Rural and Urban Differences in NHIS enrollment Among women 15-49 years: Analyses 2008-2022 Ghana Demographic and Health Survey

Rural and Urban Differences in NHIS enrollment Among women 15-49 years: Analyses 2008-2022 Ghana Demographic and Health Survey | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Rural and Urban Differences in NHIS enrollment Among women 15-49 years: Analyses 2008-2022 Ghana Demographic and Health Survey Sam Johnson, Latifatu Fagin, Joseph Eghan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6852251/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective This study examined the predictors of NHIS enrollment among women 15–49 years in Ghana, using the Ghana Demographic and Health Survey (GDHS) from 2008 to 2022. It also evaluates the association between place of residence and NHIS enrollment while recognizing key elements impelling neonatal mortality rates over time. Design: A cross-sectional study utilising secondary data from the GDHS conducted 2008, 2014, and 2022. Statistical analyses were performed using STATA version 17, with univariate, bivariate, and trend analysis applied to assess NHIS enrollment. Setting: The study is based on nationwide representative survey data from Ghana, shelling all 16 regions. The GDHS datasets provide understandings into maternal and child health, including maternal health and NHIS enrollments. Results The odds of NHIS registration increased from 2008 to 2022, indicating improved enrollment over time. Rural residence and proximity to health facilities were associated with lower registration odds, but these associations were not significant. Age, marital status, and education were significant factors, with women aged 30–39 showing the highest odds of registration. Married women had 53% higher odds of registration, and those with higher education were more likely to enroll. Wealthier women also had higher registration odds, highlighting the socio-economic factors influencing NHIS uptake. Conclusion Sociodemographic factors such as wealth, marital status, and education significantly influenced NHIS registration. Despite some associations being statistically insignificant, the findings stress the importance of addressing socio-economic disparities and optimizing NHIS services to improve coverage in Ghana. National Health Insurance Scheme Maternal Enrollment Demography Contributions to the literature This study highlights long-term trends in health insurance enrollment among Ghanaian women from 2008 to 2022. It uncovers important differences in enrollment between rural and urban women, revealing ongoing inequalities in healthcare access. The paper shows how education, marital status, and income levels affect women’s likelihood of enrolling in NHIS. It provides timely evidence to guide policy decisions aimed at improving universal health coverage in Ghana. The findings support efforts to strengthen health systems and reduce maternal health disparities in low- and middle-income countries. Background Maternal and child health remains a critical global concern, especially in low- and middle-income countries (LMICs), where maternal and infant mortality rates remain alarmingly high. Despite global efforts to reduce these figures, significant disparities in health outcomes persist, particularly among rural and marginalized populations [1,2] In many developing countries, maternal mortality rates are estimated at 480 deaths per 100,000 live births, with over half a million women dying annually due to complications from pregnancy and childbirth [3]. A key contributing factor to these high mortality rates is the underutilization of healthcare services, particularly maternal care, which remains a significant barrier to improving maternal health outcomes in these settings. Although several countries have made progress in improving health-related indicators, others have struggled to address distant but crucial determinants of women’s health, such as access to education, political participation, and social and economic opportunities [4]. Lack of empowerment in these areas prevents women from making informed decisions about their health, fertility, nutrition, and overall well-being. Moreover, women are disproportionately vulnerable to catastrophic health expenditures, which can lead to further financial hardships, exacerbating their lack of control over healthcare decisions and deepening impoverishment [2,4] To safeguard women from these challenges, measures such as universal access to maternity care have been proposed and implemented in some countries, aligning with the Sustainable Development Goals (SDG), which emphasize universal health coverage (UHC) as a vital component for improving global health outcomes [5]. The divide between rural and urban populations in accessing healthcare services is a well-documented challenge, particularly in developing nations. Rural populations face numerous barriers that impede their ability to utilize health services effectively. These barriers include lower levels of education, lower income, limited access to health insurance, and longer travel distances to healthcare facilities, all of which contribute to reduced healthcare utilization [6]. Health insurance plays a key role in mitigating these disparities by providing financial protection and improving access to essential healthcare services, including maternal and reproductive care. By pooling financial risks, health insurance schemes can ensure that individuals are protected against the financial burden of healthcare costs, which may otherwise prevent them from seeking timely medical attention [7]. In Ghana, the National Health Insurance Scheme (NHIS), established under the Health Insurance Act of 2003, aims to increase access to healthcare services, including maternal care, by providing financial protection for its members [8]. Although NHIS enrollment has expanded over time, active membership and effective utilisation of the scheme remain challenges, particularly in rural areas where both health service access and health insurance enrollment rates are lower [9]. Despite some successes, the NHIS has not fully achieved its intended goal of equitable healthcare access for all citizens. Evidence suggests that rural residents, especially women, are less likely to be enrolled in and utilize the NHIS compared to their urban counterparts. Geographic location, wealth status, and other socio-economic factors are key determinants of NHIS enrollment and healthcare utilization, with rural women facing significant barriers to access [10] Despite the significant expansion of the National Health Insurance Scheme (NHIS) in Ghana, maternal and infant health outcomes remain suboptimal, with persistently high maternal mortality ratios (MMR) and neonatal mortality rates, particularly in rural and underserved communities [9]. Although NHIS was designed to improve healthcare access and reduce the financial barriers to essential maternal care, enrollment remains uneven, with notable disparities between rural and urban populations. Evidence suggests that rural women, who face greater socio-economic challenges, including lower income, education, and access to healthcare facilities, are less likely to be enrolled in NHIS compared to their urban counterparts [10]. These disparities in NHIS enrollment contribute to unequal access to healthcare services, potentially exacerbating maternal and infant health challenges in rural areas [7]. While health insurance is an essential mechanism to reduce financial barriers to healthcare, its utilization among rural women remains critically low, limiting their access to timely and quality maternal care. Therefore, the problem lies not only in low enrollment rates but also in the fact that rural women, even when enrolled, face significant barriers to fully utilizing health insurance for the provision of maternal health services. This study aims to address these gaps by focusing on the rural-urban disparities in NHIS enrollment and healthcare utilization among Ghanaian women of reproductive age. Using data from the Ghana Demographic and Health Survey (GDHS) for the years 2008, 2014, and 2022, the study seeks to identify the key factors that influence NHIS enrollment and utilization, and to assess whether current policies and programs are effectively addressing the needs of rural women. By investigating these disparities, this research will contribute to the understanding of whether the NHIS is successful in achieving equitable healthcare access for all women, and whether targeted interventions are needed to bridge the gap between rural and urban areas. The also assessed whether current NHIS strategies are effective in bridging the gap between rural and urban areas, with a particular focus on women's access to healthcare. It will further examine whether these strategies support the achievement of universal health coverage (UHC) for all women in Ghana, particularly those residing in rural areas. Methods Study design and data source This study utilized data from the Ghana Demographic and Health Surveys (GDHS) conducted in 2008, 2014, and 2022. These three survey rounds were selected due to the introduction of the National Health Insurance Scheme (NHIS) in 2003, after which GDHS began capturing NHIS-related variables. The GDHS, implemented by the Ghana Statistical Service in collaboration with USAID and other partners, provides nationally representative data on health service utilisation, mortality, morbidity, nutrition, and health behaviors. The 2022 GDHS, the seventh in the series, surveyed 18,540 households. Data were obtained from the Measure DHS database, with questionnaires tailored to address Ghana’s specific health priorities, making the dataset essential for evaluating health programs and informing policy. Response variable The response variable for this study was NHIS enrollment, measured as a binary outcome indicating whether a woman was registered for NHIS (Yes = 1, No = 0). Independent variables The study included independent variables at the community, household, and individual levels, guided by existing literature. Community-level factors included type of residence (urban/rural). Household-level variables comprised wealth index (poorest to richer) and perceived distance to a health facility (far/not far). Individual-level variables included religion, ethnicity, and number of children. These factors were selected to capture socioeconomic, geographic, and cultural influences on NHIS enrollment. Data analysis The statistical software STATA version 17 was used to process the data. Some variables were recoded and renamed so that they would be consistent across all the rounds and all results were weighted. Univariate and bivariate and multivariate analysis were carried out. Univariate analysis dealt with descriptive statistics, including frequencies and percentages, were used to summarize the background characteristics of the study population. Bivariate analysis was done using chi-square (χ²) test was used to examine the association between neonatal mortality and key independent variables, such as ANC visits and maternal characteristics. Multivariate analysis utilised the logistic regression models were employed to determine the adjusted odds ratios (OR) and 95% confidence intervals (CI) for the association between ANC utilisation and neonatal mortality. The Variance Inflation Factor (VIF) was used to test for multicollinearity among the explanatory variables. A VIF value greater than 10 (or 5) indicates problematic multicollinearity, which can inflate standard errors and obscure the individual effects of predictors. Addressing multicollinearity is important for ensuring reliable and interpretable regression results. A sequential modeling approach was employed to analyze the relationship between NHIS enrollment and demographic, socioeconomic, and regional factors using logistic regression. Initially, a null model (Model 0) was established to serve as a baseline for understanding variations in NHIS enrollment linked to observed differences. Model 1 introduced the year of the survey to evaluate its impact on NHIS enrollment trends over time. Model 2 investigated the influence of demographic factors on NHIS enrollment while controlling for the survey year. This model provided significant insights into how characteristics such as age, marital status, and education level affected enrollment. Model 3 adjusted for socioeconomic factors, such as employment status and wealth index, with confounders retained only if they demonstrated significance at p<0.05p < 0.05p<0.05 [11] in at least one model. Results Background characteristics of the study participants Table 1 illustrated the distribution of women NHIS registration, type of place of residence aligns with Ghana’s overall population trend, showing that most sampled women resided in the rural areas across all the three surveys. Urban residency increased from 38.0% in 2008 to 48.6% in 2022. Throughout the survey years, majority of the sampled women stay closer to the nearest health facility. Richest wealth status of the sampled women increased from 2008 to 2014 but shown a slight declined in the 2022 GDHS. Greater proportion of the sampled women were Akan’s in each of the survey years followed by the Mole Dagbani’s and the Ewe’s. Christianity was the dominant religion among the women across all the three survey periods accounting for more than 65% in each of the survey year. Greater proportion of the women had one child in the last five year preceding the survey. The most populated age group (category) was 25–29 years across the three surveys. Greater proportion of the sampled women had secondary education as highest education attainment across the three survey years. Majority of the women across the survey period were married and finally, majority of the working were working. Table 1 Weighted Percentage Distributions of Sampled Women by Background Characteristics and Year of Survey Variable GDHS 2008 2014 2022 Merged data n = 2986 n = 5884 n = 9353 n = 18223 NHIS registration % N % N % N % N Yes 38.1 1178 67.6 4087 94.4 8810 66.7 14075 No 61.9 1808 32.4 1796 5.6 543 33.3 4148 Place of residence Urban 38.0 999 45.0 2344 48.6 3857 43.9 7200 Rural 62.0 1987 55.0 3540 51.4 5496 56.1 11023 Distance to health facility Far 29.4 1002 27.7 1888 25.5 2800 27.5 5690 Not far 70.6 1986 72.3 3996 74.5 6533 72.5 12533 Wealth Index Poorest 25.5 968 22.2 1886 23.7 3056 23.8 5910 Poorer 22.1 656 21.0 1304 20.4 2252 21.2 4212 Middle 18.9 504 19.6 1083 19.5 1681 19.3 3268 Richer 19.3 502 18.9 883 19.0 1338 19.1 2723 Richest 14.2 356 18.4 728 17.4 1026 16.7 2110 Ethnicity Akan 45.7 1135 47.4 2244 39.5 2629 44.2 6008 Ga/dangme 4.9 141 6.2 261 6.3 351 5.8 753 Ewe 12.3 364 12.9 654 10.2 856 11.8 1874 Guan 2.9 81 2.0 145 3.3 474 2.7 700 Mole Dagbani 20.5 765 17.3 1557 22.5 2858 20.1 5180 Grussi 3.0 161 2.7 246 3.7 472 3.1 879 Gruma 5.8 201 8.2 578 10.0 1262 8.0 2041 Mande 0.9 24 1.2 87 3.7 354 1.9 465 Other 4.0 114 2.1 111 0.90 97 2.3 322 Religion % N % N % N % N Christian 69.9 1972 75.6 4179 70.3 5847 71.9 11998 Muslim 19.0 602 17.0 1204 24.0 2969 20.0 4775 Tradition/Other 11.1 412 7.4 501 5.7 537 8.1 1450 Children in last 5yr 1 47.3 1391 48.6 2877 53.5 4796 49.8 9064 2 44.3 1336 42.4 2514 39.7 3920 42.1 7770 3+ 8.4 259 9.0 493 6.8 637 8.1 1389 Age category 15–19 yrs 4.0 119 5.6 207 3.7 353 4.4 679 20–24 yrs 19.6 583 17.1 1009 17.7 1671 18.1 3263 25–29 yrs 27.9 823 25.0 1494 23.3 2276 25.4 4593 30–34 yrs 20.3 600 24.9 1409 24.9 2266 23.4 4275 35–39 yrs 17.2 518 18.5 1061 19.2 1713 18.3 3292 40–44 yrs 7.8 242 8.6 529 8.5 819 8.3 1590 45–49 yrs 3.3 101 2.5 175 2.7 255 2.8 531 Maternal Edu. No formal edu. 32.6 1127 27.4 2042 23.2 2917 27.8 6086 Primary edu. 24.9 722 20.0 1209 15.2 1496 20.0 3427 Secondary edu. 40.1 1072 48.1 2409 52.2 4237 46.8 7718 Higher 2.4 65 4.5 224 9.4 703 5.4 992 Marital Status Single 29.2 778 36.8 1966 37.6 2873 34.5 5617 Married 70.8 2208 63.2 3918 62.4 6480 65.5 12606 Occupation Not Working 12.4 356 21.9 1266 22.2 2084 18.8 3706 Working 87.6 2630 78.1 4618 77.8 7269 81.2 14517 Source : Derived from GDHS 2008, 2014 and 2022 Bivariate Analysis Bivariate Analysis of NHIS Registration and the other Factors Table 2 presented the bivariate results between NHIS enrollment and the selected factors. Across all the three survey, the finding regarding exhibited some contradictions. we employed the chi-squared test was employed to observe statistically significance alteration among the variables in the survey years, with significance level tested at 95%. From Table 3 it was evident that the percentage of NHIS registration was higher among the urban population than the rural women across all the three survey period. Notable all the survey years shows a significant relationship (p < 0.05). Regarding the distance to the nearest health facility which will influence health service utilisation and NHIS enrollment. Across all the survey years, the percentage of the women residing closer to the health facility which in addition was statistically significant (p < 0.05). In terms of the wealth index, there was a significant relationship in NHIS registration and Wealth index across all the three survey years with those in the richest wealth category having the highest percentage of NHIS registration across the survey years. Although there was significant relationship between ethnicity and NHIS enrollment but none of the ethnicity group in Ghana had the highest percentage of NHIS enrollment across all the survey. Just like the ethnicity, religious affiliation and NHIS was significant across the survey years. Muslim religion had the highest percentage of NHIS enrollment in both the 2014 and 2022 GDHS with Christians having the highest percentage in 2008. Women who have had 3 or more birth in the last five year preceding the survey has the highest percentage of NHIS registration but the difference was not significant in both the 2014 and the 2022 GDHS. Although there was significant relationship in the age category and NHIS enrollment across the survey’s years but none of the age category had a dominant lead in terms of percentage of NHIS registration across the survey periods. Being married was a stimulus to NHIS registration with married women having the highest percentage of registration across all the survey years and the difference was significant. Finally, occupational status and NHIS registration was inconsistent with 2014 GDHS showing significant relationship. Table 2 Weighted Percentage Distribution of NHIS registration By Year of GDHS and Other Factors Variable GDHS 2008 2014 2022 Pooled data % [95% CI] % [95% CI] % [95% CI] % [95% CI] Place of residence p < 0.05 p < 0.05 p < 0.05 p < 0.05 Urban 47.8[0.48, 0.54] 72.9[0.71, 0.75] 96.7[0.96, 0.97] 82.5[0.82, 0.83] Rural 33.3[0.32, 0.37] 67.2[0.66, 0.69] 92.4[0.92, 0.93] 74.0[0.73, 0.75] Distance to hosp . p < 0.05 p < 0.05 p < 0.05 p < 0.05 Far 31.6[0.31, 0.36] 68.0[0.66, 0.70] 91.2[0.91, 0.93] 73.6[0.72, 0.75] Not far 41.8[0.41, 0.46] 70.1[0.69, 0.72] 95.2[0.95, 0.96] 79.1[0.78, 0.80] Wealth Index p < 0.05 p < 0.05 p < 0.05 p < 0.05 Poorest 25.1[0.24, 0.30] 71.1[0.69, 0.73] 90.6[0.89, 0.92] 73.9[0.73, 0.75] Poorer 33.9[0.34, 0.42] 61.8[0.59, 0.64] 93.8[0.93, 0.95] 75.2[0.76, 0.76] Middle 38.5[0.37, 0.46] 68.9[0.66, 0.71] 96.7[0.96, 0.97] 78.9[0.78, 0.80] Richer 49.3[0.50, 0.59] 70.4[0.67, 0.73] 96.9[0.96, 0.98] 80.4[0.79, 0.82] Richest 57.5[0.55, 0.65] 78.6[0.76, 0.82] 98.0[0.97, 0.99] 84.8[0.83, 0.86] Ethnicity p < 0.05 p < 0.05 p < 0.05 p < 0.05 Akan 41.2[0.40, 0.45] 62.9[0.61, 0.65] 95.8[0.95, 0.96] 73.4[0.72, 0.75] Ga/dangme 37.2[0.32, 0.48] 70.3[0.65, 0.76] 92.8[0.90, 0.95] 75.0[0.72, 0.78] Ewe 32.6[0.32. 0.48] 72.2[0.69, 0.75] 92.1[0.90, 0.94] 73.7[0.72, 0.76] Guan 38.8[0.31, 0.52] 68.0[0.60, 0.75] 91.4[0.88, 0.94] 80.7[0.78, 0.83] Mole Dagbani 39.0[0.38, 0.45] 78.1[0.76, 0.80] 95.4[0.95, 0.96] 82.3[0.81, 0.83] Grussi 46.0[0.44, 0.59] 80.8[0.75, 0.85] 95.3[0.93, 0.97] 83.3[0.81, 0.86] Gruma 28.2[0.19, 0.31] 64.0[0.60, 0.68] 90.7[0.89, 0.92] 76.6[0.74, 0.78] Mande 51.6[0.31, 0.69] 60.0[0.49, 0.70] 93.5[0.90, 0.96] 84.9[0.81, 0.88] Other 38.7[0.33, 0.50] 73.4[0.65, 0.81] 93.8[0.87, 0.97] 65.4[0.59, 0.71] Religion p < 0.05 p < 0.05 p < 0.05 p < 0.05 Christian 41.4[0.42, 0.46] 68.5[0.67, 0.70] 94.8[0.94, 0.95] 77.3[0.77, 0.78] Muslim 38.3[0.37, 0.45] 77.2[0.75, 0.80] 95.0[0.94, 0.96] 83.7[0.83, 0.85] Tradition/Other 23.5[0.18, 0.26] 58.7[0.54, 0.63] 83.6[0.80, 0.87] 57.4[0.55, 0.60] Children in last 5yrs p 0.05 p > 0.05 p < 0.05 1 42.3[0.42, 0.47] 68.5[0.67, 0.70] 94.4[0.94, 0.94] 78.5[0.78, 0.79] 2 36.4[0.34, 0.40] 70.4[0.69, 0.72] 93.9[0.93, 0.95] 76.5[0.76, 0.77] 3+ 32.3[0.29, 0.40] 70.3[0.66, 0.74] 94.7[0.93, 0.96] 74.8[0.72, 0.77] Age category p < 0.05 p < 0.05 p < 0.05 p < 0.05 15–19 yrs 25.4[0.18, 0.34] 63.2[0.57, 0.70] 92.9[0.90, 0.95] 72.0[0.68, 0.75] 20–24 yrs 33.4[0.32, 0.40] 63.1[0.60, 0.66] 92.9[0.92, 0.94] 73.6[0.72, 0.75] 25–29 yrs 39.5[0.37, 0.43] 74.0[0.72, 0.76] 94.8[0.94, 0.96] 78.2[0.77, 0.79] 30–34 yrs 45.7[0.43, 0.51] 71.4[0.69, 0.74] 95.1[0.94, 0.96] 80.6[0.79, 0.82] 35–39 yrs 42.3[0.39, 0.47] 72.4[0.70, 0.75] 94.6[0.93, 0.96] 79.3[0.78, 0.81] 40–44 yrs 39.1[0.35, 0.48] 63.7[0.60, 0.68] 92.5[0.91, 0.94] 75.2[0.73, 0.77] 45–49 yrs 20.8[0.18. 0.35] 58.9[0.51, 0.66] 92.1[0.88, 0.95] 68.5[0.64, 0.72] Maternal Edu. p < 0.05 p < 0.05 p < 0.05 p < 0.05 No formal edu. 0.32[0.29, 0.35] 68.3[0.66, 0.70] 91.2[0.90, 0.92] 72.6[0.71, 0.74] Primary edu. 32.5[0.29, 0.36] 64.2[0.61, 0.69] 91.8[0.90, 0.93] 69.6[0.68, 0.71] Secondary edu. 52.7[0.50, 0.56] 71.3[0.70, 0.73] 94.4[0.96, 0.97] 82.5[0.82, 0.83] Higher 58.4[0.46, 0.70] 88.3[0.83, 0.92] 98.3[0.97, 0.99] 93.4[0.92, 0.95] Marital Status p < 0.05 p < 0.05 p < 0.05 p 0.05 p 0.05 p < 0.05 Working 37.3[0.33, 0.42] 74.3[0.72, 0.77] 94.7[0.94, 0.96] 76.0[0.75, 0.77] Not working 40.6[0.39, 0.43] 68.1[0.67, 0.69] 94.0[0.93, 0.95] 82.5[0.81, 0.84] Source : Derived from GDHS 2008, 2014 and 2022 Multivariate Analysis The analysis showed in Table 3 below indicated that NHIS registration among women in Ghana significantly increased over time, with the odds being over three times higher in 2014 compared to 2008 and dramatically increasing in 2022 (OR ≈ 24). Living in rural areas was associated with lower odds of NHIS registration in Model 2 (OR = 0.61), but the association not significant in Model 3 (OR = 0.93) after adjusting for other variables. Individuals living closer to a health facility had slightly higher odds of NHIS registration (OR = 0.13). Compared to mothers aged 15–19 years, the odds of NHIS registration increased significantly for those aged 25–39 years, with the highest odds observed between ages 30–39, while those in the oldest age category (40–49) showed no significant difference. Wealth status was positively associated with NHIS registration, with the richest category having the highest odds (OR = 1.67). Traditional or other religious groups had 42% lower odds of NHIS registration compared to Christians. Married women exhibited 53% higher odds if NHIS registration than those who are not married. Higher education levels were associated with improved registration of NHIS, as individuals with secondary (OR = 1.89) and higher education (OR = 2.61) had significantly better odds. Ethnic disparities were evident, with the Mole Dagbani (OR = 2.13), Grussi (OR = 2.50), and Gruma (OR = 1.53) ethnic groups experiencing significantly higher odds of NHIS registration compared to the Akan group. Table 3 Logistic regression analysis of factors associated with NHIS enrollment among women in Ghana (GDHS 2008–2022) Variable Model 1 Model 2 Model 3 OR [95% CI] OR [95% CI] OR [95% CI] Year of Survey 2008 GDHS (Ref) 1.00 1.00 1.00 2014 GDHS 3.38[3.08, 3.71] * 3.33[3.04, 3.65] * 3.54[3.21, 3.91] * 2022 GDHS 24.11[21.5, 27.0] * 23.92[21.35, 26.79] * 24.17[21.42, 27.56] * Place of residence Urban (Ref) 1 1 Rural 0.61[0.56, 0.66] * 0.93[0.83, 1.03] Distance to health facility Far (Ref) 1 Not far 1.13[1.03, 1.23] * Age category in year 15–19 (Ref) 1 20–24 1.02[0.82, 1.27] 25–29 1.29[1.04, 1.60] * 30–34 1.33[1.06, 1.65] * 35–39 1.34[1.07, 1.67] * 40–44 1.08[0.84, 1.38] 45–49 0.90[0.67, 1.21] Wealth status Poorest (Ref) 1.00 Poorer 1.12[1.00, 1.26] Middle 1.41[1.22, 1.62] * Richer 1.50[1.27, 1.77] * Richest 1.67[1.37, 2.05] * Religion Christianity (Ref) 1.00 Moslem 0.99[0.88, 1.12] Traditional/Other 0.58[0.50, 0.67] * Marital status Not married (Ref) 1.00 Married 1.53[1.39, 1.69] * Highest education No education (Ref) 1.00 Primary 1.09[0.97, 1.23] Secondary 1.89[1.68, 2.14] * Higher 2.61[1.95, 3.50] * Ethnicity Akan (Ref) 1.00 Ga/dangme 1.16[0.94, 1.43] Ewe 1.18[1.02, 1.35] * Guan 1.08[0.84, 1.37] Mole Dagbani 2.13[1.87, 2.44] * Grussi 2.50[2.00, 3.12] * Gruma 1.53[1.29, 1.81] * Mande 1.22[0.89, 1.67] Other 1.41[0.97, 2.05] Source : Derived from GDHS 2008, 2014 and 2022 Discussions From Model 3 (the final model) in Table 3 , it was observed that, accounting for all other variables, the odds of NHIS registration tended to increase from 2008 to 2022 GDHS. This indicated that women sampled in the 2022 GDHS had a higher chance of registration for NHIS compared to those sampled in 2014, and 2008. This aligns with other studies which suggested that there was a notable reduction in the number of uninsured adults, with a drop of approximately 16.9 million individuals [ 12 ]. Living in rural areas was associated with lower odds of NHIS registration but the association not significant. Not surprisingly, empirical studies in Ghana, particularly those assessing rural-urban differences in health insurance enrollment, have produced mixed results. A similar study analyzed the impact of Mutual Health Organizations (MHOs) in Ghana and found higher enrollment rates in urban areas (57.6%) compared to rural areas (42.4%). This suggests that urban populations may have better access to health insurance programs [ 13 ]. Women staying closer to health facility had slightly higher odd of NHIS registration but the difference was not sufficient enough to be significant, this may be due to their knowledge on the role of health insurance on healthcare utilisation. Age is another factor that influences the likelihood of health insurance enrollment, compared with women aged 15–19 years, the odds of NHIS registration increased significantly for those aged 25–39 years, with the highest odds observed between ages 30–39, while those in the oldest age category (40–49) showed no significant difference. Wealth status was positively associated with NHIS registration, with the richest category having the highest odds (OR = 1.67). Studies show that older individuals are more likely to have health insurance coverage compared to younger individuals. This is often because older adults have higher health needs and are more likely to seek out health insurance to manage the financial risks associated with healthcare. In many countries, age-related policies also provide subsidies or benefits for older individuals, further encouraging enrollment [ 14 ]. Marital status is another important factor influencing health insurance coverage. Married individuals are more likely to be enrolled in health insurance schemes compared to their unmarried counterparts. In Table 3 (Model 3) married women exhibited 53% higher odds if NHIS registration than those who are not married. Previous studies have found that unmarried individuals, especially single or divorced persons, tend to have lower rates of health insurance coverage [ 15 ]. This is particularly evident in women, who often experience economic inequality and lower income levels compared to men. Unmarried women are less likely to be covered by employer-sponsored insurance, which contributes to their overall lower health insurance coverage. Higher education levels were associated with improved registration of NHIS, as individuals with secondary and higher education had significantly better odds. Studies have found that in many LMICs educated individuals are often more aware of the financial protection provided by SHI and are more proactive in seeking enrollment. The positive impact of education on health insurance coverage can also be attributed to the fact that educated individuals tend to have higher incomes, making it easier for them to afford premiums. Furthermore, education is linked to better health outcomes, which encourages individuals to seek comprehensive health coverage. In contrast, those with lower levels of education often have limited knowledge about health insurance programs and may not understand how they can access benefits. As a result, low-education groups tend to have lower enrollment rates and are more likely to face barriers in retaining their coverage over time [ 16 , 17 ]. Conclusion Sociodemographic factors indicated that while urban residence and proximity to health facilities were associated with higher odds of insurance registration, these factors were not statistically significant in this study. The wealth index showed that women in the richest category had registration odds compared to those in the poorest category, indicating complex socio-economic influences on NHIS registration. Marital status was also significant influence of NHIS registration. Overall, while there have been improvements in NHIS registration, the study underscored the need for continued efforts to enhance NHIS services, address socio-economic disparities, and optimize delivery methods to further improve NHIS coverage in Ghana. Abbreviations NHIS–National Health Insurance Scheme ANC – Antenatal Care CI – Confidence Intervals GDHS – Ghana Demographic Health Survey LMIC – Low- and Middle-Income Countries MMR – Maternal Mortality Rate OR – Odds Ratio LMIC– Low- and Middle-Income Countries SSA – Sub Saharan Africa Declarations Ethics approval and consent to participate As publicly available secondary data were used, ethical approval was deemed unnecessary. Consent for publication No permissions were required to publish this study, as no personal information, photographs, or videos were used. Additionally, all data utilized are publicly available in the public domain. Availability of data and materials The datasets generated and/or analysed during the current study are available in the Measure DHS repository: https://dhsprogram.com/data/available-datasets.cfm Competing interests The authors declare that they have no competing interests. Funding No funding was received for this study. Authors' contributions LF conceived the study and all authors contributed to the study design. SJ and JE acquired and analysed the data. SJ validated the results. LF and SJ drafted sections of the manuscript. All authors (SJ, LF, JE) reviewed, edited, and approved the final manuscript. SJ supervised the overall study process. Acknowledgements We acknowledge Measure DHS for providing access to the datasets used in this study. Authors’ information Not applicable. References Owoo, N. S., & Lambon-Quayefio, M. P. (2013). National health insurance, social influence and antenatal care use in Ghana. Health economics review , 3 , 1-12. Kuuire, V. Z., Kangmennaang, J., Atuoye, K. N., Antabe, R., Boamah, S. A., Vercillo, S., ... & Luginaah, I. (2017). Timing and utilisation of antenatal care service in Nigeria and Malawi. Global public health , 12 (6), 711-727. Addai, I. (2020). Determinants of use of maternal–child health services in rural Ghana. Journal of biosocial science , 32 (1), 1-15. Shao, L., Wang, Y., Wang, X., Ji, L., & Huang, R. (2022). Factors associated with health insurance ownership among women of reproductive age: a multicountry study in sub-Saharan Africa. PLoS One , 17 (4), e0264377. Watkins, D. A., Jamison, D. T., Mills, T., Atun, T., Danforth, K., Glassman, A., ... & Alwan, A. (2018). Universal health coverage and essential packages of care. Liu, X., Li, N., Liu, C., Ren, X., Liu, D., Gao, B., & Liu, Y. (2016). Urban–rural disparity in utilization of preventive care services in China. Medicine , 95 (37), e4783. Mulenga, T., Moono, M., Mwendafilumba, M., Manasyan, A., & Sharma, A. (2018). Home deliveries in the capital: a qualitative exploration of barriers to institutional deliveries in peri-urban areas of Lusaka, Zambia. BMC Pregnancy and Childbirth , 18 , 1-11. Browne, J. L., Kayode, G. A., Arhinful, D., Fidder, S. A., Grobbee, D. E., & Klipstein-Grobusch, K. (2016). Health insurance determines antenatal, delivery and postnatal care utilisation: evidence from the Ghana Demographic and Health Surveillance data. BMJ open , 6 (3), e008175. Dadjo, J., Ahinkorah, B. O., & Yaya, S. (2022). Health insurance coverage and antenatal care services utilization in West Africa. BMC Health Services Research , 22 (1), 311. Ameyaw, E. K., Kofinti, R. E., & Appiah, F. (2017). National health insurance subscription and maternal healthcare utilisation across mothers’ wealth status in Ghana. Health Economics Revew , 7 , 1-15. Snijders, T. A., & Bosker, R. (2011). Multilevel analysis: An introduction to basic and advanced multilevel modeling. Carman, K. G., Eibner, C., & Paddock, S. M. (2015). Trends in health insurance enrollment, 2013–15. Health affairs , 34 (6), 1044-1048. Duku, S. K., Fenenga, C. J., Alhassan, R. K., & Nketiah-Amponsah, E. (2013). Rural-urban differences in the determinants of enrolment in health insurance in Ghana. Paris: International union for the scientific study of population . Giri, S., Acharya, D., Adhikari, R., Sharma, M. K., & Ranabhat, C. L., 2024). Status and associated factors of health insurance enrollment among women in Nepal. World Medical & Health Policy . Stimpson, J. P., Kemmick Pintor, J., & Wilson, F. A. (2019). Association of Medicaid expansion with health insurance coverage by marital status and sex. PloS one , 14 (10), e0223556. Nsiah-Boateng, E., & Aikins, M. (2018). Trends and characteristics of enrolment in the National Health Insurance Scheme in Ghana: a quantitative analysis of longitudinal data. Global health research and policy , 3 , 1-10. Osei Afriyie, D., Krasniq, B., Hooley, B., Tediosi, F., & Fink, G. (2022). Equity in health insurance schemes enrollment in low and middle-income countries: A systematic review and meta-analysis. International Journal for Equity in Health , 21 (1), 21. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6852251","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":486923389,"identity":"baa02498-c701-4a7c-a005-776667ef512d","order_by":0,"name":"Sam Johnson","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYBACxgYeBPsBQpwNiCUIa2E2YEggQgsDA0ILmwRRWpjbe489/FFxh0G+/XRa5c8fhxkMjh9+wPCh7DCDfHQDdof1nEs35jnzjMHgTO622zwJQC1n0gwYZ5w7zGB45wB2LTNyzKQZ24AqGYBaGEBabvAwMPMCRQxnJODUIvkTqEC+/+22wh8wLX8JaJEAmclwI3cbAw9MC8heeQkcWnrOmEnznDnMY3Dj7WZpnrR0HkmgXw4CfchjgEOLYXuPmeSPisNy8v25Gz/+sLGW4zt++OGDH2XWcvI4HGbYAKHhsQNmHAAxDA5g1cEgj10YLNWAW24UjIJRMApGFAAAlmZdQB3F2GQAAAAASUVORK5CYII=","orcid":"","institution":"University of Cape Coast","correspondingAuthor":true,"prefix":"","firstName":"Sam","middleName":"","lastName":"Johnson","suffix":""},{"id":486923391,"identity":"afba1a36-2427-4fac-a191-11571c840754","order_by":1,"name":"Latifatu Fagin","email":"","orcid":"","institution":"University of Cape Coast","correspondingAuthor":false,"prefix":"","firstName":"Latifatu","middleName":"","lastName":"Fagin","suffix":""},{"id":486923395,"identity":"fa742caa-93b4-4811-863e-69222936dc1a","order_by":2,"name":"Joseph Eghan","email":"","orcid":"","institution":"University of Cape Coast","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"","lastName":"Eghan","suffix":""}],"badges":[],"createdAt":"2025-06-09 08:23:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6852251/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6852251/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95654582,"identity":"aa07f4e7-dd6b-434e-a113-9cdeb28e7df5","added_by":"auto","created_at":"2025-11-11 16:12:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1481141,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6852251/v1/9569e80c-02bc-4116-a671-470f2ec35288.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Rural and Urban Differences in NHIS enrollment Among women 15-49 years: Analyses 2008-2022 Ghana Demographic and Health Survey","fulltext":[{"header":"Contributions to the literature","content":"\u003cul type=\"disc\"\u003e\n \u003cli\u003eThis study highlights long-term trends in health insurance enrollment among Ghanaian women from 2008 to 2022.\u003c/li\u003e\n \u003cli\u003eIt uncovers important differences in enrollment between rural and urban women, revealing ongoing inequalities in healthcare access.\u003c/li\u003e\n \u003cli\u003eThe paper shows how education, marital status, and income levels affect women\u0026rsquo;s likelihood of enrolling in NHIS.\u003c/li\u003e\n \u003cli\u003eIt provides timely evidence to guide policy decisions aimed at improving universal health coverage in Ghana.\u003c/li\u003e\n \u003cli\u003eThe findings support efforts to strengthen health systems and reduce maternal health disparities in low- and middle-income countries.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Background","content":"\u003cp\u003eMaternal and child health remains a critical global concern, especially in low- and middle-income countries (LMICs), where maternal and infant mortality rates remain alarmingly high. Despite global efforts to reduce these figures, significant disparities in health outcomes persist, particularly among rural and marginalized populations [1,2] In many developing countries, maternal mortality rates are estimated at 480 deaths per 100,000 live births, with over half a million women dying annually due to complications from pregnancy and childbirth [3]. \u0026nbsp;A key contributing factor to these high mortality rates is the underutilization of healthcare services, particularly maternal care, which remains a significant barrier to improving maternal health outcomes in these settings.\u003c/p\u003e\n\u003cp\u003eAlthough several countries have made progress in improving health-related indicators, others have struggled to address distant but crucial determinants of women\u0026rsquo;s health, such as access to education, political participation, and social and economic opportunities [4]. Lack of empowerment in these areas prevents women from making informed decisions about their health, fertility, nutrition, and overall well-being. Moreover, women are disproportionately vulnerable to catastrophic health expenditures, which can lead to further financial hardships, exacerbating their lack of control over healthcare decisions and deepening impoverishment [2,4] To safeguard women from these challenges, measures such as universal access to maternity care have been proposed and implemented in some countries, aligning with the Sustainable Development Goals (SDG), which emphasize universal health coverage (UHC) as a vital component for improving global health outcomes [5].\u003c/p\u003e\n\u003cp\u003eThe divide between rural and urban populations in accessing healthcare services is a well-documented challenge, particularly in developing nations. Rural populations face numerous barriers that impede their ability to utilize health services effectively. These barriers include lower levels of education, lower income, limited access to health insurance, and longer travel distances to healthcare facilities, all of which contribute to reduced healthcare utilization [6].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHealth insurance plays a key role in mitigating these disparities by providing financial protection and improving access to essential healthcare services, including maternal and reproductive care. By pooling financial risks, health insurance schemes can ensure that individuals are protected against the financial burden of healthcare costs, which may otherwise prevent them from seeking timely medical attention [7]. In Ghana, the National Health Insurance Scheme (NHIS), established under the Health Insurance Act of 2003, aims to increase access to healthcare services, including maternal care, by providing financial protection for its members [8].\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eAlthough NHIS enrollment has expanded over time, active membership and effective utilisation of the scheme remain challenges, particularly in rural areas where both health service access and health insurance enrollment rates are lower [9]. \u0026nbsp;Despite some successes, the NHIS has not fully achieved its intended goal of equitable healthcare access for all citizens. Evidence suggests that rural residents, especially women, are less likely to be enrolled in and utilize the NHIS compared to their urban counterparts. Geographic location, wealth status, and other socio-economic factors are key determinants of NHIS enrollment and healthcare utilization, with rural women facing significant barriers to access [10]\u003c/p\u003e\n\u003cp\u003eDespite the significant expansion of the National Health Insurance Scheme (NHIS) in Ghana, maternal and infant health outcomes remain suboptimal, with persistently high maternal mortality ratios (MMR) and neonatal mortality rates, particularly in rural and underserved communities [9]. Although NHIS was designed to improve healthcare access and reduce the financial barriers to essential maternal care, enrollment remains uneven, with notable disparities between rural and urban populations.\u003c/p\u003e\n\u003cp\u003eEvidence suggests that rural women, who face greater socio-economic challenges, including lower income, education, and access to healthcare facilities, are less likely to be enrolled in NHIS compared to their urban counterparts [10]. These disparities in NHIS enrollment contribute to unequal access to healthcare services, potentially exacerbating maternal and infant health challenges in rural areas [7]. While health insurance is an essential mechanism to reduce financial barriers to healthcare, its utilization among rural women remains critically low, limiting their access to timely and quality maternal care.\u003c/p\u003e\n\u003cp\u003eTherefore, the problem lies not only in low enrollment rates but also in the fact that rural women, even when enrolled, face significant barriers to fully utilizing health insurance for the provision of maternal health services. This study aims to address these gaps by focusing on the rural-urban disparities in NHIS enrollment and healthcare utilization among Ghanaian women of reproductive age. Using data from the Ghana Demographic and Health Survey (GDHS) for the years 2008, 2014, and 2022, the study seeks to identify the key factors that influence NHIS enrollment and utilization, and to assess whether current policies and programs are effectively addressing the needs of rural women.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eBy investigating these disparities, this research will contribute to the understanding of whether the NHIS is successful in achieving equitable healthcare access for all women, and whether targeted interventions are needed to bridge the gap between rural and urban areas. The also assessed whether current NHIS strategies are effective in bridging the gap between rural and urban areas, with a particular focus on women\u0026apos;s access to healthcare. It will further examine whether these strategies support the achievement of universal health coverage (UHC) for all women in Ghana, particularly those residing in rural areas.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and data source\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized data from the Ghana Demographic and Health Surveys (GDHS) conducted in 2008, 2014, and 2022. These three survey rounds were selected due to the introduction of the National Health Insurance Scheme (NHIS) in 2003, after which GDHS began capturing NHIS-related variables. The GDHS, implemented by the Ghana Statistical Service in collaboration with USAID and other partners, provides nationally representative data on health service utilisation, mortality, morbidity, nutrition, and health behaviors. The 2022 GDHS, the seventh in the series, surveyed 18,540 households. Data were obtained from the Measure DHS database, with questionnaires tailored to address Ghana\u0026rsquo;s specific health priorities, making the dataset essential for evaluating health programs and informing policy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResponse variable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe response variable for this study was NHIS enrollment, measured as a binary outcome indicating whether a woman was registered for NHIS (Yes = 1, No = 0).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndependent variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study included independent variables at the community, household, and individual levels, guided by existing literature. Community-level factors included type of residence (urban/rural). Household-level variables comprised wealth index (poorest to richer) and perceived distance to a health facility (far/not far). Individual-level variables included religion, ethnicity, and number of children. These factors were selected to capture socioeconomic, geographic, and cultural influences on NHIS enrollment.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe statistical software STATA version 17 was used to process the data. Some variables were recoded and renamed so that they would be consistent across all the rounds and all results were weighted. Univariate and bivariate and multivariate analysis were carried out. Univariate analysis dealt with descriptive statistics, including frequencies and percentages, were used to summarize the background characteristics of the study population. \u0026nbsp;Bivariate analysis was done using\u0026nbsp;chi-square (\u0026chi;\u0026sup2;) test was used to examine the association between neonatal mortality and key independent variables, such as ANC visits and maternal characteristics. Multivariate analysis utilised the\u0026nbsp;logistic regression models were employed to determine the adjusted odds ratios (OR) and 95% confidence intervals (CI) for the association between ANC utilisation and neonatal mortality.\u003c/p\u003e\n\u003cp\u003eThe Variance Inflation Factor (VIF) was used to test for multicollinearity among the explanatory variables. A VIF value greater than 10 (or 5) indicates problematic multicollinearity, which can inflate standard errors and obscure the individual effects of predictors. Addressing multicollinearity is important for ensuring reliable and interpretable regression results. A sequential modeling approach was employed to analyze the relationship between NHIS enrollment and demographic, socioeconomic, and regional factors using logistic regression. Initially, a null model (Model 0) was established to serve as a baseline for understanding variations in NHIS enrollment linked to observed differences. Model 1 introduced the year of the survey to evaluate its impact on NHIS enrollment trends over time. Model 2 investigated the influence of demographic factors on NHIS enrollment while controlling for the survey year. This model provided significant insights into how characteristics such as age, marital status, and education level affected enrollment. Model 3 adjusted for socioeconomic factors, such as employment status and wealth index, with confounders retained only if they demonstrated significance at p\u0026lt;0.05p \u0026lt; 0.05p\u0026lt;0.05 [11] in at least one model.\u003c/p\u003e"},{"header":"Results","content":"\n\u003ch3\u003eBackground characteristics of the study participants\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrated the distribution of women NHIS registration, type of place of residence aligns with Ghana\u0026rsquo;s overall population trend, showing that most sampled women resided in the rural areas across all the three surveys. Urban residency increased from 38.0% in 2008 to 48.6% in 2022. Throughout the survey years, majority of the sampled women stay closer to the nearest health facility. Richest wealth status of the sampled women increased from 2008 to 2014 but shown a slight declined in the 2022 GDHS.\u003c/p\u003e\u003cp\u003eGreater proportion of the sampled women were Akan\u0026rsquo;s in each of the survey years followed by the Mole Dagbani\u0026rsquo;s and the Ewe\u0026rsquo;s. Christianity was the dominant religion among the women across all the three survey periods accounting for more than 65% in each of the survey year. Greater proportion of the women had one child in the last five year preceding the survey. The most populated age group (category) was 25\u0026ndash;29 years across the three surveys. Greater proportion of the sampled women had secondary education as highest education attainment across the three survey years. Majority of the women across the survey period were married and finally, majority of the working were working.\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\u003eWeighted Percentage Distributions of Sampled Women by Background Characteristics and Year of Survey\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e\u003cp\u003eGDHS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e2008\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e2014\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eMerged data\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;2986\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;5884\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;9353\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;18223\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNHIS registration\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eN\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\u003e38.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8810\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e66.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e14075\u003c/p\u003e\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\u003e61.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1808\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1796\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e33.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4148\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e48.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e43.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7200\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e51.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e56.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e11023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDistance to health facility\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e27.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5690\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot far\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e74.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e72.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e12533\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWealth Index\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoorest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e968\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5910\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoorer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e656\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e21.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4212\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\u003e18.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e504\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1681\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e19.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3268\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRicher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e883\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1338\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e19.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2723\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRichest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e16.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2110\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAkan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e39.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e44.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGa/dangme\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e753\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEwe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1874\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e700\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMole Dagbani\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e765\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1557\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2858\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5180\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrussi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e472\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e879\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGruma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e578\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2041\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMande\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e465\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\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e322\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\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eN\u003c/p\u003e\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\u003e69.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e70.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5847\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e71.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e11998\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\u003e19.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e24.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2969\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4775\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTradition/Other\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e537\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChildren in last 5yr\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1391\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e53.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4796\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e49.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e9064\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e39.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3920\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e42.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7770\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e493\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e637\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1389\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge category\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u0026ndash;19 yrs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e353\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e679\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u0026ndash;24 yrs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3263\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;29 yrs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e823\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e25.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4593\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u0026ndash;34 yrs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e24.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4275\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35\u0026ndash;39 yrs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1713\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3292\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026ndash;44 yrs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e529\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1590\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e45\u0026ndash;49 yrs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e531\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMaternal Edu.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo formal edu.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e27.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6086\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary edu.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e722\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3427\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary edu.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e52.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e46.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7718\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\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e703\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e992\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1966\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e37.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2873\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e34.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5617\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\u003e70.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e62.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6480\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e65.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e12606\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot Working\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3706\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e87.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2630\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e78.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e77.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e81.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e14517\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003eSource\u003c/em\u003e: Derived from GDHS 2008, 2014 and 2022\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eBivariate Analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eBivariate Analysis of NHIS Registration and the other Factors\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presented the bivariate results between NHIS enrollment and the selected factors. Across all the three survey, the finding regarding exhibited some contradictions. we employed the chi-squared test was employed to observe statistically significance alteration among the variables in the survey years, with significance level tested at 95%. From Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e it was evident that the percentage of NHIS registration was higher among the urban population than the rural women across all the three survey period. Notable all the survey years shows a significant relationship (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Regarding the distance to the nearest health facility which will influence health service utilisation and NHIS enrollment. Across all the survey years, the percentage of the women residing closer to the health facility which in addition was statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In terms of the wealth index, there was a significant relationship in NHIS registration and Wealth index across all the three survey years with those in the richest wealth category having the highest percentage of NHIS registration across the survey years. Although there was significant relationship between ethnicity and NHIS enrollment but none of the ethnicity group in Ghana had the highest percentage of NHIS enrollment across all the survey.\u003c/p\u003e\u003cp\u003eJust like the ethnicity, religious affiliation and NHIS was significant across the survey years. Muslim religion had the highest percentage of NHIS enrollment in both the 2014 and 2022 GDHS with Christians having the highest percentage in 2008. Women who have had 3 or more birth in the last five year preceding the survey has the highest percentage of NHIS registration but the difference was not significant in both the 2014 and the 2022 GDHS. Although there was significant relationship in the age category and NHIS enrollment across the survey\u0026rsquo;s years but none of the age category had a dominant lead in terms of percentage of NHIS registration across the survey periods. Being married was a stimulus to NHIS registration with married women having the highest percentage of registration across all the survey years and the difference was significant. Finally, occupational status and NHIS registration was inconsistent with 2014 GDHS showing significant relationship.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eWeighted Percentage Distribution of NHIS registration By Year of GDHS and Other Factors\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eGDHS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2008\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2014\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePooled data\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e% [95% CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e% [95% CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e% [95% CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e% [95% CI]\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePlace of residence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\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\u003e47.8[0.48, 0.54]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72.9[0.71, 0.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96.7[0.96, 0.97]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e82.5[0.82, 0.83]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.3[0.32, 0.37]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67.2[0.66, 0.69]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.4[0.92, 0.93]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e74.0[0.73, 0.75]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDistance to hosp\u003c/b\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31.6[0.31, 0.36]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.0[0.66, 0.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91.2[0.91, 0.93]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e73.6[0.72, 0.75]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot far\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.8[0.41, 0.46]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70.1[0.69, 0.72]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95.2[0.95, 0.96]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e79.1[0.78, 0.80]\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\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoorest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.1[0.24, 0.30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.1[0.69, 0.73]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90.6[0.89, 0.92]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e73.9[0.73, 0.75]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoorer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.9[0.34, 0.42]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.8[0.59, 0.64]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93.8[0.93, 0.95]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e75.2[0.76, 0.76]\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\u003e38.5[0.37, 0.46]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.9[0.66, 0.71]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96.7[0.96, 0.97]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e78.9[0.78, 0.80]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRicher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49.3[0.50, 0.59]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70.4[0.67, 0.73]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96.9[0.96, 0.98]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e80.4[0.79, 0.82]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRichest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57.5[0.55, 0.65]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78.6[0.76, 0.82]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e98.0[0.97, 0.99]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e84.8[0.83, 0.86]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAkan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.2[0.40, 0.45]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62.9[0.61, 0.65]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95.8[0.95, 0.96]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e73.4[0.72, 0.75]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGa/dangme\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.2[0.32, 0.48]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70.3[0.65, 0.76]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.8[0.90, 0.95]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e75.0[0.72, 0.78]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEwe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.6[0.32. 0.48]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72.2[0.69, 0.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.1[0.90, 0.94]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e73.7[0.72, 0.76]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.8[0.31, 0.52]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.0[0.60, 0.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91.4[0.88, 0.94]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e80.7[0.78, 0.83]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMole Dagbani\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39.0[0.38, 0.45]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78.1[0.76, 0.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95.4[0.95, 0.96]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e82.3[0.81, 0.83]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrussi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46.0[0.44, 0.59]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80.8[0.75, 0.85]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95.3[0.93, 0.97]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e83.3[0.81, 0.86]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGruma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.2[0.19, 0.31]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64.0[0.60, 0.68]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90.7[0.89, 0.92]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e76.6[0.74, 0.78]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMande\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51.6[0.31, 0.69]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60.0[0.49, 0.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93.5[0.90, 0.96]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e84.9[0.81, 0.88]\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\u003e38.7[0.33, 0.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73.4[0.65, 0.81]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93.8[0.87, 0.97]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65.4[0.59, 0.71]\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\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\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\u003e41.4[0.42, 0.46]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.5[0.67, 0.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94.8[0.94, 0.95]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e77.3[0.77, 0.78]\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\u003e38.3[0.37, 0.45]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77.2[0.75, 0.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95.0[0.94, 0.96]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e83.7[0.83, 0.85]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTradition/Other\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.5[0.18, 0.26]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.7[0.54, 0.63]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e83.6[0.80, 0.87]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e57.4[0.55, 0.60]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChildren in last 5yrs\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.3[0.42, 0.47]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.5[0.67, 0.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94.4[0.94, 0.94]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e78.5[0.78, 0.79]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.4[0.34, 0.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70.4[0.69, 0.72]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93.9[0.93, 0.95]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e76.5[0.76, 0.77]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.3[0.29, 0.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70.3[0.66, 0.74]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94.7[0.93, 0.96]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e74.8[0.72, 0.77]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge category\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u0026ndash;19 yrs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.4[0.18, 0.34]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.2[0.57, 0.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.9[0.90, 0.95]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e72.0[0.68, 0.75]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u0026ndash;24 yrs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.4[0.32, 0.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.1[0.60, 0.66]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.9[0.92, 0.94]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e73.6[0.72, 0.75]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;29 yrs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39.5[0.37, 0.43]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.0[0.72, 0.76]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94.8[0.94, 0.96]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e78.2[0.77, 0.79]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u0026ndash;34 yrs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.7[0.43, 0.51]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.4[0.69, 0.74]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95.1[0.94, 0.96]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e80.6[0.79, 0.82]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35\u0026ndash;39 yrs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.3[0.39, 0.47]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72.4[0.70, 0.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94.6[0.93, 0.96]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e79.3[0.78, 0.81]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026ndash;44 yrs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39.1[0.35, 0.48]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.7[0.60, 0.68]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.5[0.91, 0.94]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e75.2[0.73, 0.77]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e45\u0026ndash;49 yrs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.8[0.18. 0.35]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.9[0.51, 0.66]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.1[0.88, 0.95]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e68.5[0.64, 0.72]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMaternal Edu.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo formal edu.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.32[0.29, 0.35]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.3[0.66, 0.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91.2[0.90, 0.92]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e72.6[0.71, 0.74]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary edu.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.5[0.29, 0.36]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64.2[0.61, 0.69]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91.8[0.90, 0.93]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e69.6[0.68, 0.71]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary edu.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52.7[0.50, 0.56]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.3[0.70, 0.73]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94.4[0.96, 0.97]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e82.5[0.82, 0.83]\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\u003e58.4[0.46, 0.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88.3[0.83, 0.92]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e98.3[0.97, 0.99]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e93.4[0.92, 0.95]\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\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.6[0.31, 0.38]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59.3[0.57, 0.61]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93.2[0.92, 0.94]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e73.2[0.72, 0.74]\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\u003e42.2[0.40, 0.44]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.5[0.73, 0.76]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94.6[0.94, 0.95]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e79.2[0.78, 0.80]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.3[0.33, 0.42]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.3[0.72, 0.77]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94.7[0.94, 0.96]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e76.0[0.75, 0.77]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot working\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40.6[0.39, 0.43]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.1[0.67, 0.69]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94.0[0.93, 0.95]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e82.5[0.81, 0.84]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eSource\u003c/em\u003e: Derived from GDHS 2008, 2014 and 2022\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMultivariate Analysis\u003c/h3\u003e\n\u003cp\u003eThe analysis showed in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e below indicated that NHIS registration among women in Ghana significantly increased over time, with the odds being over three times higher in 2014 compared to 2008 and dramatically increasing in 2022 (OR\u0026thinsp;\u0026asymp;\u0026thinsp;24). Living in rural areas was associated with lower odds of NHIS registration in Model 2 (OR\u0026thinsp;=\u0026thinsp;0.61), but the association not significant in Model 3 (OR\u0026thinsp;=\u0026thinsp;0.93) after adjusting for other variables. Individuals living closer to a health facility had slightly higher odds of NHIS registration (OR\u0026thinsp;=\u0026thinsp;0.13). Compared to mothers aged 15\u0026ndash;19 years, the odds of NHIS registration increased significantly for those aged 25\u0026ndash;39 years, with the highest odds observed between ages 30\u0026ndash;39, while those in the oldest age category (40\u0026ndash;49) showed no significant difference. Wealth status was positively associated with NHIS registration, with the richest category having the highest odds (OR\u0026thinsp;=\u0026thinsp;1.67).\u003c/p\u003e\u003cp\u003eTraditional or other religious groups had 42% lower odds of NHIS registration compared to Christians. Married women exhibited 53% higher odds if NHIS registration than those who are not married. Higher education levels were associated with improved registration of NHIS, as individuals with secondary (OR\u0026thinsp;=\u0026thinsp;1.89) and higher education (OR\u0026thinsp;=\u0026thinsp;2.61) had significantly better odds. Ethnic disparities were evident, with the Mole Dagbani (OR\u0026thinsp;=\u0026thinsp;2.13), Grussi (OR\u0026thinsp;=\u0026thinsp;2.50), and Gruma (OR\u0026thinsp;=\u0026thinsp;1.53) ethnic groups experiencing significantly higher odds of NHIS registration compared to the Akan group.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLogistic regression analysis of factors associated with NHIS enrollment among women in Ghana (GDHS 2008\u0026ndash;2022)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR [95% CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOR [95% CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR [95% CI]\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYear of Survey\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2008 GDHS (Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2014 GDHS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.38[3.08, 3.71] *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.33[3.04, 3.65] *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.54[3.21, 3.91] *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2022 GDHS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24.11[21.5, 27.0] *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.92[21.35, 26.79] *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.17[21.42, 27.56] *\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban (Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.61[0.56, 0.66] *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.93[0.83, 1.03]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDistance to health facility\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFar (Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot far\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.13[1.03, 1.23] *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge category in year\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u0026ndash;19 (Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.02[0.82, 1.27]\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.29[1.04, 1.60] *\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.33[1.06, 1.65] *\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.34[1.07, 1.67] *\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.08[0.84, 1.38]\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.90[0.67, 1.21]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWealth status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoorest (Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoorer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.12[1.00, 1.26]\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.41[1.22, 1.62] *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRicher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.50[1.27, 1.77] *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRichest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.67[1.37, 2.05] *\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChristianity (Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMoslem\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.99[0.88, 1.12]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraditional/Other\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.58[0.50, 0.67] *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot married (Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.53[1.39, 1.69] *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHighest education\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo education (Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.09[0.97, 1.23]\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.89[1.68, 2.14] *\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.61[1.95, 3.50] *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAkan (Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGa/dangme\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.16[0.94, 1.43]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEwe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.18[1.02, 1.35] *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.08[0.84, 1.37]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMole Dagbani\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.13[1.87, 2.44] *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrussi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.50[2.00, 3.12] *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGruma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.53[1.29, 1.81] *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMande\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.22[0.89, 1.67]\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.41[0.97, 2.05]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eSource\u003c/em\u003e: Derived from GDHS 2008, 2014 and 2022\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussions","content":"\u003cp\u003eFrom Model 3 (the final model) in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, it was observed that, accounting for all other variables, the odds of NHIS registration tended to increase from 2008 to 2022 GDHS. This indicated that women sampled in the 2022 GDHS had a higher chance of registration for NHIS compared to those sampled in 2014, and 2008. This aligns with other studies which suggested that there was a notable reduction in the number of uninsured adults, with a drop of approximately 16.9\u0026nbsp;million individuals [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Living in rural areas was associated with lower odds of NHIS registration but the association not significant. Not surprisingly, empirical studies in Ghana, particularly those assessing rural-urban differences in health insurance enrollment, have produced mixed results. A similar study analyzed the impact of Mutual Health Organizations (MHOs) in Ghana and found higher enrollment rates in urban areas (57.6%) compared to rural areas (42.4%). This suggests that urban populations may have better access to health insurance programs [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWomen staying closer to health facility had slightly higher odd of NHIS registration but the difference was not sufficient enough to be significant, this may be due to their knowledge on the role of health insurance on healthcare utilisation. Age is another factor that influences the likelihood of health insurance enrollment, compared with women aged 15\u0026ndash;19 years, the odds of NHIS registration increased significantly for those aged 25\u0026ndash;39 years, with the highest odds observed between ages 30\u0026ndash;39, while those in the oldest age category (40\u0026ndash;49) showed no significant difference. Wealth status was positively associated with NHIS registration, with the richest category having the highest odds (OR\u0026thinsp;=\u0026thinsp;1.67). Studies show that older individuals are more likely to have health insurance coverage compared to younger individuals. This is often because older adults have higher health needs and are more likely to seek out health insurance to manage the financial risks associated with healthcare. In many countries, age-related policies also provide subsidies or benefits for older individuals, further encouraging enrollment [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMarital status is another important factor influencing health insurance coverage. Married individuals are more likely to be enrolled in health insurance schemes compared to their unmarried counterparts. In Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (Model 3) married women exhibited 53% higher odds if NHIS registration than those who are not married. Previous studies have found that unmarried individuals, especially single or divorced persons, tend to have lower rates of health insurance coverage [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This is particularly evident in women, who often experience economic inequality and lower income levels compared to men. Unmarried women are less likely to be covered by employer-sponsored insurance, which contributes to their overall lower health insurance coverage. Higher education levels were associated with improved registration of NHIS, as individuals with secondary and higher education had significantly better odds. Studies have found that in many LMICs educated individuals are often more aware of the financial protection provided by SHI and are more proactive in seeking enrollment. The positive impact of education on health insurance coverage can also be attributed to the fact that educated individuals tend to have higher incomes, making it easier for them to afford premiums. Furthermore, education is linked to better health outcomes, which encourages individuals to seek comprehensive health coverage. In contrast, those with lower levels of education often have limited knowledge about health insurance programs and may not understand how they can access benefits. As a result, low-education groups tend to have lower enrollment rates and are more likely to face barriers in retaining their coverage over time [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eSociodemographic factors indicated that while urban residence and proximity to health facilities were associated with higher odds of insurance registration, these factors were not statistically significant in this study. The wealth index showed that women in the richest category had registration odds compared to those in the poorest category, indicating complex socio-economic influences on NHIS registration. Marital status was also significant influence of NHIS registration. Overall, while there have been improvements in NHIS registration, the study underscored the need for continued efforts to enhance NHIS services, address socio-economic disparities, and optimize delivery methods to further improve NHIS coverage in Ghana.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNHIS\u0026ndash;National Health Insurance Scheme\u003c/p\u003e\n\u003cp\u003eANC \u0026ndash; Antenatal Care\u003c/p\u003e\n\u003cp\u003eCI \u0026ndash; Confidence Intervals\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGDHS \u0026ndash; Ghana Demographic Health Survey\u003c/p\u003e\n\u003cp\u003eLMIC \u0026ndash; Low- and Middle-Income Countries\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMMR \u0026ndash; Maternal Mortality Rate\u003c/p\u003e\n\u003cp\u003eOR \u0026ndash; Odds Ratio\u003c/p\u003e\n\u003cp\u003eLMIC\u0026ndash; Low- and Middle-Income Countries\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSSA \u0026ndash; Sub Saharan Africa\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003cbr\u003e\u003c/strong\u003eAs publicly available secondary data were used, ethical approval was deemed unnecessary.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003cbr\u003e\u003c/strong\u003eNo permissions were required to publish this study, as no personal information, photographs, or videos were used. Additionally, all data utilized are publicly available in the public domain.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003cbr\u003e\u003c/strong\u003eThe datasets generated and/or analysed during the current study are available in the Measure DHS repository: https://dhsprogram.com/data/available-datasets.cfm\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003cbr\u003e\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003cbr\u003e\u003c/strong\u003eNo funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003cbr\u003e\u003c/strong\u003eLF conceived the study and all authors contributed to the study design. SJ and JE acquired and analysed the data. SJ validated the results. LF and SJ drafted sections of the manuscript. All authors (SJ, LF, JE) reviewed, edited, and approved the final manuscript. SJ supervised the overall study process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003cbr\u003e\u003c/strong\u003eWe acknowledge Measure DHS for providing access to the datasets used in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; information\u003cbr\u003e\u003c/strong\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOwoo, N. S., \u0026amp; Lambon-Quayefio, M. P. (2013). National health insurance, social influence and antenatal care use in Ghana. \u003cem\u003eHealth economics review\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e, 1-12.\u003c/li\u003e\n\u003cli\u003eKuuire, V. Z., Kangmennaang, J., Atuoye, K. N., Antabe, R., Boamah, S. A., Vercillo, S., ... \u0026amp; Luginaah, I. (2017). Timing and utilisation of antenatal care service in Nigeria and Malawi. \u003cem\u003eGlobal public health\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(6), 711-727.\u003c/li\u003e\n\u003cli\u003eAddai, I. (2020). Determinants of use of maternal\u0026ndash;child health services in rural Ghana. \u003cem\u003eJournal of biosocial science\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(1), 1-15.\u003c/li\u003e\n\u003cli\u003eShao, L., Wang, Y., Wang, X., Ji, L., \u0026amp; Huang, R. (2022). Factors associated with health insurance ownership among women of reproductive age: a multicountry study in sub-Saharan Africa. \u003cem\u003ePLoS One\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(4), e0264377.\u003c/li\u003e\n\u003cli\u003eWatkins, D. A., Jamison, D. T., Mills, T., Atun, T., Danforth, K., Glassman, A., ... \u0026amp; Alwan, A. (2018). Universal health coverage and essential packages of care.\u003c/li\u003e\n\u003cli\u003eLiu, X., Li, N., Liu, C., Ren, X., Liu, D., Gao, B., \u0026amp; Liu, Y. (2016). Urban\u0026ndash;rural disparity in utilization of preventive care services in China. \u003cem\u003eMedicine\u003c/em\u003e, \u003cem\u003e95\u003c/em\u003e(37), e4783.\u003c/li\u003e\n\u003cli\u003eMulenga, T., Moono, M., Mwendafilumba, M., Manasyan, A., \u0026amp; Sharma, A. (2018). Home deliveries in the capital: a qualitative exploration of barriers to institutional deliveries in peri-urban areas of Lusaka, Zambia. \u003cem\u003eBMC Pregnancy and Childbirth\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e, 1-11.\u003c/li\u003e\n\u003cli\u003eBrowne, J. L., Kayode, G. A., Arhinful, D., Fidder, S. A., Grobbee, D. E., \u0026amp; Klipstein-Grobusch, K. (2016). Health insurance determines antenatal, delivery and postnatal care utilisation: evidence from the Ghana Demographic and Health Surveillance data. \u003cem\u003eBMJ open\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(3), e008175.\u003c/li\u003e\n\u003cli\u003eDadjo, J., Ahinkorah, B. O., \u0026amp; Yaya, S. (2022). Health insurance coverage and antenatal care services utilization in West Africa. \u003cem\u003eBMC Health Services Research\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(1), 311.\u003c/li\u003e\n\u003cli\u003eAmeyaw, E. K., Kofinti, R. E., \u0026amp; Appiah, F. (2017). National health insurance subscription and maternal healthcare utilisation across mothers\u0026rsquo; wealth status in Ghana. \u003cem\u003eHealth Economics Revew\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e, 1-15.\u003c/li\u003e\n\u003cli\u003eSnijders, T. A., \u0026amp; Bosker, R. (2011). Multilevel analysis: An introduction to basic and advanced multilevel modeling.\u003c/li\u003e\n\u003cli\u003eCarman, K. G., Eibner, C., \u0026amp; Paddock, S. M. (2015). Trends in health insurance enrollment, 2013\u0026ndash;15. \u003cem\u003eHealth affairs\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(6), 1044-1048.\u003c/li\u003e\n\u003cli\u003eDuku, S. K., Fenenga, C. J., Alhassan, R. K., \u0026amp; Nketiah-Amponsah, E. (2013). Rural-urban differences in the determinants of enrolment in health insurance in Ghana. \u003cem\u003eParis: International union for the scientific study of population\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eGiri, S., Acharya, D., Adhikari, R., Sharma, M. K., \u0026amp; Ranabhat, C. L., 2024). Status and associated factors of health insurance enrollment among women in Nepal. \u003cem\u003eWorld Medical \u0026amp; Health Policy\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eStimpson, J. P., Kemmick Pintor, J., \u0026amp; Wilson, F. A. (2019). Association of Medicaid expansion with health insurance coverage by marital status and sex. \u003cem\u003ePloS one\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(10), e0223556.\u003c/li\u003e\n\u003cli\u003eNsiah-Boateng, E., \u0026amp; Aikins, M. (2018). Trends and characteristics of enrolment in the National Health Insurance Scheme in Ghana: a quantitative analysis of longitudinal data. \u003cem\u003eGlobal health research and policy\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e, 1-10. \u003c/li\u003e\n\u003cli\u003eOsei Afriyie, D., Krasniq, B., Hooley, B., Tediosi, F., \u0026amp; Fink, G. (2022). Equity in health insurance schemes enrollment in low and middle-income countries: A systematic review and meta-analysis. \u003cem\u003eInternational Journal for Equity in Health\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(1), 21. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"National Health Insurance Scheme, Maternal, Enrollment, Demography","lastPublishedDoi":"10.21203/rs.3.rs-6852251/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6852251/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eThis study examined the predictors of NHIS enrollment among women 15\u0026ndash;49 years in Ghana, using the Ghana Demographic and Health Survey (GDHS) from 2008 to 2022. It also evaluates the association between place of residence and NHIS enrollment while recognizing key elements impelling neonatal mortality rates over time.\u003c/p\u003e\u003ch2\u003eDesign:\u003c/h2\u003e\u003cp\u003eA cross-sectional study utilising secondary data from the GDHS conducted 2008, 2014, and 2022. Statistical analyses were performed using STATA version 17, with univariate, bivariate, and trend analysis applied to assess NHIS enrollment.\u003c/p\u003e\u003ch2\u003eSetting:\u003c/h2\u003e\u003cp\u003eThe study is based on nationwide representative survey data from Ghana, shelling all 16 regions. The GDHS datasets provide understandings into maternal and child health, including maternal health and NHIS enrollments.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe odds of NHIS registration increased from 2008 to 2022, indicating improved enrollment over time. Rural residence and proximity to health facilities were associated with lower registration odds, but these associations were not significant. Age, marital status, and education were significant factors, with women aged 30\u0026ndash;39 showing the highest odds of registration. Married women had 53% higher odds of registration, and those with higher education were more likely to enroll. Wealthier women also had higher registration odds, highlighting the socio-economic factors influencing NHIS uptake.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eSociodemographic factors such as wealth, marital status, and education significantly influenced NHIS registration. Despite some associations being statistically insignificant, the findings stress the importance of addressing socio-economic disparities and optimizing NHIS services to improve coverage in Ghana.\u003c/p\u003e","manuscriptTitle":"Rural and Urban Differences in NHIS enrollment Among women 15-49 years: Analyses 2008-2022 Ghana Demographic and Health Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 12:02:19","doi":"10.21203/rs.3.rs-6852251/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1db0cd95-5a20-4d96-8bed-4d99f4682516","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T13:43:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-18 12:02:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6852251","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6852251","identity":"rs-6852251","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}