Geographic and Socioeconomic Disparities in Nutritional Status of Women in Pakistan: Secondary Analysis from Pakistan National Nutrition Survey

Geographic and Socioeconomic Disparities in Nutritional Status of Women in Pakistan: Secondary Analysis from Pakistan National Nutrition Survey | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Geographic and Socioeconomic Disparities in Nutritional Status of Women in Pakistan: Secondary Analysis from Pakistan National Nutrition Survey Kamran Sadiq, Bushra Mahmood, Sumra Kureishy, Shabina Ariff, Ghulam Mustafa, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-25852/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Pakistan is experiencing a rapid nutrition transition with a shift from underweight to overweight and obesity. This paper will examine the role of household socioeconomic position (SEP), community SEP and urbanicity on the nutritional status (underweight, overweight and obesity) of Pakistani women. Methods: We analysed data on 34,391 women aged ≥20 years enrolled in 2011 National Nutritional Survey of Pakistan (NNS). The NNS is a nationally representative survey employing a multistage stratified cluster sampling design. We assessed household SEP through a wealth index constructed using items from household possessions, utilities and housing conditions. We assessed the relationship of urbanicity, household and community SEP with categories of body mass index (BMI) using multinomial logistic regression where normal BMI (18.6-22.5 kg/m 2 ) was the reference category. Results: Overall, 15% of women were underweight (BMI<18.5 kg/m 2 ), 14% were pre-overweight (BMI 23.00-24.9 kg/m 2 ), 22% were overweight (BMI 25.0-29.99 kg/m 2 ) and 12% were obese (BMI≥30.0 kg/m 2 ). Households with higher SEP were associated with increased levels of overweight-1 (aOR: 2.91; 95%CI: 2.41-3.50), overweight-2 (aOR: 4.15; 95%CI: 3.31-5.19) and obesity (aOR: 6.20; 95%CI: 4.92-7.81) among women. Women were more likely to be obese in major urban (aOR: 2.34; 95%CI: 2.02-2.71) and urban (aOR: 1.84; 95%CI: 1.62-2.09) areas compared to rural areas. At the community level, communities in rural areas were more likely to have higher levels of underweight, while communities in urban areas were more likely to have higher levels of obesity. Furthermore, the likelihood of underweight and overweight women coexisting within the same community was low in major urban (r=0.67), urban (r=0.55) and rural (r=0.54) areas. Conclusions: In Pakistan, overweight and obesity among women is associated with urbanicity and household and community SEP. Women living in urban areas with high household and community SEP were associated with higher levels of overweight and obesity. Our findings suggest the importance of interventions targeting undernutrition in rural areas and overnutrition in urban areas. Health Policy Socioeconomic position Neighbourhood Urbanicity Obesity Underweight Overweight Nutrition transition Pakistan Figures Figure 1 Figure 2 Background Maternal and child undernutrition is a major public health problem in developing countries, such as India, Pakistan and Bangladesh [ 1 , 2 ]. However, emerging evidence suggests the presence of a nutrition transition in developing countries; where the nutritional status of the population is shifting from underweight to overweight. At the regional and country level, the nutritional transition trends differ according to economic and dietary conditions [ 3 , 4 ]. Although undernutrition still contributes to poor health outcomes in the developing world, there is sufficient evidence that the rise in overweight and obesity is resulting in an increased burden of non-communicable diseases, such as diabetes and cardiovascular diseases [ 5 ]. In Pakistan, the overall proportion of underweight women has decreased from 25–13% over the past two decades; however simultaneously there has been a rise in the proportion of overweight women (22.5–34%). The 2011 National Nutrition Survey (NNS) also identified a major shift in the nutritional status of women, with 16% being underweight and 34% being overweight and obese [ 6 ]. Similar trends have been reported in the 2013 Pakistan Demographic and Health Survey (PDHS) [ 7 ]. However, there is limited data on the reasons for this nutrition transition among Pakistani women at the national level. Other studies have postulated the overall economic development (improved employment opportunities and higher incomes) as the factor leading to changes in lifestyle and dietary habits [ 8 ]. Regardless of the economic growth in Pakistan, there has been an increase in economic disparity at the population level [ 9 – 11 ]. The economic opportunities among lower socioeconomic segments of the population remain inequitable. This inequity in economic opportunities along with inflation limits the population’s ability to access quality food in adequate quantity. According to the World Health Organization (WHO), 60% of Pakistanis live below the poverty line (< $ 2 per day), while another 21% lives on less than $ 1.25 per day [ 12 ]. Over the past decades, the population size of urban Pakistan has grown substantially [ 13 ]. The rise is linked to urbanization, which can have an impact on population health. Research has linked urbanization with improvements in access to healthcare, clean water, sanitation, education, social services and economic opportunities. These improvements have also led to an altering of the dietary and physical activity patterns with the population becoming more sedentary and experiencing a higher emergence of noncommunicable diseases and obesity [ 14 – 17 ]. We were not able to assess urbanicity on a continuum scale [ 14 , 16 ]. However, we used population size and the Federal Bureau of Statistics definition to classify urbanicity into major urban, urban and rural areas. We also did not have access to community level variables to characterize communities. For future studies, we recommend collecting community level variables to enable the assessment of community level factors affecting health and nutrition. However, the impact of urbanization on population health in Pakistan remains largely theoretical due to the limited evidence available. Therefore, we conducted a secondary analysis of the 2011 NNS to examine the association of household socioeconomic position (SEP), community SEP, and urbanization with the nutritional status of Pakistani women. Methods Data source, study setting and population Our study used the data subset of the 2011 NNS [ 12 ]. The NNS is a large cross-sectional survey that collected data on the nutritional status and health indicators of women, children and older adults (≥ 50 years old) across Pakistan. The survey was administered by trained lady health workers (LHWs) through face-to-face interviews. A structured questionnaire, semi-structured interviews and focus groups were used to capture a wide range of data on household characteristics, food security, maternal and child health and nutrition status. Stratified two-stage cluster sampling was used to select a representative sample size. This resulted in 27 963 households completing the survey. However, for our study, the study population was restricted to women (≥ 20 years old), which gave a final sample size of 34 391 women. Main exposures and covariates The main exposures of interest were household SEP and community SEP. Research suggests that questionnaires on income do not provide a good indication of the socioeconomic status in developing countries. However, the use of household assets and material possessions as indicators of the wealth index is widely used; it is validated in India. [ 19 , 20 ] The household wealth index is an indicator based on household possessions, utilities and construction [ 21 ]. Such scales have shown good validity and reliability in classifying households by their wealth in developing countries and provide a measure of income inequality in health status [ 19 , 20 , 22 ]. We performed a principle component analysis (PCA) based on household possessions, utilities (water source, cooking fuel, electricity, gas) and housing conditions (roof, wall and floor construction materials). The household wealth index score was generated by combining the score for each asset with weight for each asset derived from the PCA. The wealth index does not have a direct interpretation, since it is a constructed composite measure. Therefore, the population was divided into quintiles of the wealth index, with the 1st quintile representing the lowest SEP (poorest households) and the 5th quintile representing the highest SEP (richest households). The community wealth index was calculated by combining household wealth index scores. The communities were also divided into quintiles, with the 1st quintile representing the lowest SEP (poorest communities) and the 5th quintile representing the highest SEP (richest communities). Covariates included in the analysis were age, ethnicity, individual education, household education, occupation, urbanicity (major urban, urban and rural), and province of residence. Outcome The nutritional status of women was assessed through Body Mass Index (BMI). The World Health Organization (WHO) classification for Asian populations was used to categorize BMI into the following groups: <18.5 kg/m 2 (underweight), 18.5–22.9 kg/m 2 (normal weight), 23.0 − 24.9 kg/m 2 (pre-overweight), 25.0-27.5 kg/m 2 (overweight-1), 27.6–29.9 kg/m 2 (overweight-2) and ≥ 30 kg/m 2 (obese) [ 23 ]. Statistical analysis We computed the proportion of underweight, normal weight, pre-overweight, overweight and obese women and applied sampling weights to account for unequal sampling probabilities and clustering during data analysis. Multinomial logistic regression was used to assess the relationship between SEP and BMI, where the normal BMI was used as the reference. The association of BMI categories were further assessed across household wealth quintiles, community wealth quintiles, age, education, urbanicity, and occupation. Using the multinomial logistic regression, we calculated the adjusted odds ratios (aOR) with 95% confidence intervals. The interaction was assessed between household wealth, community wealth and urbanicity. For BMI categories, multilevel multinomial logistic regression was used to assess the variance in nutritional status between communities, with a community level random effect. The random effects used were specific to underweight and overweight, allowing for different community level factors to affect each outcome category. If correlated, the random effects for underweight and overweight would demonstrate the extent to which underweight and overweight women coexist in communities. The normal BMI was used as the reference. All analyses were performed using SAS statistical software (version 9.3). Results Participant profile From the 2011 NNS, a total of 34,391 adult women aged 20 years or older were included in the secondary data analysis. Majority of the women were housewives (90%) aged between 20–39 years (67%), from rural areas (67%), with no formal education (62%) (Table 1 ; Fig. 1 ). Most of the women were from the province of Punjab (55%). Among the employed women, 3% were identified as unskilled labourers or farm workers. Only 1% of the women were highly educated with post-secondary education and worked in the services sector. Overall, 36% of women were normal weight, 15% were underweight, 14% were pre-overweight, 22% were overweight and 12% were obese. Table 1 Distribution of BMI by participants’ characteristics, National Nutritional Survey of Pakistan 2011 Underweight Normal Pre-overweight Overweight Obesity Overall < 18.5 18.5–22.9 23.0-24.9 25.0-27.5 27.5–29.9 ≥ 30 N N (%; 95%CI) %(95%CI) %(95%CI) %(95%CI) %(95%CI) %(95%CI) %(95%CI) Total Sample 34391 - 15(15–16) 36(36–37) 14(14–15) 13(12–13) 10(9–10) 12(12–13) Urbanicity Major Urban 7028 20(19–20) 9(8–10) 27(25–28) 14(13–15) 15(14–16) 14(13–15) 22(21–24) Urban 7507 13(12–13) 11(10–12) 32(30–33) 14(13–15) 15(14–16) 11(11–12) 17(16–18) Rural 19856 67(67–68) 18(17–18) 40(39–41) 14(13–15) 12(11–12) 8(8–8) 8(8–9) Province Punjab 16665 55(54–55) 15(14–16) 36(35–37) 13(13–14) 13(12–13) 10(9–10) 13(13–14) Sindh 7480 22(22–23) 20(19–21) 38(37–40) 12(11–13) 11(10–11) 8(8–9) 11(10–12) KPK 4150 13(13–14) 6(5–8) 35(32–37) 19(16–21) 16(15–18) 12(11–13) 12(11–14) Balochistan 2483 4(4–5) 19(17–21) 41(38–43) 14(12–15) 12(11–13) 6(5–8) 8(7–10) FATA 758 2(2–2) 2(0–5) 26(20–31) 21(17–26) 25(21–30) 15(11–19) 11(7–14) AJK 1801 3(2–3) 19(16–22) 39(37–42) 16(13–18) 11(10–13) 8(6–9) 7(5–9) Gilgit 1054 1(1–1) 16(12–20) 48(45–52) 16(12–19) 10(8–12) 6(4–8) 3(2–5) Age (years) 20–29 12733 37(36–37) 19(18–19) 44(43–45) 14(13–15) 10(10–11) 7(6–7) 6(6–7) 30–39 10438 30(29–31) 13(12–13) 35(34–36) 14(14–15) 14(13–15) 10(10–11) 13(13–14) 40–49 4718 14(13–14) 11(10–12) 29(27–30) 13(12–14) 15(14–16) 13(12–14) 19(18–21) 50–59 3399 10(10–10) 11(9–12) 28(27–30) 13(11–14) 15(14–17) 13(12–14) 20(18–22) 60–69 2101 6(6–7) 16(14–18) 31(28–33) 15(13–16) 13(11–15) 11(10–13) 14(12–16) 70–79 719 2(2–2) 25(21–28) 32(28–35) 13(10–16) 12(9–14) 8(5–10) 11(9–14) ≥ 80 283 1(1–1) 33(27–39) 37(30–43) 12(7–18) 8(4–11) 6(2–9) 4(2–7) Occupation Business/shop/landlord 82 0(0–0) 15(5–24) 34(21–47) 12(5–19) 16(7–24) 10(3–18) 13(5–21) Services (higher education) 1 382 1(1–1) 9(6–13) 32(26–37) 15(11–20) 17(13–22) 13(9–18) 14(10–18) Services (lower education) 2 429 1(1–1) 16(12–20) 34(29–39) 16(12–20) 12(9–15) 9(6–13) 12(9–16) Skilled manual workers 17 0(0–0) 19(0–39) 17(0–38) 34(7–60) 5(0–15) 16(0–33) 9(0–27) Unskilled/Farm workers 832 3(2–3) 23(20–26) 48(44–52) 9(7–11) 8(6–10) 5(4–7) 6(4–8) Housewife 30976 90(90–91) 14(14–15) 36(35–37) 14(14–15) 13(13–13) 10(9–10) 13(12–13) Student/unemployed/retired 1283 4(3–4) 26(23–29) 38(35–42) 13(11–15) 10(8–11) 6(4–8) 6(5–8) Non-manual 893 2(2–2) 13(11–16) 33(29–37) 15(13–18) 14(12–17) 11(9–13) 13(10–16) Manual work 849 3(2–3) 23(20–26) 47(44–51) 10(7–12) 8(6–10) 5(4–7) 6(4–8) Education (years) No formal schooling 20856 62(61–63) 17(16–17) 39(38–39) 13(13–14) 12(12–13) 9(8–9) 10(10–11) Primary (1–5 years) 3723 11(11–12) 14(12–15) 36(35–38) 13(12–15) 12(11–14) 11(10–12) 13(12–15) Secondary (6–10 years) 6180 18(17–18) 12(11–13) 32(30–33) 16(14–17) 14(13–15) 10(9–11) 16(15–18) College (11–12 years) 1735 5(4–5) 13(11–15) 31(28–34) 15(13–18) 13(12–15) 12(10–14) 16(13–18) University (> 12 years) 1897 5(4–5) 10(8–12) 32(29–34) 16(14–18) 15(13–17) 12(10–14) 15(14–17) Household education (years) No formal schooling 7984 24(23–25) 21(20–22) 42(40–43) 13(12–14) 11(10–12) 7(7–8) 7(6–8) Primary (1–5 years) 12091 36(35–37) 16(15–17) 40(39–41) 14(13–14) 12(12–13) 8(8–9) 10(9–11) Secondary (6–10 years) 10682 30(29–31) 11(10–12) 32(31–33) 15(14–16) 14(14–15) 11(11–12) 16(15–17) College (11–12 years) 2151 6(5–6) 9(7–10) 28(26–31) 14(13–16) 14(13–16) 13(12–15) 21(18–23) University (> 12 years) 1483 4(3–4) 6(5–8) 22(19–24) 16(14–19) 15(12–17) 16(13–18) 25(23–28) Household wealth quintiles 1st quintile (Poorest) 5910 18(17–19) 27(26–29) 47(46–48) 11(10–12) 7(6–8) 4(3–5) 4(3–4) 2nd quintile 6312 19(19–20) 17(16–18) 42(40–43) 16(15–18) 12(11–13) 7(7–8) 6(5–7) 3rd quintile 6745 20(19–21) 14(13–15) 39(38–40) 14(13–15) 14(13–15) 9(8–10) 10(9–11) 4th quintile 7238 21(20–21) 11(11–12) 31(30–33) 15(14–16) 15(14–16) 12(11–12) 16(15–17) 5th quintile (Richest) 8186 22(21–23) 7(7–8) 25(24–27) 15(14–16) 15(14–16) 15(14–16) 23(21–24) 1 ≥ 14 years of education 2 < 14 years of education 1. Correlation of underweight and overweight parameters in major urban areas: r = 0.67 2. Correlation of underweight and overweight parameters in urban areas: r = 0.55 3. Correlation of underweight and overweight parameters in rural areas: r = 0.54 Household SEP and underweight A gradual decrease was observed in the proportion of underweight women across household wealth quintiles (HWQ) with 27% in the 1st HWQ (lowest SEP; poorest household) to 4% in the 5th quintile (highest SEP; richest household). After adjusting for covariates, women in the 1st HWQ were more likely to be underweight than those in the 3rd quintile (aOR: 0.73; 95%CI: 0.64–0.83) and the 5th quintile (aOR: 0.57; 95%CI: 0.48–0.68) (Table 2 ). Table 2 Multivariable model for association of household socioeconomic position and other participants’ characteristics with categories of BMI among women, National Nutritional Survey of Pakistan 2011 Adjusted ORs(95% confidence interval) Covariates BMI < 18.5 BMI 23 -24.99 BMI 25.0–27.49 BMI 27.5–29.9 BMI ≥ 30 Wealth quintile 1st quintile (Poorest) 1.00 1.00 1.00 1.00 1.00 2nd quintile 0.76(0.68–0.85) 1.4(1.21–1.62) 1.51(1.29–1.77) 1.51(1.24–1.84) 1.64(1.33–2.03) 3rd quintile 0.73(0.64–0.83) 1.32(1.14–1.53) 1.93(1.63–2.28) 1.99(1.65–2.41) 2.62(2.12–3.24) 4th quintile 0.72(0.62–0.83) 1.62(1.37–1.92) 2.49(2.09–2.97) 2.99(2.44–3.68) 4.23(3.38–5.29) 5th quintile (Richest) 0.57(0.48–0.68) 1.84(1.53–2.2) 2.91(2.41–3.5) 4.15(3.31–5.19) 6.2(4.92–7.81) Urbanicity Major urban 0.84(0.73–0.97) 1.18(1.03–1.36) 1.47(1.27–1.69) 1.56(1.33–1.83) 2.34(2.02–2.71) Urban 0.85(0.75–0.96) 1.13(1.02–1.26) 1.37(1.23–1.53) 1.4(1.23–1.59) 1.84(1.62–2.09) Rural 1.00 1.00 1.00 1.00 1.00 Household education (years) No formal schooling 1.00 1.00 1.00 1.00 1.00 Primary (1–5 years) 0.87(0.78–0.96) 1.08(0.97–1.2) 1.05(0.93–1.19) 1(0.87–1.14) 1.11(0.96–1.28) Secondary (6–10 years) 0.83(0.74–0.94) 1.34(1.18–1.53) 1.24(1.09–1.4) 1.27(1.1–1.48) 1.49(1.28–1.74) College (11–12 years) 0.77(0.62–0.97) 1.36(1.12–1.65) 1.25(1.01–1.55) 1.38(1.1–1.73) 1.72(1.35–2.19) University (> 12 years) 0.75(0.56–1.01) 1.91(1.51–2.41) 1.47(1.14–1.89) 1.76(1.37–2.26) 2.33(1.81–2.99) Province AJK 1.34(1.09–1.65) 1.09(0.84–1.41) 0.89(0.69–1.16) 0.84(0.62–1.15) 0.7(0.5–0.99) Balochistan 1.09(0.89–1.34) 0.98(0.77–1.24) 1.06(0.85–1.32) 0.77(0.56–1.07) 0.9(0.68–1.2) FATA 0.27(0.11–0.67) 2.25(1.49–3.4) 3.83(2.51–5.85) 3.47(2.07–5.83) 2.79(1.59–4.87) Gilgit 1.14(0.77–1.68) 0.93(0.62–1.38) 0.67(0.45–1.01) 0.6(0.39–0.93) 0.28(0.16–0.48) KPK 0.56(0.42–0.73) 1.26(1 -1.58) 1.26(0.98–1.6) 1.26(0.99–1.62) 1.01(0.78–1.31) Sindh 1.27(1.09–1.47) 0.93(0.79–1.09) 0.79(0.67–0.93) 0.8(0.68–0.95) 0.72(0.59–0.88) Punjab 1.00 1.00 1.00 1.00 1.00 Occupation Non-manual 1.32(0.93–1.86) 1.27(0.89–1.82) 1.28(0.89–1.85) 1.22(0.79–1.88) 0.96(0.61–1.5) Manual work 1.00 1.00 1.00 1.00 1.00 Housewife 1.06(0.87–1.3) 1.42(1.09–1.84) 1.38(1.04–1.82) 1.48(1.07–2.05) 1.41(1 -2.01) Student/Retired/Unemployed 1.9(1.45–2.49) 1.08(0.76–1.53) 0.94(0.65–1.36) 0.82(0.52–1.29) 0.72(0.45–1.14) Under age/Not reported 1.28(0.87–1.89) 0.86(0.5–1.49) 1.11(0.71–1.73) 0.85(0.5–1.42) 0.88(0.48–1.59) (N = 34391); Reference: BMI = 18.5–22.9, N = 12380 Household SEP, overweight and obesity An increase in overweight and obese women was observed with rising HWQ, with the largest proportion of overweight (30%) and obese (23%) women found in the 5th quintile (highest SEP; richest household) (Table 1 ). Furthermore, the multivariable model demonstrated an increasing gradient in adjusted odds ratios (aOR) for overweight and obesity across wealth quintiles with aORs ranging from 1.51 (2nd quintile) to 2.91 (5th quintile) for overweight-1, 1.51 (2nd quintile) to 4.15 (5th quintile) for overweight-2 and 1.64 (2nd quintile) to 6.20 (5th quintile) for obesity (Table 2 ). Community SEP, urbanicity, underweight and overweight Overall, women living in rural areas were more likely to be underweight than those living in major urban (aOR: 0.84; 96%CI: 0.73–0.97) and urban areas (aOR: 0.85; 95%CI: 0.75–0.96). The proportion of overweight and obesity was significantly different across urbanicity, with the largest amount of obese women living in major urban areas (aOR: 2.34; 95%CI: 2.02–2.71) (Table 2 , Fig. 1 ). The multivariable model for HWQ and urbanicity demonstrated a gradual increase in overweight and obesity with increasing wealth quintiles in rural and urban areas. The interaction of HWQ and urbanicity for obesity was most evident in the 5th quintile, with women living in major urban areas (aOR: 6.87; 95%CI 5.09–9.29) being more likely to be obese than those in urban areas (aOR: 5.27; 95%CI3.92-7.08) and rural areas (aOR: 4.63; 95%CI 3.48–6.17) (Table 3 ). Table 3 Multivariable models for the interaction between household socioeconomic position and urbanicity, and the interaction of household socioeconomic position for association with categories of BMI among women, National Nutritional Survey of Pakistan 2011 Adjusted ORs(95% confidence interval) Covariates BMI < 18.5 BMI 23 -24.99 BMI 25.0 -29.9 BMI ≥ 30 Household wealth quintile (HWQ) and urbanicity Major Urban 1st quintile (Poorest) 1.19(0.56–2.5) 0.84(0.37–1.91) 0.83(0.27–2.57) 0.61(0.15–2.54) 2nd quintile 0.73(0.44–1.22) 1.73(1.05–2.84) 2.01(1.21–3.33) 2.69(1.39–5.23) 3rd quintile 0.48(0.36–0.63) 1.13(0.83–1.54) 1.95(1.5–2.53) 2.86(2.01–4.07) 4th quintile 0.61(0.47–0.8) 1.82(1.39–2.39) 3.21(2.51–4.11) 5.6(4.08–7.69) 5th quintile (Richest) 0.5(0.37–0.68) 1.84(1.41–2.39) 3.38(2.65–4.29) 6.87(5.09–9.29) Urban 1st quintile (Poorest) 0.88(0.62–1.26) 1.27(0.87–1.86) 1.37(1 -1.88) 2.18(1.25–3.8) 2nd quintile 0.7(0.54–0.91) 1.34(1.03–1.73) 2.08(1.61–2.68) 2.54(1.79–3.6) 3rd quintile 0.65(0.51–0.83) 1.49(1.18–1.9) 2.09(1.68–2.6) 3.85(2.81–5.28) 4th quintile 0.52(0.41–0.66) 1.58(1.24 -2) 2.64(2.11–3.32) 4.34(3.27–5.75) 5th quintile (Richest) 0.49(0.36–0.65) 1.7(1.33–2.18) 3.16(2.5–3.99) 5.27(3.92–7.08) Rural 1st quintile (Poorest) 1.00 1.00 1.00 1.00 2nd quintile 0.75(0.67–0.85) 1.36(1.18–1.58) 1.35(1.16–1.57) 1.44(1.15–1.82) 3rd quintile 0.73(0.63–0.85) 1.27(1.08–1.49) 1.69(1.44–1.98) 2.07(1.62–2.63) 4th quintile 0.72(0.6–0.87) 1.47(1.21–1.79) 2.13(1.76–2.58) 3.06(2.35 -4) 5th quintile (Richest) 0.52(0.41–0.67) 1.76(1.41–2.21) 2.95(2.37–3.67) 4.63(3.48–6.17) Community wealth quintile 1st quintile (Poorest) 1.00 1.00 1.00 1.00 2nd quintile 1(0.87–1.14) 1.16(0.98–1.36) 1.33(1.14–1.56) 1.46(1.14–1.87) 3rd quintile 1.07(0.92–1.23) 1.11(0.94–1.32) 1.45(1.22–1.71) 1.64(1.28–2.11) 4th quintile 1.04(0.85–1.27) 1.25(1.02–1.52) 1.56(1.28–1.9) 2.19(1.67–2.88) 5th quintile (Richest) 1.03(0.81–1.31) 1.27(1 -1.61) 1.76(1.41–2.18) 2.87(2.15–3.84) (N = 34391); Reference: BMI = 18.5–22.9, N = 12380 When assessing the interaction between community wealth quintiles and nutritional status, there was an increase of the effect estimates (aORs) for obesity across community wealth (Table 3 ). The difference in underweight across community wealth quintiles was not statistically significant. A trend was observed among household education levels and obesity. Households with higher levels of education were more likely to have overweight and obese women. Moreover, the likelihood of overweight and obesity among women increased consistently with the attainment of higher education; overweight-1 (aOR: 1.47; 95%CI 1.14–1.89), overweight-2 (aOR: 1.76; 95%CI 1.37–2.26) and obesity (aOR: 2.33; 95%CI 1.81–2.99) (Table 2 ). Province of residence, underweight and overweight Among the provinces of Pakistan, the largest proportions of underweight women were in rural Sindh (20%) and rural Balochistan (19%) (Fig. 1 ). Alternatively, women living in urban KPK (23%) and urban Punjab (23%) were found to the most overweight and obese women. In comparison to Punjab, women living in AJK (aOR: 1.34; 95%CI: 1.09–1.65) and Sindh (aOR: 1.27; 95%CI 1.09–1.47) were more likely to be underweight (Table 2 ). Additionally, women in FATA (aOR: 0.27; 95%CI: 0.11–0.67) were less likely to be underweight compared to those in Punjab. As for women in Gilgit, there were less likely to be overweight (aOR: 0.60; 95%CI: 0.39–0.93) and obese (aOR: 0.28; 95%CI: 0.16–0.48) compared to women in Punjab. Co-existence of underweight and obesity at community level We evaluated the correlation of underweight and overweight parameters to assess whether underweight and overweight women coexist at the community level. The moderate negative association in major urban (r = 0.67), urban (r = 0.55) and rural (r = 0.54) communities suggest that the likelihood of underweight and overweight women coexisting within the same community is low. (Fig. 2 ) Discussion According to the 2011 NNS, the prevalence of underweight (15%) is lower than overweight and obesity combined (35%) among Pakistani women. Our findings suggest a noticeable difference in the level of underweight, overweight and obesity with household and community SEP and urbanicity. Underweight women were mainly found in poor rural areas, while overweight and obese women were mainly in rich urban areas. Furthermore, the likelihood of being overweight or obese increased drastically for women living in wealthier communities located in urban areas. We also found that underweight, overweight, and obesity did not co-exist within the same community. Overall, underweight women were more likely to be among the poorest, rural dwelling with the fewest years of formal schooling. This emphasizes that despite a decrease in the prevalence of underweight women, targeted nutrition interventions are still essential. Alternatively, an increasing trend in overweight and obesity was associated with an increase in SEP, urbanization and education. Further analyses on community characteristics would be important in developing and delivering interventions that are relevant to local needs. Similar to our findings, research conducted in other South Asian countries has found a comparable distribution of underweight, overweight and obesity across SEP. However, the overall prevalence of overweight and obesity in Bangladesh (2007: 10%,1.7%; 2011: 17%,2.9%), India (2006: 9.8%,2.9%) and Nepal (2011: 11.2%,2.2%) is lower than Pakistan (2011: 22%, 12%). [ 25 – 27 ]. In India, there has been an increase in overweight and obesity among communities with higher SEP. However, about 50% of Indian women are still underweight, with the majority living in communities with lower SEP [ 28 ]. Similar findings have been reported from Bangladesh [ 29 , 30 ]. This suggests that Pakistan is at a relatively similar stage of nutrition transition as compared to its neighbouring countries [ 11 , 31 , 32 ]. Women with higher education were associated with increased risk of overweight and obesity. In most developing countries, such as Pakistan, India and Bangladesh, a positive association between education and BMI has been reported [ 28 , 30 , 32 ]. However, in contrast to our findings, a local study from the Khairpur district showed that education was associated with lower levels of obesity [ 11 ]. The situation observed in Khairpur is identical to the advanced stage of nutrition transition seen in Brazil, where higher levels of education are associated with lower levels of obesity. [ 33 , 34 ]. However, further research is needed to assess if higher levels of education and higher SEP have an association with obesity in Pakistan. Additional factors that may have increased the proportion of overweight women are sedentary jobs, availability of household help and the perception that a plump body size is associated with higher SEP [ 35 ]. Households with higher SEP are associated with higher consumption of meats, fats and fast foods, especially in urban areas [ 36 , 37 ]. At later stages of nutrition transition, commonly seen in developed countries, BMI is inversely proportional to educational achievement since more research is needed. The risk of overweight and obesity increased with urbanicity even after controlling for SEP and education. These findings are consistent with other research conducted in developing countries, including India and Bangladesh [ 28 , 38 – 40 ]. Literature has also identified cities as promoting overweight and obesity through their obesogenic environment with increased availability of fast food and other restaurants and increasing norms of eating out, mechanized transportation, sedentary jobs and low physical activity [ 41 – 43 ]. Lack of parks, air pollution, actual and perceived safety, violence and political instability and cultural and religious norms may also constraint physical activity in an overall sedentary environment [ 44 , 45 ]. Data on the influence of neighbourhood environments, including food and physical activity, is needed from Pakistan and other developing countries to understand role of urban environment on overweight and obesity. Such data may provide useful evidence for urban planning and shaping urban environment to tackle the obesity problem. Conclusions Our results show that the overweight and obesity are significant public health problem in Pakistan, especially in urban areas. Underweight still persists among the rural poor, especially in the province of Sindh. These findings emphasize the importance of interventions targeting undernutrition in rural areas and overnutrition in urban areas. Furthermore, our study has showed that underweight and overweight do not exist in the same communities, thus providing avenues for targeted interventions according to local needs at the community level. The high levels of overweight and obesity in major urban areas highlights the need for urgent action to lower the increasing burden of chronic diseases, such as diabetes and cardiovascular disease. Also, further analyses on geospatial distribution of underweight, overweight and communities characteristics are required to improve the development and delivery of nutritional interventions relevant to the local need. Abbreviations AKU Aga Khan University; BMI:Body mass index; HWQ:Household wealth quintiles; NNS:National Nutrition Survey; PDHS:Pakistan Demographic and Health Survey; RAF; Research and Advocacy Fund; SEP:Socioeconomic position. Declarations Acknowledgements Pakistan NNS 2011 was conducted by the Aga Khan University and Ministry of Health, Pakistan and funded by UNICEF, Pakistan. This study is from a project funded by the Maternal and Newborn Health Programme - Research and Advocacy Fund (RAF) and implemented by the Aga Khan University (AKU), Karachi, Pakistan. RAF is a grant of the UK Department for International Development (DFID) and Australian Department of Foreign Affairs and Trade (DFAT). Authors' contributions ZAB conceived the idea & provided technical inputs in the design of the study, analysis. NJ interpretation of data, statistical analysis. SS provided technical inputs in the design interpretation of results and reviewed final version of the manuscript. KS & BM wrote first draft version of the manuscript, SK, SA, GM, IA, RT, GNK, MU contributed to manuscript write up and critical revisions. All authors read and approved the final manuscript. Funding This study was funded by the Maternal and Newborn Health Programme, Research and Advocacy Fund (RAF), Pakistan. The funder had no role in study design, data collection, analysis, and interpretation of data and in writing the manuscript. Availability of data and materials The datasets are available from the corresponding author on request. Ethics approval and consent to participate The original survey was approved by the Ethics Review Committee of the Aga Khan University and the National Bioethics Committee of the Government of Pakistan. A secondary analysis was conducted of the NNS, therefore exempting us from requiring further ethical review and approval. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. 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Am J Public Health. 2012;102(9):1644–54. Holsten JE. Obesity and the community food environment: a systematic review. Public Health Nutr. 2009;12(3):397–405. Larson NI, Story MT, Nelson MC. Neighborhood environments: disparities in access to healthy foods in the U.S. Am J Prev Med. 2009;36(1):74–81. Babakus WS, Thompson JL. Physical activity among South Asian women: a systematic, mixed-methods review. The International Journal of Behavioral Nutrition Physical Activity. 2012;9:150. Benjamin K, Donnelly TT. Barriers and facilitators influencing the physical activity of Arabic adults: A literature review. Avicenna. 2013;8. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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13:31:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-25852/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-25852/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":1025587,"identity":"67a9a37a-c3c2-47f7-842d-29858d31a5f7","added_by":"auto","created_at":"2020-05-04 14:22:36","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":69491,"visible":true,"origin":"","legend":"Distribution of BMI categories by province, urban and rural areas among Pakistani women","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-25852/v1/Figure1.jpg"},{"id":1025588,"identity":"753454d7-a311-490f-8d38-f15ee13ac42a","added_by":"auto","created_at":"2020-05-04 14:22:36","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":65734,"visible":true,"origin":"","legend":"Scatterplot of the community specific residuals for underweight and obesity in major urban1, urban2 and rural3 areas of Pakistan","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-25852/v1/Figure2.jpg"},{"id":13501611,"identity":"b1da9d32-169a-48b7-b5f0-bb943e8f6059","added_by":"auto","created_at":"2021-09-16 23:11:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":618340,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-25852/v1/dda4ed28-ae0f-47f2-b3b1-e1a1ce1a9e88.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eGeographic and Socioeconomic Disparities in Nutritional Status of Women in Pakistan: Secondary Analysis from Pakistan National Nutrition Survey\u003c/p\u003e","fulltext":[{"header":"Background","content":" \u003cp\u003eMaternal and child undernutrition is a major public health problem in developing countries, such as India, Pakistan and Bangladesh [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, emerging evidence suggests the presence of a nutrition transition in developing countries; where the nutritional status of the population is shifting from underweight to overweight. At the regional and country level, the nutritional transition trends differ according to economic and dietary conditions [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Although undernutrition still contributes to poor health outcomes in the developing world, there is sufficient evidence that the rise in overweight and obesity is resulting in an increased burden of non-communicable diseases, such as diabetes and cardiovascular diseases [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Pakistan, the overall proportion of underweight women has decreased from 25\u0026ndash;13% over the past two decades; however simultaneously there has been a rise in the proportion of overweight women (22.5\u0026ndash;34%). The 2011 National Nutrition Survey (NNS) also identified a major shift in the nutritional status of women, with 16% being underweight and 34% being overweight and obese [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Similar trends have been reported in the 2013 Pakistan Demographic and Health Survey (PDHS) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, there is limited data on the reasons for this nutrition transition among Pakistani women at the national level. Other studies have postulated the overall economic development (improved employment opportunities and higher incomes) as the factor leading to changes in lifestyle and dietary habits [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRegardless of the economic growth in Pakistan, there has been an increase in economic disparity at the population level [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The economic opportunities among lower socioeconomic segments of the population remain inequitable. This inequity in economic opportunities along with inflation limits the population\u0026rsquo;s ability to access quality food in adequate quantity. According to the World Health Organization (WHO), 60% of Pakistanis live below the poverty line (\u0026lt; \u003cspan\u003e$\u003c/span\u003e2 per day), while another 21% lives on less than \u003cspan\u003e$\u003c/span\u003e1.25 per day [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOver the past decades, the population size of urban Pakistan has grown substantially [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The rise is linked to urbanization, which can have an impact on population health. Research has linked urbanization with improvements in access to healthcare, clean water, sanitation, education, social services and economic opportunities. These improvements have also led to an altering of the dietary and physical activity patterns with the population becoming more sedentary and experiencing a higher emergence of noncommunicable diseases and obesity [\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe were not able to assess urbanicity on a continuum scale [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, we used population size and the Federal Bureau of Statistics definition to classify urbanicity into major urban, urban and rural areas. We also did not have access to community level variables to characterize communities. For future studies, we recommend collecting community level variables to enable the assessment of community level factors affecting health and nutrition. However, the impact of urbanization on population health in Pakistan remains largely theoretical due to the limited evidence available. Therefore, we conducted a secondary analysis of the 2011 NNS to examine the association of household socioeconomic position (SEP), community SEP, and urbanization with the nutritional status of Pakistani women.\u003c/p\u003e "},{"header":"Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source, study setting and population\u003c/h2\u003e \u003cp\u003eOur study used the data subset of the 2011 NNS [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The NNS is a large cross-sectional survey that collected data on the nutritional status and health indicators of women, children and older adults (\u0026ge;\u0026thinsp;50\u0026nbsp;years old) across Pakistan. The survey was administered by trained lady health workers (LHWs) through face-to-face interviews. A structured questionnaire, semi-structured interviews and focus groups were used to capture a wide range of data on household characteristics, food security, maternal and child health and nutrition status. Stratified two-stage cluster sampling was used to select a representative sample size. This resulted in 27 963 households completing the survey. However, for our study, the study population was restricted to women (\u0026ge;\u0026thinsp;20\u0026nbsp;years old), which gave a final sample size of 34 391 women.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMain exposures and covariates\u003c/h2\u003e \u003cp\u003eThe main exposures of interest were household SEP and community SEP. Research suggests that questionnaires on income do not provide a good indication of the socioeconomic status in developing countries. However, the use of household assets and material possessions as indicators of the wealth index is widely used; it is validated in India. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] The household wealth index is an indicator based on household possessions, utilities and construction [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Such scales have shown good validity and reliability in classifying households by their wealth in developing countries and provide a measure of income inequality in health status [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. We performed a principle component analysis (PCA) based on household possessions, utilities (water source, cooking fuel, electricity, gas) and housing conditions (roof, wall and floor construction materials). The household wealth index score was generated by combining the score for each asset with weight for each asset derived from the PCA. The wealth index does not have a direct interpretation, since it is a constructed composite measure. Therefore, the population was divided into quintiles of the wealth index, with the 1st quintile representing the lowest SEP (poorest households) and the 5th quintile representing the highest SEP (richest households).\u003c/p\u003e \u003cp\u003eThe community wealth index was calculated by combining household wealth index scores. The communities were also divided into quintiles, with the 1st quintile representing the lowest SEP (poorest communities) and the 5th quintile representing the highest SEP (richest communities). Covariates included in the analysis were age, ethnicity, individual education, household education, occupation, urbanicity (major urban, urban and rural), and province of residence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eOutcome\u003c/h2\u003e \u003cp\u003eThe nutritional status of women was assessed through Body Mass Index (BMI). The World Health Organization (WHO) classification for Asian populations was used to categorize BMI into the following groups: \u0026lt;18.5\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e (underweight), 18.5\u0026ndash;22.9\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e (normal weight), 23.0 \u0026minus;\u0026thinsp;24.9\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e (pre-overweight), 25.0-27.5\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e (overweight-1), 27.6\u0026ndash;29.9\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e (overweight-2) and \u0026ge;\u0026thinsp;30\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e (obese) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe computed the proportion of underweight, normal weight, pre-overweight, overweight and obese women and applied sampling weights to account for unequal sampling probabilities and clustering during data analysis.\u003c/p\u003e \u003cp\u003eMultinomial logistic regression was used to assess the relationship between SEP and BMI, where the normal BMI was used as the reference. The association of BMI categories were further assessed across household wealth quintiles, community wealth quintiles, age, education, urbanicity, and occupation. Using the multinomial logistic regression, we calculated the adjusted odds ratios (aOR) with 95% confidence intervals. The interaction was assessed between household wealth, community wealth and urbanicity.\u003c/p\u003e \u003cp\u003eFor BMI categories, multilevel multinomial logistic regression was used to assess the variance in nutritional status between communities, with a community level random effect. The random effects used were specific to underweight and overweight, allowing for different community level factors to affect each outcome category. If correlated, the random effects for underweight and overweight would demonstrate the extent to which underweight and overweight women coexist in communities. The normal BMI was used as the reference. All analyses were performed using SAS statistical software (version 9.3).\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":" \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eParticipant profile\u003c/h2\u003e \u003cp\u003eFrom the 2011 NNS, a total of 34,391 adult women aged 20\u0026nbsp;years or older were included in the secondary data analysis. Majority of the women were housewives (90%) aged between 20\u0026ndash;39\u0026nbsp;years (67%), from rural areas (67%), with no formal education (62%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Most of the women were from the province of Punjab (55%). Among the employed women, 3% were identified as unskilled labourers or farm workers. Only 1% of the women were highly educated with post-secondary education and worked in the services sector. Overall, 36% of women were normal weight, 15% were underweight, 14% were pre-overweight, 22% were overweight and 12% were obese.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of BMI by participants\u0026rsquo; characteristics, National Nutritional Survey of Pakistan 2011\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ePre-overweight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eOverall\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;18.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e18.5\u0026ndash;22.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e23.0-24.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003e25.0-27.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e27.5\u0026ndash;29.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eN (%; 95%CI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e%(95%CI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e%(95%CI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e%(95%CI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003e%(95%CI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e%(95%CI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e%(95%CI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal Sample\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15(15\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36(36\u0026ndash;37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14(14\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e13(12\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10(9\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12(12\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUrbanicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMajor Urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(19\u0026ndash;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(8\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27(25\u0026ndash;28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14(13\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e15(14\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14(13\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e22(21\u0026ndash;24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(12\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(10\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32(30\u0026ndash;33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14(13\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e15(14\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11(11\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e17(16\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67(67\u0026ndash;68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18(17\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40(39\u0026ndash;41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14(13\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e12(11\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8(8\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8(8\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProvince\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePunjab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55(54\u0026ndash;55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15(14\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36(35\u0026ndash;37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13(13\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e13(12\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10(9\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13(13\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSindh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(22\u0026ndash;23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(19\u0026ndash;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38(37\u0026ndash;40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12(11\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e11(10\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8(8\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11(10\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKPK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(13\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(5\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35(32\u0026ndash;37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19(16\u0026ndash;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e16(15\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12(11\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12(11\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBalochistan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(4\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(17\u0026ndash;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41(38\u0026ndash;43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14(12\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e12(11\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6(5\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8(7\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFATA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(2\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(0\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26(20\u0026ndash;31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21(17\u0026ndash;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e25(21\u0026ndash;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15(11\u0026ndash;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11(7\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAJK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(2\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(16\u0026ndash;22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39(37\u0026ndash;42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16(13\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e11(10\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8(6\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7(5\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGilgit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(1\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(12\u0026ndash;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48(45\u0026ndash;52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16(12\u0026ndash;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e10(8\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6(4\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3(2\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37(36\u0026ndash;37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(18\u0026ndash;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44(43\u0026ndash;45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14(13\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e10(10\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7(6\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6(6\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(29\u0026ndash;31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(12\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35(34\u0026ndash;36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14(14\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e14(13\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10(10\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13(13\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(13\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(10\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29(27\u0026ndash;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13(12\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e15(14\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13(12\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19(18\u0026ndash;21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(10\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(9\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28(27\u0026ndash;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13(11\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e15(14\u0026ndash;17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13(12\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e20(18\u0026ndash;22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(6\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(14\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31(28\u0026ndash;33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15(13\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e13(11\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11(10\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14(12\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(2\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25(21\u0026ndash;28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32(28\u0026ndash;35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13(10\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e12(9\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8(5\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11(9\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(1\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33(27\u0026ndash;39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37(30\u0026ndash;43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12(7\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e8(4\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6(2\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4(2\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBusiness/shop/landlord\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15(5\u0026ndash;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34(21\u0026ndash;47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12(5\u0026ndash;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e16(7\u0026ndash;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10(3\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13(5\u0026ndash;21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eServices (higher education)\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(1\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(6\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32(26\u0026ndash;37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15(11\u0026ndash;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e17(13\u0026ndash;22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13(9\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14(10\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eServices (lower education)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(1\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(12\u0026ndash;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34(29\u0026ndash;39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16(12\u0026ndash;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e12(9\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9(6\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12(9\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkilled manual workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(0\u0026ndash;39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17(0\u0026ndash;38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34(7\u0026ndash;60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e5(0\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16(0\u0026ndash;33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9(0\u0026ndash;27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnskilled/Farm workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(2\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23(20\u0026ndash;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48(44\u0026ndash;52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9(7\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e8(6\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5(4\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6(4\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90(90\u0026ndash;91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14(14\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36(35\u0026ndash;37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14(14\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e13(13\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10(9\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13(12\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudent/unemployed/retired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(3\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26(23\u0026ndash;29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38(35\u0026ndash;42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13(11\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e10(8\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6(4\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6(5\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-manual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(2\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(11\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33(29\u0026ndash;37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15(13\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e14(12\u0026ndash;17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11(9\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13(10\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManual work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(2\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23(20\u0026ndash;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47(44\u0026ndash;51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10(7\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e8(6\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5(4\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6(4\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62(61\u0026ndash;63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17(16\u0026ndash;17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39(38\u0026ndash;39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13(13\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e12(12\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9(8\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10(10\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary (1\u0026ndash;5\u0026nbsp;years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(11\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14(12\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36(35\u0026ndash;38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13(12\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e12(11\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11(10\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13(12\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary (6\u0026ndash;10\u0026nbsp;years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(17\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(11\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32(30\u0026ndash;33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16(14\u0026ndash;17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e14(13\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10(9\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16(15\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege (11\u0026ndash;12\u0026nbsp;years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(4\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(11\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31(28\u0026ndash;34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15(13\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e13(12\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12(10\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16(13\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity (\u0026gt;\u0026thinsp;12\u0026nbsp;years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(4\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(8\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32(29\u0026ndash;34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16(14\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e15(13\u0026ndash;17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12(10\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15(14\u0026ndash;17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold education (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(23\u0026ndash;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21(20\u0026ndash;22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42(40\u0026ndash;43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13(12\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e11(10\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7(7\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7(6\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary (1\u0026ndash;5\u0026nbsp;years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36(35\u0026ndash;37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(15\u0026ndash;17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40(39\u0026ndash;41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14(13\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e12(12\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8(8\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10(9\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary (6\u0026ndash;10\u0026nbsp;years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(29\u0026ndash;31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(10\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32(31\u0026ndash;33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15(14\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e14(14\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11(11\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16(15\u0026ndash;17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege (11\u0026ndash;12\u0026nbsp;years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(5\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(7\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28(26\u0026ndash;31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14(13\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e14(13\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13(12\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e21(18\u0026ndash;23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity (\u0026gt;\u0026thinsp;12\u0026nbsp;years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(3\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(5\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22(19\u0026ndash;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16(14\u0026ndash;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e15(12\u0026ndash;17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16(13\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e25(23\u0026ndash;28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold wealth quintiles\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1st quintile (Poorest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(17\u0026ndash;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27(26\u0026ndash;29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47(46\u0026ndash;48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11(10\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e7(6\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4(3\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4(3\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(19\u0026ndash;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17(16\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42(40\u0026ndash;43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16(15\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e12(11\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7(7\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6(5\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(19\u0026ndash;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14(13\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39(38\u0026ndash;40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14(13\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e14(13\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9(8\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10(9\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4th quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(20\u0026ndash;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(11\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31(30\u0026ndash;33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15(14\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e15(14\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12(11\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16(15\u0026ndash;17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5th quintile (Richest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(21\u0026ndash;23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(7\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25(24\u0026ndash;27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15(14\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e15(14\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15(14\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23(21\u0026ndash;24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e1\u0026thinsp;\u0026ge;\u0026thinsp;14\u0026nbsp;years of education\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e2\u0026thinsp;\u0026lt;\u0026thinsp;14\u0026nbsp;years of education\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e1. Correlation of underweight and overweight parameters in major urban areas: r\u0026thinsp;=\u0026thinsp;0.67\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e2. Correlation of underweight and overweight parameters in urban areas: r\u0026thinsp;=\u0026thinsp;0.55\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e3. Correlation of underweight and overweight parameters in rural areas: r\u0026thinsp;=\u0026thinsp;0.54\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eHousehold SEP and underweight\u003c/h2\u003e \u003cp\u003eA gradual decrease was observed in the proportion of underweight women across household wealth quintiles (HWQ) with 27% in the 1st HWQ (lowest SEP; poorest household) to 4% in the 5th quintile (highest SEP; richest household). After adjusting for covariates, women in the 1st HWQ were more likely to be underweight than those in the 3rd quintile (aOR: 0.73; 95%CI: 0.64\u0026ndash;0.83) and the 5th quintile (aOR: 0.57; 95%CI: 0.48\u0026ndash;0.68) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable model for association of household socioeconomic position and other participants\u0026rsquo; characteristics with categories of BMI among women, National Nutritional Survey of Pakistan 2011\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eAdjusted ORs(95% confidence interval)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCovariates\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eBMI 23 -24.99\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eBMI 25.0\u0026ndash;27.49\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eBMI 27.5\u0026ndash;29.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth quintile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1st quintile (Poorest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.76(0.68\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4(1.21\u0026ndash;1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.51(1.29\u0026ndash;1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.51(1.24\u0026ndash;1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.64(1.33\u0026ndash;2.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.73(0.64\u0026ndash;0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32(1.14\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.93(1.63\u0026ndash;2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.99(1.65\u0026ndash;2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.62(2.12\u0026ndash;3.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4th quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72(0.62\u0026ndash;0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.62(1.37\u0026ndash;1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.49(2.09\u0026ndash;2.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.99(2.44\u0026ndash;3.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.23(3.38\u0026ndash;5.29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5th quintile (Richest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.57(0.48\u0026ndash;0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.84(1.53\u0026ndash;2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.91(2.41\u0026ndash;3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.15(3.31\u0026ndash;5.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.2(4.92\u0026ndash;7.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUrbanicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMajor urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.84(0.73\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18(1.03\u0026ndash;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.47(1.27\u0026ndash;1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.56(1.33\u0026ndash;1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.34(2.02\u0026ndash;2.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85(0.75\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13(1.02\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.37(1.23\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.4(1.23\u0026ndash;1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.84(1.62\u0026ndash;2.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold education (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary (1\u0026ndash;5\u0026nbsp;years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87(0.78\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08(0.97\u0026ndash;1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05(0.93\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(0.87\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.11(0.96\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary (6\u0026ndash;10\u0026nbsp;years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83(0.74\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.34(1.18\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.24(1.09\u0026ndash;1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.27(1.1\u0026ndash;1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.49(1.28\u0026ndash;1.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege (11\u0026ndash;12\u0026nbsp;years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.77(0.62\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.36(1.12\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.25(1.01\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.38(1.1\u0026ndash;1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.72(1.35\u0026ndash;2.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity (\u0026gt;\u0026thinsp;12\u0026nbsp;years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.75(0.56\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.91(1.51\u0026ndash;2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.47(1.14\u0026ndash;1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.76(1.37\u0026ndash;2.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.33(1.81\u0026ndash;2.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProvince\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAJK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.34(1.09\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09(0.84\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89(0.69\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.84(0.62\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7(0.5\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBalochistan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.09(0.89\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98(0.77\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06(0.85\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77(0.56\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9(0.68\u0026ndash;1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFATA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.27(0.11\u0026ndash;0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.25(1.49\u0026ndash;3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.83(2.51\u0026ndash;5.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.47(2.07\u0026ndash;5.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.79(1.59\u0026ndash;4.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGilgit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.14(0.77\u0026ndash;1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93(0.62\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67(0.45\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6(0.39\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.28(0.16\u0026ndash;0.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKPK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.56(0.42\u0026ndash;0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26(1 -1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26(0.98\u0026ndash;1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.26(0.99\u0026ndash;1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01(0.78\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSindh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.27(1.09\u0026ndash;1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93(0.79\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.79(0.67\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8(0.68\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.72(0.59\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePunjab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-manual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.32(0.93\u0026ndash;1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27(0.89\u0026ndash;1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.28(0.89\u0026ndash;1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.22(0.79\u0026ndash;1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96(0.61\u0026ndash;1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManual work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06(0.87\u0026ndash;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42(1.09\u0026ndash;1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.38(1.04\u0026ndash;1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.48(1.07\u0026ndash;2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.41(1 -2.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudent/Retired/Unemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.9(1.45\u0026ndash;2.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08(0.76\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94(0.65\u0026ndash;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82(0.52\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.72(0.45\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnder age/Not reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.28(0.87\u0026ndash;1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86(0.5\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11(0.71\u0026ndash;1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85(0.5\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88(0.48\u0026ndash;1.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e(N\u0026thinsp;=\u0026thinsp;34391); Reference: BMI\u0026thinsp;=\u0026thinsp;18.5\u0026ndash;22.9, N\u0026thinsp;=\u0026thinsp;12380\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eHousehold SEP, overweight and obesity\u003c/h2\u003e \u003cp\u003eAn increase in overweight and obese women was observed with rising HWQ, with the largest proportion of overweight (30%) and obese (23%) women found in the 5th quintile (highest SEP; richest household) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Furthermore, the multivariable model demonstrated an increasing gradient in adjusted odds ratios (aOR) for overweight and obesity across wealth quintiles with aORs ranging from 1.51 (2nd quintile) to 2.91 (5th quintile) for overweight-1, 1.51 (2nd quintile) to 4.15 (5th quintile) for overweight-2 and 1.64 (2nd quintile) to 6.20 (5th quintile) for obesity (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCommunity SEP, urbanicity, underweight and overweight\u003c/h2\u003e \u003cp\u003eOverall, women living in rural areas were more likely to be underweight than those living in major urban (aOR: 0.84; 96%CI: 0.73\u0026ndash;0.97) and urban areas (aOR: 0.85; 95%CI: 0.75\u0026ndash;0.96). The proportion of overweight and obesity was significantly different across urbanicity, with the largest amount of obese women living in major urban areas (aOR: 2.34; 95%CI: 2.02\u0026ndash;2.71) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe multivariable model for HWQ and urbanicity demonstrated a gradual increase in overweight and obesity with increasing wealth quintiles in rural and urban areas. The interaction of HWQ and urbanicity for obesity was most evident in the 5th quintile, with women living in major urban areas (aOR: 6.87; 95%CI 5.09\u0026ndash;9.29) being more likely to be obese than those in urban areas (aOR: 5.27; 95%CI3.92-7.08) and rural areas (aOR: 4.63; 95%CI 3.48\u0026ndash;6.17) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable models for the interaction between household socioeconomic position and urbanicity, and the interaction of household socioeconomic position for association with categories of BMI among women, National Nutritional Survey of Pakistan 2011\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eAdjusted ORs(95% confidence interval)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCovariates\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eBMI 23 -24.99\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eBMI 25.0 -29.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold wealth quintile (HWQ) and urbanicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMajor Urban\u003c/b\u003e\u003c/p\u003e \u003cp\u003e1st quintile (Poorest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19(0.56\u0026ndash;2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84(0.37\u0026ndash;1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83(0.27\u0026ndash;2.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.61(0.15\u0026ndash;2.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.73(0.44\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.73(1.05\u0026ndash;2.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.01(1.21\u0026ndash;3.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.69(1.39\u0026ndash;5.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.48(0.36\u0026ndash;0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13(0.83\u0026ndash;1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.95(1.5\u0026ndash;2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.86(2.01\u0026ndash;4.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4th quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.61(0.47\u0026ndash;0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.82(1.39\u0026ndash;2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.21(2.51\u0026ndash;4.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.6(4.08\u0026ndash;7.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5th quintile (Richest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5(0.37\u0026ndash;0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.84(1.41\u0026ndash;2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.38(2.65\u0026ndash;4.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e6.87(5.09\u0026ndash;9.29)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUrban\u003c/b\u003e\u003c/p\u003e \u003cp\u003e1st quintile (Poorest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88(0.62\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27(0.87\u0026ndash;1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.37(1 -1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.18(1.25\u0026ndash;3.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7(0.54\u0026ndash;0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.34(1.03\u0026ndash;1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.08(1.61\u0026ndash;2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.54(1.79\u0026ndash;3.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65(0.51\u0026ndash;0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.49(1.18\u0026ndash;1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.09(1.68\u0026ndash;2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.85(2.81\u0026ndash;5.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4th quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.52(0.41\u0026ndash;0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.58(1.24 -2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.64(2.11\u0026ndash;3.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.34(3.27\u0026ndash;5.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5th quintile (Richest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.49(0.36\u0026ndash;0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7(1.33\u0026ndash;2.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.16(2.5\u0026ndash;3.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e5.27(3.92\u0026ndash;7.08)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRural\u003c/b\u003e\u003c/p\u003e \u003cp\u003e1st quintile (Poorest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.75(0.67\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.36(1.18\u0026ndash;1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35(1.16\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.44(1.15\u0026ndash;1.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.73(0.63\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27(1.08\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.69(1.44\u0026ndash;1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.07(1.62\u0026ndash;2.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4th quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72(0.6\u0026ndash;0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.47(1.21\u0026ndash;1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.13(1.76\u0026ndash;2.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.06(2.35 -4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5th quintile (Richest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.52(0.41\u0026ndash;0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.76(1.41\u0026ndash;2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.95(2.37\u0026ndash;3.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4.63(3.48\u0026ndash;6.17)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCommunity wealth quintile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1st quintile (Poorest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(0.87\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16(0.98\u0026ndash;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.33(1.14\u0026ndash;1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.46(1.14\u0026ndash;1.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07(0.92\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11(0.94\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.45(1.22\u0026ndash;1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.64(1.28\u0026ndash;2.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4th quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04(0.85\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25(1.02\u0026ndash;1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.56(1.28\u0026ndash;1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.19(1.67\u0026ndash;2.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5th quintile (Richest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03(0.81\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27(1 -1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.76(1.41\u0026ndash;2.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.87(2.15\u0026ndash;3.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e(N\u0026thinsp;=\u0026thinsp;34391); Reference: BMI\u0026thinsp;=\u0026thinsp;18.5\u0026ndash;22.9, N\u0026thinsp;=\u0026thinsp;12380\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhen assessing the interaction between community wealth quintiles and nutritional status, there was an increase of the effect estimates (aORs) for obesity across community wealth (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The difference in underweight across community wealth quintiles was not statistically significant.\u003c/p\u003e \u003cp\u003eA trend was observed among household education levels and obesity. Households with higher levels of education were more likely to have overweight and obese women. Moreover, the likelihood of overweight and obesity among women increased consistently with the attainment of higher education; overweight-1 (aOR: 1.47; 95%CI 1.14\u0026ndash;1.89), overweight-2 (aOR: 1.76; 95%CI 1.37\u0026ndash;2.26) and obesity (aOR: 2.33; 95%CI 1.81\u0026ndash;2.99) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eProvince of residence, underweight and overweight\u003c/h2\u003e \u003cp\u003eAmong the provinces of Pakistan, the largest proportions of underweight women were in rural Sindh (20%) and rural Balochistan (19%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Alternatively, women living in urban KPK (23%) and urban Punjab (23%) were found to the most overweight and obese women. In comparison to Punjab, women living in AJK (aOR: 1.34; 95%CI: 1.09\u0026ndash;1.65) and Sindh (aOR: 1.27; 95%CI 1.09\u0026ndash;1.47) were more likely to be underweight (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Additionally, women in FATA (aOR: 0.27; 95%CI: 0.11\u0026ndash;0.67) were less likely to be underweight compared to those in Punjab. As for women in Gilgit, there were less likely to be overweight (aOR: 0.60; 95%CI: 0.39\u0026ndash;0.93) and obese (aOR: 0.28; 95%CI: 0.16\u0026ndash;0.48) compared to women in Punjab.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCo-existence of underweight and obesity at community level\u003c/h2\u003e \u003cp\u003eWe evaluated the correlation of underweight and overweight parameters to assess whether underweight and overweight women coexist at the community level. The moderate negative association in major urban (r\u0026thinsp;=\u0026thinsp;0.67), urban (r\u0026thinsp;=\u0026thinsp;0.55) and rural (r\u0026thinsp;=\u0026thinsp;0.54) communities suggest that the likelihood of underweight and overweight women coexisting within the same community is low. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e "},{"header":"Discussion","content":" \u003cp\u003eAccording to the 2011 NNS, the prevalence of underweight (15%) is lower than overweight and obesity combined (35%) among Pakistani women. Our findings suggest a noticeable difference in the level of underweight, overweight and obesity with household and community SEP and urbanicity. Underweight women were mainly found in poor rural areas, while overweight and obese women were mainly in rich urban areas. Furthermore, the likelihood of being overweight or obese increased drastically for women living in wealthier communities located in urban areas. We also found that underweight, overweight, and obesity did not co-exist within the same community. Overall, underweight women were more likely to be among the poorest, rural dwelling with the fewest years of formal schooling. This emphasizes that despite a decrease in the prevalence of underweight women, targeted nutrition interventions are still essential. Alternatively, an increasing trend in overweight and obesity was associated with an increase in SEP, urbanization and education. Further analyses on community characteristics would be important in developing and delivering interventions that are relevant to local needs.\u003c/p\u003e \u003cp\u003eSimilar to our findings, research conducted in other South Asian countries has found a comparable distribution of underweight, overweight and obesity across SEP. However, the overall prevalence of overweight and obesity in Bangladesh (2007: 10%,1.7%; 2011: 17%,2.9%), India (2006: 9.8%,2.9%) and Nepal (2011: 11.2%,2.2%) is lower than Pakistan (2011: 22%, 12%). [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In India, there has been an increase in overweight and obesity among communities with higher SEP. However, about 50% of Indian women are still underweight, with the majority living in communities with lower SEP [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Similar findings have been reported from Bangladesh [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This suggests that Pakistan is at a relatively similar stage of nutrition transition as compared to its neighbouring countries [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWomen with higher education were associated with increased risk of overweight and obesity. In most developing countries, such as Pakistan, India and Bangladesh, a positive association between education and BMI has been reported [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, in contrast to our findings, a local study from the Khairpur district showed that education was associated with lower levels of obesity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The situation observed in Khairpur is identical to the advanced stage of nutrition transition seen in Brazil, where higher levels of education are associated with lower levels of obesity. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, further research is needed to assess if higher levels of education and higher SEP have an association with obesity in Pakistan. Additional factors that may have increased the proportion of overweight women are sedentary jobs, availability of household help and the perception that a plump body size is associated with higher SEP [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Households with higher SEP are associated with higher consumption of meats, fats and fast foods, especially in urban areas [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. At later stages of nutrition transition, commonly seen in developed countries, BMI is inversely proportional to educational achievement since more research is needed.\u003c/p\u003e \u003cp\u003eThe risk of overweight and obesity increased with urbanicity even after controlling for SEP and education. These findings are consistent with other research conducted in developing countries, including India and Bangladesh [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Literature has also identified cities as promoting overweight and obesity through their obesogenic environment with increased availability of fast food and other restaurants and increasing norms of eating out, mechanized transportation, sedentary jobs and low physical activity [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Lack of parks, air pollution, actual and perceived safety, violence and political instability and cultural and religious norms may also constraint physical activity in an overall sedentary environment [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Data on the influence of neighbourhood environments, including food and physical activity, is needed from Pakistan and other developing countries to understand role of urban environment on overweight and obesity. Such data may provide useful evidence for urban planning and shaping urban environment to tackle the obesity problem.\u003c/p\u003e "},{"header":"Conclusions","content":" \u003cp\u003eOur results show that the overweight and obesity are significant public health problem in Pakistan, especially in urban areas. Underweight still persists among the rural poor, especially in the province of Sindh. These findings emphasize the importance of interventions targeting undernutrition in rural areas and overnutrition in urban areas. Furthermore, our study has showed that underweight and overweight do not exist in the same communities, thus providing avenues for targeted interventions according to local needs at the community level. The high levels of overweight and obesity in major urban areas highlights the need for urgent action to lower the increasing burden of chronic diseases, such as diabetes and cardiovascular disease. Also, further analyses on geospatial distribution of underweight, overweight and communities characteristics are required to improve the development and delivery of nutritional interventions relevant to the local need.\u003c/p\u003e "},{"header":"Abbreviations","content":" \u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAKU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAga Khan University; BMI:Body mass index; HWQ:Household wealth quintiles; NNS:National Nutrition Survey; PDHS:Pakistan Demographic and Health Survey; RAF; Research and Advocacy Fund; SEP:Socioeconomic position.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePakistan NNS 2011 was conducted by the Aga Khan University and Ministry of Health, Pakistan and funded by UNICEF, Pakistan. This study is from a project funded by the Maternal and Newborn Health Programme - Research and Advocacy Fund (RAF) and implemented by the Aga Khan University (AKU), Karachi, Pakistan. RAF is a grant of the UK \u003ca href=\"http://www.dfid.gov.uk/\"\u003eDepartment for International Development (DFID)\u003c/a\u003e\u0026nbsp;and\u0026nbsp;Australian Department of Foreign Affairs and Trade (DFAT).\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZAB conceived the idea \u0026amp; provided technical inputs in the design of the study, analysis. NJ interpretation of data, statistical analysis. SS provided technical inputs in the design interpretation of results and reviewed final version of the manuscript. KS \u0026amp; BM wrote first draft version of the manuscript, SK, SA, GM, IA, RT, GNK, MU contributed to manuscript write up and critical revisions. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Maternal and Newborn Health Programme, Research and Advocacy Fund (RAF), Pakistan. The funder had no role in study design, data collection, analysis, and interpretation of data and in writing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets are available from the corresponding author on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original survey was approved by the Ethics Review Committee of the Aga Khan University and the National Bioethics Committee of the Government of Pakistan. A secondary analysis was conducted of the NNS, therefore exempting us from requiring further ethical review and approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e \u003cspan\u003eAhmed T, Hossain M, Sanin KI. Global burden of maternal and child undernutrition and micronutrient deficiencies. Annals of Nutrition Metabolism. 2012;61(Suppl 1):8\u0026ndash;17.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eBlack RE, Victora CG, Walker SP, Bhutta ZA, Christian P, de Onis M, et al. Maternal and child undernutrition and overweight in low-income and middle-income countries. 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The burden of disease from undernutrition and overnutrition in countries undergoing rapid nutrition transition: a view from Brazil. Am J Public Health. 2004;94(3):433\u0026ndash;4.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eMonteiro CA, Conde WL, Popkin BM. Income-specific trends in obesity in Brazil: 1975\u0026ndash;2003. Am J Public Health. 2007;97(10):1808\u0026ndash;12.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eSong YM. Commentary. varying relation of socioeconomic status with obesity between countries at different stages of development. Int J Epidemiol. 2006;35(1):112\u0026ndash;3.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eZhai F, Wang H, Du S, He Y, Wang Z, Ge K,et al. Lifespan nutrition and changing socio-economic conditions in China. Asia Pac J Clin Nutr. 2007;16(Suppl 1):374\u0026ndash;82.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eThang NM, Popkin BM. 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Neighborhood disparities in access to healthy foods and their effects on environmental justice. Am J Public Health. 2012;102(9):1644\u0026ndash;54.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eHolsten JE. Obesity and the community food environment: a systematic review. Public Health Nutr. 2009;12(3):397\u0026ndash;405.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eLarson NI, Story MT, Nelson MC. Neighborhood environments: disparities in access to healthy foods in the U.S. Am J Prev Med. 2009;36(1):74\u0026ndash;81.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eBabakus WS, Thompson JL. Physical activity among South Asian women: a systematic, mixed-methods review. The International Journal of Behavioral Nutrition Physical Activity. 2012;9:150.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eBenjamin K, Donnelly TT. Barriers and facilitators influencing the physical activity of Arabic adults: A literature review. Avicenna. 2013;8.\u003c/span\u003e \u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Socioeconomic position, Neighbourhood, Urbanicity, Obesity, Underweight, Overweight, Nutrition transition, Pakistan","lastPublishedDoi":"10.21203/rs.3.rs-25852/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-25852/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Pakistan is experiencing a rapid nutrition transition with a shift from underweight to overweight and obesity.\u0026nbsp;This paper will examine the role of household socioeconomic position (SEP), community SEP and urbanicity on the nutritional status (underweight, overweight and obesity) of Pakistani women.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We analysed data on 34,391 women aged ≥20 years enrolled in 2011 National Nutritional Survey of Pakistan (NNS). The NNS is a nationally representative survey employing a multistage stratified cluster sampling design. We assessed household SEP through a wealth index constructed using items from household possessions, utilities and housing conditions. We assessed the relationship of urbanicity, household and community SEP with categories of body mass index (BMI) using multinomial logistic regression where normal BMI (18.6-22.5 kg/m 2 ) was the reference category. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Overall, 15% of women were underweight (BMI\u0026lt;18.5 kg/m 2 ), 14% were pre-overweight (BMI 23.00-24.9 kg/m 2 ), 22% were overweight (BMI 25.0-29.99 kg/m 2 ) and 12% were obese (BMI≥30.0 kg/m 2 ). Households with higher SEP were associated with increased levels of overweight-1 (aOR: 2.91; 95%CI: 2.41-3.50), overweight-2 (aOR: 4.15; 95%CI: 3.31-5.19) and obesity (aOR: 6.20; 95%CI: 4.92-7.81) among women. Women were more likely to be obese in major urban (aOR: 2.34; 95%CI: 2.02-2.71) and urban (aOR: 1.84; 95%CI: 1.62-2.09) areas compared to rural areas. At the community level, communities in rural areas were more likely to have higher levels of underweight, while communities in urban areas were more likely to have higher levels of obesity. Furthermore, the likelihood of underweight and overweight women coexisting within the same community was low in major urban (r=0.67), urban (r=0.55) and rural (r=0.54) areas. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e In Pakistan, overweight and obesity among women is associated with urbanicity and household and community SEP. Women living in urban areas with high household and community SEP were associated with higher levels of overweight and obesity. Our findings suggest the importance of interventions targeting undernutrition in rural areas and overnutrition in urban areas.\u003c/p\u003e","manuscriptTitle":"Geographic and Socioeconomic Disparities in Nutritional Status of Women in Pakistan: Secondary Analysis from Pakistan National Nutrition Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-05-04 14:22:34","doi":"10.21203/rs.3.rs-25852/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"50a4ade8-a761-453f-987c-d0acadddbe75","owner":[],"postedDate":"May 4th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":93279,"name":"Health Policy"}],"tags":[],"updatedAt":"2020-05-29T02:41:51+00:00","versionOfRecord":[],"versionCreatedAt":"2020-05-04 14:22:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-25852","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-25852","identity":"rs-25852","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}