{'fraction_non_alphanumeric': 0.024495699404532934, 'fraction_numerical': 0.02898862919481159, 'mean_word_length': 4.7878706919583225, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 2, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 50, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Dual burden of underweight and overweight/obesity among adults in Botswana: prevalence, trends and sociodemographic correlates: a cross-sectional survey

Objective To estimate the prevalence and trends in underweight, overweight/obesity and identify their sociodemographic correlates among adults in Botswana from 2007 to 2017. Design The study analysed cross-sectional and nationally representative data from 2007 to 2014 Botswana STEPS Surveys and the 2017 Botswana Demographic Survey. Setting Botswana. Participants Botswana adults aged 25–64 years (n=4003 in 2007, n=2983 in 2014 and n=11 550 in 2017). Primary outcome Underweight and overweight/obesity. Results The prevalence of underweight decreased from 18.1% (95% CI 12.0% to 26.3%) in 2007 to 11.6% (95% CI 9.5% to 13.9%) in 2014 and further dropped to 8.1% (95% CI 7.5% to 8.8%) in 2017. The prevalence of overweight/obesity increased slightly from 37.4% (95% CI 34.3% to 40.7%) in 2007 to 38.6% (95% CI 35.9% to 41.3%) in 2014 to 47.3% (95% CI 46.1% to 48.4%) in 2017. Underweight was more prevalent among males than females while overweight and obesity were more prevalent among females than males. The key risk factor for underweight was being male (adjusted OR (AOR) 2.21: 95% CI 1.80 to 2.72 in 2007, AOR 1.54: 95% CI 1.06 to 2.22 in 2014 and AOR 1.51: 95% CI 1.45 to 1.58 in 2017). For overweight/obesity, the main risk factors were being female (male AOR 0.23: 95% CI 0.15 to 0.35 in 2007, AOR 0.32: 95% CI 0.25 to 0.42 in 2014 and AOR 0.30: 95% CI 0.29 to 0.31 in 2017), being old (AOR 2.18: 95% CI 1.58 to 3.01 in 2007, AOR 2.37: 95% CI 1.71 to 3.29 in 2014) and AOR 2.10: 95% CI 1.94 to 2.27 in 2017 among those aged 55–64 years) and not working (AOR 1.70: 95% CI 1.20 to 2.42 in 2007, AOR 2.05: 95% CI 1.55 to 2.69 in 2014 and AOR 1.34: 95% CI 1.27 to 1.40 in 2017). Conclusions The findings presented in this study indicate coexistence of the double burden of underweight and overweight/obesity among adults aged 25–64 years in Botswana. Although underweight prevalence is on the decline, overweight/obesity is increasing over time. The problem of underweight and overweight/obesity needs immediate and effective interventions.

under-nutrition and overweight/obesity. 1 World Health Organisation (WHO) contends that it is not uncommon to find under-nutrition and overweight/obesity co-existing within the same country, the same community and the same household. 1 The consequences of both under-nutrition and overweight and obesity are well known. While warnings about health consequences of excess weight abound 2-4 less attention appears to be paid to the simultaneous study of the double burden of both underweight and excess weight. 1 Until recently, sub-Saharan Africa (SSA) was minimally affected by the obesity/overweight epidemic due to under-nutrition and a major burden of HIV and tuberculosis. 5 Meanwhile research on overweight/obesity has started to gain significance in SSA 6-8 due to its increasing magnitude.
The rise of non-communicable diseases (NCDs) and their impact in low-and middle-income countries is currently raising concern. NCDs kill more than 36 million people each year with nearly 80% of non-communicable disease (NCD) deaths occurring in low-and middle-income countries. 9 Cardiovascular diseases account for most NCD deaths (17 million people) annually, followed by cancers (7.6 million), respiratory diseases (4.2 million), and diabetes (1.3 million). 9 These four diseases share four risk factors: tobacco use, physical inactivity, the harmful use of alcohol and unhealthy diets. 9 Being underweight or overweight is likely to lead to adverse health outcomes throughout the life course. 10 The global NCD targets of halting, by 2025, the rise in the prevalence of obesity at its 2010 level have been driven largely by concerns about the health and economic burden of increasing body-mass index (BMI). 11,12 Epidemiological studies have shown substantial risks in people with very high BMI-eg, sever (=>35 kg/m 2 ) or morbid (=>40 kg/m 2 ) obesity. 13 It should also be noted that being underweight is also associated with increased risk of morbidity and mortality and with adverse pregnancy outcomes. [14][15][16][17] There is a realisation that there are very few analyses of trends in underweight, 18   diabetes. [19][20][21] During the past 25 years, the levels of overweight and obesity have increased by more than 330%, contributing to an increase in the burden of NCDs on the continent. 6 The prevalence of obesity on the African continent is heterogeneous with southern Africa, a region comprising Botswana, Lesotho, Namibia, South Africa, and Swaziland, having the highest levels. 10 Botswana has one of the highest levels on the continent with 3.2% of males and 15.4% of females aged 20-49 years estimated to be obese and 12.9% of males and 23.3% of females in the same age group were estimated to be overweight in 2007. 22 Estimates for undernutrition in Botswana were 16.5% for males and 11.2% for females in the same period. 23 Although the growing number of obese individuals has received attention in many developing countries, SSA is still lacking research into this subject partially due to the persisting high proportion of the population classified as underweight. 24 Most of nutritional research in SSA has focussed on under-nutrition, particularly among vulnerable population subgroups such as women and children. 24 Nonetheless, the ongoing nutritional transition in these settings has been mostly linked to the rapid process of urbanisation and westernisation. [25][26][27][28][29] A worrisome trend is that in the last decades, the number of deaths from NCDs in developing countries has already exceeded those observed in developed countries. 30,31 Given this information, it is imperative for each country to know the

Study design
The current analysis is based on three cross-sectional, population-based surveys. The first Botswana STEPS Survey was conducted in 2007 while the Botswana STEPS Survey II was conducted in 2014. The 2007 Botswana STEPS Survey adopted a multi-stage cluster sampling methodology to arrive at the selected sample of adults aged 25-64. 32 The 2014 Botswana STEPS Survey used systematically sampled enumeration areas of the districts to produce representative data for 15-69 age groups. 40 A detailed report on the methodology of each Botswana STEPS Surveys has been presented elsewhere. 39 groups the survey analysis was restricted to adults aged 25-64 years old in order to make the analysis comparable between the three surveys. Since data used in the surveys represent only the participants sampled, the data were weighted to make them nationally representative of the participants aged 25-64 years.

Sampling procedure
Since a multi-stage stratified sampling procedure was used in the two STEPS Surveys, the use of standard statistical methods for analyzing the data would produce incorrect standard errors for the survey estimates because the methods assume simple random sampling procedure was used. As such during analysis of data, a complex sample module from SPSS was adopted to ensure that the estimated standard errors for the survey estimates are robust STEPS Survey was 4000 but was oversampled by 15 individuals although ultimately the refined sample size was 4003 adults aged 25-64 years, yielding a response rate of 100%. 32 The target sample size for the 2014 Botswana STEPS Survey was 6410 but 4074 individuals participated, yielding an overall response rate of 64%. 33 The targeted sample size was not reached for various reasons that include: shortage of transport, inadequate survey equipment, PDA's non-functionality, loss of data after being entered into PDA, time consuming process of finding sampled houses in enumeration areas, and long distances between households, among others. 33 The 2017 Botswana Demographic Survey target sample size was 622 enumeration areas but due to resource constraints this sample size was reduced by 23%, which yielded a sample of 478 enumeration areas. 34 The sampled 478 enumeration areas yielded a sample of 9560 households. 34

Outcome variables
The dependent variables are (i) underweight; (ii) overweight and (iii) obesity. Height was measured using portable height/length measuring board while weight was measured portal electronic weighing scale. Height was measured in centimetres during the survey but was later converted to metres during the analysis. Height was restricted to 1.0 m to 2.7 m while weight was restricted to 20kg to 350 kg as per the recommendation of the World Health Organisation. Using these restrictions, 3  self-employed were classified as working and those in non-paid/unpaid family helper, student, homemaker/housework, retired, unemployed (able to work), and unemployed (unable to work) as not working. Other key socio-demographic factors such as wealth status, place of residence and marital status were not used in the analyses because not all the datasets used had collected this information. Therefore to maintain consistency across surveys, only those variables that appeared in all the three surveys were used.

Statistical Analysis
Unweighted data were used to study the characteristics of the participants. The data are weighted to adjust for differential selection probabilities because the sample design involves more than one stage of selection. This ensures that data are representative of the target population, in this case, the 25-64 year olds. The data were weighted using sample weights. In order to ensure the changes in age distribution do not affect trends of the overall estimates, age-standardized proportions were computed using the 2011 Botswana Population and Housing Census data as a standard population. As such both unstandardized (true population prevalence rates) and standardized prevalence rates (hypothetical prevalence rates) are presented. Since the standard analytical procedures in traditional package assume that the observations in the data represent a simple random sample from the population of interests, a complex sample procedure in SPSS was used. Complex sample procedure enables the researcher to achieve more statistical valid inferences for populations measured in complex sample data because it incorporates the sample design into the survey analysis. As such complex sample module in SPSS was used to estimate For the logistic regression analysis, the outcome indicator variables were based on underweight vs. normal weight and overweight/obesity vs. normal weight categories, respectively where normal weight is the reference category. Confidence intervals were used to estimate the precision of the percentages and odds ratios. In order to determine socio-demographic correlates of underweight and overweight/obesity, all the datasets were used. Multivariable logistic regression analyses were used. Odds ratios and 95% confidence intervals after adjusting for covariates were estimated and presented. All the statistical analyses were done in SPSS version 25. 35

Ethics Statement
The current analysis is based on the use of secondary datasets from Ministry of Health and Wellness and WHO and Statistics Botswana.

Patient and Public Involvement
The study used secondary data and therefore the outcome and predictor measures used in the study were those already existent in the datasets. Therefore no patients were involved in the design, development of research question and outcome measures. The results will be disseminated to study participants through the preparation of policy briefs and presentations in conferences.

Prevalence and trends in the prevalence of underweight and overweight/obesity
The prevalence of underweight decreased from 18.1% in 2007, 11.6% in 2014 and to 8.2% in 2017 while the prevalence of overweight/obesity rose from 37.4% to 38.6% and to 47.3% during the same time period (see Figure 1).
Insert Figure 1 Underweight was more prevalent among males than females while overweight and obesity were more prevalent among females than males throughout the three periods, 2007, 2014 and 2017. The largest decrease in underweight was more pronounced among males but small among females, 10.7 percentage points between 2007 and 2014 and 3.3 percentage points between 2014 and 2017 among males (see Table 2). Another largest decline in underweight was observed among the 25-      participants who were classified as not working were 8% less likely to be underweight and in 2017 participants who were not working were 22% less likely to be underweight and all these associations were statistically significant at 95% level. All other risk factors were not statistically significant (see Table 4).   Table 5 shows odds ratios of being overweight/obese by sociodemographic factors.
These results are consistent with previous studies conducted in the country. 22 Botswana, as in other parts of the world, it has been observed that hypertension was more prevalent among those who were obese. 44 The study results also demonstrate that the prevalence of underweight is highest among males. Undernourishment is known to compromise the health of adults. For example, it has been observed that undernourished elderly have longer periods of illness, longer hospital stays, higher rates of infection, delayed wound healing, reduced appetite, increased mortality rate and adverse pregnancy outcomes. 45 The reasons for high prevalence of underweight among males remains unknown because men tend to eat food  1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59  60   F  o  r  p  e  e  r  r  e  v  i  e  w  o  n  l  y   19   richer in fats and proteins, to drink more wine, beer, spirits and sweet carbonated drink; in   general they show dietary behaviours potentially favouring overweight and obesity. 46 Further studies need to be conducted that investigates why men are underweight despite their eating behaviours which are likely to promote overweight/obesity. These studies should explore the behavioural patterns of men that increase their likelihood of becoming underweight.
Since this study is based on cross-sectional data, no causal relationships can be deduced from this type of data. However the odds ratios were used to determine how different sociodemographic factors are risk factors to either underweight or overweight/obesity. One of the major limitations is that some of the key variables known to be related to nutritional status were not included in the analysis because the datasets used did not contain them, such as wealth status, place of residence, and marital status. The study restricted itself to socio-demographic correlates and as such cannot explain the behavioural aspects of malnutrition. Apart from physical measurements of height and weight used for calculating body mass index, socio-demographic data were collected from self-reports which may not necessarily be accurate.

CONCLUSION
Botswana has a dual burden of underweight and overweight/obesity among adults.
Although the prevalence of underweight has been decreasing between 2007 and 2017, it was evident that the burden of overweight/obesity increased among adults in Botswana in the same period. The key socio-demographic correlates of underweight was being male while those of overweight/obesity were being female, being old, and not working. It is therefore imperative that immediate interventions are designed and implemented to tackle the dual burden of underweight and overweight/obesity in order to alleviate problems associated with malnutrition. Further research is necessary to unravel other risk factors associated with underweight and overweight/obesity which were not contained in the current datasets. It is also necessary to investigate the underlying behavioural factors for underweight and overweight/obesity such as reasons for low fruit and vegetable consumption, alcohol consumption and tobacco use.  Strengths and limitations of this study  To the author's knowledge, this is the first study to document the dual burden of underweight and overweight/obesity and trends in both underweight and overweight/obesity among adults in Botswana using similar nationally representative datasets.  The study provides the first picture of trends in adult body-mass index in underweight and overweight/obesity, which are of enormous clinical and public health interest.  The study also provides evidence of Botswana transitioning from an era when underweight prevalence was high to one in which more people are overweight/obese. As such Botswana can use this evidence to gauge her performance with regard to meeting the global obesity targets  The study contributes to our understanding of the socio-demographic risk factors associated underweight and overweight/obesity.  The analysis accounted for a complex survey design (clustering, stratification, sample weights) which gives accurate estimates of the population parameters.  One of the major limitations is that some of the key variables known to be related to underweight and overweight/obesity were not included in the analysis because the datasets used did not contain them, such as wealth status, place of residence, and marital status.  Because the data were not derived from randomized control trail, no causal relationships can be insinuated from the study results as the data used come from cross-sectional surveys. Because cross-sectional data refers to the situation of interest at a specific point in time, it is impossible to infer causality from it.

Introduction
Background/rationale 2 Explain the scientific background and rationale for the investigation being reported Although the growing number of obese individuals has received attention in many developing countries, SSA is still lacking research into this subject partially due to the persisting high proportion of the population classified as underweight. 31 Most of nutritional research in SSA has focussed on under-nutrition, particularly among vulnerable population subgroups such as women and children. 31 Nonetheless, the ongoing nutritional transition in these settings has been mostly linked to the rapid process of urbanisation and westernisation. [32][33][34][35][36] A worrisome trend is that in the last decades, the number of deaths from NCDs in developing countries has already exceeded those observed in developed countries. [37][38] Given this information, it is imperative for each country to know the estimated prevalence and change in underweight and overweight/obesity so as to allow government to use this evidence to develop and implement appropriate intervention strategies. At the moment,

Study design 4
Present key elements of study design early in the paper The key outcome variables are underweight and overweight/obesity while the predictors were: sex, age, citizenship, and work status. The key outcome variables were derived from the computation of the boss mass index (BMI) which is calculated as weight in kilograms divided by height in metres squared. Underweight constituted those participants who has a BMI of less than 18.5 kilograms per metre square while overweight/obesity referred to participants whose BMI was 25 kilograms per metre square or above. 7

Bias 9
Describe any efforts to address potential sources of bias  During data collection, data collectors were trained to ensure that they collected data as per the instructions. In addition, during data analysis, the author used complex sample module in SPSS to ensure that the more valid statistical estimates were obtained because it took into account that complex sampling designs were used in the three surveys.

Study size 10
Explain how the study size was arrived at In each of the three surveys used, sample sizes were estimated to ensure that minimum sample sizes were obtained and that ensured that valid estimates can be made from the data. Work status was created into two categories as working or not working. Participants who described their main work status over the past year as government employee, parastatal, non-governmental employee, self-employed were classified as working and those in non-paid/unpaid family helper, student, homemaker/housework, retired, unemployed (able to work), and unemployed (unable to work) as not working.

(a) Describe all statistical methods, including those used to control for confounding
The data are weighted to adjust for differential selection probabilities because the sample design involves more than one stage of selection. This ensures that data are representative of the target population, in this case, the 25-64 year olds. The data were weighted using sample weights. In order to ensure the changes in age distribution do not affect trends of the overall estimates, age-standardized proportions were computed using the 2011 Botswana Population and Housing Census data as a standard population. As such both unstandardized (true population prevalence rates) and standardized prevalence F o r p e e r r e v i e w o n l y rates (hypothetical prevalence rates) are presented. Since the standard analytical procedures in traditional package assume that the observations in the data represent a simple random sample from the population of interests, a complex sample procedure in SPSS was used. Complex sample procedure enables the researcher to achieve more statistical valid inferences for populations measured in complex sample data because it incorporates the sample design into the survey analysis. As such complex sample module in SPSS was used to estimate the prevalence and socio-demographic risk factors of underweight and overweight/obesity. For the logistic regression analysis, the outcome indicator variables were based on underweight vs.
normal weight and overweight/obesity vs. normal weight categories, respectively where normal weight is the reference category. Confidence intervals were used to estimate the precision of the percentages and odds ratios. In order to determine socio-demographic correlates of underweight and overweight/obesity, all the datasets were used. Multivariable logistic regression analyses were used. Odds ratios and 95% confidence intervals after adjusting for covariates were estimated and presented. All the statistical analyses were done in SPSS version 25

(c) Explain how missing data were addressed
The missing values were excluded from the analysis 7

(d) If applicable, describe analytical methods taking account of sampling strategy
Complex sample procedure enables the researcher to achieve more statistical valid inferences for populations measured in complex sample data because it incorporates the sample design into the survey analysis. As such complex sample module in SPSS was used to estimate the prevalence and socio-demographic risk factors of underweight and overweight/obesity.

(e) Describe any sensitivity analyses
None

Results
Participants 13* (a) Report numbers of individuals at each stage of study-eg numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, and analysed 6-7

(b) Give reasons for non-participation at each stage
The targeted sample size was not reached for various reasons that include: shortage of transport, inadequate survey equipment, PDA's non-functionality, loss of data after being entered into PDA, time consuming process of finding sampled houses in enumeration areas, and long distances between households, among others 7

. Make clear which confounders were adjusted for and why they were included
Underweight was more prevalent among males than females while overweight and obesity were more prevalent among females than males. The key risk factor for underweight was being male with unadjusted odds ratios (UOR) as follows: (UOR: 14-17

N/A
Other analyses 17

Key results 18
Summarise key results with reference to study objectives 18  The prevalence of underweight is lower than the prevalence of overweight/obesity and underweight is declining over time while overweight/obesity in on the increase. The key socio-demographic correlates of underweight were being male while for overweight/obesity the key socio-demographic correlates were being female, being old, and not working.

Limitations 19
Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and magnitude of any potential bias Since this study is based on cross-sectional data, no causal relationships can be deduced from this type of data. However the odds ratios were used to determine how different socio-demographic factors are risk factors to either underweight or overweight/obesity. One of the major limitations is that some of the key variables known to be related to nutritional status were not included in the analysis because the datasets used did not contain them, such as wealth status, place of residence, and marital status. The study restricted itself to socio-demographic correlates and as such cannot explain the behavioural aspects of malnutrition. Apart from physical measurements of height and weight used for calculating body mass index, sociodemographic data were collected from self-reports which may not necessarily be accurate.

Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence
Refer to 19 above 19 Generalisability 21

Discuss the generalisability (external validity) of the study results
Since the data represent adults aged 25-64 years in Botswana, the study results can be generalized to the general population in that age group.

Funding 22
Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based The study used secondary data and was not funded 19 *Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies.

Article Summary: Strengths and limitations of this study
 To the author's knowledge, this is the first study to document the dual burden of underweight and overweight/obesity and trends in both underweight and overweight/obesity among adults in Botswana using similar nationally representative datasets, which are of enormous clinical and public health interest.  The study contributes to our understanding of the socio-demographic risk factors associated with underweight and overweight/obesity.  The analysis accounted for a complex survey design (clustering, stratification, sample weights) which gives accurate estimates of the population parameters.  One of the major limitations is that some of the key variables known to be related to underweight and overweight/obesity were not included in the analysis because the datasets used did not contain them, such as wealth status, place of residence, and marital status.  Because the data were not derived from randomized control trial, no causal relationships can be inferred from the study results as the data used come from cross-sectional surveys. Because cross-sectional data refers to the situation of interest at a specific point in time, it is impossible to infer causality from it. under-nutrition and overweight/obesity. 1 World Health Organisation (WHO) contends that it is not uncommon to find under-nutrition and overweight/obesity co-existing within the same country, the same community and the same household. 1 The consequences of both under-nutrition and overweight and obesity are well known. While warnings about health consequences of excess weight abound 2-4 less attention appears to be paid to the simultaneous study of the double burden of both underweight and excess weight. 1 Until recently, sub-Saharan Africa (SSA) was minimally affected by the obesity/overweight epidemic due to under-nutrition and a major burden of HIV and tuberculosis. 5 Meanwhile research on overweight/obesity has started to gain significance in SSA 6-8 due to its increasing magnitude.
The rise of non-communicable diseases (NCDs) and their impact in low-and middle-income countries is currently raising concern. NCDs kill more than 36 million people each year with nearly 80% of NCD deaths occurring in low-and middle-income countries. 9 Cardiovascular diseases account for most NCD deaths (17 million people) annually, followed by cancers (7.6 million), respiratory diseases (4.2 million), and diabetes (1.3 million). 9 These four diseases share four risk factors: tobacco use, physical inactivity, the harmful use of alcohol and unhealthy diets. 9 Being underweight or overweight is likely to lead to adverse health outcomes throughout the life course. 10 The global NCD targets of halting, by 2025, the rise in the prevalence of obesity at its 2010 level have been driven largely by concerns about the health and economic burden of increasing body-mass index (BMI). 11,12 Epidemiological studies have shown substantial risks in people with very high BMI-eg, severe (≥35 kg/m 2 ) or morbid (≥40 kg/m 2 ) obesity. 13 It should also be noted that being underweight is also associated with increased risk of morbidity and mortality and with adverse pregnancy outcomes. [14][15][16][17] There is a realisation that there are very few analyses of trends in underweight, 18 especially for men.
Sub-Saharan African countries are experiencing rapid and major demographic and epidemiological transitions marked by an ageing population, persisting communicable diseases such as HIV and tuberculosis in some certain parts of Africa and increasing NCDs  22 Estimates for undernutrition in Botswana were 16.5% for males and 11.2% for females in the same period. 23 Although the growing number of obese individuals has received attention in many developing countries, SSA is still lacking research into this subject partially due to the persisting high proportion of the population classified as underweight. 24 Most of nutritional research in SSA has focussed on under-nutrition, particularly among vulnerable population subgroups such as women and children. 24 Nonetheless, the ongoing nutritional transition in these settings has been mostly linked to the rapid process of urbanisation and westernisation. [25][26][27][28][29] A worrisome trend is that the number of deaths from NCDs in developing countries has already exceeded those observed in developed countries. 30,31 Given this information, it is imperative for each country to know the estimated prevalence and change in underweight and overweight/obesity so that this can be used as evidence by government to develop and implement appropriate intervention strategies. At the moment,

Study design
The current analysis is based on three cross-sectional, population-based surveys. The first Botswana STEPS Survey was conducted in 2007 while the Botswana STEPS Survey II was conducted in 2014. The 2007 Botswana STEPS Survey adopted a multi-stage cluster sampling methodology to arrive at the selected sample of adults aged 25-64. 32 The 2014 Botswana STEPS Survey used systematically sampled enumeration areas of the districts to produce representative data for 15-69 age groups. 40 A detailed report on the methodology of each Botswana STEPS Surveys has been presented elsewhere. 39

Sampling procedure
Since a multi-stage stratified sampling procedure was used in the two STEPS Surveys, the use of standard statistical methods for analyzing the data would produce incorrect standard errors for the survey estimates because the methods assume simple random sampling procedure was used. As such during analysis of data, a complex sample module from SPSS was adopted to ensure that the estimated standard errors for the survey estimates are robust

Statistical Analysis
Unweighted data were used to study the characteristics of the participants. The data were weighted to adjust for differential selection probabilities because the sample design involves more than one stage of selection. This ensures that data are representative of the target population, in this case, the 25-64 year olds. The data were weighted using sample weights. In order to ensure the changes in age distribution do not affect trends of the overall estimates, age-standardized proportions were computed using the 2011 Botswana Population and Housing Census data as a standard population. As such both unstandardized (true population prevalence rates) and standardized prevalence rates (hypothetical prevalence rates) are presented. Since the standard analytical procedures in traditional statistical package assume that the observations in the data represent a simple random sample from the population of interests, a complex sample procedure in SPSS was used.
Complex sample procedure enables the researcher to achieve more statistical valid inferences for populations measured in complex sample data because it incorporates the

Ethics Statement
The current analysis is based on the use of secondary datasets from Ministry of Health and Wellness and WHO and Statistics Botswana.

Patient and Public Involvement
The study used secondary data and therefore the outcome and predictor measures used in the study were those already existent in the datasets. Therefore no patients were involved in the design, development of research question and outcome measures. The results will be disseminated to study participants through the preparation of policy briefs and presentations in conferences.

Prevalence and trends in the prevalence of underweight and overweight/obesity
The prevalence of underweight decreased from 18.1% in 2007, 11.6% in 2014 and to 8.2% in 2017 while the prevalence of overweight/obesity rose from 37.4% to 38.6% and to 47.3% during the same time period (see Figure 1).
These results are consistent with previous studies conducted in the country. 22 Botswana, as in other parts of the world, it has been observed that hypertension was more prevalent among those who were obese. 44 The study results also demonstrate that the prevalence of underweight is highest among males. Undernourishment is known to compromise the health of adults. For example, it has been observed that undernourished elderly have longer periods of illness, longer hospital stays, higher rates of infection, delayed wound healing, reduced appetite, increased mortality rate and adverse pregnancy outcomes. 45 The reasons for high prevalence of underweight among males remains unknown because men tend to eat food Further studies need to be conducted that investigates why men are underweight despite their eating behaviours which are likely to promote overweight/obesity. These studies should explore the behavioural patterns of men that increase their likelihood of becoming underweight.
Since this study is based on cross-sectional data, no causal relationships can be deduced from this type of data. However the odds ratios were used to determine how different sociodemographic factors are risk factors to either underweight or overweight/obesity. One of the major limitations is that some of the key variables known to be related to nutritional status were not included in the analysis because the datasets used did not contain them, such as wealth status, place of residence, and marital status. The study restricted itself to socio-demographic correlates and as such cannot explain the behavioural aspects of malnutrition. Apart from physical measurements of height and weight used for calculating body mass index, socio-demographic data were collected from self-reports which may not necessarily be accurate. It should also be noted that while BMI is often used as an indicator for obesity, BMI does not always reflect adiposity or body fatness. This is because BMI is unable to distinguish between fat and muscle, which tends to be heavier and can tip more toned individuals into overweight status. BMI does not accurately reflect visceral fat accumulation, the likely culprit leading to most of the metabolic and clinical consequences of obesity 47

CONCLUSION
Botswana has a dual burden of underweight and overweight/obesity among adults.
Although the prevalence of underweight has been decreasing between 2007 and 2017, it was evident that the burden of overweight/obesity increased among adults in Botswana in the same period. The key socio-demographic correlates of underweight was being male while those of overweight/obesity were being female, being old, and not working. It is therefore imperative that immediate interventions are designed and implemented to tackle the dual burden of underweight and overweight/obesity in order to alleviate problems associated with malnutrition. Further research is necessary to unravel other risk factors F o r p e e r r e v i e w o n l y

Strengths and limitations of this study
 To the author's knowledge, this is the first study to document the dual burden of underweight and overweight/obesity and trends in both underweight and overweight/obesity among adults in Botswana using similar nationally representative datasets.  The study provides the first picture of trends in adult body-mass index in underweight and overweight/obesity, which are of enormous clinical and public health interest.  The study also provides evidence of Botswana transitioning from an era when underweight prevalence was high to one in which more people are overweight/obese. As such Botswana can use this evidence to gauge her performance with regard to meeting the global obesity targets  The study contributes to our understanding of the socio-demographic risk factors associated underweight and overweight/obesity.  The analysis accounted for a complex survey design (clustering, stratification, sample weights) which gives accurate estimates of the population parameters.  One of the major limitations is that some of the key variables known to be related to underweight and overweight/obesity were not included in the analysis because the datasets used did not contain them, such as wealth status, place of residence, and marital status.  Because the data were not derived from randomized control trail, no causal relationships can be insinuated from the study results as the data used come from cross-sectional surveys. Because cross-sectional data refers to the situation of interest at a specific point in time, it is impossible to infer causality from it.

Background/rationale 2 Explain the scientific background and rationale for the investigation being reported
Although the growing number of obese individuals has received attention in many developing countries, SSA is still lacking research into this subject partially due to the persisting high proportion of the population classified as underweight. 31 Most of nutritional research in SSA has focussed on under-nutrition, particularly among vulnerable population subgroups such as women and children. 31 Nonetheless, the ongoing nutritional transition in these settings has been mostly linked to the rapid process of urbanisation and westernisation. [32][33][34][35][36] A worrisome trend is that in the last decades, the number of deaths from NCDs in developing countries has already exceeded those observed in developed countries. [37][38] Given this information, it is imperative for each country to know the estimated prevalence and change in underweight and overweight/obesity so as to allow government to use this evidence to develop and implement appropriate intervention strategies. At the moment,  The key outcome variables are underweight and overweight/obesity while the predictors were: sex, age, citizenship, and work status.

For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe comparability of assessment methods if there is more than one group
The key outcome variables were derived from the computation of the boss mass index (BMI) which is calculated as weight in kilograms divided by height in metres squared. Underweight constituted those participants who has a BMI of less than 18.5 kilograms per metre square while overweight/obesity referred to participants whose BMI was 25 kilograms per metre square or above.

7-8
Bias 9 Describe any efforts to address potential sources of bias During data collection, data collectors were trained to ensure that they collected data as per the instructions. In addition, during data analysis, the author used complex sample module in SPSS to ensure that the more valid statistical estimates The data are weighted to adjust for differential selection probabilities because the sample design involves more than one stage of selection. This ensures that data are representative of the target population, in this case, the 25-64 year olds. The data were weighted using sample weights. In order to ensure the changes in age distribution do not affect trends of the overall estimates, age-standardized proportions were computed using the 2011 Botswana Population and Housing Census data as a standard population. As such both unstandardized (true population prevalence rates) and standardized prevalence rates (hypothetical prevalence rates) are presented. Since the standard analytical procedures in traditional package assume that the observations in the data represent a simple random sample from the population of interests, a 8-9 F o r p e e r r e v i e w o n l y complex sample procedure in SPSS was used. Complex sample procedure enables the researcher to achieve more statistical valid inferences for populations measured in complex sample data because it incorporates the sample design into the survey analysis. As such complex sample module in SPSS was used to estimate the prevalence and socio-demographic risk factors of underweight and overweight/obesity. For the logistic regression analysis, the outcome indicator variables were based on underweight vs. normal weight and overweight/obesity vs. normal weight categories, respectively where normal weight is the reference category. Confidence intervals were used to estimate the precision of the percentages and odds ratios. In order to determine socio-demographic correlates of underweight and overweight/obesity, all the datasets were used. Multivariable logistic regression analyses were used. Odds ratios and 95% confidence intervals after adjusting for covariates were estimated and presented. All the statistical analyses were done in SPSS version 25

(c) Explain how missing data were addressed
The missing values were excluded from the analysis 7

(d) If applicable, describe analytical methods taking account of sampling strategy
Complex sample procedure enables the researcher to achieve more statistical valid inferences for populations measured in complex sample data because it incorporates the sample design into the survey analysis. As such complex sample module in SPSS was used to estimate the prevalence and socio-demographic risk factors of underweight and overweight/obesity.

8-9
(e) Describe any sensitivity analyses   Underweight was more prevalent among males than females while overweight and obesity were more prevalent among females than males. The key risk factor for underweight was being male with unadjusted odds ratios (UOR) as follows:

Key results 18
Summarise key results with reference to study objectives The prevalence of underweight is lower than the prevalence of overweight/obesity and underweight is declining over time while overweight/obesity in on the increase. The key socio-demographic correlates of underweight were being male while F o r p e e r r e v i e w o n l y for overweight/obesity the key socio-demographic correlates were being female, being old, and not working.

Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and magnitude of any potential bias
Since this study is based on cross-sectional data, no causal relationships can be deduced from this type of data. However the odds ratios were used to determine how different socio-demographic factors are risk factors to either underweight or overweight/obesity. One of the major limitations is that some of the key variables known to be related to nutritional status were not included in the analysis because the datasets used did not contain them, such as wealth status, place of residence, and marital status. The study restricted itself to socio-demographic correlates and as such cannot explain the behavioural aspects of malnutrition. Apart from physical measurements of height and weight used for calculating body mass index, socio-demographic data were collected from self-reports which may not necessarily be accurate. It should also be noted that while BMI is often used as an indicator for obesity, BMI does not always reflect adiposity or body fatness. This is because BMI is unable to distinguish between fat and muscle, which tends to be heavier and can tip more toned individuals Since the data represent adults aged 25-64 years in Botswana, the study results can be generalized to the general population in that age group.

Article Summary: Strengths and limitations of this study
 To the author's knowledge, this is the first study to document the dual burden of underweight and overweight/obesity and trends in both underweight and overweight/obesity among adults in Botswana using similar nationally representative datasets, which are of enormous clinical and public health interest.  The study contributes to our understanding of the socio-demographic risk factors associated with underweight and overweight/obesity.  The analysis accounted for a complex survey design (clustering, stratification, sample weights) which gives accurate estimates of the population parameters.  One of the major limitations is that some of the key variables known to be related to underweight and overweight/obesity were not included in the analysis because the datasets used did not contain them, such as wealth status, place of residence, and marital status.  Because the data were not derived from randomized control trial, no causal relationships can be inferred from the study results as the data used come from cross-sectional surveys. Because cross-sectional data refers to the situation of interest at a specific point in time, it is impossible to infer causality from it.  1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59  An increase in non-communicable diseases (NCDs) and their effect in low-and middle-income countries is a public health concern. At least 36 million people annually are killed by NCDs with almost 80% of NCD deaths taking place in low-and middleincome countries. 9 The annual contribution of each of the major NCD-related deaths are as follows: cardiovascular diseases account for 17 million deaths, cancers account for 7.6 million deaths, respiratory diseases account for 4.2 million deaths, and diabetes account for 1.3 million deaths. 9 Tobacco use, physical inactivity, harmful use of alcohol and unhealthy diet are the common risk factors shared by the four above-mentioned diseases. 9 Being underweight or overweight is likely to lead to adverse health outcomes throughout the life course. 10 The global NCD targets of halting, by 2025, the rise in the prevalence of obesity at its 2010 level have been driven largely by concerns about the health and economic burden of increasing body-mass index (BMI). 11,12 Epidemiological studies have shown substantial risks in people with very high BMI e.g. severe (≥35 kg/m 2 ) or morbid (≥40 kg/m 2 ) obesity. 13 It should also be noted that being underweight is also associated with increased risk of morbidity and mortality and with adverse pregnancy outcomes. [14][15][16][17] There is a realisation that there are very few analyses of trends in underweight, 18 especially for men. There are rapid and major demographic and epidemiological changes that are taking place in some certain parts of sub-Saharan African countries as shown by an ageing population, persisting communicable diseases such as HIV and tuberculosis and rising prevalence of NCDs such as hypertension and diabetes. [19][20][21][22] Some scholars have observed that over the past 25 years, the prevalence of overnutrition has risen by more than 330%, contributing to an upsurge in the burden of NCDs in Africa. 6 23 Estimates for undernutrition in Botswana were 16.5% for males and 11.2% for females in the same period. 24 Despite the increasing number of people who are overweight and obese, SSA still lacks research in this area partly because of the persisting high percentage of the population classified as underweight. 25 As such most of the nutritional research in SSA has tended to focus on under-nutrition, especially on the vulnerable populations such as women and children. 25 The rapid process of both urbanisation and westernization taking place in SSA is often associated with the current nutritional transition in these countries. [26][27][28][29][30] A worrisome trend is that the number of deaths from NCDs in developing countries has already exceeded those observed in developed countries. 31,32 Given this information, it is imperative for each country to know the estimated prevalence and change in underweight and overweight/obesity so that this can be used as evidence by

Study design
The current analysis is based on three cross-sectional, population-based surveys. The first Since a multi-stage stratified sampling procedure was used in the two STEPS Surveys, the use of standard statistical methods for analyzing the data would produce incorrect standard errors for the survey estimates because the methods assume simple random sampling procedure was used. As such during analysis of data, a complex sample module from SPSS was adopted to ensure that the estimated standard errors for the survey estimates are robust to bias and correctly reflect sampling variability. The target sample size for the 2007 Botswana STEPS Survey was 4000 but was oversampled by 15 individuals although ultimately the refined sample size was 4003 adults aged 25-64 years, yielding a response rate of 100%. 33 The target sample size for the 2014 Botswana STEPS Survey was 6410 but 4074 individuals participated, yielding an overall response rate of 64%. 34 The targeted sample size was not reached for various reasons that include: shortage of transport, inadequate survey equipment, PDA's non-functionality, loss of data after being entered into PDA, time consuming process of finding sampled houses in enumeration areas, and long distances between households, among others. 34 The 2017 Botswana Demographic Survey target sample size was 622 enumeration areas but due to resource constraints this sample size was reduced by 23%, which yielded a sample of 478 enumeration areas. 35 The sampled 478 enumeration areas yielded a sample of 9560 households. 35

Outcome variables
The dependent variables are (i) underweight; (ii) overweight and (iii) obesity. Height was measured using portable height/length measuring board while weight was measured using portal electronic weighing scale. Height was measured in centimetres during the survey but was later converted to metres during the analysis. Height was restricted to 1. normal weight and overweight/obesity vs. normal weight respectively for use with respect to logistic regression analyses.

Socio-demographic variables
The independent variables used for analysis in this study were selected on the basis of only those variables that appeared in all the three surveys were used.

Statistical Analysis
Unweighted data were used to study the characteristics of the participants. The data were weighted to adjust for differential selection probabilities because the sample design involves more than one stage of selection. This ensures that data are representative of the target population, in this case, the 25-64 year olds. The data were weighted using sample weights. In order to ensure the changes in age distribution do not affect trends of the overall estimates, age-standardized proportions were computed using the 2011 Botswana Population and Housing Census data as a standard population. As such both unstandardized (true population prevalence rates) and standardized prevalence rates (hypothetical prevalence rates) are presented. Since the standard analytical procedures in

Ethics Statement
The current analysis is based on the use of secondary datasets from Ministry of Health and Wellness and WHO and Statistics Botswana.

Patient and Public Involvement
The study used secondary data and therefore the outcome and predictor measures used in the study were those already existent in the datasets. Therefore no patients were involved in the design, development of research question and outcome measures. The results will be disseminated to study participants through the preparation of policy briefs and presentations in conferences.

Prevalence and trends in the prevalence of underweight and overweight/obesity
The prevalence of underweight decreased from 18.1% in 2007, 11.6% in 2014 and to 8.2% in 2017 while the prevalence of overweight/obesity rose from 37.4% to 38.6% and to 47.3% during the same time period (see Figure 1).
Insert Figure 1 Underweight was more prevalent among males than females while overweight and obesity were more prevalent among females than males throughout the three periods,  Table 2). Another largest decline in underweight was observed among the 25-34 and 35-44 year-olds especially     Not working participants were less likely to be underweight. Adjusted odds ratios showed that participants not working were 52% less likely to be underweight in 2007. All these associations were statistically significant at 5% level. In 2014, participants who were classified as not working were 8% less likely to be underweight and in 2017 participants who were not working were 22% less likely to be underweight. All other risk factors were not statistically significant (see Table 4).    Table 5 shows odds ratios of being overweight/obese by sociodemographic factors.
Since this study is based on cross-sectional data, no causal relationships can be deduced from this type of data. However the odds ratios were used to determine how different sociodemographic factors are risk factors to either underweight or overweight/obesity. One of the major limitations is that some of the key variables known to be related to nutritional status were not included in the analysis because the datasets used did not contain them, such as wealth status, place of residence, and marital status.
The study restricted itself to socio-demographic correlates and as such cannot explain the behavioural aspects of malnutrition. Apart from physical measurements of height and weight used for calculating body mass index, socio-demographic data were collected from self-reports which may not necessarily be accurate. It should also be noted that while BMI is often used as an indicator for obesity, BMI does not always reflect adiposity or body fatness. This is because BMI is unable to distinguish between fat and muscle, which tends to be heavier and can tip more toned individuals into overweight status. BMI does not accurately reflect visceral fat accumulation, the likely culprit leading to most of the metabolic and clinical consequences of obesity. 48

CONCLUSION
Botswana has a dual burden of underweight and overweight/obesity among adults.

Introduction
Background/rationale 2 Explain the scientific background and rationale for the investigation being reported Although the growing number of obese individuals has received attention in many developing countries, SSA is still lacking research into this subject partially due to the persisting high proportion of the population classified as underweight. 31 Most of nutritional research in SSA has focussed on under-nutrition, particularly among vulnerable population subgroups such as women and children. 31 Nonetheless, the ongoing nutritional transition in these settings has been mostly linked to the rapid process of urbanisation and westernisation. [32][33][34][35][36] A worrisome trend is that in the last decades, the number of deaths from NCDs in developing countries has already exceeded those observed in developed countries. [37][38] Given this information, it is imperative for each country to know the estimated prevalence and change in underweight and overweight/obesity so as to allow government to use this evidence to develop and implement appropriate intervention strategies. At the moment,  1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  The key outcome variables are underweight and overweight/obesity while the predictors were: sex, age, citizenship, and work status. The key outcome variables were derived from the computation of the boss mass index (BMI) which is calculated as weight in kilograms divided by height in metres squared. Underweight constituted those participants who has a BMI of less than 18.5 kilograms per metre square while overweight/obesity referred to participants whose BMI was 25 kilograms per metre square or above.

7-8
Bias 9 Describe any efforts to address potential sources of bias During data collection, data collectors were trained to ensure that they collected data as per the instructions. In addition, during data analysis, the author used complex sample module in SPSS to ensure that the more valid statistical estimates  1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46 F o r p e e r r e v i e w o n l y were obtained because it took into account that complex sampling designs were used in the three surveys.

Study size 10 Explain how the study size was arrived at
In each of the three surveys used, sample sizes were estimated to ensure that minimum sample sizes were obtained and that ensured that valid estimates can be made from the data. normal weight and overweight/obesity vs. normal weight respectively for use with respect to logistic regression analyses.
The following variables were collected in all the surveys: sex, age, nationality, and work. Age was categorized as follows: 25-34, 35-44, 45-54, and 55-64. Citizenship variable was created into two categories, Motswana (a citizen of Botswana) or others. Work status was created into two categories as working or not working. Participants who described their main work status over the past year as government employee, parastatal, non-governmental employee, self-employed were classified as working and those in non-paid/unpaid family helper, student, homemaker/housework, retired, unemployed (able to work), and unemployed (unable to work) as not working.

7-8
Statistical methods 12 (a) Describe all statistical methods, including those used to control for confounding The data are weighted to adjust for differential selection probabilities because the sample design involves more than one stage of selection. This ensures that data are representative of the target population, in this case, the 25-64 year olds. The data were weighted using sample weights. In order to ensure the changes in age distribution do not affect trends of the overall estimates, age-standardized proportions were computed using the 2011 Botswana Population and Housing Census data as a standard population. As such both unstandardized (true population prevalence rates) and standardized prevalence rates (hypothetical prevalence rates) are presented. Since the standard analytical procedures in traditional package assume that the observations in the data represent a simple random sample from the population of interests, a 8-9  1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46 F o r p e e r r e v i e w o n l y complex sample procedure in SPSS was used. Complex sample procedure enables the researcher to achieve more statistical valid inferences for populations measured in complex sample data because it incorporates the sample design into the survey analysis. As such complex sample module in SPSS was used to estimate the prevalence and socio-demographic risk factors of underweight and overweight/obesity. For the logistic regression analysis, the outcome indicator variables were based on underweight vs. normal weight and overweight/obesity vs. normal weight categories, respectively where normal weight is the reference category. Confidence intervals were used to estimate the precision of the percentages and odds ratios. In order to determine socio-demographic correlates of underweight and overweight/obesity, all the datasets were used. Multivariable logistic regression analyses were used. Odds ratios and 95% confidence intervals after adjusting for covariates were estimated and presented. All the statistical analyses were done in SPSS version 25

(b) Describe any methods used to examine subgroups and interactions
None (c) Explain how missing data were addressed The missing values were excluded from the analysis 7

(d) If applicable, describe analytical methods taking account of sampling strategy
Complex sample procedure enables the researcher to achieve more statistical valid inferences for populations measured in complex sample data because it incorporates the sample design into the survey analysis. As such complex sample module in SPSS was used to estimate the prevalence and socio-demographic risk factors of underweight and overweight/obesity.

8-9
(e) Describe any sensitivity analyses   Underweight was more prevalent among males than females while overweight and obesity were more prevalent among females than males. The key risk factor for underweight was being male with unadjusted odds ratios (UOR) as follows:

Key results 18
Summarise key results with reference to study objectives The prevalence of underweight is lower than the prevalence of overweight/obesity and underweight is declining over time while overweight/obesity in on the increase. The key socio-demographic correlates of underweight were being male while  1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46 F o r p e e r r e v i e w o n l y for overweight/obesity the key socio-demographic correlates were being female, being old, and not working.

Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and magnitude of any potential bias
Since this study is based on cross-sectional data, no causal relationships can be deduced from this type of data. However the odds ratios were used to determine how different socio-demographic factors are risk factors to either underweight or overweight/obesity. One of the major limitations is that some of the key variables known to be related to nutritional status were not included in the analysis because the datasets used did not contain them, such as wealth status, place of residence, and marital status. The study restricted itself to socio-demographic correlates and as such cannot explain the behavioural aspects of malnutrition. Apart from physical measurements of height and weight used for calculating body mass index, socio-demographic data were collected from self-reports which may not necessarily be accurate. It should also be noted that while BMI is often used as an indicator for obesity, BMI does not always reflect adiposity or body fatness. This is because BMI is unable to distinguish between fat and muscle, which tends to be heavier and can tip more toned individuals Since the data represent adults aged 25-64 years in Botswana, the study results can be generalized to the general population in that age group.

Funding 22
Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based The study used secondary data and was not funded 21 *Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies.  1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46