Magnitude and determinants of biological risk factors of non-communicable diseases among reproductive age women in Gofa and Basketo Zones, Southern Ethiopia: a community-based cross-sectional study

Magnitude and determinants of biological risk factors of non-communicable diseases among reproductive age women in Gofa and Basketo Zones, Southern Ethiopia: a community-based cross-sectional study | 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 Article Magnitude and determinants of biological risk factors of non-communicable diseases among reproductive age women in Gofa and Basketo Zones, Southern Ethiopia: a community-based cross-sectional study Markos Manote Domba, Salvatore Fava, Terefe Gelibo, Bahiru Mulatu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4221395/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background :- The prevalence of non-communicable diseases (NCDs) among women of reproductive age has surged two fold in various African countries. This escalation in NCD burdens combined with inadequate access to sexual and reproductive health services is progressively impacting women of reproductive age, posing substantial risks to forthcoming generations. This research endeavors to evaluate the extent of biological risk factors and their associated determinants among women of reproductive age in the Gofa and Basketo Zones of Southern Ethiopia. Methods : A community-based survey following the World Health Organization (WHO) stepwise approach was undertaken, employing a multistage cluster sampling method to select participants from the designated zones. Statistical analysis was conducted using Statistical Package for the Social Sciences (SPSS) software encompassing descriptive statistics, bivariate analysis, and multivariate logistic regression. Associations were deemed statistically significant if the p-value was ≤ 0.05. Result : Approximately 27.0% of participants exhibited one or more biological risk factors. Significant associations were observed among participants in older age groups, residing in rural areas, those with lower educational attainment, belonging to the Gofa zone, those from households with higher wealth index, widowed/divorced individuals, single individuals, government employees, merchants, and housewives. Additionally, those with larger family sizes (>4), getting no health professional advice, had a family history of NCD and were not members of a functional women development army (WDA) displayed statistically significant associations with the co-occurrence of biological risk factors. Conclusion : The escalation of biological risk factors is concerning, highlighting the urgency for targeted community-based interventions. Prioritizing older age groups, rural residents, individuals from households with higher wealth status, and lower educational attainment is advised. Implementing family-oriented changes and reinforcing healthcare systems are crucial. Policy and socio-political factors influencing the rise of NCD risk factors should also be addressed. Magnitude and determinants non-communicable diseases biological risk factors reproductive-aged women Gofa Basketo South Ethiopia Figures Figure 1 Introduction Non-communicable diseases (NCDs) are the dominant cause of global morbidity and mortality, especially in developing ( 1 , 2 ). The rising burden of NCDs is coupled with unmet needs of sexual and reproductive health services in SSA ( 3 ) and doubled among reproductive-age women of many African countries ( 4 , 5 ) and are expected to exceed infectious diseases as major causes of morbidity and mortality in Africa by the year 2035( 6 ). NCDs are mostly linked with metabolic (such as obesity, blood sugar, blood pressure, and cholesterol levels) and behavioral risk factors( 7 ). Despite efforts to improve lives and well-being, the burden of metabolic risk factors of NCDs persist( 8 ). In Ethiopia, evidence shows that 95% of the study population was found with 1–2 NCD risk factors ( 9 ). This indicates that the burden of NCDs is likely to become unbearable in the future in Ethiopia ( 9 ). Women with NCD risk factors had an increased negative impact on reproductive health as well as on fetal health ( 10 , 11 ). It also impedes progress toward sustainable development goals (SDG), mainly SDGs 1, 3, and 5 ( 12 ). Tackling NCDs in women needs a systematic understanding of major biological risk factors and determinants ( 13 , 14 ). Despite the increasing burden of the disease in Ethiopia, there is a paucity of studies focusing on reproductive-age women, especially in the peripheral setup of the country including Gofa and Basketo. Therefore, this study aims to address the above gaps and, to advocate the policy makers and regional government in preventing biological risk factors among women of reproductive age. Materials and Methods Study setting and period. The study was conducted in Gofa and Basketo Zones, Southern Ethiopia from Sept 9/2022 to Dec 6/2022. Study design A community-based cross-sectional study was conducted by the WHO a stepwise approach to the surveillance of NCD risk factors. Study population All women of reproductive age residing in the Gofa and Basketo zones during the data collection period were eligible for inclusion. This encompassed those who considered the study area their permanent residence for at least six months. Exclusions were made for non-permanent residents, pregnant women, individuals institutionalized in hospitals, prisons, nursing homes, similar facilities, as well as those residing primarily in military camps or dormitories. Additionally, critically ill, mentally disabled, and physically disabled individuals unsuitable for physical participation were excluded. Sample size determination and sampling technique A multi-stage cluster sampling approach using the Kish method was employed to select the study participants. The WHO regional office tools for assessing operational district health systems in Africa recommend that for the total number of districts between 10 to 19, sampling 50 % of them could be enough (15). Sample size was determined using a single proportion formula considering the Z-score=1.96; Proportion =50%; marginal error=0.05; Design effect =3.35; and non-response rate=10%, making the total sample size of 1,416 respondents. Study variables and measurement The dependent variable s: are biological non-communicable disease risk factors including overweight/obese, high blood pressure, raised blood glucose, and raised cholesterol. Independent variable : Socio-demographic and cultural variables include: age, place of residence, family history, parity, educational status, marital status, religion, occupation of women, wealth status of household, and social support to NCD prevention. Knowledge-related factors/variables include Knowledge of NCD risk factors, getting advice from health professionals, and using mass media. Structural factor includes membership in a functional women's development army. Data collection tool The data collection process adhered to the WHO Stepwise approach for chronic disease risk factor surveillance (STEPS). A questionnaire based on the WHO Stepwise Surveillance questionnaire was utilized as the data collection instrument. This questionnaire was translated into Gofatho and Basket languages and subsequently back-translated into English to ensure accuracy. Step 1 involved gathering socio-demographic, knowledge, and health system-related data. Step 2 comprised physical measurements such as height, weight, and blood pressure conducted using standardized instruments and protocols. Step 3 involved biochemical measurements of serum samples to assess fasting blood glucose and total cholesterol levels. Blood glucose levels were determined using dry chemistry methods, while total cholesterol serum levels were assessed using wet chemistry techniques. Data collection technique All the selected data collectors had two days of training on collecting data from all three steps before the survey. The training was imparted on STEPS instruments including interactive discussions and physical measurements. The interviewer-administered questionnaire covers socio-demographic information, knowledge, health system-related questions, and NCD risk factors. The assessment of variables was made according to suggestions by different scholars or standard guidelines such as measurement of socio-demographic variables (16), wealth index (17, 18), physical measurement (19), and biochemical measurement (20) including self-report (21). Data quality management To maintain data quality, the questionnaire underwent translation and pretesting as necessary. Following data collection, thorough checks for completeness and consistency were conducted, and coding was performed by both the supervisor and principal investigator. Data analysis SPSS software version 25 was used to conduct data analysis. Descriptive weighted analysis was done along with complex sample analysis. The presence of association was assessed using bivariate analysis and then multivariate logistic regression analysis was used to identify the independent predictors. The strength of the association was estimated by odds ratio and its 95% confidence interval. Associations with a p-value ≤0.05 are considered statistically significant. Ethical considerations The survey protocol obtained ethical clearance from the institution review boards of the Southern Regional State Public Health Institute and Selinus University of Science and Literature. A paper-based written informed consent form was administered to eligible participants. At each stage of the process, consent was indicated by signing or making a mark on the consent form on a printed copy, which was retained by the participant and the data collector. A designated head of household provided written consent for the household to take part in the survey, after which individual members were rostered during a household interview. Participants aged 15-49 years and emancipated minors ages 15-17 then provided written consent for an interview and participation in the biomarker component of the survey. For minors ages 15-17, parents /guardians provided permission which was followed by assent by the participant. Results Socio-demographic Characteristics of Participants The average age of participants was 28.9 years with a standard deviation of 7.5 years, and 41.5% fell within the 25–34 age range. A significant portion (48.2%) had no formal education, while the majorities (93.4%) were married. Predominantly, participants hailed from the Gofa zone (93.3%) and rural areas (83.3%). Furthermore, 84% resided in rural regions. In terms of wealth distribution, approximately 20.5%, 21.1%, and 20.6% of participants were categorized into the poorest, poorer, and middle wealth quintiles respectively. Housewives comprised the largest occupational group, accounting for 72% of participants. Additional socio-demographic details can be found in Table 1 . Table 1 Socio-demographic characteristics of participants in Basketo and Gofa Zones Characteristics Un-weighted count weighted percent Age 15–24 400 29.4 25–34 599 41.5 ≥ 35 405 29.1 Educational status Illiterate 519 38.6 Able to read and write 147 9.6 Primary education 400 28.7 Secondary education and above 338 23.1 Marital status Married 1297 93.4 Single 68 4.4 Widowed/Divorced 39 2.1 Residence 1. Urban 393 16.2 Rural 1011 83.8 Zone Gofa 1244 93.3 Basketo 160 6.7 Wealth index Poorest 288 20.5 Poorer 286 21.1 Middle 277 20.6 Richer 280 19.5 Richest 273 18.3 Occupational status Housewife 981 71.9 Merchant 172 12.7 Government employee 76 4.4 Other* 175 11.0 Family size ≤ 4 544 38.4 > 4 860 61.6 Family history of NCDs No 1292 92.7 Yes 112 7.3 Social support to prevent NCD risk factors No 846 60.8 Yes 558 39.2 *Others in occupational status: daily laborers, students, and maidservants Regarding knowledge of NCD risk factors, only 34.6% of the study participants had good knowledge; but the vast majority had poor knowledge. Prevalence of biological risk factors The prevalence of four biological non-communicable disease risk factors varies between the different socio-demographic sub-groups. Table 2 shows the differences of each of the risk factors stratified by the different socio-demographic characteristics (Table 2 ). Overweight and obesity The prevalence of overweight or obesity among reproductive-age women was 13.3% (95% CI 13.2:-13.5) and the overall prevalence of abdominal obesity was 37.1% (95% CI: 33.2–40.1) with the mean waist circumference of 89.9cm ± 12.0. Being overweight or obese had a positive relationship with the age of participants and household wealth status (Table 2 ). Raised blood pressure The prevalence of raised blood pressure was 12.0% (95% CI: 11.9–12.2). The prevalence of raised blood pressure increased significantly with age. 5.0% (95% CI: 3.2–7.5), 12.4% (95% CI: 9.9–15.2), and 16.8% (95% CI: 13.4–20.7) for women aged 15–24, 25–34 and 35 and above respectively. We also observed a higher prevalence of raised blood pressure among married (11.6%, 95% CI: 10.0-13.5) and single (11.8%, 95% CI: 5.7–21.0) women compared with widowed/divorced (7.7%, 95% CI: 2.2–19.1) (Table 2 ). Raised fasting blood sugar The overall prevalence of raised fasting blood sugar in the study participants was 4.0% (95% CI: 3.9%-4.1%). The prevalence was significantly increased with the age and wealth index. Raised fasting blood glucose prevalence was higher among widowed, merchants, government employees, and households having above four family sizes as compared to their counterparts (Table 2 ). Raised total cholesterol Overall prevalence of raised total cholesterol ≥ 200mg/dl was 7.8%( 95% CI: 7.7-8.0). The prevalence increased with the age of the respondents; 6.0% (95% CI: 4.0-8.6) for 15–24; 7.3% (95% CI: 5.5–9.6) for 25–34 and 9.9% (95% CI: 7.3–13.1) for age 35 and above. We also noted the lower prevalence of raised total cholesterol among married (7.5%, 95% CI: 6.1-9.0) and single (5.9%, 95% CI: (2.0-13.4) compared with widowed/divorced (17.9%, 95% CI: 8.4–32.0) (Table 2 ). Table 2 Prevalence of modifiable biological non-communicable disease risk factors by background characteristics of reproductive-aged women in Gofa and Basketo zones, 2023 Socio-demographic characteristics Raised Blood Pressure (%) Raised Fasting blood glucose (%) Raised Cholesterol Level (%) Overweight and/or obesity (%) Age 15–24 5.0(3.2, 7.5) 1.0(0.3, 2.4) 6.0(4.0, 8.6) 10.3(7.6, 13.5) 25–34 12.4(9.9, 15.2) 2.7(1.6, 4.2) 7.3(5.5, 9.6) 15.2(12.5, 18.2) ≥ 35 16.8(13.4, 20.7) 8.9(6.4, 12.0) 9.9(7.3, 13.1) 14.8(11.6, 18.5) P-value < .001 * < .001 * .109 .061 Educational status Illiterate 15.0(12.2, 18.3) 4.0(2.6, 6.0) 8.1(6.0, 10.7) 11.0(8.5, 13.9) Able to read and write 12.2(7.7, 18.3) 10.9(6.6, 16.7) 4.8(2.2, 9.1 32.0(24.8, 39.8) Primary education 11.0(8.2, 14.3) 2.0(0.9, 3.7) 8.0(5.6, 11.0) 11.0(8.2, 14.3) Secondary education and above 6.5(4.2, 9.5 3.3(1.7, 5.6) 8.0(5.5, 11.2) 13.0(9.7, 16.9) P-value .002* < .001 * .575 < .001* Marital status Married 11.6(10.0, 13.5) 4.0(3.0, 5.2) 7.5(6.1, 9.0) 14.3(12.4, 16.2) Single 11.8(5.7, 21.0) 0.0 5.9(2.0, 13.4) 5.9(2.0, 13.4) Widowed/Divorced 7.7(2.2, 19.1) 10.3(3.6, 22.6) 17.9(8.4, 32.0) 7.7(2.2, 19.1) P-value .747 .033 * .046 * .080 Residence Urban 9.2(6.6, 12.3) 4.3(2.6, 6.7) 11.5(8.6, 14.9) 16.8(13.3, 20.7) Rural 12.5(10.5, 14.6) 3.9(2.8, 5.2) 6.2(4.9, 7.8) 12.5(10.5, 14.6) P-value .082 .687 .001 * .034* Zone Gofa 11.2(9.5, 13.0) 3.7(2.8, 4.9) 8.4(6.9, 10.0) 14.3(12.4, 16.3) Basketo 14.4(9.6, 20.4) 6.3(3.3, 10.8) 2.5(0.8, 5.8) 8.8(5.1, 13.9) P-value .233 .120 .009 * .054 Wealth index Poorest 6.6(4.2, 9.9) 0.0 8.3(5.6, 11.9) 13.9(10.3, 18.2) Poorer 11.5(8.2, 15.60) 4.2(2.3, 7.0) 7.0(4.5, 10.4) 8.4(5.6, 12.0) Middle 15.9(11.9, 20.5) 1.4(0.5, 3.4) 7.2(4.6, 10.7) 13.4(9.7, 17.7) Richer 10.7(7.5, 14.7) 5.7(3.4, 8.9) 7.1(4.6, 10.6) 11.4(8.1, 15.5) Richest 13.2(9.6, 17.6) 8.8(5.9, 12.6) 8.8(5.9, 12.6) 21.6(17.0, 26.8) P-value .012 * < .001 * .905 < .001* Occupational status Housewife 11.1(9.3, 13.2) 2.9(1.9, 4.0) 7.7(6.2, 9.5) 15.1(13.0, 17.4) Merchant 18.6(13.3, 24.9) 11.6(7.5, 17.0) 7.0(3.9, 11.5) 11.6(7.5, 17.0) Government employee 13.2(7.0, 22.1) 9.2(4.2, 17.2) 10.5(5.1, 18.9) 21.1(13.1, 31.2) Other 6.3(3.4, 10.6) 0.6(0.1, 2.6) 6.9(3.8, 11.3) 4.6(2.2, 8.4) P-value .004 * < .001 * .763 4 14.3(12.1, 16.8) 5.1(3.8, 6.7) 5.6(4.2, 7.3) 14.9(12.6, 17.4) P-value < .001 * .007* < .001* .097 *Others in occupational status: daily laborers, students, and maidservants Prevalence of combined biological risk factors About 27.0% of women in the reproductive age group had at least one risk factor (see Fig. 1 ). Factors association with non-communicable diseases risk factors In logistic regression models, being overweight and/or obese showed significant associations with several factors including age, marital status, residence, zone, educational status, wealth index, occupational status, family size, receiving advice from health professionals, family history of NCDs, and membership in a functional WDA. Similarly, variables such as age, marital status, residence, zone, educational status, wealth index, occupational status, family size, receiving advice from health professionals, family history of NCDs, social support for preventing NCD risk factors, and membership in a functional WDA were significantly associated with raised blood pressure during bivariate analysis. Raised fasting blood glucose was significantly associated with several factors including age, marital status, zone, educational status, wealth index, occupational status, family size, receiving advice from health professionals, family history of NCDs and membership in a functional WDA during bivariate analysis. Raised total cholesterol levels were significantly associated with age, marital status, residence, zone, educational status, wealth index, occupational status, family size, receiving advice from health professionals, family history of NCDs, and membership in a functional WDA during bivariate analysis. Variables with a p-value of less than 0.25 in binary logistic regression were included in a multivariable logistic regression model to control for confounding effects. Detailed factors associated with biological risk factors are presented in Tables 3 , 4 , 5 , 6 , and 7 . The likelihood of overweight and/or obesity was higher among women aged 25–34 years (AOR: 1.34, 95% CI 1.28, 1.41) and those aged > 35 years (AOR: 1.19, 95% CI 1.13, 1.26) compared to those aged 15–24 years. Literate women, particularly those with primary (AOR: 1.23, 95% CI 1.17, 1.28) and secondary school education (AOR: 1.63, 95% CI 1.55, 1.72), were more likely to be overweight and/or obese compared to illiterate women. Married (AOR: 2.62, 95% CI 2.21, 3.10) and single women (AOR: 1.40, 95% CI 1.23, 1.60) had higher odds of being overweight and/or obese compared to widowed/divorced women. Residents of the Gofa zone had nearly twice the likelihood of being overweight and/or obese (AOR: 1.73, 95% CI 1.60, 1.88) compared to Basketo residents. However, urban residents (AOR: 0.85, 95% CI 0.81, 0.88) had lower odds of being overweight and/or obese compared to rural. Women from households with lower wealth statuses were less likely to be overweight and/or obese compared to the wealthiest households. Housewives (AOR: 7.83, 95% CI 7.02, 8.74), merchants (AOR: 6.28, 95% CI 5.61, 7.03), and government employees (AOR: 6.37, 95% CI 5.63, 7.21) had significantly higher odds of being overweight and/or obese compared to other occupations such as maid servants, daily laborers and students. Women from smaller households (family size ≤ 4) were less likely to be overweight and/or obese (AOR: 0.77, 95% CI 0.74, 0.81) compared to those from larger households. Lack of health professional advice had more than double risk of overweight and/or obesity (AOR: 2.53, 95% CI 2.44, 2.63), as did non-membership in functional WDA (AOR: 1.59, 95% CI 1.54, 1.65). Conversely, women without a family history of NCDs were less likely to be overweight and/or obese (AOR: 0.71, 95% CI 0.67, 0.75) compared to those with a family history. Table 3 Bivariate and multivariate logistic regression of overweight and/or obesity and independent variables among reproductive-age women in Gofa and Basketo zones, South Ethiopia Candidate Variables Overweight and/or obesity COR (95%CI) P-value AOR (95%CI) P-value Age 15–24 1 1 25–34 1.62 (1.56, 1.68) < .001 1.34 (1.28, 1.41) < .001* ≥ 35 1.45 (1.39, 1.51) < .001 1.19 (1.13, 1.26) < .001* Educational status Illiterate 1 1 Able to read and write 3.98 (3.80, 4.16) < .001 4.67 (4.45, 4.90) < .001* Primary education 0.99 (0.95, 1.03) .581 1.23 (1.17, 1.28) < .001* Secondary education and above 1.07 (1.02, 1.12) .002 1.63 (1.55, 1.72) < .001* Marital status Married 1.62(1.43, 1.84) < .001 2.62 (2.21, 3.10) < .001* Single 0.96(0.83, 1.12) .632 1.40 (1.23, 1.60) < .001* Widowed/Divorced 1 1 Residence Urban 1.41(1.36, 1.47) < .001 0.85 (0.81, 0.88) < .001* Rural 1 1 Zone Gofa 1.65(1.54, 1.78) < .001 1.73 (1.60, 1.88) < .001* Basketo 1 1 Wealth index Poorest 0.59(0.57, 0.62) < .001 0.84 (0.80, 0.89) < .001* Poorer 0.27(0.26, 0.29) < .001 0.30 (0.29, 0.32) < .001* Middle 0.61(0.58, 0.63) < .001 0.75 (0.71, 0.78) < .001* Richer 0.43(0.41, 0.45) < .001 0.38 (0.36, 0.40) < .001* Richest 1 1 Occupational status 1. Housewife 6.03(5.48, 6.65) < .001 7.83 (7.02, 8.74) < .001* 2. Merchant 5.33(4.80, 5.92) < .001 6.28 (5.61, 7.03) < .001* 3. Government employee 8.48(7.57, 9.51) < .001 6.37 (5.63, 7.21) < .001* 4. Other 1 1 Family size 1. ≤ 4 0.71(0.69, 0.74) < .001 0.77 (0.74, 0.81) 4 1 1 Getting advice from health professionals 1. No 2.04(1.97, 2.10) 2.53 (2.44, 2.63) < .001* 2. Yes 1 1 Family history of NCDs 1. No 0.59(0.56, 0.62) < .001 0.71 (0.67, 0.75) < .001* 2. Yes 1 1 Is your WDA functional? 1. No 1.74(1.68, 1.80) < .001 1.59 (1.54, 1.65) < .001* 2. Yes 1 1 COR-Crude Odds Ratio: odds ratio by bivariate analysis. 95% CI: confidence interval at the 95% level. AOR-Adjusted OR: odds ratio by multiple logistic regression 1: Referent category Women aged 15–24 years (AOR: 0.41, 95% CI 0.39, 0.44) and 25–34 years (AOR: 0.67, 95% CI 0.65, 0.70) had lower odds of raised blood pressure compared to those aged ≥ 35 years. Illiterate women (AOR: 2.64, 95% CI 2.48, 2.82), those able to read and write (AOR: 3.07, 95% CI 2.85, 3.32), and those with primary education (AOR: 2.58, 95% CI 2.41, 2.75) were approximately three times more likely to have raised blood pressure compared to those with secondary education. Married (AOR: 1.89, 95% CI 1.66, 2.16) and single women (AOR: 7.56, 95% CI 6.47, 8.82) faced higher risk of raised blood pressure compared to widowed/divorced women. Urban residents had about half the risk of raised blood pressure (AOR: 0.56, 95% CI 0.53, 0.59) compared to rural residents. Individuals from poorer (AOR: 1.08, 95% CI 1.02, 1.14) and middle wealth quartiles (AOR: 1.56, 95% CI 1.48, 1.64) were more likely to have raised blood pressure, whereas those from the poorest (AOR: 0.83, 95% CI 0.78, 0.88) and richer households (AOR: 0.62, 95% CI 0.59, 0.66) were less likely. Housewives (AOR: 1.56, 95% CI 1.45, 1.69), merchants (AOR: 2.98, 95% CI 2.75, 3.23), and government employees (AOR: 4.11, 95% CI 3.69, 4.54) faced higher risk compared to other occupations. Participants from smaller households (family size ≤ 4) had a 31% lower risk (AOR: 0.69, 95% CI 0.66, 0.72) compared to those with larger families. Lack of health professional advice increased the likelihood of raised blood pressure (AOR: 1.58, 95% CI 1.52, 1.64), as did non-membership in functional WDA (AOR: 1.53, 95% CI 1.47, 1.59). Individuals without a family history of NCDs (AOR: 0.66, 95% CI 0.62, 0.71) and lack of social support 0.95(0.92, 0.99) had lower risk of raised blood pressure. Table 4 Bivariate and multivariate logistic regression of raised blood pressure and independent variables among reproductive-age women in Gofa and Basketo zones, South Ethiopia Candidate Variables Raised Blood Pressure COR (95%CI) P-value AOR (95%CI) P-value Age 15–24 0.29(.28, 0.31) < .001 0.41 (0.39, 0.44) < .001* 25–34 0.67(0.64, 0.69) < .001 0.67 (0.65, 0.70) < .001* ≥ 35 1 1 Educational status Illiterate 2.46(2.34, 2.59) < .001 2.64 (2.48, 2.82) < .001* Able to read and write 2.50(2.35, 2.67) < .001 3.07 (2.85, 3.32) < .001* Primary education 1.93(1.83, 2.04) < .001 2.58 (2.41, 2.75) < .001* Secondary education and above 1 1 Marital status Married 1.39(1.22, 1.57) < .001 1.89 (1.66, 2.16) < .001* Single 1.94 (1.68, 2.24) < .001 7.56 (6.47, 8.82) < .001* Widowed/Divorced 1 1 Residence Urban 0.65(0.62, 0.69) < .001 0.56 (0.53, 0.59) < .001* Rural 1 1 Zone Gofa 0.80(0.76, 0.85) < .001 0.97 (0.91, 1.03) .346 Basketo 1 1 Wealth index Poorest 0.62(0.58, 0.65) < .001 0.83 (0.78, 0.88) < .001* Poorer 0.95(0.90, 0.99) .039 1.08(1.02, 1.14) .006* Middle 1.45(1.38, 1.53) < .001 1.56 (1.48, 1.64) < .001* Richer 0.73(0.69, 0.77) < .001 0.62 (0.59, 0.66) < .001* Richest 1 1 Occupational status Housewife 1.72(1.61, 1.84) < .001 1.56 (1.45, 1.69) < .001* Merchant 3.35(3.12, 3.60) < .001 2.98 (2.75, 3.23) < .001* Government employee 2.75(2.51, 3.01) < .001 4.11 (3.69, 4.54) < .001* Other 1 1 Family size ≤ 4 0.49(0.47, 0.51) < .001 0.69 (0.66, 0.72) 4 1 1 Getting advice from health professionals No 1.45(1.41, 1.50) < .001 1.58 (1.52, 1.64) < .001* Yes 1 1 Family history of NCDs No 0.58(0.55, 0.61) < .001 0.66 (0.62, 0.71) < .001* Yes 1 1 Social support to prevent NCD risk factors No 1.07(1.03, 1.10) < .001 0.95(0.92, 0.99) .009* Yes 1 Is your WDA functional? No 1.33(1.29, 1.38) < .001 1.53 (1.47, 1.59) < .001* Yes 1 1 COR-Crude Odds Ratio: odds ratio by bivariate analysis. 95% CI: confidence interval at the 95% level. AOR-Adjusted OR: odds ratio by multiple logistic regression 1: Referent category Women aged 15–24 years (AOR: 0.26, 95% CI 0.23, 0.31) and 25–34 years (AOR: 0.19, 95% CI 0.18, 0.21) had lower odds of raised fasting blood glucose compared to those aged ≥ 35 years. However, women who were able to read and write (AOR: 19.86, 95% CI 17.41, 22.64), illiterate (AOR: 3.36, 95% CI 3.00, 3.75), and had primary school education (AOR: 2.90, 95% CI 2.55, 3.31) were at significantly higher risk compared to those with secondary education. Women from the Gofa zone were about half as likely to have raised blood glucose (AOR: 0.52, 95% CI 0.46, 0.59) compared to Basketo residents, and married women were less likely to have raised blood glucose (AOR: 0.15, 95% CI 0.13, 0.17) compared to widowed or divorced. Participants from smaller households (family size ≤ 4) had a 35% lower risk (AOR: 0.65, 95% CI 0.59, 0.71) compared to those with larger families. Merchants (AOR: 57.45, 95% CI 42.90, 76.92), government employees (AOR: 11.07, 95% CI 5.17, 15.02), and housewives (AOR: 5.23, 95% CI 3.92, 6.98) had higher odds of raised fasting blood glucose compared to other occupations. Conversely, individuals from the poorer (AOR: 0.50, 95% CI 0.46, 0.54), middle (AOR: 0.12, 95% CI 0.11, 0.14), and richer wealth quartiles (AOR: 0.59, 95% CI 0.54, 0.64) were less likely to have raised fasting blood glucose compared to the richest. Lack of advice from health professionals increased the likelihood (AOR: 8.33, 95% CI 7.75, 8.95), as did non-membership in functional WDA (AOR: 2.59, 95% CI 2.38, 2.81). Table 5 Bivariate and multivariate logistic regression of raised fasting blood glucose and independent variables among reproductive-age women in Gofa and Basketo zones, South Ethiopia Candidate Variables Raised Fasting blood glucose COR (95%CI) P-value AOR (95%CI) P-value Age 15–24 0.13(0.12, 0.14) < .001 0.26 (0.23, 0.31) < .001* 25–34 0.20(0.19, 0.22) < .001 0.19 (0.18, 0.21) < .001* ≥ 35 1 1 Educational status Illiterate 1.71(1.58, 1.85) < .001 3.36 (3.00, 3.75) < .001* Able to read and write 5.74(5.27, 6.25) < .001 19.86 (17.41, 22.64) < .001* Primary education 0.85(0.77, 0.93) .001 2.90 (2.55, 3.31) < .001* Secondary education and above 1 1 Marital status Married 0.23(.20, .25) < .001 0.15 (0.13, 0.17) < .001* Single 0.00 (.00.) .969 (.00, .00) .960 Widowed/Divorced 1 1 Zone Gofa 0.60(0.55, 0.66) < .001 0.52 (0.46, 0.59) < .001* Basketo 1 1 Wealth index Poorest 0.00 (0.00, 4.68) .936 (.00, .00) .928 Poorer 0.44(0.41, 0.47) < .001 0.50 (0.46, 0.54) < .001* Middle 0.14(0.13, 0 .16) < .001 0.12 (0.11, 0.14) < .001* Richer 0.53(0.49, 0.57) < .001 0.59 (0.54, 0.64) < .001* Richest 1 1 Occupational status Housewife 7.79(6.03, 10.08) < .001 5.23 (3.92, 6.98) < .001* Merchant 36.94(28.53, 47.81) < .001 57.45 (42.90, 76.92) < .001* Government employee 23.01(17.56, 30.15) < .001 11.07 (5.17, 15.02) < .001* Other 1 1 Family size ≤ 4 0.32(.30, .34) < .001 0.65 (0.59, 0.71) 4 1 1 Getting advice from health professionals No 5.09(4.81, 5.38) < .001 8.33 (7.75, 8.95) < .001* Yes 1 1 Family history of NCDs No 0.35(.32, .37) < .001 0.96 (0.86, 1.07) .432 Yes 1 1 Is your WDA functional? No 1.89(1.78, 2.02) < .001 2.59 (2.38, 2.81) < .001* Yes 1 1 COR-Crude Odds Ratio: odds ratio by bivariate analysis. 95% CI: confidence interval at the 95% level. AOR-Adjusted OR: odds ratio by multiple logistic regression 1: Referent category Women aged 15–24 years (AOR: 0.50, 95% CI 0.47, 0.53) and 25–34 years (AOR: 0.77, 95% CI 0.73, 0.81) had half and three-quarters lower odds of raised total cholesterol, respectively, compared to women aged ≥ 35 years. Those with primary (AOR: 1.07, 95% CI 1.02, 1.13) and secondary school education (AOR: 1.09, 95% CI 1.02, 1.16) were more likely to have raised total cholesterol compared to illiterate women, while those who could read and write (AOR: 0.68, 95% CI 0.62, 0.74) were less likely to have raised total cholesterol. Married women (AOR: 0.82, 95% CI 0.73, 0.92) had 18% lower likelihood of raised total cholesterol compared to Widowed/divorced. Residents of the Gofa area were four times more likely to have raised total cholesterol (AOR: 3.90, 95% CI 3.41, 4.47) compared to Basketo residents, however urban residents had lower risk AOR: 0.83, 95% CI 0.78, 0.87) compared to the rural. Women from poorest1.10 (1.03, 1.18), poorer (AOR: 0.67, 95% CI 0.63, 0.72) and middle wealth quartiles (AOR: 0.77, 95% CI 0.71, 0.82) were less likely to have raised total cholesterol compared to the richest, while those from richer households (AOR: 1.10, 95% CI 1.03, 1.17) were more likely. Housewives 1.37 1.26, 1.48) (AOR: 1.34, 95% CI 1.23, 1.46), merchants (AOR: 1.24, 95% CI 1.12, 1.37), and government employees (AOR: 1.71, 95% CI 1.53, 1.92) had higher odds of raised total cholesterol compared to other occupations. Women from households with family size ≤ 4 were three times more likely to have raised total cholesterol (AOR: 2.88, 95% CI 2.75, 3.02) compared to those with > 4 family size. Lack of advice from health professionals increased the likelihood (AOR: 4.11, 95% CI 3.93, 4.30), as did non-membership in functional WDA (AOR: 1.69, 95% CI 1.62, 1.78). Women without family history of NCDs (AOR: 2.63, 95% CI 2.37, 2.91) had higher risk of raised total cholesterol. Table 6 Bivariate and multivariate logistic regression of raised cholesterol level and independent variables among reproductive-age women in Gofa and Basketo zones, South Ethiopia Candidate Variables Raised Cholesterol Level COR (95%CI) P-value AOR (95%CI) P-value Age 15–24 0.79(0.75, 0.83) < .001 0.50 (0.47, 0.53) < .001* 25–34 0.91(0.87, 0.95) < .001 0.77 (0.73, 0.81) < .001* ≥ 35 1 1 Educational status Illiterate 1 < .001 1 Able to read and write 0.65 (0.61, 0.71) < .001 0.68 (0.62, 0.74) < .001* Primary education 1.28 (1.22, 1.34) 0.512 1.07 (1.02, 1.13) .006* Secondary education and above 1.26 (1.21, 1.32) 1.09 (1.02, 1.16) .007* Marital status Married 0.42(0.38, 0.46) < .001 0.82 (0.73, 0.92) < .001* Single 0.41(0.36, 0.47) < .001 0.90 (0.77, 1.06) .213 Widowed/Divorced 1 1 Residence Urban 1.78(1.69, 1.86) < .001 0.83 (0.78, 0.87) < .001* Rural 1 1 Zone Gofa 3.49(3.07, 3.98) < .001 4.11 (3.59, 4.71) < .001* Basketo 1 1 Wealth index Poorest 1.21(1.15, 1.29) < .001 1.10 (1.03, 1.18) .003* Poorer 0.66(0.62, 0.71) < .001 0.67 (0.63, 0.72) < .001* Middle 0.76(0.71, 0.81) < .001 0.77 (0.715, 0.82) < .001* Richer 1.04(0.98, 1.11) .176 1.10 (1.03, 1.17) .004* Richest 1 1 Occupational status Housewife 1.25(1.17, 1.33) < .001 1.34 (1.23, 1.46) < .001* Merchant 1.02(.94, 1.12) .598 1.24 (1.12, 1.37) < .001* Government employee 2.81(2.56, 3.08) < .001 1.71 (1.53, 1.92) < .001* Other 1 1 Family size ≤ 4 2.41(2.32, 2.51) < .001 2.88 (2.75, 3.02) 4 1 1 Getting advice from health professionals No 3.72(3.58, 3.87) < .001 4.11 (3.93, 4.30) < .001* Yes 1 1 Family history of NCDs No 1.91(1.74, 2.10) < .001 2.63 (2.37, 2.91) < .001* Yes 1 1 Is your WDA functional? No 1.51(1.45, 1.58) < .001 1.69 (1.62, 1.78) < .001* Yes 1 1 COR-Crude Odds Ratio: odds ratio by bivariate analysis. 95% CI: confidence interval at the 95% level. AOR-Adjusted OR: odds ratio by multiple logistic regression 1: Referent category Clustering of biological NCD risk factors Over twenty-seven percent of participants had at least one biological risk factor, while nine percent had two or more. The prevalence of biological risk factors among reproductive-age women is depicted in Fig. 1 , and Table 7 outlines the crude and adjusted odds ratios for the clustering of risk factors among women in the Gofa and Basketo zones. Logistic regression analysis revealed that women aged 25–34 (AOR: 1.58, 95% CI 1.53, 1.64) and > = 35 years (AOR: 1.96, 95% CI 1.88, 2.04) were more likely to experience co-occurrence of risk factors compared to those aged 15–24 years. Illiterate women (AOR: 1.17, 95% CI 1.12, 1.22), those able to read and write (AOR: 2.17, 95% CI 2.06, 2.28), and those with primary education (AOR: 1.25, 95% CI 1.20, 1.30) were more likely to have co-occurrence of risk factors compared to those with secondary education. Single women (AOR: 1.50, 95% CI 1.47, 1.71) were more likely to experience co-occurrence of risk factors compared to married women, however widowed/divorced women (AOR: 0.91, 95% CI 0.83, 0.99) had lower risk. Additionally, women residing in Gofa (AOR: 1.47, 95% CI 1.39, 1.55) and rural areas (AOR: 1.42, 95% CI 1.36, 1.47) were more likely to have co-occurrence of NCD risk factors. Wealth status also showed associations, with participants in the richest (AOR: 1.16, 95% CI 1.17, 1.27) and middle (AOR: 1.19, 95% CI 1.14, 1.24) quartiles being more likely to have co-occurrence of risk factors compared to the first quartile. However, participants from the poorer (AOR: 0.74, 95% CI 0.71, 0.78) and richer (AOR: 0.76, 95% CI 0.73, 0.79) quartiles were less likely to have risk factors. Occupation-wise, government employees (AOR: 3.83, 95% CI 3.56, 4.12), merchants (AOR: 1.88, 95% CI 1.77, 1.99), and housewives (AOR: 1.81, 95% CI 1.70, 1.89) were more likely to have co-occurrence of risk factors. Non-membership in functional WDA (AOR: 1.66, 95% CI 1.62, 1.71), family history of NCDs (AOR: 1.76, 95% CI 1.68, 1.84), and lack of health professional advice (AOR: 2.51, 95% CI 2.44, 2.58) were also associated with a higher probability of co-occurrence of risk factors. Table 7 Bivariate and multivariate logistic regression of clustering of biological risk factors and independent variables among reproductive-age women in Gofa and Basketo zones, South Ethiopia Variables Clustering of biological risk factors COR (95%CI) P-value AOR (95%CI) P-value Age 15–24 1 1 25–34 1.78 (1.73, 1.84) < .001 1.58 (1.53, 1.64) < .001* ≥ 35 2.12 (2.05, 2.19) < .001 1.96 (1.88, 2.04) < .001* Educational status Illiterate 1.18 (1.14, 1.22) < .001 1.17 (1.12, 1.22) .002* Able to read and write 1.96 (1.87, 2.04) < .001 2.17 (2.06, 2.28) < .001* Primary education 1.09 (1.05, 1.12) < .001 1.25 (1.20, 1.30) .012* Secondary education and above 1 1 Marital status Married 1 1 Single 1.22 (1.15, 1.29) < .001 1.50 (1.47, 1.71) < .001* Widowed/Divorced 1.53 (1.39, 1.69) .012 0.91 (0.83, 0.99) .042* Zone Gofa 1.20 (1.15, 1.26) < .001 1.47 (1.39, 1.55) < .001* Basketo 1 1 Residence Urban 1 1 Rural 0.82 (0.81, 0.85) < .001 1.42 (1.36, 1.47) < .001* Wealth index Poorest 1 1 Poorer .84 (.81, .88) < .001 .74 (.71, .78) < .001* Middle 1.40 (1.35, 1.45) < .001 1.19 (1.14, 1.24) < .001 * Richer 1.04 (1.00, 1.08) .026 .76 (.73, .79) < .001* Richest 1.55 (1.50, 1.61) < .001 1.16 (1.12, 1.21) <.001* Occupational status Housewife 1.89 (1.81, 1.97) < .001 1.81 (1.70, 1.89) < .001* Merchant 2.06 (1.96,2.18) < .001 1.88 (1.77, 1.99) < .001* Government employee 5.31 (4.98, 5.67) < .001 3.83 (3.56, 4.12) < .001* Other 1 1 Family size ≤ 4 0.81 (0.79, 0.83) 4 1 1 Getting advice from health professionals No 2.37 (2.31, 2.43) < .001 2.51 (2.44, 2.58) < .001* Yes 1 1 Family history of NCDs No 1 < .001 1 Yes 2.15 (2.06, 2.24) 1.76 (1.68, 1.84) < .001* Is your WDA functional? No 1.60 (1.56, 1.64) < .001 1.66 (1.62, 1.71) < .001* Yes 1 1 COR-Crude Odds Ratio: odds ratio by bivariate analysis. 95% CI: confidence interval at the 95% level. AOR-Adjusted OR: odds ratio by multiple logistic regression 1: Referent category DISCUSSION The study provides data on the prevalence of biological risk factors for non-communicable diseases, including raised blood pressure, overweight or obesity, raised fasting blood glucose, and raised total cholesterol levels. It examines their relationship with significant independent variables through multivariable analysis. The findings reveal significant associations between risk factors and various socio-demographic variables. Despite variations in the distribution of risk factors across different demographic factors, the prevalence of these biological risk factors remains notably higher within the study population. The finding of the current study indicates that the combined prevalence of overweight or obesity is 13.3%. Which is slightly lower compared to previous national reports of Ethiopia (14.9%) and international surveys from Ghana(15.6%), Gabon (18.9%), Burkina Faso(19.6%), Lesotho(19.9%), Nepal (22.2%) and Uganda (25,7%) ( 22 , 23 ). Moreover, a higher prevalence of overweight/obesity was reported from women in Sub-Saharan Africa was 23.1%( 24 ). The prevalence difference could stem from social and cultural factors impacting dietary habits and physical activity levels across diverse geographic regions. Another factor could be variations in sampling designs, particularly with a majority of participants hailing from rural areas where urbanization-related shifts towards processed foods and reduced physical activity may be less pronounced. Odds of overweight and/or obesity were more likely among women aged 25–34 years and age ≥ 35 years compared with women aged 15–24 years. This is supported by a study in Ethiopian DHS ( 25 ). This could be due to raised body composition of fat and hormonal changes following increment of age, accompanied by low physical activity, leading to overweight and obesity among aged women( 26 ). Overweight and/or obesity was more than four times likely among participants who were able to read and write and it was also more likely among women with primary and secondary school education compared with those who were illiterate. This finding is supported by a study in Ethiopian DHS( 25 ). This could be in developing countries like Ethiopia, where women who have higher education are more likely to consume energy-dense foods and follow a sedentary lifestyle; hence, they are more likely to be overweight and obese compared with their counterparts( 27 ). Odds of overweight and /or obesity were more likely among married and widowed/separated compared with single women. This finding is in line with the result of multilevel analysis of Ethiopian demographic and health survey( 28 ). The reason could be single women unlike their married, widowed or divorced are less likely to be multiparous and are at lower risk of weight gain since they are less likely to have pregnancy and the postpartum period( 29 ). Another possible explanation could be single women are more likely to be younger and they have decreased risk of overweight/obesity compared with married, widowed and separated( 28 ). Further analysis of our study also revealed that the prevalence of overweight or obesity was common among government employees, family size above four, urban residents, and wealthy. This finding is in line with previous similar study findings from within Ethiopia and other international studies also reported as common NCD risk factors of overweight or obesity ( 22 – 25 , 30 – 34 ). We found that 3.6–8% lower prevalence of hypertension among the study population compared to a previous similar study of STEEPS survey of Ethiopia which accounts for 15.6%( 30 ) and Uganda (20%). This may be attributed and sociocultural factors that may influence physical activity status and intake of healthy diet consumption these would in turn facilitate reproductive-age women being prone to raise their blood pressure. However, the prevalence of our finding (12%) is slightly higher than in previous studies in Burundi (1%), sub-Saharan Africa (5.5%), and Nepal (10.5%) ( 31 , 35 ). Our study also found that the prevalence of raised blood pressure increases with an increase in age of reproductive age of women. This finding is consistent with previous national and international surveys in Ethiopia and Nepal( 23 , 35 ). We also found that raised blood pressure was significantly associated with being married, single, having low educational status, having rural residency, richest, and household has more than four in size. This may be a result of low awareness-related factors and health promotion and prevention activities will help to tackle the problem. Our finding shows that the prevalence of raised fasting blood sugar was recorded at four percent in the study population. This finding is in contrast to national estimates of Ethiopia, where a total of 5.8% of women had a blood glucose level of greater than 110 mg/dl( 30 ). A study by Bista Bet al( 1 ) reported an raised blood sugar has doubled from 3–6% in Nepal and 8% in South Asian people( 2 , 3 ). The difference in prevalence may be due to the variation in lifestyle of women in the study area and the techniques of measuring blood sugar level. Although WHO global estimates have shown that increased levels of blood sugar levels are higher than the estimates in this study (4%)( 36 ). This emphasizes the need to intervene in raised blood sugar levels via physical activity and the consumption of a healthy diet. The prevalence of raised blood sugar levels increased with increasing age groups. This finding is in line with previous research reports by Bista et al( 37 ). This similarity was reported by Chang AM and Halter JB that increasing age is associated with the combined effect of increasing adiposity, decreasing physical activity, medications, coexisting illness, and insulin secretory defects that affect blood sugar level( 38 ). Further analysis of our study indicates that a raised blood sugar level was common among the participants who were richest, merchants, government employees, widowed/divorced, and households having above four family sizes. These differences may be attributed to variations in physical activity levels, dietary habits, and urbanization levels, with these findings similar to those from previous studies(39, 40). Some of the studies have put forward a biological explanation that increased content of the glucose transporter GLUT4 in the plasma membrane of skeletal muscle cells incubated under anoxia conditions( 17 , 18 ), and in skeletal muscle cells exposed to prolonged hypoxia leads to better glucose tolerance(37, 41, 42). The current study reveals the first estimation of the prevalence of raised cholesterol among women of reproductive age group to be 7.8% in Ethiopia. However, the prevalence of the current study is lower compared to research conducted in Zambia (17.3%), Kenya(21%), Nepal(11%), Korea(48.4%), Japan(30%) and 39.3% for women in the United States( 43 – 45 ). The difference may be due to differences in intake of fat and physical exercise among nations. A previous study conducted by Kim SM( 45 ) reports that Higher intakes of fat are associated with higher levels of total cholesterol and HDL-C levels; decreasing the intake of fat increases the OR of low HDL-C levels in women. A high-fat, high-cholesterol diet raised HDL-C and the production rate of apoA-I using a mechanism that involves the regulation of translation of the apoA-ImRNA. However, this was not tested in our study and further research is recommended. A balanced diet improves the global trends in the prevalence of raised cholesterol among the study population and beyond. Our study also revealed an increase in the prevalence of raised cholesterol levels with increasing age groups. This finding is in line with from STEPS survey 2019 of Nepal( 43 ) reported a raised BP and cholesterol with an increase in the age group. In support of this finding research done by Uranga R( 46 ) indicates a reduction in the production of growth hormone with increasing age may be a causal factor contributing to the age-dependent rise in blood cholesterol, Similarly, other reports show that a higher prevalence of raised blood cholesterol level may be linked to increasing age and fluctuations in female sex hormone i.e. estrogen. Various studies have shown that estrogen helps to maintain levels of high-density lipoproteins (HDL) in adult females. However, at menopause, many females experience a change in their cholesterol levels, with total cholesterol and low-density lipoproteins (LDL) levels rising and HDL falling( 47 ). Further, the current study found that raised cholesterol was more likely among widowed/divorced, rural residents, the Gofa ethnic group, women who had no family history of NCDs and those from households’ with family size ≤ 4. This might be a consequence of diet, exercise, or physical activity and lipid metabolism variation among socio-demographic groups. Clustering of biological risk factors The result indicates that the prevalence of clustering of risk factors (≥ 1 RF) is 27%.It found that the clustering of NCD risk factors increased with age and wealth index among the study population. The finding of our study was in line with the trends of increase in NCD risk factors found in previous similar studies of other countries such as a study done by Rahman S et al in Bangladesh ( 48 ) and Sarveswaran G et al in South India ( 49 ). Likewise, another study from the Nepal STEPS survey 2019( 43 ) reported that the prevalence of clustering of risk factors is higher among the richest, Similar to individual risk factors such as overweight/obesity and hypertension, the clustering of NCDs risk factors in the richest group can be linked with the adoption of a sedentary lifestyle. In addition, Elaine H et( 50 ) further pointed out that older individuals were less likely to have simultaneous physical inactivity/ sedentary behavior and unhealthy diet. The clustering of biological risk factors was higher among participants from the Gofa zone and rural residents. The reason could be westernization of dietary patterns among specific provinces might have a role in the clustering of risk factors. However, these finding was not supported by the results from the Nepal STEPS survey( 43 ). It was also higher among those with lower education. In support of this finding, Elaine H et al( 50 ) from Brazil indicates individuals of lower educational status have less access to information on the reduction of risk factors; thus, tend to have less healthy habits ( 50 ). However, it was inconsistent with the findings from other population-based 350 studies of Bangladesh ( 51 , 52 ). The difference could be attributed to sociocultural differences among the study population. Clustering of risk factors was higher among married and single women. The finding was also supported by the study in Bangladesh( 53 ). However, it was not supported by studies in Nepal ( 31 ). This might be attributed to the difference in the study areas. Clustering of risk factors was more likely among women who were not a member of functional WDA. This finding is supported by the study in Southwest Ethiopia, 2020( 54 ). This may indicate the impact of social networks and pressure which promotes preventive behaviors ( 55 ). Further, our study shows co-occurrence of NCD risk factors was more likely among women who were government employees, merchants, and housewives, had a family history of NCD, and no health professional advice. . The co-occurrence of risk factors among study participants signifies the importance of designing an integrative intervention to tackle the multiple risk factors ( 56 ). The NCD risk factors determine several adverse health outcomes, including maternal death( 57 ). This alarming situation among reproductive-aged women might be a barrier for us to achieving Sustainable Development Goals (SDG) ( 58 ). Strengths and Limitations This pioneering study in the Gofa and Basketo zones examines the prevalence and factors linked to biological risk factors among reproductive-aged women. Its strengths lie in its community-based approach covering both rural and urban residents, ensuring broader applicability. Additionally, rigorous measures were implemented, including pilot testing of instruments, comprehensive training of data collectors and supervisors, and high response rates (99%), enhancing data quality. The study's use of weighted analysis allows for extrapolation of findings to the entire study area. It delves into socioeconomic and knowledge-related factors associated with risk factors, including their clustering. Furthermore, it measures key biomarkers like raised blood pressure, blood sugar, and cholesterol levels, filling gaps left by previous studies in the region. However, the study's cross-sectional design limits its ability to establish causal relationships. CONCLUSION The prevalence of biological risk factors among reproductive-age women in the Gofa and Basketo zones is concerning, with over one-fourth of participants exhibiting one or more of the studied NCD risk factors. Overweight and/or obesity, followed by raised blood pressure and cholesterol levels, showed the highest prevalence. Factors associated with increased risk varied, with Basketo zone residents, housewives, merchants, government employees, older women, and those with lower education levels being more susceptible to raised blood pressure and blood glucose. Conversely, younger women, married or widowed/divorced individuals, residents of the Basketo zone, and those with secondary education were less likely to exhibit these risk factors. Family size, socioeconomic status, and lifestyle factors also played significant roles in determining risk. Addressing this burden requires comprehensive awareness programs targeting younger age groups, emphasizing lifestyle modifications to counteract the alarming rise in risk factors, especially overweight/obesity, raised cholesterol, high blood pressure, and blood glucose levels. Implementing family-based interventions and leveraging healthcare advice from professionals and WDA networks are essential strategies. Moreover, tailored governmental plans considering local socio-economic and cultural contexts are necessary to combat these emerging public health threats and achieve SDG targets. Additionally, policy and socio-political factors influencing the rise of biological risk factors must be carefully considered. Declarations Acknowledgment : I thank Selinus University Faculty of Natural Health Science for selecting and giving me a chance to research such public health topics and assigning a supervisor. I also extend my gratitude to data collectors and supervisors for supporting data collection. Authors’ contributions The authors’ responsibilities were as follows: MMD: conceived, drafted, did the analysis and finalized the manuscript by incorporating the inputs from the other authors. SF and TG: Supervised the study and ensured quality of the data and made substantial contribution to the local implementation of the study and BM assisted the analysis and interpretation of the data. All authors critically reviewed the manuscript. Availability of data and materials Most of the data used to support the findings of this study are included within the article. More data are available upon reasonable request from the corresponding author. Consent for publication Not applicable. Competing interests We declare that no financial support received for the research authorship and/or publication of this article. 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Castillo O, Woolcott OO, Gonzales E, Tello V, Tello L, Villarreal C, et al. Residents at high altitude show a lower glucose profile than sea-level residents throughout 12-hour blood continuous monitoring. High altitude medicine & biology. 2007;8(4):307-11. Vitale C, Fini M, Speziale G, Chierchia S. Gender differences in the cardiovascular effects of sex hormones. Fundamental & clinical pharmacology. 2010;24(6):675-85. Cartee GD, Douen AG, Ramlal T, Klip A, Holloszy J. Stimulation of glucose transport in skeletal muscle by hypoxia. Journal of Applied Physiology. 1991;70(4):1593-600. Bista B, Dhimal M, Bhattarai S, Neupane T, Xu YY, Pandey AR, et al. Prevalence of non-communicable diseases risk factors and their determinants: Results from STEPS survey 2019, Nepal. PLoS One. 2021;16(7):e0253605. Muula AS, Rudatsikira E, Babaniyi O, Songolo P, Mulenga D, Siziya S. Factors associated with high cholesterol levels in Lusaka, Zambia: a community-based study. 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Diabetes Metab Syndr. 2020;14(5):1005-14. Nunes HE, Gonçalves EC, Vieira JA, Silva DA. Clustering of Risk Factors for Non-Communicable Diseases among Adolescents from Southern Brazil. PLoS One. 2016;11(7):e0159037. Zaman MM, Bhuiyan MR, Karim MN, Rahman MM, Akanda AW, Fernando T. Clustering of non-communicable diseases risk factors in Bangladeshi adults: An analysis of STEPS survey 2013. BMC public health. 2015;15(1):1-9. Al-Zubayer MA, Ahammed B, Sarder MA, Kundu S, Majumder UK, Islam SMS. Double and triple burden of non-communicable diseases and its determinants among adults in Bangladesh: Evidence from a recent demographic and health survey. Int J Clin Pract. 2021;75(10):e14613. Saifur Rahman Chowdhury1 MNI, Tasbeen Akhtar Sheekha2, Shirmin Bintay Kader2, Ahmed Hossain. Prevalence and determinants of non-communicable diseases risk factors among reproductive aged women of Bangladesh: Evidence from Bangladesh Demographic Health Survey 2017-2018. August 4, 2022. Zenu S, Abebe E, Dessie Y, Debalke R, Berkessa T, Reshad M. Co-occurrence of Behavioral Risk Factors of Non-communicable Diseases and Social Determinants among Adults in Urban Centers of Southwestern Ethiopia in 2020: A Community-Based Cross-Sectional Study. J Multidiscip Healthc. 2021;14:1561-70. Morrissey JL, Janz KF, Letuchy EM, Francis SL, Levy SM. The effect of family and friend support on physical activity through adolescence: a longitudinal study. Int J Behav Nutr Phys Act. 2015;12:103. Dhungana RR, Bista B, Pandey AR, de Courten M. Prevalence, clustering and sociodemographic distributions of non-communicable disease risk factors in Nepalese adolescents: secondary analysis of a nationwide school survey. BMJ Open. 2019;9(5):e028263. Storm F, Agampodi S, Eddleston M, Sørensen JB, Konradsen F, Rheinländer T. Indirect causes of maternal death. Lancet Glob Health. 2014;2(10):e566. Nations U, Bangladish. Sustainable Development Goal 3 Good Health and Well-being Ensure healthy lives and promote well-being for all at all ages. 2023. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 01 Jun, 2024 Reviews received at journal 31 May, 2024 Reviewers agreed at journal 21 May, 2024 Reviews received at journal 19 May, 2024 Reviewers agreed at journal 19 May, 2024 Reviewers invited by journal 15 May, 2024 Submission checks completed at journal 16 Apr, 2024 Editor assigned by journal 16 Apr, 2024 First submitted to journal 05 Apr, 2024 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4221395","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":288263400,"identity":"afc024e3-211c-483b-bc38-ef27f6a9344f","order_by":0,"name":"Markos Manote Domba","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYHACZgTr7z8bIMXYeIBILUCahy0NpKWBJC2HwUy8WnTbzz42+PBnm7w5e//BBxI85+3Wth8G2lJjE41Li9mZdOPEmW23DXf2HGY2MJC4nbztTCJQy7G03AZcWg6kMR/mbbjNuOFGMptEgsHtZLMDQC2MDYdxazn/jPkwz5/b9kAt7D8OJJxLNjv/kICWG2nMyTxstxNBtgCD6oCd2Q1Cttx4xmwI9EvyhjOHjaUZG5ITzG4AbUnA55fzacwSH/7ctt1wvPHhZ8YGO3uz8+kPH3yoscGpBQMkglUmEKscBOxJUTwKRsEoGAUjAwAA+nJnmGeaDCEAAAAASUVORK5CYII=","orcid":"","institution":"Public Health, Gofa Zone Health Department, South Ethiopia Regional State Health Bureau","correspondingAuthor":true,"prefix":"","firstName":"Markos","middleName":"Manote","lastName":"Domba","suffix":""},{"id":288263401,"identity":"ad98a0a7-793a-4ea4-9a3a-57cdfec6a6f5","order_by":1,"name":"Salvatore Fava","email":"","orcid":"","institution":"Selinus University of Science and Literature, Distance Learning Online University","correspondingAuthor":false,"prefix":"","firstName":"Salvatore","middleName":"","lastName":"Fava","suffix":""},{"id":288263402,"identity":"28e7b450-7c53-4fe6-ac45-17e3f17c7258","order_by":2,"name":"Terefe Gelibo","email":"","orcid":"","institution":"Public Health Department, Colombia University, Mailman School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Terefe","middleName":"","lastName":"Gelibo","suffix":""},{"id":288263403,"identity":"ec886f55-b6ac-42df-a067-134ad577f060","order_by":3,"name":"Bahiru Mulatu","email":"","orcid":"","institution":"Arba Minch Collage of Health Science","correspondingAuthor":false,"prefix":"","firstName":"Bahiru","middleName":"","lastName":"Mulatu","suffix":""}],"badges":[],"createdAt":"2024-04-05 07:42:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4221395/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4221395/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54863149,"identity":"5a7ee2fa-3990-4634-bc8e-697520151da4","added_by":"auto","created_at":"2024-04-17 20:22:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":8726,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence by the number of biological risk factors among reproductive age group women in Gofa and Basketo zones, Southern Ethiopia\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4221395/v1/ca5eb7ff247755a6c2a0167c.png"},{"id":54863855,"identity":"120d2550-1697-4453-ab57-6a9f5e67a79e","added_by":"auto","created_at":"2024-04-17 20:30:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1073688,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4221395/v1/4fde4230-d999-40bd-9000-87e3b2d830df.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Magnitude and determinants of biological risk factors of non-communicable diseases among reproductive age women in Gofa and Basketo Zones, Southern Ethiopia: a community-based cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNon-communicable diseases (NCDs) are the dominant cause of global morbidity and mortality, especially in developing (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The rising burden of NCDs is coupled with unmet needs of sexual and reproductive health services in SSA (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) and doubled among reproductive-age women of many African countries (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) and are expected to exceed infectious diseases as major causes of morbidity and mortality in Africa by the year 2035(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). NCDs are mostly linked with metabolic (such as obesity, blood sugar, blood pressure, and cholesterol levels) and behavioral risk factors(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Despite efforts to improve lives and well-being, the burden of metabolic risk factors of NCDs persist(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In Ethiopia, evidence shows that 95% of the study population was found with 1\u0026ndash;2 NCD risk factors (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e9\u003c/span\u003e). This indicates that the burden of NCDs is likely to become unbearable in the future in Ethiopia (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Women with NCD risk factors had an increased negative impact on reproductive health as well as on fetal health (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e11\u003c/span\u003e). It also impedes progress toward sustainable development goals (SDG), mainly SDGs 1, 3, and 5 (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Tackling NCDs in women needs a systematic understanding of major biological risk factors and determinants (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Despite the increasing burden of the disease in Ethiopia, there is a paucity of studies focusing on reproductive-age women, especially in the peripheral setup of the country including Gofa and Basketo. Therefore, this study aims to address the above gaps and, to advocate the policy makers and regional government in preventing biological risk factors among women of reproductive age.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy setting and period.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in Gofa and Basketo Zones, Southern Ethiopia from Sept 9/2022 to Dec 6/2022.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA community-based cross-sectional study was conducted by the WHO a stepwise approach to the surveillance of NCD risk factors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll women\u0026nbsp;of\u0026nbsp;reproductive age\u0026nbsp;residing\u0026nbsp;in the Gofa and Basketo zones during the data collection period were\u0026nbsp;eligible for inclusion.\u0026nbsp;This\u0026nbsp;encompassed those\u0026nbsp;who considered the study area their permanent residence\u0026nbsp;for at least six months. Exclusions\u0026nbsp;were\u0026nbsp;made for non-permanent residents,\u0026nbsp;pregnant\u0026nbsp;women, individuals\u0026nbsp;institutionalized in hospitals, prisons, nursing homes, similar\u0026nbsp;facilities, as well as those residing primarily in\u0026nbsp;military camps or dormitories. Additionally, critically ill, mentally disabled, and\u0026nbsp;physically disabled individuals unsuitable for\u0026nbsp;physical\u0026nbsp;participation were excluded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample size determination and sampling technique\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA multi-stage cluster sampling approach using the Kish method was employed to select the study participants. The WHO regional office tools for assessing operational district health systems in Africa recommend that for the total number of districts between 10 to 19, \u0026nbsp;sampling 50 % of them could be enough\u0026nbsp;(15). Sample size was determined using a single proportion formula considering the Z-score=1.96; Proportion =50%; marginal error=0.05; Design effect =3.35; and non-response rate=10%, making the total sample size of 1,416 respondents. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy variables\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and measurement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe dependent variable\u003c/em\u003e\u003cstrong\u003es:\u003c/strong\u003e are biological non-communicable disease risk factors including overweight/obese, high blood pressure, raised blood glucose, and raised cholesterol.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIndependent variable\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e Socio-demographic and cultural variables include:\u0026nbsp;age, place of residence, family history, parity, educational status, marital status, religion, occupation of women, wealth status of household,\u0026nbsp;and\u0026nbsp;social support to NCD prevention.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eKnowledge-related factors/variables include Knowledge of NCD risk factors, getting advice from health professionals, and using mass media.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eStructural factor includes membership in a functional women's development army.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eData collection tool\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data collection process adhered to the WHO Stepwise approach for chronic disease risk factor surveillance (STEPS).\u0026nbsp;A questionnaire based on the WHO Stepwise Surveillance questionnaire was utilized as the data collection instrument. This questionnaire was translated into Gofatho and Basket languages and subsequently back-translated into English to ensure accuracy. Step 1 involved gathering socio-demographic, knowledge, and health system-related data. Step 2 comprised physical measurements such as height, weight, and blood pressure conducted using standardized instruments and protocols. Step 3 involved biochemical measurements of serum samples to assess fasting blood glucose and total cholesterol levels. Blood glucose levels were determined using dry chemistry methods, while total cholesterol serum levels were assessed using wet chemistry techniques.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection technique\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the selected data collectors had two days of training on collecting data from all three steps before the survey. The training was imparted on STEPS instruments including interactive discussions and physical measurements. The interviewer-administered questionnaire covers socio-demographic information, knowledge, health system-related questions, and NCD risk factors. The assessment of variables was made according to suggestions by different scholars or standard guidelines such as measurement of socio-demographic variables\u0026nbsp;(16), wealth index\u0026nbsp;(17, 18), physical measurement\u0026nbsp;(19), and biochemical measurement\u0026nbsp;(20)\u0026nbsp;including self-report\u0026nbsp;(21).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003equality management\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo maintain data quality, the questionnaire underwent translation and pretesting as necessary. Following data collection, thorough checks for completeness and consistency were conducted, and coding was performed by both the supervisor and principal investigator.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSPSS software version 25 was used to conduct data analysis. Descriptive weighted analysis was done along with complex sample analysis. The presence of association was assessed using bivariate analysis and then multivariate logistic regression analysis was used to identify the independent predictors. \u0026nbsp;The strength of the association was estimated by odds ratio and its 95% confidence interval. Associations with a p-value ≤0.05 are considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe survey protocol obtained ethical clearance from the institution review boards of the Southern Regional State Public Health Institute and Selinus University of Science and Literature. A paper-based written informed consent form was administered to eligible participants. At each stage of the process, consent was indicated by signing or making a mark on the consent form on a printed copy, which was retained by the participant and the data collector. A designated head of household provided written consent for the household to take part in the survey, after which individual members were rostered during a household interview. Participants aged 15-49 years and emancipated minors ages 15-17 then provided written consent for an interview and participation in the biomarker component of the survey. For minors ages 15-17, parents /guardians provided permission which was followed by assent by the participant.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eSocio-demographic Characteristics of Participants\u003c/p\u003e \u003cp\u003eThe average age of participants was 28.9 years with a standard deviation of 7.5 years, and 41.5% fell within the 25\u0026ndash;34 age range. A significant portion (48.2%) had no formal education, while the majorities (93.4%) were married. Predominantly, participants hailed from the Gofa zone (93.3%) and rural areas (83.3%). Furthermore, 84% resided in rural regions. In terms of wealth distribution, approximately 20.5%, 21.1%, and 20.6% of participants were categorized into the poorest, poorer, and middle wealth quintiles respectively. Housewives comprised the largest occupational group, accounting for 72% of participants. Additional socio-demographic details can be found in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eSocio-demographic characteristics of participants in Basketo and Gofa Zones\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUn-weighted count\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eweighted percent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational status\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAble to read and write\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary education and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed/Divorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.2\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZone\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGofa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasketo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWealth index\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupational status\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMerchant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily size\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of NCDs\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial support to prevent NCD risk factors\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e*Others in occupational status: daily laborers, students, and maidservants\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegarding knowledge of NCD risk factors, only 34.6% of the study participants had good knowledge; but the vast majority had poor knowledge.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePrevalence of biological risk factors\u003c/h2\u003e \u003cp\u003eThe prevalence of four biological non-communicable disease risk factors varies between the different socio-demographic sub-groups. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the differences of each of the risk factors stratified by the different socio-demographic characteristics (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eOverweight and obesity\u003c/h2\u003e \u003cp\u003eThe prevalence of overweight or obesity among reproductive-age women was 13.3% (95% CI 13.2:-13.5) and the overall prevalence of abdominal obesity was 37.1% (95% CI: 33.2\u0026ndash;40.1) with the mean waist circumference of 89.9cm\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0. Being overweight or obese had a positive relationship with the age of participants and household wealth status (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eRaised blood pressure\u003c/h2\u003e \u003cp\u003eThe prevalence of raised blood pressure was 12.0% (95% CI: 11.9\u0026ndash;12.2). The prevalence of raised blood pressure increased significantly with age. 5.0% (95% CI: 3.2\u0026ndash;7.5), 12.4% (95% CI: 9.9\u0026ndash;15.2), and 16.8% (95% CI: 13.4\u0026ndash;20.7) for women aged 15\u0026ndash;24, 25\u0026ndash;34 and 35 and above respectively. We also observed a higher prevalence of raised blood pressure among married (11.6%, 95% CI: 10.0-13.5) and single (11.8%, 95% CI: 5.7\u0026ndash;21.0) women compared with widowed/divorced (7.7%, 95% CI: 2.2\u0026ndash;19.1) (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eRaised fasting blood sugar\u003c/h2\u003e \u003cp\u003eThe overall prevalence of raised fasting blood sugar in the study participants was 4.0% (95% CI: 3.9%-4.1%). The prevalence was significantly increased with the age and wealth index. Raised fasting blood glucose prevalence was higher among widowed, merchants, government employees, and households having above four family sizes as compared to their counterparts (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eRaised total cholesterol\u003c/h2\u003e \u003cp\u003eOverall prevalence of raised total cholesterol\u0026thinsp;\u0026ge;\u0026thinsp;200mg/dl was 7.8%( 95% CI: 7.7-8.0). The prevalence increased with the age of the respondents; 6.0% (95% CI: 4.0-8.6) for 15\u0026ndash;24; 7.3% (95% CI: 5.5\u0026ndash;9.6) for 25\u0026ndash;34 and 9.9% (95% CI: 7.3\u0026ndash;13.1) for age 35 and above. We also noted the lower prevalence of raised total cholesterol among married (7.5%, 95% CI: 6.1-9.0) and single (5.9%, 95% CI: (2.0-13.4) compared with widowed/divorced (17.9%, 95% CI: 8.4\u0026ndash;32.0) (Table \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\u003ePrevalence of modifiable biological non-communicable disease risk factors by background characteristics of reproductive-aged women in Gofa and Basketo zones, 2023\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocio-demographic\u003c/p\u003e \u003cp\u003echaracteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaised Blood Pressure (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRaised Fasting blood glucose (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRaised Cholesterol Level (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOverweight and/or obesity (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.0(3.2, 7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0(0.3, 2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0(4.0, 8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.3(7.6, 13.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.4(9.9, 15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.7(1.6, 4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.3(5.5, 9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.2(12.5, 18.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.8(13.4, 20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.9(6.4, 12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.9(7.3, 13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.8(11.6, 18.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational status\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\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.0(12.2, 18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.0(2.6, 6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.1(6.0, 10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.0(8.5, 13.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAble to read and write\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.2(7.7, 18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.9(6.6, 16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.8(2.2, 9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.0(24.8, 39.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.0(8.2, 14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0(0.9, 3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.0(5.6, 11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.0(8.2, 14.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary education and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.5(4.2, 9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.3(1.7, 5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.0(5.5, 11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.0(9.7, 16.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.002*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\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\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.6(10.0, 13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.0(3.0, 5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.5(6.1, 9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.3(12.4, 16.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.8(5.7, 21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.9(2.0, 13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.9(2.0, 13.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed/Divorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.7(2.2, 19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.3(3.6, 22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.9(8.4, 32.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.7(2.2, 19.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.033\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.046\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence\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\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.2(6.6, 12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.3(2.6, 6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.5(8.6, 14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.8(13.3, 20.7)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.5(10.5, 14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.9(2.8, 5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.2(4.9, 7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.5(10.5, 14.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.034*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eZone\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\u003eGofa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.2(9.5, 13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.7(2.8, 4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.4(6.9, 10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.3(12.4, 16.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasketo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.4(9.6, 20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.3(3.3, 10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.5(0.8, 5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.8(5.1, 13.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.009\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth index\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\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.6(4.2, 9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.3(5.6, 11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.9(10.3, 18.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.5(8.2, 15.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.2(2.3, 7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.0(4.5, 10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.4(5.6, 12.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.9(11.9, 20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.4(0.5, 3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.2(4.6, 10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.4(9.7, 17.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.7(7.5, 14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.7(3.4, 8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.1(4.6, 10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.4(8.1, 15.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.2(9.6, 17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.8(5.9, 12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.8(5.9, 12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.6(17.0, 26.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.012\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupational status\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\u003eHousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.1(9.3, 13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.9(1.9, 4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.7(6.2, 9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.1(13.0, 17.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMerchant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.6(13.3, 24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.6(7.5, 17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.0(3.9, 11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.6(7.5, 17.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.2(7.0, 22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.2(4.2, 17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.5(5.1, 18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.1(13.1, 31.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.3(3.4, 10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6(0.1, 2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.9(3.8, 11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.6(2.2, 8.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.004\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily size\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\u003e\u0026le;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.2(5.2, 9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.2(1.2, 3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.0(8.6, 13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.8(9.3, 14.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.3(12.1, 16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.1(3.8, 6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.6(4.2, 7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.9(12.6, 17.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.007*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Others in occupational status: daily laborers, students, and maidservants\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePrevalence of combined biological risk factors\u003c/h2\u003e \u003cp\u003eAbout 27.0% of women in the reproductive age group had at least one risk factor (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eFactors association with non-communicable diseases risk factors\u003c/h2\u003e \u003cp\u003eIn logistic regression models, being overweight and/or obese showed significant associations with several factors including age, marital status, residence, zone, educational status, wealth index, occupational status, family size, receiving advice from health professionals, family history of NCDs, and membership in a functional WDA. Similarly, variables such as age, marital status, residence, zone, educational status, wealth index, occupational status, family size, receiving advice from health professionals, family history of NCDs, social support for preventing NCD risk factors, and membership in a functional WDA were significantly associated with raised blood pressure during bivariate analysis. Raised fasting blood glucose was significantly associated with several factors including age, marital status, zone, educational status, wealth index, occupational status, family size, receiving advice from health professionals, family history of NCDs and membership in a functional WDA during bivariate analysis. Raised total cholesterol levels were significantly associated with age, marital status, residence, zone, educational status, wealth index, occupational status, family size, receiving advice from health professionals, family history of NCDs, and membership in a functional WDA during bivariate analysis. Variables with a p-value of less than 0.25 in binary logistic regression were included in a multivariable logistic regression model to control for confounding effects. Detailed factors associated with biological risk factors are presented in Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, and \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe likelihood of overweight and/or obesity was higher among women aged 25\u0026ndash;34 years (AOR: 1.34, 95% CI 1.28, 1.41) and those aged\u0026thinsp;\u0026gt;\u0026thinsp;35 years (AOR: 1.19, 95% CI 1.13, 1.26) compared to those aged 15\u0026ndash;24 years. Literate women, particularly those with primary (AOR: 1.23, 95% CI 1.17, 1.28) and secondary school education (AOR: 1.63, 95% CI 1.55, 1.72), were more likely to be overweight and/or obese compared to illiterate women. Married (AOR: 2.62, 95% CI 2.21, 3.10) and single women (AOR: 1.40, 95% CI 1.23, 1.60) had higher odds of being overweight and/or obese compared to widowed/divorced women. Residents of the Gofa zone had nearly twice the likelihood of being overweight and/or obese (AOR: 1.73, 95% CI 1.60, 1.88) compared to Basketo residents. However, urban residents (AOR: 0.85, 95% CI 0.81, 0.88) had lower odds of being overweight and/or obese compared to rural. Women from households with lower wealth statuses were less likely to be overweight and/or obese compared to the wealthiest households.\u003c/p\u003e \u003cp\u003eHousewives (AOR: 7.83, 95% CI 7.02, 8.74), merchants (AOR: 6.28, 95% CI 5.61, 7.03), and government employees (AOR: 6.37, 95% CI 5.63, 7.21) had significantly higher odds of being overweight and/or obese compared to other occupations such as maid servants, daily laborers and students. Women from smaller households (family size\u0026thinsp;\u0026le;\u0026thinsp;4) were less likely to be overweight and/or obese (AOR: 0.77, 95% CI 0.74, 0.81) compared to those from larger households. Lack of health professional advice had more than double risk of overweight and/or obesity (AOR: 2.53, 95% CI 2.44, 2.63), as did non-membership in functional WDA (AOR: 1.59, 95% CI 1.54, 1.65). Conversely, women without a family history of NCDs were less likely to be overweight and/or obese (AOR: 0.71, 95% CI 0.67, 0.75) compared to those with a family history.\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\u003eBivariate and multivariate logistic regression of overweight and/or obesity and independent variables among reproductive-age women in Gofa and Basketo zones, South Ethiopia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCandidate Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eOverweight and/or obesity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.62 (1.56, 1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.34 (1.28, 1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.45 (1.39, 1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19 (1.13, 1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational status\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\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003eAble to read and write\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.98 (3.80, 4.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.67 (4.45, 4.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.95, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23 (1.17, 1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary education and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07 (1.02, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.63 (1.55, 1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\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\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.62(1.43, 1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.62 (2.21, 3.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96(0.83, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.40 (1.23, 1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed/Divorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eResidence\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\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.41(1.36, 1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85 (0.81, 0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\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\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eZone\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\u003eGofa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.65(1.54, 1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.73 (1.60, 1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasketo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eWealth index\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\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.59(0.57, 0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84 (0.80, 0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.27(0.26, 0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30 (0.29, 0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.61(0.58, 0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75 (0.71, 0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.43(0.41, 0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38 (0.36, 0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eOccupational status\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\u003e1. Housewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.03(5.48, 6.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.83 (7.02, 8.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Merchant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.33(4.80, 5.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.28 (5.61, 7.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Government employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.48(7.57, 9.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.37 (5.63, 7.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. Other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eFamily size\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\u003e1. \u0026le;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.71(0.69, 0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77 (0.74, 0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. \u0026gt;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eGetting advice from health professionals\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\u003e1. No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.04(1.97, 2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.53 (2.44, 2.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eFamily history of NCDs\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\u003e1. No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.59(0.56, 0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71 (0.67, 0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eIs your WDA functional?\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\u003e1. No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.74(1.68, 1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.59 (1.54, 1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCOR-Crude Odds Ratio: odds ratio by bivariate analysis. 95% CI: confidence interval at the 95% level.\u003c/p\u003e \u003cp\u003eAOR-Adjusted OR: odds ratio by multiple logistic regression 1: Referent category\u003c/p\u003e \u003cp\u003eWomen aged 15\u0026ndash;24 years (AOR: 0.41, 95% CI 0.39, 0.44) and 25\u0026ndash;34 years (AOR: 0.67, 95% CI 0.65, 0.70) had lower odds of raised blood pressure compared to those aged\u0026thinsp;\u0026ge;\u0026thinsp;35 years. Illiterate women (AOR: 2.64, 95% CI 2.48, 2.82), those able to read and write (AOR: 3.07, 95% CI 2.85, 3.32), and those with primary education (AOR: 2.58, 95% CI 2.41, 2.75) were approximately three times more likely to have raised blood pressure compared to those with secondary education. Married (AOR: 1.89, 95% CI 1.66, 2.16) and single women (AOR: 7.56, 95% CI 6.47, 8.82) faced higher risk of raised blood pressure compared to widowed/divorced women. Urban residents had about half the risk of raised blood pressure (AOR: 0.56, 95% CI 0.53, 0.59) compared to rural residents.\u003c/p\u003e \u003cp\u003eIndividuals from poorer (AOR: 1.08, 95% CI 1.02, 1.14) and middle wealth quartiles (AOR: 1.56, 95% CI 1.48, 1.64) were more likely to have raised blood pressure, whereas those from the poorest (AOR: 0.83, 95% CI 0.78, 0.88) and richer households (AOR: 0.62, 95% CI 0.59, 0.66) were less likely. Housewives (AOR: 1.56, 95% CI 1.45, 1.69), merchants (AOR: 2.98, 95% CI 2.75, 3.23), and government employees (AOR: 4.11, 95% CI 3.69, 4.54) faced higher risk compared to other occupations. Participants from smaller households (family size\u0026thinsp;\u0026le;\u0026thinsp;4) had a 31% lower risk (AOR: 0.69, 95% CI 0.66, 0.72) compared to those with larger families. Lack of health professional advice increased the likelihood of raised blood pressure (AOR: 1.58, 95% CI 1.52, 1.64), as did non-membership in functional WDA (AOR: 1.53, 95% CI 1.47, 1.59). Individuals without a family history of NCDs (AOR: 0.66, 95% CI 0.62, 0.71) and lack of social support 0.95(0.92, 0.99) had lower risk of raised blood pressure.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBivariate and multivariate logistic regression of raised blood pressure and independent variables among reproductive-age women in Gofa and Basketo zones, South Ethiopia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCandidate Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eRaised Blood Pressure\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.29(.28, 0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41 (0.39, 0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67(0.64, 0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67 (0.65, 0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eEducational status\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\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.46(2.34, 2.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.64 (2.48, 2.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAble to read and write\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.50(2.35, 2.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.07 (2.85, 3.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.93(1.83, 2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.58 (2.41, 2.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary education and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eMarital status\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\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.39(1.22, 1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.89 (1.66, 2.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.94 (1.68, 2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.56 (6.47, 8.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed/Divorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eResidence\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\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65(0.62, 0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56 (0.53, 0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\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\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eZone\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\u003eGofa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80(0.76, 0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97 (0.91, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.346\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasketo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eWealth index\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\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.62(0.58, 0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83 (0.78, 0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95(0.90, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08(1.02, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.006*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.45(1.38, 1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.56 (1.48, 1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.73(0.69, 0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.62 (0.59, 0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eOccupational status\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\u003eHousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.72(1.61, 1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.56 (1.45, 1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMerchant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.35(3.12, 3.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.98 (2.75, 3.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.75(2.51, 3.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.11 (3.69, 4.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eFamily size\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\u003e\u0026le;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.49(0.47, 0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69 (0.66, 0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eGetting advice from health professionals\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.45(1.41, 1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.58 (1.52, 1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eFamily history of NCDs\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.58(0.55, 0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.66 (0.62, 0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eSocial support to prevent NCD risk factors\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07(1.03, 1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95(0.92, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.009*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eIs your WDA functional?\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.33(1.29, 1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.53 (1.47, 1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCOR-Crude Odds Ratio: odds ratio by bivariate analysis. 95% CI: confidence interval at the 95% level.\u003c/p\u003e \u003cp\u003eAOR-Adjusted OR: odds ratio by multiple logistic regression 1: Referent category\u003c/p\u003e \u003cp\u003eWomen aged 15\u0026ndash;24 years (AOR: 0.26, 95% CI 0.23, 0.31) and 25\u0026ndash;34 years (AOR: 0.19, 95% CI 0.18, 0.21) had lower odds of raised fasting blood glucose compared to those aged\u0026thinsp;\u0026ge;\u0026thinsp;35 years. However, women who were able to read and write (AOR: 19.86, 95% CI 17.41, 22.64), illiterate (AOR: 3.36, 95% CI 3.00, 3.75), and had primary school education (AOR: 2.90, 95% CI 2.55, 3.31) were at significantly higher risk compared to those with secondary education. Women from the Gofa zone were about half as likely to have raised blood glucose (AOR: 0.52, 95% CI 0.46, 0.59) compared to Basketo residents, and married women were less likely to have raised blood glucose (AOR: 0.15, 95% CI 0.13, 0.17) compared to widowed or divorced. Participants from smaller households (family size\u0026thinsp;\u0026le;\u0026thinsp;4) had a 35% lower risk (AOR: 0.65, 95% CI 0.59, 0.71) compared to those with larger families. Merchants (AOR: 57.45, 95% CI 42.90, 76.92), government employees (AOR: 11.07, 95% CI 5.17, 15.02), and housewives (AOR: 5.23, 95% CI 3.92, 6.98) had higher odds of raised fasting blood glucose compared to other occupations. Conversely, individuals from the poorer (AOR: 0.50, 95% CI 0.46, 0.54), middle (AOR: 0.12, 95% CI 0.11, 0.14), and richer wealth quartiles (AOR: 0.59, 95% CI 0.54, 0.64) were less likely to have raised fasting blood glucose compared to the richest. Lack of advice from health professionals increased the likelihood (AOR: 8.33, 95% CI 7.75, 8.95), as did non-membership in functional WDA (AOR: 2.59, 95% CI 2.38, 2.81).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBivariate and multivariate logistic regression of raised fasting blood glucose and independent variables among reproductive-age women in Gofa and Basketo zones, South Ethiopia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCandidate Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eRaised Fasting blood glucose\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.13(0.12, 0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.26 (0.23, 0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.20(0.19, 0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19 (0.18, 0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eEducational status\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\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.71(1.58, 1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.36 (3.00, 3.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAble to read and write\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.74(5.27, 6.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.86 (17.41, 22.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85(0.77, 0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.90 (2.55, 3.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary education and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eMarital status\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\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.23(.20, .25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15 (0.13, 0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00 (.00.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.00, .00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.960\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed/Divorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eZone\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\u003eGofa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.60(0.55, 0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52 (0.46, 0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasketo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eWealth index\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\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00 (0.00, 4.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.00, .00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.928\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.44(0.41, 0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50 (0.46, 0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.14(0.13, 0 .16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12 (0.11, 0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.53(0.49, 0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.59 (0.54, 0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eOccupational status\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\u003eHousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.79(6.03, 10.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.23 (3.92, 6.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMerchant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.94(28.53, 47.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.45 (42.90, 76.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.01(17.56, 30.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.07 (5.17, 15.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eFamily size\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\u003e\u0026le;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.32(.30, .34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65 (0.59, 0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eGetting advice from health professionals\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.09(4.81, 5.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.33 (7.75, 8.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eFamily history of NCDs\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.35(.32, .37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96 (0.86, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.432\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eIs your WDA functional?\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.89(1.78, 2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.59 (2.38, 2.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCOR-Crude Odds Ratio: odds ratio by bivariate analysis. 95% CI: confidence interval at the 95% level.\u003c/p\u003e \u003cp\u003eAOR-Adjusted OR: odds ratio by multiple logistic regression 1: Referent category\u003c/p\u003e \u003cp\u003eWomen aged 15\u0026ndash;24 years (AOR: 0.50, 95% CI 0.47, 0.53) and 25\u0026ndash;34 years (AOR: 0.77, 95% CI 0.73, 0.81) had half and three-quarters lower odds of raised total cholesterol, respectively, compared to women aged\u0026thinsp;\u0026ge;\u0026thinsp;35 years. Those with primary (AOR: 1.07, 95% CI 1.02, 1.13) and secondary school education (AOR: 1.09, 95% CI 1.02, 1.16) were more likely to have raised total cholesterol compared to illiterate women, while those who could read and write (AOR: 0.68, 95% CI 0.62, 0.74) were less likely to have raised total cholesterol. Married women (AOR: 0.82, 95% CI 0.73, 0.92) had 18% lower likelihood of raised total cholesterol compared to Widowed/divorced. Residents of the Gofa area were four times more likely to have raised total cholesterol (AOR: 3.90, 95% CI 3.41, 4.47) compared to Basketo residents, however urban residents had lower risk AOR: 0.83, 95% CI 0.78, 0.87) compared to the rural.\u003c/p\u003e \u003cp\u003eWomen from poorest1.10 (1.03, 1.18), poorer (AOR: 0.67, 95% CI 0.63, 0.72) and middle wealth quartiles (AOR: 0.77, 95% CI 0.71, 0.82) were less likely to have raised total cholesterol compared to the richest, while those from richer households (AOR: 1.10, 95% CI 1.03, 1.17) were more likely. Housewives 1.37 1.26, 1.48) (AOR: 1.34, 95% CI 1.23, 1.46), merchants (AOR: 1.24, 95% CI 1.12, 1.37), and government employees (AOR: 1.71, 95% CI 1.53, 1.92) had higher odds of raised total cholesterol compared to other occupations. Women from households with family size\u0026thinsp;\u0026le;\u0026thinsp;4 were three times more likely to have raised total cholesterol (AOR: 2.88, 95% CI 2.75, 3.02) compared to those with \u0026gt;\u0026thinsp;4 family size. Lack of advice from health professionals increased the likelihood (AOR: 4.11, 95% CI 3.93, 4.30), as did non-membership in functional WDA (AOR: 1.69, 95% CI 1.62, 1.78). Women without family history of NCDs (AOR: 2.63, 95% CI 2.37, 2.91) had higher risk of raised total cholesterol.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBivariate and multivariate logistic regression of raised cholesterol level and independent variables among reproductive-age women in Gofa and Basketo zones, South Ethiopia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCandidate Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eRaised Cholesterol Level\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79(0.75, 0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50 (0.47, 0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91(0.87, 0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77 (0.73, 0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eEducational status\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\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003eAble to read and write\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65 (0.61, 0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68 (0.62, 0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.28 (1.22, 1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07 (1.02, 1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.006*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary education and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.26 (1.21, 1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09 (1.02, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.007*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\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\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.42(0.38, 0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82 (0.73, 0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.41(0.36, 0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90 (0.77, 1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed/Divorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eResidence\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\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.78(1.69, 1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83 (0.78, 0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\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\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eZone\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\u003eGofa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.49(3.07, 3.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.11 (3.59, 4.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasketo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eWealth index\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\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.21(1.15, 1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10 (1.03, 1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.003*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.66(0.62, 0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67 (0.63, 0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.76(0.71, 0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77 (0.715, 0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04(0.98, 1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10 (1.03, 1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.004*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eOccupational status\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\u003eHousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.25(1.17, 1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.34 (1.23, 1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMerchant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02(.94, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.24 (1.12, 1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.81(2.56, 3.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.71 (1.53, 1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eFamily size\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\u003e\u0026le;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.41(2.32, 2.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.88 (2.75, 3.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eGetting advice from health professionals\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.72(3.58, 3.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.11 (3.93, 4.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eFamily history of NCDs\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.91(1.74, 2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.63 (2.37, 2.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eIs your WDA functional?\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.51(1.45, 1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.69 (1.62, 1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCOR-Crude Odds Ratio: odds ratio by bivariate analysis. 95% CI: confidence interval at the 95% level.\u003c/p\u003e \u003cp\u003eAOR-Adjusted OR: odds ratio by multiple logistic regression 1: Referent category\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eClustering of biological NCD risk factors\u003c/h2\u003e \u003cp\u003eOver twenty-seven percent of participants had at least one biological risk factor, while nine percent had two or more. The prevalence of biological risk factors among reproductive-age women is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e outlines the crude and adjusted odds ratios for the clustering of risk factors among women in the Gofa and Basketo zones. Logistic regression analysis revealed that women aged 25\u0026ndash;34 (AOR: 1.58, 95% CI 1.53, 1.64) and \u0026gt;\u0026thinsp;=\u0026thinsp;35 years (AOR: 1.96, 95% CI 1.88, 2.04) were more likely to experience co-occurrence of risk factors compared to those aged 15\u0026ndash;24 years. Illiterate women (AOR: 1.17, 95% CI 1.12, 1.22), those able to read and write (AOR: 2.17, 95% CI 2.06, 2.28), and those with primary education (AOR: 1.25, 95% CI 1.20, 1.30) were more likely to have co-occurrence of risk factors compared to those with secondary education. Single women (AOR: 1.50, 95% CI 1.47, 1.71) were more likely to experience co-occurrence of risk factors compared to married women, however widowed/divorced women (AOR: 0.91, 95% CI 0.83, 0.99) had lower risk. Additionally, women residing in Gofa (AOR: 1.47, 95% CI 1.39, 1.55) and rural areas (AOR: 1.42, 95% CI 1.36, 1.47) were more likely to have co-occurrence of NCD risk factors. Wealth status also showed associations, with participants in the richest (AOR: 1.16, 95% CI 1.17, 1.27) and middle (AOR: 1.19, 95% CI 1.14, 1.24) quartiles being more likely to have co-occurrence of risk factors compared to the first quartile. However, participants from the poorer (AOR: 0.74, 95% CI 0.71, 0.78) and richer (AOR: 0.76, 95% CI 0.73, 0.79) quartiles were less likely to have risk factors. Occupation-wise, government employees (AOR: 3.83, 95% CI 3.56, 4.12), merchants (AOR: 1.88, 95% CI 1.77, 1.99), and housewives (AOR: 1.81, 95% CI 1.70, 1.89) were more likely to have co-occurrence of risk factors. Non-membership in functional WDA (AOR: 1.66, 95% CI 1.62, 1.71), family history of NCDs (AOR: 1.76, 95% CI 1.68, 1.84), and lack of health professional advice (AOR: 2.51, 95% CI 2.44, 2.58) were also associated with a higher probability of co-occurrence of risk factors.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBivariate and multivariate logistic regression of clustering of biological risk factors and independent variables among reproductive-age women in Gofa and Basketo zones, South Ethiopia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eClustering of biological risk factors\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.78 (1.73, 1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.58 (1.53, 1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.12 (2.05, 2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.96 (1.88, 2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.18 (1.14, 1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.17 (1.12, 1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.002*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAble to read and write\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.96 (1.87, 2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.17 (2.06, 2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.09 (1.05, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.25 (1.20, 1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.012*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary education and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.22 (1.15, 1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.50 (1.47, 1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed/Divorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.53 (1.39, 1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91 (0.83, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.042*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eZone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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\u003eGofa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20 (1.15, 1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.47 (1.39, 1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasketo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.82 (0.81, 0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42 (1.36, 1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.84 (.81, .88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.74 (.71, .78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.40 (1.35, 1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.19 (1.14, 1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04 (1.00, 1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.76 (.73, .79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.55 (1.50, 1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.16 (1.12, 1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupational status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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\u003eHousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.89 (1.81, 1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.81 (1.70, 1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMerchant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.06 (1.96,2.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.88 (1.77, 1.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.31 (4.98, 5.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.83 (3.56, 4.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eFamily size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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\u003e\u0026le;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81 (0.79, 0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01 (.98, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.708\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eGetting advice from health professionals\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.37 (2.31, 2.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.51 (2.44, 2.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003e\u003cb\u003eFamily history of NCDs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.15 (2.06, 2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.76 (1.68, 1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIs your WDA functional?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.60 (1.56, 1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.66 (1.62, 1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCOR-Crude Odds Ratio: odds ratio by bivariate analysis. 95% CI: confidence interval at the 95% level.\u003c/p\u003e \u003cp\u003eAOR-Adjusted OR: odds ratio by multiple logistic regression 1: Referent category\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe study provides data on the prevalence of biological risk factors for non-communicable diseases, including raised blood pressure, overweight or obesity, raised fasting blood glucose, and raised total cholesterol levels. It examines their relationship with significant independent variables through multivariable analysis. The findings reveal significant associations between risk factors and various socio-demographic variables. Despite variations in the distribution of risk factors across different demographic factors, the prevalence of these biological risk factors remains notably higher within the study population.\u003c/p\u003e \u003cp\u003eThe finding of the current study indicates that the combined prevalence of overweight or obesity is 13.3%. Which is slightly lower compared to previous national reports of Ethiopia (14.9%) and international surveys from Ghana(15.6%), Gabon (18.9%), Burkina Faso(19.6%), Lesotho(19.9%), Nepal (22.2%) and Uganda (25,7%) (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Moreover, a higher prevalence of overweight/obesity was reported from women in Sub-Saharan Africa was 23.1%(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The prevalence difference could stem from social and cultural factors impacting dietary habits and physical activity levels across diverse geographic regions. Another factor could be variations in sampling designs, particularly with a majority of participants hailing from rural areas where urbanization-related shifts towards processed foods and reduced physical activity may be less pronounced.\u003c/p\u003e \u003cp\u003eOdds of overweight and/or obesity were more likely among women aged 25\u0026ndash;34 years and age\u0026thinsp;\u0026ge;\u0026thinsp;35 years compared with women aged 15\u0026ndash;24 years. This is supported by a study in Ethiopian DHS (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e25\u003c/span\u003e). This could be due to raised body composition of fat and hormonal changes following increment of age, accompanied by low physical activity, leading to overweight and obesity among aged women(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Overweight and/or obesity was more than four times likely among participants who were able to read and write and it was also more likely among women with primary and secondary school education compared with those who were illiterate. This finding is supported by a study in Ethiopian DHS(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e25\u003c/span\u003e). This could be in developing countries like Ethiopia, where women who have higher education are more likely to consume energy-dense foods and follow a sedentary lifestyle; hence, they are more likely to be overweight and obese compared with their counterparts(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Odds of overweight and /or obesity were more likely among married and widowed/separated compared with single women. This finding is in line with the result of multilevel analysis of Ethiopian demographic and health survey(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The reason could be single women unlike their married, widowed or divorced are less likely to be multiparous and are at lower risk of weight gain since they are less likely to have pregnancy and the postpartum period(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Another possible explanation could be single women are more likely to be younger and they have decreased risk of overweight/obesity compared with married, widowed and separated(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurther analysis of our study also revealed that the prevalence of overweight or obesity was common among government employees, family size above four, urban residents, and wealthy. This finding is in line with previous similar study findings from within Ethiopia and other international studies also reported as common NCD risk factors of overweight or obesity (\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR24\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan additionalcitationids=\"CR31 CR32 CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe found that 3.6\u0026ndash;8% lower prevalence of hypertension among the study population compared to a previous similar study of STEEPS survey of Ethiopia which accounts for 15.6%(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e30\u003c/span\u003e) and Uganda (20%). This may be attributed and sociocultural factors that may influence physical activity status and intake of healthy diet consumption these would in turn facilitate reproductive-age women being prone to raise their blood pressure. However, the prevalence of our finding (12%) is slightly higher than in previous studies in Burundi (1%), sub-Saharan Africa (5.5%), and Nepal (10.5%) (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study also found that the prevalence of raised blood pressure increases with an increase in age of reproductive age of women. This finding is consistent with previous national and international surveys in Ethiopia and Nepal(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e35\u003c/span\u003e). We also found that raised blood pressure was significantly associated with being married, single, having low educational status, having rural residency, richest, and household has more than four in size. This may be a result of low awareness-related factors and health promotion and prevention activities will help to tackle the problem.\u003c/p\u003e \u003cp\u003eOur finding shows that the prevalence of raised fasting blood sugar was recorded at four percent in the study population. This finding is in contrast to national estimates of Ethiopia, where a total of 5.8% of women had a blood glucose level of greater than 110 mg/dl(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e30\u003c/span\u003e). A study by Bista Bet al(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) reported an raised blood sugar has doubled from 3\u0026ndash;6% in Nepal and 8% in South Asian people(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The difference in prevalence may be due to the variation in lifestyle of women in the study area and the techniques of measuring blood sugar level. Although WHO global estimates have shown that increased levels of blood sugar levels are higher than the estimates in this study (4%)(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e36\u003c/span\u003e). This emphasizes the need to intervene in raised blood sugar levels via physical activity and the consumption of a healthy diet.\u003c/p\u003e \u003cp\u003eThe prevalence of raised blood sugar levels increased with increasing age groups. This finding is in line with previous research reports by Bista et al(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e37\u003c/span\u003e). This similarity was reported by Chang AM and Halter JB that increasing age is associated with the combined effect of increasing adiposity, decreasing physical activity, medications, coexisting illness, and insulin secretory defects that affect blood sugar level(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Further analysis of our study indicates that a raised blood sugar level was common among the participants who were richest, merchants, government employees, widowed/divorced, and households having above four family sizes. These differences may be attributed to variations in physical activity levels, dietary habits, and urbanization levels, with these findings similar to those from previous studies(39, 40). Some of the studies have put forward a biological explanation that increased content of the glucose transporter GLUT4 in the plasma membrane of skeletal muscle cells incubated under anoxia conditions(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e18\u003c/span\u003e), and in skeletal muscle cells exposed to prolonged hypoxia leads to better glucose tolerance(37, 41, 42).\u003c/p\u003e \u003cp\u003eThe current study reveals the first estimation of the prevalence of raised cholesterol among women of reproductive age group to be 7.8% in Ethiopia. However, the prevalence of the current study is lower compared to research conducted in Zambia (17.3%), Kenya(21%), Nepal(11%), Korea(48.4%), Japan(30%) and 39.3% for women in the United States(\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e45\u003c/span\u003e). The difference may be due to differences in intake of fat and physical exercise among nations. A previous study conducted by Kim SM(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e45\u003c/span\u003e) reports that Higher intakes of fat are associated with higher levels of total cholesterol and HDL-C levels; decreasing the intake of fat increases the OR of low HDL-C levels in women. A high-fat, high-cholesterol diet raised HDL-C and the production rate of apoA-I using a mechanism that involves the regulation of translation of the apoA-ImRNA. However, this was not tested in our study and further research is recommended. A balanced diet improves the global trends in the prevalence of raised cholesterol among the study population and beyond.\u003c/p\u003e \u003cp\u003eOur study also revealed an increase in the prevalence of raised cholesterol levels with increasing age groups. This finding is in line with from STEPS survey 2019 of Nepal(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) reported a raised BP and cholesterol with an increase in the age group. In support of this finding research done by Uranga R(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e46\u003c/span\u003e) indicates a reduction in the production of growth hormone with increasing age may be a causal factor contributing to the age-dependent rise in blood cholesterol, Similarly, other reports show that a higher prevalence of raised blood cholesterol level may be linked to increasing age and fluctuations in female sex hormone i.e. estrogen. Various studies have shown that estrogen helps to maintain levels of high-density lipoproteins (HDL) in adult females. However, at menopause, many females experience a change in their cholesterol levels, with total cholesterol and low-density lipoproteins (LDL) levels rising and HDL falling(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurther, the current study found that raised cholesterol was more likely among widowed/divorced, rural residents, the Gofa ethnic group, women who had no family history of NCDs and those from households\u0026rsquo; with family size\u0026thinsp;\u0026le;\u0026thinsp;4. This might be a consequence of diet, exercise, or physical activity and lipid metabolism variation among socio-demographic groups.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eClustering of biological risk factors\u003c/h2\u003e \u003cp\u003eThe result indicates that the prevalence of clustering of risk factors (\u0026ge;\u0026thinsp;1 RF) is 27%.It found that the clustering of NCD risk factors increased with age and wealth index among the study population. The finding of our study was in line with the trends of increase in NCD risk factors found in previous similar studies of other countries such as a study done by Rahman S et al in Bangladesh (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e48\u003c/span\u003e) and Sarveswaran G et al in South India (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Likewise, another study from the Nepal STEPS survey 2019(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) reported that the prevalence of clustering of risk factors is higher among the richest, Similar to individual risk factors such as overweight/obesity and hypertension, the clustering of NCDs risk factors in the richest group can be linked with the adoption of a sedentary lifestyle. In addition, Elaine H et(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e50\u003c/span\u003e) further pointed out that older individuals were less likely to have simultaneous physical inactivity/ sedentary behavior and unhealthy diet. The clustering of biological risk factors was higher among participants from the Gofa zone and rural residents. The reason could be westernization of dietary patterns among specific provinces might have a role in the clustering of risk factors. However, these finding was not supported by the results from the Nepal STEPS survey(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). It was also higher among those with lower education. In support of this finding, Elaine H et al(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e50\u003c/span\u003e) from Brazil indicates individuals of lower educational status have less access to information on the reduction of risk factors; thus, tend to have less healthy habits (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e50\u003c/span\u003e). However, it was inconsistent with the findings from other population-based 350 studies of Bangladesh (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e52\u003c/span\u003e). The difference could be attributed to sociocultural differences among the study population. Clustering of risk factors was higher among married and single women. The finding was also supported by the study in Bangladesh(\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e53\u003c/span\u003e). However, it was not supported by studies in Nepal (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e31\u003c/span\u003e). This might be attributed to the difference in the study areas. Clustering of risk factors was more likely among women who were not a member of functional WDA. This finding is supported by the study in Southwest Ethiopia, 2020(\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e54\u003c/span\u003e). This may indicate the impact of social networks and pressure which promotes preventive behaviors (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Further, our study shows co-occurrence of NCD risk factors was more likely among women who were government employees, merchants, and housewives, had a family history of NCD, and no health professional advice. .\u003c/p\u003e \u003cp\u003eThe co-occurrence of risk factors among study participants signifies the importance of designing an integrative intervention to tackle the multiple risk factors (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e56\u003c/span\u003e). The NCD risk factors determine several adverse health outcomes, including maternal death(\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e57\u003c/span\u003e). This alarming situation among reproductive-aged women might be a barrier for us to achieving Sustainable Development Goals (SDG) (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e58\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eThis pioneering study in the Gofa and Basketo zones examines the prevalence and factors linked to biological risk factors among reproductive-aged women. Its strengths lie in its community-based approach covering both rural and urban residents, ensuring broader applicability. Additionally, rigorous measures were implemented, including pilot testing of instruments, comprehensive training of data collectors and supervisors, and high response rates (99%), enhancing data quality. The study's use of weighted analysis allows for extrapolation of findings to the entire study area. It delves into socioeconomic and knowledge-related factors associated with risk factors, including their clustering. Furthermore, it measures key biomarkers like raised blood pressure, blood sugar, and cholesterol levels, filling gaps left by previous studies in the region. However, the study's cross-sectional design limits its ability to establish causal relationships.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe prevalence of biological risk factors among reproductive-age women in the Gofa and Basketo zones is concerning, with over one-fourth of participants exhibiting one or more of the studied NCD risk factors. Overweight and/or obesity, followed by raised blood pressure and cholesterol levels, showed the highest prevalence. Factors associated with increased risk varied, with Basketo zone residents, housewives, merchants, government employees, older women, and those with lower education levels being more susceptible to raised blood pressure and blood glucose. Conversely, younger women, married or widowed/divorced individuals, residents of the Basketo zone, and those with secondary education were less likely to exhibit these risk factors. Family size, socioeconomic status, and lifestyle factors also played significant roles in determining risk. Addressing this burden requires comprehensive awareness programs targeting younger age groups, emphasizing lifestyle modifications to counteract the alarming rise in risk factors, especially overweight/obesity, raised cholesterol, high blood pressure, and blood glucose levels. Implementing family-based interventions and leveraging healthcare advice from professionals and WDA networks are essential strategies. Moreover, tailored governmental plans considering local socio-economic and cultural contexts are necessary to combat these emerging public health threats and achieve SDG targets. Additionally, policy and socio-political factors influencing the rise of biological risk factors must be carefully considered.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eI thank Selinus University Faculty of Natural Health Science for selecting and giving me a chance to research such public health topics and assigning a supervisor. I also extend my gratitude to data collectors and supervisors for supporting data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors\u0026rsquo; responsibilities were as follows:\u003c/p\u003e\n\u003cp\u003eMMD: conceived, drafted, did the analysis and finalized the manuscript by incorporating the inputs from the other authors. SF and TG: Supervised the study and ensured quality of the data and made substantial contribution to the local implementation of the study and BM assisted the analysis and interpretation of the data. All authors critically reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMost of the data used to support the findings of this study are included within the article. More data are available upon reasonable request from the corresponding author.\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\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare that no financial support received for the research authorship and/or publication of this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eNCD Countdown 2030: worldwide trends in non-communicable disease mortality and progress towards Sustainable Development Goal target 3.4. Lancet. 2018;392(10152):1072-88.\u003c/li\u003e\n \u003cli\u003eOrganization WH. Noncommunicable diseases country profiles 2018. 2018.\u003c/li\u003e\n \u003cli\u003eCollaborators GRF. 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Lancet Glob Health. 2014;2(10):e566.\u003c/li\u003e\n \u003cli\u003eNations U, Bangladish. Sustainable Development Goal 3 Good Health and Well-being Ensure healthy lives and promote well-being for all at all ages. 2023.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-health-population-and-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"johp","sideBox":"Learn more about [Journal of Health, Population and Nutrition](http://jhpn.biomedcentral.com/)","snPcode":"41043","submissionUrl":"https://submission.nature.com/new-submission/41043/3","title":"Journal of Health, Population and Nutrition","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Magnitude and determinants, non-communicable diseases, biological risk factors, reproductive-aged women, Gofa, Basketo, South Ethiopia ","lastPublishedDoi":"10.21203/rs.3.rs-4221395/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4221395/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e:- The prevalence of non-communicable diseases (NCDs) among women of reproductive age has surged two fold in various African countries. This escalation in NCD burdens combined with inadequate access to sexual and reproductive health services is progressively impacting women of reproductive age, posing substantial risks to forthcoming generations. This research endeavors to evaluate the extent of biological risk factors and their associated determinants among women of reproductive age in the Gofa and Basketo Zones of Southern Ethiopia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A community-based survey following the World Health Organization (WHO) stepwise approach was undertaken, employing a multistage cluster sampling method to select participants from the designated zones. Statistical analysis was conducted using Statistical Package for the Social Sciences (SPSS) software encompassing descriptive statistics, bivariate analysis, and multivariate logistic regression. Associations were deemed statistically significant if the p-value was ≤ 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult\u003c/strong\u003e: Approximately 27.0% of participants exhibited one or more biological risk factors. Significant associations were observed among participants in older age groups, residing in rural areas, those with lower educational attainment, belonging to the Gofa zone, those from households with higher wealth index, widowed/divorced individuals, single individuals, government employees, merchants, and housewives. Additionally, those with larger family sizes (\u0026gt;4), getting no health professional advice, had a family history of NCD and were not members of a functional women development army (WDA) displayed statistically significant associations with the co-occurrence of biological risk factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: The escalation of biological risk factors is concerning, highlighting the urgency for targeted community-based interventions. Prioritizing older age groups, rural residents, individuals from households with higher wealth status, and lower educational attainment is advised. Implementing family-oriented changes and reinforcing healthcare systems are crucial. Policy and socio-political factors influencing the rise of NCD risk factors should also be addressed.\u003c/p\u003e","manuscriptTitle":"Magnitude and determinants of biological risk factors of non-communicable diseases among reproductive age women in Gofa and Basketo Zones, Southern Ethiopia: a community-based cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-17 20:22:21","doi":"10.21203/rs.3.rs-4221395/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-01T22:12:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-31T05:33:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"70221242045248567339139532227650792779","date":"2024-05-21T19:22:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-19T07:31:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"75222955110939478043238651017057565701","date":"2024-05-19T04:20:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-15T22:16:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-16T13:22:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-16T13:22:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Health, Population and Nutrition","date":"2024-04-05T07:40:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-health-population-and-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"johp","sideBox":"Learn more about [Journal of Health, Population and Nutrition](http://jhpn.biomedcentral.com/)","snPcode":"41043","submissionUrl":"https://submission.nature.com/new-submission/41043/3","title":"Journal of Health, Population and Nutrition","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9a3d0f49-4c49-4ff5-9f81-4554aabd15bb","owner":[],"postedDate":"April 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-03-30T12:38:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-17 20:22:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4221395","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4221395","identity":"rs-4221395","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}