Sociodemographic, functional disability and severe illness predict extreme fatigue among older adults in Ghana

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Meanwhile, studies investigating impact of sociodemographic and health-related factors on extreme fatigue among older adults in Ghana are limited. This study, therefore, examined the prevalence and predictors of extreme fatigue among older adults in Ghana. Methods We analyzed cross-sectional data among community-dwelling older adults aged 50+ (N = 4,838) extracted from the 2023 Ghana Annual Household Income and Expenditure Survey (AHIES). Descriptive statistics were applied to estimate the prevalence of extreme fatigue. A multivariable model estimated adjusted associations, with significance at p < 0.05. Results Overall, 17.03% of participants reported experiencing extreme fatigue. In the multivariable model, severe illness (aOR = 5.16, 95% CI: 4.21–6.31), functional disability (aOR = 1.31, 95% CI: 1.05–1.63), rural residence (aOR = 1.26, 95% CI: 1.06–1.50), and basic labor occupations (aOR = 1.41, 95% CI: 1.13–1.78) predicted higher likelihood of experiencing extreme fatigue. Also, older adults of Gurma (aOR = 2.94, 95% CI: 2.05–4.19) and other ethnic groups (aOR = 1.73, 95% CI: 1.12–2.65) had higher odds of experiencing extreme fatigue. On the other hand, older adults in Northern (aOR = 0.31, 95% CI: 0.22–0.43) and Southern Ghana (aOR = 0.48, 95% CI: 0.40–0.58) were less likely to report extreme fatigue. Conclusion Chronic illness, functional disability, occupation, and regional disparities emerged as key predictors, underscoring the need for targeted health interventions such as tailored self-management education and Community-based exercise programs. Fatigue Critical Illness Older Adults Chronic diseases AHIES Ghana 1. Introduction The landscape of aging and chronic diseases in Ghana are being shaped by transitions in demographics and epidemiology [ 1 – 4 ]. The proportion of people over 60 years continue to increase steadily worldwide as a result of an increase in longevity and a concurrent decline in fertility [ 5 , 6 ]. In Ghana, this proportion has seen a 4.5% growth, an increase from 213,447 in 1960 to 1,991,736 in 2021 [ 7 ]. According to most recent projections, this number is expected to reach 6.3 million by 2050 [ 8 ]. The increasing older adults population has resulted in increasingly substantial pressure on families and primary care services as this shift occurs alongside a constrained health and social system [ 9 ]. Extreme fatigue, which is a condition characterized by persistent and overwhelming sense of tiredness which is unable to resolve after the individual attains rest [ 10 ], is common but rarely recognized in older adults [ 11 ]. The prevalence of extreme fatigue varies across diverse populations and clinical contexts. A systematic review and meta-analysis revealed that, about 22% of cancer patients have been reported to experience fatigue[ 12 ]. Park et al. [ 58 ] reported that, about 80% of patients who experience chronic illnesses like multiple sclerosis, experience fatigue. About 80% of older patients in the Netherlands, especially those who are hospitalized reported extreme fatigue that limits their daily activities [ 13 ]. Moreover, a seroprevalence Coronavirus (SEROCoV) Population-Based Study during the COVID-19 pandemic show that about 40% of adults still experience extreme fatigue long after infection with the virus [ 14 ]. Extreme fatigue is a common and disabling marker of serious physical and mental health issues in older populations. In the latter stages of life, extreme fatigue has been shown to reduce psychological resilience, erode the independence of older adults and most often influence negatively the management of chronic diseases [ 15 , 16 ]. Studies conducted worldwide have shown extreme fatigue is a risk factor for sleep associated disorders, a depressive state and multimorbidity in older adults [ 17 – 19 ]. A systematic review and meta-analysis of 21 studies involving 17843 participants showed that slower gait, declining quality of life and a high rate of incident disability are experienced by older adults who frequently report fatigue [ 20 ]. The findings from these studies suggest that extreme fatigue is not just a symptom of lack of rest but an important indicator of vulnerability in the older population, which requires research, policy and clinical attention. Several sociodemographic and health-related factors such as chronic health conditions, functional disability, age, gender, marital status and occupation may serve as correlates of extreme fatigue among older adults including [ 21 ]. Noncommunicable diseases and multimorbidity are key epidemiologic profiles of older populations including Ghanaian older cohort, both of which are key players of extreme fatigue [ 22 , 23 ]. A study conducted by the World Health Organization (WHO) on Global AGEing and adult health (SAGE) highlights the burden cardiometabolic diseases poses on the adult population in Ghana [ 24 ]. The difficulty in treatment and management of non-communicable diseases, and the associated psychosocial stress increase the likelihood of developing extreme fatigue. Furthermore, a study conducted in Ghana noted that patients with non-communicable diseases often complained of extreme fatigue [ 25 ]. For instance, there are reports of post-stroke fatigue affecting approximately 60% of people within the first month after stroke [ 26 ]. Meanwhile, a population level estimate of how chronic health conditions predict extreme fatigue in Ghana is lacking. Loss of functional ability is also considered both a correlate and a consequence of extreme fatigue [ 27 ]. Studies have reported that, older adults who report “feeling tired most of the time” display a reduced performance of physical activity and a greater difficulty in handling their daily routine [ 28 , 29 ]. A vicious cycle is highlighted by this relationship: activity is limited by extreme fatigue, hastens deconditioning which subsequently worsens fatigue. In settings with limited resources especially where devices meant for assistance and rehabilitation programs are less accessible, the functional toll of extreme fatigue is highly felt increasing the likelihood of social isolation, utilizing health-care and becoming dependent [ 30 ]. How extreme fatigue is experienced and handled is further shaped by sociodemographic characteristics [ 31 ]. There have been consistent differences in reports of fatigue among sexes with women having a higher frequency in global and regional studies [ 32 ]. Also, food insecurity, socioeconomic status and limited social support remain crucial determinants of extreme fatigue [ 33 ]. There are notable gaps in age-friendly infrastructures and geriatric care capacity which inadvertently worsens the effect fatigue has on daily life [ 34 , 35 ]. It is therefore important to understand how other sociodemographic factors such as level of education, structure of household, sex, age and employment status predict extreme fatigue to guide the creation of equitable and responsive interventions. Collectively, population level estimates of extreme fatigue and its associated sociodemographic and health related factors among older Ghanaians are limited despite its importance in guiding healthy ageing, clinical practice, public health interventions and shaping policy and resource allocation for geriatric care in Ghana [ 36 – 38 ]. Most of the studies around this topic focus on either specific disease of surrounding geriatric conditions like depression, vision impairment and frailty [ 39 – 41 ]. These studies are able to highlight substantial loss of functional ability in later life, but they do not capture the prevalence and predictors of extreme fatigue. This study aims to address this gap by investigating the prevalence and predictors of extreme fatigue among older adults in Ghana. The results from this study are in direct support of targets 3.4 and 3.8 of the Sustainable Development Goal 3 which seeks to reduce premature mortality from NCDs and promote well-being and achieve universal health coverage respectively. Findings could help identify prevalence and predictors of extreme fatigue among older adults informing early detection, integrated care and interventions that ensure healthy ageing and overall well-being. 2. Materials and methods 2.1 Data Source This study was based on existing data from the Ghana Annual Household Income and Expenditure Survey (AHIES), conducted quarterly by the Ghana Statistical Service (GSS) between 2021 and 2023 [ 42 ]. The AHIES is Ghana’s first high-frequency national survey, collecting detailed quarterly and annual information on household finances, demographics, and well-being. It provides essential data for generating macroeconomic indicators and supports research, policymaking, and monitoring of national development programs and the Sustainable Development Goals (SDGs). The AHIES sample was based on the 2021 Population and Housing Census. A total of 10,800 households were selected from 600 Enumeration Areas (EAs), with 304 (50.67%) urban and 296 (49.33%) rural areas. In each EA, 18 households were randomly selected, creating a nationally and regionally representative sample for expenditure and GDP estimates. For this study, a subset of participants aged 50 years and above was analyzed due to the evidence that extreme fatigue is more prevalent among older adults [ 51 ]. Comprehensive details on the survey design, sampling framework, and field procedures are available in the Annual Household Income and Expenditure Survey (AHIES) report [ 42 ]. 2.2 Measures The dependent variable in this study was extreme fatigue among older adults. Extreme fatigue was defined as “a condition characterized by persistent and overwhelming sense of tiredness which is unable to resolve after the individual attains rest”. Respondents were asked whether they experienced extreme fatigue, and answers were recorded in binary form (Yes or No). Responses were coded as “0” for “No” and “1” for “Yes.” Treating extreme fatigue as a binary variable is consistent with previous population-based studies examining fatigue outcomes [ 43 , 44 ]. Sociodemographic and health-related factors were included as predictors of extreme fatigue and independent variables. Key measures included illness status, functional disability, body mass index (BMI), educational attainment, and occupational classification. Functional disability was derived from three activities of daily living (ADLs): walking or climbing steps, performing self-care tasks such as washing or dressing, and communicating effectively (including both understanding and being understood). Each of these items was coded dichotomously, with responses indicating either no difficulty (0) or some level of difficulty, assistance, or complete inability to perform the task (1). The responses were aggregated into a continuous disability score ranging from 0 to 3. Individuals who reported no difficulty across all three ADLs were categorized as having no disability (None). Those who reported difficulty with one or two tasks were considered to have moderate disability (Moderate), while those indicating difficulty with all three tasks were classified as having severe functional disability (Severe). This approach to classifying functional disability aligns with prior published research [ 45 ] Severe illness was defined from self-reports of illness or injury. Respondents were classified as having severe illness if they reported either (a) an illness not serious but not preventing work, (b) a serious illness not preventing work, or (c) illness/injury that temporarily or permanently stopped work. Those reporting no illness or injury were coded as having no severe illness. BMI was regrouped using WHO criteria: underweight (< 18.5), healthy weight (18.5–24.9), overweight (25.0–29.9), and obese (≥ 30.0). Educational status was coded as “Formal education” for any class-based schooling or “No formal education” for otherwise. Occupations were regrouped into three categories: white-collar for professional and managerial jobs, blue-collar/basic labor for manual and technical trades, and service/administrative for clerical and support roles. Finally, the 16 administrative regions of Ghana were grouped into three zones to simplify our analysis: Central Ghana (Bono, Bono East, Ahafo, Ashanti, Eastern and Oti), Southern Ghana (Central, Greater Accra, Volta, Western and Western North), and Northern Ghana (Upper West, Upper East, North East, Northern and Savannah). 2.3 Data analysis Statistical analysis was conducted using R programming software (version 4.4.3). The analysis began by importing the AHIES dataset, from which a targeted sample of individuals aged 50 and above was extracted for the study. The overall prevalence of extreme fatigue among older adults from 50 years and above was calculated. The relationship between each independent variable and the dependent variable, extreme fatigue, was first evaluated using bivariate logistic regression (Model 1). The model assessed the crude, unadjusted relationship between each independent variable and extreme fatigue. Next, in a multivariable analysis, Model 2 was built by including all independent variables to examine their adjusted associations with extreme fatigue. For all analysis, a p-value of less than 0.05 was statistically significant. 3. Results 3.1 Background characteristics and prevalence of extreme fatigue Table 1 present the descriptive characteristics of participants and prevalence of extreme fatigue. Overall, as shown in Table 1 , 824 participants (17.03%) reported experiencing extreme fatigue. In Table 1 , the chi-square analysis showed Occupation class (χ² = 30.46, p < 0.001), place of residence/settlement (urban vs. rural) (χ² = 22.72, p < 0.001), regional group (χ² = 128.24, p < 0.001), and ethnicity (χ² = 89.36, p < 0.001) were all significantly associated with the distribution of extreme fatigue. Furthermore, severe illness status (χ² = 319.8, p < 0.001) and functional disability level (χ² = 18.38, p < 0.001) exhibited particularly strong associations. Table 1 Distribution of presence of extreme fatigue (n = 4838) Variable Levels No (%) Yes (%) \(\:\varvec{p}-\varvec{v}\varvec{a}\varvec{l}\varvec{u}\varvec{e}\:(\varvec{X}²\) ) Extreme fatigue 4014(82.97) 824 (17.03) Sex Male 1795 (44.7) 364 (44.2) 0.80 Female 2219 (55.3) 460 (55.8) Age (years) 50–59 2340 (58.3) 445 (54.0) 0.132 60–609 1134 (28.3) 256 (31.1) 70–79 411 (10.2) 97 (11.8) 80+ 129 (3.2) 26 (3.2) Marital status Never married 53 (1.3) 12 (1.5) 0.242 Divorced 222 (5.5) 38 (4.6) Informal/living together 198 (4.9) 56 (6.8) Married 2643 (65.8) 535 (64.9) Separated 118 (2.9) 29 (3.5) Widowed 780 (19.4 154 (18.7) Education status No formal education 1274 (31.7) 268 (32.5) O.689 Formal education 2740 (68.3) 556 (67.5) Occupation Administrative & services 1001 (24.9) 148 (18.0) < 0.001 Blue-collar/Basic labor 2731 (68.0) 640 (77.7) White-collar Occupations 282 (7.0) 36 (4.4) Settlement RURAL 1989 (49.6) 484 (58.7) < 0.001 URBAN 2025 (50.4) 340 (41.3) Region Norther Ghana 897 (22.3) 99 (12.0) < 0.001 Central Ghana 1574 (39.2) 497 (60.3) Southern Ghana 1543 (38.4) 228 (27.7) Ethnicity Akan 1901 (47.4) 389 (47.2) < 0.001 Ewe 574 (14.3) 140 (17.0) Ga-Dangwe 253 (6.3) 45 (5.5) Grusi 97 (2.4) 6 (0.7) Guan 119 (3.0) 27 (3.3) Gurma 168 (4.2) 85 (10.3) Mande 26 (0.6) 4 (0.5) Mole Dagbani 759 (18.9) 93 (11.3) All Other Tribes (Hausa, Baribari, Zabrama) 117 (2.9) 35 (4.2) Current Health Insurance Status No 1473 (36.7) 320 (38.8) 0.264 Yes, covered 2541 (63.3) 504 (61.2) Body type Underweight 362 (9.0) 87 (10.6) 0.234 Healthy body 2093 (52.1) 442 (53.6) Overweight 1031 (25.7) 202 (24.5) Obese 528 (13.2) 93 (11.3) Severe illness status No 3713 (92.5) 584 (70.9) < 0.001 Yes 301 (7.5) 240 (29.1) Functional disability None 3496 (87.1) 671 (81.4) < 0.001 Moderate 512 (12.8) 151 (18.3) Severe 6 (0.1) 2 (0.2) 3.2 Predictors of extreme fatigue In a logistic regression analysis, Table 2 presents the odds ratios (OR), adjusted odds ratios(aOR), 95% confidence intervals (CI), and respective p-values for both the bivariable (Model 1) and multivariable (Model 2) models. In the multivariable model (Model 2), health related factors including severe illness and functional disability, as well as sociodemographic factors such as settlement/locality type, ethnicity, regions and occupation predicted the likelihood of experiencing extreme fatigue among older adults in this study. Specifically, older adults with severe illness were 5.16 times more likely to experience extreme fatigue compared to those with no illness (aOR = 5.16, 95% CI: 4.21–6.31, p < 0.001). Compared to those with no functional disability, older adults with functional disability were 1.31 times more likely to experience extreme fatigue (aOR = 1.31, 95% CI: 1.05–1.63). Also, older adults working as basic laborers were 1.41 times more likely to experience extreme fatigue compared to those working in Administrative & Service occupations (adjusted aOR = 1.41, 95% CI: 1.13–1.78). Older adults living in rural areas were 1.26 times more likely to experience extreme fatigue compared to those in urban areas (aOR = 1.26, 95% CI: 1.06–1.50). Compared with the Akan ethnic group, older adults who identified with Gurma (aOR = 2.94, 95% CI: 2.05–4.19) and ‘other ethnic groups’ (aOR = 1.73, 95% CI: 1.12–2.65) were 2.94 and 1.73 times, respectively, more likely to experience extreme fatigue. On the other hand, older adults in the Northern and Southern Ghana were 0.31 (aOR = 0.31, 95% CI: 0.22–0.43) and 0.48 (aOR = 0.48, 95% CI: 0.40–0.58) times less likely to experience extreme fatigue, respectively, compared to those in Central Ghana. Table 2 Bivariable and Multivariable Predictors of Extreme Fatigue among Older Adults Variable Category Bivariable Odds Ratio [95% CI] Multivariable Odds Ratio [95% CI] Odds Ratio [95% CI] p-value Odds Ratio [95% CI] p-value Severe illness status No - Ref Yes 5.07[4.19–6.13] < 0.001 5.16[4.21–6.31] < 0.001 Functional disability None - Ref Moderate 1.53[1.26–1.87] < 0.001 1.31[1.05–1.63] 0.017 Severe 1.73[0.25–7.56] 0.50 0.95[0.11–4.97] 0.96 Occupation Administrative & Service - Ref Blue-Collar or Basic Labor 1.59[1.31–1.93] < 0.001 1.41[1.13–1.78] 0.0029 White-Collar occupations 0.86[0.58–1.26] 0.458 1.18[0.77–1.76] 0.43 Regional group Central Ghana - Ref Northern Ghana 0.34[0.28–0.44] < 0.001 0.31[0.22–0.43] < 0.001 Southern Ghana 0.46[0.39–0.56] < 0.001 0.48[0.40–0.58] < 0.001 Settlement URBAN - Ref RURAL 1.44[1.25–1.68] < 0.001 1.26[1.06–1.50] 0.0087 Ethnicity Akan - Ref Ewe 1.19[0.96–1.47] 0.11 1.21[0.95–1.53] 0.11 Ga-Dangme 0.86[0.61–1.20] 0.41 1.06[0.73–1.49] 0.77 Grusi 0.30[0.12–0.64] 0.0042 0.56[0.2–1.29] 0.21 Guan 1.10[0.71–1.68] 0.64 1.08[0.67–1.70] 0.73 Gurma 2.47[1.86–3.27] < 0.001 2.94[2.05–4.19] < 0.001 Mande 0.75[0.22–1.94] 0.60 0.96[0.27–2.61] 0.94 Mole-Dagbani 0.59[0.47–0.76] < 0.001 1.07[0.75–1.52] 0.70 Other Tribes (Hausa, Baribari, Zabrama) 1.46[0.97–2.14] 0.058 1.73[1.11–2.64] 0.0137 Sex Male - Ref Female 1.02[0.88–1.19] 0.775 1.13[0.94–1.37] 0.203 Age (years) 50–59 - Ref 60–69 1.19[1.00–1.41] 0.047 1.15[0.96–1.39] 0.133 70–79 1.24[0.97–1.58] 0.082 1.02[0.77–1.34] 0.87 80+ 1.06[0.67–1.61] 0.793 0.86[0.52–1.38] 0.55 Marital status Never married - Ref Divorced 0.76[0.38–1.60] 0.44 0.71[0.33–1.57] 0.38 Informal/living together 1.25[0.64–2.60] 0.53 0.86[0.41–1.88] 0.68 Married 0.89[0.49–1.76] 0.73 0.90[0.47–1.87] 0.76 Separated 1.09[0.52–2.36] 0.83 1.10[0.50–2.53] 0.81 Widowed 0.87[0.47–1.75] 0.63 0.89[0.45–1.88] 0.75 Education status No formal education - Ref Formal education 0.96[0.82–1.13] 0.66 1.03[0.84–1.27] 0.44 Current Health Insurance Status No - Ref Yes, covered 0.91[0.78–1.07] 0.247 0.95[0.81–1.13] 0.72 Body type Healthy body - Ref Underweight 1.14[0.88–1.46] 0.32 1.12[0.84–1.47] 0.93 Overweight 0.93[0.77–1.11] 0.42 1.04[0.85–1.27] 0.59 Obese 0.83[0.65–1.06] 0.14 0.99[0.73–1.30] 0.78 4. Discussion 4.1 Principal findings This study sought to ascertain the prevalence of extreme fatigue and associated sociodemographic and health related predictors among older adults in Ghana. The results showed 17.03% of older adults in this study experienced extreme fatigue. Our regression analysis established that health related factors including severe illness and functional disability, as well as sociodemographic factors such as settlement/locality type, ethnicity, regions and occupation predicted the likelihood of experiencing extreme fatigue among older adults in this study. Specifically, older adults with severe illness were 5.16 times more likely to experience extreme fatigue. Those with functional disability were 1.31 times more likely to experience extreme fatigue. Also, older adults working as basic laborers were 1.41 times more likely to experience extreme fatigue. Older adults who identified with Gurma and other ethnic groups and living in rural areas were 2.94, 1.73 and 1.26 times more likely to experience extreme fatigue, respectively. On the other hand, older adults in the Northern and Southern Ghana were 0.31 and 0.48 times less likely to experience extreme fatigue, respectively. 4.2 Interpretation and relation to previous literature Globally, fatigue prevalence varies significantly across studies and countries. The prevalence of extreme fatigue found in our study is within the range of previously reported rates [ 46 – 48 ]. However it is also lower than rates reported in other studies [ 49 , 50 ]. For instance, in Korea, a study by Son et al. [ 47 ] reported a prevalence of 17.7% among the Korean study participants, which is almost the same as the rate found in our study. However, higher rates have been reported in other studies. A study by Hu et al. [ 50 ] found that the prevalence of fatigue in older adults was 42.6%. Similarly, in sub-Saharan Africa, a recent community-based study in Ethiopia also reported a notably higher prevalence of 37.9% among older adults, potentially reflecting regional differences driven by socioeconomic factors such as poverty and low income levels; unemployment or underemployment; low educational status; rural residence and poor infrastructure; food insecurity and malnutrition and high healthcare costs; healthcare access, or varying definitions of extreme fatigue [ 51 ]. However, the comparatively lower prevalence of extreme fatigue in the Ghanaian sample may be due to its focus on severe cases rather than general fatigue. Furthermore, cultural factors in Ghana, such as stoicism or the tendency to normalize age-related tiredness among older adults, may contribute to underreporting, potentially influencing the reported prevalence rate [ 52 ]. A qualitative study of Ghanaians in Inner London reported that participants downplayed struggles as a form of resilience [ 53 ]. These findings suggest that similar cultural dynamics in Ghana may contribute to the underreporting of symptoms such as fatigue, as they are either normalized or managed privately through faith rather than expressed as medical concerns [ 54 ]. In this study, older adults with severe illnesses were found to be over five times more likely to experience extreme fatigue compared to those without severe health conditions such as cardiovascular diseases like heart failure, stroke, chronic respiratory diseases, advanced HIV/AIDS, or other chronic infections, highlighting the significant influence of chronic illness and comorbidities on fatigue experiences. Persistent fatigue is associated with a broad spectrum of health issues, including the exacerbation of pre-existing conditions [ 55 ]. Severe and chronic fatigue are highly prevalent in chronic diseases, with elevated rates observed in conditions such as multiple sclerosis, particularly in the context of multimorbidity [ 56 , 57 ]. Thus, our observed association aligns with findings from global and African literature, which consistently link chronic conditions such as cardiovascular diseases, diabetes, and arthritis to heightened fatigue [ 57 – 59 ]. Chronic illness may contribute to extreme fatigue through physiological mechanisms, including inflammation, pain, and impaired oxygen delivery [ 60 ]. For example, in chronic kidney disease, increased fatigue is associated with progression toward dialysis initiation [ 61 ]. Similarly, in chronic obstructive pulmonary disease, fatigue is linked to increased mortality risk [ 62 ]. Thus, this finding underscores the need for targeted healthcare interventions such as pulmonary rehabilitation programs, optimized pharmacological therapy, nutritional support, and psychosocial counseling to address fatigue in these populations. Occupation was a notable predictor, with blue-collar or basic laborers 1.41 times more likely to report extreme fatigue than those in administrative or service roles. This may be attributed to the physical demands of manual labor, which predominate in Ghana's agrarian and informal sectors, leading to cumulative wear and tear on older bodies. Studies conducted in the United States and China have found that fatigue is a prevalent and significant issue across diverse workplaces, stemming from intense physical labor or mental effort [ 63 ]. This is especially important because fatigue, accompanied by reduced motivation and vigilance, significantly contributes to an elevated risk of accidents and injuries [ 64 ]. Those with functional disability were 1.31 times more likely to experience extreme fatigue reflecting how limitations in daily activities perpetuate a cycle of deconditioning and energy depletion. Individuals with central nervous system trauma such as stroke, neurological diseases or degenerative muscle diseases like muscular dystrophy frequently experience fatigue as a secondary consequence of their primary impairments, which can intensify over time and adversely affect their health-related quality of life [ 65 ]. The data indicate significant ethnic and regional variations in fatigue prevalence among older adults in Ghana. The Gurma group retains a significantly elevated risk with the Hausa, Baribari, and Zabrama tribes showing a notable association with fatigue suggesting ethnicity-specific factors may influence fatigue outcomes. This trend could be due to some historical and structural factors such as poverty and limited access to health facilities in regions where these ethnic groups are dominated. For instance, the Ghana Poverty Reduction Strategy revealed that the three northern regions of Ghana are still in the leading pack of administrative regions contributing approximately 40% of the poor in Ghana [ 66 ]. Abdulai et al. [ 67 ] demonstrates that colonial policies deliberately underdeveloped Northern Ghana, turning it into a labor reservoir while neglecting education, infrastructure, and economic growth, which entrenched ethnic and regional disparities. Groups such as Gurma, Dagbon, Wala, and others bore the brunt of this neglect, facing poverty and limited opportunities, unlike their southern counterparts. These structural disadvantages created lasting health inequities, with some ethnic groups showing heightened vulnerability [ 67 ]. On the other hand, older adults in the Northern and Southern Ghana were 0.31 and 0.48 times less likely to experience extreme fatigue, respectively as compared to older adults in the central region. The reason for this trend is, however, unclear and more studies including qualitative evidence are needed to provide nuanced understanding and explanation for these findings. 5. Implications for policy, practice and research This study provides essential evidence on the prevalence and determinants of extreme fatigue among older adults in Ghana, with 17.03% of respondents reporting this outcome. It enriches the literature by identifying key sociodemographic and health-related predicators such as severe illness, functionality disability, occupation, ethnicity, locality type and region that significantly shape fatigue risk. The magnitude of associations, particularly the fivefold higher odds among persons with severe illness, offers robust epidemiological insight. These results contribute to global scholarship on ageing while addressing a critical gap in public health research in sub–Saharan Africa. The findings highlight the need for targeted policy measures that incorporate fatigue screening and management within chronic disease care, especially for older adults with severe illness or functional limitations. The elevated risk among manual laborers underscores the importance of occupational health strategies, ergonomic protections, flexible work options and retirement planning for ageing workers in informal sectors. Marked ethnic and regional disparities, higher risks among Gurma, other minority groups, and rural residents demand equity-oriented health planning. Policymakers should prioritize culturally responsive outreach, infrastructure investment, and resource allocation in underserved areas. The comparatively lower fatigue prevalence in Northern and Southern Ghana suggests protective factors worthy of further exploration and replication. Healthcare providers should routinely assess fatigue in older adults, particularly those with chronic conditions, functional disabilities, or physically demanding work. Fatigue screening tools should be embedded in geriatric assessments and chronic disease protocols. Community based interventions must be tailored to high-risk populations using culturally appropriate messaging to encourage disclosure and care seeking. In rural settings, mobile clinics and community health workers can play a critical role in detection and management. Recognizing the influence of cultural norms such as stoicism or spiritual coping on underreporting, practitioners should employ empathetic, context-aware communication to build trust and engagement. This study opens several avenues for future inquiry. Qualitative research is needed to examine how cultural beliefs, gender roles, and spiritual practices influence perceptions and reporting of fatigue among older Ghanaian adults. Longitudinal studies could assess the progress of fatigue and its effects on morbidity, disability and mortality. Further studies of the apparent protective factors in Northern and Southern Ghana may reveal resilience mechanisms and adaptive behaviors. Additionally, intervention studies should test integrated fatigue management approaches, combining physiotherapy, nutrition, and psychosocial support to improve health outcomes and quality of life among older adults. 6. Strengths and limitations This study is constrained by its reliance on self-reported information, which may be affected by recall bias. Although the Ghana Statistical Service (GSS) employed trained enumerators to enhance data quality, the accuracy of responses ultimately depended on participants’ ability to recall events and details preceding data collection. Despite the limitations, the use of the Ghana Annual Household Income and Expenditure Survey (AHIES) provides a large, nationally representative dataset, ensuring that the findings are generalizable to the wider Ghanaian older adult population. 7. Conclusion This study sought to examine population-level estimates of extreme fatigue and its associated sociodemographic and health-related factors among older Ghanaians. The findings showed 17.03% of older adults in this study experienced extreme fatigue. Further analysis established that health-related factors, including severe illness and functional disability, as well as sociodemographic factors such as settlement/locality type, ethnicity, regions and occupation, predicted the likelihood of experiencing extreme fatigue among older adults in this study. The findings indicate the need to increase primary care screening protocols across all sixteen regions of Ghana, and also to do more sensitization on interventions that reduce the effect of fatigue to help the older adults affected maintain a healthy level of independence. Abbreviations ADLs Activities of Daily Living AHIES Annual Household Income and Expenditure Survey aOR Adjusted Odds Ratio BMI Body Mass Index CI Confidence Interval EAs Enumeration Areas GDP Gross Domestic Product GSS Ghana Statistical Service GPRSI Ghana Poverty Reduction Strategy I OR Odds Ratios p p-value R R programming software SAGE Global AGEing and Adult Health SDGs Sustainable Development Goals WHO World Health Organization χ² Chi-square statistic Declarations Competing interests The authors declare no competing interests. Ethics Statement This study is a secondary analysis of publicly available, anonymized data from the Ghana Statistical Service (GSS) derived from the Annual Household Income and Expenditure Survey (AHIES). The original data collection by the GSS was conducted in accordance with the Statistical Service Act, 2019 (Act 1003), following all required ethical standards and informed consent procedures. As the authors of this study did not have any direct interaction with study participants and the dataset contains no personal identifiers, additional ethical and institutional review board (IRB) approval was not required. Clinical trial number Not applicable. Consent to participate Not applicable. The study used secondary, anonymized data obtained from publicly available national datasets; therefore, informed consent was not required. Consent to Publish authors have read and approved the manuscript and consent to its submission to Discover Public Health. Funding statement The authors did not receive any funding for this study. Author Contribution D.F. contributed to the study concept, data analysis, results formulation, manuscript writing, and final revision. R.A., D.A., E.P.A, M.A.P. and B.N.D contributed to the study concept, manuscript writing, and review of the final manuscript. P.P. and W.A.D. conceptualized and supervised the study. Acknowledgement We extend our gratitude to the Ghana Statistical Service (GSS) for providing access to the 2023 Annual Household Income and Expenditure Survey data. Data Availability The dataset (2023 Ghana Annual Household Income and Expenditure Survey-AHIES) analyzed in the current study is publicly available upon request from the Ghana Statistical Service website. 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University of Alberta; 2020. Dovie DA. The Status of Older Adult Care in Contemporary Ghana: A Profile of Some Emerging Issues. Front Sociol [Internet]. 2019 Apr 11 [cited 2025 Sept 22];4. Available from: https://www.frontiersin.org/journals/sociology/articles/ 10.3389/fsoc.2019.00025/full Mba CJ. Population Ageing in Ghana: Research Gaps and the Way Forward. J Aging Res 2010 Sept 29;2010:672157. World Health Organization. Ghana country assessment report on ageing and health [Internet]. Geneva: World Health Organization. 2014 [cited 2025 Sept 22]. 34 p. Available from: https://iris.who.int/handle/10665/126341 Choi S, Harrison T. The Roles of Stress, Sleep, and Fatigue on Depression in People with Visual Impairments. Biol Res Nurs. 2023;25(4):550–8. Uslu A, Canbolat O. Relationship Between Frailty and Fatigue in Older Cancer Patients. Semin Oncol Nurs. 2021;37(4):151179. Wright A, Fisher PL, Baker N, O’Rourke L, Cherry MG. Perfectionism, depression and anxiety in chronic fatigue syndrome: A systematic review. J Psychosom Res. 2021;140:110322. GSS AHIES. Ghana - Annual Household Income and Expenditure Survey (AHIES) – 2023 [Internet]. 2023 [cited 2025 Sept 19]. Available from: https://microdata.statsghana.gov.gh/index.php/catalog/119 Galland-Decker C, Marques-Vidal P, Vollenweider P. Prevalence and factors associated with fatigue in the Lausanne middle-aged population: a population-based, cross-sectional survey. BMJ Open. 2019;9(8):e027070. Mekuria BA, Fentanew M, Anteneh YE, Suleman J, Belet Y, Getie K, et al. Risk factors of fatigue among community-dwelling older adults in Bahir Dar, Northwest Ethiopia: a community-based cross-sectional study. Front Public Health. 2024;12:1491287. Hung WW, Ross JS, Boockvar KS, Siu AL. Recent trends in chronic disease, impairment and disability among older adults in the United States. BMC Geriatr. 2011;11(1):47. Galland-Decker C, Marques-Vidal P, Vollenweider P. Prevalence and factors associated with fatigue in the Lausanne middle-aged population: a population-based, cross-sectional survey. BMJ Open. 2019;9(8):e027070. Son CG. Case Report of Chronic Fatigue Syndrome Treated with Salt-Indirect Moxibustion. J Korean Med. 2012;33(4):81–5. Yoon JH, Park NH, Kang YE, Ahn YC, Lee EJ, Son CG. The demographic features of fatigue in the general population worldwide: a systematic review and meta-analysis. Front Public Health. 2023 July;28:11:1192121. Hertanti NS, Nguyen TV, Chuang YH. Global prevalence and risk factors of fatigue and post-infectious fatigue among patients with dengue: a systematic review and meta-analysis. eClinicalMedicine. 2025;80:103041. Hu T, Wang F, Duan Q, Zhao X, Yang F. Prevalence of fatigue and perceived fatigability in older adults: a systematic review and meta-analysis. Sci Rep. 2025;15(1):4818. Mekuria BA, Fentanew M, Anteneh YE, Suleman J, Belet Y, Getie K, et al. Risk factors of fatigue among community-dwelling older adults in Bahir Dar, Northwest Ethiopia: a community-based cross-sectional study. Front Public Health. 2024;12:1491287. Wilson TK, Gentzler AL. Emotion regulation and coping with racial stressors among African Americans across the lifespan. Dev Rev. 2021 Sept;61:100967. Isiwele A, Stokes G, Callender C, Rivas C. Navigating trauma and strength: experiences of Ghanaian and Nigerian youth in inner London. Discov Ment Health 2025 July 9;5(1):103. Kpobi LNA, Swartz L. The threads in his mind have torn’: conceptualization and treatment of mental disorders by neo-prophetic Christian healers in Accra, Ghana. Int J Ment Health Syst. 2018;12(1):40. Maisel P, Baum E, Donner-Banzhoff N. Fatigue as the Chief Complaint. Dtsch Ärztebl Int [Internet]. 2021 Aug 23 [cited 2025 Oct 11]; Available from: https://www.aerzteblatt.de/ 10.3238/arztebl.m2021.0192 Fiest KM, Fisk JD, Patten SB, Tremlett H, Wolfson C, Warren S, et al. Fatigue and Comorbidities in Multiple Sclerosis. Int J MS Care. 2016;18(2):96–104. Goërtz YMJ, Braamse AMJ, Spruit MA, Janssen DJA, Ebadi Z, Van Herck M, et al. Fatigue in patients with chronic disease: results from the population-based Lifelines Cohort Study. Sci Rep. 2021;11(1):20977. Overman CL, Kool MB, Da Silva JAP, Geenen R. The prevalence of severe fatigue in rheumatic diseases: an international study. Clin Rheumatol. 2016;35(2):409–15. Park NH, Kang YE, Yoon JH, Ahn YC, Lee EJ, Park BJ, et al. Comparative study for fatigue prevalence in subjects with diseases: a systematic review and meta-analysis. Sci Rep. 2024;14(1):23348. Van Steenbergen HW, Tsonaka R, Huizinga TWJ, Van Boonen A. Der Helm-van Mil AHM. Fatigue in rheumatoid arthritis; a persistent problem: a large longitudinal study. RMD Open. 2015;1(1):e000041. Gregg LP, Jain N, Carmody T, Minhajuddin AT, Rush AJ, Trivedi MH, et al. Fatigue in Nondialysis Chronic Kidney Disease: Correlates and Association with Kidney Outcomes. Am J Nephrol. 2019;50(1):37–47. Andersson M, Stridsman C, Rönmark E, Lindberg A, Emtner M. Physical activity and fatigue in chronic obstructive pulmonary disease – A population based study. Respir Med. 2015;109(8):1048–57. Lu L, Megahed FM, Sesek RF, Cavuoto LA. A survey of the prevalence of fatigue, its precursors and individual coping mechanisms among U.S. manufacturing workers. Appl Ergon. 2017;65:139–51. Swaen GMH, Van Amelsvoort LGPM, Bültmann U, Kant I. Fatigue as a risk factor for being injured in an occupational accident: results from the Maastricht Cohort Study. Occup Environ Med. 2003 June;60(suppl 1):i88–92. Widerström-Noga E, Finlayson ML. Aging with a Disability: Physical Impairment, Pain, and Fatigue. Phys Med Rehabil Clin N Am. 2010;21(2):321–37. Kuu-Ire S. Poverty reduction in Northern Ghana: a review of colonial and post-independence development strategies. Ghana J Dev Stud. 2009;6(1):175–203. Abdulai AG. The Political Economy of Maternal Healthcare in Ghana. SSRN Electron J [Internet]. 2018 [cited 2025 Oct 2]; Available from: https://www.ssrn.com/abstract=3272848 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 19 Apr, 2026 Read the published version in Discover Public Health → Version 1 posted Editorial decision: Revision requested 23 Jan, 2026 Reviews received at journal 21 Jan, 2026 Reviewers agreed at journal 14 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviews received at journal 17 Dec, 2025 Reviewers agreed at journal 17 Dec, 2025 Reviewers agreed at journal 17 Dec, 2025 Reviewers invited by journal 15 Dec, 2025 Editor assigned by journal 15 Dec, 2025 Editor invited by journal 26 Nov, 2025 Submission checks completed at journal 12 Nov, 2025 First submitted to journal 12 Nov, 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. 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Introduction","content":"\u003cp\u003eThe landscape of aging and chronic diseases in Ghana are being shaped by transitions in demographics and epidemiology [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The proportion of people over 60 years continue to increase steadily worldwide as a result of an increase in longevity and a concurrent decline in fertility [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In Ghana, this proportion has seen a 4.5% growth, an increase from 213,447 in 1960 to 1,991,736 in 2021 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. According to most recent projections, this number is expected to reach 6.3\u0026nbsp;million by 2050 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The increasing older adults population has resulted in increasingly substantial pressure on families and primary care services as this shift occurs alongside a constrained health and social system [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eExtreme fatigue, which is a condition characterized by persistent and overwhelming sense of tiredness which is unable to resolve after the individual attains rest [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], is common but rarely recognized in older adults [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The prevalence of extreme fatigue varies across diverse populations and clinical contexts. A systematic review and meta-analysis revealed that, about 22% of cancer patients have been reported to experience fatigue[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Park et al. [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] reported that, about 80% of patients who experience chronic illnesses like multiple sclerosis, experience fatigue. About 80% of older patients in the Netherlands, especially those who are hospitalized reported extreme fatigue that limits their daily activities [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Moreover, a seroprevalence Coronavirus (SEROCoV) Population-Based Study during the COVID-19 pandemic show that about 40% of adults still experience extreme fatigue long after infection with the virus [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Extreme fatigue is a common and disabling marker of serious physical and mental health issues in older populations. In the latter stages of life, extreme fatigue has been shown to reduce psychological resilience, erode the independence of older adults and most often influence negatively the management of chronic diseases [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Studies conducted worldwide have shown extreme fatigue is a risk factor for sleep associated disorders, a depressive state and multimorbidity in older adults [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A systematic review and meta-analysis of 21 studies involving 17843 participants showed that slower gait, declining quality of life and a high rate of incident disability are experienced by older adults who frequently report fatigue [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The findings from these studies suggest that extreme fatigue is not just a symptom of lack of rest but an important indicator of vulnerability in the older population, which requires research, policy and clinical attention.\u003c/p\u003e\u003cp\u003eSeveral sociodemographic and health-related factors such as chronic health conditions, functional disability, age, gender, marital status and occupation may serve as correlates of extreme fatigue among older adults including [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Noncommunicable diseases and multimorbidity are key epidemiologic profiles of older populations including Ghanaian older cohort, both of which are key players of extreme fatigue [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. A study conducted by the World Health Organization (WHO) on Global AGEing and adult health (SAGE) highlights the burden cardiometabolic diseases poses on the adult population in Ghana [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The difficulty in treatment and management of non-communicable diseases, and the associated psychosocial stress increase the likelihood of developing extreme fatigue. Furthermore, a study conducted in Ghana noted that patients with non-communicable diseases often complained of extreme fatigue [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. For instance, there are reports of post-stroke fatigue affecting approximately 60% of people within the first month after stroke [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Meanwhile, a population level estimate of how chronic health conditions predict extreme fatigue in Ghana is lacking.\u003c/p\u003e\u003cp\u003eLoss of functional ability is also considered both a correlate and a consequence of extreme fatigue [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Studies have reported that, older adults who report \u0026ldquo;feeling tired most of the time\u0026rdquo; display a reduced performance of physical activity and a greater difficulty in handling their daily routine [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A vicious cycle is highlighted by this relationship: activity is limited by extreme fatigue, hastens deconditioning which subsequently worsens fatigue. In settings with limited resources especially where devices meant for assistance and rehabilitation programs are less accessible, the functional toll of extreme fatigue is highly felt increasing the likelihood of social isolation, utilizing health-care and becoming dependent [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHow extreme fatigue is experienced and handled is further shaped by sociodemographic characteristics [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. There have been consistent differences in reports of fatigue among sexes with women having a higher frequency in global and regional studies [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Also, food insecurity, socioeconomic status and limited social support remain crucial determinants of extreme fatigue [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. There are notable gaps in age-friendly infrastructures and geriatric care capacity which inadvertently worsens the effect fatigue has on daily life [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. It is therefore important to understand how other sociodemographic factors such as level of education, structure of household, sex, age and employment status predict extreme fatigue to guide the creation of equitable and responsive interventions.\u003c/p\u003e\u003cp\u003eCollectively, population level estimates of extreme fatigue and its associated sociodemographic and health related factors among older Ghanaians are limited despite its importance in guiding healthy ageing, clinical practice, public health interventions and shaping policy and resource allocation for geriatric care in Ghana [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Most of the studies around this topic focus on either specific disease of surrounding geriatric conditions like depression, vision impairment and frailty [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. These studies are able to highlight substantial loss of functional ability in later life, but they do not capture the prevalence and predictors of extreme fatigue. This study aims to address this gap by investigating the prevalence and predictors of extreme fatigue among older adults in Ghana. The results from this study are in direct support of targets 3.4 and 3.8 of the Sustainable Development Goal 3 which seeks to reduce premature mortality from NCDs and promote well-being and achieve universal health coverage respectively. Findings could help identify prevalence and predictors of extreme fatigue among older adults informing early detection, integrated care and interventions that ensure healthy ageing and overall well-being.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data Source\u003c/h2\u003e\u003cp\u003eThis study was based on existing data from the Ghana Annual Household Income and Expenditure Survey (AHIES), conducted quarterly by the Ghana Statistical Service (GSS) between 2021 and 2023 [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The AHIES is Ghana\u0026rsquo;s first high-frequency national survey, collecting detailed quarterly and annual information on household finances, demographics, and well-being. It provides essential data for generating macroeconomic indicators and supports research, policymaking, and monitoring of national development programs and the Sustainable Development Goals (SDGs).\u003c/p\u003e\u003cp\u003eThe AHIES sample was based on the 2021 Population and Housing Census. A total of 10,800 households were selected from 600 Enumeration Areas (EAs), with 304 (50.67%) urban and 296 (49.33%) rural areas. In each EA, 18 households were randomly selected, creating a nationally and regionally representative sample for expenditure and GDP estimates. For this study, a subset of participants aged 50 years and above was analyzed due to the evidence that extreme fatigue is more prevalent among older adults [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Comprehensive details on the survey design, sampling framework, and field procedures are available in the Annual Household Income and Expenditure Survey (AHIES) report [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Measures\u003c/h2\u003e\u003cp\u003eThe dependent variable in this study was extreme fatigue among older adults. Extreme fatigue was defined as \u0026ldquo;a condition characterized by persistent and overwhelming sense of tiredness which is unable to resolve after the individual attains rest\u0026rdquo;. Respondents were asked whether they experienced extreme fatigue, and answers were recorded in binary form (Yes or No). Responses were coded as \u0026ldquo;0\u0026rdquo; for \u0026ldquo;No\u0026rdquo; and \u0026ldquo;1\u0026rdquo; for \u0026ldquo;Yes.\u0026rdquo; Treating extreme fatigue as a binary variable is consistent with previous population-based studies examining fatigue outcomes [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSociodemographic and health-related factors were included as predictors of extreme fatigue and independent variables. Key measures included illness status, functional disability, body mass index (BMI), educational attainment, and occupational classification.\u003c/p\u003e\u003cp\u003eFunctional disability was derived from three activities of daily living (ADLs): walking or climbing steps, performing self-care tasks such as washing or dressing, and communicating effectively (including both understanding and being understood). Each of these items was coded dichotomously, with responses indicating either no difficulty (0) or some level of difficulty, assistance, or complete inability to perform the task (1). The responses were aggregated into a continuous disability score ranging from 0 to 3. Individuals who reported no difficulty across all three ADLs were categorized as having no disability (None). Those who reported difficulty with one or two tasks were considered to have moderate disability (Moderate), while those indicating difficulty with all three tasks were classified as having severe functional disability (Severe). This approach to classifying functional disability aligns with prior published research [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eSevere illness was defined from self-reports of illness or injury. Respondents were classified as having severe illness if they reported either (a) an illness not serious but not preventing work, (b) a serious illness not preventing work, or (c) illness/injury that temporarily or permanently stopped work. Those reporting no illness or injury were coded as having no severe illness.\u003c/p\u003e\u003cp\u003eBMI was regrouped using WHO criteria: underweight (\u0026lt;\u0026thinsp;18.5), healthy weight (18.5\u0026ndash;24.9), overweight (25.0\u0026ndash;29.9), and obese (\u0026ge;\u0026thinsp;30.0).\u003c/p\u003e\u003cp\u003eEducational status was coded as \u0026ldquo;Formal education\u0026rdquo; for any class-based schooling or \u0026ldquo;No formal education\u0026rdquo; for otherwise. Occupations were regrouped into three categories: white-collar for professional and managerial jobs, blue-collar/basic labor for manual and technical trades, and service/administrative for clerical and support roles.\u003c/p\u003e\u003cp\u003eFinally, the 16 administrative regions of Ghana were grouped into three zones to simplify our analysis: Central Ghana (Bono, Bono East, Ahafo, Ashanti, Eastern and Oti), Southern Ghana (Central, Greater Accra, Volta, Western and Western North), and Northern Ghana (Upper West, Upper East, North East, Northern and Savannah).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Data analysis\u003c/h2\u003e\u003cp\u003eStatistical analysis was conducted using R programming software (version 4.4.3). The analysis began by importing the AHIES dataset, from which a targeted sample of individuals aged 50 and above was extracted for the study. The overall prevalence of extreme fatigue among older adults from 50 years and above was calculated. The relationship between each independent variable and the dependent variable, extreme fatigue, was first evaluated using bivariate logistic regression (Model 1). The model assessed the crude, unadjusted relationship between each independent variable and extreme fatigue. Next, in a multivariable analysis, Model 2 was built by including all independent variables to examine their adjusted associations with extreme fatigue. For all analysis, a p-value of less than 0.05 was statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Background characteristics and prevalence of extreme fatigue\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e present the descriptive characteristics of participants and prevalence of extreme fatigue. Overall, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, 824 participants (17.03%) reported experiencing extreme fatigue. In Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the chi-square analysis showed Occupation class (χ\u0026sup2; = 30.46, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), place of residence/settlement (urban vs. rural) (χ\u0026sup2; = 22.72, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), regional group (χ\u0026sup2; = 128.24, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and ethnicity (χ\u0026sup2; = 89.36, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were all significantly associated with the distribution of extreme fatigue. Furthermore, severe illness status (χ\u0026sup2; = 319.8, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and functional disability level (χ\u0026sup2; = 18.38, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) exhibited particularly strong associations.\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 presence of extreme fatigue (n\u0026thinsp;=\u0026thinsp;4838)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\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\u003eLevels\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{p}-\\varvec{v}\\varvec{a}\\varvec{l}\\varvec{u}\\varvec{e}\\:(\\varvec{X}\u0026sup2;\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eExtreme fatigue\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4014(82.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e824\u003cb\u003e(17.03)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1795 (44.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e364 (44.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2219 (55.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e460 (55.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50\u0026ndash;59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2340 (58.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e445 (54.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60\u0026ndash;609\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1134 (28.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e256 (31.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70\u0026ndash;79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e411 (10.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97 (11.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e129 (3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26 (3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNever married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e53 (1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDivorced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e222 (5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38 (4.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInformal/living together\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e198 (4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56 (6.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2643 (65.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e535 (64.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSeparated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e118 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29 (3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWidowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e780 (19.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e154 (18.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo formal education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1274 (31.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e268 (32.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eO.689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFormal education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2740 (68.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e556 (67.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAdministrative \u0026amp; services\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1001 (24.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e148 (18.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBlue-collar/Basic labor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2731 (68.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e640 (77.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhite-collar Occupations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e282 (7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSettlement\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRURAL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1989 (49.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e484 (58.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eURBAN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2025 (50.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e340 (41.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\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\u003cp\u003eNorther Ghana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e897 (22.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e99 (12.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCentral Ghana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1574 (39.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e497 (60.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSouthern Ghana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1543 (38.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e228 (27.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAkan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1901 (47.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e389 (47.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEwe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e574 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e140 (17.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGa-Dangwe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e253 (6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45 (5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrusi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e97 (2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGuan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e119 (3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27 (3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGurma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e168 (4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85 (10.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMande\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26 (0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4 (0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMole Dagbani\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e759 (18.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e93 (11.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll Other Tribes (Hausa, Baribari, Zabrama)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e117 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35 (4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCurrent Health Insurance Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1473 (36.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e320 (38.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes, covered\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2541 (63.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e504 (61.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBody type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e362 (9.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e87 (10.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHealthy body\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2093 (52.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e442 (53.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1031 (25.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e202 (24.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e528 (13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e93 (11.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSevere illness status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3713 (92.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e584 (70.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e301 (7.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e240 (29.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFunctional disability\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3496 (87.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e671 (81.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e512 (12.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e151 (18.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSevere\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6 (0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2 (0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Predictors of extreme fatigue\u003c/h2\u003e\u003cp\u003eIn a logistic regression analysis, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the odds ratios (OR), adjusted odds ratios(aOR), 95% confidence intervals (CI), and respective p-values for both the bivariable (Model 1) and multivariable (Model 2) models. In the multivariable model (Model 2), health related factors including severe illness and functional disability, as well as sociodemographic factors such as settlement/locality type, ethnicity, regions and occupation predicted the likelihood of experiencing extreme fatigue among older adults in this study. Specifically, older adults with severe illness were 5.16 times more likely to experience extreme fatigue compared to those with no illness (aOR\u0026thinsp;=\u0026thinsp;5.16, 95% CI: 4.21\u0026ndash;6.31, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Compared to those with no functional disability, older adults with functional disability were 1.31 times more likely to experience extreme fatigue (aOR\u0026thinsp;=\u0026thinsp;1.31, 95% CI: 1.05\u0026ndash;1.63). Also, older adults working as basic laborers were 1.41 times more likely to experience extreme fatigue compared to those working in Administrative \u0026amp; Service occupations (adjusted aOR\u0026thinsp;=\u0026thinsp;1.41, 95% CI: 1.13\u0026ndash;1.78). Older adults living in rural areas were 1.26 times more likely to experience extreme fatigue compared to those in urban areas (aOR\u0026thinsp;=\u0026thinsp;1.26, 95% CI: 1.06\u0026ndash;1.50). Compared with the Akan ethnic group, older adults who identified with Gurma (aOR\u0026thinsp;=\u0026thinsp;2.94, 95% CI: 2.05\u0026ndash;4.19) and \u0026lsquo;other ethnic groups\u0026rsquo; (aOR\u0026thinsp;=\u0026thinsp;1.73, 95% CI: 1.12\u0026ndash;2.65) were 2.94 and 1.73 times, respectively, more likely to experience extreme fatigue. On the other hand, older adults in the Northern and Southern Ghana were 0.31 (aOR\u0026thinsp;=\u0026thinsp;0.31, 95% CI: 0.22\u0026ndash;0.43) and 0.48 (aOR\u0026thinsp;=\u0026thinsp;0.48, 95% CI: 0.40\u0026ndash;0.58) times less likely to experience extreme fatigue, respectively, compared to those in Central Ghana.\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\u003eBivariable and Multivariable Predictors of Extreme Fatigue among Older Adults\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\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\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBivariable Odds Ratio [95% CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMultivariable Odds Ratio [95% CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOdds Ratio\u003c/p\u003e\u003cp\u003e[95% CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOdds Ratio [95% CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSevere illness status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.07[4.19\u0026ndash;6.13]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.16[4.21\u0026ndash;6.31]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;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\u003eFunctional disability\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.53[1.26\u0026ndash;1.87]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.31[1.05\u0026ndash;1.63]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSevere\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.73[0.25\u0026ndash;7.56]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.95[0.11\u0026ndash;4.97]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAdministrative \u0026amp; Service\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBlue-Collar or Basic Labor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.59[1.31\u0026ndash;1.93]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.41[1.13\u0026ndash;1.78]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.0029\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhite-Collar occupations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.86[0.58\u0026ndash;1.26]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.458\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.18[0.77\u0026ndash;1.76]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRegional group\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCentral Ghana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNorthern Ghana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.34[0.28\u0026ndash;0.44]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.31[0.22\u0026ndash;0.43]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSouthern Ghana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.46[0.39\u0026ndash;0.56]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.48[0.40\u0026ndash;0.58]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;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\u003eSettlement\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eURBAN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRURAL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.44[1.25\u0026ndash;1.68]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.26[1.06\u0026ndash;1.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.0087\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\u003cp\u003eAkan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEwe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.19[0.96\u0026ndash;1.47]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.21[0.95\u0026ndash;1.53]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGa-Dangme\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.86[0.61\u0026ndash;1.20]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.06[0.73\u0026ndash;1.49]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrusi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.30[0.12\u0026ndash;0.64]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.0042\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.56[0.2\u0026ndash;1.29]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGuan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.10[0.71\u0026ndash;1.68]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.08[0.67\u0026ndash;1.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGurma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.47[1.86\u0026ndash;3.27]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.94[2.05\u0026ndash;4.19]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMande\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.75[0.22\u0026ndash;1.94]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.96[0.27\u0026ndash;2.61]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMole-Dagbani\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.59[0.47\u0026ndash;0.76]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.07[0.75\u0026ndash;1.52]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther Tribes (Hausa, Baribari, Zabrama)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.46[0.97\u0026ndash;2.14]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.73[1.11\u0026ndash;2.64]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.0137\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.02[0.88\u0026ndash;1.19]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.13[0.94\u0026ndash;1.37]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.203\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50\u0026ndash;59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60\u0026ndash;69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.19[1.00\u0026ndash;1.41]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.15[0.96\u0026ndash;1.39]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.133\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70\u0026ndash;79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.24[0.97\u0026ndash;1.58]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.02[0.77\u0026ndash;1.34]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.06[0.67\u0026ndash;1.61]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.86[0.52\u0026ndash;1.38]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNever married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDivorced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.76[0.38\u0026ndash;1.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.71[0.33\u0026ndash;1.57]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInformal/living together\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.25[0.64\u0026ndash;2.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.86[0.41\u0026ndash;1.88]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.89[0.49\u0026ndash;1.76]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.90[0.47\u0026ndash;1.87]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSeparated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.09[0.52\u0026ndash;2.36]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.10[0.50\u0026ndash;2.53]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWidowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.87[0.47\u0026ndash;1.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.89[0.45\u0026ndash;1.88]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo formal education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFormal education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.96[0.82\u0026ndash;1.13]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.03[0.84\u0026ndash;1.27]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCurrent Health Insurance Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes, covered\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.91[0.78\u0026ndash;1.07]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.95[0.81\u0026ndash;1.13]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBody type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHealthy body\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.14[0.88\u0026ndash;1.46]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.12[0.84\u0026ndash;1.47]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.93[0.77\u0026ndash;1.11]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.04[0.85\u0026ndash;1.27]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.83[0.65\u0026ndash;1.06]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.99[0.73\u0026ndash;1.30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Principal findings\u003c/h2\u003e\u003cp\u003eThis study sought to ascertain the prevalence of extreme fatigue and associated sociodemographic and health related predictors among older adults in Ghana. The results showed 17.03% of older adults in this study experienced extreme fatigue. Our regression analysis established that health related factors including severe illness and functional disability, as well as sociodemographic factors such as settlement/locality type, ethnicity, regions and occupation predicted the likelihood of experiencing extreme fatigue among older adults in this study. Specifically, older adults with severe illness were 5.16 times more likely to experience extreme fatigue. Those with functional disability were 1.31 times more likely to experience extreme fatigue. Also, older adults working as basic laborers were 1.41 times more likely to experience extreme fatigue. Older adults who identified with Gurma and other ethnic groups and living in rural areas were 2.94, 1.73 and 1.26 times more likely to experience extreme fatigue, respectively. On the other hand, older adults in the Northern and Southern Ghana were 0.31 and 0.48 times less likely to experience extreme fatigue, respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Interpretation and relation to previous literature\u003c/h2\u003e\u003cp\u003eGlobally, fatigue prevalence varies significantly across studies and countries. The prevalence of extreme fatigue found in our study is within the range of previously reported rates [\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. However it is also lower than rates reported in other studies [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. For instance, in Korea, a study by Son et al. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] reported a prevalence of 17.7% among the Korean study participants, which is almost the same as the rate found in our study. However, higher rates have been reported in other studies. A study by Hu et al. [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] found that the prevalence of fatigue in older adults was 42.6%. Similarly, in sub-Saharan Africa, a recent community-based study in Ethiopia also reported a notably higher prevalence of 37.9% among older adults, potentially reflecting regional differences driven by socioeconomic factors such as poverty and low income levels; unemployment or underemployment; low educational status; rural residence and poor infrastructure; food insecurity and malnutrition and high healthcare costs; healthcare access, or varying definitions of extreme fatigue [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. However, the comparatively lower prevalence of extreme fatigue in the Ghanaian sample may be due to its focus on severe cases rather than general fatigue. Furthermore, cultural factors in Ghana, such as stoicism or the tendency to normalize age-related tiredness among older adults, may contribute to underreporting, potentially influencing the reported prevalence rate [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. A qualitative study of Ghanaians in Inner London reported that participants downplayed struggles as a form of resilience [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. These findings suggest that similar cultural dynamics in Ghana may contribute to the underreporting of symptoms such as fatigue, as they are either normalized or managed privately through faith rather than expressed as medical concerns [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this study, older adults with severe illnesses were found to be over five times more likely to experience extreme fatigue compared to those without severe health conditions such as cardiovascular diseases like heart failure, stroke, chronic respiratory diseases, advanced HIV/AIDS, or other chronic infections, highlighting the significant influence of chronic illness and comorbidities on fatigue experiences. Persistent fatigue is associated with a broad spectrum of health issues, including the exacerbation of pre-existing conditions [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Severe and chronic fatigue are highly prevalent in chronic diseases, with elevated rates observed in conditions such as multiple sclerosis, particularly in the context of multimorbidity [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Thus, our observed association aligns with findings from global and African literature, which consistently link chronic conditions such as cardiovascular diseases, diabetes, and arthritis to heightened fatigue [\u003cspan additionalcitationids=\"CR58\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Chronic illness may contribute to extreme fatigue through physiological mechanisms, including inflammation, pain, and impaired oxygen delivery [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. For example, in chronic kidney disease, increased fatigue is associated with progression toward dialysis initiation [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Similarly, in chronic obstructive pulmonary disease, fatigue is linked to increased mortality risk [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Thus, this finding underscores the need for targeted healthcare interventions such as pulmonary rehabilitation programs, optimized pharmacological therapy, nutritional support, and psychosocial counseling to address fatigue in these populations.\u003c/p\u003e\u003cp\u003eOccupation was a notable predictor, with blue-collar or basic laborers 1.41 times more likely to report extreme fatigue than those in administrative or service roles. This may be attributed to the physical demands of manual labor, which predominate in Ghana's agrarian and informal sectors, leading to cumulative wear and tear on older bodies. Studies conducted in the United States and China have found that fatigue is a prevalent and significant issue across diverse workplaces, stemming from intense physical labor or mental effort [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. This is especially important because fatigue, accompanied by reduced motivation and vigilance, significantly contributes to an elevated risk of accidents and injuries [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThose with functional disability were 1.31 times more likely to experience extreme fatigue reflecting how limitations in daily activities perpetuate a cycle of deconditioning and energy depletion. Individuals with central nervous system trauma such as stroke, neurological diseases or degenerative muscle diseases like muscular dystrophy frequently experience fatigue as a secondary consequence of their primary impairments, which can intensify over time and adversely affect their health-related quality of life [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe data indicate significant ethnic and regional variations in fatigue prevalence among older adults in Ghana. The Gurma group retains a significantly elevated risk with the Hausa, Baribari, and Zabrama tribes showing a notable association with fatigue suggesting ethnicity-specific factors may influence fatigue outcomes. This trend could be due to some historical and structural factors such as poverty and limited access to health facilities in regions where these ethnic groups are dominated. For instance, the Ghana Poverty Reduction Strategy revealed that the three northern regions of Ghana are still in the leading pack of administrative regions contributing approximately 40% of the poor in Ghana [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Abdulai et al. [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] demonstrates that colonial policies deliberately underdeveloped Northern Ghana, turning it into a labor reservoir while neglecting education, infrastructure, and economic growth, which entrenched ethnic and regional disparities. Groups such as Gurma, Dagbon, Wala, and others bore the brunt of this neglect, facing poverty and limited opportunities, unlike their southern counterparts. These structural disadvantages created lasting health inequities, with some ethnic groups showing heightened vulnerability [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. On the other hand, older adults in the Northern and Southern Ghana were 0.31 and 0.48 times less likely to experience extreme fatigue, respectively as compared to older adults in the central region. The reason for this trend is, however, unclear and more studies including qualitative evidence are needed to provide nuanced understanding and explanation for these findings.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Implications for policy, practice and research","content":"\u003cp\u003eThis study provides essential evidence on the prevalence and determinants of extreme fatigue among older adults in Ghana, with 17.03% of respondents reporting this outcome. It enriches the literature by identifying key sociodemographic and health-related predicators such as severe illness, functionality disability, occupation, ethnicity, locality type and region that significantly shape fatigue risk. The magnitude of associations, particularly the fivefold higher odds among persons with severe illness, offers robust epidemiological insight. These results contribute to global scholarship on ageing while addressing a critical gap in public health research in sub\u0026ndash;Saharan Africa.\u003c/p\u003e\u003cp\u003eThe findings highlight the need for targeted policy measures that incorporate fatigue screening and management within chronic disease care, especially for older adults with severe illness or functional limitations. The elevated risk among manual laborers underscores the importance of occupational health strategies, ergonomic protections, flexible work options and retirement planning for ageing workers in informal sectors. Marked ethnic and regional disparities, higher risks among Gurma, other minority groups, and rural residents demand equity-oriented health planning. Policymakers should prioritize culturally responsive outreach, infrastructure investment, and resource allocation in underserved areas. The comparatively lower fatigue prevalence in Northern and Southern Ghana suggests protective factors worthy of further exploration and replication.\u003c/p\u003e\u003cp\u003eHealthcare providers should routinely assess fatigue in older adults, particularly those with chronic conditions, functional disabilities, or physically demanding work. Fatigue screening tools should be embedded in geriatric assessments and chronic disease protocols.\u003c/p\u003e\u003cp\u003eCommunity based interventions must be tailored to high-risk populations using culturally appropriate messaging to encourage disclosure and care seeking. In rural settings, mobile clinics and community health workers can play a critical role in detection and management. Recognizing the influence of cultural norms such as stoicism or spiritual coping on underreporting, practitioners should employ empathetic, context-aware communication to build trust and engagement.\u003c/p\u003e\u003cp\u003eThis study opens several avenues for future inquiry. Qualitative research is needed to examine how cultural beliefs, gender roles, and spiritual practices influence perceptions and reporting of fatigue among older Ghanaian adults. Longitudinal studies could assess the progress of fatigue and its effects on morbidity, disability and mortality. Further studies of the apparent protective factors in Northern and Southern Ghana may reveal resilience mechanisms and adaptive behaviors. Additionally, intervention studies should test integrated fatigue management approaches, combining physiotherapy, nutrition, and psychosocial support to improve health outcomes and quality of life among older adults.\u003c/p\u003e"},{"header":"6. Strengths and limitations","content":"\u003cp\u003eThis study is constrained by its reliance on self-reported information, which may be affected by recall bias. Although the Ghana Statistical Service (GSS) employed trained enumerators to enhance data quality, the accuracy of responses ultimately depended on participants\u0026rsquo; ability to recall events and details preceding data collection. Despite the limitations, the use of the Ghana Annual Household Income and Expenditure Survey (AHIES) provides a large, nationally representative dataset, ensuring that the findings are generalizable to the wider Ghanaian older adult population.\u003c/p\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eThis study sought to examine population-level estimates of extreme fatigue and its associated sociodemographic and health-related factors among older Ghanaians. The findings showed 17.03% of older adults in this study experienced extreme fatigue. Further analysis established that health-related factors, including severe illness and functional disability, as well as sociodemographic factors such as settlement/locality type, ethnicity, regions and occupation, predicted the likelihood of experiencing extreme fatigue among older adults in this study.\u003c/p\u003e\u003cp\u003eThe findings indicate the need to increase primary care screening protocols across all sixteen regions of Ghana, and also to do more sensitization on interventions that reduce the effect of fatigue to help the older adults affected maintain a healthy level of independence.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eADLs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eActivities of Daily Living\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAHIES\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAnnual Household Income and Expenditure Survey\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eaOR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAdjusted Odds Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBody Mass Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfidence Interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEAs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEnumeration Areas\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGDP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGross Domestic Product\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGSS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGhana Statistical Service\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGPRSI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGhana Poverty Reduction Strategy I\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOdds Ratios\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ep\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eR programming software\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSAGE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGlobal AGEing and Adult Health\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSDGs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSustainable Development Goals\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWorld Health Organization\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eχ\u0026sup2;\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eChi-square statistic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eEthics Statement\u003c/h2\u003e\u003cp\u003eThis study is a secondary analysis of publicly available, anonymized data from the Ghana Statistical Service (GSS) derived from the Annual Household Income and Expenditure Survey (AHIES). The original data collection by the GSS was conducted in accordance with the Statistical Service Act, 2019 (Act 1003), following all required ethical standards and informed consent procedures. As the authors of this study did not have any direct interaction with study participants and the dataset contains no personal identifiers, additional ethical and institutional review board (IRB) approval was not required.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConsent to participate\u003c/h2\u003e\u003cp\u003eNot applicable. The study used secondary, anonymized data obtained from publicly available national datasets; therefore, informed consent was not required.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConsent to Publish\u003c/h2\u003e\u003cp\u003eauthors have read and approved the manuscript and consent to its submission to Discover Public Health.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding statement\u003c/h2\u003e\u003cp\u003eThe authors did not receive any funding for this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eD.F. contributed to the study concept, data analysis, results formulation, manuscript writing, and final revision. R.A., D.A., E.P.A, M.A.P. and B.N.D contributed to the study concept, manuscript writing, and review of the final manuscript. P.P. and W.A.D. conceptualized and supervised the study.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe extend our gratitude to the Ghana Statistical Service (GSS) for providing access to the 2023 Annual Household Income and Expenditure Survey data.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe dataset (2023 Ghana Annual Household Income and Expenditure Survey-AHIES) analyzed in the current study is publicly available upon request from the Ghana Statistical Service website. ( [https://microdata.statsghana.gov.gh/index.php/catalog/119/get-microdata](https:/microdata.statsghana.gov.gh/index.php/catalog/119/get-microdata) )\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgyemang-Duah W, Braimah JA, Asante D, Appiah JO, Peprah P, Awuviry-Newton K et al. 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SSRN Electron J [Internet]. 2018 [cited 2025 Oct 2]; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ssrn.com/abstract=3272848\u003c/span\u003e\u003cspan address=\"https://www.ssrn.com/abstract=3272848\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Fatigue, Critical Illness, Older Adults, Chronic diseases, AHIES, Ghana","lastPublishedDoi":"10.21203/rs.3.rs-7958304/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7958304/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eExtreme fatigue is a disabling but under-recognized condition among older adults. Meanwhile, studies investigating impact of sociodemographic and health-related factors on extreme fatigue among older adults in Ghana are limited. This study, therefore, examined the prevalence and predictors of extreme fatigue among older adults in Ghana.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe analyzed cross-sectional data among community-dwelling older adults aged 50+ (N\u0026thinsp;=\u0026thinsp;4,838) extracted from the 2023 Ghana Annual Household Income and Expenditure Survey (AHIES). Descriptive statistics were applied to estimate the prevalence of extreme fatigue. A multivariable model estimated adjusted associations, with significance at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOverall, 17.03% of participants reported experiencing extreme fatigue. In the multivariable model, severe illness (aOR\u0026thinsp;=\u0026thinsp;5.16, 95% CI: 4.21\u0026ndash;6.31), functional disability (aOR\u0026thinsp;=\u0026thinsp;1.31, 95% CI: 1.05\u0026ndash;1.63), rural residence (aOR\u0026thinsp;=\u0026thinsp;1.26, 95% CI: 1.06\u0026ndash;1.50), and basic labor occupations (aOR\u0026thinsp;=\u0026thinsp;1.41, 95% CI: 1.13\u0026ndash;1.78) predicted higher likelihood of experiencing extreme fatigue. Also, older adults of Gurma (aOR\u0026thinsp;=\u0026thinsp;2.94, 95% CI: 2.05\u0026ndash;4.19) and other ethnic groups (aOR\u0026thinsp;=\u0026thinsp;1.73, 95% CI: 1.12\u0026ndash;2.65) had higher odds of experiencing extreme fatigue. On the other hand, older adults in Northern (aOR\u0026thinsp;=\u0026thinsp;0.31, 95% CI: 0.22\u0026ndash;0.43) and Southern Ghana (aOR\u0026thinsp;=\u0026thinsp;0.48, 95% CI: 0.40\u0026ndash;0.58) were less likely to report extreme fatigue.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eChronic illness, functional disability, occupation, and regional disparities emerged as key predictors, underscoring the need for targeted health interventions such as tailored self-management education and Community-based exercise programs.\u003c/p\u003e","manuscriptTitle":"Sociodemographic, functional disability and severe illness predict extreme fatigue among older adults in Ghana","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-05 11:14:09","doi":"10.21203/rs.3.rs-7958304/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-23T14:07:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-21T08:58:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278553220779186104545201471273318798256","date":"2026-01-14T07:49:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"139376978222370050483831914144902471387","date":"2026-01-12T15:55:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-17T13:51:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29124071586575067742670125037424395096","date":"2025-12-17T13:25:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"45578804941072211753700717129997683366","date":"2025-12-17T13:02:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-15T12:46:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-15T12:42:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-26T10:20:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-13T01:51:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2025-11-13T01:48:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bdd6cdde-717a-4c06-9561-160a01cff81a","owner":[],"postedDate":"December 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:03:27+00:00","versionOfRecord":{"articleIdentity":"rs-7958304","link":"https://doi.org/10.1186/s12982-026-01918-x","journal":{"identity":"discover-public-health","isVorOnly":false,"title":"Discover Public Health"},"publishedOn":"2026-04-19 15:57:41","publishedOnDateReadable":"April 19th, 2026"},"versionCreatedAt":"2025-12-05 11:14:09","video":"","vorDoi":"10.1186/s12982-026-01918-x","vorDoiUrl":"https://doi.org/10.1186/s12982-026-01918-x","workflowStages":[]},"version":"v1","identity":"rs-7958304","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7958304","identity":"rs-7958304","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}