Experience of intimate-partner controlling behaviours among women in Ghana: a novel three step latent class analysis approach with survey-weighted fractional-logit regression

Experience of intimate-partner controlling behaviours among women in Ghana: a novel three step latent class analysis approach with survey-weighted fractional-logit regression | 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 Experience of intimate-partner controlling behaviours among women in Ghana: a novel three step latent class analysis approach with survey-weighted fractional-logit regression Justice Moses K. Aheto, Patience Otobia Wellington, Irene Kafui Vorsah Amponsah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7041744/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Nov, 2025 Read the published version in BMC Women's Health → Version 1 posted 11 You are reading this latest preprint version Abstract Background Intimate partner controlling behavior manifesting as jealousy, accusations of infidelity, social restrictions, and monitoring, undermines women’s autonomy, well-being and poses a critical public health issue, yet its heterogeneity remains underexplored in Ghana. Objective To identify distinct classes of controlling behaviors among Ghanaian women and to examine how key sociodemographic factors predict membership in each class. Methods We analyzed data from 5,137 ever-married women in the 2022 Ghana Demographic and Health Survey domestic‐violence module. Five binary indicators of partner control (jealousy, accusations of unfaithfulness, social isolation, family contact restrictions, and whereabouts monitoring) were subjected to latent class analysis (LCA) using unweighted generalized structural equation modeling. Model fit was compared across two‐ to four‐class solutions using the Bayesian Information Criterion (BIC), with a four‐class model selected. We then computed each woman’s posterior class‐membership probabilities and regressed these fractional outcomes on age, education, residence, region, religion, ethnicity, wealth, media exposure, partner education, partner alcohol use, and employment status via survey‐weighted fractional‐logit generalized linear models. Results Four distinct classes emerged: ( 1 ) Minimal Monitoring (59.2%), ( 2 ) Multi-Domain Surveillance (11.2%), ( 3 ) Jealousy and Location Monitoring (22.2%), and ( 4 ) Pervasive High‐Severity Control (7.4%). Younger age (20–34 years) and partner alcohol use were strongly associated with higher probabilities of membership in Classes 2–4, while secondary or higher education conferred protection, especially against Class 4 (adjusted odds ratio 0.42; 95% CI 0.21–0.83). Regional disparities were also evident, with women in northern and Savannah regions facing two‐ to six‐fold greater odds of severe control. Conclusions Partner controlling behaviors in Ghana are heterogeneous and disproportionately affect young, less-educated women and those with alcohol‐consuming partners. Interventions should include early recognition of controlling acts, empowerment through education, and community dialogue to challenge patriarchal norms. Addressing these patterns may prevent escalation to physical and sexual IPV and reduce the substantial public health burden of coercive control. Intimate partner violence (IPV) Psychological abuse Latent class analysis (LCA) Vermunt ML correction Demographic and Health Survey (DHS) Sub-Saharan Africa (SSA) Ghana Sociodemographic predictors Women’s autonomy BACKGROUND Intimate partner violence (IPV) is recognized as a widespread public health crisis and human rights violation. Globally about one in three women experience physical and/or sexual violence by a partner in their lifetime ( 1 ). The WHO emphasizes that “most of this violence is intimate partner violence,” and notes that IPV can have pervasive effects on women’s physical, mental, sexual and reproductive health ( 1 ). IPV encompasses not only physical and sexual abuse but also psychological aggression and controlling behaviors (e.g. monitoring a partner’s movements, isolating her from friends/family, restricting finances, dictating daily activities) ( 1 , 2 ). Such controlling tactics are subtle but insidious, undermining women’s autonomy and often foreshadowing more severe violence ( 2 , 3 ). Indeed, recent reviews confirm that exposure to coercive control is strongly linked to adverse mental health outcomes (e.g. PTSD, depression, anxiety) ( 3 ). IPV is also highly prevalent in sub-Saharan Africa (SSA): roughly one-third of women experience IPV over their lifetime, with similar regional figures reported in West Africa ( 3 ). These statistics underscore an urgent need for intensified prevention and research on IPV globally. Controlling behavior is a specific form of IPV whereby a partner seeks to dominate by limiting social and economic freedom. Studies describe common controlling acts as spying on a partner’s movements, severing her ties to friends or family, controlling money, and making unilateral decisions about her daily life ( 2 ). Data from DHS surveys across SSA suggest that such behaviors are widespread – for example, DHS-based studies report that 20–50% of women in SSA experience at least one controlling act by a partner ( 2 ) In Eastern Africa, 35–44% of women reported partner control (e.g. jealousy, accusations of infidelity, social isolation) ( 2 ). Controlling behaviors often coexist with other abuses: they can escalate into physical or sexual violence and are considered a critical element of the IPV continuum ( 4 ). Culturally, controlling actions are enabled by patriarchal norms. Controlling behaviors inflict serious health harms. Psychologically, victims often suffer chronic stress, low self-worth and long-term mental illness. Control in a relationship also worsen physical health: victims are more likely to report headaches, chronic pain and cardiovascular symptoms ( 4 , 5 ). Moreover, there are critical sexual and reproductive impacts. IPV is linked to unintended pregnancy, low contraceptive use, sexually transmitted infections (STIs) including HIV, and pregnancy complications ( 5 ). For example, controlling partners may sabotage birth control or pressure women into unwanted pregnancies ( 5 ), raising risks of mistimed or unwanted births. In Ghana, women who have experienced IPV are significantly less likely to use contraceptives and more likely to report unintended pregnancy and STIs. Taken together, the evidence shows that controlling behaviors, as part of the broader IPV spectrum, undermine women’s psychological well-being, physical health and reproductive autonomy ( 4 , 5 ). Ghanaian women face a substantial burden of IPV and controlling abuse. Recent nationally representative data show that approximately 6 in 10 women (61%) aged 15–49 who have ever had a husband or intimate partner have experienced at least one form of controlling behavior from their current or most recent partner. The most frequently reported controlling acts included a partner insisting on always knowing their whereabouts (44%) and expressing jealousy or anger when they interact with other men (42%). Additionally, 21% of women reported being falsely accused of infidelity by their husband or intimate partner ( 6 ). This high prevalence in Ghana is rooted in socio-cultural norms. Ghana remains a largely patriarchal society where women’s autonomy can be restricted by tradition and law. For example, customary practices like bride-price can enhance the perception of men’s “ownership” of women. Gender inequalities – including lower female education and economic dependence – leave many Ghanaian women vulnerable to partner control ( 2 ). These dynamics perpetuate a cycle: controlling husbands isolate women socially and economically, while women’s limited power and resources make it difficult to resist. Traditional, variable-centered analyses of intimate partner abuse typically aggregate controlling behaviors into a single score or assume that effects are uniform across all individuals. Such approaches can mask important heterogeneity in how these behaviors co-occur. For example, summative or dichotomous measures of abuse may “conceal patterns of overlap between individual types of experiences” , because they ignore distinct combinations of behaviors ( 7 – 9 ). In contrast, a person-centered approach like Latent Class Analysis (LCA) groups women into subpopulations based on their response patterns. LCA thus explicitly seeks “qualitatively different subgroups” defined by characteristic patterns of abuse ( 10 , 11 ) In short, person-centered models like LCA can detect latent patterns of controlling behaviors and identify subgroups at differential risk, information that variable-centered regression or factor analyses simply cannot provide ( 7 , 10 ). To our knowledge, few studies in sub-Saharan Africa have and no study in Ghana has applied person-centered LCA to IPV or controlling behavior. Thus, this work will fill an important gap in the IPV literature. By combining a rigorous mixture-modeling framework with current, nationally representative data, the study will contribute new evidence on the heterogeneity of partner abuse in Ghana and more broadly in sub-Saharan Africa, informing both research and policy aimed at reducing intimate partner violence. METHODS Data Source and Analytical Sample This study drew on the 2022 Ghana Demographic and Health Survey (GDHS) domestic violence module, which collects nationally representative data on ever‑married women. After rigorous data cleaning and removing cases with missing responses on any of the five key indicators and covariates, we arrived at an analytical sample of 5,137 women. All subsequent analyses refer to this cleaned sample. Survey Design and Weighting To ensure that our estimates reflect the GDHS’s complex multistage sampling, we applied the DHS domestic violence weight (variable d005), rescaled by 1,000,000 as recommended in DHS user‑support forums. Primary sampling units and sampling strata (the cross-classification of region and urban/rural residence) were declared in Stata 17 so that all standard errors account for clustering and stratification. This survey setup provided correct design‐based variance estimates in all downstream analyses. Latent Class Analysis We employed latent class analysis (LCA) to uncover subgroups defined by women’s affirmative responses to five binary items (d101a_bin through d101e_bin). Each item was treated as a Bernoulli outcome in Stata’s generalized structural equation (gsem) modeling framework. Because estimating information criteria under complex-survey settings in gsem precludes post‐estimation of AIC and BIC, we estimated all latent‐class models on the unweighted data without declaring the survey design. This approach enabled us to obtain the BIC and select the optimal number of classes. We compared two-, three-, and four-class solutions, ultimately selecting the four-class model, the lowest BIC solution, a choice supported by simulation evidence that BIC generally outperforms alternate indices in large-sample LCA settings (( 12 – 15 ). Mixture Model Formulation Let \(\:{Y}_{ij}\) denote the response of individual \(\:i\:\) to binary item \(\:j\:\) (where \(\:j\:=\:1,\:2,\:\dots\:,\:4\:\:\) and \(\:\:{Y}_{ij}\in\:\text{0,1}\) ), and let \(\:{C}_{i}\) represent the latent class membership for individual \(\:\:i\:\) . The probability of observing a particular response pattern is modeled as a finite mixture: \(\:P\left({\varvec{Y}}_{\varvec{i}}={\varvec{y}}_{\varvec{i}}\right)={\sum\:}_{c=1}^{K}{\pi\:}_{c{\prod\:}_{j=1}^{5}P\left({Y}_{ij}={y}_{ij}|{C}_{i}=c\right)}\) ……………………………………………………….1 Where: \(\:{\pi\:}_{c}\) is the prior probability (prevalence) of latent class \(\:c\:\) \(\:K\:\) is the number of latent classes \(\:P\left({Y}_{ij}=1|{C}_{i}=c\right)={\rho\:}_{jc}\) represents the item-response probability for item \(\:j\:\) in class \(\:c\:\) Item-Response Model Each binary indicator follows a Bernoulli distribution within each latent class: \(\:P\left({Y}_{ij}=1|{C}_{i}=c\right)={\rho\:}_{jc}\) …................ 2 Where \(\:{\rho\:}_{jc}\) is the probability of endorsing item \(\:j\:\) for individuals in class \(\:\:c\) . The complete data log-likelihood function is: \(\:\text{log}L={\sum\:}_{i=1}^{N}\text{log}\left[{\sum\:}_{c=1}^{K}{\pi\:}_{c{\prod\:}_{j=1}^{5}{\rho\:}_{jc}^{{y}_{ij}}{\left(1-{\rho\:}_{jc}\right)}^{1-{y}_{ij}}}\right]\) ……………………………………………………….. 3 Class Enumeration and Selection Model comparison primarily relied on the Bayesian Information Criterion which balances model fit against parsimony and demonstrates superior performance in large-sample LCA simulations ( 15 , 16 ). After fitting two-, three-, and four-class models, we observed a monotonic decline in BIC values, with the four-class solution achieving the lowest BIC. Entropy values (0.70) indicated adequate class separation, though we note entropy should not be used for model selection due to overfitting risks ( 16 , 17 ). All classes exceeded 5% prevalence, avoiding spurious small-class solutions ( 16 ). Class specific item-response probabilities were examined to ensure each class represented a distinct and substantively interpretable response pattern, with probabilities differing mostly by > 0.40 between classes to confirm meaningful separation ( 12 , 17 , 18 ). Posterior Class Membership Probabilities Following LCA estimation, posterior membership probabilities for each individual across all four latent classes were computed using Bayes' theorem: \(\:P\left({C}_{i}=c|{\varvec{Y}}_{\varvec{i}}={\varvec{y}}_{\varvec{i}}\right)=\frac{{\pi\:}_{c{\prod\:}_{j=1}^{5}{\rho\:}_{jc}^{{y}_{ij}}{\left(1-{\rho\:}_{jc}\right)}^{1-{y}_{ij}}}}{{\sum\:}_{k=1}^{K}{\pi\:}_{k{\prod\:}_{j=1}^{5}{\rho\:}_{jk}^{{y}_{ij}}{\left(1-{\rho\:}_{jk}\right)}^{1-{y}_{ij}}}}\) ………………………………………….. 4 These posterior probabilities represent the probability that individual \(\:i\:\) belongs to class \(\:c\:\) given their observed response pattern ( 19 ). Fractional Logit Regression: Mathematical Specification and Quasi-Maximum Likelihood Estimation To examine associations between latent class profiles and sociodemographic covariates, we treated the posterior class membership probabilities as fractional outcomes and applied the fractional logit regression model developed by Papke and Wooldridge (1996) ( 20 , 21 ). Fractional Response Model Specification For each latent class \(\:c\:\) , let \(\:{p}_{ic}\) denote the posterior probability that individual \(\:i\:\) belongs to class \(\:i\:\) , where \(\:\:0\:\le\:\:{p}_{ic}\:\le\:\:1\) and \(\:\:{\sum\:}_{c=1}^{K}\:{p}_{ic}\:=\:1\) . The conditional expectation of the fractional response is modeled as: \(\:E\left({p}_{ic}∣{X}_{i}\right)=G\left(Xi\beta\:c\right)\) ………………………………………………………………… 5 where \(\:\:{X}_{i}\) is a vector of covariates for individual \(\:i\:\) , \(\:\beta\:c\:\) is a vector of coefficients specific to class \(\:c\:\) , and \(\:G\left(\cdot\:\right)\) is a distribution function that ensures \(\:E\left({p}_{ic}|{\varvec{X}}_{\varvec{i}}\right)\) . We employed the logistic distribution function: \(\:E\left({p}_{ic}∣{X}_{i}\right)=\frac{\text{exp}\left({X}_{i}{\beta\:}_{c}\right)}{1+\text{exp}\left({X}_{i}{\beta\:}_{c}\right)}\) ……………………………………………………………… 6 Quasi-Maximum Likelihood Estimation Following Papke and Wooldridge (1996), we applied quasi-maximum likelihood estimation (QMLE) using the Bernoulli log-likelihood function ( 22 ). For each class \(\:\:c\:\) , the quasi-log-likelihood is: \(\:{\mathcal{l}}_{c}\left({\beta\:}_{c}\right)={\sum\:}_{i=1}^{N}\left[{p}_{ic}\text{log}G\left({X}_{i}{\beta\:}_{c}\right)+\left(1-{p}_{ic}\right)\text{log}\left(1-G\left({X}_{i}{\beta\:}_{c}\right)\right)\right]\) ………………………….7 The QMLE is consistent for the conditional mean parameters as long as \(\:E\left({p}_{ic}∣{\varvec{X}}_{\varvec{i}}\right)\) “is correctly specified, regardless of the actual distribution of \(\:{p}_{ic}\) ” ( 23 ). This robustness property makes QMLE particularly suitable for fractional outcomes that may not follow a standard probability distribution. Variance Specification and Robust Standard Errors The fractional logit model assumes conditional variance of the form: \(\:Var\left({p}_{ic}|{X}_{i}\right)={\sigma\:}^{2}G\left({X}_{i}{\beta\:}_{c}\right)\left[1-G\left({X}_{i}{\beta\:}_{c}\right)\right]\) ………………………………………………8 Where \(\:{\sigma\:}^{2}\) is an unknown scale parameter estimated by the GLM procedure. Fractional‑Membership Regression of Class Probabilities Following class enumeration, we generated posterior membership probabilities for each individual in all four latent classes. To explore associations between latent‑class profiles and socio‑demographic covariates, we treated these class probabilities as fractional outcomes in survey‑weighted generalized linear models. Specifically, we fitted binomial-family, logit‐link regressions of each class probability on the full set of covariates, using factor‑variable notation to handle categorical predictors automatically. RESULTS Women in the analytic sample were almost evenly split between urban (48.1%) and rural (51.9%) areas. The largest age groups were 25–29 (19.2%) and 30–34 (19.2%), while only 8.7% were aged 45–49. Half of the participants completed secondary education (51.0%), nearly a quarter had no formal schooling (24.9%), and 9.4% had higher education. Pentecostal/Charismatic Christians comprised the largest religious group (36.9%), followed by Muslims (25.5%) and Catholics (10.6%). Akan (35.6%) and Mole‑Dagbani (25.9%) were the most common ethnicities. The poorest two wealth quintiles together accounted for 45.9% of women. Three‑quarters of partners did not drink alcohol (74.6%). By occupation, half of women worked in services (50.3%), 14.8% were not working, and smaller proportions held professional, clerical, or manual jobs. Among the 5137 partnered women included in the analysis, age was evenly distributed across the reproductive span, with the largest proportions in the 25–29 (19.2%) and 30–34 (19.2%) age group and the smallest in the oldest group (45–49 years, 8.7%). Educational attainment was moderate: half of women had completed secondary school (51.0%), nearly a quarter had no formal education (24.9%), and 9.4% had attained higher education. Residence was balanced between urban (48.1%) and rural (51.9%) settings. All sixteen administrative regions contributed similarly to the sample (ranging from 5.2% in Western North to 7.9% in Ashanti). In terms of religion, Pentecostal/Charismatic affiliations predominated (36.9%), followed by Islam (25.5%) and Catholicism (10.6%). Ethnically, Akan (35.6%) and Mole‑Dagbani (25.9%) women constituted the majority. Socioeconomic status was skewed toward the lower quintiles: 45.9% of women fell into the poorest or poorer groups. Three‑quarters of partners did not consume alcohol (74.6%), and women’s occupations were dominated by the services sector (50.3%), with 14.8% not engaged in paid work (Table 1 ). Table 1 Distribution of Participant Characteristics (N = 5,137) Covariate N (%) Age 15–19 332 (6.46) 20–24 886 (17.25) 25–29 988 (19.23) 30–34 985 (19.17) 35–39 850 (16.55) 40–44 651 (12.67) 45–49 445 (8.66) Total 5137(100) Education level No education 1 280 (24.92) Primary 754 (14.68) Secondary 2 619 (50.98) Higher 484 (9.42) Total 5137(100) Residence Urban 2 470 (48.08) Rural 2 667 (51.92) Total 5137(100) Region Western 293 (5.70) Central 311 (6.05) Greater Accra 366 (7.12) Volta 288 (5.61) Eastern 313 (6.09) Ashanti 405 (7.88) Western North 266 (5.18) Ahafo 307 (5.98) Bono 284 (5.53) Bono East 317 (6.17) Oti 300 (5.84) Northern 371 (7.22) Savannah 334 (6.50) North East 316 (6.15) Upper East 330 (6.42) Upper West 336 (6.54) Total 5137(100) Religion Catholic 546 (10.63) Anglican 41 (0.80) Methodist 208 (4.05) Presbyterian 259 (5.04) Pentecostal/Charismatic 1 897 (36.93) Other Christian 665 (12.95) Islam 1 310 (25.50) Traditional/Spiritualist 100 (1.95) No religion 106 (2.06) Don’t know 5 (0.10) Total 5137(100) Ethnicity Akan 1 829 (35.60) Ga/Dangme 211 (4.11) Ewe 567 (11.04) Guan 224 (4.36) Mole‑Dagbani 1 329 (25.87) Grusi 256 (4.98) Gurma 522 (10.16) Mande 153 (2.98) Don’t know 46 (0.90) Total 5137(100) Wealth quintile Poorest 1 254 (24.41) Poorer 1 102 (21.45) Middle 1 017 (19.80) Richer 969 (18.86) Richest 795 (15.48) Total 5137(100) Partner drinks alcohol No 3 831 (74.58) Yes 1 306 (25.42) Total 5137(100) Occupation (grouped) Not working 762 (14.83) Professional/technical/managerial 329 (6.40) Clerical 76 (1.48) Sales 471 (9.17) Agricultural – self employed 17 (0.33) Agricultural – employee 252 (4.91) Services 2 582 (50.26) Skilled manual 583 (11.35) Unskilled manual 24 (0.47) Don’t know 41 (0.80) Total 5137(100) Across all five behaviors, jealousy and insistence on knowing the respondent’s whereabouts were the most frequently reported controlling tactics, with 40.8% of women indicating that their partner became jealous when they spoke with other men and 45.1% reporting that their partner insisted on always knowing their location. In contrast, more restrictive actions, such as preventing contact with female friends and limiting family visits, were less common (16.0% and 7.1%, respectively). Accusations of unfaithfulness was reported by 18.9% of women (Table 2 ). Table 2 Prevalence of Partner Controlling Behaviors (N = 5,137) Indicator No (0) N (%) Yes ( 1 ) N (%) Jealous if respondent talks with other men 3 042 (59.22%) 2 095 (40.78%) Accuses respondent of unfaithfulness 4 164 (81.06%) 973 (18.94%) Does not permit respondent to meet female friends 4 316 (84.02%) 821 (15.98%) Tries to limit respondent’s contact with family 4 773 (92.91%) 364 (7.09%) Insists on knowing where respondent is 2 819 (54.88%) 2 318 (45.12%) A four‑class latent class model best captured the patterns of partner monitoring and control among 5,137 ever‑married Ghanaian women. Class one, constituting 59.2% of the sample, exhibited uniformly low probabilities across all five indicators (0.01–0.22), and was labeled “Minimal or Occasional Monitoring.” Class two (11.2%) showed moderate‑to‑high probabilities on most items—particularly location insistence (d101e = 0.84) and jealousy (d101a = 0.53)—earning the label “Multi‑Domain Control with Surveillance.” Class three (22.2%) was defined by very high jealousy (d101a = 0.94) and location monitoring (d101e = 0.72), but low social isolation, thus “Jealousy and Location Monitoring.” Finally, Class four (7.4%) reported extremely high endorsement (> 0.80) of nearly every behavior and was defined as “Pervasive, High‑Severity Control.” These four profiles reflect qualitatively distinct subgroups: the majority experiencing little to no control (Class 1), a small but important group under intense surveillance (Class 2), a cohort marked by jealousy without broader isolation (Class 3), and a high‑severity minority subjected to pervasive control (Class 4). This classification lays the foundation for examining how socio‑demographic covariates predict membership in each subgroup (Tables 3 and 4 ). Table 3 Latent Class Prevalence Class Description Prevalence (%) 95% CI 1 Minimal or Occasional Monitoring 59.21 55.78–62.54 2 Multi‑Domain Control with Surveillance 11.23 4.38–25.88 3 Jealousy and Location Monitoring 22.15 13.97–33.27 4 Pervasive, High‑Severity Control 7.41 4.92–11.01 Table 4 Conditional ItemResponse Probabilities by Class Indicator Class 1 Class 2 Class 3 Class 4 Pr(std. err) Pr(std. err) Pr(std. err) Pr(std. err) Jealous if talks with other men 0.11 (0.014) 0.53 (0.164) 0.94 (0.044) 0.99 (0.033) Accuses of unfaithfulness 0.03 (0.005) 0.17 (0.072) 0.40 (0.034) 0.89 (0.128) Prohibits meeting female friends 0.03 (0.005) 0.49 (0.094) 0.13 (0.051) 0.80 (0.054) Limits contact with family 0.01 (0.002) 0.24 (0.055) 0.02 (0.035) 0.48 (0.050) Insists on knowing whereabouts 0.22 (0.012) 0.84 (0.049) 0.72 (0.031) 0.92 (0.024) We evaluated solutions containing one through five latent classes. The four-class model achieved the lowest Akaike Information Criterion (AIC = 22 852.01) and the lowest Bayesian Information Criterion (BIC = 23 002.52), outperforming both the three‑class (AIC = 22 933.77; BIC = 23 045.02) and five‑class (AIC = 22 858.67; BIC = 23 048.27) solutions. The four-class solution also exhibited adequate entropy (0.70) and high average latent class posterior probabilities (ALCPP = 0.8392), reflecting clear separation among the identified subgroups. Based on these fit indices, particularly the BIC, and considerations of parsimony and interpretability, the four-class model was selected for all subsequent analyses (Table 5 ). Table 5 Fit Statistics and Diagnostics for 1- to 4-Class Latent Class Models Model LL AIC BIC SABIC CAIC LR Entropy ALCPP Class 1 -13073.8 26157.68 26190.40 26174.52 26195.40 3351.405 1 Class 2 -11539.9 23101.82 23173.80 23138.85 23184.80 283.5364 0.72 0.9236 Class 3 -11449.9 22933.77 23045.02 22991.00 23062.02 103.4936 0.7 0.8636 Class 4 -11403.0 22852.01 23002.52 22929.43 23025.52 9.722973 0.7 0.8392 Class 5 -11400.2 22858.67 23048.27 22956.12 23077.27 4.210027 0.6 0.7397 Among women in the “Multi-Domain Surveillance” class (Class 2), age emerged as a strong predictor of membership. In the fully adjusted model, women aged 20–24 had 46% increased odds of membership in this class relative to adolescents 15–19 (aOR 1.46; 95% CI 1.16–1.83; p = 0.001), and women aged 30–34 exhibited a similar increase (aOR 1.38; 95% CI 1.05–1.81; p = 0.020). The 25–29 year group also showed a significant increase in odds (aOR 1.31; 95% CI 1.01–1.70; p = 0.044). No other age categories differed significantly from the 15–19 reference. Regional residence was another key correlate. Compared with Western region, women in Ahafo (aOR = 1.52; 1.12–2.07; p = 0.008), Oti (aOR = 1.66; 1.14–2.43; p = 0.009), Northern (aOR = 1.64; 1.15–2.32; p = 0.006), Savannah (aOR = 1.78; 1.22–2.60; p = 0.003), and Upper West (aOR = 1.54; 1.01–2.35; p = 0.047) exhibited increased odds of membership in class 2. Although Bono East residence was associated with reduced unadjusted odds (uOR = 0.71; 0.51–0.99; p = 0.044), this association was attenuated in multivariable model (aOR = 0.74; 0.52–1.04; p = 0.084). Finally, partner alcohol use was significantly associated with Class 2 membership: women whose partners drank alcohol had 22%-increased odds of being in the Multi-Domain Surveillance class (AOR 1.22; 95% CI 1.05–1.42; p = 0.011). Education level, residence, religion, ethnicity, wealth quintile, and occupation did not retain significance in adjusted analyses. These findings identify young adulthood (20–34 years), specific regional contexts, and partner alcohol use as key determinants of experiencing a predominantly monitoring pattern of partner control (Table 6 ). Table 6 Socio-Demographic Factors Associated with Membership in Class 2: Unadjusted and Adjusted Fractional-Logit Models Variable UOR [95% CI] p‑value AOR [95% CI] p‑value Age 15–19 (ref) reference — reference — 20–24 1.418 [1.129–1.782] 0.003 1.455 [1.155–1.832] 0.001 25–29 1.247 [0.985–1.578] 0.067 1.310 [1.008–1.702] 0.044 30–34 1.364 [1.059–1.757] 0.016 1.380 [1.053–1.809] 0.02 35–39 0.979 [0.752–1.275] 0.874 0.990 [0.746–1.314] 0.945 40–44 1.139 [0.851–1.526] 0.381 1.120 [0.823–1.524] 0.471 45–49 0.957 [0.673–1.361] 0.806 0.922 [0.643–1.321] 0.656 Education No education (ref) reference — reference — Primary 0.849 [0.676–1.067] 0.16 0.942 [0.736–1.206] 0.634 Secondary 0.904 [0.752–1.087] 0.284 1.009 [0.801–1.272] 0.937 Higher 0.796 [0.600–1.056] 0.114 0.912 [0.654–1.274] 0.59 Residence Urban (ref) reference — reference — Rural 1.058 [0.921–1.216] 0.424 0.854 [0.716–1.017] 0.077 Region Western (ref) reference — reference — Central 1.029 [0.813–1.303] 0.812 1.024 [0.797–1.315] 0.855 Greater Accra 0.758 [0.570–1.008] 0.057 0.737 [0.536–1.015] 0.061 Volta 1.289 [0.898–1.851] 0.169 1.361 [0.897–2.064] 0.147 Eastern 0.886 [0.664–1.182] 0.409 0.920 [0.685–1.236] 0.581 Ashanti 1.099 [0.839–1.440] 0.493 1.144 [0.876–1.495] 0.322 Western North 0.903 [0.665–1.227] 0.514 0.958 [0.697–1.317] 0.791 Ahafo 1.499 [1.113–2.018] 0.008 1.519 [1.116–2.066] 0.008 Bono 0.717 [0.507–1.013] 0.059 0.737 [0.521–1.042] 0.084 Bono East 0.709 [0.507–0.991] 0.044 0.735 [0.518–1.042] 0.084 Oti 1.599 [1.132–2.257] 0.008 1.662 [1.138–2.426] 0.009 Northern 1.465 [1.079–1.990] 0.015 1.635 [1.153–2.318] 0.006 Savannah 1.503 [1.094–2.065] 0.012 1.780 [1.218–2.599] 0.003 North East 1.182 [0.798–1.751] 0.403 1.332 [0.855–2.077] 0.205 Upper East 1.028 [0.774–1.364] 0.849 1.245 [0.880–1.760] 0.215 Upper West 1.186 [0.817–1.721] 0.37 1.538 [1.005–2.352] 0.047 Religion Catholic (ref) reference — reference — Anglican 0.928 [0.411–2.095] 0.857 1.064 [0.461–2.458] 0.885 Methodist 0.989 [0.698–1.402] 0.952 1.047 [0.715–1.535] 0.813 Presbyterian 1.105 [0.763–1.601] 0.597 1.216 [0.828–1.787] 0.318 Pentecostal/Charismatic 1.092 [0.848–1.405] 0.494 1.150 [0.878–1.507] 0.31 Other Christian 1.110 [0.817–1.507] 0.505 1.134 [0.822–1.566] 0.442 Islam 1.049 [0.797–1.381] 0.731 0.994 [0.728–1.359] 0.971 Trad./Spiritualist 1.608 [1.014–2.551] 0.044 1.086 [0.676–1.747] 0.732 No religion 0.997 [0.657–1.511] 0.988 0.874 [0.584–1.307] 0.511 Don’t know 0.678 [0.279–1.646] 0.389 0.596 [0.238–1.491] 0.268 Ethnicity Akan (ref) reference — reference — Ga/Dangme 0.880 [0.640–1.210] 0.431 1.012 [0.722–1.418] 0.944 Ewe 1.022 [0.808–1.293] 0.857 0.931 [0.710–1.220] 0.604 Guan 1.028 [0.766–1.381] 0.853 0.856 [0.625–1.172] 0.331 Mole‑Dagbani 0.990 [0.828–1.182] 0.91 0.848 [0.685–1.049] 0.128 Grusi 1.049 [0.777–1.416] 0.755 0.936 [0.665–1.319] 0.707 Gurma 1.388 [1.060–1.817] 0.017 0.990 [0.740–1.324] 0.944 Mande 1.042 [0.757–1.435] 0.799 1.084 [0.763–1.541] 0.652 Don’t know 0.837 [0.514–1.362] 0.473 0.877 [0.511–1.504] 0.632 Combined Wealth quintile Poorest (ref) reference — reference — Poorer 0.945 [0.755–1.182] 0.619 1.015 [0.812–1.268] 0.897 Middle 0.808 [0.648–1.008] 0.059 0.894 [0.702–1.140] 0.366 Richer 0.814 [0.661–1.002] 0.052 0.881 [0.686–1.358] 0.323 Richest 0.758 [0.598–0.960] 0.022 0.895 [0.672–1.193] 0.449 Partner drinks alcohol No (ref) reference — reference — Yes 1.117 [0.961–1.298] 0.149 1.217 [1.046–1.415] 0.011 Occupation Not working (ref) reference — reference — Prof/Tech/Managerial 0.797 [0.575–1.105] 0.173 0.874 [0.618–1.236] 0.446 Clerical 0.999 [0.602–1.658] 0.997 1.054 [0.630–1.764] 0.841 Sales 0.877 [0.678–1.135] 0.318 0.950 [0.729–1.239] 0.706 Agri – self‑employed 1.388 [0.333–5.785] 0.652 1.155 [0.233–5.726] 0.86 Agri – employee 1.254 [0.840–1.871] 0.268 1.202 [0.814–1.774] 0.355 Services 0.993 [0.817–1.207] 0.947 0.988 [0.806–1.210] 0.907 Skilled manual 0.905 [0.715–1.145] 0.406 0.872 [0.680–1.119] 0.28 Unskilled manual 1.604 [0.581–4.428] 0.361 1.826 [0.655–5.092] 0.25 Don’t know 0.920 [0.523–1.617] 0.771 0.944 [0.523–1.704] 0.848 Women’s odds of belonging to Class 3 declined with age: In unadjusted analyses, women aged 30–34 years had 41% reduced odds of experiencing the “Jealousy and Location Monitoring” partner behaviour relative to adolescents aged 15–19 years (uOR = 0.59; 95% CI = 0.43–0.81; p = 0.001), and this protective association persisted after adjustment (aOR = 0.65; 0.46–0.92; p = 0.015). Similarly, women 35–39 (uOR = 0.54; 0.39–0.74; p < 0.001; aOR = 0.58; 0.41–0.81; p = 0.001), 40–44 (uOR = 0.52; 0.37–0.73; p < 0.001; aOR = 0.55; 0.38–0.78; p = 0.001), and 45–49 (uOR = 0.43; 0.30–0.62; p < 0.001; aOR = 0.45; 0.31–0.66; p < 0.001) also had significantly reduced odds compared with the youngest group. Educational attainment showed the opposite pattern: in adjusted models, women with primary schooling had 30%-increased odds of experiencing “Jealousy and Location Monitoring” partner behavior (aOR = 1.30; 1.03–1.64; p = 0.030), and those with secondary education had 29%-increased odds (aOR = 1.29; 1.05–1.58; p = 0.014), relative to women with no formal education. No significant associations were observed for residence, nor for any of the individual regions, in the multivariable model. Ethnicity and most religious affiliations were also not associated with class membership; however, women in the very small women who do not know their religion (“Don’t know) had over fivefold increased odds (aOR = 5.39; 1.66–17.57; p = 0.005), though this estimate is based on few observations. Women in the poorer (second) wealth quintile exhibited 25% increased adjusted odds versus the poorest quintile (aOR = 1.25; 1.02–1.53; p = 0.030), while no significant differences emerged for higher quintiles. Partner characteristics mattered as well: alcohol consumption by the partner was associated with 36%-increased odds of class 3 membership in both adjusted and unadjusted models (aOR = 1.36; 1.16–1.60; p < 0.001). Partner education beyond no schooling did not retain significance after adjustment. Taken together, these findings suggest that younger women and those with lower educational attainment are particularly vulnerable to partners’ jealous and monitoring behaviors and that partner alcohol use and modest differences in wealth may further modulate this risk (Table 7 ). Table 7 Socio-Demographic Factors Associated with Membership in Class 3: Unadjusted and Adjusted Fractional-Logit Models Variable UOR [95% CI] p‑value AOR [95% CI] p‑value Age 15–19 (ref) reference — reference — 20–24 0.932 [0.683–1.273] 0.657 0.990 [0.728–1.347] 0.949 25–29 0.862 [0.637–1.168] 0.338 0.948 [0.697–1.291] 0.735 30–34 0.588 [0.425–0.814] 0.001 0.652 [0.462–0.921] 0.015 35–39 0.535 [0.388–0.738] < 0.001 0.577 [0.412–0.807] 0.001 40–44 0.518 [0.368–0.727] < 0.001 0.547 [0.384–0.779] 0.001 45–49 0.432 [0.302–0.618] < 0.001 0.454 [0.312–0.661] < 0.001 Education No education (ref) reference — reference — Primary 1.439 [1.166–1.777] 0.001 1.295 [1.026–1.635] 0.03 Secondary 1.546 [1.317–1.814] < 0.001 1.290 [1.053–1.579] 0.014 Higher 1.196 [0.932–1.535] 0.16 0.965 [0.660–1.409] 0.853 Residence Urban (ref) reference — reference — Rural 0.995 [0.869–1.140] 0.946 0.982 [0.838–1.150] 0.818 Region Western (ref) reference — reference — Central 1.216 [0.931–1.589] 0.151 1.209 [0.931–1.571] 0.155 Greater Accra 0.979 [0.718–1.335] 0.893 1.007 [0.728–1.394] 0.966 Volta 0.961 [0.723–1.279] 0.786 1.060 [0.754–1.490] 0.739 Eastern 1.001 [0.768–1.305] 0.992 1.040 [0.798–1.357] 0.769 Ashanti 0.989 [0.777–1.260] 0.93 1.089 [0.850–1.395] 0.499 Western North 0.906 [0.681–1.205] 0.496 0.976 [0.727–1.310] 0.87 Ahafo 0.997 [0.755–1.317] 0.984 1.059 [0.791–1.419] 0.698 Bono 0.910 [0.632–1.310] 0.612 0.975 [0.663–1.434] 0.899 Bono East 0.684 [0.508–0.919] 0.012 0.755 [0.552–1.033] 0.079 Oti 0.952 [0.708–1.279] 0.742 1.002 [0.708–1.418] 0.99 Northern 0.972 [0.750–1.259] 0.828 1.331 [0.952–1.862] 0.094 Savannah 0.961 [0.688–1.341] 0.813 1.101 [0.737–1.646] 0.638 North East 0.786 [0.534–1.155] 0.219 0.957 [0.625–1.466] 0.839 Upper East 0.801 [0.613–1.047] 0.105 0.883 [0.620–1.259] 0.493 Upper West 0.754 [0.570–0.996] 0.047 0.860 [0.595–1.244] 0.423 Religion Catholic (ref) reference — reference — Anglican 1.121 [0.600–2.093] 0.721 1.223 [0.660–2.267] 0.522 Methodist 1.473 [1.009–2.149] 0.045 1.396 [0.970–2.009] 0.072 Presbyterian 0.949 [0.647–1.392] 0.787 0.986 [0.679–1.434] 0.943 Pentec/Charis 1.155 [0.911–1.465] 0.234 1.112 [0.872–1.420] 0.391 Other Christian 0.898 [0.684–1.178] 0.437 0.876 [0.659–1.164] 0.361 Islam 0.942 [0.737–1.204] 0.633 0.960 [0.730–1.261] 0.767 Trad/Spiritualist 0.780 [0.540–1.128] 0.186 0.839 [0.566–1.244] 0.381 No religion 1.239 [0.724–2.119] 0.434 1.165 [0.687–1.976] 0.571 Don’t know 6.501 [1.678–25.195] 0.007 5.393 [1.655–17.566] 0.005 Ethnicity Akan (ref) reference — reference — Ga/Dangme 1.095 [0.838–1.432] 0.505 1.162 [0.866–1.558] 0.316 Ewe 0.950 [0.776–1.163] 0.617 0.966 [0.749–1.247] 0.793 Guan 1.041 [0.673–1.612] 0.858 1.081 [0.686–1.704] 0.738 Mole‑Dagbani 0.897 [0.747–1.077] 0.242 1.104 [0.851–1.431] 0.457 Grusi 0.772 [0.566–1.053] 0.102 1.005 [0.688–1.468] 0.978 Gurma 0.872 [0.683–1.114] 0.271 1.025 [0.742–1.415] 0.881 Mande 0.796 [0.548–1.154] 0.228 1.103 [0.762–1.695] 0.653 Don’t know 1.611 [0.824–3.149] 0.163 2.109 [1.071–4.153] 0.031 Combined Wealth quintile Poorest (ref) reference — reference — Poorer 1.362 [1.114–1.665] 0.003 1.252 [1.022–1.533] 0.03 Middle 1.376 [1.133–1.671] 0.001 1.206 [0.942–1.544] 0.136 Richer 1.204 [1.006–1.441] 0.043 1.001 [0.781–1.284] 0.991 Richest 1.111 [0.905–1.363] 0.316 0.976 [0.739–1.290] 0.865 Partner drinks alcohol No (ref) reference — reference — Yes 1.205 [1.033–1.406] 0.018 1.361 [1.160–1.595] < 0.001 Occupation Not working (ref) reference — reference — Prof/Tech/Managerial 0.809 [0.589–1.111] 0.19 1.188 [0.785–1.796] 0.415 Clerical 0.816 [0.472–1.409] 0.465 0.968 [0.549–1.707] 0.911 Sales 0.754 [0.569–1.000] 0.049 0.904 [0.678–1.207] 0.494 Agri – self‑emp’d 0.690 [0.368–1.295] 0.247 1.103 [0.544–2.237] 0.762 Agri – employee 0.663 [0.469–0.937] 0.02 0.819 [0.578–1.161] 0.262 Services 0.732 [0.602–0.890] 0.002 0.911 [0.743–1.116] 0.368 Skilled manual 0.840 [0.643–1.097] 0.199 0.904 [0.690–1.185] 0.465 Unskilled manual 0.971 [0.459–2.053] 0.939 0.979 [0.427–2.242] 0.959 Don’t know 0.876 [0.455–1.686] 0.691 1.135 [0.573–2.247] 0.716 Table 8 presents associations between socio-demographic factors and membership in Class 4 (“Pervasive, High-Severity Control”). Women aged 20–24 years experienced significantly increased odds of the Pervasive Control profile compared with adolescents aged 15–19 years (aOR 1.81; 95% CI 1.18–2.77; p = 0.007), whereas no other age groups differed after adjustment. Educational attainment showed a protective effect at the highest level: women with higher education had 44% reduced odds of Class 4 membership compared with those with no education (aOR 0.42; 95% CI 0.21–0.83; p = 0.013) in both the adjusted and unadjusted model. Primary and secondary schooling were not significant. Regionally, compared with Western Region residents, women in Central (aOR 2.62; 95% CI 1.49–4.59; p = 0.001), Volta (AOR 2.04; 95% CI 1.06–3.92; p = 0.032), Eastern (AOR 2.07; 95% CI 1.18–3.63; p = 0.011), Northern (AOR 2.42; 95% CI 1.22–4.81; p = 0.011), Savannah (AOR 6.02; 95% CI 2.90–12.49; p < 0.001), North East (AOR 2.63; 95% CI 1.12–6.19; p = 0.027), Upper East (AOR 2.38; 95% CI 1.22–4.65; p = 0.011), and Upper West (AOR 3.37; 95% CI 1.66–6.81; p = 0.001) all had significantly higher odds of Class 4 membership. Other regions did not differ significantly in adjusted analyses. Partner drinking was associated with a 95% increase in the odds of Class 4 membership compared with women whose partners did not drink (AOR 1.95; 95% (1.594–2.393); p < 0.001), indicating that partner alcohol use is a strong correlate of experiencing multiple, severe controlling behaviors. Ethnic affiliation conferred protection for several groups: compared with Akan women, those of Mole-Dagbani (aOR 0.60; 0.42–0.87; p = 0.007), Grusi (aOR 0.50; 0.28–0.89; p = 0.019), Gurma (aOR 0.49; 0.32–0.76; p = 0.001), and women who do not know their ethnicities (aOR 0.17; 0.09–0.33; p < 0.001) had significantly reduced odds. Significant independent associations were not observed for wealth quintile, religion, or occupation (Table 8 ). Table 8 Socio-Demographic Factors Associated with Membership in Class 4: Unadjusted and Adjusted Fractional-Logit Models Variable UOR [95% CI] p‑value AOR [95% CI] p‑value Age 15–19 (ref) reference — reference — 20–24 1.584 [1.037–2.420] 0.033 1.808 [1.181–2.770] 0.007 25–29 1.036 [0.696–1.541] 0.863 1.186 [0.782–1.798] 0.422 30–34 1.154 [0.765–1.742] 0.493 1.198 [0.778–1.844] 0.412 35–39 1.190 [0.771–1.836] 0.431 1.165 [0.723–1.879] 0.529 40–44 1.337 [0.857–2.085] 0.201 1.142 [0.702–1.856] 0.592 45–49 1.098 [0.635–1.897] 0.737 0.847 [0.469–1.529] 0.58 Education No education (ref) reference — reference — Primary 1.408 [0.937–2.116] 0.1 1.223 [0.774–1.933] 0.387 Secondary 1.057 [0.780–1.433] 0.72 0.936 [0.598–1.466] 0.773 Higher 0.559 [0.331–0.944] 0.03 0.416 [0.208–0.831] 0.013 Residence Urban (ref) reference — reference — Rural 1.003 [0.785–1.282] 0.979 0.760 [0.576–1.004] 0.053 Region Western (ref) reference — reference — Central 2.567 [1.461–4.509] 0.001 2.617 [1.491–4.594] 0.001 Greater Accra 0.895 [0.514–1.559] 0.695 0.957 [0.530–1.730] 0.885 Volta 1.972 [1.107–3.511] 0.021 2.042 [1.064–3.917] 0.032 Eastern 1.703 [0.994–2.918] 0.053 2.070 [1.182–3.626] 0.011 Ashanti 1.284 [0.778–2.117] 0.328 1.534 [0.912–2.580] 0.107 Western North 0.872 [0.454–1.674] 0.68 0.992 [0.507–1.942] 0.982 Ahafo 1.076 [0.645–1.794] 0.779 1.374 [0.783–2.412] 0.267 Bono 1.502 [0.786–2.869] 0.218 1.850 [0.902–3.794] 0.093 Bono East 0.651 [0.319–1.329] 0.238 0.895 [0.417–1.923] 0.776 Oti 1.018 [0.598–1.734] 0.947 1.469 [0.801–2.695] 0.214 Northern 1.230 [0.692–2.186] 0.479 2.424 [1.222–4.810] 0.011 Savannah 3.139 [1.611–6.118] 0.001 6.017 [2.89912.489] < 0.001 North East 1.177 [0.546–2.536] 0.677 2.627 [1.115–6.185] 0.027 Upper East 1.146 [0.625–2.101] 0.66 2.378 [1.216–4.653] 0.011 Upper West 1.424 [0.802–2.530] 0.227 3.366 [1.664–6.806] 0.001 Religion Catholic (ref) reference — reference — Anglican 2.293 [0.674–7.799] 0.183 2.774 [0.787–9.775] 0.112 Methodist 1.338 [0.649–2.757] 0.429 1.147 [0.565–2.326] 0.704 Presbyterian 1.373 [0.746–2.530] 0.308 1.255 [0.684–2.303] 0.463 Pentecostal/Charismatic 1.136 [0.776–1.664] 0.511 1.038 [0.696–1.546] 0.856 Other Christian 1.206 [0.770–1.889] 0.412 1.033 [0.659–1.619] 0.889 Islam 0.849 [0.560–1.289] 0.442 0.881 [0.531–1.464] 0.625 Trad./Spiritualist 1.866 [0.934–3.730] 0.077 1.948 [0.901–4.210] 0.09 No religion 1.455 [0.640–3.311] 0.371 1.288 [0.565–2.937] 0.547 Don’t know 1.148 [0.587–2.243] 0.687 0.852 [0.463–1.569] 0.607 Ethnicity Akan (ref) reference — reference — Ga/Dangme 0.748 [0.434–1.287] 0.294 0.800 [0.468–1.368] 0.414 Ewe 1.128 [0.792–1.608] 0.503 0.988 [0.667–1.463] 0.951 Guan 1.103 [0.631–1.927] 0.731 0.827 [0.475–1.441] 0.502 Mole‑Dagbani 0.664 [0.499–0.884] 0.005 0.603 [0.417–0.871] 0.007 Grusi 0.567 [0.339–0.951] 0.031 0.500 [0.280–0.894] 0.019 Gurma 0.671 [0.425–1.061] 0.087 0.491 [0.319–0.756] 0.001 Mande 0.858 [0.436–1.690] 0.658 1.176 [0.536–2.581] 0.686 Don’t know 0.178 [0.093–0.340] < 0.001 0.168 [0.087–0.326] < 0.001 Combined Wealth quintile Poorest (ref) reference — reference — Poorer 1.161 [0.833–1.619] 0.377 1.148 [0.801–1.645] 0.453 Middle 1.241 [0.832–1.851] 0.288 1.096 [0.699–1.719] 0.689 Richer 1.040 [0.733–1.476] 0.826 0.965 [0.614–1.517] 0.878 Richest 0.723 [0.491–1.065] 0.101 0.857 [0.503–1.460] 0.571 Partner drinks alcohol No (ref) reference — reference — Yes 1.655 [1.534–2.491] < 0.001 1.951 [1.594–2.393] < 0.001 Occupation Not working (ref) reference — reference — Prof/Tech/Managerial 0.824 [0.523–1.298] 0.404 1.639 [0.929–2.889] 0.088 Clerical 0.899 [0.378–2.139] 0.81 1.034 [0.427–2.506] 0.941 Sales 1.451 [0.959–2.194] 0.078 1.464 [0.937–2.287] 0.094 Agri – self‑employed 0.647 [0.157–2.664] 0.546 0.367 [0.074–1.808] 0.217 Agri – employee 1.117 [0.669–1.864] 0.672 1.443 [0.813–2.561] 0.21 Services 1.131 [0.831–1.540] 0.434 1.037 [0.737–1.458] 0.836 Skilled manual 0.790 [0.537–1.164] 0.233 0.684 [0.452–1.035] 0.072 Unskilled manual 0.435 [0.119–1.591] 0.208 0.691 [0.170–2.809] 0.605 Don’t know 0.482 [0.194–1.199] 0.116 0.500 [0.209–1.195] 0.119 Discussion Using latent class analysis and fractional-logit regression on GDHS data, we identified four distinct patterns of partner control—ranging from minimal monitoring to pervasive, high‐severity coercion—and uncovered key predictors such as age, education, partner alcohol use, and region. While most women experience little control, a substantial minority endure jealousy, surveillance, and social isolation. Given that coercive control often precedes physical and sexual violence and carries its own mental-health burdens, understanding these patterns is critical for targeted prevention and control strategies. This study identified four distinct classes of partner control among Ghanaian women: Class 1 (59%) with minimal monitoring, Class 2 (11%) with broad multi-domain surveillance, Class 3 (22%) with primarily jealousy and location tracking, and Class 4 (7%) with pervasive, high-severity control. In other words, while a majority of women experience little to no partner control, a substantial minority endure systematic coercion. Each class suggests different dynamics. Women in Class 2 (“Multi-Domain Surveillance”) face multiple restrictions, especially insistence on whereabouts and social surveillance, but not necessarily extreme isolation. This pattern resembles “coercive surveillance” noted in other studies, where jealous monitoring co-occurs with restrictive rules ( 24 ). Class three (“Jealousy and Location Monitoring”) women endure high jealousy and tracking yet maintain social ties. This echoes prior findings that jealousy and accusations are common forms of control in Ghana and predict abuse ( 24 , 25 ). Class four (“Pervasive High-Severity Control”) captures women experiencing virtually all controlling behaviors at high levels. This profile likely represents relationships with entrenched abuse, consistent with the notion that men who exert severe control are “more prone to commit physical, sexual, and emotional abuse” ( 26 , 27 ). Indeed, controlling behaviors often co-occur with other forms of IPV ( 24 , 25 , 28 ), and our data suggest Class 4 women may be at high risk of further violence. Overall, the spectrum from Class 1 to 4 reflects increasing violation of women’s autonomy, mirroring typologies seen in the literature. Several key predictors differentiated the classes. Age was strongly related: younger women (particularly those 20–34) had higher odds of being in the surveillance and high-control class, whereas older women were less likely to be in the jealousy-dominated class. This finding corresponds with the linear assumption that younger women are universally at higher risk but suggests that different forms of control may emerge at different life stages ( 29 ). Education was protective: women with secondary or higher schooling were significantly less likely to be in the high-severity control class, echoing prior findings that women with higher education have reduced odds of any IPV ( 4 ). Partner alcohol use emerged as a consistent risk factor: women whose partners drank had significantly increased odds of being in the surveillance, jealousy, or pervasive-control classes ( 4 , 30 ). This is plausible, as partner alcohol abuse is known to exacerbate aggression and controlling tendencies ( 4 , 28 – 30 ). We also found regional variation: residence in certain regions (e.g. Ahafo, Northern, and Savannah) doubled or tripled the odds of being in the surveillance or high-control classes, suggesting cultural or socio-economic factors in these areas heighten controlling behaviors. Notably, after adjustment, variables like religion, wealth, and urban/rural residence had minimal associations. This implies partner control cuts across socioeconomic lines, though it is shaped strongly by age, education, partner drinking, and locale. Globally, controlling behaviors are recognized as a common precursor and component of IPV. For example, a study in rural South Africa found that accusations of infidelity, restrictions on friends/family, and jealousy strongly predicted subsequent violence; likewise our classes highlight these same behaviors (Class 3 and 4) in Ghana ( 24 ). Dickson et al. (2024) using 2022 Ghana data reported that women whose partners got jealous or accused them of unfaithfulness were significantly more likely to suffer IPV, reinforcing the link between our control classes and violence risk ( 4 ). Similarly, Issahaku (2016) found in northern Ghana that jealousy and accusations were the dominant controlling tactics associated with abuse ( 31 ). In short, the specific behaviors defining our classes (jealousy, social isolation, surveillance) match those identified in Ghana and elsewhere as core elements of coercive control. Where we add depth is by quantifying distinct subgroups: not all controlled women are alike, and interventions can be tailored (e.g., Class 3 women may benefit from strategies addressing psychological abuse without social isolation). Some findings merit further thought. The inverse age gradient (younger women more controlled) likely reflects life-course dynamics: younger wives or girlfriends may have less established power in the relationship. The protective effect of education suggests that empowerment and autonomy (through schooling) can reduce male control ( 4 ). Our strong alcohol effect supports programs that target heavy drinking as part of violence prevention. The pronounced regional differences – with the most rural, traditionally patriarchal zones bearing higher control – echo literature on Ghana’s gender inequalities and underscore where interventions might concentrate. Limitations Because the GDHS is a single-time-point survey, we cannot establish temporal or causal relationships. For example, while low education and partner alcohol use are associated with higher probabilities of belonging to severe-control classes, we cannot determine whether these factors preceded or resulted from controlling behaviors. Also, all partner-control indicators rely on women’s self-reports, which may be subject to recall error or social-desirability bias. Women might under-report controlling acts due to stigma or fear, or over-report if they interpret questions differently, potentially misclassifying their true experience. Finally, to retrieve AIC and BIC, we estimated the LCA on unweighted data. This may bias class prevalence estimates if the domestic-violence weight correlates with the control indicators. Although we reinstated weights in the fractional-logit regressions, class enumeration itself did not account for the survey design. The results from this study have important public health and policy implications for Ghana and similar populations in other countries. Partner controlling behaviors, even in the absence of physical violence – have serious health consequences (stress, depression, isolation, reproductive coercion) and often signal broader abuse ( 32 , 33 ). Currently Ghana’s Domestic Violence Act (2007) and related policies focus on physical and sexual violence, but our findings suggest the need to explicitly address psychological and controlling abuse as integral forms of gender-based violence. The study recommends that the Ministries of Gender, Education and NGOs should develop community-based relationship literacy and advocacy programs that teach equitable, respectful relationships. These could include school curricula on consent and communication, radio campaigns targeting men and women, and faith-based workshops. Such education would directly confront the patriarchal norms identified as underpinning control. Programs that involve men as allies (e.g. religious groups, chieftaincy networks, “MenEngage” alliances) can shift norms that condone male dominance. Campaigns (like the UNFPA-supported “16 Days of Activism”) should include male-targeted messages about the harms of controlling their partners. Positive role models and peer education can promote non-violent masculinity. The findings from this study and some previous studies show that higher levels of female education correlate with lower control and IPV. Continuing policies like Ghana’s free Senior High School (SHS) and scholarships for girls will be beneficial. Vocational training and economic empowerment programs (especially for young women in vulnerable regions) can increase women’s autonomy. Education ministries, Microfinance agencies and women’s NGOs should align to create scholarships and skills workshops aimed at girls from early adolescence onward. Future research should employ longitudinal designs to examine transitions between controlling behavior classes and identify factors that promote resilience or escalation. Understanding how women move between classes over time would inform the timing and targeting of precise and high impact policy and intervention strategies. Conclusion In summary, our study underscores that partner control is a prevalent public health issue in Ghana, with identifiable patterns and at-risk groups. Addressing it requires multi-sectoral strategies: legal reform, community education, healthcare screening, and empowerment of women. Doing so will not only reduce psychological abuse, but also likely lower the incidence of physical and sexual IPV that often co-occurs. These insights and recommendations fill a gap in Ghana’s GBV policy by highlighting the “hidden” dimension of coercion and suggesting concrete steps to combat it. Abbreviations AIC Akaike Information Criterion ALCPP Average Latent Class Posterior Probability AOR Adjusted Odds Ratio BIC Bayesian Information Criterion CAIC Consistent Akaike Information Criterion DHS Demographic and Health Surveys GDHS Ghana Demographic and Health Survey GBV GenderBased Violence IPV Intimate Partner Violence LCA Latent Class Analysis LR Likelihood Ratio SABIC SampleSize Adjusted Bayesian Information Criterion UOR Unadjusted Odds Ratio Declarations Funding The authors received no funding for this study. Author information Authors and Affiliations Department of Biostatistics, School of Public Health, College of Health Sciences, University of Ghana, Accra, Ghana Authors’ Contributions P.O.W. secured and analysed the data and wrote the first draft manuscript. P.O.W. wrote and reviewed the various sections of the manuscript. P.O.W. reviewed the final version of the manuscript before submission. P.O.W. read and approved the final manuscript. Justice Moses K. Aheto (J.M.K.A.) J.M.K.A. wrote reviewed the various sections of the manuscript. J.M.K.A. reviewed the final version of the manuscript before submission. J.M.K.A. read and approved the final manuscript. Irene Kafui Vorsah Amponsah (I.K.V.A) I.K.V.A reviewed the initial draft of the manuscript. I.K.V.A reviewed and analysed the various sections of the final manuscript. Ethics declarations Ethics approval and consent to participate This secondary analysis used de‑identified, publicly available data from the 2022 Ghana Demographic and Health Survey. The original survey protocol was approved by the Ghana Health Service Ethical Review Committee and the ICF Institutional Review Board. The research has been performed in accordance with Declaration of Helsinki. Details about ethical standards are available at The DHS Program - Protecting the Privacy of DHS Survey Respondents Consent for publication Not applicable. Availability of data and materials The datasets used in this study are publicly available from the DHS Program. Researchers may access the 2022 Ghana Demographic and Health Survey data by registering for a free account and requesting permission at The DHS Program - Available Datasets Competing interests The author declares that she has no competing interests. Acknowledgements Thank you to the MEASURE DHS Program for granting access and making the data freely available for the study. References Violence against women Key facts Overview. 2024. Andualem F, Nakie G, Rtbey G, Melkam M, Tinsae T, Kibralew G, et al. Magnitude and determinants of intimate partner controlling behavior among women in sub-Saharan African countries from the recent demographic and health survey data: a multilevel analysis. BMC Public Health [Internet]. 2025 May 15;25(1):1787. Available from: https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-025-23004-8 Dokkedahl S, Kok RN, Murphy S, Kristensen TR, Bech-Hansen D, Elklit A. The psychological subtype of intimate partner violence and its effect on mental health: Protocol for a systematic review and meta-analysis. Vol. 8, Systematic Reviews. BioMed Central Ltd.; 2019. Dickson KS, Ayebeng C, Okyere J. Unveiling Shadows: Investigating women’s experience of intimate partner violence in Ghana through the lens of the 2022 Demographic and Health Survey. PLoS One. 2024 Aug 1;19(8). Tenkorang EY. Women’s autonomy and intimate partner violence in Ghana. Int Perspect Sex Reprod Health. 2019 Jun 1;44(2):51–61. Ghana Demographic and Health Survey. 2022. Lanier P, Maguire-Jack K, Lombardi B, Frey J, Rose RA. Adverse Childhood Experiences and Child Health Outcomes: Comparing Cumulative Risk and Latent Class Approaches. Matern Child Health J. 2018 Mar 1;22(3):288–97. Clarke K, Patalay P, Allen E, Knight L, Naker D, Devries K. Patterns and predictors of violence against children in Uganda: a latent class analysis. Available from: http://dx.doi.org/10.1136/bmjopen-2015-010443 Miedema SS, Le VD, Chiang L, Ngann T, Wu Shortt J. Adverse Childhood Experiences and Intimate Partner Violence Among Youth in Cambodia: A Latent Class Analysis. J Interpers Violence. 2023 Jan 1;38(1–2):NP1446–72. Weiss NH, Dixon-Gordon KL, Peasant C, Jaquier V, Johnson C, Sullivan TP. A latent profile analysis of intimate partner victimization and aggression and examination of between-class differences in psychopathology symptoms and risky behaviors. Psychol Trauma. 2017 May 1;9(3):370–8. Weller BE, Bowen NK, Faubert SJ. Latent Class Analysis: A Guide to Best Practice. Journal of Black Psychology. 2020 May 1;46(4):287–311. STATA STRUCTURAL EQUATION MODELING REFERENCE MANUAL [Internet]. 1985. Available from: www.stata.com Vermunt JK, Magidson J. LATENT GOLD 5.0 UPGRADE MANUAL 1. Asparouhov T. Sampling Weights in Latent Variable Modeling [Internet]. 2005. Available from: http://www.statmodel.com Nylund KL, Asparouhov T, Muthén BO. Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study. Vol. 14, STRUCTURAL EQUATION MODELING. 2007. Sinha P, Calfee CS, Delucchi KL. Practitioner’s Guide to Latent Class Analysis: Methodological Considerations and Common Pitfalls. Vol. 49, Critical Care Medicine. Lippincott Williams and Wilkins; 2021. p. E63–79. Ulbricht CM, Chrysanthopoulou SA, Levin L, Lapane KL. The use of latent class analysis for identifying subtypes of depression: A systematic review. Vol. 266, Psychiatry Research. Elsevier Ireland Ltd; 2018. p. 228–46. Introduction to Latent Class Analyses. Vermunt JK, Magidson J. Linear Logistic Scoring Equations for Latent Class and Latent Profile Models: A Simple Method for Classifying New Cases. Structural Equation Modeling. Routledge; 2024. Papke LE. Econometric methods for fractional response variables with an application to 401 (k) plan participation rates. Journal of Applied Econometrics. 1996;11(6):619–32. Papke LE, Wooldridge JM. Panel data methods for fractional response variables with an application to test pass rates. J Econom. 2008 Jul;145(1–2):121–33. Oberhofer H, Pfaffermayr M. Fractional response models - A replication exercise of Papke and Wooldridge (1996). Contemporary Economics. 2012 Oct 29;6(3):56–64. Mullahy J, Burns M, Craig B, Holly A, Koch S, Murteira J, et al. NBER WORKING PAPER SERIES MULTIVARIATE FRACTIONAL REGRESSION ESTIMATION OF ECONOMETRIC SHARE MODELS [Internet]. 2010. Available from: http://www.nber.org/papers/w16354 Sulaiman LAR, Ojogiwa OT, Ajayi CE. Intimate partner controlling behaviour and intimate partner violence among married women in rural areas in South Africa. BMC Womens Health. 2025 Dec 1;25(1). Ahorsu K, Biveridge F, Peter Sarpong BK, William Gaines Reviewed by Timothy Quashigah BC, Naa Dedei Botchwey C, Dey K. GHANA SOCIAL SCIENCE JOURNAL ARTICLES Academic Capitalism: Globalization, Universities and the Paradox of the Neoliberal Marketplace James Dzisah. 1-33 Ghana’s Foreign Policy Choices in Relation to Wielding Oil and Gas Resource for Regional Integration Archaeological Perspectives of the Danish-Dangbe Encounter along the Eastern Coastal Belt of Ghana and their Implications for Understanding Dangbe Culture. Vol. 13, Ghana Social Science Journal. 2016. Ahinkorah BO, Aboagye RG, Okyere J, Seidu AA, Budu E, Yaya S. Child marriage and its association with partner controlling behaviour against adolescent girls and young women in sub-Saharan Africa. BMC Global and Public Health. 2023 Jul 31;1(1). Herbert A, Fraser A, Howe LD, Szilassy E, Barnes M, Feder G, et al. Categories of Intimate Partner Violence and Abuse Among Young Women and Men: Latent Class Analysis of Psychological, Physical, and Sexual Victimization and Perpetration in a UK Birth Cohort. J Interpers Violence. 2023 Jan 1;38(1–2):NP931–54. Alangea DO, Addo-Lartey AA, Sikweyiya Y, Chirwa ED, Coker-Appiah D, Jewkes R, et al. Prevalence and risk factors of intimate partner violence among women in four districts of the central region of Ghana: Baseline findings from a cluster randomised controlled trial. PLoS One. 2018 Jul 1;13(7). Issahaku PA. Correlates of Intimate Partner Violence in Ghana. Sage Open. 2017 Jun 1;7(2). Okyere J, Salu S, Ayebeng C, Dickson KS. Shedding light on hidden dynamics: partner controlling behavior and women’s alcohol consumption in Ghana. Discover Public Health. 2024 Jun 11;21(1). Issahaku P. Intimate partner violence: The controlling behaviours of men towards women in Northern Ghana. Vol. 13, Ghana Social Science Journal. 2016. Lohmann S, Cowlishaw S, Ney L, O’Donnell M, Felmingham K. The Trauma and Mental Health Impacts of Coercive Control: A Systematic Review and Meta-Analysis. Vol. 25, Trauma, Violence, and Abuse. SAGE Publications Ltd; 2024. p. 630–47. Antai D. Controlling behavior, power relations within intimate relationships and intimate partner physical and sexual violence against women in Nigeria. BMC Public Health. 2011;11. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 25 Nov, 2025 Read the published version in BMC Women's Health → Version 1 posted Editorial decision: Revision requested 15 Sep, 2025 Reviews received at journal 29 Aug, 2025 Reviewers agreed at journal 15 Aug, 2025 Reviews received at journal 15 Aug, 2025 Reviewers agreed at journal 11 Aug, 2025 Reviewers agreed at journal 06 Aug, 2025 Reviewers invited by journal 04 Aug, 2025 Editor assigned by journal 30 Jul, 2025 Editor invited by journal 10 Jul, 2025 Submission checks completed at journal 08 Jul, 2025 First submitted to journal 08 Jul, 2025 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-7041744","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":496647565,"identity":"a29eda59-afce-45e6-ba8a-06133f1f9070","order_by":0,"name":"Justice Moses K. Aheto","email":"","orcid":"","institution":"University of Ghana","correspondingAuthor":false,"prefix":"","firstName":"Justice","middleName":"Moses K.","lastName":"Aheto","suffix":""},{"id":496647566,"identity":"09fa850b-f00f-4a85-b026-d59c26a8b291","order_by":1,"name":"Patience Otobia Wellington","email":"data:image/png;base64,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","orcid":"","institution":"University of Ghana","correspondingAuthor":true,"prefix":"","firstName":"Patience","middleName":"Otobia","lastName":"Wellington","suffix":""},{"id":496647567,"identity":"b12afb9d-08a8-444e-8fc1-3991f0947a5b","order_by":2,"name":"Irene Kafui Vorsah Amponsah","email":"","orcid":"","institution":"University of Cape Coast","correspondingAuthor":false,"prefix":"","firstName":"Irene","middleName":"Kafui Vorsah","lastName":"Amponsah","suffix":""}],"badges":[],"createdAt":"2025-07-03 23:53:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7041744/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7041744/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12905-025-04144-w","type":"published","date":"2025-11-25T15:58:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":97179634,"identity":"676b0ea0-3a0b-4561-b5da-884fe5b364ef","added_by":"auto","created_at":"2025-12-01 16:16:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2628701,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7041744/v1/3b53a80c-0675-471f-9228-409023350f0d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Experience of intimate-partner controlling behaviours among women in Ghana: a novel three step latent class analysis approach with survey-weighted fractional-logit regression","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eIntimate partner violence (IPV) is recognized as a widespread public health crisis and human rights violation. Globally about one in three women experience physical and/or sexual violence by a partner in their lifetime (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The WHO emphasizes that \u003cem\u003e“most of this violence is intimate partner violence,”\u003c/em\u003e and notes that IPV can have pervasive effects on women’s physical, mental, sexual and reproductive health (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). IPV encompasses not only physical and sexual abuse but also psychological aggression and controlling behaviors (e.g. monitoring a partner’s movements, isolating her from friends/family, restricting finances, dictating daily activities) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Such controlling tactics are subtle but insidious, undermining women’s autonomy and often foreshadowing more severe violence (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Indeed, recent reviews confirm that exposure to coercive control is strongly linked to adverse mental health outcomes (e.g. PTSD, depression, anxiety) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIPV is also highly prevalent in sub-Saharan Africa (SSA): roughly one-third of women experience IPV over their lifetime, with similar regional figures reported in West Africa (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). These statistics underscore an urgent need for intensified prevention and research on IPV globally.\u003c/p\u003e\u003cp\u003eControlling behavior is a specific form of IPV whereby a partner seeks to dominate by limiting social and economic freedom. Studies describe common controlling acts as spying on a partner’s movements, severing her ties to friends or family, controlling money, and making unilateral decisions about her daily life (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Data from DHS surveys across SSA suggest that such behaviors are widespread – for example, DHS-based studies report that 20–50% of women in SSA experience at least one controlling act by a partner (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) In Eastern Africa, 35–44% of women reported partner control (e.g. jealousy, accusations of infidelity, social isolation) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Controlling behaviors often coexist with other abuses: they can escalate into physical or sexual violence and are considered a critical element of the IPV continuum (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Culturally, controlling actions are enabled by patriarchal norms.\u003c/p\u003e\u003cp\u003eControlling behaviors inflict serious health harms. Psychologically, victims often suffer chronic stress, low self-worth and long-term mental illness. Control in a relationship also worsen physical health: victims are more likely to report headaches, chronic pain and cardiovascular symptoms (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Moreover, there are critical sexual and reproductive impacts. IPV is linked to unintended pregnancy, low contraceptive use, sexually transmitted infections (STIs) including HIV, and pregnancy complications (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). For example, controlling partners may sabotage birth control or pressure women into unwanted pregnancies (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), raising risks of mistimed or unwanted births. In Ghana, women who have experienced IPV are significantly less likely to use contraceptives and more likely to report unintended pregnancy and STIs. Taken together, the evidence shows that controlling behaviors, as part of the broader IPV spectrum, undermine women’s psychological well-being, physical health and reproductive autonomy (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGhanaian women face a substantial burden of IPV and controlling abuse. Recent nationally representative data show that approximately 6 in 10 women (61%) aged 15–49 who have ever had a husband or intimate partner have experienced at least one form of controlling behavior from their current or most recent partner. The most frequently reported controlling acts included a partner insisting on always knowing their whereabouts (44%) and expressing jealousy or anger when they interact with other men (42%). Additionally, 21% of women reported being falsely accused of infidelity by their husband or intimate partner (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). This high prevalence in Ghana is rooted in socio-cultural norms. Ghana remains a largely patriarchal society where women’s autonomy can be restricted by tradition and law. For example, customary practices like bride-price can enhance the perception of men’s “ownership” of women. Gender inequalities – including lower female education and economic dependence – leave many Ghanaian women vulnerable to partner control (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). These dynamics perpetuate a cycle: controlling husbands isolate women socially and economically, while women’s limited power and resources make it difficult to resist.\u003c/p\u003e\u003cp\u003eTraditional, variable-centered analyses of intimate partner abuse typically aggregate controlling behaviors into a single score or assume that effects are uniform across all individuals. Such approaches can mask important heterogeneity in how these behaviors co-occur. For example, summative or dichotomous measures of abuse may \u003cem\u003e“conceal patterns of overlap between individual types of experiences”\u003c/em\u003e, because they ignore distinct combinations of behaviors (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e–\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In contrast, a person-centered approach like Latent Class Analysis (LCA) groups women into subpopulations based on their response patterns. LCA thus explicitly seeks “qualitatively different subgroups” defined by characteristic patterns of abuse (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) In short, person-centered models like LCA can detect latent patterns of controlling behaviors and identify subgroups at differential risk, information that variable-centered regression or factor analyses simply cannot provide (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo our knowledge, few studies in sub-Saharan Africa have and no study in Ghana has applied person-centered LCA to IPV or controlling behavior. Thus, this work will fill an important gap in the IPV literature. By combining a rigorous mixture-modeling framework with current, nationally representative data, the study will contribute new evidence on the heterogeneity of partner abuse in Ghana and more broadly in sub-Saharan Africa, informing both research and policy aimed at reducing intimate partner violence.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cb\u003eData Source and Analytical Sample\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study drew on the 2022 Ghana Demographic and Health Survey (GDHS) domestic violence module, which collects nationally representative data on ever‑married women. After rigorous data cleaning and removing cases with missing responses on any of the five key indicators and covariates, we arrived at an analytical sample of 5,137 women. All subsequent analyses refer to this cleaned sample.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSurvey Design and Weighting\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo ensure that our estimates reflect the GDHS’s complex multistage sampling, we applied the DHS domestic violence weight (variable d005), rescaled by 1,000,000 as recommended in DHS user‑support forums. Primary sampling units and sampling strata (the cross-classification of region and urban/rural residence) were declared in Stata 17 so that all standard errors account for clustering and stratification. This survey setup provided correct design‐based variance estimates in all downstream analyses.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLatent Class Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe employed latent class analysis (LCA) to uncover subgroups defined by women’s affirmative responses to five binary items (d101a_bin through d101e_bin). Each item was treated as a Bernoulli outcome in Stata’s generalized structural equation (gsem) modeling framework. Because estimating information criteria under complex-survey settings in gsem precludes post‐estimation of AIC and BIC, we estimated all latent‐class models on the unweighted data without declaring the survey design. This approach enabled us to obtain the BIC and select the optimal number of classes. We compared two-, three-, and four-class solutions, ultimately selecting the four-class model, the lowest BIC solution, a choice supported by simulation evidence that BIC generally outperforms alternate indices in large-sample LCA settings ((\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e–\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMixture Model Formulation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLet \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{ij}\\)\u003c/span\u003e\u003c/span\u003e denote the response of individual \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\:\\)\u003c/span\u003e\u003c/span\u003e to binary item \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\:\\)\u003c/span\u003e\u003c/span\u003e (where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\:=\\:1,\\:2,\\:\\dots\\:,\\:4\\:\\:\\)\u003c/span\u003e\u003c/span\u003e and\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{Y}_{ij}\\in\\:\\text{0,1}\\)\u003c/span\u003e\u003c/span\u003e), and let \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{i}\\)\u003c/span\u003e\u003c/span\u003e represent the latent class membership for individual\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:i\\:\\)\u003c/span\u003e\u003c/span\u003e. The probability of observing a particular response pattern is modeled as a finite mixture:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:P\\left({\\varvec{Y}}_{\\varvec{i}}={\\varvec{y}}_{\\varvec{i}}\\right)={\\sum\\:}_{c=1}^{K}{\\pi\\:}_{c{\\prod\\:}_{j=1}^{5}P\\left({Y}_{ij}={y}_{ij}|{C}_{i}=c\\right)}\\)\u003c/span\u003e\u003c/span\u003e ……………………………………………………….1\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\pi\\:}_{c}\\)\u003c/span\u003e\u003c/span\u003e is the prior probability (prevalence) of latent class \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:c\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:K\\:\\)\u003c/span\u003e\u003c/span\u003e is the number of latent classes\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:P\\left({Y}_{ij}=1|{C}_{i}=c\\right)={\\rho\\:}_{jc}\\)\u003c/span\u003e\u003c/span\u003e represents the item-response probability for item \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\:\\)\u003c/span\u003e\u003c/span\u003e in class \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:c\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003e\u003cb\u003eItem-Response Model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEach binary indicator follows a Bernoulli distribution within each latent class:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:P\\left({Y}_{ij}=1|{C}_{i}=c\\right)={\\rho\\:}_{jc}\\)\u003c/span\u003e\u003c/span\u003e …................ 2\u003c/p\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\rho\\:}_{jc}\\)\u003c/span\u003e\u003c/span\u003eis the probability of endorsing item \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\:\\)\u003c/span\u003e\u003c/span\u003e for individuals in class\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:c\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe complete data log-likelihood function is:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{log}L={\\sum\\:}_{i=1}^{N}\\text{log}\\left[{\\sum\\:}_{c=1}^{K}{\\pi\\:}_{c{\\prod\\:}_{j=1}^{5}{\\rho\\:}_{jc}^{{y}_{ij}}{\\left(1-{\\rho\\:}_{jc}\\right)}^{1-{y}_{ij}}}\\right]\\)\u003c/span\u003e\u003c/span\u003e ……………………………………………………….. 3\u003c/p\u003e\u003cp\u003e\u003cb\u003eClass Enumeration and Selection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eModel comparison primarily relied on the Bayesian Information Criterion which balances model fit against parsimony and demonstrates superior performance in large-sample LCA simulations (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). After fitting two-, three-, and four-class models, we observed a monotonic decline in BIC values, with the four-class solution achieving the lowest BIC. Entropy values (0.70) indicated adequate class separation, though we note entropy should not be used for model selection due to overfitting risks (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). All classes exceeded 5% prevalence, avoiding spurious small-class solutions (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Class specific item-response probabilities were examined to ensure each class represented a distinct and substantively interpretable response pattern, with probabilities differing mostly by \u0026gt; 0.40 between classes to confirm meaningful separation (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003ePosterior Class Membership Probabilities\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFollowing LCA estimation, posterior membership probabilities for each individual across all four latent classes were computed using Bayes' theorem:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:P\\left({C}_{i}=c|{\\varvec{Y}}_{\\varvec{i}}={\\varvec{y}}_{\\varvec{i}}\\right)=\\frac{{\\pi\\:}_{c{\\prod\\:}_{j=1}^{5}{\\rho\\:}_{jc}^{{y}_{ij}}{\\left(1-{\\rho\\:}_{jc}\\right)}^{1-{y}_{ij}}}}{{\\sum\\:}_{k=1}^{K}{\\pi\\:}_{k{\\prod\\:}_{j=1}^{5}{\\rho\\:}_{jk}^{{y}_{ij}}{\\left(1-{\\rho\\:}_{jk}\\right)}^{1-{y}_{ij}}}}\\)\u003c/span\u003e\u003c/span\u003e ………………………………………….. 4\u003c/p\u003e\u003cp\u003eThese posterior probabilities represent the probability that individual \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\:\\)\u003c/span\u003e\u003c/span\u003e belongs to class \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:c\\:\\)\u003c/span\u003e\u003c/span\u003e given their observed response pattern (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eFractional Logit Regression: Mathematical Specification and Quasi-Maximum Likelihood Estimation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo examine associations between latent class profiles and sociodemographic covariates, we treated the posterior class membership probabilities as fractional outcomes and applied the fractional logit regression model developed by Papke and Wooldridge (1996) (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eFractional Response Model Specification\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor each latent class \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:c\\:\\)\u003c/span\u003e\u003c/span\u003e, let \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{ic}\\)\u003c/span\u003e\u003c/span\u003e denote the posterior probability that individual \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\:\\)\u003c/span\u003e\u003c/span\u003e belongs to class \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\:\\)\u003c/span\u003e\u003c/span\u003e, where\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:0\\:\\le\\:\\:{p}_{ic}\\:\\le\\:\\:1\\)\u003c/span\u003e\u003c/span\u003e and\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{\\sum\\:}_{c=1}^{K}\\:{p}_{ic}\\:=\\:1\\)\u003c/span\u003e\u003c/span\u003e. The conditional expectation of the fractional response is modeled as:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E\\left({p}_{ic}∣{X}_{i}\\right)=G\\left(Xi\\beta\\:c\\right)\\)\u003c/span\u003e\u003c/span\u003e………………………………………………………………… 5\u003c/p\u003e\u003cp\u003ewhere\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{X}_{i}\\)\u003c/span\u003e\u003c/span\u003e is a vector of covariates for individual \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\:\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:c\\:\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003eis a vector of coefficients specific to class\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:c\\:\\)\u003c/span\u003e\u003c/span\u003e, \u003cem\u003eand\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:G\\left(\\cdot\\:\\right)\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003eis a distribution function that ensures\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E\\left({p}_{ic}|{\\varvec{X}}_{\\varvec{i}}\\right)\\)\u003c/span\u003e\u003c/span\u003e. We employed the logistic distribution function:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E\\left({p}_{ic}∣{X}_{i}\\right)=\\frac{\\text{exp}\\left({X}_{i}{\\beta\\:}_{c}\\right)}{1+\\text{exp}\\left({X}_{i}{\\beta\\:}_{c}\\right)}\\)\u003c/span\u003e\u003c/span\u003e ……………………………………………………………… 6\u003c/p\u003e\u003cp\u003e\u003cb\u003eQuasi-Maximum Likelihood Estimation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFollowing Papke and Wooldridge (1996), we applied quasi-maximum likelihood estimation (QMLE) using the Bernoulli log-likelihood function (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). For each class\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:c\\:\\)\u003c/span\u003e\u003c/span\u003e, the quasi-log-likelihood is:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mathcal{l}}_{c}\\left({\\beta\\:}_{c}\\right)={\\sum\\:}_{i=1}^{N}\\left[{p}_{ic}\\text{log}G\\left({X}_{i}{\\beta\\:}_{c}\\right)+\\left(1-{p}_{ic}\\right)\\text{log}\\left(1-G\\left({X}_{i}{\\beta\\:}_{c}\\right)\\right)\\right]\\)\u003c/span\u003e\u003c/span\u003e………………………….7\u003c/p\u003e\u003cp\u003eThe QMLE is consistent for the conditional mean parameters as long as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E\\left({p}_{ic}∣{\\varvec{X}}_{\\varvec{i}}\\right)\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e“is correctly specified, regardless of the actual distribution of\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{ic}\\)\u003c/span\u003e\u003c/span\u003e” (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). This robustness property makes QMLE particularly suitable for fractional outcomes that may not follow a standard probability distribution.\u003c/p\u003e\u003cp\u003e\u003cb\u003eVariance Specification and Robust Standard Errors\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe fractional logit model assumes conditional variance of the form:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Var\\left({p}_{ic}|{X}_{i}\\right)={\\sigma\\:}^{2}G\\left({X}_{i}{\\beta\\:}_{c}\\right)\\left[1-G\\left({X}_{i}{\\beta\\:}_{c}\\right)\\right]\\)\u003c/span\u003e\u003c/span\u003e………………………………………………8\u003c/p\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}^{2}\\)\u003c/span\u003e\u003c/span\u003e is an unknown scale parameter estimated by the GLM procedure.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFractional‑Membership Regression of Class Probabilities\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFollowing class enumeration, we generated posterior membership probabilities for each individual in all four latent classes. To explore associations between latent‑class profiles and socio‑demographic covariates, we treated these class probabilities as fractional outcomes in survey‑weighted generalized linear models. Specifically, we fitted binomial-family, logit‐link regressions of each class probability on the full set of covariates, using factor‑variable notation to handle categorical predictors automatically.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eWomen in the analytic sample were almost evenly split between urban (48.1%) and rural (51.9%) areas. The largest age groups were 25\u0026ndash;29 (19.2%) and 30\u0026ndash;34 (19.2%), while only 8.7% were aged 45\u0026ndash;49. Half of the participants completed secondary education (51.0%), nearly a quarter had no formal schooling (24.9%), and 9.4% had higher education. Pentecostal/Charismatic Christians comprised the largest religious group (36.9%), followed by Muslims (25.5%) and Catholics (10.6%). Akan (35.6%) and Mole‑Dagbani (25.9%) were the most common ethnicities. The poorest two wealth quintiles together accounted for 45.9% of women. Three‑quarters of partners did not drink alcohol (74.6%). By occupation, half of women worked in services (50.3%), 14.8% were not working, and smaller proportions held professional, clerical, or manual jobs. Among the 5137 partnered women included in the analysis, age was evenly distributed across the reproductive span, with the largest proportions in the 25\u0026ndash;29 (19.2%) and 30\u0026ndash;34 (19.2%) age group and the smallest in the oldest group (45\u0026ndash;49 years, 8.7%). Educational attainment was moderate: half of women had completed secondary school (51.0%), nearly a quarter had no formal education (24.9%), and 9.4% had attained higher education. Residence was balanced between urban (48.1%) and rural (51.9%) settings. All sixteen administrative regions contributed similarly to the sample (ranging from 5.2% in Western North to 7.9% in Ashanti). In terms of religion, Pentecostal/Charismatic affiliations predominated (36.9%), followed by Islam (25.5%) and Catholicism (10.6%). Ethnically, Akan (35.6%) and Mole‑Dagbani (25.9%) women constituted the majority. Socioeconomic status was skewed toward the lower quintiles: 45.9% of women fell into the poorest or poorer groups. Three‑quarters of partners did not consume alcohol (74.6%), and women\u0026rsquo;s occupations were dominated by the services sector (50.3%), with 14.8% not engaged in paid work (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of Participant Characteristics (N\u0026thinsp;=\u0026thinsp;5,137)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCovariate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u0026nbsp;(%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u0026ndash;19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e332\u0026nbsp;(6.46)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u0026ndash;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e886\u0026nbsp;(17.25)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e988\u0026nbsp;(19.23)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e985\u0026nbsp;(19.17)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35\u0026ndash;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e850\u0026nbsp;(16.55)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e651\u0026nbsp;(12.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e45\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e445\u0026nbsp;(8.66)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5137(100)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026nbsp;280\u0026nbsp;(24.92)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e754\u0026nbsp;(14.68)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u0026nbsp;619\u0026nbsp;(50.98)\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\u003e484\u0026nbsp;(9.42)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5137(100)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\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\u003e2\u0026nbsp;470\u0026nbsp;(48.08)\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\u003e2\u0026nbsp;667\u0026nbsp;(51.92)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5137(100)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRegion\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWestern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e293\u0026nbsp;(5.70)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e311\u0026nbsp;(6.05)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGreater Accra\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e366\u0026nbsp;(7.12)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVolta\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e288\u0026nbsp;(5.61)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEastern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e313\u0026nbsp;(6.09)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAshanti\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e405\u0026nbsp;(7.88)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWestern North\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e266\u0026nbsp;(5.18)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAhafo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e307\u0026nbsp;(5.98)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBono\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e284\u0026nbsp;(5.53)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBono East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e317\u0026nbsp;(6.17)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOti\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e300\u0026nbsp;(5.84)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorthern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e371\u0026nbsp;(7.22)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSavannah\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e334\u0026nbsp;(6.50)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e316\u0026nbsp;(6.15)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e330\u0026nbsp;(6.42)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper West\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e336\u0026nbsp;(6.54)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5137(100)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCatholic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e546\u0026nbsp;(10.63)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnglican\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41\u0026nbsp;(0.80)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMethodist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e208\u0026nbsp;(4.05)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresbyterian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e259\u0026nbsp;(5.04)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePentecostal/Charismatic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026nbsp;897\u0026nbsp;(36.93)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Christian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e665\u0026nbsp;(12.95)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIslam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026nbsp;310\u0026nbsp;(25.50)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraditional/Spiritualist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100\u0026nbsp;(1.95)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo religion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e106\u0026nbsp;(2.06)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDon\u0026rsquo;t know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u0026nbsp;(0.10)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5137(100)\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\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\u003e1\u0026nbsp;829\u0026nbsp;(35.60)\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\u003e211\u0026nbsp;(4.11)\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\u003e567\u0026nbsp;(11.04)\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\u003e224\u0026nbsp;(4.36)\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\u003e1\u0026nbsp;329\u0026nbsp;(25.87)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrusi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e256\u0026nbsp;(4.98)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGurma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e522\u0026nbsp;(10.16)\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\u003e153\u0026nbsp;(2.98)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDon\u0026rsquo;t know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46\u0026nbsp;(0.90)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5137(100)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWealth quintile\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoorest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026nbsp;254\u0026nbsp;(24.41)\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\u003e1\u0026nbsp;102\u0026nbsp;(21.45)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026nbsp;017\u0026nbsp;(19.80)\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\u003e969\u0026nbsp;(18.86)\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\u003e795\u0026nbsp;(15.48)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5137(100)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePartner drinks alcohol\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u0026nbsp;831\u0026nbsp;(74.58)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026nbsp;306\u0026nbsp;(25.42)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5137(100)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOccupation (grouped)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\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\u003e762\u0026nbsp;(14.83)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProfessional/technical/managerial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e329\u0026nbsp;(6.40)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClerical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76\u0026nbsp;(1.48)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e471\u0026nbsp;(9.17)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgricultural \u0026ndash; self employed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17\u0026nbsp;(0.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgricultural \u0026ndash; employee\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e252\u0026nbsp;(4.91)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eServices\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u0026nbsp;582\u0026nbsp;(50.26)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSkilled manual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e583\u0026nbsp;(11.35)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnskilled manual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24\u0026nbsp;(0.47)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDon\u0026rsquo;t know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41\u0026nbsp;(0.80)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5137(100)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAcross all five behaviors, jealousy and insistence on knowing the respondent\u0026rsquo;s whereabouts were the most frequently reported controlling tactics, with 40.8% of women indicating that their partner became jealous when they spoke with other men and 45.1% reporting that their partner insisted on always knowing their location. In contrast, more restrictive actions, such as preventing contact with female friends and limiting family visits, were less common (16.0% and 7.1%, respectively). Accusations of unfaithfulness was reported by 18.9% of women (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePrevalence of Partner Controlling Behaviors (N\u0026thinsp;=\u0026thinsp;5,137)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndicator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo (0) N (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) N (%)\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\u003eJealous if respondent talks with other men\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3 042 (59.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2 095 (40.78%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAccuses respondent of unfaithfulness\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4 164 (81.06%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e973 (18.94%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDoes not permit respondent to meet female friends\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4 316 (84.02%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e821 (15.98%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTries to limit respondent\u0026rsquo;s contact with family\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4 773 (92.91%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e364 (7.09%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInsists on knowing where respondent is\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 819 (54.88%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2 318 (45.12%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eA four‑class latent class model best captured the patterns of partner monitoring and control among 5,137 ever‑married Ghanaian women. Class one, constituting 59.2% of the sample, exhibited uniformly low probabilities across all five indicators (0.01\u0026ndash;0.22), and was labeled \u0026ldquo;Minimal or Occasional Monitoring.\u0026rdquo; Class two (11.2%) showed moderate‑to‑high probabilities on most items\u0026mdash;particularly location insistence (d101e\u0026thinsp;=\u0026thinsp;0.84) and jealousy (d101a\u0026thinsp;=\u0026thinsp;0.53)\u0026mdash;earning the label \u0026ldquo;Multi‑Domain Control with Surveillance.\u0026rdquo; Class three (22.2%) was defined by very high jealousy (d101a\u0026thinsp;=\u0026thinsp;0.94) and location monitoring (d101e\u0026thinsp;=\u0026thinsp;0.72), but low social isolation, thus \u0026ldquo;Jealousy and Location Monitoring.\u0026rdquo; Finally, Class four (7.4%) reported extremely high endorsement (\u0026gt;\u0026thinsp;0.80) of nearly every behavior and was defined as \u0026ldquo;Pervasive, High‑Severity Control.\u0026rdquo; These four profiles reflect qualitatively distinct subgroups: the majority experiencing little to no control (Class 1), a small but important group under intense surveillance (Class 2), a cohort marked by jealousy without broader isolation (Class 3), and a high‑severity minority subjected to pervasive control (Class 4). This classification lays the foundation for examining how socio‑demographic covariates predict membership in each subgroup (Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLatent Class Prevalence\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePrevalence (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMinimal or Occasional Monitoring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e55.78\u0026ndash;62.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMulti‑Domain Control with Surveillance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.38\u0026ndash;25.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJealousy and Location Monitoring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.97\u0026ndash;33.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePervasive, High‑Severity Control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.92\u0026ndash;11.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eConditional ItemResponse Probabilities by Class\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndicator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClass\u0026nbsp;1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClass\u0026nbsp;2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClass\u0026nbsp;3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eClass\u0026nbsp;4\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePr(std. err)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePr(std. err)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePr(std. err)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePr(std. err)\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\u003eJealous if talks with other men\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.11 (0.014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.53 (0.164)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.94 (0.044)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.99 (0.033)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAccuses of unfaithfulness\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.03 (0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.17 (0.072)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.40 (0.034)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.89 (0.128)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eProhibits meeting female friends\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.03 (0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.49 (0.094)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.13 (0.051)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.80 (0.054)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLimits contact with family\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.01 (0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.24 (0.055)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.02 (0.035)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.48 (0.050)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInsists on knowing whereabouts\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.22 (0.012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.84 (0.049)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.72 (0.031)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.92 (0.024)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWe evaluated solutions containing one through five latent classes. The four-class model achieved the lowest Akaike Information Criterion (AIC\u0026thinsp;=\u0026thinsp;22 852.01) and the lowest Bayesian Information Criterion (BIC\u0026thinsp;=\u0026thinsp;23 002.52), outperforming both the three‑class (AIC\u0026thinsp;=\u0026thinsp;22 933.77; BIC\u0026thinsp;=\u0026thinsp;23 045.02) and five‑class (AIC\u0026thinsp;=\u0026thinsp;22 858.67; BIC\u0026thinsp;=\u0026thinsp;23 048.27) solutions. The four-class solution also exhibited adequate entropy (0.70) and high average latent class posterior probabilities (ALCPP\u0026thinsp;=\u0026thinsp;0.8392), reflecting clear separation among the identified subgroups. Based on these fit indices, particularly the BIC, and considerations of parsimony and interpretability, the four-class model was selected for all subsequent analyses (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFit Statistics and Diagnostics for 1- to 4-Class Latent Class Models\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSABIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCAIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eEntropy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eALCPP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-13073.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26157.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26190.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e26174.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e26195.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3351.405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-11539.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23101.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23173.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23138.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e23184.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e283.5364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.9236\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-11449.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22933.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23045.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e22991.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e23062.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e103.4936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.8636\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-11403.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22852.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23002.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e22929.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e23025.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e9.722973\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.8392\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-11400.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22858.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23048.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e22956.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e23077.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.210027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.7397\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAmong women in the \u0026ldquo;Multi-Domain Surveillance\u0026rdquo; class (Class 2), age emerged as a strong predictor of membership. In the fully adjusted model, women aged 20\u0026ndash;24 had 46% increased odds of membership in this class relative to adolescents 15\u0026ndash;19 (aOR 1.46; 95% CI 1.16\u0026ndash;1.83; p\u0026thinsp;=\u0026thinsp;0.001), and women aged 30\u0026ndash;34 exhibited a similar increase (aOR 1.38; 95% CI 1.05\u0026ndash;1.81; p\u0026thinsp;=\u0026thinsp;0.020). The 25\u0026ndash;29 year group also showed a significant increase in odds (aOR 1.31; 95% CI 1.01\u0026ndash;1.70; p\u0026thinsp;=\u0026thinsp;0.044). No other age categories differed significantly from the 15\u0026ndash;19 reference. Regional residence was another key correlate. Compared with Western region, women in Ahafo (aOR\u0026thinsp;=\u0026thinsp;1.52; 1.12\u0026ndash;2.07; p\u0026thinsp;=\u0026thinsp;0.008), Oti (aOR\u0026thinsp;=\u0026thinsp;1.66; 1.14\u0026ndash;2.43; p\u0026thinsp;=\u0026thinsp;0.009), Northern (aOR\u0026thinsp;=\u0026thinsp;1.64; 1.15\u0026ndash;2.32; p\u0026thinsp;=\u0026thinsp;0.006), Savannah (aOR\u0026thinsp;=\u0026thinsp;1.78; 1.22\u0026ndash;2.60; p\u0026thinsp;=\u0026thinsp;0.003), and Upper West (aOR\u0026thinsp;=\u0026thinsp;1.54; 1.01\u0026ndash;2.35; p\u0026thinsp;=\u0026thinsp;0.047) exhibited increased odds of membership in class 2. Although Bono East residence was associated with reduced unadjusted odds (uOR\u0026thinsp;=\u0026thinsp;0.71; 0.51\u0026ndash;0.99; p\u0026thinsp;=\u0026thinsp;0.044), this association was attenuated in multivariable model (aOR\u0026thinsp;=\u0026thinsp;0.74; 0.52\u0026ndash;1.04; p\u0026thinsp;=\u0026thinsp;0.084). Finally, partner alcohol use was significantly associated with Class 2 membership: women whose partners drank alcohol had 22%-increased odds of being in the Multi-Domain Surveillance class (AOR 1.22; 95% CI 1.05\u0026ndash;1.42; p\u0026thinsp;=\u0026thinsp;0.011). Education level, residence, religion, ethnicity, wealth quintile, and occupation did not retain significance in adjusted analyses. These findings identify young adulthood (20\u0026ndash;34 years), specific regional contexts, and partner alcohol use as key determinants of experiencing a predominantly monitoring pattern of partner control (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSocio-Demographic Factors Associated with Membership in Class 2: Unadjusted and Adjusted Fractional-Logit Models\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUOR\u0026nbsp;[95%\u0026nbsp;CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep‑value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAOR\u0026nbsp;[95%\u0026nbsp;CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep‑value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u0026ndash;19 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u0026ndash;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.418\u0026nbsp;[1.129\u0026ndash;1.782]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.455\u0026nbsp;[1.155\u0026ndash;1.832]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.247\u0026nbsp;[0.985\u0026ndash;1.578]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.310\u0026nbsp;[1.008\u0026ndash;1.702]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.044\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.364\u0026nbsp;[1.059\u0026ndash;1.757]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.380\u0026nbsp;[1.053\u0026ndash;1.809]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35\u0026ndash;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.979\u0026nbsp;[0.752\u0026ndash;1.275]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.874\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.990\u0026nbsp;[0.746\u0026ndash;1.314]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.945\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.139\u0026nbsp;[0.851\u0026ndash;1.526]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.120\u0026nbsp;[0.823\u0026ndash;1.524]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.471\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e45\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.957\u0026nbsp;[0.673\u0026ndash;1.361]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.806\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.922\u0026nbsp;[0.643\u0026ndash;1.321]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.656\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo education (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.849\u0026nbsp;[0.676\u0026ndash;1.067]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.942\u0026nbsp;[0.736\u0026ndash;1.206]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.634\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.904\u0026nbsp;[0.752\u0026ndash;1.087]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.009\u0026nbsp;[0.801\u0026ndash;1.272]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.937\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\u003e0.796\u0026nbsp;[0.600\u0026ndash;1.056]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.912\u0026nbsp;[0.654\u0026ndash;1.274]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.058\u0026nbsp;[0.921\u0026ndash;1.216]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.424\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.854\u0026nbsp;[0.716\u0026ndash;1.017]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRegion\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWestern (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.029\u0026nbsp;[0.813\u0026ndash;1.303]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.812\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.024\u0026nbsp;[0.797\u0026ndash;1.315]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.855\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGreater\u0026nbsp;Accra\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.758\u0026nbsp;[0.570\u0026ndash;1.008]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.737\u0026nbsp;[0.536\u0026ndash;1.015]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVolta\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.289\u0026nbsp;[0.898\u0026ndash;1.851]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.361\u0026nbsp;[0.897\u0026ndash;2.064]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.147\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEastern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.886\u0026nbsp;[0.664\u0026ndash;1.182]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.920\u0026nbsp;[0.685\u0026ndash;1.236]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.581\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAshanti\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.099\u0026nbsp;[0.839\u0026ndash;1.440]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.493\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.144\u0026nbsp;[0.876\u0026ndash;1.495]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.322\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWestern\u0026nbsp;North\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.903\u0026nbsp;[0.665\u0026ndash;1.227]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.958\u0026nbsp;[0.697\u0026ndash;1.317]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.791\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAhafo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.499\u0026nbsp;[1.113\u0026ndash;2.018]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.519\u0026nbsp;[1.116\u0026ndash;2.066]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBono\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.717\u0026nbsp;[0.507\u0026ndash;1.013]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.737\u0026nbsp;[0.521\u0026ndash;1.042]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBono\u0026nbsp;East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.709\u0026nbsp;[0.507\u0026ndash;0.991]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.044\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.735\u0026nbsp;[0.518\u0026ndash;1.042]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOti\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.599\u0026nbsp;[1.132\u0026ndash;2.257]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.662\u0026nbsp;[1.138\u0026ndash;2.426]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorthern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.465\u0026nbsp;[1.079\u0026ndash;1.990]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.635\u0026nbsp;[1.153\u0026ndash;2.318]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSavannah\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.503\u0026nbsp;[1.094\u0026ndash;2.065]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.780\u0026nbsp;[1.218\u0026ndash;2.599]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth\u0026nbsp;East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.182\u0026nbsp;[0.798\u0026ndash;1.751]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.332\u0026nbsp;[0.855\u0026ndash;2.077]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.205\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper\u0026nbsp;East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.028\u0026nbsp;[0.774\u0026ndash;1.364]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.849\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.245\u0026nbsp;[0.880\u0026ndash;1.760]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.215\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper\u0026nbsp;West\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.186\u0026nbsp;[0.817\u0026ndash;1.721]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.538\u0026nbsp;[1.005\u0026ndash;2.352]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCatholic (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnglican\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.928\u0026nbsp;[0.411\u0026ndash;2.095]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.064\u0026nbsp;[0.461\u0026ndash;2.458]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.885\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMethodist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.989\u0026nbsp;[0.698\u0026ndash;1.402]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.952\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.047\u0026nbsp;[0.715\u0026ndash;1.535]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.813\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresbyterian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.105\u0026nbsp;[0.763\u0026ndash;1.601]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.216\u0026nbsp;[0.828\u0026ndash;1.787]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.318\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePentecostal/Charismatic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.092\u0026nbsp;[0.848\u0026ndash;1.405]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.150\u0026nbsp;[0.878\u0026ndash;1.507]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u0026nbsp;Christian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.110\u0026nbsp;[0.817\u0026ndash;1.507]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.134\u0026nbsp;[0.822\u0026ndash;1.566]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.442\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIslam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.049\u0026nbsp;[0.797\u0026ndash;1.381]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.731\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.994\u0026nbsp;[0.728\u0026ndash;1.359]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.971\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrad./Spiritualist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.608\u0026nbsp;[1.014\u0026ndash;2.551]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.086\u0026nbsp;[0.676\u0026ndash;1.747]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.732\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u0026nbsp;religion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.997\u0026nbsp;[0.657\u0026ndash;1.511]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.874\u0026nbsp;[0.584\u0026ndash;1.307]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.511\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDon\u0026rsquo;t know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.678\u0026nbsp;[0.279\u0026ndash;1.646]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.596\u0026nbsp;[0.238\u0026ndash;1.491]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.268\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\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\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\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\u003e0.880\u0026nbsp;[0.640\u0026ndash;1.210]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.012\u0026nbsp;[0.722\u0026ndash;1.418]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.944\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\u003e1.022\u0026nbsp;[0.808\u0026ndash;1.293]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.931\u0026nbsp;[0.710\u0026ndash;1.220]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.604\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\u003e1.028\u0026nbsp;[0.766\u0026ndash;1.381]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.853\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.856\u0026nbsp;[0.625\u0026ndash;1.172]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.331\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\u003e0.990\u0026nbsp;[0.828\u0026ndash;1.182]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.848\u0026nbsp;[0.685\u0026ndash;1.049]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrusi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.049\u0026nbsp;[0.777\u0026ndash;1.416]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.755\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.936\u0026nbsp;[0.665\u0026ndash;1.319]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.707\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGurma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.388\u0026nbsp;[1.060\u0026ndash;1.817]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.990\u0026nbsp;[0.740\u0026ndash;1.324]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.944\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\u003e1.042\u0026nbsp;[0.757\u0026ndash;1.435]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.799\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.084\u0026nbsp;[0.763\u0026ndash;1.541]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.652\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDon\u0026rsquo;t know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.837\u0026nbsp;[0.514\u0026ndash;1.362]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.877\u0026nbsp;[0.511\u0026ndash;1.504]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.632\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCombined Wealth quintile\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoorest (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoorer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.945\u0026nbsp;[0.755\u0026ndash;1.182]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.015\u0026nbsp;[0.812\u0026ndash;1.268]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.897\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.808\u0026nbsp;[0.648\u0026ndash;1.008]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.894\u0026nbsp;[0.702\u0026ndash;1.140]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.366\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRicher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.814\u0026nbsp;[0.661\u0026ndash;1.002]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.881\u0026nbsp;[0.686\u0026ndash;1.358]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.323\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\u003e0.758\u0026nbsp;[0.598\u0026ndash;0.960]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.895\u0026nbsp;[0.672\u0026ndash;1.193]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.449\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePartner drinks alcohol\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.117\u0026nbsp;[0.961\u0026ndash;1.298]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.217\u0026nbsp;[1.046\u0026ndash;1.415]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot working (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProf/Tech/Managerial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.797\u0026nbsp;[0.575\u0026ndash;1.105]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.874\u0026nbsp;[0.618\u0026ndash;1.236]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.446\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClerical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.999\u0026nbsp;[0.602\u0026ndash;1.658]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.054\u0026nbsp;[0.630\u0026ndash;1.764]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.841\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.877\u0026nbsp;[0.678\u0026ndash;1.135]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.318\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.950\u0026nbsp;[0.729\u0026ndash;1.239]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.706\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgri \u0026ndash; self‑employed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.388\u0026nbsp;[0.333\u0026ndash;5.785]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.652\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.155\u0026nbsp;[0.233\u0026ndash;5.726]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgri \u0026ndash; employee\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.254\u0026nbsp;[0.840\u0026ndash;1.871]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.202\u0026nbsp;[0.814\u0026ndash;1.774]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.355\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eServices\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.993\u0026nbsp;[0.817\u0026ndash;1.207]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.947\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.988\u0026nbsp;[0.806\u0026ndash;1.210]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.907\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSkilled manual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.905\u0026nbsp;[0.715\u0026ndash;1.145]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.872\u0026nbsp;[0.680\u0026ndash;1.119]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnskilled manual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.604\u0026nbsp;[0.581\u0026ndash;4.428]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.361\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.826\u0026nbsp;[0.655\u0026ndash;5.092]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDon\u0026rsquo;t know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.920\u0026nbsp;[0.523\u0026ndash;1.617]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.771\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.944\u0026nbsp;[0.523\u0026ndash;1.704]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.848\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWomen\u0026rsquo;s odds of belonging to Class 3 declined with age: In unadjusted analyses, women aged 30\u0026ndash;34 years had 41% reduced odds of experiencing the \u0026ldquo;Jealousy and Location Monitoring\u0026rdquo; partner behaviour relative to adolescents aged 15\u0026ndash;19 years (uOR\u0026thinsp;=\u0026thinsp;0.59; 95% CI\u0026thinsp;=\u0026thinsp;0.43\u0026ndash;0.81; p\u0026thinsp;=\u0026thinsp;0.001), and this protective association persisted after adjustment (aOR\u0026thinsp;=\u0026thinsp;0.65; 0.46\u0026ndash;0.92; p\u0026thinsp;=\u0026thinsp;0.015). Similarly, women 35\u0026ndash;39 (uOR\u0026thinsp;=\u0026thinsp;0.54; 0.39\u0026ndash;0.74; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; aOR\u0026thinsp;=\u0026thinsp;0.58; 0.41\u0026ndash;0.81; p\u0026thinsp;=\u0026thinsp;0.001), 40\u0026ndash;44 (uOR\u0026thinsp;=\u0026thinsp;0.52; 0.37\u0026ndash;0.73; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; aOR\u0026thinsp;=\u0026thinsp;0.55; 0.38\u0026ndash;0.78; p\u0026thinsp;=\u0026thinsp;0.001), and 45\u0026ndash;49 (uOR\u0026thinsp;=\u0026thinsp;0.43; 0.30\u0026ndash;0.62; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; aOR\u0026thinsp;=\u0026thinsp;0.45; 0.31\u0026ndash;0.66; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) also had significantly reduced odds compared with the youngest group. Educational attainment showed the opposite pattern: in adjusted models, women with primary schooling had 30%-increased odds of experiencing \u0026ldquo;Jealousy and Location Monitoring\u0026rdquo; partner behavior (aOR\u0026thinsp;=\u0026thinsp;1.30; 1.03\u0026ndash;1.64; p\u0026thinsp;=\u0026thinsp;0.030), and those with secondary education had 29%-increased odds (aOR\u0026thinsp;=\u0026thinsp;1.29; 1.05\u0026ndash;1.58; p\u0026thinsp;=\u0026thinsp;0.014), relative to women with no formal education. No significant associations were observed for residence, nor for any of the individual regions, in the multivariable model. Ethnicity and most religious affiliations were also not associated with class membership; however, women in the very small women who do not know their religion (\u0026ldquo;Don\u0026rsquo;t know) had over fivefold increased odds (aOR\u0026thinsp;=\u0026thinsp;5.39; 1.66\u0026ndash;17.57; p\u0026thinsp;=\u0026thinsp;0.005), though this estimate is based on few observations. Women in the poorer (second) wealth quintile exhibited 25% increased adjusted odds versus the poorest quintile (aOR\u0026thinsp;=\u0026thinsp;1.25; 1.02\u0026ndash;1.53; p\u0026thinsp;=\u0026thinsp;0.030), while no significant differences emerged for higher quintiles. Partner characteristics mattered as well: alcohol consumption by the partner was associated with 36%-increased odds of class 3 membership in both adjusted and unadjusted models (aOR\u0026thinsp;=\u0026thinsp;1.36; 1.16\u0026ndash;1.60; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Partner education beyond no schooling did not retain significance after adjustment. Taken together, these findings suggest that younger women and those with lower educational attainment are particularly vulnerable to partners\u0026rsquo; jealous and monitoring behaviors and that partner alcohol use and modest differences in wealth may further modulate this risk (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSocio-Demographic Factors Associated with Membership in Class 3: Unadjusted and Adjusted Fractional-Logit Models\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUOR\u0026nbsp;[95%\u0026nbsp;CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep‑value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAOR\u0026nbsp;[95%\u0026nbsp;CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep‑value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u0026ndash;19\u0026nbsp;(ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u0026ndash;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.932\u0026nbsp;[0.683\u0026ndash;1.273]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.657\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.990\u0026nbsp;[0.728\u0026ndash;1.347]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.949\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.862\u0026nbsp;[0.637\u0026ndash;1.168]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.338\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.948\u0026nbsp;[0.697\u0026ndash;1.291]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.735\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.588\u0026nbsp;[0.425\u0026ndash;0.814]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.652\u0026nbsp;[0.462\u0026ndash;0.921]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35\u0026ndash;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.535\u0026nbsp;[0.388\u0026ndash;0.738]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026nbsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.577\u0026nbsp;[0.412\u0026ndash;0.807]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.518\u0026nbsp;[0.368\u0026ndash;0.727]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026nbsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.547\u0026nbsp;[0.384\u0026ndash;0.779]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e45\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.432\u0026nbsp;[0.302\u0026ndash;0.618]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026nbsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.454\u0026nbsp;[0.312\u0026ndash;0.661]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026nbsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u0026nbsp;education\u0026nbsp;(ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.439\u0026nbsp;[1.166\u0026ndash;1.777]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.295\u0026nbsp;[1.026\u0026ndash;1.635]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.546\u0026nbsp;[1.317\u0026ndash;1.814]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026nbsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.290\u0026nbsp;[1.053\u0026ndash;1.579]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\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\u003e1.196\u0026nbsp;[0.932\u0026ndash;1.535]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.965\u0026nbsp;[0.660\u0026ndash;1.409]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.853\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u0026nbsp;(ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\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\u003e0.995\u0026nbsp;[0.869\u0026ndash;1.140]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.946\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.982\u0026nbsp;[0.838\u0026ndash;1.150]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.818\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRegion\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWestern\u0026nbsp;(ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.216\u0026nbsp;[0.931\u0026ndash;1.589]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.209\u0026nbsp;[0.931\u0026ndash;1.571]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.155\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGreater\u0026nbsp;Accra\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.979\u0026nbsp;[0.718\u0026ndash;1.335]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.007\u0026nbsp;[0.728\u0026ndash;1.394]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.966\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVolta\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.961\u0026nbsp;[0.723\u0026ndash;1.279]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.060\u0026nbsp;[0.754\u0026ndash;1.490]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.739\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEastern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.001\u0026nbsp;[0.768\u0026ndash;1.305]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.040\u0026nbsp;[0.798\u0026ndash;1.357]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.769\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAshanti\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.989\u0026nbsp;[0.777\u0026ndash;1.260]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.089\u0026nbsp;[0.850\u0026ndash;1.395]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.499\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWestern\u0026nbsp;North\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.906\u0026nbsp;[0.681\u0026ndash;1.205]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.976\u0026nbsp;[0.727\u0026ndash;1.310]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAhafo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.997\u0026nbsp;[0.755\u0026ndash;1.317]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.984\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.059\u0026nbsp;[0.791\u0026ndash;1.419]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.698\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBono\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.910\u0026nbsp;[0.632\u0026ndash;1.310]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.975\u0026nbsp;[0.663\u0026ndash;1.434]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.899\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBono\u0026nbsp;East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.684\u0026nbsp;[0.508\u0026ndash;0.919]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.755\u0026nbsp;[0.552\u0026ndash;1.033]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOti\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.952\u0026nbsp;[0.708\u0026ndash;1.279]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.742\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.002\u0026nbsp;[0.708\u0026ndash;1.418]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorthern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.972\u0026nbsp;[0.750\u0026ndash;1.259]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.331\u0026nbsp;[0.952\u0026ndash;1.862]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.094\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSavannah\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.961\u0026nbsp;[0.688\u0026ndash;1.341]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.813\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.101\u0026nbsp;[0.737\u0026ndash;1.646]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.638\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth\u0026nbsp;East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.786\u0026nbsp;[0.534\u0026ndash;1.155]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.957\u0026nbsp;[0.625\u0026ndash;1.466]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.839\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper\u0026nbsp;East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.801\u0026nbsp;[0.613\u0026ndash;1.047]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.883\u0026nbsp;[0.620\u0026ndash;1.259]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.493\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper\u0026nbsp;West\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.754\u0026nbsp;[0.570\u0026ndash;0.996]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.860\u0026nbsp;[0.595\u0026ndash;1.244]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.423\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCatholic\u0026nbsp;(ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnglican\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.121\u0026nbsp;[0.600\u0026ndash;2.093]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.721\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.223\u0026nbsp;[0.660\u0026ndash;2.267]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.522\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMethodist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.473\u0026nbsp;[1.009\u0026ndash;2.149]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.396\u0026nbsp;[0.970\u0026ndash;2.009]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresbyterian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.949\u0026nbsp;[0.647\u0026ndash;1.392]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.787\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.986\u0026nbsp;[0.679\u0026ndash;1.434]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.943\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePentec/Charis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.155\u0026nbsp;[0.911\u0026ndash;1.465]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.112\u0026nbsp;[0.872\u0026ndash;1.420]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.391\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u0026nbsp;Christian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.898\u0026nbsp;[0.684\u0026ndash;1.178]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.876\u0026nbsp;[0.659\u0026ndash;1.164]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.361\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIslam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.942\u0026nbsp;[0.737\u0026ndash;1.204]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.633\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.960\u0026nbsp;[0.730\u0026ndash;1.261]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.767\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrad/Spiritualist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.780\u0026nbsp;[0.540\u0026ndash;1.128]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.839\u0026nbsp;[0.566\u0026ndash;1.244]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.381\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u0026nbsp;religion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.239\u0026nbsp;[0.724\u0026ndash;2.119]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.434\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.165\u0026nbsp;[0.687\u0026ndash;1.976]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.571\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDon\u0026rsquo;t know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.501\u0026nbsp;[1.678\u0026ndash;25.195]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.393\u0026nbsp;[1.655\u0026ndash;17.566]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAkan\u0026nbsp;(ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\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\u003e1.095\u0026nbsp;[0.838\u0026ndash;1.432]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.162\u0026nbsp;[0.866\u0026ndash;1.558]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.316\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\u003e0.950\u0026nbsp;[0.776\u0026ndash;1.163]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.966\u0026nbsp;[0.749\u0026ndash;1.247]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.793\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\u003e1.041\u0026nbsp;[0.673\u0026ndash;1.612]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.858\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.081\u0026nbsp;[0.686\u0026ndash;1.704]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.738\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\u003e0.897\u0026nbsp;[0.747\u0026ndash;1.077]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.104\u0026nbsp;[0.851\u0026ndash;1.431]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.457\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrusi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.772\u0026nbsp;[0.566\u0026ndash;1.053]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.005\u0026nbsp;[0.688\u0026ndash;1.468]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.978\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGurma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.872\u0026nbsp;[0.683\u0026ndash;1.114]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.025\u0026nbsp;[0.742\u0026ndash;1.415]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.881\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\u003e0.796\u0026nbsp;[0.548\u0026ndash;1.154]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.103\u0026nbsp;[0.762\u0026ndash;1.695]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.653\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDon\u0026rsquo;t know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.611\u0026nbsp;[0.824\u0026ndash;3.149]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.109\u0026nbsp;[1.071\u0026ndash;4.153]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCombined Wealth quintile\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoorest\u0026nbsp;(ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\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\u003e1.362\u0026nbsp;[1.114\u0026ndash;1.665]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.252\u0026nbsp;[1.022\u0026ndash;1.533]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.376\u0026nbsp;[1.133\u0026ndash;1.671]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.206\u0026nbsp;[0.942\u0026ndash;1.544]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.136\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRicher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.204\u0026nbsp;[1.006\u0026ndash;1.441]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.043\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.001\u0026nbsp;[0.781\u0026ndash;1.284]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.991\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRichest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.111\u0026nbsp;[0.905\u0026ndash;1.363]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.976\u0026nbsp;[0.739\u0026ndash;1.290]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.865\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePartner drinks alcohol\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u0026nbsp;(ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.205\u0026nbsp;[1.033\u0026ndash;1.406]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.361\u0026nbsp;[1.160\u0026ndash;1.595]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026nbsp;0.001\u003c/b\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot working\u0026nbsp;(ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProf/Tech/Managerial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.809\u0026nbsp;[0.589\u0026ndash;1.111]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.188\u0026nbsp;[0.785\u0026ndash;1.796]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.415\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClerical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.816\u0026nbsp;[0.472\u0026ndash;1.409]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.465\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.968\u0026nbsp;[0.549\u0026ndash;1.707]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.911\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.754\u0026nbsp;[0.569\u0026ndash;1.000]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.904\u0026nbsp;[0.678\u0026ndash;1.207]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.494\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgri\u0026nbsp;\u0026ndash; self‑emp\u0026rsquo;d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.690\u0026nbsp;[0.368\u0026ndash;1.295]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.103\u0026nbsp;[0.544\u0026ndash;2.237]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.762\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgri\u0026nbsp;\u0026ndash; employee\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.663\u0026nbsp;[0.469\u0026ndash;0.937]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.819\u0026nbsp;[0.578\u0026ndash;1.161]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.262\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eServices\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.732\u0026nbsp;[0.602\u0026ndash;0.890]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.911\u0026nbsp;[0.743\u0026ndash;1.116]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.368\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSkilled manual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.840\u0026nbsp;[0.643\u0026ndash;1.097]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.904\u0026nbsp;[0.690\u0026ndash;1.185]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.465\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnskilled manual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.971\u0026nbsp;[0.459\u0026ndash;2.053]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.939\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.979\u0026nbsp;[0.427\u0026ndash;2.242]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.959\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDon\u0026rsquo;t know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.876\u0026nbsp;[0.455\u0026ndash;1.686]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.691\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.135\u0026nbsp;[0.573\u0026ndash;2.247]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.716\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents associations between socio-demographic factors and membership in Class 4 (\u0026ldquo;Pervasive, High-Severity Control\u0026rdquo;). Women aged 20\u0026ndash;24 years experienced significantly increased odds of the Pervasive Control profile compared with adolescents aged 15\u0026ndash;19 years (aOR 1.81; 95% CI 1.18\u0026ndash;2.77; p\u0026thinsp;=\u0026thinsp;0.007), whereas no other age groups differed after adjustment. Educational attainment showed a protective effect at the highest level: women with higher education had 44% reduced odds of Class 4 membership compared with those with no education (aOR 0.42; 95% CI 0.21\u0026ndash;0.83; p\u0026thinsp;=\u0026thinsp;0.013) in both the adjusted and unadjusted model. Primary and secondary schooling were not significant. Regionally, compared with Western Region residents, women in Central (aOR 2.62; 95% CI 1.49\u0026ndash;4.59; p\u0026thinsp;=\u0026thinsp;0.001), Volta (AOR 2.04; 95% CI 1.06\u0026ndash;3.92; p\u0026thinsp;=\u0026thinsp;0.032), Eastern (AOR 2.07; 95% CI 1.18\u0026ndash;3.63; p\u0026thinsp;=\u0026thinsp;0.011), Northern (AOR 2.42; 95% CI 1.22\u0026ndash;4.81; p\u0026thinsp;=\u0026thinsp;0.011), Savannah (AOR 6.02; 95% CI 2.90\u0026ndash;12.49; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), North East (AOR 2.63; 95% CI 1.12\u0026ndash;6.19; p\u0026thinsp;=\u0026thinsp;0.027), Upper East (AOR 2.38; 95% CI 1.22\u0026ndash;4.65; p\u0026thinsp;=\u0026thinsp;0.011), and Upper West (AOR 3.37; 95% CI 1.66\u0026ndash;6.81; p\u0026thinsp;=\u0026thinsp;0.001) all had significantly higher odds of Class 4 membership. Other regions did not differ significantly in adjusted analyses. Partner drinking was associated with a 95% increase in the odds of Class 4 membership compared with women whose partners did not drink (AOR 1.95; 95% (1.594\u0026ndash;2.393); p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that partner alcohol use is a strong correlate of experiencing multiple, severe controlling behaviors. Ethnic affiliation conferred protection for several groups: compared with Akan women, those of Mole-Dagbani (aOR 0.60; 0.42\u0026ndash;0.87; p\u0026thinsp;=\u0026thinsp;0.007), Grusi (aOR 0.50; 0.28\u0026ndash;0.89; p\u0026thinsp;=\u0026thinsp;0.019), Gurma (aOR 0.49; 0.32\u0026ndash;0.76; p\u0026thinsp;=\u0026thinsp;0.001), and women who do not know their ethnicities (aOR 0.17; 0.09\u0026ndash;0.33; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) had significantly reduced odds. Significant independent associations were not observed for wealth quintile, religion, or occupation (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eSocio-Demographic Factors Associated with Membership in Class 4: Unadjusted and Adjusted Fractional-Logit Models\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUOR\u0026nbsp;[95%\u0026nbsp;CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep‑value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAOR\u0026nbsp;[95%\u0026nbsp;CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep‑value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u0026ndash;19 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u0026ndash;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.584\u0026nbsp;[1.037\u0026ndash;2.420]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.808\u0026nbsp;[1.181\u0026ndash;2.770]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.036\u0026nbsp;[0.696\u0026ndash;1.541]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.186\u0026nbsp;[0.782\u0026ndash;1.798]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.422\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.154\u0026nbsp;[0.765\u0026ndash;1.742]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.493\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.198\u0026nbsp;[0.778\u0026ndash;1.844]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.412\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35\u0026ndash;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.190\u0026nbsp;[0.771\u0026ndash;1.836]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.165\u0026nbsp;[0.723\u0026ndash;1.879]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.529\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.337\u0026nbsp;[0.857\u0026ndash;2.085]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.142\u0026nbsp;[0.702\u0026ndash;1.856]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.592\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e45\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.098\u0026nbsp;[0.635\u0026ndash;1.897]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.737\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.847\u0026nbsp;[0.469\u0026ndash;1.529]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo education (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.408\u0026nbsp;[0.937\u0026ndash;2.116]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.223\u0026nbsp;[0.774\u0026ndash;1.933]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.387\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.057\u0026nbsp;[0.780\u0026ndash;1.433]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.936\u0026nbsp;[0.598\u0026ndash;1.466]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.773\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\u003e0.559\u0026nbsp;[0.331\u0026ndash;0.944]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.416\u0026nbsp;[0.208\u0026ndash;0.831]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.003\u0026nbsp;[0.785\u0026ndash;1.282]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.979\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.760\u0026nbsp;[0.576\u0026ndash;1.004]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRegion\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWestern (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.567\u0026nbsp;[1.461\u0026ndash;4.509]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.617\u0026nbsp;[1.491\u0026ndash;4.594]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGreater\u0026nbsp;Accra\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.895\u0026nbsp;[0.514\u0026ndash;1.559]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.695\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.957\u0026nbsp;[0.530\u0026ndash;1.730]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.885\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVolta\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.972\u0026nbsp;[1.107\u0026ndash;3.511]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.042\u0026nbsp;[1.064\u0026ndash;3.917]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEastern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.703\u0026nbsp;[0.994\u0026ndash;2.918]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.070\u0026nbsp;[1.182\u0026ndash;3.626]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAshanti\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.284\u0026nbsp;[0.778\u0026ndash;2.117]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.534\u0026nbsp;[0.912\u0026ndash;2.580]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWestern\u0026nbsp;North\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.872\u0026nbsp;[0.454\u0026ndash;1.674]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.992\u0026nbsp;[0.507\u0026ndash;1.942]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.982\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAhafo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.076\u0026nbsp;[0.645\u0026ndash;1.794]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.779\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.374\u0026nbsp;[0.783\u0026ndash;2.412]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.267\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBono\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.502\u0026nbsp;[0.786\u0026ndash;2.869]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.850\u0026nbsp;[0.902\u0026ndash;3.794]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBono\u0026nbsp;East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.651\u0026nbsp;[0.319\u0026ndash;1.329]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.895\u0026nbsp;[0.417\u0026ndash;1.923]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.776\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOti\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.018\u0026nbsp;[0.598\u0026ndash;1.734]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.947\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.469\u0026nbsp;[0.801\u0026ndash;2.695]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.214\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorthern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.230\u0026nbsp;[0.692\u0026ndash;2.186]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.479\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.424\u0026nbsp;[1.222\u0026ndash;4.810]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSavannah\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.139\u0026nbsp;[1.611\u0026ndash;6.118]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.017\u0026nbsp;[2.89912.489]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026nbsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth\u0026nbsp;East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.177\u0026nbsp;[0.546\u0026ndash;2.536]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.677\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.627\u0026nbsp;[1.115\u0026ndash;6.185]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper\u0026nbsp;East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.146\u0026nbsp;[0.625\u0026ndash;2.101]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.378\u0026nbsp;[1.216\u0026ndash;4.653]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper\u0026nbsp;West\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.424\u0026nbsp;[0.802\u0026ndash;2.530]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.366\u0026nbsp;[1.664\u0026ndash;6.806]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCatholic (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnglican\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.293\u0026nbsp;[0.674\u0026ndash;7.799]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.774\u0026nbsp;[0.787\u0026ndash;9.775]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMethodist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.338\u0026nbsp;[0.649\u0026ndash;2.757]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.147\u0026nbsp;[0.565\u0026ndash;2.326]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.704\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresbyterian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.373\u0026nbsp;[0.746\u0026ndash;2.530]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.255\u0026nbsp;[0.684\u0026ndash;2.303]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.463\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePentecostal/Charismatic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.136\u0026nbsp;[0.776\u0026ndash;1.664]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.038\u0026nbsp;[0.696\u0026ndash;1.546]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.856\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u0026nbsp;Christian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.206\u0026nbsp;[0.770\u0026ndash;1.889]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.033\u0026nbsp;[0.659\u0026ndash;1.619]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.889\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIslam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.849\u0026nbsp;[0.560\u0026ndash;1.289]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.881\u0026nbsp;[0.531\u0026ndash;1.464]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.625\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrad./Spiritualist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.866\u0026nbsp;[0.934\u0026ndash;3.730]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.948\u0026nbsp;[0.901\u0026ndash;4.210]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u0026nbsp;religion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.455\u0026nbsp;[0.640\u0026ndash;3.311]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.288\u0026nbsp;[0.565\u0026ndash;2.937]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.547\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDon\u0026rsquo;t know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.148\u0026nbsp;[0.587\u0026ndash;2.243]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.852\u0026nbsp;[0.463\u0026ndash;1.569]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.607\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\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\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\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\u003e0.748\u0026nbsp;[0.434\u0026ndash;1.287]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.800\u0026nbsp;[0.468\u0026ndash;1.368]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.414\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\u003e1.128\u0026nbsp;[0.792\u0026ndash;1.608]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.503\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.988\u0026nbsp;[0.667\u0026ndash;1.463]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.951\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\u003e1.103\u0026nbsp;[0.631\u0026ndash;1.927]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.731\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.827\u0026nbsp;[0.475\u0026ndash;1.441]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.502\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\u003e0.664\u0026nbsp;[0.499\u0026ndash;0.884]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.603\u0026nbsp;[0.417\u0026ndash;0.871]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrusi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.567\u0026nbsp;[0.339\u0026ndash;0.951]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.500\u0026nbsp;[0.280\u0026ndash;0.894]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGurma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.671\u0026nbsp;[0.425\u0026ndash;1.061]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.491\u0026nbsp;[0.319\u0026ndash;0.756]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\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\u003e0.858\u0026nbsp;[0.436\u0026ndash;1.690]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.658\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.176\u0026nbsp;[0.536\u0026ndash;2.581]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.686\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDon\u0026rsquo;t know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.178\u0026nbsp;[0.093\u0026ndash;0.340]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026nbsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.168\u0026nbsp;[0.087\u0026ndash;0.326]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026nbsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCombined Wealth quintile\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoorest (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\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\u003e1.161\u0026nbsp;[0.833\u0026ndash;1.619]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.377\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.148\u0026nbsp;[0.801\u0026ndash;1.645]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.453\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.241\u0026nbsp;[0.832\u0026ndash;1.851]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.096\u0026nbsp;[0.699\u0026ndash;1.719]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.689\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRicher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.040\u0026nbsp;[0.733\u0026ndash;1.476]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.826\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.965\u0026nbsp;[0.614\u0026ndash;1.517]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.878\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\u003e0.723\u0026nbsp;[0.491\u0026ndash;1.065]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.857\u0026nbsp;[0.503\u0026ndash;1.460]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.571\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePartner drinks alcohol\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.655\u0026nbsp;[1.534\u0026ndash;2.491]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026nbsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.951\u0026nbsp;[1.594\u0026ndash;2.393]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026nbsp;0.001\u003c/b\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot working (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ereference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProf/Tech/Managerial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.824\u0026nbsp;[0.523\u0026ndash;1.298]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.639\u0026nbsp;[0.929\u0026ndash;2.889]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClerical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.899\u0026nbsp;[0.378\u0026ndash;2.139]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.034\u0026nbsp;[0.427\u0026ndash;2.506]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.941\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.451\u0026nbsp;[0.959\u0026ndash;2.194]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.464\u0026nbsp;[0.937\u0026ndash;2.287]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.094\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgri \u0026ndash; self‑employed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.647\u0026nbsp;[0.157\u0026ndash;2.664]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.546\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.367\u0026nbsp;[0.074\u0026ndash;1.808]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgri \u0026ndash; employee\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.117\u0026nbsp;[0.669\u0026ndash;1.864]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.672\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.443\u0026nbsp;[0.813\u0026ndash;2.561]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eServices\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.131\u0026nbsp;[0.831\u0026ndash;1.540]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.434\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.037\u0026nbsp;[0.737\u0026ndash;1.458]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.836\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSkilled manual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.790\u0026nbsp;[0.537\u0026ndash;1.164]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.684\u0026nbsp;[0.452\u0026ndash;1.035]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnskilled manual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.435\u0026nbsp;[0.119\u0026ndash;1.591]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.691\u0026nbsp;[0.170\u0026ndash;2.809]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.605\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDon\u0026rsquo;t know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.482\u0026nbsp;[0.194\u0026ndash;1.199]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.500\u0026nbsp;[0.209\u0026ndash;1.195]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eUsing latent class analysis and fractional-logit regression on GDHS data, we identified four distinct patterns of partner control\u0026mdash;ranging from minimal monitoring to pervasive, high‐severity coercion\u0026mdash;and uncovered key predictors such as age, education, partner alcohol use, and region. While most women experience little control, a substantial minority endure jealousy, surveillance, and social isolation. Given that coercive control often precedes physical and sexual violence and carries its own mental-health burdens, understanding these patterns is critical for targeted prevention and control strategies. This study identified four distinct classes of partner control among Ghanaian women: Class 1 (59%) with minimal monitoring, Class 2 (11%) with broad multi-domain surveillance, Class 3 (22%) with primarily jealousy and location tracking, and Class 4 (7%) with pervasive, high-severity control. In other words, while a majority of women experience little to no partner control, a substantial minority endure systematic coercion.\u003c/p\u003e\u003cp\u003eEach class suggests different dynamics. Women in Class 2 (\u0026ldquo;Multi-Domain Surveillance\u0026rdquo;) face multiple restrictions, especially insistence on whereabouts and social surveillance, but not necessarily extreme isolation. This pattern resembles \u0026ldquo;coercive surveillance\u0026rdquo; noted in other studies, where jealous monitoring co-occurs with restrictive rules (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Class three (\u0026ldquo;Jealousy and Location Monitoring\u0026rdquo;) women endure high jealousy and tracking yet maintain social ties. This echoes prior findings that jealousy and accusations are common forms of control in Ghana and predict abuse (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Class four (\u0026ldquo;Pervasive High-Severity Control\u0026rdquo;) captures women experiencing virtually all controlling behaviors at high levels. This profile likely represents relationships with entrenched abuse, consistent with the notion that men who exert severe control are \u0026ldquo;more prone to commit physical, sexual, and emotional abuse\u0026rdquo; (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Indeed, controlling behaviors often co-occur with other forms of IPV (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), and our data suggest Class 4 women may be at high risk of further violence. Overall, the spectrum from Class 1 to 4 reflects increasing violation of women\u0026rsquo;s autonomy, mirroring typologies seen in the literature.\u003c/p\u003e\u003cp\u003eSeveral key predictors differentiated the classes. Age was strongly related: younger women (particularly those 20\u0026ndash;34) had higher odds of being in the surveillance and high-control class, whereas older women were less likely to be in the jealousy-dominated class. This finding corresponds with the linear assumption that younger women are universally at higher risk but suggests that different forms of control may emerge at different life stages (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Education was protective: women with secondary or higher schooling were significantly less likely to be in the high-severity control class, echoing prior findings that women with higher education have reduced odds of any IPV (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Partner alcohol use emerged as a consistent risk factor: women whose partners drank had significantly increased odds of being in the surveillance, jealousy, or pervasive-control classes (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). This is plausible, as partner alcohol abuse is known to exacerbate aggression and controlling tendencies (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). We also found regional variation: residence in certain regions (e.g. Ahafo, Northern, and Savannah) doubled or tripled the odds of being in the surveillance or high-control classes, suggesting cultural or socio-economic factors in these areas heighten controlling behaviors. Notably, after adjustment, variables like religion, wealth, and urban/rural residence had minimal associations. This implies partner control cuts across socioeconomic lines, though it is shaped strongly by age, education, partner drinking, and locale.\u003c/p\u003e\u003cp\u003eGlobally, controlling behaviors are recognized as a common precursor and component of IPV. For example, a study in rural South Africa found that accusations of infidelity, restrictions on friends/family, and jealousy strongly predicted subsequent violence; likewise our classes highlight these same behaviors (Class 3 and 4) in Ghana (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Dickson \u003cem\u003eet al.\u003c/em\u003e (2024) using 2022 Ghana data reported that women whose partners got jealous or accused them of unfaithfulness were significantly more likely to suffer IPV, reinforcing the link between our control classes and violence risk (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Similarly, Issahaku (2016) found in northern Ghana that jealousy and accusations were the dominant controlling tactics associated with abuse (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). In short, the specific behaviors defining our classes (jealousy, social isolation, surveillance) match those identified in Ghana and elsewhere as core elements of coercive control. Where we add depth is by quantifying distinct subgroups: not all controlled women are alike, and interventions can be tailored (e.g., Class 3 women may benefit from strategies addressing psychological abuse without social isolation).\u003c/p\u003e\u003cp\u003eSome findings merit further thought. The inverse age gradient (younger women more controlled) likely reflects life-course dynamics: younger wives or girlfriends may have less established power in the relationship. The protective effect of education suggests that empowerment and autonomy (through schooling) can reduce male control (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Our strong alcohol effect supports programs that target heavy drinking as part of violence prevention. The pronounced regional differences \u0026ndash; with the most rural, traditionally patriarchal zones bearing higher control \u0026ndash; echo literature on Ghana\u0026rsquo;s gender inequalities and underscore where interventions might concentrate.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003cp\u003eBecause the GDHS is a single-time-point survey, we cannot establish temporal or causal relationships. For example, while low education and partner alcohol use are associated with higher probabilities of belonging to severe-control classes, we cannot determine whether these factors preceded or resulted from controlling behaviors. Also, all partner-control indicators rely on women\u0026rsquo;s self-reports, which may be subject to recall error or social-desirability bias. Women might under-report controlling acts due to stigma or fear, or over-report if they interpret questions differently, potentially misclassifying their true experience. Finally, to retrieve AIC and BIC, we estimated the LCA on unweighted data. This may bias class prevalence estimates if the domestic-violence weight correlates with the control indicators. Although we reinstated weights in the fractional-logit regressions, class enumeration itself did not account for the survey design.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eThe results from this study have important public health and policy implications for Ghana and similar populations in other countries. Partner controlling behaviors, even in the absence of physical violence \u0026ndash; have serious health consequences (stress, depression, isolation, reproductive coercion) and often signal broader abuse (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Currently Ghana\u0026rsquo;s Domestic Violence Act (2007) and related policies focus on physical and sexual violence, but our findings suggest the need to explicitly address psychological and controlling abuse as integral forms of gender-based violence. The study recommends that the Ministries of Gender, Education and NGOs should develop community-based relationship literacy and advocacy programs that teach equitable, respectful relationships. These could include school curricula on consent and communication, radio campaigns targeting men and women, and faith-based workshops. Such education would directly confront the patriarchal norms identified as underpinning control. Programs that involve men as allies (e.g. religious groups, chieftaincy networks, \u0026ldquo;MenEngage\u0026rdquo; alliances) can shift norms that condone male dominance. Campaigns (like the UNFPA-supported \u0026ldquo;16 Days of Activism\u0026rdquo;) should include male-targeted messages about the harms of controlling their partners. Positive role models and peer education can promote non-violent masculinity. The findings from this study and some previous studies show that higher levels of female education correlate with lower control and IPV. Continuing policies like Ghana\u0026rsquo;s free Senior High School (SHS) and scholarships for girls will be beneficial. Vocational training and economic empowerment programs (especially for young women in vulnerable regions) can increase women\u0026rsquo;s autonomy. Education ministries, Microfinance agencies and women\u0026rsquo;s NGOs should align to create scholarships and skills workshops aimed at girls from early adolescence onward. Future research should employ longitudinal designs to examine transitions between controlling behavior classes and identify factors that promote resilience or escalation. Understanding how women move between classes over time would inform the timing and targeting of precise and high impact policy and intervention strategies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, our study underscores that partner control is a prevalent public health issue in Ghana, with identifiable patterns and at-risk groups. Addressing it requires multi-sectoral strategies: legal reform, community education, healthcare screening, and empowerment of women. Doing so will not only reduce psychological abuse, but also likely lower the incidence of physical and sexual IPV that often co-occurs. These insights and recommendations fill a gap in Ghana\u0026rsquo;s GBV policy by highlighting the \u0026ldquo;hidden\u0026rdquo; dimension of coercion and suggesting concrete steps to combat it.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eAIC\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAkaike Information Criterion\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eALCPP\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAverage Latent Class Posterior Probability\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eAOR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAdjusted Odds Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eBIC\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBayesian Information Criterion\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCAIC\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConsistent Akaike Information Criterion\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eDHS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDemographic and Health Surveys\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eGDHS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGhana Demographic and Health Survey\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eGBV\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGenderBased Violence\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eIPV\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eIntimate Partner Violence\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eLCA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLatent Class Analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eLR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLikelihood Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eSABIC\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSampleSize Adjusted Bayesian Information Criterion\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eUOR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUnadjusted Odds Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no funding for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Biostatistics, School of Public Health, College of Health Sciences, University of Ghana, Accra, Ghana\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eP.O.W. secured and analysed the data and wrote the first draft manuscript. P.O.W. wrote and reviewed the various sections of the manuscript. P.O.W. reviewed the final version of the manuscript before submission. P.O.W. read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eJustice Moses K. Aheto (J.M.K.A.)\u003c/p\u003e\n\u003cp\u003eJ.M.K.A. wrote reviewed the various sections of the manuscript. J.M.K.A. reviewed the final version of the manuscript before submission. \u0026nbsp;J.M.K.A. read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eIrene Kafui Vorsah Amponsah (I.K.V.A)\u003c/p\u003e\n\u003cp\u003eI.K.V.A reviewed the initial draft of the manuscript. \u0026nbsp;I.K.V.A reviewed and analysed the various sections of the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis secondary analysis used de‑identified, publicly available data from the 2022 Ghana Demographic and Health Survey. The original survey protocol was approved by the Ghana Health Service Ethical Review Committee and the ICF Institutional Review Board. The research has been performed in accordance with Declaration of Helsinki. Details about ethical standards are available at The DHS Program - Protecting the Privacy of DHS Survey Respondents\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used in this study are publicly available from the DHS Program. Researchers may access the 2022 Ghana Demographic and Health Survey data by registering for a free account and requesting permission at The DHS Program - Available Datasets \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares that she has no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThank you to the MEASURE DHS Program for granting access and making the data freely available for the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eViolence against women Key facts Overview. 2024. \u003c/li\u003e\n\u003cli\u003eAndualem F, Nakie G, Rtbey G, Melkam M, Tinsae T, Kibralew G, et al. Magnitude and determinants of intimate partner controlling behavior among women in sub-Saharan African countries from the recent demographic and health survey data: a multilevel analysis. BMC Public Health [Internet]. 2025 May 15;25(1):1787. Available from: https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-025-23004-8\u003c/li\u003e\n\u003cli\u003eDokkedahl S, Kok RN, Murphy S, Kristensen TR, Bech-Hansen D, Elklit A. The psychological subtype of intimate partner violence and its effect on mental health: Protocol for a systematic review and meta-analysis. Vol. 8, Systematic Reviews. BioMed Central Ltd.; 2019. \u003c/li\u003e\n\u003cli\u003eDickson KS, Ayebeng C, Okyere J. Unveiling Shadows: Investigating women\u0026rsquo;s experience of intimate partner violence in Ghana through the lens of the 2022 Demographic and Health Survey. PLoS One. 2024 Aug 1;19(8). \u003c/li\u003e\n\u003cli\u003eTenkorang EY. Women\u0026rsquo;s autonomy and intimate partner violence in Ghana. Int Perspect Sex Reprod Health. 2019 Jun 1;44(2):51\u0026ndash;61. \u003c/li\u003e\n\u003cli\u003eGhana Demographic and Health Survey. 2022. \u003c/li\u003e\n\u003cli\u003eLanier P, Maguire-Jack K, Lombardi B, Frey J, Rose RA. Adverse Childhood Experiences and Child Health Outcomes: Comparing Cumulative Risk and Latent Class Approaches. Matern Child Health J. 2018 Mar 1;22(3):288\u0026ndash;97. \u003c/li\u003e\n\u003cli\u003eClarke K, Patalay P, Allen E, Knight L, Naker D, Devries K. Patterns and predictors of violence against children in Uganda: a latent class analysis. Available from: http://dx.doi.org/10.1136/bmjopen-2015-010443\u003c/li\u003e\n\u003cli\u003eMiedema SS, Le VD, Chiang L, Ngann T, Wu Shortt J. Adverse Childhood Experiences and Intimate Partner Violence Among Youth in Cambodia: A Latent Class Analysis. J Interpers Violence. 2023 Jan 1;38(1\u0026ndash;2):NP1446\u0026ndash;72. \u003c/li\u003e\n\u003cli\u003eWeiss NH, Dixon-Gordon KL, Peasant C, Jaquier V, Johnson C, Sullivan TP. A latent profile analysis of intimate partner victimization and aggression and examination of between-class differences in psychopathology symptoms and risky behaviors. Psychol Trauma. 2017 May 1;9(3):370\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eWeller BE, Bowen NK, Faubert SJ. Latent Class Analysis: A Guide to Best Practice. Journal of Black Psychology. 2020 May 1;46(4):287\u0026ndash;311. \u003c/li\u003e\n\u003cli\u003eSTATA STRUCTURAL EQUATION MODELING REFERENCE MANUAL [Internet]. 1985. Available from: www.stata.com\u003c/li\u003e\n\u003cli\u003eVermunt JK, Magidson J. LATENT GOLD 5.0 UPGRADE MANUAL 1. \u003c/li\u003e\n\u003cli\u003eAsparouhov T. Sampling Weights in Latent Variable Modeling [Internet]. 2005. Available from: http://www.statmodel.com\u003c/li\u003e\n\u003cli\u003eNylund KL, Asparouhov T, Muth\u0026eacute;n BO. Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study. Vol. 14, STRUCTURAL EQUATION MODELING. 2007. \u003c/li\u003e\n\u003cli\u003eSinha P, Calfee CS, Delucchi KL. Practitioner\u0026rsquo;s Guide to Latent Class Analysis: Methodological Considerations and Common Pitfalls. Vol. 49, Critical Care Medicine. Lippincott Williams and Wilkins; 2021. p. E63\u0026ndash;79. \u003c/li\u003e\n\u003cli\u003eUlbricht CM, Chrysanthopoulou SA, Levin L, Lapane KL. The use of latent class analysis for identifying subtypes of depression: A systematic review. Vol. 266, Psychiatry Research. Elsevier Ireland Ltd; 2018. p. 228\u0026ndash;46. \u003c/li\u003e\n\u003cli\u003eIntroduction to Latent Class Analyses. \u003c/li\u003e\n\u003cli\u003eVermunt JK, Magidson J. Linear Logistic Scoring Equations for Latent Class and Latent Profile Models: A Simple Method for Classifying New Cases. Structural Equation Modeling. Routledge; 2024. \u003c/li\u003e\n\u003cli\u003ePapke LE. Econometric methods for fractional response variables with an application to 401 (k) plan participation rates. Journal of Applied Econometrics. 1996;11(6):619\u0026ndash;32. \u003c/li\u003e\n\u003cli\u003ePapke LE, Wooldridge JM. Panel data methods for fractional response variables with an application to test pass rates. J Econom. 2008 Jul;145(1\u0026ndash;2):121\u0026ndash;33. \u003c/li\u003e\n\u003cli\u003eOberhofer H, Pfaffermayr M. Fractional response models - A replication exercise of Papke and Wooldridge (1996). Contemporary Economics. 2012 Oct 29;6(3):56\u0026ndash;64. \u003c/li\u003e\n\u003cli\u003eMullahy J, Burns M, Craig B, Holly A, Koch S, Murteira J, et al. NBER WORKING PAPER SERIES MULTIVARIATE FRACTIONAL REGRESSION ESTIMATION OF ECONOMETRIC SHARE MODELS [Internet]. 2010. Available from: http://www.nber.org/papers/w16354\u003c/li\u003e\n\u003cli\u003eSulaiman LAR, Ojogiwa OT, Ajayi CE. Intimate partner controlling behaviour and intimate partner violence among married women in rural areas in South Africa. BMC Womens Health. 2025 Dec 1;25(1). \u003c/li\u003e\n\u003cli\u003eAhorsu K, Biveridge F, Peter Sarpong BK, William Gaines Reviewed by Timothy Quashigah BC, Naa Dedei Botchwey C, Dey K. GHANA SOCIAL SCIENCE JOURNAL ARTICLES Academic Capitalism: Globalization, Universities and the Paradox of the Neoliberal Marketplace James Dzisah. 1-33 Ghana\u0026rsquo;s Foreign Policy Choices in Relation to Wielding Oil and Gas Resource for Regional Integration Archaeological Perspectives of the Danish-Dangbe Encounter along the Eastern Coastal Belt of Ghana and their Implications for Understanding Dangbe Culture. Vol. 13, Ghana Social Science Journal. 2016. \u003c/li\u003e\n\u003cli\u003eAhinkorah BO, Aboagye RG, Okyere J, Seidu AA, Budu E, Yaya S. Child marriage and its association with partner controlling behaviour against adolescent girls and young women in sub-Saharan Africa. BMC Global and Public Health. 2023 Jul 31;1(1). \u003c/li\u003e\n\u003cli\u003eHerbert A, Fraser A, Howe LD, Szilassy E, Barnes M, Feder G, et al. Categories of Intimate Partner Violence and Abuse Among Young Women and Men: Latent Class Analysis of Psychological, Physical, and Sexual Victimization and Perpetration in a UK Birth Cohort. J Interpers Violence. 2023 Jan 1;38(1\u0026ndash;2):NP931\u0026ndash;54. \u003c/li\u003e\n\u003cli\u003eAlangea DO, Addo-Lartey AA, Sikweyiya Y, Chirwa ED, Coker-Appiah D, Jewkes R, et al. Prevalence and risk factors of intimate partner violence among women in four districts of the central region of Ghana: Baseline findings from a cluster randomised controlled trial. PLoS One. 2018 Jul 1;13(7). \u003c/li\u003e\n\u003cli\u003eIssahaku PA. Correlates of Intimate Partner Violence in Ghana. Sage Open. 2017 Jun 1;7(2). \u003c/li\u003e\n\u003cli\u003eOkyere J, Salu S, Ayebeng C, Dickson KS. Shedding light on hidden dynamics: partner controlling behavior and women\u0026rsquo;s alcohol consumption in Ghana. Discover Public Health. 2024 Jun 11;21(1). \u003c/li\u003e\n\u003cli\u003eIssahaku P. Intimate partner violence: The controlling behaviours of men towards women in Northern Ghana. Vol. 13, Ghana Social Science Journal. 2016. \u003c/li\u003e\n\u003cli\u003eLohmann S, Cowlishaw S, Ney L, O\u0026rsquo;Donnell M, Felmingham K. The Trauma and Mental Health Impacts of Coercive Control: A Systematic Review and Meta-Analysis. Vol. 25, Trauma, Violence, and Abuse. SAGE Publications Ltd; 2024. p. 630\u0026ndash;47. \u003c/li\u003e\n\u003cli\u003eAntai D. Controlling behavior, power relations within intimate relationships and intimate partner physical and sexual violence against women in Nigeria. BMC Public Health. 2011;11.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Intimate partner violence (IPV), Psychological abuse, Latent class analysis (LCA), Vermunt ML correction, Demographic and Health Survey (DHS), Sub-Saharan Africa (SSA), Ghana, Sociodemographic predictors, Women’s autonomy","lastPublishedDoi":"10.21203/rs.3.rs-7041744/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7041744/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIntimate partner controlling behavior manifesting as jealousy, accusations of infidelity, social restrictions, and monitoring, undermines women\u0026rsquo;s autonomy, well-being and poses a critical public health issue, yet its heterogeneity remains underexplored in Ghana.\u003c/p\u003e\u003cp\u003e\u003cb\u003eObjective\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo identify distinct classes of controlling behaviors among Ghanaian women and to examine how key sociodemographic factors predict membership in each class.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe analyzed data from 5,137 ever-married women in the 2022 Ghana Demographic and Health Survey domestic‐violence module. Five binary indicators of partner control (jealousy, accusations of unfaithfulness, social isolation, family contact restrictions, and whereabouts monitoring) were subjected to latent class analysis (LCA) using unweighted generalized structural equation modeling. Model fit was compared across two‐ to four‐class solutions using the Bayesian Information Criterion (BIC), with a four‐class model selected. We then computed each woman\u0026rsquo;s posterior class‐membership probabilities and regressed these fractional outcomes on age, education, residence, region, religion, ethnicity, wealth, media exposure, partner education, partner alcohol use, and employment status via survey‐weighted fractional‐logit generalized linear models.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFour distinct classes emerged: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Minimal Monitoring (59.2%), (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Multi-Domain Surveillance (11.2%), (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Jealousy and Location Monitoring (22.2%), and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Pervasive High‐Severity Control (7.4%). Younger age (20\u0026ndash;34 years) and partner alcohol use were strongly associated with higher probabilities of membership in Classes 2\u0026ndash;4, while secondary or higher education conferred protection, especially against Class 4 (adjusted odds ratio 0.42; 95% CI 0.21\u0026ndash;0.83). Regional disparities were also evident, with women in northern and Savannah regions facing two‐ to six‐fold greater odds of severe control.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePartner controlling behaviors in Ghana are heterogeneous and disproportionately affect young, less-educated women and those with alcohol‐consuming partners. Interventions should include early recognition of controlling acts, empowerment through education, and community dialogue to challenge patriarchal norms. Addressing these patterns may prevent escalation to physical and sexual IPV and reduce the substantial public health burden of coercive control.\u003c/p\u003e","manuscriptTitle":"Experience of intimate-partner controlling behaviours among women in Ghana: a novel three step latent class analysis approach with survey-weighted fractional-logit regression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-08 09:04:43","doi":"10.21203/rs.3.rs-7041744/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-16T00:29:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-29T12:05:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"151051375474157634469330844982676900306","date":"2025-08-15T07:51:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-15T06:34:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"156687269838631577869857967283586070667","date":"2025-08-11T18:46:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"67733032358411215065477599942991178598","date":"2025-08-06T13:52:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-04T12:11:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-30T10:46:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-10T13:41:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-08T16:23:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Women's Health","date":"2025-07-08T15:42:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fb6ea7d0-5f7b-43db-9b82-5f3dc2672ba5","owner":[],"postedDate":"August 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T16:13:53+00:00","versionOfRecord":{"articleIdentity":"rs-7041744","link":"https://doi.org/10.1186/s12905-025-04144-w","journal":{"identity":"bmc-womens-health","isVorOnly":false,"title":"BMC Women's Health"},"publishedOn":"2025-11-25 15:58:19","publishedOnDateReadable":"November 25th, 2025"},"versionCreatedAt":"2025-08-08 09:04:43","video":"","vorDoi":"10.1186/s12905-025-04144-w","vorDoiUrl":"https://doi.org/10.1186/s12905-025-04144-w","workflowStages":[]},"version":"v1","identity":"rs-7041744","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7041744","identity":"rs-7041744","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}