The Open Public Health Journal




ISSN: 1874-9445 ― Volume 13, 2020
RESEARCH ARTICLE

Socio-demographic Determinants of Overweight and Obesity Among Mothers of Primary School Children Living in a Rural Health and Demographic Surveillance System Site, South Africa



Perpetua Modjadji1, *
1 School of Health Care Sciences, Department of Public Health, Sefako Makgatho Health Sciences University, 1 Molotlegi Street, Ga-Rankuwa, 0208, South Africa

Abstract

Background:

South Africa continues to have significant high prevalence rate of overweight/obesity relative to its African counterparts, particularly, among women, owing to several factors such as nutrition transition and socio-demographic factors. Nonetheless, little is known about the socio-demographic determinants of overweight/obesity, especially in the rural settings.

Objective:

To investigate the socio-demographic determinants of overweight and obesity among mothers of primary school children living in a rural Dikgale Health and Demographic Surveillance System Site in South Africa

Methods:

A cross-sectional study was conducted among 508 mothers of primary school children from a rural setting. Body mass index (BMI) was calculated by dividing the body weight by height squared and the prevalence of overweight (BMI ≥ 25–29.9 kg/m2) and obesity (BMI ≥30 kg/m2) were determined. The socio-demographic variables were collected using an interviewer-administered questionnaire. Multiple logistic regression analysis was used to ascertain any relationships with overweight/obesity as an outcome measure. Data were analyzed using STATA 14.

Results:

The response rate was 98%. The mean age of mothers was 37±7years. Mothers were charecterized by singlehood (63%), unemployed (82%) and low literacy (41%). The odds of being overweight/obese were significantly higher among mothers living with spouses as household heads (AOR=3.5 95%CI: 1.97-6.31), had two to three pregnancies (AOR=2.4, 95%CI: 1.40-4.20), and five pregnancies and above (AOR=2.5, 95%CI: 1.0-6.37). Mothers who lived in households with a monthly income between $344.84 and $524,60 were less likely to be overweight or obese (AOR=0.31 95%CI: 0.14-0.70). Additionally, age, marital status and age at first pregnancy were significantly associated with being overweight/obese (χ2 test, p<0.05).

Conclusion:

The key determinants of overweight/obesity were living in spouse-headed household, household monthly income and more than one pregnancy. Evidence-based strategies that focus on strengthening the social aspects while addressing overweight and obesity among mothers of primary school children living in a rural Dikgale HDSS site, South Africa.

Keywords: Socio-demographic factors, Obstetric history, Overweight/obesity, Mothers of primary school children, Rural Dikgale HDSS site, South Africa.


Article Information


Identifiers and Pagination:

Year: 2020
Volume: 13
First Page: 518
Last Page: 528
Publisher Id: TOPHJ-13-518
DOI: 10.2174/1874944502013010518

Article History:

Received Date: 9/5/2020
Revision Received Date: 7/8/2020
Acceptance Date: 14/8/2020
Electronic publication date: 20/10/2020
Collection year: 2020

© 2020 Perpetua Modjadji.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


* Address correspondence to this author at the Department of Public Health, Sefako Makgatho Health Sciences University, School of Health Care Sciences, PO Box 215, Ga-Rankuwa MEDUNSA, 0204, South Africa; Tel: +2712 521 3664; E-mail: Perpetua.modjadji@smu.ac.za





1. INTRODUCTION AND BACKGROUND

According to the World Health Organization, more than 1.2 billion adults are either overweight or obese, with overweight affecting more than 1 billion and obesity, 300 million, globally [1WHO. Obesity and overweight 2018. Available from: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight]. Approximately 39% of adults are overweight, while 13% are obese worldwide. Thirty nine percent of men and 40% of women are overweight while 11% men and 15% women are obese [1WHO. Obesity and overweight 2018. Available from: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight]. Historically, the burden of overweight and obesity was once associated with High-income Countries (HICs), but lately, increasing rates are observed in Low-and-middle-income Countries (LMICs) [2Cois A, Day C. Obesity trends and risk factors in the South African adult population. BMC Obes 2015; 2(1): 42.
[http://dx.doi.org/10.1186/s40608-015-0072-2] [PMID: 26617987]
].

The increasing burden of overweight and obesity in LMICs is well documented, particularly in Africa [3Amugsi DA, Dimbuene ZT, Mberu B, Muthuri S, Ezeh AC. Prevalence and time trends in overweight and obesity among urban women: An analysis of demographic and health surveys data from 24 African countries, 1991-2014. BMJ Open 2017; 7(10)e017344
[http://dx.doi.org/10.1136/bmjopen-2017-017344] [PMID: 29079606]
]. More than one third of women and a quarter of men are overweight in Africa, and these proportions are expected to increase by 41% and 30%, respectively, over the next ten years [4Gbary AR, Kpozehouen A, Houehanou YC, Djrolo F, Amoussou MP, Tchabi Y, et al. Prevalence and risk factors of overweight and obesity: Findings from a cross-sectional community-based survey in Benin. Global Epidemic Obesity 2014; 2(1): 3.
[http://dx.doi.org/10.7243/2052-5966-2-3]
]. Amugsi et al. have reported the outstanding high prevalence of overweight and obesity among urban women in Zimbabwe (28% and13%, respectively) and Egypt (36% and 34%) out of the 24 African countries studied [3Amugsi DA, Dimbuene ZT, Mberu B, Muthuri S, Ezeh AC. Prevalence and time trends in overweight and obesity among urban women: An analysis of demographic and health surveys data from 24 African countries, 1991-2014. BMJ Open 2017; 7(10)e017344
[http://dx.doi.org/10.1136/bmjopen-2017-017344] [PMID: 29079606]
]. While on the other hand, the prevalence of overweight and obesity among rural women has ranged from 5.6% to 27.7%, and 1.1% to 23%, respectively, in 32 Sub-Saharan countries [5Neupane S, Prakash KC, Doku DT. Overweight and obesity among women: analysis of demographic and health survey data from 32 Sub-Saharan African Countries. BMC Public Health 2016; 16(1): 30.
[http://dx.doi.org/10.1186/s12889-016-2698-5] [PMID: 26758204]
]. In South Africa, an increase in the overall prevalence of overweight and obesity in women was estimated from 56% to 68% between 1998 and 2016 [6South Africa Demographic Health Survey SADHS 2016. Available online: https://dhsprogramcom/pubs/pdf/FR337/FR337pdf, 7Department of Health. South Africa Demographic and Health Survey: Final Report. Department of Health 1998. Available online: https://www.dhsprogram.com/pubs/pdf/FR131/FR131.pdf].

The global rise of overweight/obesity is primarily influenced by nutrition transitions, explained as the shift from a plant-based diet to obesogenic diets and reduction in energy expenditure [8Nnyepi MS, Gwisai N, Lekgoa M, Seru T. Evidence of nutrition transition in Southern Africa. Proc Nutr Soc 2015; 74(4): 478-86.
[http://dx.doi.org/10.1017/S0029665115000051] [PMID: 25686639]
-10Vorster HH, Kruger A, Margetts BM. The nutrition transition in Africa: Can it be steered into a more positive direction? Nutrients 2011; 3(4): 429-41.
[http://dx.doi.org/10.3390/nu3040429] [PMID: 22254104]
]. Furthermore, the rise in overweight/ob esity rates is attributable to socio-demographic [11Nienaber-Rousseau C, Sotunde OF, Ukegbu PO, et al. Socio-demographic and lifestyle factors predict 5-year changes in adiposity among a group of black South African adults. Int J Environ Res Public Health 2017; 14(9): 1089.
[http://dx.doi.org/10.3390/ijerph14091089] [PMID: 28930196]
], environmental [12Micklesfield LK, Lambert EV, Hume DJ, et al. Socio-cultural, environmental and behavioural determinants of obesity in black South African women. Cardiovasc J Afr 2013; 24(9-10): 369-75.
[http://dx.doi.org/10.5830/CVJA-2013-069] [PMID: 24051701]
], behavioral [12Micklesfield LK, Lambert EV, Hume DJ, et al. Socio-cultural, environmental and behavioural determinants of obesity in black South African women. Cardiovasc J Afr 2013; 24(9-10): 369-75.
[http://dx.doi.org/10.5830/CVJA-2013-069] [PMID: 24051701]
] and genetic factors [13Yako YY, Echouffo-Tcheugui JB, Balti EV, et al. Genetic association studies of obesity in Africa: A systematic review. Obes Rev 2015; 16(3): 259-72.
[http://dx.doi.org/10.1111/obr.12260] [PMID: 25641693]
]. Women have been reported to have a higher risk of overweight/obesity compared to men, while being employed and having a higher level of education were associated with increased risks for overweight and obesity [14Mchiza ZJ-R, Parker W-A, Hossin MZ, et al. Social and Psychological Predictors of Body Mass Index among South Africans 15 Years and Older: SANHANES-1. Int J Environ Res Public Health 2019; 16(20): 3919.
[http://dx.doi.org/10.3390/ijerph16203919] [PMID: 31618952]
]. Higher educational attainment in women and higher socioeconomic status (SES) in men were associated with higher BMI [15Wagner RG, Crowther NJ, Gómez-Olivé FX, Kabudula C, Kahn K, Mhembere M, et al. Sociodemographic, socioeconomic, clinical and behavioural predictors of body mass index vary by sex in rural South African adults-findings from the AWI-Gen study. Global health action 2018; 11(sup2): 1549436.]. In both rural and urban settings, a higher SES was associated with an increased likelihood of being obese in both men and women in developing countries [12Micklesfield LK, Lambert EV, Hume DJ, et al. Socio-cultural, environmental and behavioural determinants of obesity in black South African women. Cardiovasc J Afr 2013; 24(9-10): 369-75.
[http://dx.doi.org/10.5830/CVJA-2013-069] [PMID: 24051701]
, 16Mashinya F, Alberts M, Cook I, Ntuli S. Determinants of body mass index by gender in the dikgale health and demographic surveillance system site, south africa. Global health action 2018; 11(sup2): 1537613.]. In developed countries, obesity is widely considered a condition that affects people of lower socioeconomic status (SES) more so than those of higher SES [17Wang Y, Beydoun MA. The obesity epidemic in the United States--gender, age, socioeconomic, racial/ethnic, and geographic characteristics: A systematic review and meta-regression analysis. Epidemiol Rev 2007; 29(1): 6-28.
[http://dx.doi.org/10.1093/epirev/mxm007] [PMID: 17510091]
]. Although overweight/obesity is prevalent among women, generally, in Africa, older women are more affected than younger women [18Uthman OA. Patterns, distribution, and determinants of under-and overnutrition among women in Nigeria: A population-based analysis. J Public Health (Bangkok) 2009; 17(5): 289-99.
[http://dx.doi.org/10.1007/s10389-009-0251-z]
, 19Puoane T, Steyn K, Bradshaw D, et al. Obesity in South Africa: The South African demographic and health survey. Obes Res 2002; 10(10): 1038-48.
[http://dx.doi.org/10.1038/oby.2002.141] [PMID: 12376585]
].

Overweight/obesity is an important risk factor for non-communicable diseases (NCDs), such as cardiovascular diseases, diabetes, hypertension and certain cancers [20Kim HC, Oh SM. Noncommunicable diseases: Current status of major modifiable risk factors in Korea. J Prev Med Public Health 2013; 46(4): 165-72.
[http://dx.doi.org/10.3961/jpmph.2013.46.4.165] [PMID: 23946874]
]. Further detrimental consequences of overweight/obesity among women of reproductive age increase the risk of preterm birth and low birth weight [21Han Z, Mulla S, Beyene J, Liao G, McDonald SD. Maternal underweight and the risk of preterm birth and low birth weight: A systematic review and meta-analyses. Int J Epidemiol 2011; 40(1): 65-101.
[http://dx.doi.org/10.1093/ije/dyq195] [PMID: 21097954]
], preeclampsia and risk of adverse neonatal outcome [22Doherty DA, Magann EF, Francis J, Morrison JC, Newnham JP. Pre-pregnancy body mass index and pregnancy outcomes. Int J Gynaecol Obstet 2006; 95(3): 242-7.
[http://dx.doi.org/10.1016/j.ijgo.2006.06.021] [PMID: 17007857]
]

South Africa continues to have significantly high prevalence rates relative to its African counterparts. In addition, the NCDs burden is also on the rise and already among the top causes of death [23Nojilana B, Bradshaw D, Pillay-van Wyk V, et al. Emerging trends in non-communicable disease mortality in South Africa, 1997 - 2010. S Afr Med J 2016; 106(5): 58.
[http://dx.doi.org/10.7196/SAMJ.2016.v106i5.10674] [PMID: 27138667]
]. Dikgale Health and Demographic Surveillance System Site (Dikgale HDSS site) are known for persistent childhood undernutrition and a high prevalence of overweight/obesity among adults associated with socioeconomic, demographic and behavioral factors [16Mashinya F, Alberts M, Cook I, Ntuli S. Determinants of body mass index by gender in the dikgale health and demographic surveillance system site, south africa. Global health action 2018; 11(sup2): 1537613., 24Maimela E, Alberts M, Modjadji SE, et al. The prevalence and determinants of chronic non-communicable disease risk factors amongst adults in the Dikgale health demographic and surveillance system (HDSS) site, Limpopo Province of South Africa. PLoS One 2016; 11(2)e0147926
[http://dx.doi.org/10.1371/journal.pone.0147926] [PMID: 26882033]
-26Ramsay M, Crowther NJ, Agongo G, Ali SA, Asiki G, Boua RP, et al. Regional and sex-specific variation in BMI distribution in four sub-Saharan African countries: The H3Africa AWI-Gen study. Global health action 2018; 11(sup2): 1556561.]. Despite the Dikgale HDSS site being well researched, overweight/obesity has been better studied among adults aged 40-60 years [16Mashinya F, Alberts M, Cook I, Ntuli S. Determinants of body mass index by gender in the dikgale health and demographic surveillance system site, south africa. Global health action 2018; 11(sup2): 1537613., 26Ramsay M, Crowther NJ, Agongo G, Ali SA, Asiki G, Boua RP, et al. Regional and sex-specific variation in BMI distribution in four sub-Saharan African countries: The H3Africa AWI-Gen study. Global health action 2018; 11(sup2): 1556561.]. Research on the socio-demographic determinants of overweight/obesity among women of reproductive age is crucial. In view of this, this study investigated the socio-demographic determinants of overweight and obesity among women of reproductive age in a rural Dikgale HDSS site, South Africa.

2. MATERIALS AND METHODS

2.1. Study Design and Setting

This paper was taken out of a doctoral thesis written by the author. The main aim of the study was to determine the growth patterns of primary school children and the maternal factors influencing these growth patterns. The study used a convergent mixed method design with parallel data collection for the quantitative and qualitative phases and conducted from August 2017 to December 2017. This paper reports the cross-sectional quantitative survey conducted to determine the socio-demographic determinants of overweight/obesity among women who are mothers of primary school children. A detailed study design was described in the first two papers published by the author [25Modjadji P, Madiba S. Childhood undernutrition and its predictors in a rural health and demographic surveillance system site in south africa. Int J Environ Res Public Health 2019; 16(17): 3021.
[http://dx.doi.org/10.3390/ijerph16173021] [PMID: 31438531]
, 27Modjadji P, Madiba S. The double burden of malnutrition in a rural health and demographic surveillance system site in South Africa: A study of primary schoolchildren and their mothers. BMC Public Health 2019; 19(1): 1087.
[http://dx.doi.org/10.1186/s12889-019-7412-y] [PMID: 31399048]
]. The study population consisted of 508 mothers whose children were attending one of the five largest primary schools in the Dikgale HDSS site.

Dikgale HDSS Site, a rural site in the Limpopo Province of South Africa and forms part of the International Network for the Demographic Evaluation of Populations and their Health (INDEPTH). The study setting has previously been reported in details [28Alberts M, Dikotope SA, Choma SR, et al. Health & demographic surveillance system profile: The Dikgale health and demographic surveillance system. Int J Epidemiol 2015; 44(5): 1565-71.
[http://dx.doi.org/10.1093/ije/dyv157] [PMID: 26275454]
]. The sample size of 515 was calculated using the Rao software calculator [29Hightower C, Scott K. Infer more, describe less: More powerful survey conclusions through easy inferential tests 2012.], taking into consideration the enrolment number of children (n=7772) in the primary schools of Dikgale HDSS site (EMIS, 2016). Selected school children were paired with their mothers and a final sample size of 515 was obtained. In the original study, children who were younger than 5 years, or had physical disabilities that compromised their stature, or whose biological mothers were not available to participate were excluded from the study [30Modjadji P. Growth patterns and socio-cultural beliefs and prcatices in Dikgale, Limpopo Province: A mixed method study of primary school children and their mothers 2019.]. Mothers who reported to be pregnant at the time of the study were also exluded from the study. The nutritional status of the school children were reported in the first two published papers [25Modjadji P, Madiba S. Childhood undernutrition and its predictors in a rural health and demographic surveillance system site in south africa. Int J Environ Res Public Health 2019; 16(17): 3021.
[http://dx.doi.org/10.3390/ijerph16173021] [PMID: 31438531]
, 27Modjadji P, Madiba S. The double burden of malnutrition in a rural health and demographic surveillance system site in South Africa: A study of primary schoolchildren and their mothers. BMC Public Health 2019; 19(1): 1087.
[http://dx.doi.org/10.1186/s12889-019-7412-y] [PMID: 31399048]
].

2.2. Data Collection

Trained research assistants collected the data between a period of August 2017 to December 2017 using a previously tested questionnaire, including the sociodemographic status of women’s personal and household information, and maternity history. Socio-demographic factors used in this study were based on the adapted theoretical framework for available multilevel factors driving adult obesity in South Africa [31Sartorius B, Veerman LJ, Manyema M, Chola L, Hofman K. Determinants of obesity and associated population attributability, South Africa: Empirical evidence from a national panel survey, 2008-2012. PLoS One 2015; 10(6)e0130218
[http://dx.doi.org/10.1371/journal.pone.0130218] [PMID: 26061419]
]. The questionnaire covered a range of socio-demographic characteristics and the household situation of women, in accordance with the variables used in other studies conducted in Dikgale HDSS site [16Mashinya F, Alberts M, Cook I, Ntuli S. Determinants of body mass index by gender in the dikgale health and demographic surveillance system site, south africa. Global health action 2018; 11(sup2): 1537613., 24Maimela E, Alberts M, Modjadji SE, et al. The prevalence and determinants of chronic non-communicable disease risk factors amongst adults in the Dikgale health demographic and surveillance system (HDSS) site, Limpopo Province of South Africa. PLoS One 2016; 11(2)e0147926
[http://dx.doi.org/10.1371/journal.pone.0147926] [PMID: 26882033]
, 32Modjadji SEP. Nutritional factors involved in development of neural tube defects in offspring of women residing in a high risk area: University of Limpopo (Turfloop campus) 2009.], as well as, in other developing countries [33Mawa R. Age, Educational Attainment and Household Socio-Economic Status Influence the Risk of Overweight and Obesity Among Women in Uganda. Journal of Food and Nutrition Sciences 2018; 6: 96-105.-35Kirunda BE, Fadnes LT, Wamani H, Van den Broeck J, Tylleskär T. Population-based survey of overweight and obesity and the associated factors in peri-urban and rural Eastern Uganda. BMC Public Health 2015; 15(1): 1168.
[http://dx.doi.org/10.1186/s12889-015-2506-7] [PMID: 26602893]
]. Age in years was classified into three groups, namely below 35 years, 36-45 years and >45 years. Marital status was categorized according to single and ever married. Level of educational status was classified into low literacy (i.e. did not go to school, primary school and did not complete secondary school) and high literacy (i.e. completed grade 12 and post Grade 12). Employment status was classified into unemployed and employed. Household structure entailed the type of house (brick or non-brick), household head (self, spouse or other family member), household size (1-4 or ≥5), electricity (yes or no), refrigerator use (yes or no), water access (yes or no), and toilet type (pit toilet or flush toilet). Household monthly income was categorized into four groups; ≤$52,20, $53,29–$262,26, $344,84–$524,60, and >$524,65. The three categories for the variables; parity (1, 2-4 and ≥5) and number of pregnancies (1, 2-4 and ≥5), while the age of mothers at first pregnancy was divided into ≤30 years and >30 years.

All measurements were done according to WHO recommendations [36WHO. Obesity: Preventing and Managing The Global Epidemic 2000.]. Weight and height were measured in duplicate and recorded as the average of the two measurements using a smart D-quip electronic scale and a stadiometer, respectively. A non-stretchable plastic tape measure was used to measure the waist and hip circumferences of the women. All the measurements for weight, height and waist and hip circumferences were measured to the nearest 0.1kg, 0.1cm and 0.1 cm, respectively. Body mass index (BMI) was calculated by dividing an individual’s weight in kilograms (kg) by height in meters squared. Overweight and obesity, defined as BMI ≥25kg/m2 and ≥30kg/m2, respectively, based on the WHO adult BMI classification. Central obesity was defined by a waist circumference ≥88cm [37WHO. Physical status: the use and interpretation of anthropometry 1995.] and a waist–hip ratio (WHR), ≥0.85. The waist–hip ratio (WHR) was computed as the waist circumference divided by the hip circumference. Waist-to-height ratio (WHtR≥0.5) is a proxy for central (visceral) adipose tissue [38Ashwell M, Gibson S. Waist-to-height ratio as an indicator of ‘early health risk’: Simpler and more predictive than using a ‘matrix’ based on BMI and waist circumference. BMJ Open 2016; 6(3)e010159
[http://dx.doi.org/10.1136/bmjopen-2015-010159] [PMID: 26975935]
, 39Ashwell M, Cole TJ, Dixon AK. Ratio of waist circumference to height is strong predictor of intra-abdominal fat. BMJ 1996; 313(7056): 559-60.
[http://dx.doi.org/10.1136/bmj.313.7056.559d] [PMID: 8790002]
].

2.3. Data Analysis

At data analysis, seven questionnaires had missing data above 10% and were excluded for a final sample of 508 women, considered in this paper. Data were stored in Microsoft Excel and analyzed using Stata (Intercooled Stata® Version 14, College Station, TX). Descriptive statistics numerical and categorical variables were computed. To determine the association of overweight/obesity with independent variables, 2% (n=12) of mothers who were underweight were excluded from the analyses for a final sample size of 496, which did not compromise the sample power. Backward stepwise elimination procedure was used in a multivariate logistic regression analysis to determine the association between overweight/ obesity and independent variables, and the reference group was normal. We put all the independent variables with a p-value ≤0.20 during bivariate analyses were included in the model. Gradually, variables were eliminated from the regression model at each step to find a reduced model that best explained the data, controlled for confounders. Adjusted odds ratios (AOR) with a 95% confidence interval (CI) were generated and used to determine the independent strength of the associations. Results are presented as median (IQR), frequency (n), percentages (percentage) and AOR (95%CI). Significance was considered at p <0.05.

3. RESULTS

3.1. Socio-demographic and Obstetric Characteristics of Mothers

Data were complete for 508 mothers of primary school children. The socio-demographic and obstetric characteristics of mothers are presented in Table 1. The mean age of mothers was 37±7years. Forty eight percent (48%) of women in this study were younger (24-35years), while 39% were middle-aged and 13% aged above 45years. Mothers in this study were single (63%), unemployed (82%) and 41% had low literacy. Thirty eight percent (38%) of women lived in houses headed by spouses, while 34% were self-headed and 29% by other family members, while 36% lived in household with household size of five members and above. Most mothers (69%) had two to four pregnancies and 71% had a parity of two to four.

3.2. Overweight/obesity Among Mothers

The analysis of the means for weight, height, BMI, WC, HC, WHR, WHtR by age group, using Kruskal Wallis test, are reported in Table 2. Significant differences in weight (p=0.011), BMI (p=0.001), WC (p=0.001), WHR (p=0.001) and WHtR (p=0.0004) were observed by age groups. No significant difference was observed for height and HC. Medians for weight, BMI, WC, WHR and WHtR increased significantly with age group. Table 3 presents the prevalence of BMI, WC, WHR and WHtR by age group, using a chi-square test (χ2). The prevalence of overweight and obesity was 27% and 42%, respectively, while only 2% of the mothers were underweight. Abdominal obesity was prevalent, as indicated by increased WC in 53%, WHR in 33% and WHtR in 41% of mothers. All nutritional indicators were significantly different from the age groups. The prevalence of obesity increased with increasing age, with the highest prevalence of obesity observed in the oldest group, by BMI (65%), WC (75%), WHR (46%) and WHtR (63%).

Table 1
Demographic and obstetric characteristics of women.


Table 2
Comparison of medians for nutritional indicators by age group.


Table 3
Comparison of the nutritional status indicators by age group.


3.3. Weight Status of Mothers by their Characteristics

Weight status of mothers is compared by their characteristics in Table 4. Significant associations between being overweight or obese were observed by age category (p=0.042), age of childbirth (p=0.038), marital status (p=0.011), household head (p=≤0.0001) and household monthly income (p=0.053). Overweight/obesity was more prevalent among mothers who gave birth after 30 years (78%) compared to those who gave birth before 30 years (69%). The prevalence of overweight/obesity was significantly higher among mothers who were married (78%) as compared to those who were single (67%). In addition, overweight/obesity was significantly higher among mothers in households with a monthly income of >$524,65 (82%), in comparison to households with a monthly income of ≤$52,50 (71%), $53,29–$262,26 (73%), and $344,84–$524,60 (53%). The prevalence of overweight/obesity was significantly higher in mothers living in households headed by their spouses (83%), compared to those living in a household headed by themselves (65%) or other family members (63%).

Table 4
Association of weight status of women with socio-demographic and obstetric charecteristics.


Table 5
Multiple logistic analysis; factors associated with overweight/obesity among women.


3.4. Factors Associated with Overweight/Obesity

In the bivariate logistic regression, the age of mothers, marital status, education, household size, household income, household head, and age at first pregnancy were associated with overweight/obesity (p<0.20). To determine the association of overweight/obesity with socio-demographic factors and obstetric history, multivariate logistic regression analysis was performed. Results are presented in Table 5 and showed a significant association between overweight/obesity and household monthly income, household head and number of pregnancies after controlling for potential confounders. The odds of being overweight/obese were significantly higher among mothers living with spouses as household heads (AOR=3.5 95%CI: 1.97-6.31), had two to three pregnancies (AOR=2.4, 95%CI: 1.40-4.20), and five pregnancies and above (AOR=2.5, 95%CI: 1.0-6.37). Mothers who lived in households with a monthly income between $344,84 and $524,60 were less likely to be overweight or obese (AOR=0.31 95%CI: 0.14-0.70).

4. DISCUSSION

The main objective of the study was to determine the socio-demographic determinants of overweight and obesity among mothers of primary school children living in a rural Dikgale HDSS site in South Africa. Mothers in this study were underprivileged due to poor socioeconomic status, as described by high rates of singleness (72%), unemployment (82%), and a household monthly income below $262,26 (86%). These socioeconomic components suggest poverty or poor living conditions in the Dikgale HDSS site, consistent with other studies conducted in the area [24Maimela E, Alberts M, Modjadji SE, et al. The prevalence and determinants of chronic non-communicable disease risk factors amongst adults in the Dikgale health demographic and surveillance system (HDSS) site, Limpopo Province of South Africa. PLoS One 2016; 11(2)e0147926
[http://dx.doi.org/10.1371/journal.pone.0147926] [PMID: 26882033]
, 28Alberts M, Dikotope SA, Choma SR, et al. Health & demographic surveillance system profile: The Dikgale health and demographic surveillance system. Int J Epidemiol 2015; 44(5): 1565-71.
[http://dx.doi.org/10.1093/ije/dyv157] [PMID: 26275454]
]. Poor living conditions in South Africa, are predominant, countrywide [40National Department of Health (NDoH) S South Africa Demographic and Health Survey 2019., 41Shisana OLD, Rehle T, et al. South African National Health and Nutrition Examination Survey (SANHNES-1) 2013.].

South Africa is still battling with issues of poverty, inequality, unemployment and hunger, two decades after democracy [42StatisticsSouthAfrica Millennium Development Goals Country Report 2013. Available from: www.statssagovza/MDG/MDGR_2013.pdf]. A high prevalence of unemployment (87%) and poverty (59%) have been reported nationally, with the highest prevalence of poverty in Limpopo Province; the province in which the current study was conducted [43Ipsos People’s Poll: The “People’s Agenda” puts job creation as priority 2014. Available from: http://wwwipsoscoza/SitePages/Ipsos%20Poll%20The%20“People’s%20Agenda”%20puts%20Job%20Creation%20as%20priorityaspx, 44Statistics South Africa Poverty Trends in South Africa An examination of absolute poverty between 2006 and 2015 2017. Available from: https://wwwstatssagovza/publications/Report-03-10-06/Report-03-10-062015pdf]. Literature documents that low-income individuals and families face a number of challenges in the acquisition of sufficient, nutritious food for a healthy and active lifestyle [45Laraia BA, Leak TM, Tester JM, Leung CW. Biobehavioral factors that shape nutrition in low-income populations: A narrative review 2017.
[http://dx.doi.org/10.1016/j.amepre.2016.08.003]
]. SANHANES-1 found that 51% of people living in rural areas reported that they did not have enough money for basic necessities such as foods [41Shisana OLD, Rehle T, et al. South African National Health and Nutrition Examination Survey (SANHNES-1) 2013.]. According to Govendor et al., lack of access to nutritious and balanced diets remain a major impediment to the health and well-being of people living in rural areas [46Govender L, Pillay K, Siwela M, Modi A, Mabhaudhi T. Food and nutrition insecurity in selected rural communities of KwaZulu-Natal, South Africa—Linking human nutrition and agriculture. Int J Environ Res Public Health 2016; 14(1): 17.
[http://dx.doi.org/10.3390/ijerph14010017] [PMID: 28036008]
].

Socioeconomic status has been linked to both higher rates of overweight/obesity and poor dietary quality, particularly among women [2Cois A, Day C. Obesity trends and risk factors in the South African adult population. BMC Obes 2015; 2(1): 42.
[http://dx.doi.org/10.1186/s40608-015-0072-2] [PMID: 26617987]
, 12Micklesfield LK, Lambert EV, Hume DJ, et al. Socio-cultural, environmental and behavioural determinants of obesity in black South African women. Cardiovasc J Afr 2013; 24(9-10): 369-75.
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, 47Steyn NP, McHiza ZJ. Obesity and the nutrition transition in Sub-Saharan Africa. Ann N Y Acad Sci 2014; 1311(1): 88-101.
[http://dx.doi.org/10.1111/nyas.12433] [PMID: 24725148]
]. Although the mechanism behind the link is unclear, poverty has been associated with unhealthy behaviours [48Lynch JW, Kaplan GA, Salonen JT. Why do poor people behave poorly? Variation in adult health behaviours and psychosocial characteristics by stages of the socioeconomic lifecourse. Soc Sci Med 1997; 44(6): 809-19.
[http://dx.doi.org/10.1016/S0277-9536(96)00191-8] [PMID: 9080564]
, 49Moore CJ, Cunningham SA. Social position, psychological stress, and obesity: A systematic review. J Acad Nutr Diet 2012; 112(4): 518-26.
[http://dx.doi.org/10.1016/j.jand.2011.12.001] [PMID: 22709702]
]. The importance of the association of socioeconomic with living conditions is explained through factors such as education and income. For example, education is considered a fundamental component of socioeconomic as it provides knowledge and life skills that allow better-educated individuals to gain better access to information and resources for the promotion of health. In addition, higher household incomes provide better nutrition, housing, schooling, and recreation [50Adler NE, Newman K. Socioeconomic disparities in health: Pathways and policies. Health Aff (Millwood) 2002; 21(2): 60-76.
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].

The prevalence of overweight/obesity (69%) observed among mothers of primary school children in this study was high. Abdominal/central obesity was also prevalent among these women, as evident by an increased WHtR (41%) and WC (53%). Both prevalence of overweighty/obeity and abdmoninal obesity were higher among the oldest women in the current study. The high prevalence of overweight/obesity and abdominal obesity among black South African women is a public health concern. In South Africa, various studies in different parts of the country have reported the prevalence of overweight/obesity to be between 54% and 76% [16Mashinya F, Alberts M, Cook I, Ntuli S. Determinants of body mass index by gender in the dikgale health and demographic surveillance system site, south africa. Global health action 2018; 11(sup2): 1537613., 24Maimela E, Alberts M, Modjadji SE, et al. The prevalence and determinants of chronic non-communicable disease risk factors amongst adults in the Dikgale health demographic and surveillance system (HDSS) site, Limpopo Province of South Africa. PLoS One 2016; 11(2)e0147926
[http://dx.doi.org/10.1371/journal.pone.0147926] [PMID: 26882033]
, 51Tydeman-Edwards R. Obsesity, undernutrition and the double burden of disease in the Free State 2012.], while the country-wide prevalence is estimated to be in the range of 65% to 68% [40National Department of Health (NDoH) S South Africa Demographic and Health Survey 2019., 41Shisana OLD, Rehle T, et al. South African National Health and Nutrition Examination Survey (SANHNES-1) 2013.]. The findings of the current study are comparable with the countrywide prevalence estimate as well as prevalence reports from various parts of the country. In addition, an increase in the prevalence of overweight/obesity with an increase in age has been reported in previous studies. Literature suggests that body weight increases with age and the prevalence of overweight/obesity are higher among older women, as compared to younger women [18Uthman OA. Patterns, distribution, and determinants of under-and overnutrition among women in Nigeria: A population-based analysis. J Public Health (Bangkok) 2009; 17(5): 289-99.
[http://dx.doi.org/10.1007/s10389-009-0251-z]
, 19Puoane T, Steyn K, Bradshaw D, et al. Obesity in South Africa: The South African demographic and health survey. Obes Res 2002; 10(10): 1038-48.
[http://dx.doi.org/10.1038/oby.2002.141] [PMID: 12376585]
, 52Al-Ghamdi S, Shubair MM, Aldiab A, et al. Prevalence of overweight and obesity based on the body mass index; A cross-sectional study in Alkharj, Saudi Arabia. Lipids Health Dis 2018; 17(1): 134.
[http://dx.doi.org/10.1186/s12944-018-0778-5] [PMID: 29871648]
].

The overall high prevalence of overweight/obesity observed in the current study may be explained by the intake of more energy-dense food as well as a reduction in the level of physical activity [53Alemu F. Assessment of the impact of malnutrition on children at Dilla referral hospital and unity pediatric clinic, Ethiopia. Int J Nutr Metab 2013; 5(6): 105-13.-55Dieffenbach S, Stein AD. Stunted child/overweight mother pairs represent a statistical artifact, not a distinct entity. J Nutr 2012; 142(4): 771-3.
[http://dx.doi.org/10.3945/jn.111.153387] [PMID: 22378330]
]. Previous studies conducted in Dikgale HDSS site have reported a high prevalence of physical inactivity in women (70.8%) has been reported [24Maimela E, Alberts M, Modjadji SE, et al. The prevalence and determinants of chronic non-communicable disease risk factors amongst adults in the Dikgale health demographic and surveillance system (HDSS) site, Limpopo Province of South Africa. PLoS One 2016; 11(2)e0147926
[http://dx.doi.org/10.1371/journal.pone.0147926] [PMID: 26882033]
] as well as the possibility of consumption of energy dense foods contributing to overweight/obesity among women [27Modjadji P, Madiba S. The double burden of malnutrition in a rural health and demographic surveillance system site in South Africa: A study of primary schoolchildren and their mothers. BMC Public Health 2019; 19(1): 1087.
[http://dx.doi.org/10.1186/s12889-019-7412-y] [PMID: 31399048]
]. It is believed that the environment in South Africa has changed over the past decades, with increasing availability and accessibility of energy-dense food. In addition, the World Health survey reported that, in general, women worldwide were physically inactive [56Guthold R, Ono T, Strong KL, Chatterji S, Morabia A. Worldwide variability in physical inactivity a 51-country survey. Am J Prev Med 2008; 34(6): 486-94.
[http://dx.doi.org/10.1016/j.amepre.2008.02.013] [PMID: 18471584]
]. The high rates of overweight and obesity could also be explained by the nutrition transition and urbanization that Africa is facing [19Puoane T, Steyn K, Bradshaw D, et al. Obesity in South Africa: The South African demographic and health survey. Obes Res 2002; 10(10): 1038-48.
[http://dx.doi.org/10.1038/oby.2002.141] [PMID: 12376585]
].

The prevalence of overweight and obesity among rural women in our study was higher than the prevalence reported in other countries, such as Lesotho, Zimbabwe, Kenya and Nigeria [57Adienbo OM. Hart voowa. high prevalence of obesity among indigenous residents of a nigerian ethnic group: the kalabaris in the niger delta region of south-south nigeria. Greener Journal of Medical Sciences 2012; 2(6): 152-6.-62Fadzai Mukora-Mutseyekwa HZ, Lydia Nengomasha and Nicholas Kofi Adjei. Trends in prevalence and related risk factors of overweight and obesity among women of reproductive age in Zimbabwe, 2005–2015. Int J Environ Res Public Health 2019; 16: 2758.
[http://dx.doi.org/10.3390/ijerph16152758]
]. In Lesotho, the prevalence of overweight/obesity among women was estimated to be 44.4% by BMI, and 54.1% by WC. A prevalence of 35% has been reported in Zimbabwe [62Fadzai Mukora-Mutseyekwa HZ, Lydia Nengomasha and Nicholas Kofi Adjei. Trends in prevalence and related risk factors of overweight and obesity among women of reproductive age in Zimbabwe, 2005–2015. Int J Environ Res Public Health 2019; 16: 2758.
[http://dx.doi.org/10.3390/ijerph16152758]
], while in Kenya the prevalence was estimated to be between 20.5% and 43% [58Ettarh R, Van de Vijver S, Oti S, Kyobutungi C. Peer reviewed: overweight, obesity, and perception of body image among slum residents in Nairobi, Kenya, 2008–2009. Prev Chronic Dis 2013; 10., 59Mkuu RS, Epnere K, Chowdhury MAB. Peer reviewed: Prevalence and predictors of overweight and obesity among kenyan women. Prev Chronic Dis 2018; 15., 61Steyn NP, Nel JH, Parker WA, Ayah R, Mbithe D. Dietary, social, and environmental determinants of obesity in Kenyan women. Scand J Public Health 2011; 39(1): 88-97.
[http://dx.doi.org/10.1177/1403494810384426] [PMID: 20851847]
]. In rural Nigeria, a prevalence of 49.3% has been reported among women [57Adienbo OM. Hart voowa. high prevalence of obesity among indigenous residents of a nigerian ethnic group: the kalabaris in the niger delta region of south-south nigeria. Greener Journal of Medical Sciences 2012; 2(6): 152-6.]. These studies attributed the increase in overweight/obesity among women to nutrition transition associated with frequent intake of processed and sugary, as well as high-calorie and high fat diets, cultural lifestyles, dietary choices and socio-demographic characteristics [57Adienbo OM. Hart voowa. high prevalence of obesity among indigenous residents of a nigerian ethnic group: the kalabaris in the niger delta region of south-south nigeria. Greener Journal of Medical Sciences 2012; 2(6): 152-6., 60Rothman M, Ranneileng M, Nel R, Walsh C. Nutritional status and food intake of women residing in rural and urban areas of Lesotho. South Afr J Clin Nutr 2019; 32(1): 21-7.
[http://dx.doi.org/10.1080/16070658.2017.1415783]
, 62Fadzai Mukora-Mutseyekwa HZ, Lydia Nengomasha and Nicholas Kofi Adjei. Trends in prevalence and related risk factors of overweight and obesity among women of reproductive age in Zimbabwe, 2005–2015. Int J Environ Res Public Health 2019; 16: 2758.
[http://dx.doi.org/10.3390/ijerph16152758]
, 63Steyn NP, Labadarios D, Nel J, Kruger HS, Maunder EM. What is the nutritional status of children of obese mothers in South Africa? Nutrition 2011; 27(9): 904-11.
[http://dx.doi.org/10.1016/j.nut.2010.10.007] [PMID: 21367580]
]. In contrast to these findings, some countries in sub-Saharan Africa still report a higher prevalence of underweight in comparison to the findings of the current study (2%). High prevalence of underweight/undernutrition has been reported in Ethiopia (30%), Uganda (12%) and Tanzania (11%) [64Mtumwa AH, Paul E, Vuai SA. Determinants of undernutrition among women of reproductive age in Tanzania mainland. South Afr J Clin Nutr 2016; 29(2): 75-81.
[http://dx.doi.org/10.1080/16070658.2016.1216509]
].

Women who lived in households with a middle-income bracket were less likely to be overweight or obese. Studies have suggested that the prevalence of obesity varies with income level, although patterns differ between high-income and low-income countries [65Davis MA, Murphy SP, Neuhaus JM, Gee L, Quiroga SS. Living arrangements affect dietary quality for U.S. adults aged 50 years and older: NHANES III 1988-1994. J Nutr 2000; 130(9): 2256-64.
[http://dx.doi.org/10.1093/jn/130.9.2256] [PMID: 10958821]
, 66Deshmukh-Taskar P, Nicklas TA, Yang S-J, Berenson GS. Does food group consumption vary by differences in socioeconomic, demographic, and lifestyle factors in young adults? The Bogalusa Heart Study. J Am Diet Assoc 2007; 107(2): 223-34.
[http://dx.doi.org/10.1016/j.jada.2006.11.004] [PMID: 17258958]
]. As explained by Drewsnoski and Spector, the association between low household income and overweight/obesity in this study could be mediated, in part, by the low cost of energy-dense foods and may be reinforced by the consumption of high sugar and fat [67Lipowicz A, Gronkiewicz S, Malina RM. Body mass index, overweight and obesity in married and never married men and women in Poland. Am J Hum Biol 2002; 14(4): 468-75.
[http://dx.doi.org/10.1002/ajhb.10062] [PMID: 12112568]
].

A significant association between weight status and household heads was observed. Further analysis showed that women who lived in households headed by spouses were 3.5 times more likely (95%CI: 1.97-6.31) to be overweight or obese in comparison to those who lived in self-headed households. This might be that married individuals tend to eat a greater number of meals per day and have a higher total intake of energy [65Davis MA, Murphy SP, Neuhaus JM, Gee L, Quiroga SS. Living arrangements affect dietary quality for U.S. adults aged 50 years and older: NHANES III 1988-1994. J Nutr 2000; 130(9): 2256-64.
[http://dx.doi.org/10.1093/jn/130.9.2256] [PMID: 10958821]
-67Lipowicz A, Gronkiewicz S, Malina RM. Body mass index, overweight and obesity in married and never married men and women in Poland. Am J Hum Biol 2002; 14(4): 468-75.
[http://dx.doi.org/10.1002/ajhb.10062] [PMID: 12112568]
]. This may account for the higher prevalence of overweight and obesity observed in mothers who are married in the current study, compared to those who are not. Furthermore, the likelihood of mothers who are married being overweight or obesity has been previously reported in South Africa, and may be related to food affordability [15Wagner RG, Crowther NJ, Gómez-Olivé FX, Kabudula C, Kahn K, Mhembere M, et al. Sociodemographic, socioeconomic, clinical and behavioural predictors of body mass index vary by sex in rural South African adults-findings from the AWI-Gen study. Global health action 2018; 11(sup2): 1549436.]. A similar association has been reported in both Kenya and Poland [67Lipowicz A, Gronkiewicz S, Malina RM. Body mass index, overweight and obesity in married and never married men and women in Poland. Am J Hum Biol 2002; 14(4): 468-75.
[http://dx.doi.org/10.1002/ajhb.10062] [PMID: 12112568]
, 68Masibo P, Buluku E, Menya D, Malit V. Prevalence and determinants of under-and over-nutrition among adult Kenyan women; evidence from the Kenya Demographic and Health survey 2008-09. East Afr J Public Health 2013; 10: 611-22.]. In contrast, for women in the self-headed household, this likely reflects the challenges of being both the sole provider and caretaker within a household. The effect of cultural dimensions on most single parent families headed by women has been reported, with single mothers being placed at a greater risk for poverty and food insecurity, and to some extent, obesity [69Martin MA, Lippert AM. Feeding her children, but risking her health: The intersection of gender, household food insecurity and obesity. Soc Sci Med 2012; 74(11): 1754-64.
[http://dx.doi.org/10.1016/j.socscimed.2011.11.013] [PMID: 22245381]
].

In Ethiopia, another LMIC, adults in the highest income quintile were 3.16 times (95% CI: 1.88–5.30) more likely to be overweight/obese as compared to adults from the lowest quintile [70Darebo T, Mesfin A, Gebremedhin S. Prevalence and factors associated with overweight and obesity among adults in Hawassa city, southern Ethiopia: A community based cross-sectional study. BMC Obes 2019; 6(1): 8.
[http://dx.doi.org/10.1186/s40608-019-0227-7] [PMID: 30867934]
]. Other studies from developing countries have documented an increase in obesity with wealth, partially explained by individuals from countries in transition overeating because of economic access to food [71Monteiro CA, Conde WL, Popkin BM. Independent effects of income and education on the risk of obesity in the Brazilian adult population. J Nutr 2001; 131(3): 881S-6S.
[http://dx.doi.org/10.1093/jn/131.3.881S] [PMID: 11238779]
, 72Dinsa GD, Goryakin Y, Fumagalli E, Suhrcke M. Obesity and socioeconomic status in developing countries: A systematic review. Obes Rev 2012; 13(11): 1067-79.
[http://dx.doi.org/10.1111/j.1467-789X.2012.01017.x] [PMID: 22764734]
]. In contrast, in developed countries, studies suggest that poverty is associated with a higher risk for obesity, because economically disadvantaged people are more likely to consume junk and empty-calorie food, important risk factors for obesity [73Kim TJ, von dem Knesebeck O. Income and obesity: What is the direction of the relationship? A systematic review and meta-analysis. BMJ Open 2018; 8(1)e019862
[PMID: 29306894]
, 74McLaren L. Socioeconomic status and obesity. Epidemiol Rev 2007; 29(1): 29-48.
[http://dx.doi.org/10.1093/epirev/mxm001] [PMID: 17478442]
].

The current study showed that women who had been pregnant two to three times (AOR=2.4, 95%CI: 1.40-4.20) or five times and more (AOR=2.5, 95%CI: 0.99 – 6.37) were more likely to be overweight or obese than those reported to have been pregnant once. The literature on the association of the number of historical pregnancies and overweight/obesity is scant. Martinéz et al. reported that women with ≥4 pregnancies, relative to those with 1–2 pregnancies, were 1.59 (95%CI: 1.01–2.47) more likely to be obese [75Martínez ME, Pond E, Wertheim BC, et al. Association between parity and obesity in Mexican and Mexican-American women: Findings from the Ella binational breast cancer study. J Immigr Minor Health 2013; 15(2): 234-43.
[http://dx.doi.org/10.1007/s10903-012-9649-8] [PMID: 22618357]
]. Parity, which is closely related to the number of pregnancies, was not associated with overweight and obesity in the current study. In agreement with our findings, higher parity (≥4 pregnancies) was not significantly associated with a higher BMI in the study by Martinéz et al. [75Martínez ME, Pond E, Wertheim BC, et al. Association between parity and obesity in Mexican and Mexican-American women: Findings from the Ella binational breast cancer study. J Immigr Minor Health 2013; 15(2): 234-43.
[http://dx.doi.org/10.1007/s10903-012-9649-8] [PMID: 22618357]
]. In contrast, most studies reported a positive association between parity and weight gain or BMI [76Abrams B, Heggeseth B, Rehkopf D, Davis E. Parity and body mass index in US women: A prospective 25-year study. Obesity (Silver Spring) 2013; 21(8): 1514-8.
[http://dx.doi.org/10.1002/oby.20503] [PMID: 23630108]
-79Mansour AA, Ajeel NA. Parity is associated with increased waist circumference and other anthropometric indices of obesity. Eat Weight Disord 2009; 14(2-3): e50-5.
[http://dx.doi.org/10.1007/BF03327800] [PMID: 19934637]
]. Among Chinese middle- and older-aged women in Shanghai, weight gain was associated with increasing parity [80Wen W, Gao YT, Shu XO, et al. Sociodemographic, behavioral, and reproductive factors associated with weight gain in Chinese women. Int J Obes Relat Metab Disord 2003; 27(8): 933-40.
[http://dx.doi.org/10.1038/sj.ijo.0802318] [PMID: 12861234]
]. In Guangzhou in China, a positive correlation between parity and obesity, as measured by BMI, was reported [81Lao XQ, Thomas GN, Jiang CQ, et al. Parity and the metabolic syndrome in older Chinese women: The Guangzhou Biobank Cohort Study. Clin Endocrinol (Oxf) 2006; 65(4): 460-9.
[http://dx.doi.org/10.1111/j.1365-2265.2006.02615.x] [PMID: 16984238]
].

The mechanisms underlying the association between parity and obesity are complicated and remain unknown [82Mannan M, Doi SA, Mamun AA. Association between weight gain during pregnancy and postpartum weight retention and obesity: A bias-adjusted meta-analysis. Nutr Rev 2013; 71(6): 343-52.
[http://dx.doi.org/10.1111/nure.12034] [PMID: 23731445]
]. Gestational weight gain has been found to be associated with higher postpartum weight retention [83Parihar M. Obesity and infertility. Rev Gynaecol Pract 2003; 3(3): 120-6.
[http://dx.doi.org/10.1016/S1471-7697(03)00061-3]
] especially long term [84Dufour DL, Reina JC, Spurr G. Energy intake and expenditure of free-living, pregnant Colombian women in an urban setting. Am J Clin Nutr 1999; 70(2): 269-76.
[http://dx.doi.org/10.1093/ajcn.70.2.269] [PMID: 10426705]
], suggesting that maternal transition during pregnancy may partially explain postpartum obesity [85Magiakou MA, Mastorakos G, Rabin D, et al. The maternal hypothalamic-pituitary-adrenal axis in the third trimester of human pregnancy. Clin Endocrinol (Oxf) 1996; 44(4): 419-28.
[http://dx.doi.org/10.1046/j.1365-2265.1996.683505.x] [PMID: 8706308]
]. A prospective study indicated that childbearing might increase visceral adipose tissue, independent of an overall increase in body fat [86Gunderson EP, Sternfeld B, Wellons MF, et al. Childbearing may increase visceral adipose tissue independent of overall increase in body fat. Obesity (Silver Spring) 2008; 16(5): 1078-84.
[http://dx.doi.org/10.1038/oby.2008.40] [PMID: 18356843]
]. However, it is worth noting that there is no consensus in the literature regarding a linear association between parity and obesity [87Gunderson EP. Childbearing and obesity in women: Weight before, during, and after pregnancy. Obstet Gynecol Clin North Am 2009; 36(2): 317-332, ix.
[http://dx.doi.org/10.1016/j.ogc.2009.04.001] [PMID: 19501316]
].

4.1. Limitations of the Study

The current study had similar limitations to earlier studies [25Modjadji P, Madiba S. Childhood undernutrition and its predictors in a rural health and demographic surveillance system site in south africa. Int J Environ Res Public Health 2019; 16(17): 3021.
[http://dx.doi.org/10.3390/ijerph16173021] [PMID: 31438531]
, 27Modjadji P, Madiba S. The double burden of malnutrition in a rural health and demographic surveillance system site in South Africa: A study of primary schoolchildren and their mothers. BMC Public Health 2019; 19(1): 1087.
[http://dx.doi.org/10.1186/s12889-019-7412-y] [PMID: 31399048]
], extracted from the larger study [30Modjadji P. Growth patterns and socio-cultural beliefs and prcatices in Dikgale, Limpopo Province: A mixed method study of primary school children and their mothers 2019.]. The use of a cross-sectional study design resulted in only inferences being made about the associations of overweight/obesity with socio-demographic factors. The study could not establish causality or temporality of events. Nonetheless, the inferences estimated in this study could be a good measure of the association between the identified factors and overweight/obesity among women in the study area. It is worth mentioning that sampling for the larger study was primarily based on a representative sample of primary schools and schoolchildren in Dikgale HDSS site, hence, the women who participated in this study matched with their children. The results of this study are applicable to Dikgale HDSS site and cannot be generalized to other areas in South Africa, because this site is a very small rural area and the circumstances may vary considerably in urban areas.

CONCLUSION

This study aimed to investigate the socio-demographic determinants of overweight and obesity among women living in a rural Dikgale HDSS site in South Africa. The prevalence of overweight and obesity in women was 27% and 42%, respectively. The findings further showed that the key determinants were living in spouse-headed household, household monthly income and more than one pregnancy. With evidence on the rise of NCDs and the indisputable impact overweight and obesity have, there is a need for urgent action. Tailor-made women program focusing on strengthening the social aspects that promote overweight and obesity are necessary to address the high prevalence, in order to avert its consequences.

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

All procedures involving human subjects were approved by the Sefako Makgatho Health Sciences University Research and Ethics Committee, South Africa [SMUREC/H/161/2016: PG]. Permission to conduct the study was obtained from the Limpopo Province Department of Education, South Africa.

HUMAN AND ANIMAL RIGHTS

No animals were used in this research. All human research procedures followed were in accordance with ethical standards of the committee responsible fur human experiments (institutional and national), and with the Helenski Declaration of 1975, as revised in 2013.

CONSENT FOR PUBLICATION

Written informed consent was obtained from each participant prior to the study.

AVAILABILITY OF DATA AND MATERIALS

The data supporting the findings of the article is available from the corresponding author [PM] upon reasonable request.

FUNDING

The Research Development Grant (RDG) from Sefako Makgatho Health Sciences University (D105-ModjadjiRDG) funded this research and Vlaamse Interuniversitaire Raad (VLIR), Belgium funded the pilot study

CONFLICT OF INTEREST

The author declares no conflict of interest, financial or otherwise.

ACKNOWLEDGEMENTS

The author would like to thank the Department of education, Limpopo Province (South Africa) for permission to conduct the study. She also appreciates the mothers of primary school children in Dikgale HDSS site, for full participation in the study. She acknowledges Professor Sphiwe Madiba, her doctoral promoter/supervisor, and Professor Marianne Alberts, the founder of the Dikgale HDSS site. She appreciate the support of the South African Medical Research Council (MRC) through its Division of Research Capacity Development under the Mid-Career Scientist Programme.

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