Factors associated with underweight, overweight, and obesity in reproductive age Tanzanian women

Kedir Y Ahmed; Abdon G Rwabilimbo; Solomon Abrha; Andrew Page; Amit Arora; Fentaw Tadese; Tigistu Yemane Beyene; Abdulaziz Seiko; Abdulhafiz A Endris; Kingsley E Agho; Felix Akpojene Ogbo; on behalf of the Global Maternal and Child Health Research collaboration (GloMACH)

doi:10.1371/journal.pone.0237720

. 2020 Aug 24;15(8):e0237720. doi: 10.1371/journal.pone.0237720

Factors associated with underweight, overweight, and obesity in reproductive age Tanzanian women

Kedir Y Ahmed ^1,^2,^*, Abdon G Rwabilimbo ^2,^3,^‡, Solomon Abrha ^4,^‡, Andrew Page ², Amit Arora ^2,^5,^6,⁷, Fentaw Tadese ⁸, Tigistu Yemane Beyene ⁹, Abdulaziz Seiko ¹⁰, Abdulhafiz A Endris ¹¹, Kingsley E Agho ², Felix Akpojene Ogbo ^2,¹²; on behalf of the Global Maternal and Child Health Research collaboration (GloMACH)^¶

Editor: Hajo Zeeb¹³

¹College of Medicine and Health Sciences, Samara University, Samara, Ethiopia

²Translational Health Research Institute, Western Sydney University, Sydney, NSW, Australia

³Chato District Council, Geita Region, Northwestern Tanzania

⁴School of Public Health, College of Medicine and Health Science, Wolayta Sodo University, Wolayta Sodo, Ethiopia

⁵School of Health Sciences, Western Sydney University, Sydney, NSW, Australia

⁶Oral Health Services, Sydney Local Health District and Sydney Dental Hospital, NSW Health, Surry Hills, NSW, Australia

⁷Discipline of Child and Adolescent Health, Sydney Medical School, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia

⁸College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia

⁹CDT Africa, College of Health Science, Addis Ababa University, Addis Ababa, Ethiopia

¹⁰CARE Ethiopia, Afar, Ethiopia

¹¹Ethiopian Public Health Institute, Addis Ababa, Ethiopia

¹²General Practice Unit, Prescot Specialist Medical Centre Makurdi, Makurdi, Benue State, Nigeria

¹³Leibniz Institute for Prevention Research and Epidemiology BIPS, GERMANY

Competing Interests: The authors declare that they have no competing interests.

‡ These authors are joint second authors on this work.

¶ Members of GloMACH are provided in the acknowledgments.

^✉

* E-mail: kedirymam331@gmail.com

Roles

Kedir Y Ahmed: Conceptualization, Data curation, Formal analysis, Methodology, Software, Writing – original draft, Writing – review & editing

Abdon G Rwabilimbo: Conceptualization, Writing – original draft, Writing – review & editing

Solomon Abrha: Conceptualization, Writing – original draft, Writing – review & editing

Andrew Page: Writing – review & editing

Amit Arora: Writing – review & editing

Fentaw Tadese: Writing – review & editing

Tigistu Yemane Beyene: Writing – review & editing

Abdulaziz Seiko: Writing – review & editing

Abdulhafiz A Endris: Writing – review & editing

Kingsley E Agho: Writing – review & editing

Felix Akpojene Ogbo: Conceptualization, Writing – review & editing

Hajo Zeeb: Editor

PMCID: PMC7444815 PMID: 32834011

Abstract

Background

Underweight, overweight, and obesity are major public health challenges among reproductive-age women of lower- and middle-income countries (including Tanzania). In those settings, obesogenic factors (attributes that promote excessive body weight gain) are increasing in the context of an existing high burden of undernutrition. The present study investigated factors associated with underweight, overweight, and obesity among reproductive age women in Tanzania.

Methods

This study used 2015–16 Tanzania Demographic and Health Survey data (n = 11735). To account for the hierarchical nature of the data (i.e., reproductive age women nested within clusters), multilevel multinomial logistic regression models were used to investigate the association between individual-level (socioeconomic, demographic and behavioural) and community-level factors with underweight, overweight, and obesity.

Results

Reproductive age women who were informally employed (relative risk ratio [RRR] = 0.79; 95% confidence interval [CI]: 0.64, 0.96), those who were currently married (RRR = 0.59; 95% CI: 0.43, 0.82) and those who used contraceptives (RRR = 0.70; 95% CI: 0.54, 0.90) were less likely to be underweight. Reproductive age women who attained secondary or higher education (RRR = 1.48; 95% CI: 1.11, 1.96), those who resided in wealthier households (RRR = 2.31; 95% CI: 1.78, 3.03) and those who watched the television (RRR = 1.26; 95% CI: 1.06, 1.50) were more likely to be overweight. The risk of experiencing obesity was higher among reproductive age women who attained secondary or higher education (RRR = 1.79; 95% CI: 1.23, 2.61), those who were formally employed (RRR = 1.50; 95% CI: 1.14, 1.98), those who resided in wealthier households (RRR = 4.77; 95% CI: 3.03, 7.50), those who used alcohol (RRR = 1.43; 95% CI: 1.12, 1.82) and/or watched the television (RRR = 1.70; 95% CI: 1.35, 2.13).

Conclusion

Our study suggests that relevant government jurisdictions need to identify, promote, and implement evidence-based interventions that can simultaneously address underweight and overweight/obesity among reproductive age women in Tanzania.

Introduction

Underweight and overweight (and obesity) have historically been considered as separate public health challenges, but recent global evidence is showing that underweight and overweight/obesity can co-exist within similar communities [1]. This is particularly common in low and middle-income countries (LMICs) where obesogenic factors (attributes that promote excessive body weight gain) are increasing in the context of an existing high burden of undernutrition [2]. The World Health Organization (WHO) describes the coexistence of undernutrition along with overweight, obesity or diet-related non-communicable diseases (NCDs) as the “double burden of malnutrition” (DBM) [3].

In reproductive aged women, the DBM is associated with adverse health and reproductive outcomes. For example, during the early pubertal period, overweight and obesity is associated with psychosocial problems and abnormal uterine bleeding due to irregularity in the menstrual cycle from peripheral conversion of androgens to oestrogen [4–6]. During pregnancy and labour, overweight and obesity are associated with an increased risk of gestational diabetes and pre-eclampsia, haemorrhage, caesarean birthing, and maternal and early neonatal death [7, 8]. In all populations, overweight and obesity are associated with an increased risk of NCDs such as Type 2 diabetes mellitus, cardiovascular and respiratory diseases [9]. In addition, underweight among women is associated with lower economic productivity and can also lead to increased rates of morbidity and mortality [10].

Globally, nearly one-third of the population is affected by at least one form of malnutrition (underweight, overweight, or obesity) [3, 11]. In 2016, more than 600 million adults were underweight, while nearly two billion adults were overweight or obese [12–14]. Evidence has shown that both underweight and overweight are higher among women than men, potentially due to women’s reproductive health status, lower social status, poverty, and a lack of education [15–17]. Evidence from LMICs, has suggested that South Asian and sub-Saharan Africa countries (including Tanzania) are facing nutrition and epidemiologic transitions. These mean that there is a shift towards less nutritious and cheaper foods increasing sedentary behaviours, respectively [2, 18–21]. Nutrition and epidemiologic transition has been attributable to changes to available food quantity and quality, rapid economic growth and urbanization [2, 18, 19, 22, 23].

In the past two and half decades in Tanzania, while there has been no reduction in the prevalence of undernutrition, the issues of overweight and obesity are emerging as major public health challenges, particularly in reproductive age women [24]. The Tanzania Demographic and Health Survey (TDHS) report showed that the prevalence of overweight and obesity in reproductive age women increased from 11% in 1991 to 28% in 2016 [24]. However, the prevalence of underweight did not improve over the same period [24]. Previously published studies in Tanzania have shown that overweight or obese reproductive aged women were more likely to be older, educated, married, and resided in wealthier households and urban areas [25, 26]. In contrast, women who were never married, those who resided in poor households, and attained lower education were more likely to be underweight [25, 26]. Although useful, these studies have a number of limitation. First, the studies were based on the older 2010 TDHS data, which may not reflect the current socio-economic, demographic and health situation of the country. Second, the previous studies did not consider the relevant methodological approach (including the hierarchical nature of data) in handling the large dataset. Finally, our study used a statistical modelling technique (multinomial logistic regression and adjusted for relevant confounders), an important statistical approach that was not employed in the past studies conducted in Tanzania.

Understanding of the factors associated with underweight, overweight and obesity among reproductive age Tanzanian women would be helpful for relevant health practitioners to implement evidence based and effective interventions to address both underweight and overweight (and obesity) [27, 28]. This context-specific information is also crucial to national and international stakeholders given the current commitment to achieve Sustainable Development Goal 3 (SDG–3, to end all forms of malnutrition) [29] and the Global Action Plan for the Prevention and Control of NCDs target 9 (to halt the rise in obesity) in Tanzania [30]. Accordingly, the present study investigated factors associated with underweight, overweight, and obesity among reproductive age women in Tanzania.

Methods

Data source

The study used the 2015–16 TDHS data (n = 11735). The TDHS was collected by the National Bureau of Statistics, Office of the Chief Government Statistician (OCGS) in Zanzibar and Inner City Fund (ICF) International. The funding for the TDHS was from the Government of Tanzania, Global Affairs Canada and the United States Agency for International Development [24]. The TDHS collected relevant information on maternal and child health indicators, including height and weight measurements for the reproductive age women.

The TDHS used a two-stage stratified cluster sampling technique to select the study participants. In stage one, enumeration areas (EAs) were selected proportional to each geographical zones in Tanzania. The EAs were based on the 2012 Tanzania Population and Housing Censuses [31]. In stage two, a systematic random sampling technique was used to select households after the complete household listing was conducted in each EAs. Out of 13,376 households included in the survey, 13,158 reproductive age women who were permanent residents or who spent the night in the selected households the night before the survey provided weight and height measurements.

For this study, women who were pregnant, or who had given birth in the two months preceding the survey were excluded to minimize measurement bias due to initial weight gain from pregnancy and childbirth, consistent with the TDHS report [24] and previously published studies [18, 25, 26, 32]. A total of sample 11,735 reproductive age women were included in the study. The detailed methodology of the TDHS is provided elsewhere [24].

Outcome variable

The main outcome variable was the nutritional status of reproductive age women, measured using the WHO adult body mass index (BMI) classification [13], and used by the Tanzanian National Bureau of Statistics and ICF International [24]. BMI was defined as a woman’s weight in kilograms divided by the square of her height in meters (kg/m²). The survey used an electronic SECA 874 flat scale designed for mobile use to measure weight and Shorr measuring board to assess height. BMI was classified into four groups, and these subsequently formed the study outcome variables:

Underweight: BMI < 18.5 kg/m²
Normal weight: BMI ≥ 18.5 kg/m² and BMI ≤ 24.9 kg/m²
Overweight: BMI ≥ 25.0 kg/m² and BMI ≤ 29.9 kg/m2
Obesity: BMI ≥ 30.0 kg/m²

Independent variables

Independent variables were selected based on previous studies from LMICs [18, 19, 25, 26, 32–37] and data availability in the TDHS. These factors were broadly classified as socio-economic, demographic, behavioural and community-level factors.

Socioeconomic factors included women’s education (categorised as ‘no schooling’, ‘primary education’, or ‘secondary or higher education’), women’s employment (categorised as ‘no employment, ‘informal employment or ‘formal employment), and household wealth index (categorised as ‘poor’, ‘middle’ or ‘rich’). Informal employment included agricultural jobs, skilled manual, and unskilled manual works, while formal employment included professional work, technical, management, sales and clerical jobs. Household wealth index was computed by the National Bureau of Statistics and ICF International using principal component analysis (PCA). The PCA considers the ownership of household assets such as toilets, electricity, television, radio, fridge, and bicycle, as well as the availability of a source of drinking water and floor material of the main house [38].

Demographic factors included women’s age (categorised as ‘15–24 years’, ‘25–34 years’ or ‘35 and above years’), women’s parity (classified as ‘none’, ‘1–4 children’ ‘5 or more children’), and contraceptive use (classified as “Yes” or “No”). Behavioural factors included women’s exposure to the media (radio, magazine/newspaper or television), alcohol drinking (classified as “Yes” or “No”) and cigarette smoking (classified as “Yes” or “No”). Women who were exposed to the media at least once a week were classified as ‘Yes’ otherwise were classified as ‘No’.

Community-level factors included the place of residence (classified as rural or urban) and region of residence (classified as ‘Western Zone’, ‘Northern Zone’, ‘Southern Highlands’, ‘Southern Zone’, ‘South West Highlands’, ‘Lake Zone’, ‘Eastern Zone’, ‘Central Zone’, and ‘Zanzibar’).

Statistical analysis

Descriptive analyses involved the calculation of frequencies and percentages for each of the study variables. This was followed by the estimation of the prevalence of underweight, overweight, and obesity by socioeconomic, demographic, behavioural and community-level factors.

Multilevel multinomial logistic regression modelling was used to examine the association between individual-level (socioeconomic, demographic, behavioural factors), and community-level factors and (i) underweight, (ii) overweight, and (iii) obesity using normal weight groups as a reference category. For this study, using multilevel modelling has the following advantages over the classical single-level logistic regression models. Firstly, multilevel modelling accounts the hierarchical nature of the data (reproductive age women [level I] were conceptualised as being nested within clusters [level II]), consistent with previously published studies [32, 37, 39]. Failing to recognise hierarchical structures underestimates the standard errors of regression coefficients, leading to an overstatement of statistical significance Secondly, multilevel modelling helps to consider the dependence of observations in the same clusters (i.e., reproductive age women in the same cluster tend to be more similar in their nutritional status than in different clusters) [40, 41]. Finally, multilevel modelling allows estimating cluster-level effects (random effects) simultaneously with the measure of associations for community-level predictors (e.g., place of residence).

Regression models were specified in four stages. In stage one, we developed a null unconditional model without any study variable. In stage two, individual-level factors (socio-economic, demographic and behavioural factors) were included in the model. In stage three, community-level factors (place of residence and region of residence) were included without variables from stage two. In the final model, both individual and community-level factors were included in the model. This final model consisting of both the individual and community-level factors model was exported in the results, as it had the smallest deviance and best explains the variation in the outcome variables. S2 Table presented the random variation and model fitness test results of fitted models.

Relative risk ratios with corresponding 95% confidence intervals (CIs) were estimated as the measure of association between the study factors and outcome variables. Multicollinearity was checked using ‘vif’ command and no significant results were evident. All statistical analyses were conducted using Stata version 14.0 (StataCorp, USA) with ‘svy’ command to adjust for sampling weights, clustering effects and stratification, and the ‘gsem’ function was used for multinomial models.

Ethics approval and consent to participate

The survey was conducted after ethical approval was obtained from the National Institute for Medical Research in Tanzania. During the survey written informed consent were obtained from study participants before the commencement of data collection. Approval to use the data was sought from Measure DHS for this study and permission was granted.

Results

Characteristics of the study participants

Among reproductive age women, nearly half (49.6%) were from wealthy households, and 61.6% attained primary education. More than two-fifth (43.7%) of the study participants had no employment, and more than half (53.5%) of women watched the television. Among study participants, 16.3% used alcohol, and 0.4% of them were smokers. Nearly two-third (63.1%) of women resided in rural households (S1 Table).

Prevalence of underweight, overweight and obesity among reproductive age women in Tanzania

The prevalence of underweight was higher among reproductive age women who had no employment (14.5%) compared to those who were formally employed (5.6%). Women who resided in wealthy households had a higher prevalence of overweight (23.9%) compared to those who resided in poor-level households (11.5%). The prevalence of obesity was higher among reproductive age women who attained secondary or higher education (13.1%) compared to those who had no schooling (4.9%) [Table 1].

Table 1. Prevalence of underweight, overweight and obesity by study factors in reproductive age Tanzanian women, TDHS 2015–16.

Variables	Underweight	Normal weight	Overweight	Obesity	^**P value
Variables	^*n (%)	n (%)	n (%)	n (%)	^**P value
Socioeconomic factors
Women’s education
No schooling	166 (9.9)	1152 (69.1)	269 (16.1)	81 (4.9)	<0.001
Primary school	663 (9.2)	4539 (62.8)	1303 (18.0)	724 (10.0)
Secondary and above	277 (9.7)	1604 (56.4)	591 (20.8)	371 (13.1)
Women’s employment
No employment	394 (14.5)	1708 (63.0)	422 (15.6)	188 (6.9)	<0.001
Formal employment	52 (5.6)	416 (44.2)	269 (28.6)	203 (21.6)
Informal employment	171 (6.7)	1370 (53.6)	572 (22.4)	443 (17.3)
Marital status
Not married	501 (15.7)	2118 (66.4)	437 (13.7)	133 (4.2)	<0.001
Currently married	485 (7.0)	4224 (60.6)	1409 (20.2)	848 (12.2)
Formerly married	121 (7.6)	953 (60.1)	317 (20.0)	195 (12.3)
Household wealth status
Poor	494 (12.9)	2810 (73.2)	442 (11.5)	92 (2.4)	<0.001
Middle	184 (8.8)	1468 (70.6)	332 (16.0)	94 (4.5)
Rich	428 (7.4)	3016 (51.8)	1388 (23.9)	990 (17.0)
Demographic factors
Women’s age
15–24 years	631 (13.4)	3363 (71.7)	562 (12.0)	133 (2.8)	<0.001
25–34 years	205 (6.2)	1987 (60.0)	724 (21.9)	395 (11.9)
35–49 years	270 (7.2)	1943 (52.0)	875 (23.4)	648 (17.3)
Parity
None	491 (16.1)	2031 (66.4)	408 (13.4)	125 (4.1)	<0.001
1–4 children	398 (6.8)	3503 (59.8)	1196 (20.4)	759 (13.0)
5+ children	217 (7.7)	1761 (62.3)	557 (19.7)	292 (10.3)
Behavioural factors
Listening radio
No	309 (12.2)	1680 (66.1)	396 (15.6)	156 (6.1)	<0.001
Yes	796 (8.7)	5615 (61.0)	1767 (19.2)	1020 (11.1)	<0.001
Read magazine
No	658 (10.1)	4307 (66.1)	1079 (16.6)	473 (7.3)	<0.001
Yes	447 (8.9)	2987 (57.2)	1082 (20.7)	703 (13.5)	<0.001
Watch television
No	613 (11.3)	3764 (69.0)	802 (14.7)	274 (5.0)	<0.001
Yes	492 (7.8)	3530 (56.2)	1361 (21.7)	902 (14.4)	<0.001
Alcohol use
No	991 (10.1)	6181 (62.9)	1753 (17.8)	900 (9.2)	<0.001
Yes	115 (6.0)	1113 (58.2)	410 (21.4)	276 (14.4)	<0.001
Smoking
No	1097 (9.4)	7264 (62.1)	2162 (18.5)	1166 (10.0)	0.018
Yes	8 (16.7)	30 (61.3)	1 (1.4)	10 (20.6)	0.018
Contraceptive use
No	865 (11.6)	4925 (64.6)	1199 (16.1)	581 (7.8)	<0.001
Yes	241 (5.7)	2469 (57.80	963 (22.6)	596 (14.0)	<0.001
Community-level factors
Place of residence
Urban	319 (7.4)	2213 (52.2)	1033 (23.9)	765 (17.7)	<0.001
Rural	786 (10.6)	5082 (68.6)	1129 (15.3)	411 (5.6)	<0.001
Region of residence
Western Zone	112 (10.4)	733 (67.7)	163 (15.1)	74 (6.8)	<0.001
Northern Zone	142 (9.9)	770 (53.8)	297 (20.8)	74 (6.8)
Southern Highlands	41 (7.4)	500 (68.0)	128 (17.4)	223 (15.6)
Southern Zone	54 (8.4)	407 (63.2	128 (19.9)	58 (7.9)
Southwest Zone	43 (4.4)	712 (65.4)	214 (19.7)	111 (10.2)
Lake Zone	218 (10.1)	2126 (71.1)	394 (13.2)	143 (4.8)
Eastern Zone	147 (6.6)	1130 950.5)	562 (25.1)	398 (17.8)
Central zone	122 (14.8)	738 (63.4)	198 (17.0)	53 (4.6)
Zanzibar	62 (11.9)	178 (49.2)	78 (21.7)	62 (17.2)

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*n indicates the weighted count

**P value indicates x² test results in biavariate analysis

Factors associated with underweight in Tanzanian women

Reproductive age women who were informally employed were less likely to be underweight compared to those who had no employment (relative risk ratio [RRR] = 0.79; 95% confidence interval [CI]: 0.64, 0.96). Women who were currently married were less likely to be underweight compared to those who were single (RRR = 0.59; 95% CI: 0.43, 0.82). Reproductive age women who used contraceptives were less likely to be underweight compared to their counterparts (RRR = 0.70; 95% CI: 0.54, 0.90). The risk of being underweight was significantly higher among women who smoked cigarettes and those who resided in Southern Tanzania compared to those who did not smoke cigarettes or resided in Western Tanzania, respectively (RRR = 4.31; 95% CI: 1.46, 12.74 for smoking and RRR = 0.33; 95% CI: 0.17, 0.61 for region) [Table 2].

Table 2. Factors associated with underweight, overweight and obesity in reproductive age Tanzanian women, TDHS 2015–16.

Variables	Underweight	Overweight	Obesity
	RRR (95% CI)	RRR (95% CI)	RRR (95% CI)
Socioeconomic factors
Women’s education
No schooling	1.00	1.00	1.00
Primary school	1.22 (0.88, 1.69)	1.26 (0.97, 1.64)	1.60 (1.12, 2.28)
Secondary and above	1.16 (0.81, 1.67)	1.48 (1.11, 1.96)	1.79 (1.23, 2.61)
Women’s employment
No employment	1.00	1.00	1.00
Formal employment	0.77 (0.55, 1.08)	1.23 (0.98, 1.53)	1.50 (1.14, 1.98)
Informal employment	0.79 (0.64, 0.96)	1.00 (0.85, 1.17)	1.27 (1.03, 1.58)
Marital status
Not married	1.00	1.00	1.00
Currently married	0.59 (0.43, 0.82)	1.47 (1.13, 1.91)	1.78 (1.25, 2.54)
Formerly married	0.64 (0.41, 0.98)	1.19 (0.87, 1.64)	1.56 (1.03, 2.34)
Household wealth status
Poor	1.00	1.00	1.00
Middle	0.75 (0.57, 1.00)	1.51 (1.13, 2.02)	1.89 (1.14, 3.17)
Rich	0.77 (0.60, 1.00)	2.31 (1.78, 3.03)	4.77 (3.03, 7.50)
Demographic factors
Women’s age
15–24 years	1.00	1.00	1.00
25–34 years	0.93 (0.69, 1.23)	2.19 (1.79, 2.68)	3.92 (2.93, 5.24)
35–49 years	1.36 (0.94, 1.96)	3.31 (2.59, 4.23)	9.94 (7.20, 13.73)
Parity
None	1.00	1.00	1.00
1–4 children	0.87 (0.63, 1.20)	1.19 (0.92, 1.55)	1.23 (0.86, 1.76)
5+ children	0.71 (0.45, 1.13)	1.02 (0.73, 1.42)	0.99 (0.65, 1.52)
Behavioural factors
Listening radio
No	1.00	1.00	1.00
Yes	0.87 (0.70, 1.08)	1.00 (0.82, 1.21)	1.03 (0.79, 1.35)
Read magazine
No	1.00	1.00	1.00
Yes	0.95 (0.79, 1.15)	1.15 (0.99, 1.33)	1.33 (1.10, 1.60)
Watch television
No	1.00	1.00	1.00
Yes	0.90 (0.73, 1.10)	1.26 (1.06, 1.50)	1.70 (1.35, 2.13)
Alcohol use
No	1.00	1.00	1.00
Yes	0.93 (0.66, 1.31)	1.17 (0.96, 1.31)	1.43 (1.12, 1.82)
Smoking
No	1.00	1.00	1.00
Yes	4.31 (1.46, 12.74)	0.43 (0.90, 2.10)	1.07 (0.29, 3.95)
Contraceptive use
No	1.00	1.00	1.00
Yes	0.70 (0.54, 0.90)	1.12 (0.96, 1.31)	1.19 (0.98, 1.44)
Community-level factors
Place of residence
Urban	1.00	1.00	1.00
Rural	1.22 (0.99, 1.52)	0.89 (0.76, 1.06)	0.70 (0.57, 0.86)
Region of residence
Western Zone	1.00	1.00	1.00
Northern Zone	0.79 (0.47, 1.31)	1.08 (0.76, 1.54)	1.21 (0.81, 1.82)
Southern Highlands	0.64 (0.37, 1.11)	0.82 (0.57, 1.18)	0.51 (0.34, 0.76)
Southern Zone	1.00 (0.52, 1.94)	0.95 (0.64, 1.42)	1.02 (0.57, 1.81)
Southwest Zone	0.33 (0.17, 0.61)	1.12 (0.77, 1.62)	0.72 (0.46, 1.12)
Lake Zone	0.84 (0.52, 1.36)	0.77 (0.55, 1.09)	0.50 (0.33, 0.75)
Eastern Zone	0.89 (0.53, 1.49)	1.12 (0.80, 1.56)	0.91 (0.62, 1.34)
Central Zone	1.06 (0.64, 1.75)	0.86 (0.59, 1.25)	0.61 (0.35, 1.05)
Zanzibar	1.03 (0.64, 1.66)	1.14 (0.82, 1.59)	1.27 (0.89, 1.82)

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RRR: Relative risk ratio; 95% CI: confidence interval

Factors associated with overweight in Tanzanian women

Reproductive age women who attained secondary or higher education were more likely to be overweight compared to those who had no schooling (RRR = 1.48; 95% CI: 1.11, 1.96), and those who were currently married had a higher risk of being overweight compared to those who were not married (RRR = 1.47; 95% CI: 1.13, 1.91). Women who resided in wealthier households (RRR = 2.31; 95% CI: 1.78, 3.03) and those who reported watching television (RRR = 1.26; 95% CI: 1.06, 1.50) were more likely to be overweight compared to counterparts, respectively. Compared to women ≤ 24 years of age, older women were more likely to be overweight (RRR = 3.31; 95% CI: 2.59, 4.23 for 35–49 years) [Table 2].

Factors associated with obesity in Tanzanian women

Reproductive age women who attained secondary or higher education had a higher risk of being obese compared to those who had no education (RRR = 1.79; 95% CI: 1.23, 2.61), and formally employed women were more likely to be obese compared to those who had no employment (RRR = 1.50; 95% CI: 1.14, 1.98). Women who were currently married (RRR = 1.78; 95% CI: 1.25, 2.54), and those who resided in wealthier households (RRR = 4.77; 95% CI: 3.03, 7.50) had a higher risk of being obese compared to those who were not married and/or resided in poorer households, respectively. The risk of being obese was higher among older women aged (≥25 years) compared to those who were younger (15–24 years, RRR = 3.92; 95% CI: 2.93, 5.24 for 25–34 years and RRR = 9.94; 95% CI: 7.20, 13.73 for 35–49 years). Women who watched the television were more likely to be obese compared to those who did not watch the television (RRR = 1.70; 95% CI: 1.35, 2.13). The risk of being obese was higher among women who used alcohol compared to those who did not use alcohol (RRR = 1.43; 95% CI: 1.12, 1.82). Women who resided in Northern Zone (RRR = 0.51; 95% CI: 0.34, 0.76) and Lake Zone (RRR = 0.50; 95% CI: 0.33, 0.75) of Tanzania were less likely to be obese compared to those who resided in Western Zone (Table 2).

Discussion

Our study showed that reproductive age women who were underweight were more likely to be informally employed, currently married, watched the television and resided in the Southern Zone of Tanzania. The risk of being overweight and/or obese was higher among reproductive age women who attained secondary or higher education, resided in wealthy households, were currently married and watched television. Women who had formal employment and used alcohol also had higher risk of obesity. Residing in rural households, and Southern Highlands and Lake Zone of Tanzania was associated with lower risk of obesity.

Research indicates that women who are from wealthy households of LMICs have a higher risk of being overweight or obese, while women from wealthy households in higher-income countries have a lower risk of being overweight or obese [42–45]. Our study showed that women who resided in wealthy households were more likely to be overweight or obese compared to those who resided in poor households. The results of this study are consistent with previous studies from South Asian [16, 46] and sub-Saharan Africa countries [18, 34], which showed that women from wealthy households were at a higher risk of overweight or obesity. The likely reason for the relationship between wealthy households, and overweight and obesity could be that women who resided in wealthy households have a reduced level of socio-economical stress and physical activity, and with a less healthy dietary habit (such as poor consumption of fruits and vegetables, and a higher intake of highly caloric foods) compared to those in poor households [47, 48]. The findings of this study suggest that intervention to reduce the burden of overweight or obesity should target on reproductive age women from both middle- and rich-level households.

Global evidence indicates that higher educational attainment is associated with the better health status of the community, due to the improvement in socioeconomic status [49, 50], health literacy and health behaviours [49–51], and self-control and empowerment [50, 51]. This is not always the case in LMIC settings where those with higher education are more likely to be overweight or obese [52, 53]. Consistent with other studies in LMIC settings, we found that reproductive-age women who attained secondary or higher education were more likely to be overweight or obese, similar to studies from Ghana [34], Bangladesh [17], and Ethiopia [18, 19]. Women with higher education are more likely to have a higher socioeconomic status and material resources, and have ready access to energy-dense foods (e.g., sugary drinks) and more sedentary employment (for example, office work) [18, 54]. The perception among the sub-Saharan Africa community (including Tanzania) that a round-body frame as a marker of socioeconomic success is also the likely explanation for the observed relationship between higher education and overweight and obesity [34, 55]. National policy makers should target on measures and policies that improve physical activity and ensure access to healthy foods by all reproductive-age women.

Increases in women’s employment, which is an important determinant of the immediate causes of undernutrition (such as feeding practices and ill-health) and more distal causes of undernutrition (such as income, food security, and education), has a great potential to improve the nutritional status of women [56]. Our study showed that women who were informally employed (in agriculture and manual jobs) had a lower risk of underweight compared to those who had no employment. The negative association between informal employment and underweight can be explained via three pathways [56, 57]. Firstly, the effect of women’s employment on their empowerment and household decision making. Secondly, women’s employment as a source of income for food and non-food expenditures. Finally, women’s employment in agriculture is also a source of food and household consumption.

Moreover, our study showed that women who had formal employment were more likely to be obese compared to those who had no employment. This finding was consistent with previously published studies from 38 LMICs that showed formally employed women were more likely to be overweight or obese [58]. The positive association between formal employment and overweight or obesity can be due to the positive energy balance among formally employed women [58–60]. This positive energy balance may emanate from the less physically active nature of formal jobs (e.g., office works), and also increases in consumption of energy-dense foods (such as sugary drinks) as a result of improvement in the food purchasing power of women [58–60].

Consistent with past studies conducted in Ghana [34], Bangladesh [61], and Myanmar [62], women who watched television had a higher relative risk of overweight or obesity compared to those who did not watch television. Previous studies documented that sedentary behaviours (including watching television) and inadequate physical activity as major risk factors for overweight and obesity [61, 63]. The possible reason for the relationship between watching television and overweight and obesity could be that women who watched television had a reduced level of physical activity as a result of increased sitting time [61, 63]. In LMICs, having a television can also be considered as a proxy indicator for the higher socioeconomic status of women, which in turn increases the risk of exposure to energy-dense and junk foods [47, 48]. Our study indicated that interventions focused on reproductive age Tanzanian women are required to improve their awareness on the impacts of watching television and sedentary life style.

Social characteristics (including marital status) can influence women’s body weight by its moderating effect on diet and physical activity [64, 65]. Our study found that women who were currently married were less likely to be underweight compared to never-married women, but more likely to be overweight or obesity. These findings are consistent with previously published studies conducted in Ethiopia [18], Ghana [34] and Bangladesh [66]. The possible explanation for the relationship between marital status and body weight could be seen in two perspectives [67, 68]. The first perspective related to resources that married women anticipated to have a person with who to eat regularly, which increases the chance to gain bodyweight. The second perspective is based on the attractiveness model that currently married women are less concerned with their body weight as compared to never-married women which are always trying to minimize weight gain to attract a partner.

Our study indicated that reproductive age women who resided in rural households were less likely to be obese compared to those who resided in urban households. The relationship between place of residence and obesity is supported by previously published studies from LMICs [36, 69, 70]. Women from rural households may be engaged in occupational physical activities such as agricultural occupations subject them to labour-intensive activities (manual work) which promotes weight loss and less excess weight gain [58]. Additionally, the reduced chances of rural women to consume processed, packed and refrigerated foods could be the possible explanation for the negative relationship between rural residence and obesity.

Strengths and limitations

This study has various limitations. First, these findings are limited by the use of cross-sectional data which presents difficulties in establishing a temporal association between the independent variables and the outcome measures. Nevertheless, the observed associations are consistent with studies from other LMICs [18, 34, 61, 62]. Second, BMI does not reflect the location or amount of body fat of women which could be seen as a potential limitation of this study. Despite this, studies have shown that BMI is correlated to more direct measures of body fat, such as underwater weighing and dual-energy x-ray absorptiometry [71]. Third, the study was limited by a lack of data on key factors such as length of time in watching TV, physical activity and total energy expenditure of the urban women, as the TDHS did not collect information on these variables. Fourth, the study factors were measured based on self-report questionnaires which would be a source of measurement bias. Fifth, a lack of subnational assessment given differences across regions may also be considered as a potential limitation. Despite these limitations, the national representativeness of the data and using a standardized questionnaire can be considered as the potential strength of the current study.

Conclusion

Our study showed varied factors that are associated with the nutritional status of reproductive age women in Tanzania. Underweight was less likely to be evident among women who were informally employed, currently married and used contraception. The risk of being overweight or obese was higher in women who were formally employed, attained secondary or higher education, resided in wealthier households, currently married, and watched television. These results suggest that there is an increasing need Tanzanian stakeholders to identify, promote, and implement policy interventions that simultaneously address underweight, overweight, and obesity in reproductive age women.

Appropriate intervention strategies focused on reproductive age Tanzanian women–especially on women with risk factors–that promote healthy adult lifestyles (such as physical activity, reducing the intake of sugary drinks, eating fruits and vegetables, and avoiding excessive alcohol consumption) could be implemented to reduce the burdens and impacts of overweight and obesity. In addition, given that Tanzanian women perceive weight gain as an economic success, we propose targeted educational programs that can instil a self-consciousness behaviour on women’s weight control. Finally, interventional studies that evaluate the current policy initiatives in addressing underweight, overweight and obesity should be key priorities to improve women’s health outcomes.

Supporting information

S1 Table. Characteristic of the study participants of reproductive age Tanzanian women, 2015–16.

(DOCX)

Click here for additional data file.^{(16.3KB, docx)}

S2 Table. Factors associated underweight, overweight and obesity among reproductive age women in Tanzania, 2015–16.

(DOCX)

Click here for additional data file.^{(25.1KB, docx)}

Acknowledgments

The authors are grateful to Measure DHS, ICF International, Rockville, MD, USA, for providing the data for analysis. KYA, AGR, KEA, and FAO acknowledge the support of Global Maternal and Child Health Research collaboration.

GloMACH members are Kingsley E. Agho, Felix Akpojene Ogbo, Thierno Diallo, Osita E Ezeh, Osuagwu L Uchechukwu, Pramesh R. Ghimire, Blessing Jaka Akombi, Pascal Ogeleka, Tanvir Abir, Abukari I. Issaka, Kedir Yimam Ahmed, Abdon Gregory Rwabilimbo, Daarwin Subramanee, Nilu Nagdev and Mansi Dhami.

Felix Akpojene Ogbo (f.ogbo@westernsydney.edu.au)–the leader of the collaboration

Kingsley E. Agho, Felix Akpojene Ogbo, Kedir Yimam Ahmed, and Mansi Dhami

Translational Health Research Institute, Western Sydney University, NSW, Australia

Thierno Diallo

School of Psychology, Western Sydney University, NSW, Australia

Osita E. Ezeh, Pramesh R. Ghimire, Tanvir Abir, Abukari I. Issaka, Daarwin Subramanee, and Nilu Nagdev

School of Health Science, Western Sydney University, NSW, Australia

Osuagwu L. Uchechukwu

School of Medicine, Diabetes Obesity and Metabolism Translational Research Unit (DOMTRU), Macarthur Clinical School, NSW, Australia

Blessing Jaka Akombi

School of Public Health and Community Medicine, Faculty of Medicine, University of New South Wales, NSW, Australia

Pascal Ogeleka

Prescot Specialist Medical Centre, Welfare Quarters, Makurdi, Benue State, Nigeria

Abdon Gregory Rwabilimbo

Chato District Council, Geita Region, Northwestern Tanzania

List of abbreviations

BMI: Body Mass Index
CI: Confidence Interval
DHS: Demographic and Health Survey
EA: Enumeration Areas
ICF: Inner City Fund
LMICs: Lower and Middle income Countries
MoHCDEC: Ministry of Health Community Development Elderly and children
MoFEA: Ministry of Finance and Economic Affairs
MoCLA: Ministry of Constitution and Legal Affairs
NCD: Non-Communicable Diseases
NIMR: National Institute for Medical Research
OCGS: Chief Government Statistician
RRR: Relative Risk Ratio;SDGs: Sustainable Development Goals
TDHS: Tanzania Demographic and Health Survey
USAID: United State of America Aid
WHO: World Health Organization
MoHCDEC: Ministry of Health Community Development Elderly and children
MoFEA: Ministry of Finance and Economic Affairs
MoCLA: Ministry of Constitution and Legal Affairs

Data Availability

The analysis was based on the datasets for Tanzania Demographic Health Survey (TDHS). The 2015-16 TDHS datasets can be downloaded [with Measure DHS approval] from the data page with dataset file name ‘TZIR7BDT.ZIP’ for STATA https://dhsprogram.com/data/dataset/Tanzania_Standard-DHS_2015.cfm?flag=0. The authors did not have special access privileges.

Funding Statement

This study received no grant from any funding agency in public, commercial or not for profit sectors.

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PLoS One. doi: 10.1371/journal.pone.0237720.r001

Decision Letter 0

Hajo Zeeb

20 May 2020

PONE-D-20-06054

Determinants of underweight, overweight, and obesity in reproductive age Tanzanian women: evidence from the 2015–16 demographic and health survey

PLOS ONE

Dear Mr. Ahmed,

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Reviewer #1: COMMENTS FOR THE AUTHORS

1) This paper investigated factors associated with underweight, overweight, and obesity using multinomial logistic regressions. The paper adds some data to the literature around the determinants of nutritional status of reproductive-aged Tanzanian women. Notwithstanding, there are some issues that the paper needs to address, inter alia, possible confounders, interpretation of results, and grammar.

2) The authors examined only distal determinants of underweight, overweight, and obesity. Factors and confounders such as food habits of women and other behavioural determinants such as smoking and alcohol consumption that have been consistently considered as proximate determinants of nutritional status were not included in the models. Moreover, the authors on page 15 noted that “poor consumption of fruits and vegetables might be a likely reason…” for the poor nutritional status among women. My advice to the authors is to include these risk factors in the models and further discuss them.

3) Because multinomial logistic regression models were used to investigate the associations, one would expect that the results are interpreted as relative risk ratios (RRR), with reference to the base outcome, in this case, “normal weight.” However, the authors interpreted the results and findings as binary logistic regression, leading to incorrect interpretation of results and contradictory statements throughout the manuscript. For instance, “reproductive-age women who were UNDERWEIGHT were more likely to be currently married”…….” “The odds of OBESITY was higher among currently married women” (page 15). The authors should re-write the entire results and interpret the findings as relative risk ratios (RRR), by comparing the results of underweight, overweight, and obesity to NORMAL WEIGHT. Furthermore, the descriptive results of the “characteristics of the study participants” should be expanded to include other socioeconomic variables.

4) The paper should have a deeper discussion on the status of adult’s health and lifestyle in Tanzania. At the moment, there is no in-depth discussion of the public health policies and intervention programmes targeted at improving the lifestyles and reducing malnutrition. Some further reflection is needed on these issues.

Minor comments

5) The authors need the assistance of an English writer. Many of the points made were obscured by incomplete sentences, difficult phrasing and language. See, for example, page 19,”……. currently married, and watched”. It was a bit difficult for me to follow some of the arguments in the manuscript. Please fix them.

Reviewer #2: In the introduction section first paragraph the word "individuals" does not make sense in the sentence. Likewise, the authors defined double burden instead of showing/describing how LMICs are currently facing a double burden.

The authors have stated, "no previous national-level studies have been conducted to assess the determinants of underweight, overweight, and obesity in reproductive-age women" this is not correct there are other studies in the similar subject matter such as underweight using other terms such as undernutrition as well as obesity using national-level data. The only difference is that they used previous TDHS 2010. It could be more clear for authors to show they have covered that during the literature review and clearly state what will be new in their study. Perhaps using a new model (multinomial), study both underweight, overweight and obesity concurrently using more current (2015-16 TDHS) national data etc.

In the methodology section more explanation on how they reached the sample size of 11,735 is required. From how many households. Did the 11,735 samples account for all reproductive women regardless of whether they have pregnant or not? There should be also further explanations on how they have taken into account the correlation between women/subjects from the same households and sampling weight during analysis.

The authors have stated "the main outcome variables were underweight, overweight, and obesity" in fact this are just categories of nutritional status rather than outcome variables! They should revise this appropriately. To be more clear, authors should use words like exposure, independent, explanatory variables etc rather than study factors! Is there any reason why they did not combine ‘informal employment and ‘formal employment together? I don't see any concrete reason why not to do that.

In the case of results section Table 1 does not add anything crucial relating to study subject matter. Could be more meaningful if combined with the outcome variable of interest i.e. nutrition status categories of overweight, underweight, normal and obese. Therefore, they should combine table 1 and table 2 if necessary.

The first sentence of the conclusion is not clear. Authors should consider revising. The authors have stated "Our study showed that reproductive-age women who were underweight were more likely to be informally employed, currently married, and watched" The word watched does not make sense. I guess perhaps they mean watched television!

Reviewer #3: The manuscripts covers an overall important topic. However, I have some major concerns related to the presentation of results and the discussion.

- First of all, the analysis is based on cross-sectional data (DHS survey). Generall, this is fine, but you cannot claim to analyse "determinants" in this case. Please change it to "Factors associated...".

- The authors stated that there is no previous study on this issue. I seriously doubt this statement. For example, you can refer to a study published on 2019, which is also based on Tanzanian DHS data (10.1186/s12937-019-0505-8).

- The authors claim to have conducted a multilevel analysis. In the abstract it is not clear what these levels are. In the main text it even seems as if it is a hierarchical model. I cannot see the multilevel approach, although individual- and community-level factors have been included in the regression model. This needs to be clarified and maybe even modified with a new analysis.

- Introduction: In how far does the obesogenic environment (which impacts on weight gain) relate to underweight?

- Introduction: Poverty and education directly relate to the social status. In the current formulation it seems as if these are distinguished issues.

- Methods: I suggest to call it "Independent variables" instead of "Study factors".

- Methods: Why did you call the descriptive analysis "preliminary analyses"?

- Methods: I am missing information about the prerequisities of the regression analysis. For example, did you check for multicollinearity? And what is about the model fit?

- Results: The description of the study characteristics is very short.

- Results: I am missing p-values in table 2 to show differences between independent variables and the outcome alread at the bivariate level.

- Results/Discussion: TV watching seems to be associated with underweight, overweight, and obesity. What is the implication of this result? This is only one example for further issues which should be thought during data analysis and interpretation.

- Discussion: Why did you recommend to ensure access to healthy foods only to women with higher educational attainment?

**********

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Reviewer #1: No

Reviewer #2: Yes: Edwin Paul

Reviewer #3: Yes: Florian Fischer

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PLoS One. 2020 Aug 24;15(8):e0237720. doi: 10.1371/journal.pone.0237720.r002

Author response to Decision Letter 0

13 Jul 2020

July 13 2020

Professor Hajo Zeeb

Scientific Editor

PLOS ONE

Dear Professor Zeeb,

RE: Manuscript resubmission – [PONE-D-20-06054R1] Determinants of underweight, overweight, and obesity in reproductive age Tanzanian women: evidence from the 2015–16 demographic and health survey

Thank you for the invitation to revise our subject-titled manuscript and for the very constructive comments from the reviewers and editor. A revised manuscript (clean and version with track changes) reflecting the following point-by-point response to the reviewers’ comments have been submitted for your consideration.

Academic Editor

General comments

1. The manuscript needs to be revised thoroughly for methodological precision. Importantly, the interpretation of the estimates must be reviewed, and causal language should be checked very carefully. Overall, language editing is necessary, all reviewers have remarked substantial shortcomings in this regard.

Response:

We thank the editor for the comment and note the methods, results interpretations [as well as casual inference language] and English language in the revised manuscript has been extensively edited by senior authors whose native language is English.

2. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

http://www.journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf

http://www.journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Response:

The revised manuscript meets PLOS ONE's style requirements

3. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

In your revised cover letter, please address the following prompts:

Response: Response:

Data availability statement has been revised (Page 23), in line with our previously published manuscript with PLoS One that used similar data [Ahmed et al., 2020].

The following text has been added in the revised manuscript

“The analysis was based on the datasets for Tanzania Demographic Health Survey (TDHS). The 2015-16 TDHS datasets can be downloaded [with Measure DHS approval] from the data page with dataset file name ‘TZIR7BDT.ZIP’ for STATA https://dhsprogram.com/data/dataset/Tanzania_Standard-DHS_2015.cfm?flag=0. The authors did not have special access privileges.”

Reference:

1. Ahmed, K. Y., Page, A., Arora, A., Ogbo, F. A., Global, M., & Child Health Research, c. (2020). Associations between infant and young child feeding practices and acute respiratory infection and diarrhoea in Ethiopia: A propensity score matching approach. PLoS One, 15(4), e0230978.

We will update your Data Availability statement on your behalf to reflect the information you provide.

Response:

As noted above, the data availability statement has been revised (Page 23), in line with our previously published manuscript with PLoS One that used similar data [Ahmed et al., 2020]

Reference:

4. One of the noted authors is a group or consortium [Global Maternal and Child Health Research collaboration (GloMACH)]. In addition to naming the author group, please list the individual authors and affiliations within this group in the acknowledgments section of your manuscript. Please also indicate clearly a lead author for this group along with a contact email address.

Response:

Revision done (Page 23 and 24)

Reviewer #1

Comments to the Author

This paper investigated factors associated with underweight, overweight, and obesity using multinomial logistic regressions. The paper adds some data to the literature around the determinants of nutritional status of reproductive-aged Tanzanian women. Notwithstanding, there are some issues that the paper needs to address, inter alia, possible confounders, interpretation of results, and grammar.

Response:

As noted above and in response to the editor comment, the methods, results interpretations [as well as casual inference language] and English language in the revised manuscript has been extensively edited by senior authors whose native language is English.

The authors examined only distal determinants of underweight, overweight, and obesity. Factors and confounders such as food habits of women and other behavioural determinants such as smoking and alcohol consumption that have been consistently considered as proximate determinants of nutritional status were not included in the models. Moreover, the authors on page 15 noted that “poor consumption of fruits and vegetables might be a likely reason…” for the poor nutritional status among women. My advice to the authors is to include these risk factors in the models and further discuss them.

Response:

We have some reasons to believe that the reviewer has good knowledge of subject matter – thank you for the comment. We have considered additional variables [based on availability in DHS datasets over the study period] in the revised manuscript as suggested by the reviewer.

Because multinomial logistic regression models were used to investigate the associations, one would expect that the results are interpreted as relative risk ratios (RRR), with reference to the base outcome, in this case, “normal weight.” However, the authors interpreted the results and findings as binary logistic regression, leading to incorrect interpretation of results and contradictory statements throughout the manuscript. For instance, “reproductive-age women who were UNDERWEIGHT were more likely to be currently married” …….” “The odds of OBESITY was higher among currently married women” (page 15). The authors should re-write the entire results and interpret the findings as relative risk ratios (RRR), by comparing the results of underweight, overweight, and obesity to NORMAL WEIGHT.

Response:

Thank you for the critical observation. We apologize for the incorrect interpretation of findings and note that the entire results section has been edited and extensively reviewed by senior authors.

Furthermore, the descriptive results of the “characteristics of the study participants” should be expanded to include other socioeconomic variables.

Response:

Revision done (Page 12, Paragraph 1)

The paper should have a deeper discussion on the status of adult’s health and lifestyle in Tanzania. At the moment, there is no in-depth discussion of the public health policies and intervention programmes targeted at improving the lifestyles and reducing malnutrition. Some further reflection is needed on these issues.

Response:

Thanks for the comment. Revision done (Page 22 paragraph 2)

The authors need the assistance of an English writer. Many of the points made were obscured by incomplete sentences, difficult phrasing and language. See, for example, page 19,”……. currently married, and watched”. It was a bit difficult for me to follow some of the arguments in the manuscript. Please fix them.

Response:

We apologize for the difficulty caused to the reviewer in reading our manuscript and note [as above] that the language in the revised manuscript has been extensively edited by senior authors whose native language is English.

Reviewer #2

In the introduction section first paragraph the word "individuals" does not make sense in the sentence.

Response:

Now revised (Page 4 paragraph 1)

Likewise, the authors defined double burden instead of showing/describing how LMICs are currently facing a double burden

Response:

Point appreciated and the situation of DBM in low and middle income countries described (Page 5 paragraph 1)

Response:

Revision done (Page 5 and 6).

Response:

Point appreciated and revised accordingly (Page 7, Paragraph 2 and 3)

There should be also further explanations on how they have taken into account the correlation between women/subjects from the same households and sampling weight during analysis.

Response:

In the statistical, sampling weight was account for using the ‘svy’ command – this was noted in the original manuscript.

In our preliminary analyses, we tried to account for the household-level variance, but given the low number of reproductive age women in each households, the dataset could not support inclusion of separate household-level covariates at the household level. We also note that past studies have been conducted on same topic (Tareke et al. 2020, Yeshaw et al., 2020 and Ntenda et al., 2018)

References:

1. Tareke, A. A., & Abate, M. G. (2020). Nutritional paradox in Ethiopian women: Multilevel multinomial analysis. Clinical nutrition ESPEN, 36, 60-68.

2. Yeshaw, Y., Kebede, S. A., Liyew, A. M., Tesema, G. A., Agegnehu, C. D., Teshale, A. B., & Alem, A. Z. (2020). Determinants of overweight/obesity among reproductive age group women in Ethiopia: multilevel analysis of Ethiopian demographic and health survey. 10(3), e034963. doi:10.1136/bmjopen-2019-034963 %J BMJ Open

3. Ntenda, P. A. M., & Kazambwe, J. F. (2018). A multilevel analysis of overweight and obesity among non-pregnant women of reproductive age in Malawi: evidence from the 2015–16 Malawi Demographic and Health Survey. International health, 11(6), 496-506.

Response:

Point appreciated and now revised (Page 8 paragraph 2)

To be more clear, authors should use words like exposure, independent, explanatory variables etc rather than study factors!

Response:

Revision done (Page 8 Paragraph 3)

Is there any reason why they did not combine ‘informal employment and ‘formal employment together? I don't see any concrete reason why not to do that.

Response:

We appreciate the reviewer’s concern. We, however, note that people who are formally employed (e.g. office workers) may be more likely to have sedentary lifestyle (positive energy balance), particularly in a less developed country like Tanzania with extremely limited availability of physical activity options compared to developed countries like Sweden or Norway with varied options for formally employed workers to attend work. In contrast, informally employed women (e.g., agricultural worker and manual labourers) would have negative energy balance because majority of these women walked hundreds of kilometers weekly to their farm or workplace – in the context of a lack of mechanized agriculture or industrial machineries in a LMIC like Tanzania compared to developed countries. We believe that this classification provided critical information for recommending context-specific strategies for at risk population in Tanzania.

Response:

Point appreciated and now reflected in the revised manuscript; Table 1 now supplementary table (S1_Table).

Response:

Now revised (Page 21 and 22)

Reviewer #3

The manuscripts covers an overall important topic. However, I have some major concerns related to the presentation of results and the discussion.

Thank you for the comment. The reviewer’s specific comments are addressed below in this rebuttal.

First of all, the analysis is based on cross-sectional data (DHS survey). Generall, this is fine, but you cannot claim to analyse "determinants" in this case. Please change it to "Factors associated...".

Response:

The text has been revised in the entire manuscript, where applicable.

The authors stated that there is no previous study on this issue. I seriously doubt this statement. For example, you can refer to a study published on 2019, which is also based on Tanzanian DHS data (10.1186/s12937-019-0505-8).

Response:

Now revised (page 5 and 6)

The authors claim to have conducted a multilevel analysis. In the abstract it is not clear what these levels are. In the main text it even seems as if it is a hierarchical model. I cannot see the multilevel approach, although individual- and community-level factors have been included in the regression model. This needs to be clarified and maybe even modified with a new analysis.

Response:

We have revised the text in response to the reviewer comment (page 10 paragraph 2). We note that using multilevel modelling has the following advantages over classical single level logistic regression modelling:

1. Firstly, multilevel modelling was used to accounts for the hierarchical nature of the data (reproductive age women [level I] were conceptualised as being nested within clusters [level II]), consistent with previously published studies (Tareke et al. 2020, Yeshaw et al., 2020 and Ntenda et al., 2018). Failing to recognise hierarchical structures underestimates the standard errors of regression coefficients, leading to an overstatement of statistical significance

2. Secondly, multilevel modelling helps to consider the dependence of observations in the same clusters (i.e., reproductive age women in the same cluster tend to be more similar in their nutritional status than in different clusters) (Peugh et al., 2009 and Leyland et al., 2003).

3. Finally, multilevel modelling allows estimating cluster-level effects (random effects) simultaneously with the measure of associations for community-level predictors (e.g., place of residence).

This text is now included in the revised manuscript. The random effects and the model fitness output of all fitted models also included as supplementary information (S2_Table) for further information on the multilevel modelling fitted.

We would also like to clarify for the reviewer that:

Multilevel modelling is designed to explore and analyse data that come from populations which have a hierarchical and non-hierarchical complex data structures. Hierarchical multilevel modelling works when the lower-level unit nests in one and only one higher-level unit (for example, a child may be nested in only one school) [Snijders et al, 1993].

The non-hierarchical multilevel modelling helps to deal with all the different types of designs, realities and research questions that meet ross-classified structures and multiple membership structures. For example, atomic units (individuals) can be nested within more than one unit from a higher-level classification.

In the present study, the hierarchical multilevel modelling was employed to consider the hierarchical nested data structure (i.e., reproductive age women were only nested to a single cluster). Similar analytical strategy was also applied in previously published studies based on demographic health survey data (Tareke et al. 2020, Yeshaw et al., 2020 and Ntenda et al., 2018).

References

1. Tareke, A. A., & Abate, M. G. (2020). Nutritional paradox in Ethiopian women: Multilevel multinomial analysis. Clinical nutrition ESPEN, 36, 60-68.

4. Peugh JL. A practical guide to multilevel modeling. J Sch Psychol. 2010;48(1):85-112. Epub 2009/12/17.

5. Leyland AH, Groenewegen PP. Multilevel modelling and public health policy. 2003;31(4):267-74.

6. Snijders, T.A.B., and Bosker, R.J. (1993). Standard errors and sample sizes for two-level research. J. Educational Statist., 18, 237-259.

Introduction: In how far does the obesogenic environment (which impacts on weight gain) relate to underweight?

Response:

The text has been clarified in the revised manuscript (Page 4 Paragraph 1).

Introduction: Poverty and education directly relate to the social status. In the current formulation it seems as if these are distinguished issues.

Response:

We agree with the reviewer that poverty and education directly relate to the social status. In our study, we have used these variable as presented in the TDHS and used in previously published studies [Rwabilimbo et al., 2020]. We have discussed household wealth and education variables at the beginning of discussion in indicating the importance of these variables (as they are modifiable factors) compared to other social characteristics such as marital status.

Reference

Rwabilimbo, A. G., Ahmed, K. Y., Page, A., & Ogbo, F. A. (2020). Trends and factors associated with the utilisation of antenatal care services during the Millennium Development Goals era in Tanzania. Trop. Med. Health, 48, 38-38.

Methods: I suggest to call it "Independent variables" instead of "Study factors".

Response:

Revision done suggested by the reviewer (Page 8 Paragraph 3)

Methods: Why did you call the descriptive analysis "preliminary analyses"?

Response:

Now revised (Page 10 Paragraph 1)

Methods: I am missing information about the prerequisities of the regression analysis. For example, did you check for multicollinearity? And what is about the model fit?

Response:

Revision done. Reviewers concern reflected in the revised manuscript (Page 10 and 11, S2_Table)

Results: The description of the study characteristics is very short.

Response:

Revision done (Page 12 paragraph 1).

Results: I am missing p-values in table 2 to show differences between independent variables and the outcome already at the bivariate level.

Response:

Revision done as suggested by the reviewer (Table 1)

Results/Discussion: TV watching seems to be associated with underweight, overweight, and obesity. What is the implication of this result? This is only one example for further issues which should be thought during data analysis and interpretation.

Response:

Point appreciated and reflected in the revised manuscript (Page 19 Paragraph 3)

Discussion: Why did you recommend to ensure access to healthy foods only to women with higher educational attainment?

Response:

The text has been clarified in the revised manuscript (Page 18 Paragraph 1)

We thank the reviewers for the valuable comments and time in reading our manuscript.

We look forward to your final discussion in due course. Please contact me should you require any further information.

Sincerely,

Kedir Yimam Ahmed

Corresponding author

Attachment

Submitted filename: Response to reviewers.docx

Click here for additional data file.^{(47.7KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0237720.r003

Decision Letter 1

Hajo Zeeb

24 Jul 2020

PONE-D-20-06054R1

Factors associated with underweight, overweight, and obesity in reproductive age Tanzanian women

PLOS ONE

Dear Dr. Ahmed,

The authors have extensively revised the manuscript according to comments made. However, a few issues remain: (Line numbers refer to manuscript with highlighted changes)

L 275 63.1% is not one-third

L279-280 - the highlighting of high or low prevalences according to very different grouping characteristics appears awkward (mentioning smoking and Southwest Tanzania in one sentence, while these categories may overlap). Rather make separate sentences and compare prevalences according to joint characteristics, e.g. by economic status.

L314 Simply state that compared to women≤ 24 years of age, older women were more likely to be overweight. Do not repeat all numbers from the table in the text, but perhaps chose one to exemplify.

Overall, my suggestion is to more strongly group the findings regarding overweight and obesity, as the associated factors are very similar, and in the discussion you are doing this already in most sections. The first discussion section, however, should also be more condensed with regard to overweight/obesity findings.

L 316 check the value 2.192

L.355 check the wording (reported does not fit here)

L. 448 an "a" was added - why?

L 474 Sentence starting "In addition" lacks a verb

Please submit your revised manuscript by Sep 07 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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PLoS One. 2020 Aug 24;15(8):e0237720. doi: 10.1371/journal.pone.0237720.r004

Author response to Decision Letter 1

25 Jul 2020

July 25 2020

Professor Hajo Zeeb

Scientific Editor

PLOS ONE

Dear Professor Zeeb,

RE: Manuscript resubmission – [PONE-D-20-06054R2] Factors associated with underweight, overweight, and obesity in reproductive age Tanzanian women

Thank you for the invitation to revise our subject-titled manuscript and for the very constructive comments. A revised manuscript (clean and version with track changes) reflecting the following point-by-point response have been submitted for your consideration.

Comment: L 275 63.1% is not one-third

Response:

Thank you for the observation! Revision done now (line 250).

Comment: L279-280 - the highlighting of high or low prevalences according to very different grouping characteristics appears awkward (mentioning smoking and Southwest Tanzania in one sentence, while these categories may overlap). Rather make separate sentences and compare prevalences according to joint characteristics, e.g. by economic status.

Response:

Revision done (Line 255-267)

Comment: L314 Simply state that compared to women≤ 24 years of age, older women were more likely to be overweight. Do not repeat all numbers from the table in the text, but perhaps chose one to exemplify.

Response:

Revision done (Line 288-292)

Comment: Overall, my suggestion is to more strongly group the findings regarding overweight and obesity, as the associated factors are very similar, and in the discussion you are doing this already in most sections. The first discussion section, however, should also be more condensed with regard to overweight/obesity findings.

Response:

Revision done (Line 320-326)

Comment: L 316 check the value 2.192

Response:

Revision done (Line 288-292)

Comment: L.355 check the wording (reported does not fit here)

Response:

Now revised (Line 326-327)

Comment: L. 448 an "a" was added - why?

Response:

Now revised (Line 419)

L 474 Sentence starting "In addition" lacks a verb

Response:

Revision done (Line 442-446)

We thank the Editor and reviewers for the valuable comments and time in reading our manuscript.

We look forward to your final discussion in due course. Please contact me should you require any further information.

Sincerely,

Kedir Yimam Ahmed, MPH

Corresponding author

Attachment

Submitted filename: Response to reviewers.docx

Click here for additional data file.^{(32.1KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0237720.r005

Decision Letter 2

Hajo Zeeb

3 Aug 2020

Factors associated with underweight, overweight, and obesity in reproductive age Tanzanian women

PONE-D-20-06054R2

Dear Dr. Ahmed,

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

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

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Kind regards,

Hajo Zeeb

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0237720.r006

Acceptance letter

Hajo Zeeb

6 Aug 2020

PONE-D-20-06054R2

Factors associated with underweight, overweight, and obesity in reproductive age Tanzanian women

Dear Dr. Ahmed:

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

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

If we can help with anything else, please email us at plosone@plos.org.

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

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Prof. Hajo Zeeb

Academic Editor

PLOS ONE

Associated Data

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

Supplementary Materials

S1 Table. Characteristic of the study participants of reproductive age Tanzanian women, 2015–16.

(DOCX)

Click here for additional data file.^{(16.3KB, docx)}

S2 Table. Factors associated underweight, overweight and obesity among reproductive age women in Tanzania, 2015–16.

(DOCX)

Click here for additional data file.^{(25.1KB, docx)}

Attachment

Submitted filename: Response to reviewers.docx

Click here for additional data file.^{(47.7KB, docx)}

Attachment

Submitted filename: Response to reviewers.docx

Click here for additional data file.^{(32.1KB, docx)}

Data Availability Statement

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PERMALINK

Factors associated with underweight, overweight, and obesity in reproductive age Tanzanian women

Kedir Y Ahmed

Abdon G Rwabilimbo

Solomon Abrha

Andrew Page

Amit Arora

Fentaw Tadese

Tigistu Yemane Beyene

Abdulaziz Seiko

Abdulhafiz A Endris

Kingsley E Agho

Felix Akpojene Ogbo

Roles

Abstract

Background

Methods

Results

Conclusion

Introduction

Methods

Data source

Outcome variable

Independent variables

Statistical analysis

Ethics approval and consent to participate

Results

Characteristics of the study participants

Prevalence of underweight, overweight and obesity among reproductive age women in Tanzania

Table 1. Prevalence of underweight, overweight and obesity by study factors in reproductive age Tanzanian women, TDHS 2015–16.

Factors associated with underweight in Tanzanian women

Table 2. Factors associated with underweight, overweight and obesity in reproductive age Tanzanian women, TDHS 2015–16.

Factors associated with overweight in Tanzanian women

Factors associated with obesity in Tanzanian women

Discussion

Strengths and limitations

Conclusion

Supporting information

Acknowledgments

List of abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Hajo Zeeb

Roles

Author response to Decision Letter 0

Decision Letter 1

Hajo Zeeb

Roles

Author response to Decision Letter 1

Decision Letter 2

Hajo Zeeb

Roles

Acceptance letter

Hajo Zeeb

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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