, strategies, synergies, and challenges. Food Nutr Res. 2021;26;65. doi: 10.29219/fnr.v65.7686. eCollection 2021.
Statistics Sierra Leone (Stats SL) and ICF. 2020. Sierra Leone Demographic and Health Survey 2019. Freetown, Sierra Leone, Rockville, Maryland, USA: Stats SL and ICF. 2020. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://dhsprogram.com/pubs/pdf/FR365/FR365.pdf
Worldometer. The population of Sierra Leone. 2022. https://www.worldometers.info/world-population/sierra-leone-population/
Sierra Leone NHSSP. National Health Sector Strategic Plan 2017–2021. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://extranet.who.int/ countryplanningcycles/sites/default/files/planning_cycle_repository/sierra_leone/sierra_leone_nhssp_2017-21_final_sept2017.pdf
World Food Program (WFP). State of Food Security in Sierra Leone 2015 Comprehensive Food Security and Vulnerability Analysis Data collected September - October 2015. 2015 Sierra Leone CFSV. https://efaidnbmnnnibpcajpcglclefindmkaj/https://documents.wfp.org/stellent/groups/public/documents/ena/wfp288316.pdf?iframe.
World Bank (WB). Microdata in Sierra Leone, 2019. Demographic and Health Survey 2019. Microdata Library. 2019. https://microdata.worldbank.org/index.php/catalog/3826.
Lee PH, Burstyn I. Identification of confounder in epidemiologic data contaminated by measurement error in covariates. BMC Med Res Methodol. 2016;16:54.
Maldonado G, Greenland S. Simulation study of confounder-selection strategies. Am J Epidemiol. 1993;138(11):923–36.
Mickey RM, Greenland S. The impact of confounder selection criteria on effect estimation. Am J Epidemiol. 1989;129(1):125–37.
Kenya National Bureau of Statistics, Ministry of Health/Kenya, National AIDS Control Council/Kenya, Kenya Medical Research Institute., Population NCf, Development/Kenya: Kenya Demographic and Health Survey 2014. Rockville; 2015.
Ministry of Health CD, Gender, Elderly C-MoHCDGEC/Tanzania. Mainland, Ministry of Health - MoH/Zanzibar, National Bureau of Statistics - NBS/Tanzania, Office of Chief Government Statistician - OCGS/Zanzibar, ICF: Tanzania Demographic and Health Survey and Malaria Indicator Survey 2015–2016. Dar es Salaam: MoHCDGEC, MoH, NBS, OCGS, and ICF; 2016.
Senbanjo IO, Olayiwola IO, Afolabi WA, Senbanjo OC. Maternal and child under-nutrition in rural and urban communities of Lagos state, Nigeria: the relationship and risk factors. BMC Res Notes. 2013;6:286.
World Food Program. : East Africa Regional Food Security & Nutrition Update (November 2019): https://reliefweb.int/report/ethiopia/east-africa-regional-food-security-nutrition-update-november-2019.
Pengpid S, Peltzer K. The Prevalence of Underweight, Overweight/Obesity and their related lifestyle factors in Indonesia, 2014–15. AIMS Public Heal. 2017;4:633–649. https://doi.org/10.3934/public health. 2017.6.633.
Akseer N, Bhatti Z, Mashal T, Soofi S, Moineddin R, Black RE, et al. Geospatial inequalities and determinants of nutritional status among women and children in Afghanistan: an observational study. Lancet Glob Health. 2018;6(4):e447–59. https://doi.org/10.1016/S2214-109X(18)30025-1.
Pengpid S, Peltzer K. The prevalence and associated factors of underweight and overweight/Obesity among adults in Kenya: evidence from a national cross-sectional community survey. Pan Afr Med J. 2020;36:338. https://doi.org/10.11604/pamj.2020.36.338.21215.
Biswas T, Garnett SP, Pervin S, Rawal LB. The prevalence of underweight, overweight, and Obesity in Bangladeshi adults: data from a national survey. PLoS ONE. 2017;12(5):e0177395. https://doi.org/10.1371/journal.pone.01773. 95 eCollection 2017.
Rawal LB, Kanda K, Mahmud RA, Joshi D, Mehata S, Shrestha N, et al. Prevalence of underweight, overweight and Obesity and their associated risk factors in Nepalese adults: Data from a nationwide survey, 2016. PLoS ONE. 2018;13:e0205912. 10.1371/journal.pone.0205912.
Peng W, Mu Y, Hu Y, Li B, Raman J, Sui Z. Double burden of Malnutrition in the Asia-Pacific region-a systematic review and meta-analysis. J Epidemiol Glob Health. 2020;10(1):16–27. https://doi.org/10.2991/jegh.k.191117.002.
Popkin BM. An overview on the nutrition transition and its health implications: the Bellagio meeting. Public transition and its health implications: the Bellagio meeting. Public Health Nutr. 2002;5(1A):93–103. https://doi.org/10.1079/phn2001280.
Jaacks LM, Sliding MM, Popkin BM. Recent trends in the prevalence of under- and overweight among adolescent girls in low- and middle-income countries. Pediatr Obes. 2015;10(6):428–35. https://doi.org/10.1111/ijpo.12 000.
Haddad L, Cameron L, Barnett I. The double burden of Malnutrition in SE Asia and the Pacific: priorities, policies, and politics. Health Policy Plan. 2015;30(9):1193–206. https://doi.org/10.1093/heapol/czu110.
Weiderpass E, Botteri E, Longenecker JC, Alkandari A, Al-Wotayan R, Al Duwairi Q et al. The prevalence of overweight and Obesity in an adult Kuwaiti population in 2014. Front Endocrinol. 2019;10:449. https://doi.org/1 0.3389/fendo.2019.00449.
Anne M, Williams J, Guo OY, Addo S, Ismaily, Sorrel ML, Namaste, Brietta M, Oaks, et al. Intraindividual double burden of overweight or obesity and micronutrient deficiencies or anemia among women of reproductive age in 17 population-based surveys. Am J Clin Nutr. 2020;112(Suppl):468S–77.
Pengpid S, Peltzer K. Associations between behavioral risk factors and overweight and Obesity among adults in population-based samples from 31 countries. Obes Res Clin Pract. 2017;11(2):158–66. https://doi.org/10.1016/j. orcp.2016.08.001.
Carnauba RA, Chaves DF, Baptistella AB, Paschoal V, Naves A, Buehler AM. Association between high Consumption of phytochemical-rich foods and anthropometric measures: a systematic review. Int J Food Sci Nutr. 2017;68(2):158–66. https://doi.org/10.1080/09637486.2016.1229761.
Affinity SG, Franko DL, Striegel-Moore RH, Thompson D. Behavioral determinants of Obesity: research findings and policy implications. J Obes. 2012;2012:150732–4. https://doi.org/10.1155/2012/150732.
Allison SJ. Metabolism: high salt intake as a driver of Obesity. Nat Rev Nephrol. 2018;14(5):285. https://doi.org/10.1038/nrneph.2018.23.
Askari M, Heshmati J, Shahinfar H, Tripathi N, Daneshzad E. Ultra-processed food, and the risk of overweight and Obesity: a systematic review and meta-analysis of observational studies. Int J Obes. 2020;44(10):2080–91. https://doi.org/10.1038/s41366-020-00650-z.
Al-Tawil NG, Abdulla MM, Abdul Ameer AJ. Prevalence of and factors associated with overweight and Obesity among a group of Iraqi women. East Mediterr Health J. 2007;13(2):420–9.
Saeed KM. Prevalence and associated risk factors for Obesity in Jalalabad city – Afghanistan. Alexandria J Med. 2015;51(4):347–52. https://doi.org/10.1016/j.ajme.2014.12.004.
Birhane T. Urban food insecurity in the context of high food prices: a community-based cross-sectional study in Addis Ababa, Ethiopia. BMC Public Health. 2014;14:680.
Asfaw HA, Gebrehiwot EM, Solomon Shiferaw. Effect of shit-work on Hypertension among Factory Workers in Ethiopia. Am J Clin Exp Med. 2015;3(4):142–8.
Wai WS, Dhami RS, Gelaye B, Girma B, Lemma S, Berhane Y, et al. Comparison of measures of adiposity in identifying cardiovascular disease risk among Ethiopian adults.Obesity. 2012;20(9):1887–95.
Addis Continental Institute of Public Health (ACIPH)., University of Washington. Survey of Noncommunicable Diseases among Bank Employees and Teachers in Addis Ababa, Ethiopia. Preliminary result. 2010.
Tebekaw Y, Teller C, Ramos K. The burden of underweight and overweight among women in Addis Ababa, Ethiopia. BMC Public Health. 2014;14:1126.
Tamiru S, Alemseged F. Risk Factors for Cardiovascular Diseases. Ethiop J Health Sci. 2010;20(2):123–26.
Kandala NB, Stranges S. Geographic variation of overweight and Obesity among women in Nigeria: a case for nutritional transition in Sub-Saharan Africa. PLoS ONE. 2014;9(6):e101103.
Uthman OA. Patterns, distribution, and determinants of under- and over-nutrition among women in Nigeria: a population-based analysis. J Public Health. 2009;17(5):289–99.
Abdhalah KZ, Jean CF, Rhone O. Overweight, and Obesity in urban Africa: A problem of the rich or the poor? BMC Public Health. 2009;9:465.
Gbary AR, Kpozehouen A, Houehanou YC. Prevalence and risk factors of overweight and obesity: findings from a cross-sectional community-based survey in Benin. Glob Epidemic Obes. 2014;2:3.
Puoane T, Steyn K, Bradshaw D. Obesity in South Africa: the South African demographic and health survey. Obes Res. 2002;10(10):1038–48.
Mohsen J, Masoud A, Willett WC. A first nationwide survey of the prevalence of overweight, underweight, and abdominal Obesity in Iranian adults. Obesity. 2007;15(11):2797–808.
Kaur G, Singh SP, Singh AP. Prevalence of overweight and Obesity in Urban and Rural women of Punjab. Hum Bio Rev. 2013;2(4):06–313.
Teferi Mekonnen W, Animaw, Yeshaneh Seyum. Overweight/obesity among adults in Northwestern Ethiopia: A community-based cross-sectional study. Archives of Public Health. 2018;76:18. https://doi.org/10.1186/s13690-018-0262-8.
Workineh Tefera M, Shuremu A, Tadelle, Daba Abdissa and Zelalem Banjaw. The magnitude of central Obesity and associated factors among adults working in government offices in Bedele town, Buno Bedele zone, Southwest Ethiopia. SAGE Open Medicine. 2022;10:1–10. 10.1177/20503121221105993 journals.sagepub.
Tagbo SO, Abebe D, Oguoma VM. Overweight and Obesity among non-pregnant women of reproductive age in Nigeria: findings from the 2008–2018 Nigerian Demographic and Health Survey. Public Health. 2021;198:348–57. 10.1016/j.puhe.2021.07.036. Epub 2021.
Carlini B, Eduardo V. Nationally representative surveys show recent increases in the prevalence of overweight and Obesity among women of reproductive age in Bangladesh, Nepal, and India. J Nutr. 2009;139(11):2139–44.
Neuman M, Finlay JE, Davey Smith G, Subramanian SV. The poor stay thinner: stable socioeconomic gradients in BMI among women in lower- and middle-income countries. Am J Clin Nutr. 2011;94(5):1348–57.
Alemu E, Atnafu A, Yitayal M, Yimam K. Prevalence of Overweight and Obesity and Associated Factors among High School Adolescents in Arada Sub-city, Addis Ababa, Ethiopia. J Nutr Food Sci. 2014;4:261.
Subramanian SV, Perkins JM, O¨zaltin E, Davey Smith G. Weight of nations: a socioeconomic analysis of women in low- to middle-income countries. Am J Clin Nutr. 2011;93(2):413–21.
World Health Organization. Preventing and managing the Global Obesity Epidemic. Geneva: Report of WHO Consultation on Obesity; 1998.
Solomon Abrha S, Shiferaw, Kedir Y, Ahmed. Overweight and Obesity and its sociodemographic correlates among urban Ethiopian women: evidence from the 2011 EDHS. BMC Public Health. 2016;16:636.
Dominique Van De Walle. Poverty is falling faster for female-headed households in Africa. World Bank Blogs, published on Africa end poverty. 2015. https://blogs.worldbank.org/africacan/poverty-is-falling-faster-for-female-headed-households-in-africa
Carroll GJ, Lama SD, Martinez-Brockman JL, Pérez-Escamilla R. Evaluation of Nutrition Interventions in Children in Conflict Zones: A Narrative Review. Adv Nutr. 2017;8(5):770–9.
Ok KJ, Mukumbang FC, Mathole T, Levitt N, Puoane T. Perceptions of body size, obesity threat and the willingness to lose weight among black South African adults: a qualitative study. BMC Public Health. 2016;16(1):365.
Food, Organization A, World Food Program, and World Health Organization. International Fund for Agricultural Development, UNICEF,. The State of Food Security and Nutrition in the World 2018. Building climate resilience for food security and nutrition. Rome, Food and Agriculture Organization; 2018.
Holmes MD, Dalal S, Sewram V, Diamond MB, Adebamowo SN, Ajayi IO, et al. Consumption of processed food dietary patterns in four African populations. Public Health Nutr. 2018;21(8):1529–37.
Michelle A, Mendez CA, Monteiro, Barry M, Popkin. Overweight exceeds underweight among women in most developing countries. Am J Clin Nutr. 2005;81:714–21.
Mohsen Janghorbani M, Amini WC, Willett MM, Gouya A, Delavari S, Alikhani et al. First Nationwide Survey of Prevalence of Overweight, Underweight, and Abdominal Obesity in Iranian Adults. Obesity.2007;15(11):2797–2808.
Tamiru S, Alemseged F. Risk Factors for Cardiovascular Diseases Among Diabetic Patients in Southwest Ethiopia. Ethiop J Health Sci. 2010;20(2):121–8.

No competing interests reported.

A Double Burden of Malnutrition Among Women in the Reproductive Age (15-49 years) in Sierra Leone: A Secondary Data Analysis of the Demographic Health Survey of 2019 (SLDH-2019)

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Methods

Results

Discussion

Declarations

References

Additional Declarations

Status:

Journal Publication

Version 1