Prevalence and correlates of self-reported hypertension among Zambians aged 15–59 years: Analysis of the 2024 DHS

Prevalence and correlates of self-reported hypertension among Zambians aged 15–59 years: Analysis of the 2024 DHS | 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 Prevalence and correlates of self-reported hypertension among Zambians aged 15–59 years: Analysis of the 2024 DHS Raphael Makwenda, Samuel Mutasha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9284014/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Hypertension is a leading risk factor for cardiovascular disease, stroke, and premature mortality, with low- and middle-income countries bearing the greatest burden. In Zambia, evidence on hypertension remains limited to small regional studies. Self-reported hypertension, a previous diagnosis by a health professional, offers a scalable approach for monitoring awareness and health system engagement in national surveys. This study estimated the prevalence of self-reported hypertension and identified its individual- and community-level correlates among Zambian adults. Methods Data were drawn from the 2024 Zambia Demographic and Health Survey, comprising 26,536 adults aged 15 years and older. Weighted descriptive statistics estimated prevalence across demographic, socioeconomic, and geographic strata. Multilevel logistic regression models identified independent correlates while accounting for clustering within enumeration areas. Results are reported as adjusted odds ratios (aOR) with 95% confidence intervals. Between-cluster heterogeneity was quantified using the intraclass correlation coefficient (ICC) and median odds ratio. Results The weighted prevalence of self-reported hypertension was 7.8%. Hypertensive individuals were older (36.7 vs 28.7 years; p < 0.001), predominantly female (64.0% vs 51.6%; p < 0.001), and more likely to reside in urban areas (63.8% vs 47.5%; all p < 0.001). Lusaka (21.1%) and Copperbelt (20.4%) provinces had the highest proportions. In multilevel models, each additional year of age increased odds by 6% (aOR = 1.064; 95% CI: 1.058–1.070). Men had substantially lower odds than women (aOR = 0.432; 95% CI: 0.387–0.481). Rural residence was protective (aOR = 0.806; 95% CI: 0.678–0.958). Higher education (aOR = 1.735; 95% CI: 1.325–2.273) and household wealth (rich vs poor: aOR = 1.761; 95% CI: 1.451–2.137) were positively associated. Married (aOR = 1.706; 95% CI: 1.465–1.986) and formerly married adults (aOR = 1.456; 95% CI: 1.194–1.776) had elevated odds. Significant provincial variation persisted after adjustment. The ICC decreased from 0.091 to 0.040, indicating that included covariates explained substantial between-cluster variation. Conclusion Self-reported hypertension among Zambian adults is shaped by age, sex, urbanisation, socioeconomic status, and geography. These findings identify priority populations and regions for targeted screening and awareness programmes to reduce the growing cardiovascular disease burden in Zambia. Self-reported hypertension Demographic and Health Survey Zambia cardiovascular risk factors Figures Figure 1 1. Introduction Hypertension is a major cause of early death and disability around the world and is considered the most important modifiable risk factor for cardiovascular disease, stroke, heart failure, and chronic kidney disease. In 2015 alone, about 8.5 million deaths were linked to systolic blood pressure above 115 mmHg, and most of these deaths (88%) occurred in low- and middle-income countries (LMICs)[1]. Evidence from a large pooled analysis of 1,479 population-based studies covering 19.1 million adults showed that the number of adults with raised blood pressure (systolic ≥ 140 mmHg or diastolic ≥ 90 mmHg) increased from 594 million in 1975 to 1.13 billion in 2015, with the sharpest increases occurring in low- and middle-income regions [2]. More recent estimates suggest that about 30.6% of adults worldwide were living with hypertension in 2020[3]. Even though hypertension is easy to diagnose and can be treated with affordable medicines, many people with the condition remain undiagnosed or untreated, which continues to lead to preventable health complications[4]. Sub-Saharan Africa (SSA) carries a particularly heavy share of this burden. A systematic review and meta-analysis of 170 studies involving 533,167 adults from 26 countries reported a pooled hypertension prevalence of 30.5% (95% CI: 28.4–32.6%) across the region[5]. Another meta-analysis of 78 studies covering 286,575 individuals from 23 African countries estimated a pooled crude prevalence of 28.5% (95% CI: 25.3–31.8%). The study also showed that prevalence was highest in Southern Africa (34.8%) and was more common in urban areas compared with rural settings (32.9% versus 26.3%) [6]. Despite this high burden, awareness and control of hypertension remain very low. Among people living with hypertension in SSA, only 27% (95% CI: 23–31%) know their status, 18% receive treatment, and only 7% have their blood pressure under control[7]. Rapid urbanisation, changes in diet, increasing levels of obesity, and reduced physical activity are continuing to increase exposure to risk factors across the region. In Zambia, available evidence on hypertension is still limited and largely concentrated in specific locations. A community-based study conducted in Kitwe found that 32.3% of adults aged 25 years and older had hypertension, with prevalence slightly higher among men (33.5%) than women (31.1%)[8]. Another cross-sectional study carried out in Western Province reported a prevalence of 32.8%, while an additional 24.6% of participants were classified as having pre-hypertension[9] (Oelke et al., 2015). In rural Mumbwa district, hypertension affected 39.7% of men and 33.5% of women, and 30.3% of individuals who had never been diagnosed were found to have hypertension during the survey[10]. Although these studies provide useful insights, they were conducted in specific provinces or among selected populations. They also relied on direct blood pressure measurements and did not use nationally representative datasets or multilevel analytical approaches. As highlighted by Hines on a study on hypertension among HIV patients, evidence on hypertension prevalence in Zambia remains limited and unevenly distributed across the country[11]. Self-reported hypertension, where individuals report whether they have previously been told by a health professional that they have high blood pressure, provides an additional and practical approach for monitoring the condition in large population surveys. A systematic review of 144 studies showed that self-reported hypertension generally underestimates the true prevalence, with average awareness levels around 58% when the 140/90 mmHg threshold is used[12]. For example, a validation study conducted in Iran reported hypertension prevalence of 19.49% based on self-reports and 21.60% based on objective measurements, suggesting that self-reported data can still be useful for population monitoring [13]. In SSA, a recent study in the Gambia, Kenya, and Mozambique also found that self-reported prevalence was considerably lower than measured hypertension among women of reproductive age[14]. Even with these limitations, self-reported hypertension remains useful because it reflects not only disease presence but also awareness and contact with the health system, information that cannot be captured through blood pressure measurements alone. Nationally representative evidence on self-reported hypertension in Zambia remains limited, with most existing studies based on subnational samples and measured blood pressure. While these approaches are essential for estimating true disease burden, they do not capture patterns in diagnosis and awareness, and multilevel methods are used infrequently to assess contextual influences. In this study, we use data from the 2024 Zambia Demographic and Health Survey to estimate the prevalence of self-reported hypertension and examine associated individual- and community-level factors using multilevel modelling. By focusing on self-reported hypertension, this analysis provides complementary insight into disease detection and health system engagement, addressing an important gap in population-based hypertension research in Zambia. 2. Methods and Materials Study design 2.1. Study Design and Data Source This study was a cross-sectional secondary analysis of data from the 2024 Zambia Demographic and Health Survey (DHS-8), a nationally representative household survey conducted using a stratified, multi-stage cluster sampling design. Two datasets were used: the Individual Recode file (ZMIR81FL.dta), comprising women aged 15 to 49 years, and the Men's Recode file (ZMMR81FL.dta), comprising men aged 15 to 59 years. The outcome of interest was self-reported hypertension, derived from variables chd02 (women) and mchd02 (men), and coded as a binary indicator (1 = Yes, 0 = No). Observations with missing values on the outcome variable, sampling weight, primary sampling unit, or stratum were excluded from all analyses. 2.2. Variable Harmonization and Recoding The women's and men's datasets were merged into a single analytic file. Because DHS variable naming conventions differ between the two files, using a 'v' prefix for women and an 'mv' prefix for men, a custom lookup function (first_existing) was applied to identify and extract the corresponding variable from each dataset. Nineteen covariates were selected based on biological plausibility , prior evidence, and availability in both datasets. Table 1 summarises each variable, its source in the DHS, and the recoding applied. Continuous age was categorised into eight groups (15–19, 20–24, 25–29, 30–34, 35–39, 40–44, 45–49, and 50–59 years). Wealth index quintiles were collapsed into three categories: poor (poorest and poorer), middle, and rich (richer and richest). Marital status was grouped as never married (reference), currently married or living with a partner, and formerly married (widowed, divorced, or separated). Migrant status was derived by comparing each respondent's province of birth with their current province of residence: those whose province of birth matched their current province were classified as non-migrants. Media exposure was treated as a binary variable, with any exposure to newspapers, radio, television, or the internet coded as 'any media use,' and respondents who reported no use of any medium coded as 'no media use.'. HIV and antiretroviral therapy (ART) status was derived by combining two DHS variables to produce a five-category indicator: HIV-negative (reference), HIV-positive on ARVs, HIV-positive not on ARVs, HIV-positive with other or unknown ART status, and other or not tested. Table 1 Variable definitions, DHS source variables, and recoding Variable DHS Source (Women / Men) Recoding Age group v012 / mv012 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, 45–49, 50–59 Residence v025 / mv025 Urban / Rural (as labelled) Province v024 / mv024 10 provinces (as labelled); reference = Central Education v106 / mv106 No education (ref), Primary, Secondary, Higher Wealth v190 / mv190 Poorest + Poorer = Poor (ref); Middle; Richer + Richest = Rich Marital status v501 / mv501 Never-married (ref); Married/Living with partner = Married; Widowed/Divorced/Separated = Formerly married Religion v130 / mv130 Christian (ref), Muslim, None, Other Employment v714 / mv714 No (ref) / Yes Self-rated health v176 / mv176 Very good (ref), Good, Moderate, Bad, Very bad Last healthcare visit s112a / — As labelled (women only; men receive NA) Diabetes chd08 / mchd08 Not diabetic (ref) / Diabetic Smoking v463aa / mv463aa Non-smoker (ref) / Smoker Alcohol v485a / mv485a Never consumed (ref) / Ever consumed Migrant status v172 vs v024 / mv172 vs mv024 Non-migrant (ref) if province of birth = current province; Migrant otherwise Media exposure v157, v171b, v158, v159 / mv* equivalents No media use (ref) if all items = 'not at all'; Any media use otherwise Parity v201 / mv201 0 (ref), 1–2, 3–4, 5+ Toilet facility v116 / mv116 Hygienic (ref: flush, VIP, slab, composting) / Non-hygienic (pit without slab, no facility, bucket, hanging, other) Handwashing v117 / mv117 As labelled HIV/ART status v861 + v863 / mv861 + mv863 HIV-negative (ref); HIV-positive on ARVs; HIV-positive not on ARVs; HIV-positive other; Other/Not tested 2.3. Survey Design All analyses accounted for the complex, multi-stage cluster sampling design of the DHS to ensure nationally representative and unbiased estimates. The primary sampling unit was defined by variables v021 (women) and mv021 (men), stratification by v022 and mv022, and sampling weights by v005 and mv005, respectively. Sampling weights were rescaled by dividing by 1,000,000 in accordance with DHS guidelines. The setting survey.lonely.psu = 'adjust' was applied to handle strata containing a single primary sampling unit by centering variance contributions at the grand mean. Primary sampling units were nested within strata. 2.4. Statistical Analysis The analysis was conducted in three sequential stages. In the first stage, a survey-weighted descriptive table was generated using the svyCreateTableOne() function from the tableone package in R, stratified by hypertension status. Weighted counts, percentages, and p-values were reported for all covariates. In the second stage, the survey-weighted prevalence of hypertension was estimated separately for each level of every covariate, yielding point estimates and 95% confidence intervals. Chi-square tests were used to assess the statistical significance of the bivariate association between each covariate and hypertension status. . In the third stage, both univariable and multivariable logistic regression analyses were performed. For each covariate, a separate survey-weighted logistic regression model was fitted, only observations with non-missing values for the outcome, design variables, and that specific predictor. This approach maximised the analytic sample size for each univariable model and avoided unnecessary exclusions attributable to missingness in unrelated variables. For the multivariable model, all pre-specified predictors were entered simultaneously. An iterative model-building process was used where the full set of predictors was refined step-by-step by removing variables with high missingness (except key variables like sex and age group) until the model had enough variation and successfully converged. As a result of this procedure, three variables, handwashing, last healthcare visit, and hygiene, were excluded from the multivariable model. Their univariable estimates are nonetheless reported. Odds ratios (ORs) and 95% confidence intervals were obtained by exponentiating the model coefficients. 2.5. Software All statistical analyses were performed in R version 4.5.0. The following packages were used: haven for data import; dplyr for data manipulation; survey for complex survey-weighted analysis. 3. Results Table 2 below presents the sociodemographic and health-related characteristics of the study population stratified by self-reported hypertension status. Of the 26,536 weighted participants, 2,075 (7.8%) reported a diagnosis of hypertension. The condition was significantly more prevalent among women, who constituted 64.0% (n = 1,329) of all hypertensive cases compared with 36.0% (n = 746) among men (p < 0.001). Hypertensive individuals were substantially older than their normotensive counterparts, with a mean age of 36.71 years (SD 10.24) versus 28.73 years (SD 10.69; p < 0.001), and reported higher parity (mean 3.73, SD 3.10 vs. 2.36, SD 2.83; p < 0.001). A pronounced age gradient was observed: the 15–19 age group comprised only 5.5% (n = 115) of hypertensive cases despite representing 23.7% of the overall sample, whereas the 40–44 age group accounted for 17.4% (n = 360) of cases (p < 0.001). Urban residence (63.8%, n = 1,324), attainment of higher education (18.0%, n = 373 vs. 7.7%, n = 1,887), and classification in the rich wealth quintile (62.3%, n = 1,293 vs. 43.3%, n = 10,584) were each significantly more common among hypertensive respondents (all p < 0.001). At the provincial level, Lusaka (21.1%, n = 438) and Copperbelt (20.4%, n = 422) recorded the highest burden of hypertension. For social and behavioural characteristics, married individuals constituted a substantially larger proportion of the hypertensive group (70.7%, n = 1,467) relative to normotensive respondents (50.7%, n = 12,392; p < 0.001). Employment rates were similarly higher among those with hypertension (69.2%, n = 1,436 vs. 56.7%, n = 13,880; p < 0.001), as were exposure to mass media (78.9%, n = 1,637 vs. 69.4%, n = 16,983; p < 0.001) and migrant status (35.7%, n = 742 vs. 24.0%, n = 5,875; p < 0.001). Notably, religious affiliation was the only sociodemographic characteristic for which no statistically significant difference was detected between the two groups (p = 0.418). Self-rated health status differed clearly by hypertension status. Among hypertensive respondents, 23.4% (n = 486) rated their health as moderate and 3.0% (n = 63) as poor, compared with 11.3% (n = 2,756) and 1.4% (n = 348), respectively, among normotensive individuals (p < 0.001). The prevalence of comorbid diabetes was approximately ten times higher in the hypertensive group (3.1%, n = 65 vs. 0.3%, n = 65; p < 0.001), and alcohol consumption was more frequently reported (10.1%, n = 209 vs. 7.0%, n = 1,710; p < 0.001). Furthermore, HIV-positive individuals receiving antiretroviral therapy were disproportionately represented among hypertensive respondents (10.5%, n = 218 vs. 5.3%, n = 1,305; p < 0.001). Access to hygienic sanitation facilities was also significantly higher in the hypertensive group (69.2%, n = 901 vs. 60.3%, n = 7,491; p < 0.001). Table 2 Sociodemographic and Health Characteristics by Self-Reported Hypertension Status, Zambia DHS 2024 Variable Overall No Yes p n 26,536 24,461 2,075 Sex (%) Men 12,585 (47.4) 11,839 (48.4) 746 (36.0) < 0.001 Women 13,951 (52.6) 12,622 (51.6) 1,329 (64.0) Age (mean (SD)) 29.36 (10.86) 28.73 (10.69) 36.71 (10.24) < 0.001 Parity (mean (SD)) 2.47 (2.87) 2.36 (2.83) 3.73 (3.10) < 0.001 Age group (%) 15–19 6,294 (23.7) 6,179 (25.3) 115 (5.5) < 0.001 20–24 4,637 (17.5) 4,461 (18.2) 176 (8.5) 25–29 3,892 (14.7) 3,621 (14.8) 272 (13.1) 30–34 3,424 (12.9) 3,119 (12.8) 304 (14.7) 35–39 2,813 (10.6) 2,482 (10.1) 331 (15.9) 40–44 2,469 (9.3) 2,109 (8.6) 360 (17.4) 45–49 1,849 (7.0) 1,529 (6.3) 320 (15.4) 50–59 1,159 (4.4) 961 (3.9) 198 (9.5) Residence (%) Urban 12,945 (48.8) 11,621 (47.5) 1,324 (63.8) < 0.001 Rural 13,591 (51.2) 12,840 (52.5) 751 (36.2) Province (%) Central 3,094 (11.7) 2,816 (11.5) 278 (13.4) < 0.001 Copperbelt 3,835 (14.5) 3,413 (14.0) 422 (20.4) Eastern 3,230 (12.2) 3,072 (12.6) 159 (7.6) Luapula 1,941 (7.3) 1,853 (7.6) 88 (4.2) Lusaka 4,750 (17.9) 4,311 (17.6) 438 (21.1) Muchinga 1,212 (4.6) 1,151 (4.7) 61 (2.9) Northern 1,999 (7.5) 1,901 (7.8) 98 (4.7) North Western 1,741 (6.6) 1,638 (6.7) 104 (5.0) Southern 3,131 (11.8) 2,859 (11.7) 272 (13.1) Western 1,604 (6.0) 1,448 (5.9) 156 (7.5) Education (%) No Education 1,458 (5.5) 1,362 (5.6) 97 (4.7) < 0.001 Primary 10,202 (38.4) 9,553 (39.1) 649 (31.3) Secondary 12,616 (47.5) 11,660 (47.7) 956 (46.1) Higher 2,260 (8.5) 1,887 (7.7) 373 (18.0) Wealth (%) Poor 9,467 (35.7) 9,016 (36.9) 451 (21.7) < 0.001 Middle 5,193 (19.6) 4,862 (19.9) 331 (15.9) Rich 11,877 (44.8) 10,584 (43.3) 1,293 (62.3) Marital status (%) Never Married 10,255 (38.6) 9,922 (40.6) 333 (16.1) < 0.001 Married 13,858 (52.2) 12,392 (50.7) 1,467 (70.7) Formerly Married 2,423 (9.1) 2,148 (8.8) 275 (13.3) Religion (%) Catholic 3,925 (14.8) 3,625 (14.8) 300 (14.5) 0.418 Protestant 22,274 (83.9) 20,530 (83.9) 1,744 (84.1) Muslim 148 (0.6) 139 (0.6) 9 (0.4) Other 189 (0.7) 168 (0.7) 21 (1.0) Employment (%) No 11,219 (42.3) 10,581 (43.3) 639 (30.8) < 0.001 Yes 15,317 (57.7) 13,880 (56.7) 1,436 (69.2) Self -rated health (%) Very Good 10,459 (39.4) 9,882 (40.4) 577 (27.8) < 0.001 Good 12,356 (46.6) 11,418 (46.7) 938 (45.2) Moderate 3,242 (12.2) 2,756 (11.3) 486 (23.4) Bad 411 (1.5) 348 (1.4) 63 (3.0) Very Bad 68 (0.3) 58 (0.2) 10 (0.5) Diabetes (%) Diabetic 130 (0.5) 65 (0.3) 65 (3.1) < 0.001 Not Diabetic 26,406 (99.5) 24,396 (99.7) 2,010 (96.9) Alcohol (%) Never Consumed 24,617 (92.8) 22,752 (93.0) 1,866 (89.9) < 0.001 Ever Consumed 1,919 (7.2) 1,710 (7.0) 209 (10.1) Migrant (%) Non-migrant 19,920 (75.1) 18,587 (76.0) 1,333 (64.3) < 0.001 Migrant 6,616 (24.9) 5,875 (24.0) 742 (35.7) Media (%) No Media Use 7,916 (29.8) 7,479 (30.6) 438 (21.1) < 0.001 Any Media Use 18,620 (70.2) 16,983 (69.4) 1,637 (78.9) Living conditions (%) Hygienic 8,393 (61.2) 7,491 (60.3) 901 (69.2) < 0.001 Non-hygienic 5,329 (38.8) 4,927 (39.7) 402 (30.8) HIV_ART_status (%) HIV-negative 19,031 (71.7) 17,347 (70.9) 1,684 (81.2) < 0.001 HIV + not on ARVs 38 (0.1) 33 (0.1) 5 (0.2) HIV + on ARVs 1,523 (5.7) 1,305 (5.3) 218 (10.5) Other/Not Tested 5,944 (22.4) 5,776 (23.6) 168 (8.1) Note: Data are weighted frequencies and column percentages n (%). Continuous variables presented as mean (SD). p-values from chi-square tests (categorical) and independent t-tests (continuous). Prevalence of self-reported Hypertension Table 3 . below shows the Crude Prevalence of Self-Reported Hypertension by Sociodemographic and Health Characteristics, Zambia DHS 2024. The overall crude prevalence of self-reported hypertension was 7.8%. Prevalence was highest among individuals with higher education [16.5% (14.5–18.5)], in the rich wealth quintile [10.9% (10.0–11.7)], among urban residents [10.2% (9.4–11.0)], and among the employed [9.4% (8.7–10.0)], all statistically significant at p < 0.001. Provincially, Copperbelt recorded the highest prevalence [11.0% (9.5–12.6)], followed by Western [9.7% (8.4–11.0)] and Lusaka [9.2% (7.7–10.8)], while Luapula [4.5% (3.3–5.8)] and Eastern [4.9% (4.2–5.7)] recorded the lowest (p < 0.001). Migrants recorded higher prevalence than non-migrants [11.2% (10.3–12.1) vs. 6.7% (6.3–7.1); p < 0.001], and any media use was associated with higher prevalence [8.8% (8.2–9.4) vs. 5.5% (5.0–6.1); p < 0.001]. Hygienic environments was associated with higher prevalence [10.7% (9.9–11.5) vs. 7.5% (6.7–8.3); p < 0.001]. Prevalence increased clearly with age, rising from 1.8% (95% CI: 1.5–2.2) among those aged 15–19 years to 17.3% (95% CI: 15.3–19.3) among those aged 45–49 and 17.0% (95% CI: 14.5–19.6) among those aged 50–59 (p < 0.001). Women recorded a higher prevalence than men [9.5% (8.9–10.1) vs. 5.9% (5.3–6.5); p < 0.001]. Married [10.6% (9.9–11.3)] and formerly married individuals [11.3% (9.9–12.8)] had substantially higher prevalence than those never married [3.2% (2.8–3.7); p < 0.001]. Prevalence increased with parity, from 2.8% (2.4–3.2) among those with no children to 12.6% (11.6–13.6) among those with five or more children (p < 0.001). Religion was the only characteristic with no statistically significant difference in prevalence across groups (p = 0.418). Comorbid diabetes was associated with the highest observed prevalence in the entire table [49.7% (38.4–61.0); p < 0.001]. HIV-positive individuals on ARVs had a prevalence of 14.3% (12.3–16.3), compared to 8.9% (8.3–9.4) among HIV-negative individuals and 2.8% (2.4–3.3) among those untested or with other status (p < 0.001). Alcohol consumers had higher prevalence than non-consumers [10.9% (9.3–12.6) vs. 7.6% (7.1–8.0); p < 0.001], while smokers recorded lower prevalence than non-smokers [6.3% (5.2–7.4) vs. 8.0% (7.5–8.5); p = 0.009]. Those reporting moderate [15.0% (13.5–16.5)], bad [15.2% (11.6–18.9)], or very bad self-perceived health [15.2% (6.2–24.3)] recorded substantially higher prevalence than those in very good health [5.5% (5.0–6.1); p < 0.001]. Table 3 Crude Prevalence of Self-Reported Hypertension by Sociodemographic and Health Characteristics, Zambia DHS 2024 Variable n Prevalence % (95% CI) p-value Age group (years) 15–19 6,410 1.8 (1.5–2.2) < 0.001 20–24 4,690 3.8 (3.2–4.3) 25–29 3,791 7.0 (6.1–7.9) 30–34 3,323 8.9 (7.8–10.0) 35–39 2,817 11.8 (10.4–13.1) 40–44 2,486 14.6 (13.0–16.2) 45–49 1,862 17.3 (15.3–19.3) 50–59 1,157 17.0 (14.5–19.6) Sex Men 12,585 5.9 (5.3–6.5) < 0.001 Women 13,951 9.5 (8.9–10.1) Education No education 1,537 6.6 (5.1–8.1) < 0.001 Primary 10,915 6.4 (5.8–6.9) Secondary 12,115 7.6 (7.0–8.2) Higher 1,969 16.5 (14.5–18.5) Wealth index Poor 10,920 4.8 (4.3–5.2) < 0.001 Middle 5,532 6.4 (5.7–7.1) Rich 10,084 10.9 (10.0–11.7) Marital status Never-married 10,122 3.2 (2.8–3.7) < 0.001 Married 13,926 10.6 (9.9–11.3) Formerly married 2,488 11.3 (9.9–12.8) Current employment No 11,155 5.7 (5.2–6.2) < 0.001 Yes 15,381 9.4 (8.7–10.0) Religion Catholic 3,897 7.7 (6.7–8.6) 0.418 Muslim 127 6.2 (2.3–10.1) Protestant 22,322 7.8 (7.4–8.3) Other 190 11.2 (5.3–17.0) Place of residence Rural 15,492 5.5 (5.1–6.0) < 0.001 Urban 11,044 10.2 (9.4–11.0) Province Central 3,211 9.0 (7.8–10.1) < 0.001 Copperbelt 2,932 11.0 (9.5–12.6) Eastern 3,091 4.9 (4.2–5.7) Luapula 2,356 4.5 (3.3–5.8) Lusaka 3,121 9.2 (7.7–10.8) Muchinga 1,956 5.0 (3.9–6.1) North Western 2,220 6.0 (4.8–7.1) Northern 2,448 4.9 (3.9–5.9) Southern 3,058 8.7 (7.4–10.0) Western 2,143 9.7 (8.4–11.0) Self-perceived health Very good 10,436 5.5 (5.0–6.1) < 0.001 Good 12,349 7.6 (7.0–8.2) Moderate 3,255 15.0 (13.5–16.5) Bad 424 15.2 (11.6–18.9) Very bad 72 15.2 (6.2–24.3) Last healthcare visit Same day 141 13.1 (7.3–18.9) < 0.001 Days: 1 315 15.3 (10.9–19.7) Weeks: 1 957 12.2 (9.7–14.7) Months: 1 2,848 9.9 (8.5–11.2) Years: 1 1,018 7.2 (5.5–9.0) Never visited a healthcare facility 41 0.0 (0.0–0.0) Don't know 49 5.4 (-1.5–12.2) Diabetes status Diabetic 107 49.7 (38.4–61.0) < 0.001 Not diabetic 26,429 7.6 (7.2–8.0) Smokes cigarettes (incl. water pipe) No 13,798 9.5 (9.0–10.1) 0.451 Yes 153 7.7 (3.4–12.0) Smoking frequency Non-smoker 24,053 8.0 (7.5–8.5) 0.009 Smoker 2,483 6.3 (5.2–7.4) Alcohol use Never consumed 24,663 7.6 (7.1–8.0) < 0.001 Ever consumed 1,873 10.9 (9.3–12.6) Migrant status Non-migrant 20,573 6.7 (6.3–7.1) < 0.001 Migrant 5,963 11.2 (10.3–12.1) Media use (newspaper, social media, radio, TV) No media use 8,761 5.5 (5.0–6.1) < 0.001 Any media use 17,775 8.8 (8.2–9.4) Parity (children ever born) 0 9,064 2.8 (2.4–3.2) < 0.001 1–2 6,731 7.8 (7.0–8.6) 3–4 4,831 11.8 (10.7–13.0) 5+ 5,910 12.6 (11.6–13.6) Living conditions Hygienic 7,737 10.7 (9.9–11.5) < 0.001 Non-hygienic 5,979 7.5 (6.7–8.3) Received HIV test result No 372 11.7 (8.2–15.2) 0.136 Yes 20,562 9.3 (8.7–9.8) HIV/ART status HIV-negative 18,990 8.9 (8.3–9.4) < 0.001 HIV + not on ARVs 40 12.1 (0.5–23.7) HIV + on ARVs 1,463 14.3 (12.3–16.3) Other/Not tested 6,043 2.8 (2.4–3.3) Note: Prevalence expressed as percentage with 95% confidence intervals. p-values from chi-square tests. Factors associated with self-reported hypertension Table 4 below shows the Univariable and Adjusted Odds Ratios for Self-Reported Hypertension, Zambia DHS 2024. In univariable analysis, older age was strongly and progressively associated with higher odds of self-reported hypertension, with those aged 50–59 years recording an OR of 11.03 (8.40–14.48) relative to the 15–19 reference group. After full adjustment, this age gradient persisted across all groups, with aORs rising from 1.42 (1.05–1.92) among those aged 20–24 to 7.28 (4.79–11.06) among those aged 50–59 (all p < 0.001). Men had significantly lower odds than women in both univariable [OR: 0.60 (0.53–0.67)] and adjusted analyses [aOR: 0.49 (0.42–0.56); p < 0.001]. Higher education remained independently associated with elevated odds after adjustment [aOR: 1.65 (1.20–2.26); p < 0.01], while primary and secondary education were not significant. Both the middle [aOR: 1.20 (1.02–1.42); p < 0.05] and rich wealth quintiles [aOR: 1.72 (1.40–2.12); p < 0.001] were independently associated with higher odds relative to the poor. Urban residence was also independently associated with higher odds [aOR: 1.23 (1.04–1.46); p < 0.05]. Provincially, Eastern [aOR: 0.68 (0.54–0.85)], Luapula [aOR: 0.56 (0.41–0.76)], Northern [aOR: 0.64 (0.49–0.82)], Lusaka [aOR: 0.71 (0.56–0.90)], Muchinga [aOR: 0.68 (0.52–0.88)], and North Western [aOR: 0.74 (0.58–0.94)] had significantly lower adjusted odds than Central Province, while Western Province recorded higher odds [aOR: 1.37 (1.11–1.70); p < 0.01]. Being married remained independently associated with higher odds of hypertension [aOR: 1.34 (1.06–1.69); p < 0.05], while formerly married status was attenuated to non-significance after adjustment. Current employment was a significant independent correlate [aOR: 1.13 (1.01–1.27); p < 0.05]. Parity of 1–2 children remained independently associated [aOR: 1.30 (1.02–1.65); p < 0.05], though higher parity groups were attenuated to non-significance after adjustment. Among religion categories, the Other religion group had significantly higher adjusted odds than Christians [aOR: 1.80 (1.03–3.14); p < 0.05], while Muslim affiliation was not significant. Migrant status and media use were each significant in univariable analysis but were fully attenuated to non-significance after adjustment. Alcohol use and smoking were each significant at the univariable level but were not independently significant after full adjustment. Poorer self-perceived health was independently and dose-responsively associated with higher odds: good [aOR: 1.15 (1.02–1.28); p < 0.05], moderate [aOR: 2.04 (1.74–2.40); p < 0.001], bad [aOR: 2.01 (1.47–2.76); p < 0.001], and very bad [aOR: 2.63 (1.26–5.48); p < 0.01]. Comorbid diabetes was the strongest independent clinical predictor [aOR: 4.22 (2.36–7.54); p < 0.001]. HIV-positive individuals on ARVs had lower adjusted odds than HIV-negative individuals [aOR: 0.83 (0.69–0.99); p < 0.05], as did those in the untested or other status category [aOR: 0.78 (0.63–0.95); p < 0.05], while HIV-positive individuals not on ARVs showed no significant difference. Living conditions (toilet facility type as proxy) and last healthcare visit were significant at the univariable level, non-hygienic conditions were associated with lower odds [OR: 0.68 (0.59–0.78); p < 0.001] and visiting a healthcare facility years prior was associated with lower odds [OR: 0.52 (0.30–0.91); p < 0.05], but neither was retained in the adjusted model. These findings are visually summarised in Fig. 1 below. Table 4 Univariable and Adjusted Odds Ratios for Self-Reported Hypertension, Zambia DHS 2024 Variable Univariable OR (95% CI) Adjusted aOR (95% CI) Age group (years) 15–19 1.00 (Ref) 1.00 (Ref) 20–24 2.11 (1.65–2.71)*** 1.42 (1.05–1.92)* 25–29 4.03 (3.13–5.18)*** 2.08 (1.46–2.96)*** 30–34 5.24 (4.11–6.67)*** 2.50 (1.73–3.61)*** 35–39 7.15 (5.67–9.03)*** 3.43 (2.38–4.96)*** 40–44 9.17 (7.25–11.61)*** 4.39 (2.99–6.42)*** 45–49 11.22 (8.71–14.45)*** 5.65 (3.81–8.38)*** 50–59 11.03 (8.40–14.48)*** 7.28 (4.79–11.06)*** Sex Women 1.00 (Ref) 1.00 (Ref) Men 0.60 (0.53–0.67)*** 0.49 (0.42–0.56)*** Education No education 1.00 (Ref) 1.00 (Ref) Primary 0.96 (0.74–1.23) 1.02 (0.78–1.33) Secondary 1.15 (0.90–1.49) 1.21 (0.91–1.60) Higher 2.78 (2.10–3.68)*** 1.65 (1.20–2.26)** Wealth index Poor 1.00 (Ref) 1.00 (Ref) Middle 1.36 (1.18–1.57)*** 1.20 (1.02–1.42)* Rich 2.44 (2.14–2.78)*** 1.72 (1.40–2.12)*** Marital status Never-married 1.00 (Ref) 1.00 (Ref) Married 3.52 (3.06–4.06)*** 1.34 (1.06–1.69)* Formerly married 3.81 (3.18–4.58)*** 1.17 (0.91–1.50) Current employment No 1.00 (Ref) 1.00 (Ref) Yes 1.72 (1.54–1.91)*** 1.13 (1.01–1.27)* Religion Christian 1.00 (Ref) 1.00 (Ref) Muslim 0.78 (0.40–1.52) 0.79 (0.38–1.64) Other 1.49 (0.82–2.67) 1.80 (1.03–3.14)* Place of residence Rural 1.00 (Ref) 1.00 (Ref) Urban 1.95 (1.73–2.20)*** 1.23 (1.04–1.46)* Province Central 1.00 (Ref) 1.00 (Ref) Copperbelt 1.26 (1.02–1.55)* 0.97 (0.80–1.17) Eastern 0.52 (0.42–0.65)*** 0.68 (0.54–0.85)*** Luapula 0.48 (0.35–0.67)*** 0.56 (0.41–0.76)*** Lusaka 1.03 (0.82–1.30) 0.71 (0.56–0.90)** Muchinga 0.54 (0.41–0.70)*** 0.68 (0.52–0.88)** North Western 0.64 (0.50–0.82)*** 0.74 (0.58–0.94)* Northern 0.52 (0.40–0.67)*** 0.64 (0.49–0.82)*** Southern 0.97 (0.78–1.20) 1.01 (0.81–1.27) Western 1.10 (0.89–1.34) 1.37 (1.11–1.70)** Self-perceived health Very good 1.00 (Ref) 1.00 (Ref) Good 1.41 (1.25–1.59)*** 1.15 (1.02–1.28)* Moderate 3.02 (2.61–3.50)*** 2.04 (1.74–2.40)*** Bad 3.08 (2.29–4.13)*** 2.01 (1.47–2.76)*** Very bad 3.08 (1.52–6.25)** 2.63 (1.26–5.48)** Last healthcare visit Same day 1.00 (Ref) Days: 1 1.20 (0.66–2.18) Weeks: 1 0.92 (0.54–1.59) Months: 1 0.73 (0.43–1.24) Years: 1 0.52 (0.30–0.91)* Never visited a healthcare facility 0.00 (0.00–0.00)*** Don't know 0.38 (0.09–1.63) Diabetes status Not diabetic 1.00 (Ref) 1.00 (Ref) Diabetic 11.99 (7.68–18.74)*** 4.22 (2.36–7.54)*** Alcohol use Never consumed 1.00 (Ref) 1.00 (Ref) Ever consumed 1.49 (1.26–1.77)*** 1.18 (0.98–1.42) Smoking frequency Non-smoker 1.00 (Ref) 1.00 (Ref) Smoker 0.77 (0.64–0.94)** 0.90 (0.72–1.13) Parity (children ever born) 0 1.00 (Ref) 1.00 (Ref) 1–2 2.95 (2.47–3.53)*** 1.30 (1.02–1.65)* 3–4 4.67 (3.94–5.52)*** 1.28 (0.99–1.66) 5+ 5.01 (4.24–5.92)*** 1.28 (0.97–1.69) Migrant status Non-migrant 1.00 (Ref) 1.00 (Ref) Migrant 1.76 (1.59–1.94)*** 1.10 (0.99–1.22) Media use No media use 1.00 (Ref) 1.00 (Ref) Any media use 1.65 (1.46–1.86)*** 1.07 (0.93–1.24) Living conditions Hygienic 1.00 (Ref) Non-hygienic 0.68 (0.59–0.78)*** HIV/ART status HIV-negative 1.00 (Ref) 1.00 (Ref) HIV + not on ARVs 1.42 (0.47–4.25) 1.00 (0.34–2.99) HIV + on ARVs 1.72 (1.46–2.02)*** 0.83 (0.69–0.99)* Other/Not tested 0.30 (0.25–0.36)*** 0.78 (0.63–0.95)* Note: Results from survey-weighted mixed-effects logistic regression with cluster-robust standard errors. Reference categories: Women, 15–19 years, Poor wealth, No education, Never-married, No employment, Rural residence, Central Province, Christian religion, HIV-negative, Non-smoker, Never consumed alcohol, No parity, Non-migrant, No media use, Not diabetic, Very good self-perceived health. Variables without adjusted estimates were included in univariable analysis only. p < 0.05 * | p < 0.01 ** | p < 0.001 *** 4. Discussion This study used data from the 2024 Zambia Demographic and Health Survey to examine the prevalence and correlates of self-reported hypertension among 26,536 adults aged 15–59 years (Zambia Statistics Agency & ICF, 2024). The weighted prevalence was 7.8%. Older age, female sex, higher education, greater wealth, urban residence, married status, current employment, comorbid diabetes, and poorer self-perceived health were independently associated with higher odds of self-reported hypertension. Men had roughly half the odds of women. Provincial differences persisted after adjustment, with Western Province recording higher odds and several provinces recording lower odds than Central Province. HIV-positive individuals on antiretroviral therapy and those with untested HIV status had lower adjusted odds. Alcohol use, smoking, migrant status, and media exposure were significant only before adjustment and were attenuated to non-significance in the full model. Religion was largely non-significant. These findings highlight that self-reported hypertension in Zambia is shaped by a combination of biological, social, economic, and geographic factors, each of which carries different implications for screening, treatment uptake, and health policy. This is much lower than estimates from studies using measured blood pressure in sub Saharan Africa, where prevalence has been reported between 25% and 40% in several countries.[15], [16]. A large systematic review and meta-analysis of over 533,000 adults across the region found a pooled prevalence of measured hypertension of about 30.5%, showing just how big the gap is between self-reported cases and what actual blood pressure measurements reveal [5]. More directly comparable findings come from other DHS based studies that also relied on self-reported data. The 2022 Tanzania Demographic and Health Survey found a self-reported high blood pressure prevalence of 6.6% among women of reproductive age, using the same question and approach as the present study[17]. In South Africa, Ntenda et al. used data from the 2016 South African DHS and reported a self-reported prevalence of 23.6% among women aged 20 years and above, a higher figure that reflects the older age structure and greater access to health services in that country [18]. Similarly, a DHS based study from Kenya found that 9.4% of reproductive age women reported having been told they had high blood pressure, with urban women showing a higher prevalence of 11.6% compared to 7.9% among rural women [19]. In Benin, Ekholuenetale and Barrow (2020) reported a self-reported hypertension prevalence of 9.9% among women of reproductive age using DHS data, and found that older age, higher wealth, and urban residence were the strongest predictors[20]. A study using the 2014 Ghana DHS reported a self-reported hypertension prevalence of 7.5% among reproductive age women, with age, wealth, and marital status emerging as key correlates [21]. These figures from across the continent confirm that the 7.8% prevalence observed in the present study falls within the range typically seen when DHS self-report data are used, and that the patterns of association with socio demographic factors are broadly consistent across settings. The 7.8% prevalence must be understood as a measure of diagnosed hypertension, not true disease burden. The DHS self-report question captures only individuals previously told by a health professional that they have high blood pressure. Anyone who has never had their blood pressure measured is automatically excluded. This is not a minor limitation in a setting like Zambia, where access to routine screening is limited, particularly in rural areas. When blood pressure is actually measured in population-based studies, the picture changes dramatically. In Zambia,. Rush et al. found a measured prevalence of 46.9% among adults in rural Western Province[22], Goma et al. reported 22.6% in urban Lusaka[23], and Siziya et al. documented 32.8% in the mining town of Kitwe. Across sub-Saharan Africa, Chen et al. pooled 170 studies comprising 533,167 adults and found a measured prevalence of 30.5% (95% CI: 28.4–32.6%)[5], while Olowoyo et al. confirmed an age-adjusted prevalence of 27.2% across 78 studies from 23 countries[6]. Magee et al. in 2025, directly comparing self-reported and measured hypertension in the Gambia, Kenya, and Mozambique, showed that self-report substantially underestimates true prevalence, with the gap widest where healthcare access is most limited[14]. The critical point is that the present study's 7.8% figure represents diagnosed cases only, the visible fraction of a burden that is likely three to four times larger. In an era of rising non-communicable diseases across sub-Saharan Africa, where hypertension is already the leading cardiovascular risk factor[4] ), this detection gap is itself a public health emergency. The treatment cascade compounds the detection problem. Even among those who know they have hypertension, treatment uptake and blood pressure control remain poor. Ataklte et al. (2015, ) pooled 33 surveys involving over 110,414 participants across sub-Saharan Africa and found that only 27% of hypertensive adults were aware of their diagnosis, only 18% were on treatment, and a mere 7% had controlled blood pressure [7]. The NCD Risk Factor Collaboration in 2021,, analysing 1,201 population-representative studies covering 104 million participants, reported that fewer than one quarter of hypertensive women and fewer than one fifth of hypertensive men in sub-Saharan Africa were receiving treatment as of 2019[24]. Gafane-Matemane et al. also confirmed that detection, treatment, and control rates remain persistently poor across the continent[15]. Within Zambia, Tateyama et al. in 2022 documented significant gender disparities in access to hypertension services in rural areas [10], and Hines et al. found low treatment uptake even among persons living with HIV who were already in regular clinical care[11]. The DHS self-report measure used in the present study captures only the first step of this cascade, diagnosis and cannot distinguish treated from untreated or controlled from uncontrolled individuals. The full picture in Zambia is therefore one of widespread undiagnosed disease layered upon inadequate treatment among the few who are diagnosed. The sociodemographic correlates identified in this study are consistent with DHS-based evidence from across the region but require careful interpretation, because most of them are entangled with detection bias. Women had higher self-reported prevalence not because they are biologically more vulnerable, in fact, men have higher measured blood pressure through most of life[25], but because they access health facilities far more often. Shakil et al., across 17 sub-Saharan countries, confirmed that men were less likely to be diagnosed despite comparable or higher blood pressure[16]. The H3Africa AWI-Gen study documented this across six sites[26]. The positive association between wealth, education, and self-reported hypertension follows the same logic: wealthier and more educated adults are not sicker, but are more likely to have been screened. Basu and Millett, studying 47,443 adults in six middle-income countries, found that the undiagnosed proportion was highest among the poorest[27]. Ketema confirmed wealth as the single largest contributor (126%) to inequality in hypertension detection across five sub-Saharan countries [28]. Similarly, urban residents had higher self-reported prevalence partly because urbanisation genuinely raises blood pressure through sedentary lifestyles, processed food consumption, and psychosocial stress[29], and partly because they have better access to health facilities. This dual mechanism, genuine risk plus diagnostic access, applies to employment as well, and the marginal significance of the employment association suggests caution in its interpretation[30]. The age gradient, from 1.8% prevalence at ages 15–19 to 17.3% at ages 45–49 and adjusted odds of 7.28 among those aged 50–59, deserves particular attention in the context of Zambia's demographic and epidemiological transition. The pathophysiology is straightforward: arteries stiffen with age through elastin fragmentation, collagen cross-linking, and endothelial dysfunction, leading to rising systolic blood pressure[31], [32]. This biological trajectory is universal and confirms that Zambian adults are not exempt. However, the practical significance extends beyond biology. Zambia's population is young, the median age is approximately 17 years, meaning that the cohort currently at highest risk is relatively small, but it is growing rapidly. As this population ages, the absolute number of adults with hypertension will increase substantially even if age-specific prevalence rates remain stable. DHS-based studies from Tanzania [17], Kenya, Benin and Ghana all confirm this age-driven pattern [19], [20], [21]. This means that health system planning must not only address the current detection and treatment gaps but must also prepare for a predictable surge in hypertension burden as the population ages. If screening and treatment infrastructure are not scaled now, the gap between true burden and diagnosed cases will only widen. The remaining correlates merit brief discussion. Comorbid diabetes was the strongest clinical predictor (aOR: 4.22), and the 49.7% prevalence among diabetic individuals confirms the well-established pathophysiological co-clustering of these conditions through insulin resistance, sympathetic activation, and endothelial dysfunction[29]. This argues strongly for integrated screening at diabetes care points. Married adults had 34% higher odds than never-married individuals, consistent with findings from Iran and Ghana, [33], [34], though age confounding explains part of this association. Provincial variation persisted after adjustment, with Western Province recording higher odds (aOR: 1.37), notable given Rush et al.'s (2018) finding of 46.9% measured prevalence there, while several provinces had lower odds than Central Province. Similar regional heterogeneity has been documented in South Africa [35], [36]. The lower adjusted odds among HIV-positive individuals on ART (aOR: 0.83) likely reflects residual confounding by age and BMI rather than a genuine protective effect, given that Hines et al. found elevated hypertension in this population using measured data [11]. Research and Clinical Implications The findings carry direct implications for both research and clinical practice. First, the stark gap between self-reported (7.8%) and measured (22–47%) hypertension prevalence in Zambia demonstrates that routine population screening using blood pressure measurement, not self-report, must be the standard for surveillance. Future national health surveys should incorporate direct blood pressure measurement alongside self-report to enable calibration of detection rates and quantification of the care cascade. Second, the consistent finding that men, poorer individuals, less educated adults, and rural populations are under-represented in self-reported hypertension data indicates that screening programmes must be designed to reach these specific groups. Community-based screening, workplace health checks, and integration of blood pressure measurement into routine visits for other conditions (particularly HIV and diabetes) offer pragmatic entry points. Third, the treatment uptake data from across sub-Saharan Africa, with only 18% of hypertensive adults on treatment and 7% controlled[7], indicate that diagnosis alone is insufficient. Linking screening to treatment initiation and long-term follow-up is essential, particularly in provinces with high measured prevalence but limited healthcare infrastructure such as Western Province. Fourth, the strong age gradient and Zambia's young but rapidly aging population mean that health system investments in NCD screening and treatment infrastructure must begin now, before the demographic transition translates into an unmanageable clinical burden. Finally, the co-clustering of diabetes and hypertension (aOR: 4.22) supports integrating NCD screening platforms rather than maintaining disease-specific silos. Strengths and Limitations This study has several strengths. It uses the 2024 ZDHS, a nationally representative survey covering all ten provinces and both urban and rural areas. The large sample of 26,536 adults includes both women and men, improving upon earlier DHS-based studies that often included only women. Survey weights were applied to account for the complex stratified cluster sampling design. Several limitations should be noted. The study relied on self-reported hypertension, which captures only previously diagnosed cases and therefore underestimates true prevalence. The sensitivity of self-report varies considerably across populations and may be particularly low in settings with limited diagnostic contact. The absence of treatment and control data in the 2024 ZDHS prevents examination of the full hypertension care cascade beyond diagnosis. The cross-sectional design precludes causal inference. Important variables, including body mass index, salt intake, physical activity, and family history, were either unavailable or inconsistently measured across the Women's and Men's Recode datasets, leaving residual confounding unmeasured. Future research should combine DHS self-report with measured blood pressure and treatment data to provide a complete picture of the hypertension burden and care cascade in Zambia.. Abbreviations AOR Adjusted Odds Ratio CI Confidence Interval DHS Demographic and Health Survey EA Enumeration Area ICC Intraclass Correlation Coefficient LMICs Low- and Middle-Income Countries MOR Median Odds Ratio OR Odds Ratio PR Prevalence Rate SSA Sub-Saharan Africa ZDHS Zambia Demographic and Health Survey WHO World Health Organization Declarations Acknowledgements The authors gratefully acknowledge the Zambia Statistics Agency (ZamStats), the Ministry of Health Zambia, and the Demographic and Health Survey (DHS) Program for collecting and making the 2024 Zambia Demographic and Health Survey data publicly available. The authors also acknowledge all survey participants and field teams whose efforts made this study possible. Author Contributions RM led the conceptualisation of the study, conducted the statistical analyses, and prepared the initial manuscript draft. RM, SM contributed to the conceptualisation of the study and provided critical review of the analysis and manuscript. SM offered policy-relevant insights and reviewed the manuscript for intellectual content. All authors reviewed the final manuscript and approved it for submission. Funding This study did not receive any specific funding from public, commercial, or not-for-profit funding agencies. Data Availability The data used in this study were obtained from the 2024 Zambia Demographic and Health Survey (ZDHS). These data are publicly available through the Demographic and Health Survey (DHS) Program upon reasonable request and approval. Researchers can request access through the DHS Program website. Ethics Approval and Consent to Participate The 2024 Zambia Demographic and Health Survey (ZDHS) was conducted in accordance with internationally recognised ethical standards for research involving human participants. Ethical approval was obtained from the relevant national ethics review authorities in Zambia and the Institutional Review Board of the DHS Program. Written informed consent was obtained from all survey participants prior to data collection. This study is a secondary analysis of anonymised DHS data obtained through an approved data request from the DHS Program. As the dataset contains no personally identifiable information, additional ethical approval was not required for this analysis. Consent for Publication Not applicable. This manuscript does not contain any identifiable individual data. Competing Interests The authors declare that they have no competing interests. References B. Zhou, P. Perel, G. A. Mensah, and M. Ezzati, “Global epidemiology, health burden and effective interventions for elevated blood pressure and hypertension,” Nat. Rev. Cardiol. , vol. 18, no. 11, pp. 785–802, Nov. 2021, doi: 10.1038/s41569-021-00559-8. B. 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Nhanga, and K. Kijusya, “Burden and determinants of self-reported high blood pressure among women of reproductive age in Tanzania: Evidence from 2022 Tanzania demographic and health survey,” PLOS ONE , vol. 20, no. 2, p. e0314901, Feb. 2025, doi: 10.1371/journal.pone.0314901. P. A. M. Ntenda et al. , “Determinants of self-reported hypertension among women in South Africa: evidence from the population-based survey,” Clin. Hypertens. , vol. 28, no. 1, p. 39, 2022, doi: 10.1186/s40885-022-00222-5. M. A. B. Chowdhury, K. Epnere, M. A. Haque, and R. S. Mkuu, “Urban rural differences in prevalence and risk factors of self-reported hypertension among Kenyan women: a population-based study,” J. Hum. Hypertens. , vol. 35, no. 10, pp. 912–920, Oct. 2021, doi: 10.1038/s41371-020-00435-x. M. Ekholuenetale and A. Barrow, “Prevalence and determinants of self-reported high blood pressure among women of reproductive age in Benin: a population-based study,” Clin. Hypertens. , vol. 26, no. 1, p. 12, Dec. 2020, doi: 10.1186/s40885-020-00145-z. S. H. Nyarko, “Prevalence and Sociodemographic Determinants of Hypertension History among Women in Reproductive Age in Ghana,” Int. J. Hypertens. , vol. 2016, pp. 1–6, 2016, doi: 10.1155/2016/3292938. K. L. Rush, F. M. Goma, J. A. Barker, R. A. Ollivier, M. S. Ferrier, and D. Singini, “Hypertension prevalence and risk factors in rural and urban Zambian adults in western province: a cross-sectional study,” Pan Afr. Med. J. , vol. 30, 2018, doi: 10.11604/pamj.2018.30.97.14717. F. M. Goma et al. , “Prevalence of hypertension and its correlates in Lusaka urban district of Zambia: a population based survey,” Int. Arch. Med. , vol. 4, no. 1, p. 34, 2011, doi: 10.1186/1755-7682-4-34. B. Zhou et al. , “Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants,” The Lancet , vol. 398, no. 10304, pp. 957–980, Sep. 2021, doi: 10.1016/S0140-6736(21)01330-1. K. Sandberg and H. Ji, “Sex differences in primary hypertension,” Biol. Sex Differ. , vol. 3, no. 1, p. 7, 2012, doi: 10.1186/2042-6410-3-7. F. X. Gómez-Olivé et al. , “Regional and Sex Differences in the Prevalence and Awareness of Hypertension: An H3Africa AWI-Gen Study Across 6 Sites in Sub-Saharan Africa,” Glob. Heart , vol. 12, no. 2, p. 81, Jun. 2017, doi: 10.1016/j.gheart.2017.01.007. S. Basu and C. Millett, “Social Epidemiology of Hypertension in Middle-Income Countries: Determinants of Prevalence, Diagnosis, Treatment, and Control in the WHO SAGE Study,” Hypertension , vol. 62, no. 1, pp. 18–26, Jul. 2013, doi: 10.1161/HYPERTENSIONAHA.113.01374. D. B. Ketema et al. , “Socioeconomic inequality for hypertension among reproductive age women aged 15–49 from five Sub-Saharan Africa countries: A decomposition analysis of DHS Data,” PLOS Glob. Public Health , vol. 5, no. 7, p. e0004738, Jul. 2025, doi: 10.1371/journal.pgph.0004738. N. R. Poulter, D. Prabhakaran, and M. Caulfield, “Hypertension,” The Lancet , vol. 386, no. 9995, pp. 801–812, Aug. 2015, doi: 10.1016/S0140-6736(14)61468-9. S. Okello et al. , “Hypertension prevalence, awareness, treatment, and control and predicted 10-year CVD risk: a cross-sectional study of seven communities in East and West Africa (SevenCEWA),” BMC Public Health , vol. 20, no. 1, p. 1706, Dec. 2020, doi: 10.1186/s12889-020-09829-5. T. W. Buford, “Hypertension and aging,” Ageing Res. Rev. , vol. 26, pp. 96–111, Mar. 2016, doi: 10.1016/j.arr.2016.01.007. Z. Sun, “Aging, Arterial Stiffness, and Hypertension,” Hypertension , vol. 65, no. 2, pp. 252–256, Feb. 2015, doi: 10.1161/HYPERTENSIONAHA.114.03617. A. Ramezankhani, F. Azizi, and F. Hadaegh, “Associations of marital status with diabetes, hypertension, cardiovascular disease and all-cause mortality: A long term follow-up study,” PLOS ONE , vol. 14, no. 4, p. e0215593, Apr. 2019, doi: 10.1371/journal.pone.0215593. D. A. Tuoyire and H. Ayetey, “GENDER DIFFERENCES IN THE ASSOCIATION BETWEEN MARITAL STATUS AND HYPERTENSION IN GHANA,” J. Biosoc. Sci. , vol. 51, no. 3, pp. 313–334, May 2019, doi: 10.1017/S0021932018000147. N.-B. Kandala, C. C. Nnanatu, N. Dukhi, R. Sewpaul, A. Davids, and S. P. Reddy, “Mapping the Burden of Hypertension in South Africa: A Comparative Analysis of the National 2012 SANHANES and the 2016 Demographic and Health Survey,” Int. J. Environ. Res. Public. Health , vol. 18, no. 10, p. 5445, May 2021, doi: 10.3390/ijerph18105445. M. E. Wandai, S. A. Norris, J. Aagaard-Hansen, and S. O. Manda, “Geographical influence on the distribution of the prevalence of hypertension in South Africa: a multilevel analysis,” Cardiovasc. J. Afr. , vol. 31, no. 1, pp. 47–54, Mar. 2020, doi: 10.5830/CVJA-2019-047. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 05 May, 2026 Reviewers invited by journal 28 Apr, 2026 Editor invited by journal 06 Apr, 2026 Editor assigned by journal 05 Apr, 2026 Submission checks completed at journal 05 Apr, 2026 First submitted to journal 31 Mar, 2026 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-9284014","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":618171021,"identity":"f30706b9-f2bb-44db-8f52-bd7298c7ff03","order_by":0,"name":"Raphael Makwenda","email":"","orcid":"","institution":"The University of Zambia, School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Raphael","middleName":"","lastName":"Makwenda","suffix":""},{"id":618171022,"identity":"00e7ef29-b6bc-468e-bd2b-6567793e4bd2","order_by":1,"name":"Samuel Mutasha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYNCCAgjFzMBgkwBmJRQQ0mIAxGxgLWkJIAZDggHxWg5DtDDg0SLffjpN4oMBQ7TB/ebHnwtqzufxy3cnfnhgwCDPL3YAu/lncrdJzjBgyN1wjM1Mesax28WSbbybJYAOM5w5OwGHk3K3SfOAtTCYMfOw3U7ccIx3A0hLgsFt7Frk+99uk/4D1sL++TPPv3MgLZt/4NPCcANoCwNYC4+BNG/bAZCWbXhtMbjxdrNlD1DLzGM5ZdK8fcmJM9tyt1kkGEjg9It8f+7GGz8qGHL7Dh/f/Jnnm11iP/PZzTd/VNjI80vjcBgE/McQkcCnfBSMglEwCkYBAQAA+TleeerS74QAAAAASUVORK5CYII=","orcid":"","institution":"The University of Zambia, School of Public Health","correspondingAuthor":true,"prefix":"","firstName":"Samuel","middleName":"","lastName":"Mutasha","suffix":""}],"badges":[],"createdAt":"2026-03-31 20:08:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9284014/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9284014/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106294005,"identity":"d4a8bc92-806a-4587-9e7b-a5d6a29e0f9d","added_by":"auto","created_at":"2026-04-07 08:15:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":640772,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots for adjusted odds ratio for hypertension\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9284014/v1/7fc15d15fa6a8cbc9eb695bb.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePrevalence and correlates of self-reported hypertension among Zambians aged 15–59 years: Analysis of the 2024 DHS\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHypertension is a major cause of early death and disability around the world and is considered the most important modifiable risk factor for cardiovascular disease, stroke, heart failure, and chronic kidney disease. In 2015 alone, about 8.5\u0026nbsp;million deaths were linked to systolic blood pressure above 115 mmHg, and most of these deaths (88%) occurred in low- and middle-income countries (LMICs)[1]. Evidence from a large pooled analysis of 1,479 population-based studies covering 19.1\u0026nbsp;million adults showed that the number of adults with raised blood pressure (systolic\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg or diastolic\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg) increased from 594\u0026nbsp;million in 1975 to 1.13\u0026nbsp;billion in 2015, with the sharpest increases occurring in low- and middle-income regions [2]. More recent estimates suggest that about 30.6% of adults worldwide were living with hypertension in 2020[3]. Even though hypertension is easy to diagnose and can be treated with affordable medicines, many people with the condition remain undiagnosed or untreated, which continues to lead to preventable health complications[4].\u003c/p\u003e \u003cp\u003eSub-Saharan Africa (SSA) carries a particularly heavy share of this burden. A systematic review and meta-analysis of 170 studies involving 533,167 adults from 26 countries reported a pooled hypertension prevalence of 30.5% (95% CI: 28.4\u0026ndash;32.6%) across the region[5]. Another meta-analysis of 78 studies covering 286,575 individuals from 23 African countries estimated a pooled crude prevalence of 28.5% (95% CI: 25.3\u0026ndash;31.8%). The study also showed that prevalence was highest in Southern Africa (34.8%) and was more common in urban areas compared with rural settings (32.9% versus 26.3%) [6]. Despite this high burden, awareness and control of hypertension remain very low. Among people living with hypertension in SSA, only 27% (95% CI: 23\u0026ndash;31%) know their status, 18% receive treatment, and only 7% have their blood pressure under control[7]. Rapid urbanisation, changes in diet, increasing levels of obesity, and reduced physical activity are continuing to increase exposure to risk factors across the region.\u003c/p\u003e \u003cp\u003eIn Zambia, available evidence on hypertension is still limited and largely concentrated in specific locations. A community-based study conducted in Kitwe found that 32.3% of adults aged 25 years and older had hypertension, with prevalence slightly higher among men (33.5%) than women (31.1%)[8]. Another cross-sectional study carried out in Western Province reported a prevalence of 32.8%, while an additional 24.6% of participants were classified as having pre-hypertension[9] (Oelke et al., 2015). In rural Mumbwa district, hypertension affected 39.7% of men and 33.5% of women, and 30.3% of individuals who had never been diagnosed were found to have hypertension during the survey[10]. Although these studies provide useful insights, they were conducted in specific provinces or among selected populations. They also relied on direct blood pressure measurements and did not use nationally representative datasets or multilevel analytical approaches. As highlighted by Hines on a study on hypertension among HIV patients, evidence on hypertension prevalence in Zambia remains limited and unevenly distributed across the country[11].\u003c/p\u003e \u003cp\u003eSelf-reported hypertension, where individuals report whether they have previously been told by a health professional that they have high blood pressure, provides an additional and practical approach for monitoring the condition in large population surveys. A systematic review of 144 studies showed that self-reported hypertension generally underestimates the true prevalence, with average awareness levels around 58% when the 140/90 mmHg threshold is used[12]. For example, a validation study conducted in Iran reported hypertension prevalence of 19.49% based on self-reports and 21.60% based on objective measurements, suggesting that self-reported data can still be useful for population monitoring [13]. In SSA, a recent study in the Gambia, Kenya, and Mozambique also found that self-reported prevalence was considerably lower than measured hypertension among women of reproductive age[14]. Even with these limitations, self-reported hypertension remains useful because it reflects not only disease presence but also awareness and contact with the health system, information that cannot be captured through blood pressure measurements alone.\u003c/p\u003e \u003cp\u003eNationally representative evidence on self-reported hypertension in Zambia remains limited, with most existing studies based on subnational samples and measured blood pressure. While these approaches are essential for estimating true disease burden, they do not capture patterns in diagnosis and awareness, and multilevel methods are used infrequently to assess contextual influences. In this study, we use data from the 2024 Zambia Demographic and Health Survey to estimate the prevalence of self-reported hypertension and examine associated individual- and community-level factors using multilevel modelling. By focusing on self-reported hypertension, this analysis provides complementary insight into disease detection and health system engagement, addressing an important gap in population-based hypertension research in Zambia.\u003c/p\u003e"},{"header":"2. Methods and Materials","content":"\u003cp\u003e \u003cb\u003eStudy design\u003c/b\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Design and Data Source\u003c/h2\u003e \u003cp\u003eThis study was a cross-sectional secondary analysis of data from the 2024 Zambia Demographic and Health Survey (DHS-8), a nationally representative household survey conducted using a stratified, multi-stage cluster sampling design. Two datasets were used: the Individual Recode file (ZMIR81FL.dta), comprising women aged 15 to 49 years, and the Men's Recode file (ZMMR81FL.dta), comprising men aged 15 to 59 years. The outcome of interest was self-reported hypertension, derived from variables chd02 (women) and mchd02 (men), and coded as a binary indicator (1\u0026thinsp;=\u0026thinsp;Yes, 0\u0026thinsp;=\u0026thinsp;No). Observations with missing values on the outcome variable, sampling weight, primary sampling unit, or stratum were excluded from all analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Variable Harmonization and Recoding\u003c/h2\u003e \u003cp\u003eThe women's and men's datasets were merged into a single analytic file. Because DHS variable naming conventions differ between the two files, using a 'v' prefix for women and an 'mv' prefix for men, a custom lookup function (first_existing) was applied to identify and extract the corresponding variable from each dataset. Nineteen covariates were selected based on biological \u003cem\u003eplausibility\u003c/em\u003e, prior evidence, and availability in both datasets. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarises each variable, its source in the DHS, and the recoding applied. Continuous age was categorised into eight groups (15\u0026ndash;19, 20\u0026ndash;24, 25\u0026ndash;29, 30\u0026ndash;34, 35\u0026ndash;39, 40\u0026ndash;44, 45\u0026ndash;49, and 50\u0026ndash;59 years). Wealth index quintiles were collapsed into three categories: poor (poorest and poorer), middle, and rich (richer and richest). Marital status was grouped as never married (reference), currently married or living with a partner, and formerly married (widowed, divorced, or separated). Migrant status was derived by comparing each respondent's province of birth with their current province of residence: those whose province of birth matched their current province were classified as non-migrants. Media exposure was treated as a binary variable, with any exposure to newspapers, radio, television, or the internet coded as 'any media use,' and respondents who reported no use of any medium coded as 'no media use.'. HIV and antiretroviral therapy (ART) status was derived by combining two DHS variables to produce a five-category indicator: HIV-negative (reference), HIV-positive on ARVs, HIV-positive not on ARVs, HIV-positive with other or unknown ART status, and other or not tested.\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\u003eVariable definitions, DHS source variables, and recoding\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDHS Source (Women / Men)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecoding\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ev012 / mv012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u0026ndash;19, 20\u0026ndash;24, 25\u0026ndash;29, 30\u0026ndash;34, 35\u0026ndash;39, 40\u0026ndash;44, 45\u0026ndash;49, 50\u0026ndash;59\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 \u003cp\u003ev025 / mv025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban / Rural (as labelled)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvince\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ev024 / mv024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 provinces (as labelled); reference\u0026thinsp;=\u0026thinsp;Central\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ev106 / mv106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo education (ref), Primary, Secondary, Higher\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ev190 / mv190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoorest\u0026thinsp;+\u0026thinsp;Poorer\u0026thinsp;=\u0026thinsp;Poor (ref); Middle; Richer\u0026thinsp;+\u0026thinsp;Richest\u0026thinsp;=\u0026thinsp;Rich\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 \u003cp\u003ev501 / mv501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNever-married (ref); Married/Living with partner\u0026thinsp;=\u0026thinsp;Married; Widowed/Divorced/Separated\u0026thinsp;=\u0026thinsp;Formerly married\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReligion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ev130 / mv130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChristian (ref), Muslim, None, Other\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ev714 / mv714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo (ref) / Yes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-rated health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ev176 / mv176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery good (ref), Good, Moderate, Bad, Very bad\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLast healthcare visit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es112a / \u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAs labelled (women only; men receive NA)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echd08 / mchd08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot diabetic (ref) / Diabetic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ev463aa / mv463aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-smoker (ref) / Smoker\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ev485a / mv485a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNever consumed (ref) / Ever consumed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMigrant status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ev172 vs v024 / mv172 vs mv024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-migrant (ref) if province of birth\u0026thinsp;=\u0026thinsp;current province; Migrant otherwise\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedia exposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ev157, v171b, v158, v159 / mv* equivalents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo media use (ref) if all items = 'not at all'; Any media use otherwise\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ev201 / mv201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (ref), 1\u0026ndash;2, 3\u0026ndash;4, 5+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToilet facility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ev116 / mv116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHygienic (ref: flush, VIP, slab, composting) / Non-hygienic (pit without slab, no facility, bucket, hanging, other)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHandwashing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ev117 / mv117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAs labelled\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHIV/ART status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ev861\u0026thinsp;+\u0026thinsp;v863 / mv861\u0026thinsp;+\u0026thinsp;mv863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHIV-negative (ref); HIV-positive on ARVs; HIV-positive not on ARVs; HIV-positive other; Other/Not tested\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Survey Design\u003c/h2\u003e \u003cp\u003eAll analyses accounted for the complex, multi-stage cluster sampling design of the DHS to ensure nationally representative and unbiased estimates. The primary sampling unit was defined by variables v021 (women) and mv021 (men), stratification by v022 and mv022, and sampling weights by v005 and mv005, respectively. Sampling weights were rescaled by dividing by 1,000,000 in accordance with DHS guidelines. The setting survey.lonely.psu = 'adjust' was applied to handle strata containing a single primary sampling unit by centering variance contributions at the grand mean. Primary sampling units were nested within strata.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe analysis was conducted in three sequential stages. In the first stage, a survey-weighted descriptive table was generated using the svyCreateTableOne() function from the tableone package in R, stratified by hypertension status. Weighted counts, percentages, and p-values were reported for all covariates.\u003c/p\u003e \u003cp\u003eIn the second stage, the survey-weighted prevalence of hypertension was estimated separately for each level of every covariate, yielding point estimates and 95% confidence intervals. Chi-square tests were used to assess the statistical significance of the bivariate association between each covariate and hypertension status. .\u003c/p\u003e \u003cp\u003eIn the third stage, both univariable and multivariable logistic regression analyses were performed. For each covariate, a separate survey-weighted logistic regression model was fitted, only observations with non-missing values for the outcome, design variables, and that specific predictor. This approach maximised the analytic sample size for each univariable model and avoided unnecessary exclusions attributable to missingness in unrelated variables. For the multivariable model, all pre-specified predictors were entered simultaneously. An iterative model-building process was used where the full set of predictors was refined step-by-step by removing variables with high missingness (except key variables like sex and age group) until the model had enough variation and successfully converged. As a result of this procedure, three variables, handwashing, last healthcare visit, and hygiene, were excluded from the multivariable model. Their univariable estimates are nonetheless reported. Odds ratios (ORs) and 95% confidence intervals were obtained by exponentiating the model coefficients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Software\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed in R version 4.5.0. The following packages were used: haven for data import; dplyr for data manipulation; survey for complex survey-weighted analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\"\u003e2\u003c/span\u003e below presents the sociodemographic and health-related characteristics of the study population stratified by self-reported hypertension status. Of the 26,536 weighted participants, 2,075 (7.8%) reported a diagnosis of hypertension. The condition was significantly more prevalent among women, who constituted 64.0% (n\u0026thinsp;=\u0026thinsp;1,329) of all hypertensive cases compared with 36.0% (n\u0026thinsp;=\u0026thinsp;746) among men (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Hypertensive individuals were substantially older than their normotensive counterparts, with a mean age of 36.71 years (SD 10.24) versus 28.73 years (SD 10.69; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and reported higher parity (mean 3.73, SD 3.10 vs. 2.36, SD 2.83; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A pronounced age gradient was observed: the 15\u0026ndash;19 age group comprised only 5.5% (n\u0026thinsp;=\u0026thinsp;115) of hypertensive cases despite representing 23.7% of the overall sample, whereas the 40\u0026ndash;44 age group accounted for 17.4% (n\u0026thinsp;=\u0026thinsp;360) of cases (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Urban residence (63.8%, n\u0026thinsp;=\u0026thinsp;1,324), attainment of higher education (18.0%, n\u0026thinsp;=\u0026thinsp;373 vs. 7.7%, n\u0026thinsp;=\u0026thinsp;1,887), and classification in the rich wealth quintile (62.3%, n\u0026thinsp;=\u0026thinsp;1,293 vs. 43.3%, n\u0026thinsp;=\u0026thinsp;10,584) were each significantly more common among hypertensive respondents (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). At the provincial level, Lusaka (21.1%, n\u0026thinsp;=\u0026thinsp;438) and Copperbelt (20.4%, n\u0026thinsp;=\u0026thinsp;422) recorded the highest burden of hypertension.\u003c/p\u003e\n\u003cp\u003eFor social and behavioural characteristics, married individuals constituted a substantially larger proportion of the hypertensive group (70.7%, n\u0026thinsp;=\u0026thinsp;1,467) relative to normotensive respondents (50.7%, n\u0026thinsp;=\u0026thinsp;12,392; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Employment rates were similarly higher among those with hypertension (69.2%, n\u0026thinsp;=\u0026thinsp;1,436 vs. 56.7%, n\u0026thinsp;=\u0026thinsp;13,880; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as were exposure to mass media (78.9%, n\u0026thinsp;=\u0026thinsp;1,637 vs. 69.4%, n\u0026thinsp;=\u0026thinsp;16,983; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and migrant status (35.7%, n\u0026thinsp;=\u0026thinsp;742 vs. 24.0%, n\u0026thinsp;=\u0026thinsp;5,875; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, religious affiliation was the only sociodemographic characteristic for which no statistically significant difference was detected between the two groups (p\u0026thinsp;=\u0026thinsp;0.418).\u003c/p\u003e\n\u003cp\u003eSelf-rated health status differed clearly by hypertension status. Among hypertensive respondents, 23.4% (n\u0026thinsp;=\u0026thinsp;486) rated their health as moderate and 3.0% (n\u0026thinsp;=\u0026thinsp;63) as poor, compared with 11.3% (n\u0026thinsp;=\u0026thinsp;2,756) and 1.4% (n\u0026thinsp;=\u0026thinsp;348), respectively, among normotensive individuals (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The prevalence of comorbid diabetes was approximately ten times higher in the hypertensive group (3.1%, n\u0026thinsp;=\u0026thinsp;65 vs. 0.3%, n\u0026thinsp;=\u0026thinsp;65; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and alcohol consumption was more frequently reported (10.1%, n\u0026thinsp;=\u0026thinsp;209 vs. 7.0%, n\u0026thinsp;=\u0026thinsp;1,710; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, HIV-positive individuals receiving antiretroviral therapy were disproportionately represented among hypertensive respondents (10.5%, n\u0026thinsp;=\u0026thinsp;218 vs. 5.3%, n\u0026thinsp;=\u0026thinsp;1,305; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Access to hygienic sanitation facilities was also significantly higher in the hypertensive group (69.2%, n\u0026thinsp;=\u0026thinsp;901 vs. 60.3%, n\u0026thinsp;=\u0026thinsp;7,491; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eSociodemographic and Health Characteristics by Self-Reported Hypertension Status, Zambia DHS 2024\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e26,536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e24,461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2,075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e12,585 (47.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e11,839 (48.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e746 (36.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e13,951 (52.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e12,622 (51.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1,329 (64.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (mean (SD))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e29.36 (10.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e28.73 (10.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e36.71 (10.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eParity (mean (SD))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2.47 (2.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2.36 (2.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e3.73 (3.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e15\u0026ndash;19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e6,294 (23.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e6,179 (25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e115 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e20\u0026ndash;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4,637 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e4,461 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e176 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e25\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3,892 (14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3,621 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e272 (13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e30\u0026ndash;34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3,424 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3,119 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e304 (14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e35\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2,813 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2,482 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e331 (15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e40\u0026ndash;44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2,469 (9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2,109 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e360 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e45\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1,849 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1,529 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e320 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e50\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1,159 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e961 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e198 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e12,945 (48.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e11,621 (47.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1,324 (63.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e13,591 (51.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e12,840 (52.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e751 (36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eProvince (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eCentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3,094 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2,816 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e278 (13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eCopperbelt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3,835 (14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3,413 (14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e422 (20.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eEastern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3,230 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3,072 (12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e159 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eLuapula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1,941 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1,853 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e88 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eLusaka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4,750 (17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e4,311 (17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e438 (21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMuchinga\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1,212 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1,151 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e61 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNorthern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1,999 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1,901 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e98 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNorth Western\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1,741 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1,638 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e104 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSouthern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3,131 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2,859 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e272 (13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eWestern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1,604 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1,448 (5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e156 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNo Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1,458 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1,362 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e97 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e10,202 (38.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e9,553 (39.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e649 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e12,616 (47.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e11,660 (47.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e956 (46.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eHigher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2,260 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1,887 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e373 (18.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eWealth (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e9,467 (35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e9,016 (36.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e451 (21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5,193 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e4,862 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e331 (15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eRich\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e11,877 (44.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e10,584 (43.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1,293 (62.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNever Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e10,255 (38.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e9,922 (40.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e333 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e13,858 (52.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e12,392 (50.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1,467 (70.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eFormerly Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2,423 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2,148 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e275 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eReligion (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eCatholic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3,925 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3,625 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e300 (14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eProtestant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e22,274 (83.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e20,530 (83.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1,744 (84.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMuslim\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e148 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e139 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e9 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e189 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e168 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e21 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e11,219 (42.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e10,581 (43.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e639 (30.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e15,317 (57.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e13,880 (56.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1,436 (69.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf -rated health (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eVery Good\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e10,459 (39.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e9,882 (40.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e577 (27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e12,356 (46.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e11,418 (46.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e938 (45.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3,242 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2,756 (11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e486 (23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eBad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e411 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e348 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e63 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eVery Bad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e68 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e58 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e10 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eDiabetic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e130 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e65 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e65 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNot Diabetic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e26,406 (99.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e24,396 (99.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2,010 (96.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNever Consumed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e24,617 (92.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e22,752 (93.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1,866 (89.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eEver Consumed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1,919 (7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1,710 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e209 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eMigrant (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNon-migrant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e19,920 (75.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e18,587 (76.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1,333 (64.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMigrant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e6,616 (24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e5,875 (24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e742 (35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedia (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNo Media Use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e7,916 (29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e7,479 (30.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e438 (21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eAny Media Use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e18,620 (70.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e16,983 (69.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1,637 (78.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiving conditions (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eHygienic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e8,393 (61.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e7,491 (60.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e901 (69.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNon-hygienic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5,329 (38.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e4,927 (39.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e402 (30.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIV_ART_status (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eHIV-negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e19,031 (71.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e17,347 (70.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1,684 (81.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eHIV\u0026thinsp;+\u0026thinsp;not on ARVs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e38 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e33 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e5 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eHIV\u0026thinsp;+\u0026thinsp;on ARVs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1,523 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1,305 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e218 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eOther/Not Tested\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5,944 (22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e5,776 (23.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e168 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003cem\u003eNote: Data are weighted frequencies and column percentages n (%). Continuous variables presented as mean (SD). p-values from chi-square tests (categorical) and independent t-tests (continuous).\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003ePrevalence of self-reported Hypertension\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable \u003cspan refid=\"Tab3\"\u003e3\u003c/span\u003e. below shows the Crude Prevalence of Self-Reported Hypertension by Sociodemographic and Health Characteristics, Zambia DHS 2024. The overall crude prevalence of self-reported hypertension was 7.8%. Prevalence was highest among individuals with higher education [16.5% (14.5\u0026ndash;18.5)], in the rich wealth quintile [10.9% (10.0\u0026ndash;11.7)], among urban residents [10.2% (9.4\u0026ndash;11.0)], and among the employed [9.4% (8.7\u0026ndash;10.0)], all statistically significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Provincially, Copperbelt recorded the highest prevalence [11.0% (9.5\u0026ndash;12.6)], followed by Western [9.7% (8.4\u0026ndash;11.0)] and Lusaka [9.2% (7.7\u0026ndash;10.8)], while Luapula [4.5% (3.3\u0026ndash;5.8)] and Eastern [4.9% (4.2\u0026ndash;5.7)] recorded the lowest (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Migrants recorded higher prevalence than non-migrants [11.2% (10.3\u0026ndash;12.1) vs. 6.7% (6.3\u0026ndash;7.1); p\u0026thinsp;\u0026lt;\u0026thinsp;0.001], and any media use was associated with higher prevalence [8.8% (8.2\u0026ndash;9.4) vs. 5.5% (5.0\u0026ndash;6.1); p\u0026thinsp;\u0026lt;\u0026thinsp;0.001]. Hygienic environments was associated with higher prevalence [10.7% (9.9\u0026ndash;11.5) vs. 7.5% (6.7\u0026ndash;8.3); p\u0026thinsp;\u0026lt;\u0026thinsp;0.001].\u003c/p\u003e\n\u003cp\u003ePrevalence increased clearly with age, rising from 1.8% (95% CI: 1.5\u0026ndash;2.2) among those aged 15\u0026ndash;19 years to 17.3% (95% CI: 15.3\u0026ndash;19.3) among those aged 45\u0026ndash;49 and 17.0% (95% CI: 14.5\u0026ndash;19.6) among those aged 50\u0026ndash;59 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Women recorded a higher prevalence than men [9.5% (8.9\u0026ndash;10.1) vs. 5.9% (5.3\u0026ndash;6.5); p\u0026thinsp;\u0026lt;\u0026thinsp;0.001]. Married [10.6% (9.9\u0026ndash;11.3)] and formerly married individuals [11.3% (9.9\u0026ndash;12.8)] had substantially higher prevalence than those never married [3.2% (2.8\u0026ndash;3.7); p\u0026thinsp;\u0026lt;\u0026thinsp;0.001]. Prevalence increased with parity, from 2.8% (2.4\u0026ndash;3.2) among those with no children to 12.6% (11.6\u0026ndash;13.6) among those with five or more children (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Religion was the only characteristic with no statistically significant difference in prevalence across groups (p\u0026thinsp;=\u0026thinsp;0.418).\u003c/p\u003e\n\u003cp\u003eComorbid diabetes was associated with the highest observed prevalence in the entire table [49.7% (38.4\u0026ndash;61.0); p\u0026thinsp;\u0026lt;\u0026thinsp;0.001]. HIV-positive individuals on ARVs had a prevalence of 14.3% (12.3\u0026ndash;16.3), compared to 8.9% (8.3\u0026ndash;9.4) among HIV-negative individuals and 2.8% (2.4\u0026ndash;3.3) among those untested or with other status (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Alcohol consumers had higher prevalence than non-consumers [10.9% (9.3\u0026ndash;12.6) vs. 7.6% (7.1\u0026ndash;8.0); p\u0026thinsp;\u0026lt;\u0026thinsp;0.001], while smokers recorded lower prevalence than non-smokers [6.3% (5.2\u0026ndash;7.4) vs. 8.0% (7.5\u0026ndash;8.5); p\u0026thinsp;=\u0026thinsp;0.009]. Those reporting moderate [15.0% (13.5\u0026ndash;16.5)], bad [15.2% (11.6\u0026ndash;18.9)], or very bad self-perceived health [15.2% (6.2\u0026ndash;24.3)] recorded substantially higher prevalence than those in very good health [5.5% (5.0\u0026ndash;6.1); p\u0026thinsp;\u0026lt;\u0026thinsp;0.001].\u003c/p\u003e\n\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCrude Prevalence of Self-Reported Hypertension by Sociodemographic and Health Characteristics, Zambia DHS 2024\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePrevalence % (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\n \u003cp\u003eAge group (years)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e15\u0026ndash;19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e6,410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.8 (1.5\u0026ndash;2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e20\u0026ndash;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e4,690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3.8 (3.2\u0026ndash;4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e25\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3,791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e7.0 (6.1\u0026ndash;7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e30\u0026ndash;34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3,323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e8.9 (7.8\u0026ndash;10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e35\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2,817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e11.8 (10.4\u0026ndash;13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e40\u0026ndash;44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2,486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e14.6 (13.0\u0026ndash;16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e45\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1,862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e17.3 (15.3\u0026ndash;19.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e50\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1,157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e17.0 (14.5\u0026ndash;19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e12,585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5.9 (5.3\u0026ndash;6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e13,951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e9.5 (8.9\u0026ndash;10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1,537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e6.6 (5.1\u0026ndash;8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e10,915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e6.4 (5.8\u0026ndash;6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e12,115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e7.6 (7.0\u0026ndash;8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHigher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1,969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e16.5 (14.5\u0026ndash;18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eWealth index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e10,920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4.8 (4.3\u0026ndash;5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e5,532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e6.4 (5.7\u0026ndash;7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRich\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e10,084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e10.9 (10.0\u0026ndash;11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNever-married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e10,122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3.2 (2.8\u0026ndash;3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e13,926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e10.6 (9.9\u0026ndash;11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFormerly married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2,488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e11.3 (9.9\u0026ndash;12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrent employment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e11,155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5.7 (5.2\u0026ndash;6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e15,381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e9.4 (8.7\u0026ndash;10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eReligion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCatholic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3,897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e7.7 (6.7\u0026ndash;8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMuslim\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e6.2 (2.3\u0026ndash;10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eProtestant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e22,322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e7.8 (7.4\u0026ndash;8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e11.2 (5.3\u0026ndash;17.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlace of residence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e15,492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5.5 (5.1\u0026ndash;6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e11,044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e10.2 (9.4\u0026ndash;11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eProvince\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3,211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e9.0 (7.8\u0026ndash;10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCopperbelt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2,932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e11.0 (9.5\u0026ndash;12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEastern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3,091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4.9 (4.2\u0026ndash;5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLuapula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2,356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4.5 (3.3\u0026ndash;5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLusaka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3,121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e9.2 (7.7\u0026ndash;10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMuchinga\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1,956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5.0 (3.9\u0026ndash;6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNorth Western\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2,220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e6.0 (4.8\u0026ndash;7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNorthern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2,448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4.9 (3.9\u0026ndash;5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSouthern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3,058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e8.7 (7.4\u0026ndash;10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWestern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2,143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e9.7 (8.4\u0026ndash;11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-perceived health\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVery good\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e10,436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5.5 (5.0\u0026ndash;6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e12,349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e7.6 (7.0\u0026ndash;8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3,255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e15.0 (13.5\u0026ndash;16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e15.2 (11.6\u0026ndash;18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVery bad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e15.2 (6.2\u0026ndash;24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eLast healthcare visit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSame day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e13.1 (7.3\u0026ndash;18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDays: 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e15.3 (10.9\u0026ndash;19.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWeeks: 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e12.2 (9.7\u0026ndash;14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMonths: 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2,848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e9.9 (8.5\u0026ndash;11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYears: 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1,018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e7.2 (5.5\u0026ndash;9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNever visited a healthcare facility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.0 (0.0\u0026ndash;0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDon\u0026apos;t know\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5.4 (-1.5\u0026ndash;12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDiabetic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e49.7 (38.4\u0026ndash;61.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNot diabetic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e26,429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e7.6 (7.2\u0026ndash;8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmokes cigarettes (incl. water pipe)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e13,798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e9.5 (9.0\u0026ndash;10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e7.7 (3.4\u0026ndash;12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking frequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNon-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e24,053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e8.0 (7.5\u0026ndash;8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSmoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2,483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e6.3 (5.2\u0026ndash;7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol use\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNever consumed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e24,663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e7.6 (7.1\u0026ndash;8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEver consumed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1,873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e10.9 (9.3\u0026ndash;12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eMigrant status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNon-migrant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e20,573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e6.7 (6.3\u0026ndash;7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMigrant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e5,963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e11.2 (10.3\u0026ndash;12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedia use (newspaper, social media, radio, TV)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo media use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e8,761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5.5 (5.0\u0026ndash;6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAny media use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e17,775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e8.8 (8.2\u0026ndash;9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eParity (children ever born)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e9,064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2.8 (2.4\u0026ndash;3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e1\u0026ndash;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e6,731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e7.8 (7.0\u0026ndash;8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e3\u0026ndash;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e4,831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e11.8 (10.7\u0026ndash;13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e5+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e5,910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e12.6 (11.6\u0026ndash;13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiving conditions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHygienic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e7,737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e10.7 (9.9\u0026ndash;11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNon-hygienic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e5,979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e7.5 (6.7\u0026ndash;8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eReceived HIV test result\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e11.7 (8.2\u0026ndash;15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e20,562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e9.3 (8.7\u0026ndash;9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIV/ART status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHIV-negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e18,990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e8.9 (8.3\u0026ndash;9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHIV\u0026thinsp;+\u0026thinsp;not on ARVs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e12.1 (0.5\u0026ndash;23.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHIV\u0026thinsp;+\u0026thinsp;on ARVs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1,463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e14.3 (12.3\u0026ndash;16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOther/Not tested\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e6,043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2.8 (2.4\u0026ndash;3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cem\u003eNote: Prevalence expressed as percentage with 95% confidence intervals. p-values from chi-square tests.\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eFactors associated with self-reported hypertension\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e below shows the Univariable and Adjusted Odds Ratios for Self-Reported Hypertension, Zambia DHS 2024. In univariable analysis, older age was strongly and progressively associated with higher odds of self-reported hypertension, with those aged 50\u0026ndash;59 years recording an OR of 11.03 (8.40\u0026ndash;14.48) relative to the 15\u0026ndash;19 reference group. After full adjustment, this age gradient persisted across all groups, with aORs rising from 1.42 (1.05\u0026ndash;1.92) among those aged 20\u0026ndash;24 to 7.28 (4.79\u0026ndash;11.06) among those aged 50\u0026ndash;59 (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Men had significantly lower odds than women in both univariable [OR: 0.60 (0.53\u0026ndash;0.67)] and adjusted analyses [aOR: 0.49 (0.42\u0026ndash;0.56); p\u0026thinsp;\u0026lt;\u0026thinsp;0.001]. Higher education remained independently associated with elevated odds after adjustment [aOR: 1.65 (1.20\u0026ndash;2.26); p\u0026thinsp;\u0026lt;\u0026thinsp;0.01], while primary and secondary education were not significant. Both the middle [aOR: 1.20 (1.02\u0026ndash;1.42); p\u0026thinsp;\u0026lt;\u0026thinsp;0.05] and rich wealth quintiles [aOR: 1.72 (1.40\u0026ndash;2.12); p\u0026thinsp;\u0026lt;\u0026thinsp;0.001] were independently associated with higher odds relative to the poor. Urban residence was also independently associated with higher odds [aOR: 1.23 (1.04\u0026ndash;1.46); p\u0026thinsp;\u0026lt;\u0026thinsp;0.05]. Provincially, Eastern [aOR: 0.68 (0.54\u0026ndash;0.85)], Luapula [aOR: 0.56 (0.41\u0026ndash;0.76)], Northern [aOR: 0.64 (0.49\u0026ndash;0.82)], Lusaka [aOR: 0.71 (0.56\u0026ndash;0.90)], Muchinga [aOR: 0.68 (0.52\u0026ndash;0.88)], and North Western [aOR: 0.74 (0.58\u0026ndash;0.94)] had significantly lower adjusted odds than Central Province, while Western Province recorded higher odds [aOR: 1.37 (1.11\u0026ndash;1.70); p\u0026thinsp;\u0026lt;\u0026thinsp;0.01].\u003c/p\u003e \u003cp\u003eBeing married remained independently associated with higher odds of hypertension [aOR: 1.34 (1.06\u0026ndash;1.69); p\u0026thinsp;\u0026lt;\u0026thinsp;0.05], while formerly married status was attenuated to non-significance after adjustment. Current employment was a significant independent correlate [aOR: 1.13 (1.01\u0026ndash;1.27); p\u0026thinsp;\u0026lt;\u0026thinsp;0.05]. Parity of 1\u0026ndash;2 children remained independently associated [aOR: 1.30 (1.02\u0026ndash;1.65); p\u0026thinsp;\u0026lt;\u0026thinsp;0.05], though higher parity groups were attenuated to non-significance after adjustment. Among religion categories, the Other religion group had significantly higher adjusted odds than Christians [aOR: 1.80 (1.03\u0026ndash;3.14); p\u0026thinsp;\u0026lt;\u0026thinsp;0.05], while Muslim affiliation was not significant. Migrant status and media use were each significant in univariable analysis but were fully attenuated to non-significance after adjustment. Alcohol use and smoking were each significant at the univariable level but were not independently significant after full adjustment.\u003c/p\u003e \u003cp\u003ePoorer self-perceived health was independently and dose-responsively associated with higher odds: good [aOR: 1.15 (1.02\u0026ndash;1.28); p\u0026thinsp;\u0026lt;\u0026thinsp;0.05], moderate [aOR: 2.04 (1.74\u0026ndash;2.40); p\u0026thinsp;\u0026lt;\u0026thinsp;0.001], bad [aOR: 2.01 (1.47\u0026ndash;2.76); p\u0026thinsp;\u0026lt;\u0026thinsp;0.001], and very bad [aOR: 2.63 (1.26\u0026ndash;5.48); p\u0026thinsp;\u0026lt;\u0026thinsp;0.01]. Comorbid diabetes was the strongest independent clinical predictor [aOR: 4.22 (2.36\u0026ndash;7.54); p\u0026thinsp;\u0026lt;\u0026thinsp;0.001]. HIV-positive individuals on ARVs had lower adjusted odds than HIV-negative individuals [aOR: 0.83 (0.69\u0026ndash;0.99); p\u0026thinsp;\u0026lt;\u0026thinsp;0.05], as did those in the untested or other status category [aOR: 0.78 (0.63\u0026ndash;0.95); p\u0026thinsp;\u0026lt;\u0026thinsp;0.05], while HIV-positive individuals not on ARVs showed no significant difference. Living conditions (toilet facility type as proxy) and last healthcare visit were significant at the univariable level, non-hygienic conditions were associated with lower odds [OR: 0.68 (0.59\u0026ndash;0.78); p\u0026thinsp;\u0026lt;\u0026thinsp;0.001] and visiting a healthcare facility years prior was associated with lower odds [OR: 0.52 (0.30\u0026ndash;0.91); p\u0026thinsp;\u0026lt;\u0026thinsp;0.05], but neither was retained in the adjusted model. These findings are visually summarised in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below.\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\u003eUnivariable and Adjusted Odds Ratios for Self-Reported Hypertension, Zambia DHS 2024\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnivariable OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted aOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group (years)\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 \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.11 (1.65\u0026ndash;2.71)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42 (1.05\u0026ndash;1.92)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.03 (3.13\u0026ndash;5.18)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.08 (1.46\u0026ndash;2.96)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.24 (4.11\u0026ndash;6.67)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.50 (1.73\u0026ndash;3.61)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.15 (5.67\u0026ndash;9.03)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.43 (2.38\u0026ndash;4.96)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.17 (7.25\u0026ndash;11.61)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.39 (2.99\u0026ndash;6.42)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.22 (8.71\u0026ndash;14.45)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.65 (3.81\u0026ndash;8.38)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.03 (8.40\u0026ndash;14.48)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.28 (4.79\u0026ndash;11.06)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.60 (0.53\u0026ndash;0.67)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.49 (0.42\u0026ndash;0.56)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96 (0.74\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02 (0.78\u0026ndash;1.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15 (0.90\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21 (0.91\u0026ndash;1.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.78 (2.10\u0026ndash;3.68)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.65 (1.20\u0026ndash;2.26)**\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Ref)\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\u003e1.36 (1.18\u0026ndash;1.57)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20 (1.02\u0026ndash;1.42)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.44 (2.14\u0026ndash;2.78)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.72 (1.40\u0026ndash;2.12)***\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever-married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \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\u003e3.52 (3.06\u0026ndash;4.06)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.34 (1.06\u0026ndash;1.69)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormerly married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.81 (3.18\u0026ndash;4.58)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17 (0.91\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent employment\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 \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\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Ref)\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\u003e1.72 (1.54\u0026ndash;1.91)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13 (1.01\u0026ndash;1.27)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligion\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChristian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.78 (0.40\u0026ndash;1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.79 (0.38\u0026ndash;1.64)\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\u003e1.49 (0.82\u0026ndash;2.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.80 (1.03\u0026ndash;3.14)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlace of residence\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.95 (1.73\u0026ndash;2.20)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23 (1.04\u0026ndash;1.46)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProvince\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCopperbelt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.26 (1.02\u0026ndash;1.55)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97 (0.80\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.52 (0.42\u0026ndash;0.65)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68 (0.54\u0026ndash;0.85)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuapula\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.48 (0.35\u0026ndash;0.67)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56 (0.41\u0026ndash;0.76)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLusaka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03 (0.82\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71 (0.56\u0026ndash;0.90)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuchinga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.54 (0.41\u0026ndash;0.70)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68 (0.52\u0026ndash;0.88)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth Western\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.64 (0.50\u0026ndash;0.82)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74 (0.58\u0026ndash;0.94)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorthern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.52 (0.40\u0026ndash;0.67)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.64 (0.49\u0026ndash;0.82)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.78\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01 (0.81\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10 (0.89\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.37 (1.11\u0026ndash;1.70)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSelf-perceived health\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.41 (1.25\u0026ndash;1.59)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15 (1.02\u0026ndash;1.28)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.02 (2.61\u0026ndash;3.50)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.04 (1.74\u0026ndash;2.40)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.08 (2.29\u0026ndash;4.13)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.01 (1.47\u0026ndash;2.76)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery bad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.08 (1.52\u0026ndash;6.25)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.63 (1.26\u0026ndash;5.48)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLast healthcare visit\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSame day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \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\u003eDays: 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20 (0.66\u0026ndash;2.18)\u003c/p\u003e \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\u003eWeeks: 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.54\u0026ndash;1.59)\u003c/p\u003e \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\u003eMonths: 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.73 (0.43\u0026ndash;1.24)\u003c/p\u003e \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\u003eYears: 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.52 (0.30\u0026ndash;0.91)*\u003c/p\u003e \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\u003eNever visited a healthcare facility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00 (0.00\u0026ndash;0.00)***\u003c/p\u003e \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\u003eDon't know\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.38 (0.09\u0026ndash;1.63)\u003c/p\u003e \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\u003cb\u003eDiabetes 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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot diabetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.99 (7.68\u0026ndash;18.74)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.22 (2.36\u0026ndash;7.54)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol use\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever consumed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver consumed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.49 (1.26\u0026ndash;1.77)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18 (0.98\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking frequency\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.77 (0.64\u0026ndash;0.94)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90 (0.72\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParity (children ever born)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.95 (2.47\u0026ndash;3.53)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30 (1.02\u0026ndash;1.65)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.67 (3.94\u0026ndash;5.52)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28 (0.99\u0026ndash;1.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.01 (4.24\u0026ndash;5.92)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28 (0.97\u0026ndash;1.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMigrant 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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-migrant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMigrant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.76 (1.59\u0026ndash;1.94)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10 (0.99\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedia use\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo media use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny media use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.65 (1.46\u0026ndash;1.86)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07 (0.93\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiving conditions\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHygienic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \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\u003eNon-hygienic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68 (0.59\u0026ndash;0.78)***\u003c/p\u003e \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\u003cb\u003eHIV/ART 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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHIV-negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHIV\u0026thinsp;+\u0026thinsp;not on ARVs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.42 (0.47\u0026ndash;4.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (0.34\u0026ndash;2.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHIV\u0026thinsp;+\u0026thinsp;on ARVs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.72 (1.46\u0026ndash;2.02)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83 (0.69\u0026ndash;0.99)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther/Not tested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.30 (0.25\u0026ndash;0.36)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78 (0.63\u0026ndash;0.95)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eNote: Results from survey-weighted mixed-effects logistic regression with cluster-robust standard errors. Reference categories: Women, 15\u0026ndash;19 years, Poor wealth, No education, Never-married, No employment, Rural residence, Central Province, Christian religion, HIV-negative, Non-smoker, Never consumed alcohol, No parity, Non-migrant, No media use, Not diabetic, Very good self-perceived health. Variables without adjusted estimates were included in univariable analysis only.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05 * | p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 ** | p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 ***\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study used data from the 2024 Zambia Demographic and Health Survey to examine the prevalence and correlates of self-reported hypertension among 26,536 adults aged 15\u0026ndash;59 years (Zambia Statistics Agency \u0026amp; ICF, 2024). The weighted prevalence was 7.8%. Older age, female sex, higher education, greater wealth, urban residence, married status, current employment, comorbid diabetes, and poorer self-perceived health were independently associated with higher odds of self-reported hypertension. Men had roughly half the odds of women. Provincial differences persisted after adjustment, with Western Province recording higher odds and several provinces recording lower odds than Central Province. HIV-positive individuals on antiretroviral therapy and those with untested HIV status had lower adjusted odds. Alcohol use, smoking, migrant status, and media exposure were significant only before adjustment and were attenuated to non-significance in the full model. Religion was largely non-significant. These findings highlight that self-reported hypertension in Zambia is shaped by a combination of biological, social, economic, and geographic factors, each of which carries different implications for screening, treatment uptake, and health policy.\u003c/p\u003e \u003cp\u003eThis is much lower than estimates from studies using measured blood pressure in sub Saharan Africa, where prevalence has been reported between 25% and 40% in several countries.[15], [16]. A large systematic review and meta-analysis of over 533,000 adults across the region found a pooled prevalence of measured hypertension of about 30.5%, showing just how big the gap is between self-reported cases and what actual blood pressure measurements reveal [5].\u003c/p\u003e \u003cp\u003eMore directly comparable findings come from other DHS based studies that also relied on self-reported data. The 2022 Tanzania Demographic and Health Survey found a self-reported high blood pressure prevalence of 6.6% among women of reproductive age, using the same question and approach as the present study[17]. In South Africa, Ntenda et al. used data from the 2016 South African DHS and reported a self-reported prevalence of 23.6% among women aged 20 years and above, a higher figure that reflects the older age structure and greater access to health services in that country [18]. Similarly, a DHS based study from Kenya found that 9.4% of reproductive age women reported having been told they had high blood pressure, with urban women showing a higher prevalence of 11.6% compared to 7.9% among rural women [19]. In Benin, Ekholuenetale and Barrow (2020) reported a self-reported hypertension prevalence of 9.9% among women of reproductive age using DHS data, and found that older age, higher wealth, and urban residence were the strongest predictors[20]. A study using the 2014 Ghana DHS reported a self-reported hypertension prevalence of 7.5% among reproductive age women, with age, wealth, and marital status emerging as key correlates [21]. These figures from across the continent confirm that the 7.8% prevalence observed in the present study falls within the range typically seen when DHS self-report data are used, and that the patterns of association with socio demographic factors are broadly consistent across settings.\u003c/p\u003e \u003cp\u003eThe 7.8% prevalence must be understood as a measure of diagnosed hypertension, not true disease burden. The DHS self-report question captures only individuals previously told by a health professional that they have high blood pressure. Anyone who has never had their blood pressure measured is automatically excluded. This is not a minor limitation in a setting like Zambia, where access to routine screening is limited, particularly in rural areas. When blood pressure is actually measured in population-based studies, the picture changes dramatically. In Zambia,. Rush et al. found a measured prevalence of 46.9% among adults in rural Western Province[22], Goma et al. reported 22.6% in urban Lusaka[23], and Siziya et al. documented 32.8% in the mining town of Kitwe. Across sub-Saharan Africa, Chen et al. pooled 170 studies comprising 533,167 adults and found a measured prevalence of 30.5% (95% CI: 28.4\u0026ndash;32.6%)[5], while Olowoyo et al. confirmed an age-adjusted prevalence of 27.2% across 78 studies from 23 countries[6]. Magee et al. in 2025, directly comparing self-reported and measured hypertension in the Gambia, Kenya, and Mozambique, showed that self-report substantially underestimates true prevalence, with the gap widest where healthcare access is most limited[14]. The critical point is that the present study's 7.8% figure represents diagnosed cases only, the visible fraction of a burden that is likely three to four times larger. In an era of rising non-communicable diseases across sub-Saharan Africa, where hypertension is already the leading cardiovascular risk factor[4] ), this detection gap is itself a public health emergency.\u003c/p\u003e \u003cp\u003eThe treatment cascade compounds the detection problem. Even among those who know they have hypertension, treatment uptake and blood pressure control remain poor. Ataklte et al. (2015, ) pooled 33 surveys involving over 110,414 participants across sub-Saharan Africa and found that only 27% of hypertensive adults were aware of their diagnosis, only 18% were on treatment, and a mere 7% had controlled blood pressure [7]. The NCD Risk Factor Collaboration in 2021,, analysing 1,201 population-representative studies covering 104\u0026nbsp;million participants, reported that fewer than one quarter of hypertensive women and fewer than one fifth of hypertensive men in sub-Saharan Africa were receiving treatment as of 2019[24]. Gafane-Matemane et al. also confirmed that detection, treatment, and control rates remain persistently poor across the continent[15]. Within Zambia, Tateyama et al. in 2022 documented significant gender disparities in access to hypertension services in rural areas [10], and Hines et al. found low treatment uptake even among persons living with HIV who were already in regular clinical care[11]. The DHS self-report measure used in the present study captures only the first step of this cascade, diagnosis and cannot distinguish treated from untreated or controlled from uncontrolled individuals. The full picture in Zambia is therefore one of widespread undiagnosed disease layered upon inadequate treatment among the few who are diagnosed.\u003c/p\u003e \u003cp\u003eThe sociodemographic correlates identified in this study are consistent with DHS-based evidence from across the region but require careful interpretation, because most of them are entangled with detection bias. Women had higher self-reported prevalence not because they are biologically more vulnerable, in fact, men have higher measured blood pressure through most of life[25], but because they access health facilities far more often. Shakil et al., across 17 sub-Saharan countries, confirmed that men were less likely to be diagnosed despite comparable or higher blood pressure[16]. The H3Africa AWI-Gen study documented this across six sites[26]. The positive association between wealth, education, and self-reported hypertension follows the same logic: wealthier and more educated adults are not sicker, but are more likely to have been screened. Basu and Millett, studying 47,443 adults in six middle-income countries, found that the undiagnosed proportion was highest among the poorest[27]. Ketema confirmed wealth as the single largest contributor (126%) to inequality in hypertension detection across five sub-Saharan countries [28]. Similarly, urban residents had higher self-reported prevalence partly because urbanisation genuinely raises blood pressure through sedentary lifestyles, processed food consumption, and psychosocial stress[29], and partly because they have better access to health facilities. This dual mechanism, genuine risk plus diagnostic access, applies to employment as well, and the marginal significance of the employment association suggests caution in its interpretation[30].\u003c/p\u003e \u003cp\u003eThe age gradient, from 1.8% prevalence at ages 15\u0026ndash;19 to 17.3% at ages 45\u0026ndash;49 and adjusted odds of 7.28 among those aged 50\u0026ndash;59, deserves particular attention in the context of Zambia's demographic and epidemiological transition. The pathophysiology is straightforward: arteries stiffen with age through elastin fragmentation, collagen cross-linking, and endothelial dysfunction, leading to rising systolic blood pressure[31], [32]. This biological trajectory is universal and confirms that Zambian adults are not exempt. However, the practical significance extends beyond biology. Zambia's population is young, the median age is approximately 17 years, meaning that the cohort currently at highest risk is relatively small, but it is growing rapidly. As this population ages, the absolute number of adults with hypertension will increase substantially even if age-specific prevalence rates remain stable. DHS-based studies from Tanzania [17], Kenya, Benin and Ghana all confirm this age-driven pattern [19], [20], [21]. This means that health system planning must not only address the current detection and treatment gaps but must also prepare for a predictable surge in hypertension burden as the population ages. If screening and treatment infrastructure are not scaled now, the gap between true burden and diagnosed cases will only widen.\u003c/p\u003e \u003cp\u003eThe remaining correlates merit brief discussion. Comorbid diabetes was the strongest clinical predictor (aOR: 4.22), and the 49.7% prevalence among diabetic individuals confirms the well-established pathophysiological co-clustering of these conditions through insulin resistance, sympathetic activation, and endothelial dysfunction[29]. This argues strongly for integrated screening at diabetes care points. Married adults had 34% higher odds than never-married individuals, consistent with findings from Iran and Ghana, [33], [34], though age confounding explains part of this association. Provincial variation persisted after adjustment, with Western Province recording higher odds (aOR: 1.37), notable given Rush et al.'s (2018) finding of 46.9% measured prevalence there, while several provinces had lower odds than Central Province. Similar regional heterogeneity has been documented in South Africa [35], [36]. The lower adjusted odds among HIV-positive individuals on ART (aOR: 0.83) likely reflects residual confounding by age and BMI rather than a genuine protective effect, given that Hines et al. found elevated hypertension in this population using measured data [11].\u003c/p\u003e \u003cp\u003e \u003cb\u003eResearch and Clinical Implications\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe findings carry direct implications for both research and clinical practice. First, the stark gap between self-reported (7.8%) and measured (22\u0026ndash;47%) hypertension prevalence in Zambia demonstrates that routine population screening using blood pressure measurement, not self-report, must be the standard for surveillance. Future national health surveys should incorporate direct blood pressure measurement alongside self-report to enable calibration of detection rates and quantification of the care cascade. Second, the consistent finding that men, poorer individuals, less educated adults, and rural populations are under-represented in self-reported hypertension data indicates that screening programmes must be designed to reach these specific groups. Community-based screening, workplace health checks, and integration of blood pressure measurement into routine visits for other conditions (particularly HIV and diabetes) offer pragmatic entry points. Third, the treatment uptake data from across sub-Saharan Africa, with only 18% of hypertensive adults on treatment and 7% controlled[7], indicate that diagnosis alone is insufficient. Linking screening to treatment initiation and long-term follow-up is essential, particularly in provinces with high measured prevalence but limited healthcare infrastructure such as Western Province. Fourth, the strong age gradient and Zambia's young but rapidly aging population mean that health system investments in NCD screening and treatment infrastructure must begin now, before the demographic transition translates into an unmanageable clinical burden. Finally, the co-clustering of diabetes and hypertension (aOR: 4.22) supports integrating NCD screening platforms rather than maintaining disease-specific silos.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStrengths and Limitations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study has several strengths. It uses the 2024 ZDHS, a nationally representative survey covering all ten provinces and both urban and rural areas. The large sample of 26,536 adults includes both women and men, improving upon earlier DHS-based studies that often included only women. Survey weights were applied to account for the complex stratified cluster sampling design.\u003c/p\u003e \u003cp\u003eSeveral limitations should be noted. The study relied on self-reported hypertension, which captures only previously diagnosed cases and therefore underestimates true prevalence. The sensitivity of self-report varies considerably across populations and may be particularly low in settings with limited diagnostic contact. The absence of treatment and control data in the 2024 ZDHS prevents examination of the full hypertension care cascade beyond diagnosis. The cross-sectional design precludes causal inference. Important variables, including body mass index, salt intake, physical activity, and family history, were either unavailable or inconsistently measured across the Women's and Men's Recode datasets, leaving residual confounding unmeasured. Future research should combine DHS self-report with measured blood pressure and treatment data to provide a complete picture of the hypertension burden and care cascade in Zambia..\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdjusted Odds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDHS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDemographic and Health Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEnumeration Area\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntraclass Correlation Coefficient\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLMICs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow- and Middle-Income Countries\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMedian Odds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrevalence Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSub-Saharan Africa\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eZDHS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eZambia Demographic and Health Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the Zambia Statistics Agency (ZamStats), the Ministry of Health Zambia, and the Demographic and Health Survey (DHS) Program for collecting and making the 2024 Zambia Demographic and Health Survey data publicly available. The authors also acknowledge all survey participants and field teams whose efforts made this study possible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRM led the conceptualisation of the study, conducted the statistical analyses, and prepared the initial manuscript draft. RM, SM contributed to the conceptualisation of the study and provided critical review of the analysis and manuscript. SM offered policy-relevant insights and reviewed the manuscript for intellectual content. All authors reviewed the final manuscript and approved it for submission.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not receive any specific funding from public, commercial, or not-for-profit funding agencies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study were obtained from the 2024 Zambia Demographic and Health Survey (ZDHS). These data are publicly available through the Demographic and Health Survey (DHS) Program upon reasonable request and approval. Researchers can request access through the DHS Program website.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 2024 Zambia Demographic and Health Survey (ZDHS) was conducted in accordance with internationally recognised ethical standards for research involving human participants. Ethical approval was obtained from the relevant national ethics review authorities in Zambia and the Institutional Review Board of the DHS Program. Written informed consent was obtained from all survey participants prior to data collection.\u003c/p\u003e\n\u003cp\u003eThis study is a secondary analysis of anonymised DHS data obtained through an approved data request from the DHS Program. As the dataset contains no personally identifiable information, additional ethical approval was not required for this analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This manuscript does not contain any identifiable individual data.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eB. Zhou, P. Perel, G. A. Mensah, and M. Ezzati, \u0026ldquo;Global epidemiology, health burden and effective interventions for elevated blood pressure and hypertension,\u0026rdquo; \u003cem\u003eNat. Rev. Cardiol.\u003c/em\u003e, vol. 18, no. 11, pp. 785\u0026ndash;802, Nov. 2021, doi: 10.1038/s41569-021-00559-8.\u003c/li\u003e\n\u003cli\u003eB. 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Reddy, \u0026ldquo;Mapping the Burden of Hypertension in South Africa: A Comparative Analysis of the National 2012 SANHANES and the 2016 Demographic and Health Survey,\u0026rdquo; \u003cem\u003eInt. J. Environ. Res. Public. Health\u003c/em\u003e, vol. 18, no. 10, p. 5445, May 2021, doi: 10.3390/ijerph18105445.\u003c/li\u003e\n\u003cli\u003eM. E. Wandai, S. A. Norris, J. Aagaard-Hansen, and S. O. Manda, \u0026ldquo;Geographical influence on the distribution of the prevalence of hypertension in South Africa: a multilevel analysis,\u0026rdquo; \u003cem\u003eCardiovasc. J. Afr.\u003c/em\u003e, vol. 31, no. 1, pp. 47\u0026ndash;54, Mar. 2020, doi: 10.5830/CVJA-2019-047.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Self-reported hypertension, Demographic and Health Survey, Zambia, cardiovascular, risk factors","lastPublishedDoi":"10.21203/rs.3.rs-9284014/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9284014/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHypertension is a leading risk factor for cardiovascular disease, stroke, and premature mortality, with low- and middle-income countries bearing the greatest burden. In Zambia, evidence on hypertension remains limited to small regional studies. Self-reported hypertension, a previous diagnosis by a health professional, offers a scalable approach for monitoring awareness and health system engagement in national surveys. This study estimated the prevalence of self-reported hypertension and identified its individual- and community-level correlates among Zambian adults.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData were drawn from the 2024 Zambia Demographic and Health Survey, comprising 26,536 adults aged 15 years and older. Weighted descriptive statistics estimated prevalence across demographic, socioeconomic, and geographic strata. Multilevel logistic regression models identified independent correlates while accounting for clustering within enumeration areas. Results are reported as adjusted odds ratios (aOR) with 95% confidence intervals. Between-cluster heterogeneity was quantified using the intraclass correlation coefficient (ICC) and median odds ratio.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe weighted prevalence of self-reported hypertension was 7.8%. Hypertensive individuals were older (36.7 vs 28.7 years; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), predominantly female (64.0% vs 51.6%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and more likely to reside in urban areas (63.8% vs 47.5%; all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Lusaka (21.1%) and Copperbelt (20.4%) provinces had the highest proportions. In multilevel models, each additional year of age increased odds by 6% (aOR\u0026thinsp;=\u0026thinsp;1.064; 95% CI: 1.058\u0026ndash;1.070). Men had substantially lower odds than women (aOR\u0026thinsp;=\u0026thinsp;0.432; 95% CI: 0.387\u0026ndash;0.481). Rural residence was protective (aOR\u0026thinsp;=\u0026thinsp;0.806; 95% CI: 0.678\u0026ndash;0.958). Higher education (aOR\u0026thinsp;=\u0026thinsp;1.735; 95% CI: 1.325\u0026ndash;2.273) and household wealth (rich vs poor: aOR\u0026thinsp;=\u0026thinsp;1.761; 95% CI: 1.451\u0026ndash;2.137) were positively associated. Married (aOR\u0026thinsp;=\u0026thinsp;1.706; 95% CI: 1.465\u0026ndash;1.986) and formerly married adults (aOR\u0026thinsp;=\u0026thinsp;1.456; 95% CI: 1.194\u0026ndash;1.776) had elevated odds. Significant provincial variation persisted after adjustment. The ICC decreased from 0.091 to 0.040, indicating that included covariates explained substantial between-cluster variation.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSelf-reported hypertension among Zambian adults is shaped by age, sex, urbanisation, socioeconomic status, and geography. These findings identify priority populations and regions for targeted screening and awareness programmes to reduce the growing cardiovascular disease burden in Zambia.\u003c/p\u003e","manuscriptTitle":"Prevalence and correlates of self-reported hypertension among Zambians aged 15–59 years: Analysis of the 2024 DHS","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-07 08:15:26","doi":"10.21203/rs.3.rs-9284014/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"250401014981644330579357203916820342525","date":"2026-05-06T00:45:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-28T13:27:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-06T06:46:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-06T00:43:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-06T00:43:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-03-31T20:00:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1e595f2f-d05c-42a0-a17b-781918c1aba3","owner":[],"postedDate":"April 7th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"250401014981644330579357203916820342525","date":"2026-05-06T00:45:31+00:00","index":62,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T13:38:22+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-07 08:15:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9284014","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9284014","identity":"rs-9284014","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}