Geographic region and health outcomes: Examining health determinants and outcomes of older adults across Ghana’s North‒South divide

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This study used the harmonized Research on Early Life and Aging Trends and Effects (RELATE) dataset to compare health outcomes across the North‒South divide of Ghana and determine the factors underlying the difference in health outcomes. Although the literature indicates that the South has more health resources and better health indicators, the study found that health outcomes in the North were comparatively better than those in the South. According to the optimal health index scores, people in the North are living at 86 percent of their optimal health, while 82 percent are living at their optimal health in the South. In both regions, optimal health was substantially influenced by work-related physical activity and age. Older adults whose work involved vigorous activities had better health outcomes, but health outcomes decreased as people aged. The study also revealed that healthcare use and sex influence health outcomes, particularly in the southern region. The results showed that older adult women in the South have poorer health outcomes than their male counterparts. It has also been shown that older adults in the South are more likely to contact health professionals for medical conditions. Health Economics & Outcomes Research Social Work Social Policy Nursing Optimal Health Ghana Health Determinants Regional Disparities Health Outcomes Introduction Healthcare services and outcomes are often not evenly distributed across geographic regions. While the presence or absence of health services can boost or limit health outcomes for people within a geographic region, these outcomes are substantially influenced by the demographic, socioeconomic, and political characteristics of the region (World Health Organization, n.d.). An unequal distribution of health resources may not only result in poor health in certain parts of a country but also be a source of regional tension and agitation. Hence, our personal and collective obligation is to ensure a fair distribution of health services and outcomes across regions. This paper focuses on Ghana’s North‒South regional divide. Despite efforts by postcolonial political administrations over the years to bridge this gap, the literature shows that the divide still persists and is evident across several dimensions, including health. This study used the harmonized Research on Early Life and Aging Trends and Effects (RELATE) dataset to compare the health outcomes of respondents across regions and to determine the factors underlying the differences in health outcomes. Literature Review Based on colonial history and the level of economic development, Ghana may be divided into two broad geographical regions: the North and the South (Tsikata & Seini, 2004 ; Mancini, 2009 ). The North comprises the five current administrative regions located in the northern part of the country (Northern, North East, Savannah, Upper East and Upper West regions), and the South comprises eleven other administrative regions (Ahafo, Ashanti, Bono, Bono East, Central, Eastern, Greater Accra, Oti, Volta, Western, and Western North). Several studies have shown that the North lags behind the South in several economic and health indicators (Cooke, Hague & McKay, 2016 ; Molini & Pierella, 2015 ; Musah, Ibrahim & Adam, 2016 ; Shepherd, Gyimah-Boadi, Gariba, Plagerson & Musa, 2005). While the country in general faces challenges in the healthcare system, the challenges are more daunting in the North. For instance, the North lags behind the South in terms of skilled health professionals. According to Ghana’s Ministry of Health (2020) human resource profile, the overwhelming majority of highly skilled health care professionals, such as doctors, nurses, pharmacists, and other allied health professionals, are concentrated in the southern region. For instance, the Korle Bu Teaching Hospital in the South alone has more than 25 percent of the country's doctors, while the entire North region has only seven percent. As a result of the unequal distribution, some areas in the North have a doctor-to-patient ratio of 1:18,257, whereas certain areas in the South have a ratio of 1:4,099. The situation is the same for nurses. The nurse‒patient ratio is 1:495 in most parts of the North, compared to 1:339 in the South (Ghana Health Service, 2023). Generally, Ghana has a significant shortage of critical care (ICU) beds, but the few beds in the country are located mainly in the South. Of the 71 adult ICU beds available, only 10 are located in the North at Tamale, and the rest are located in hospitals in the South (Siaw-Frimpong, Touray & Sefa, 2021 ). Currently, there is no psychiatric hospital in the North. The three in the country are all located in the southern region (Roberts, Mogan & Asare, 2014 ). Overall, infrastructures that support good health are comparatively limited in the North. For instance, most areas in the North lack access to portable water and adequate health care facilities (Heyden-Perschon, 2005). Dalinjong, Wang and Homer ( 2018 ) surveyed selected health facilities in rural areas in the North to assess their capacity to provide basic quality childbirth services. They found that only 14 percent of facilities had clean water, 36 percent had electricity and only seven percent had emergency transportation. Distance to health care facilities is critical in accessing healthcare, particularly emergency care. The WHO recommends that communities be within five kilometers of the nearest health facility. A study by Ashiagbor, Ofori-Asenso, Forkuo and Agyei-Frimpong ( 2020 ) revealed that, on average, communities in the Ashanti Region (in the South) live approximately five kilometers from the nearest health facility. A similar study by Kuupiel, Adu, Bawontuo & Mashamba-Thompson ( 2019 ) revealed that 85% of PHC clinics in the Upper East Region (in the North) are located more than 10 kilometers away from the nearest health facility. For the past two decades, Ghana has made progress toward better health (USAID, 2023). Nonetheless, the North continues to lag behind the South. The level of malnutrition among children under five years of age and the child mortality rate in the North are high, while the childhood vaccination rate is low (USAID, 2023). Approximately one-quarter of newborns in the North have adverse health outcomes, while fewer than four out of every 10 pregnant women have the required antenatal contact before delivery (Akum, Offei, Kpordoxah, Yeboah, Issah & Boah, 2023 ). Given the general lack of health infrastructure and services in the North and the health outcomes of children and mothers, we can infer that the health outcomes of older people in the North lag behind those of their counterparts in the South. Objective of the Study Generally, aging is associated with declining health. Age-related health conditions are related to physiological changes that occur in organ systems that reduce the body’s ability to fight infections and chronic illnesses (Flint & Tadi, 2023). Improving health care services and outcomes for older adults in the North and minimizing health disparities in general will require increased funding; improved infrastructure, equipment, and medical technology; training and development of the workforce; and public health education. Given Ghana’s current negative macroeconomic and financial situation (African Development Bank, 2024), the government may not have the resources to substantially improve healthcare services and health outcomes in the North sooner. In the absence of immediate access to medical resources, we can explore nonmedical factors or socioeconomic factors that can be influenced to mitigate the health outcomes of older people. The socioeconomic factors that influence health outcomes include “the conditions in which people are born, grow, work, live, and age, and the wider set of forces and systems shaping the conditions of daily life” (CSDH, 2008 ). In some cases, socioeconomic factors have been found to be more influential in moderating health outcomes than genetic factors and access to health services (Office of Disease Prevention and Health Promotion, n.d.). Identifying critical socioeconomic factors and focusing currently limited resources on them can help improve health outcomes for older people. To this end, the current study draws on the harmonized Research on Early Life and Aging Trends and Effects (RELATE) survey data to identify the socioeconomic factors that influence health outcomes for older people in the North and South regions of Ghana and factors that may contribute to their differences. Methodology Source of Data The data for this study were drawn from the Research on Early Life and Aging Trends and Effects (RELATE) study, a harmonized cross-sectional dataset with 147,278 respondents from 14 countries: Argentina, Barbados, Brazil, Chile, China, Costa Rica, Cuba, Ghana, India, Mexico, Puerto Rico, Russia, South Africa, and Uruguay (McEniry, 2015 ). Samples from the following surveys were harmonized in the RELATE database: the Costa Rican Study of Longevity and Healthy Aging (CRELES); Puerto Rican Elderly: Health Conditions (PREHCO); the WHO Study on Global Aging and Adult Health (WHO‐SAGE) in India, Ghana, South Africa, Mexico, and Russia; the China Health and Nutrition Study (CHNS); and the Study of Aging Survey on Health and Well Being of Elders (SABE) for Barbados, Brazil, Chile, Cuba, Mexico and Uruguay. This study pooled all 4724 respondents from Ghana who were included in the harmonized survey. In fact, only respondents aged 50 years or older were included in the harmonized survey. Approximately 20% of the sample for this study came from the North, and 80% came from the South. However, the samples were not weighted for analyses because the North and the South comprised 17% and 83%, respectively, of the actual Ghana national population (Ghana Statistical Service, 2012 ). Dependent Variable Good Health Index and Optimal Health Index : The primary dependent variable for this study was “overall health outcome”, identified in the dataset as Good Health . The index for good health was constructed using four dimensions of health: (1) self-reported good health status, (2) BMI score, (3) frailty or functionality score, and (4) chronic health conditions. Self-reported good health status was a compilation of responses of “very good”, “good” or “moderate” to the question “In general, how would you rate your health today?”. BMI indicates the respondent’s body mass index (calculated using weight/height 2 ). Frailty or functionality was measured through self-reports of physical limitations or activities of daily living (ADLs). Functionality harmonized four ADLs that were common across most countries included in the harmonized data (bathing, dressing, transferring, and toileting). Chronic conditions reflected the number of chronic conditions respondents had either been diagnosed with and/or reported (e.g., hypertension, cancer, respiratory disease, heart disease, diabetes, stroke, and arthritis). Only the four common ADLs (bath, dress, transfer, and toilet) and only two chronic conditions (heart disease and diabetes) were included in the harmonized good health index . To have a valid score for good health , a respondent had to have at least two non-missing responses. For each respondent, all the non-missing responses for the dimensions of health were summed and then rescaled to a 0-100 scale, normalizing the scale to the minimum and maximum sum possible. Preliminary analysis of the good health index showed that it had good construct validity (McEniry, 2009 ). For the technical report and details about the development of the Good Health measure, see McEniry, Moen, & McDermott ( 2013 ). The authors of the current study decided to use the harmonized good health index constructed for the RELATE study without any changes. However, they chose to referred the good health index as the Optimal Health Index to capture its multiple dimensions of health and to better highlight the focus of the current study. As stated earlier, the score of the good health index or optimal health index , as is referred to in the rest of the paper, was rescaled to 0-100, where a score of “100” or 100% represents “optimal” health without disability or health risks. Higher scores move people closer to optimal health, while lower scores move them away from optimal health. For example, individuals with optimal health index scores of 80 live at 80% of their optimal health; 20% of their optimal health is affected by adverse health conditions (Blue Cross Blue Shield, 2019 ). Covariates/Independent Variables: The current study assessed several demographic and socioeconomic indicators generally known to affect health status. However, only the indicators that had relationships with the main dependent variable, optimal health, were considered for further analyses. Given this premise, the demographic and socioeconomic indicators included for further analyses were sex, residential location, age, marital status, education level, number of people in the household, and wealth index (note: household characteristics and assets were used to construct a wealth index – piped water, toilet inside the house, refrigerator, gas/electricity for cooking, telephone, washing machine, cooking stove, and television). All analyses were conducted using SPSS Version 29. Results Table 1 shows the demographic and socioeconomic characteristics of the respondents in each region. The percentage of respondents differed by sex across the regions; the majority of respondents in the North were male, but females composed the majority in the South. The proportion of married people in the North was greater (75%) than that in the South (54%). Another demographic indicator that differed across the regions was location of residence. While slightly more than half of the South respondents lived in rural areas, the overwhelming majority of respondents from the North lived in rural areas. The households in the North are comparatively larger, with eight versus five people per household. In terms of education, the ratio of primary school respondents in the South to those in the North is 5:1, the ratio for secondary school is 6:1, and the ratio of postsecondary education is 3:1. The majority of respondents in both the North and the South fall in the lower bracket of the wealth indicator variable. However, the proportions that fall within the high- and middle-level wealth brackets in the South are greater than those in the North. The proportion for the former bracket is five times, while that for the latter is seven times. Table 1 Demographic and Socioeconomic Characteristics of the Respondents by Region Variable Percentage within each region who are … South Region n = 3788 (80.2%) North Region n = 936 (19.8%) Gender Male 46.7% 61.9% Female 53.3% 38.1% Residence Urban 46.1% 18.9% Rural 53.9% 81.1% Age 50–59 40.1% 38.8% 60–69 26.9% 30.6% 70–79 23.0% 21.5% 80+ 10.0% 9.2% Marital Status Currently Married 54.3% 74.8% Currently Not Married 47.7% 25.2% Level of Education No School 47.5% 89.4% Primary 14.7% 3.2% Secondary 28.5% 4.5% Post-Secondary 9.3% 2.8% Household Size M = 4.96 (SD = 2.94) M = 8.43 (SD = 3.65) Wealth Index High 11.6% 2.3% Middle 18.3% 2.8% Low 70.2% 94.9% Smoke No 78.2% 55.8% Yes 21.8% 44.2% Drink No 42.2% 38.3% Yes 57.8% 61.7% Vigorous Exercise No 59.1% 37.7% Yes 40.9% 62.3% Health Use (past 12 months?) No 31.9% 55.6% Yes 68.1% 44.4% Self-Reported Health Status Good 81.6% 89.9% Poor 18.4% 10.1% BMI Categories Severely Underweight (< 16.5) 3.9% 6.8% Underweight (16.5 to < 18.5) 10.3% 12.9% Normal Weight (18.5 to 30) 19.9% 13.7% Obese (30 >) 11.3% 2.8% Number of Chronic Diseases 0 condition 68.0% 87.5% 1 condition 23.7% 10.7% 2 or more conditions 8.3% 1.8% Number of ADLs 0 ADL 64.0% 63.9% 1 ADL 17.5% 18.2% 2 ADLs 8.5% 9.7% 3 or more ADLs 10.1% 8.2% Optimal Health Index M = 81.68 (SD = 18.51) M = 85.90 (SD = 16.51) Four lifestyle choice variables—drinking, smoking, rigorous exercise, and utilization of health services—were considered in the study. The results show that the majority of respondents in the North and the South drink but that the majority in both regions do not smoke. For those who smoke, the proportion in the North is twice the proportion in the South. A greater percentage (62% vs. 41%) of respondents in the North engaged in activities that required physical exercise than did those in the South. For the self-rated health outcome variable, a greater proportion of residents in the North than in the South rated their health status as good. The percentage of respondents who reported having difficulties with activities of daily (ADLs) did not differ much by region, but the proportion of respondents who reported having at least one chronic disease was greater in the South (30%) than in the North (12.5%). The body mass index (BMI) data in Table 1 show that the majority of respondents in both regions had a normal BMI, but the proportion of obese respondents in the southern region was 4 times greater than that in the northern region. In regard to health service use, the proportion of residents in the North who saw health care professionals within the last 12 months of the survey was lower than that in the South. The national mean score for the optimal health index was M = 82.45 (SD = 18.24). The North had a greater mean score for optimal health (M = 85.90, SD = 16.51) than did the South (M = 81.68, SD = 18.51), t (4300) = 6.33, p < .001. We performed two separate regressions, one with the data for the North only and one with the data for the South only, to determine the predictors of health outcome for each region. Variables that had a significant correlation with a particular region’s optimal health index were included in that region’s regression model. Five variables correlated with the index for the North, while nine correlated with the index for the South. The results of the regression analyses are shown in Table 2 . Table 2 Multiple Regression: Indicators of Optimal Health by Region North South Variable B SE B Beta B SE B Beta Constant 116.884 5.388 107.85 2.83 Gender -5.459 1.934 − .123 -3.726 .951 − .084* Residential Location .874 .866 .020 Marital Status .633 2.013 .014 -1.035 .921 − .023 Age − .520 .069 − .264* − .405 .039 − .196* Education .138 .084 .034 Wealth − .806 .264 − .063 Smoking -4.343 1.420 − .105 Vigorous Exercise 6.692 1.494 .158* 5.914 .808 .131* Drinking − .507 .782 − .011 Health Use -4.656 .808 − .097* * p < .001 The regression analyses showed that two variables, exercise, and age, had significant influences on optimal health in both the North and South regions. The beta value for vigorous exercise represents the shift in the change in optimal health scores if a respondent “did not perform vigorous exercise” (baseline category) compared to if a respondent “did vigorous exercise”. In both regions, the test score for vigorous exercise was significant and positive, meaning that the change in optimal health increased as a respondent “changed” from “not exercising vigorously” to “exercising vigorously.” The other variable that had a significant influence on optimal health in both regions was age. Unlike the vigorous exercise variable, age has an inverse relationship with optimal health in both regions. The inverse relationship implies that as the age of the respondents increases, their optimal health decreases. The South had two additional variables that were significant – healthcare use or “contact with health professionals” and sex. Both variables have inverse relationships with optimal health in the South. For sex, the baseline category is men, and since the sex test score is negative, the odds of optimal health decrease when the respondent is female. “No contact” was the baseline category for health use; a negative test score implied that respondents who contacted health professionals had comparatively low optimal health. Discussion Achieving optimal health requires a balance in all dimensions (Brussow, 2013) of health. Health, as defined by the WHO, goes beyond the absence of diseases and infirmaries; it includes physical, social, psychological, nutritional, intellectual, and spiritual dimensions (Constitution of the World Health Organization, n.d.). Because health and its outcomes are expansive and subjective, there is no single definition for overall optimal health and no universal measure for capturing it. Thus, optimal health measures are often unique to particular studies. For this study, the index of optimal health included respondents’ scores on four indicators—BMI, ADLs, chronic health conditions, and self-reports. The respondents’ scores were summed and then rescaled to a 0-100 scale, where 100% indicates perfect optimal health. As shown in Table 1 , the optimal health index for the North is greater (86) than that for the South (82). According to the index scores, 86 percent of people in the North are living at their optimal health, while 82 percent in the South are living at their optimal health. Several demographic and socioeconomic factors were added to the models to assess their impact on health outcomes. The results show that several of the key socioeconomic factors often cited in the literature as determinants of health did not have an impact on the health outcome of our older adult respondents. Unlike some of the earlier studies on health in Ghana (Abalo, Mensah, Agyemang-Duah, Peprah, Budu, Gyasi, et al., 2018; Buor, 2004 ; Peters, Baker, Dieckmann, Leon, & Collins, 2010 ), the present study did not find any significant relationships between health outcomes and household income, wealth, level of poverty, marital status, or level of education. However, it was found that healthcare use or contact with health professionals significantly influences health outcomes, but the influence was only in the southern region. While most health studies find a positive relationship between healthcare access and health outcomes (Shi, 2012 ; Starfield, Shi, & Macinko, 2005 ), the current research shows an inverse relationship between the two. One possible reason for the inverse relationship could be the health status of the respondents. Compared to those from the North, more respondents from the South rated their health as poor. The South also has a greater proportion of people who are obese and/or have more chronic health problems. A negative relationship could mean that respondents who were in poor health and needed medical diagnosis and cure for their diseases contacted medical professionals. Another factor that has a significant influence on health outcomes in the South is sex. The current study showed that women in the South have poorer health outcomes than men. While older women face the same health conditions that affect older men, such as fractures and cardiovascular diseases, they also tend to experience multiple chronic health conditions, such as osteoporosis, hypertension, arthritis, and depression (Harvard Health Publishing, 2019 ; Crimmins, Shim, Zhang, & Kim, 2019 ; Gerberding, 2004 ). In addition to these biological processes, gender and sex combined to negatively impact the health of women and girls in Ghana and in most developing countries. In these countries, several women lack decision-making powers, have a high illiteracy rate, lack access to health information and are limited by multiple gender norms that make them vulnerable to illnesses (WHO, 2021 ). While healthcare use and sex were significant only for health outcomes in the South region, exercise and age were significant across both regions. We should note that the survey question for vigorous exercise was framed within the context of work. The question was “Does your work involve vigorous-intensity activity that causes large increases in breathing or heart rate, [such as heavy lifting, digging or chopping wood] for at least 10 minutes continuously?”. This means the work that most respondents do provides them with opportunities for physical activities that have a positive impact on their health. The majority of Ghanaians work in the primary sectors of the economy—agriculture, forestry, fishing, mining, and quarrying. The agricultural sector alone employs approximately 45 percent of the population (Ministry of Food and Agriculture (2024). Several women are employed in trading, small-scale manufacturing, and food processing (Amu, 2005 ). In addition to their economic roles, traditional gender roles assign home and most house chores to women (gender roles, work, and structural transformation … n.d.). The findings of the present research suggest that the physical and manual labor associated with primary economic activities and housework have moderating effects on the health outcomes of respondents across regions. With an optimal health index score of 86 percent in the North and 82 percent in the South, we can argue that a vast majority of our respondents across both regions are currently in good health. Nonetheless, natural aging processes and associated health conditions are inevitable. The current study showed that health outcomes decline as respondents age. As people age, they are more likely to encounter multiple health conditions (e.g., frailty, osteoarthritis, urinary incontinence, dementia, etc.) that negatively impact their health (WHO, 2022 ). Aging processes and health experiences differ for individuals, but generally, decreasing health often leads to decreasing physical and mental capacity among older people, particularly in advanced age (WHO, 2022 ). Conclusion This study explored how Ghana’s North‒South regional divide manifests in the health outcomes of older people. The literature on healthcare suggests that the North lacks healthcare infrastructure and personnel and is more likely to carry a heavier health burden and poorer health outcomes. The results of the present study showed the opposite results. Compared to those in the southern region, health outcomes in the northern region were comparatively better. In both regions, optimal health was substantially influenced by work-related physical activities. Older adults whose work involved vigorous activities had better health outcomes. However, with increasing age, the health outcome of older adults in both regions decreased. The current study showed that healthcare use and sex influence health outcomes, particularly in the southern region. The study suggested that older adult women in the South had poorer health outcomes than did their male counterparts. It was also found that older adults in the South who were in poor health used available healthcare services. Limitations The data used for the current study were harmonized across different countries to meet the needs of another study. Data harmonization sometimes requires the exclusion of some data, a situation that may affect how variables relate to each other. Nonetheless, the optimal health index variable used in the current study included broad and multiple dimensions of health indicators that were appropriate and applicable to the current study. Another limitation of the study is the use of cross-sectional data. Cross-sectional data provide a snapshot in time, which limits the ability of these methods to establish causal associations between variables. Several of the survey items required respondents to provide subjective interpretations of life situations and recall past activities. Furthermore, the North-South regions are artificial geographic creations. The cultural and life experiences captured by the survey data cut across the regions; therefore, the artificial North‒South regions likely affected the spatial distribution of the survey data. Thus, the cross-sectional and recalled data and the artificial regional demarcation may introduce bias and could explain the presence and/or lack of effects of some variables. Recommendations and Policy Implications Despite the limitations of the current study, it provides useful information on health outcomes for older people in Ghana. The current study identified several socioeconomic determinants that can influence health outcomes across geographic regions. The study also revealed the nuances of the concept of healthcare determinants, resources, and health outcomes. It is logical to presume that the South, which has relatively better health resources, would have better health outcomes than the North. However, the current study has shown that the link between healthcare resources and healthiness has subtle nuances that warrant attention from policymakers and healthcare providers. The findings of the current study can help policymakers and healthcare providers account for factors such as age, sex, health use and exercise, as they seek to establish healthcare policies and provide healthcare services to older adults. The current study reemphasized the positive impact of physical exercise on health outcomes. Participation in vigorous work-related exercises is found to lead to positive health outcomes for older adults. While the physical exercise studied in the current research was within the context of older people’s work life, work exercise routines can be harnessed to keep older people active and healthy as they age. For instance, policymakers can design programs that can encourage older people to take up gardening and other activities that require mild physical activity, such as hobbies and/or part-time work, as they “retire” from their physically demanding work that is currently keeping them healthy. Working in the garden or on hobbies would mimic some of the physical activities they currently perform. Gardening can result in a level of physical exercise that can help older adults maintain their health status as they age. References Abalo, E. M., Mensah, C. M., Agyemang-Duah, W., Peprah, P., Budu, H. I., Gyasi, R. M., et al., (2018). Geographical differences in perceived health status among older adults in Ghana: Do gender and educational status matter? Gerontology & Geriatric Medicine, 4 . 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Working paper #2009-04, Center for Demography & Ecology, University of Wisconsin-Madison. McEniry, M. Moen, S. & McDermott, J. (2013). Methods report on the compilation of the RELATE cross-national dataset on older adults from 20 low-, middle- and high-income countries . Ann Arbor, MI: Interuniversity Consortium for Political and Social Research [distributor]. McEniry, M. (2015). Research on Early Life and Aging Trends and Effects (RELATE): A Cross- national study . Ann Arbor, MI: Interuniversity Consortium for Political and Social Research [distributor]. Retrieved from: https://doi.org/10.3886/ICPSR34241.v2 Ministry of Food and Agriculture (MOFA). Ministry of Agriculture (2024), 45 percent of Ghanaians are employed in the agricultural sector. https://mofa.gov.gh/site/components-eugap/65-agribusiness/investment-areas/354-agric-investment-guide Ministry of Health (2007). Service availability mapping (SAM). Retrieved from: https://www.who.int/healthinfo/systems/sara_reports/en/index1.html Molini, V. & Pierella, P. (2015). Poverty Reduction in Ghana – Progress and Challenges: Overview . Ghana in Brief. World Bank, Washington, DC. Author. https://openknowledge.worldbank.org/bitstream/handle/10986/22733/K8480.pdf?sequence=4&isAllowed=y Musah, A., Ibrahim, M. & Adam, I. O. (2016). Poverty, income diversification and welfare in Northern Ghana. Journal of African Political Economy & Development, 1 . Retrieved from: http://afec-japed.com/wp-content/uploads/2017/05/Poverty-and-income-diversification.pdf Peters, E., Baker, D. P., Dieckmann, N. F., Leon, J. & Collins, J. (2010). Explaining the effect of education on health: A field study in Ghana. Psychological Science, 21 (10). 1369-1376. Retrieved from https://www.jstor.org/stable/pdf/41062491.pdf?refreqid=excelsior%3A1a9517653063888a2469982705d2907c Roberts, M., Mogan, C. & Asare, J. B. (2014). An overview of Ghana's mental health system: Results from an assessment using the World Health Organization's Assessment Instrument for Mental Health Systems (WHO-AIMS). International Journal of Mental Health System, (4) 8-16. doi: 10.1186/1752-4458-8-16. PMID: 24817908; PMCID: PMC4016652. Shi, L. (2012). The impact of primary care: A focused review. Scientifica , 2012. Siaw-Frimpong, M., Touray, S. & Sefa, N (2021). Capacity of intensive care units in Ghana. Journal of Critical Care, (61) 76-81. DOI: 10.1016/j.jcrc.2020.10.009. PMID: 33099204; PMCID: PMC7560159. Starfield, B., Shi, L., & Macinko, J. (2005). Contribution of primary care to health systems and health. Milbank Quarterly, 83 (3), 457–502. doi: 10.1111/j.1468-0009.2005.00409.x Tsikata, D. & Seini, W. (2004). Identities, Inequalities and Conflicts in Ghana. CRISE Working Paper 5. Oxford: Centre for Research on Inequality, Human Security and Ethnicity, Department of International Development, University of Oxford UNICEF Ghana (n.d.). Reaching out to those missing out on school. Retrieve from https://www.unicef.org/ghana/education.html WHO (n.d.). Determinants of health . https://www.who.int/hia/evidence/doh/en/ WHO (2021). Gender and health. https://www.who.int/news-room/questions-and-answers/item/gender-and-health WHO (2022). Aging and health. https://www.who.int/news-room/fact-sheets/detail/ageing-and-health#:~:text=Common%20conditions%20in%20older%20age,conditions%20at%20the%20same%20time. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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While the presence or absence of health services can boost or limit health outcomes for people within a geographic region, these outcomes are substantially influenced by the demographic, socioeconomic, and political characteristics of the region (World Health Organization, n.d.). An unequal distribution of health resources may not only result in poor health in certain parts of a country but also be a source of regional tension and agitation. Hence, our personal and collective obligation is to ensure a fair distribution of health services and outcomes across regions. This paper focuses on Ghana\u0026rsquo;s North‒South regional divide. Despite efforts by postcolonial political administrations over the years to bridge this gap, the literature shows that the divide still persists and is evident across several dimensions, including health. This study used the harmonized Research on Early Life and Aging Trends and Effects (RELATE) dataset to compare the health outcomes of respondents across regions and to determine the factors underlying the differences in health outcomes.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eBased on colonial history and the level of economic development, Ghana may be divided into two broad geographical regions: the North and the South (Tsikata \u0026amp; Seini, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Mancini, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The North comprises the five current administrative regions located in the northern part of the country (Northern, North East, Savannah, Upper East and Upper West regions), and the South comprises eleven other administrative regions (Ahafo, Ashanti, Bono, Bono East, Central, Eastern, Greater Accra, Oti, Volta, Western, and Western North). Several studies have shown that the North lags behind the South in several economic and health indicators (Cooke, Hague \u0026amp; McKay, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Molini \u0026amp; Pierella, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Musah, Ibrahim \u0026amp; Adam, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Shepherd, Gyimah-Boadi, Gariba, Plagerson \u0026amp; Musa, 2005).\u003c/p\u003e \u003cp\u003eWhile the country in general faces challenges in the healthcare system, the challenges are more daunting in the North. For instance, the North lags behind the South in terms of skilled health professionals. According to Ghana\u0026rsquo;s Ministry of Health (2020) human resource profile, the overwhelming majority of highly skilled health care professionals, such as doctors, nurses, pharmacists, and other allied health professionals, are concentrated in the southern region. For instance, the Korle Bu Teaching Hospital in the South alone has more than 25 percent of the country's doctors, while the entire North region has only seven percent. As a result of the unequal distribution, some areas in the North have a doctor-to-patient ratio of 1:18,257, whereas certain areas in the South have a ratio of 1:4,099. The situation is the same for nurses. The nurse‒patient ratio is 1:495 in most parts of the North, compared to 1:339 in the South (Ghana Health Service, 2023). Generally, Ghana has a significant shortage of critical care (ICU) beds, but the few beds in the country are located mainly in the South. Of the 71 adult ICU beds available, only 10 are located in the North at Tamale, and the rest are located in hospitals in the South (Siaw-Frimpong, Touray \u0026amp; Sefa, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Currently, there is no psychiatric hospital in the North. The three in the country are all located in the southern region (Roberts, Mogan \u0026amp; Asare, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, infrastructures that support good health are comparatively limited in the North. For instance, most areas in the North lack access to portable water and adequate health care facilities (Heyden-Perschon, 2005). Dalinjong, Wang and Homer (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) surveyed selected health facilities in rural areas in the North to assess their capacity to provide basic quality childbirth services. They found that only 14 percent of facilities had clean water, 36 percent had electricity and only seven percent had emergency transportation. Distance to health care facilities is critical in accessing healthcare, particularly emergency care. The WHO recommends that communities be within five kilometers of the nearest health facility. A study by Ashiagbor, Ofori-Asenso, Forkuo and Agyei-Frimpong (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) revealed that, on average, communities in the Ashanti Region (in the South) live approximately five kilometers from the nearest health facility. A similar study by Kuupiel, Adu, Bawontuo \u0026amp; Mashamba-Thompson (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) revealed that 85% of PHC clinics in the Upper East Region (in the North) are located more than 10 kilometers away from the nearest health facility.\u003c/p\u003e \u003cp\u003eFor the past two decades, Ghana has made progress toward better health (USAID, 2023). Nonetheless, the North continues to lag behind the South. The level of malnutrition among children under five years of age and the child mortality rate in the North are high, while the childhood vaccination rate is low (USAID, 2023). Approximately one-quarter of newborns in the North have adverse health outcomes, while fewer than four out of every 10 pregnant women have the required antenatal contact before delivery (Akum, Offei, Kpordoxah, Yeboah, Issah \u0026amp; Boah, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Given the general lack of health infrastructure and services in the North and the health outcomes of children and mothers, we can infer that the health outcomes of older people in the North lag behind those of their counterparts in the South.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eObjective of the Study\u003c/h2\u003e \u003cp\u003eGenerally, aging is associated with declining health. Age-related health conditions are related to physiological changes that occur in organ systems that reduce the body\u0026rsquo;s ability to fight infections and chronic illnesses (Flint \u0026amp; Tadi, 2023). Improving health care services and outcomes for older adults in the North and minimizing health disparities in general will require increased funding; improved infrastructure, equipment, and medical technology; training and development of the workforce; and public health education. Given Ghana\u0026rsquo;s current negative macroeconomic and financial situation (African Development Bank, 2024), the government may not have the resources to substantially improve healthcare services and health outcomes in the North sooner. In the absence of immediate access to medical resources, we can explore nonmedical factors or socioeconomic factors that can be influenced to mitigate the health outcomes of older people.\u003c/p\u003e \u003cp\u003eThe socioeconomic factors that influence health outcomes include \u0026ldquo;the conditions in which people are born, grow, work, live, and age, and the wider set of forces and systems shaping the conditions of daily life\u0026rdquo; (CSDH, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In some cases, socioeconomic factors have been found to be more influential in moderating health outcomes than genetic factors and access to health services (Office of Disease Prevention and Health Promotion, n.d.). Identifying critical socioeconomic factors and focusing currently limited resources on them can help improve health outcomes for older people. To this end, the current study draws on the harmonized Research on Early Life and Aging Trends and Effects (RELATE) survey data to identify the socioeconomic factors that influence health outcomes for older people in the North and South regions of Ghana and factors that may contribute to their differences.\u003c/p\u003e \u003c/div\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSource of Data\u003c/h2\u003e \u003cp\u003eThe data for this study were drawn from the Research on Early Life and Aging Trends and Effects (RELATE) study, a harmonized cross-sectional dataset with 147,278 respondents from 14 countries: Argentina, Barbados, Brazil, Chile, China, Costa Rica, Cuba, Ghana, India, Mexico, Puerto Rico, Russia, South Africa, and Uruguay (McEniry, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Samples from the following surveys were harmonized in the RELATE database: the Costa Rican Study of Longevity and Healthy Aging (CRELES); Puerto Rican Elderly: Health Conditions (PREHCO); the WHO Study on Global Aging and Adult Health (WHO‐SAGE) in India, Ghana, South Africa, Mexico, and Russia; the China Health and Nutrition Study (CHNS); and the Study of Aging Survey on Health and Well Being of Elders (SABE) for Barbados, Brazil, Chile, Cuba, Mexico and Uruguay. This study pooled all 4724 respondents from Ghana who were included in the harmonized survey. In fact, only respondents aged 50 years or older were included in the harmonized survey. Approximately 20% of the sample for this study came from the North, and 80% came from the South. However, the samples were not weighted for analyses because the North and the South comprised 17% and 83%, respectively, of the actual Ghana national population (Ghana Statistical Service, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDependent Variable\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e\u003cem\u003eGood Health Index and Optimal Health Index\u003c/em\u003e:\u003c/h2\u003e \u003cp\u003eThe primary dependent variable for this study was \u0026ldquo;overall health outcome\u0026rdquo;, identified in the dataset as \u003cem\u003eGood Health\u003c/em\u003e. The index for good health was constructed using four dimensions of health: (1) self-reported good health status, (2) BMI score, (3) frailty or functionality score, and (4) chronic health conditions. \u003cem\u003eSelf-reported good health status\u003c/em\u003e was a compilation of responses of \u0026ldquo;very good\u0026rdquo;, \u0026ldquo;good\u0026rdquo; or \u0026ldquo;moderate\u0026rdquo; to the question \u0026ldquo;In general, how would you rate your health today?\u0026rdquo;. \u003cem\u003eBMI\u003c/em\u003e indicates the respondent\u0026rsquo;s body mass index (calculated using weight/height\u003csup\u003e2\u003c/sup\u003e). \u003cem\u003eFrailty or functionality\u003c/em\u003e was measured through self-reports of physical limitations or activities of daily living (ADLs). Functionality harmonized four ADLs that were common across most countries included in the harmonized data (bathing, dressing, transferring, and toileting). \u003cem\u003eChronic conditions\u003c/em\u003e reflected the number of chronic conditions respondents had either been diagnosed with and/or reported (e.g., hypertension, cancer, respiratory disease, heart disease, diabetes, stroke, and arthritis). Only the four common ADLs (bath, dress, transfer, and toilet) and only two chronic conditions (heart disease and diabetes) were included in the harmonized \u003cem\u003egood health index\u003c/em\u003e. To have a valid score for \u003cem\u003egood health\u003c/em\u003e, a respondent had to have at least two non-missing responses. For each respondent, all the non-missing responses for the dimensions of health were summed and then rescaled to a 0-100 scale, normalizing the scale to the minimum and maximum sum possible. Preliminary analysis of the \u003cem\u003egood health index\u003c/em\u003e showed that it had good construct validity (McEniry, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). For the technical report and details about the development of the Good Health measure, see McEniry, Moen, \u0026amp; McDermott (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe authors of the current study decided to use the harmonized \u003cem\u003egood health index\u003c/em\u003e constructed for the RELATE study without any changes. However, they chose to referred the \u003cem\u003egood health index\u003c/em\u003e as the \u003cem\u003eOptimal Health Index\u003c/em\u003e to capture its multiple dimensions of health and to better highlight the focus of the current study. As stated earlier, the score of the \u003cem\u003egood health index\u003c/em\u003e or \u003cem\u003eoptimal health index\u003c/em\u003e, as is referred to in the rest of the paper, was rescaled to 0-100, where a score of \u0026ldquo;100\u0026rdquo; or 100% represents \u0026ldquo;optimal\u0026rdquo; health without disability or health risks. Higher scores move people closer to optimal health, while lower scores move them away from optimal health. For example, individuals with optimal health index scores of 80 live at 80% of their optimal health; 20% of their optimal health is affected by adverse health conditions (Blue Cross Blue Shield, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCovariates/Independent Variables:\u003c/h2\u003e \u003cp\u003eThe current study assessed several demographic and socioeconomic indicators generally known to affect health status. However, only the indicators that had relationships with the main dependent variable, optimal health, were considered for further analyses. Given this premise, the demographic and socioeconomic indicators included for further analyses were sex, residential location, age, marital status, education level, number of people in the household, and wealth index (note: household characteristics and assets were used to construct a wealth index \u0026ndash; piped water, toilet inside the house, refrigerator, gas/electricity for cooking, telephone, washing machine, cooking stove, and television). All analyses were conducted using SPSS Version 29.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the demographic and socioeconomic characteristics of the respondents in each region. The percentage of respondents differed by sex across the regions; the majority of respondents in the North were male, but females composed the majority in the South. The proportion of married people in the North was greater (75%) than that in the South (54%). Another demographic indicator that differed across the regions was location of residence. While slightly more than half of the South respondents lived in rural areas, the overwhelming majority of respondents from the North lived in rural areas. The households in the North are comparatively larger, with eight versus five people per household. In terms of education, the ratio of primary school respondents in the South to those in the North is 5:1, the ratio for secondary school is 6:1, and the ratio of postsecondary education is 3:1. The majority of respondents in both the North and the South fall in the lower bracket of the wealth indicator variable. However, the proportions that fall within the high- and middle-level wealth brackets in the South are greater than those in the North. The proportion for the former bracket is five times, while that for the latter is seven times.\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\u003eDemographic and Socioeconomic Characteristics of the Respondents by Region\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \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\u003ePercentage within each region who are \u0026hellip;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSouth Region\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;3788 (80.2%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNorth Region\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;936 (19.8%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\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\u003e46.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.9%\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\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.9%\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\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\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\u003e40.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.8%\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\u003e60\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.6%\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\u003e23.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.5%\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\u003e10.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrently Married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.8%\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\u003eCurrently Not Married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel of Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89.4%\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\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.2%\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\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5%\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\u003ePost-Secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;4.96 (SD\u0026thinsp;=\u0026thinsp;2.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;8.43 (SD\u0026thinsp;=\u0026thinsp;3.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWealth Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.3%\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\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.8%\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\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke\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\u003e78.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.8%\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrink\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\u003e42.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.3%\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVigorous Exercise\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\u003e59.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.7%\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth Use (past 12 months?)\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\u003e31.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.6%\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-Reported Health Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89.9%\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\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI Categories\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeverely Underweight (\u0026lt;\u0026thinsp;16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.8%\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\u003eUnderweight (16.5 to \u0026lt;\u0026thinsp;18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.9%\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\u003eNormal Weight (18.5 to \u0026lt;\u0026thinsp;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.8%\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 (25 to \u0026gt;\u0026thinsp;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.7%\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 (30 \u0026gt;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Chronic Diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.5%\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\u003e1 condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.7%\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\u003e2 or more conditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of ADLs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 ADL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.9%\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\u003e1 ADL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.2%\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\u003e2 ADLs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.7%\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\u003e3 or more ADLs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOptimal Health Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;81.68 (SD\u0026thinsp;=\u0026thinsp;18.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;85.90 (SD\u0026thinsp;=\u0026thinsp;16.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFour lifestyle choice variables\u0026mdash;drinking, smoking, rigorous exercise, and utilization of health services\u0026mdash;were considered in the study. The results show that the majority of respondents in the North and the South drink but that the majority in both regions do not smoke. For those who smoke, the proportion in the North is twice the proportion in the South. A greater percentage (62% vs. 41%) of respondents in the North engaged in activities that required physical exercise than did those in the South. For the self-rated health outcome variable, a greater proportion of residents in the North than in the South rated their health status as good. The percentage of respondents who reported having difficulties with activities of daily (ADLs) did not differ much by region, but the proportion of respondents who reported having at least one chronic disease was greater in the South (30%) than in the North (12.5%). The body mass index (BMI) data in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e show that the majority of respondents in both regions had a normal BMI, but the proportion of obese respondents in the southern region was 4 times greater than that in the northern region. In regard to health service use, the proportion of residents in the North who saw health care professionals within the last 12 months of the survey was lower than that in the South. The national mean score for the optimal health index was M\u0026thinsp;=\u0026thinsp;82.45 (SD\u0026thinsp;=\u0026thinsp;18.24). The North had a greater mean score for optimal health (M\u0026thinsp;=\u0026thinsp;85.90, SD\u0026thinsp;=\u0026thinsp;16.51) than did the South (M\u0026thinsp;=\u0026thinsp;81.68, SD\u0026thinsp;=\u0026thinsp;18.51), \u003cem\u003et\u003c/em\u003e(4300)\u0026thinsp;=\u0026thinsp;6.33, p\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/p\u003e \u003cp\u003eWe performed two separate regressions, one with the data for the North only and one with the data for the South only, to determine the predictors of health outcome for each region. Variables that had a significant correlation with a particular region\u0026rsquo;s optimal health index were included in that region\u0026rsquo;s regression model. Five variables correlated with the index for the North, while nine correlated with the index for the South. The results of the regression analyses are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple Regression: Indicators of Optimal Health by Region\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eNorth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE B\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBeta\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eSE B\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eBeta\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e116.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e107.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.084*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidential Location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.264*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.196*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVigorous Exercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.158*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.131*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.097*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe regression analyses showed that two variables, exercise, and age, had significant influences on optimal health in both the North and South regions. The beta value for vigorous exercise represents the shift in the change in optimal health scores if a respondent \u0026ldquo;did not perform vigorous exercise\u0026rdquo; (baseline category) compared to if a respondent \u0026ldquo;did vigorous exercise\u0026rdquo;. In both regions, the test score for vigorous exercise was significant and positive, meaning that the change in optimal health increased as a respondent \u0026ldquo;changed\u0026rdquo; from \u0026ldquo;not exercising vigorously\u0026rdquo; to \u0026ldquo;exercising vigorously.\u0026rdquo; The other variable that had a significant influence on optimal health in both regions was age. Unlike the vigorous exercise variable, age has an inverse relationship with optimal health in both regions. The inverse relationship implies that as the age of the respondents increases, their optimal health decreases. The South had two additional variables that were significant \u0026ndash; healthcare use or \u0026ldquo;contact with health professionals\u0026rdquo; and sex. Both variables have inverse relationships with optimal health in the South. For sex, the baseline category is men, and since the sex test score is negative, the odds of optimal health decrease when the respondent is female. \u0026ldquo;No contact\u0026rdquo; was the baseline category for health use; a negative test score implied that respondents who contacted health professionals had comparatively low optimal health.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAchieving optimal health requires a balance in all dimensions (Brussow, 2013) of health. Health, as defined by the WHO, goes beyond the absence of diseases and infirmaries; it includes physical, social, psychological, nutritional, intellectual, and spiritual dimensions (Constitution of the World Health Organization, n.d.). Because health and its outcomes are expansive and subjective, there is no single definition for overall optimal health and no universal measure for capturing it. Thus, optimal health measures are often unique to particular studies. For this study, the index of optimal health included respondents\u0026rsquo; scores on four indicators\u0026mdash;BMI, ADLs, chronic health conditions, and self-reports. The respondents\u0026rsquo; scores were summed and then rescaled to a 0-100 scale, where 100% indicates perfect optimal health. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the optimal health index for the North is greater (86) than that for the South (82). According to the index scores, 86 percent of people in the North are living at their optimal health, while 82 percent in the South are living at their optimal health.\u003c/p\u003e \u003cp\u003eSeveral demographic and socioeconomic factors were added to the models to assess their impact on health outcomes. The results show that several of the key socioeconomic factors often cited in the literature as determinants of health did not have an impact on the health outcome of our older adult respondents. Unlike some of the earlier studies on health in Ghana (Abalo, Mensah, Agyemang-Duah, Peprah, Budu, Gyasi, et al., 2018; Buor, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Peters, Baker, Dieckmann, Leon, \u0026amp; Collins, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), the present study did not find any significant relationships between health outcomes and household income, wealth, level of poverty, marital status, or level of education. However, it was found that healthcare use or contact with health professionals significantly influences health outcomes, but the influence was only in the southern region. While most health studies find a positive relationship between healthcare access and health outcomes (Shi, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Starfield, Shi, \u0026amp; Macinko, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), the current research shows an inverse relationship between the two. One possible reason for the inverse relationship could be the health status of the respondents. Compared to those from the North, more respondents from the South rated their health as poor. The South also has a greater proportion of people who are obese and/or have more chronic health problems. A negative relationship could mean that respondents who were in poor health and needed medical diagnosis and cure for their diseases contacted medical professionals.\u003c/p\u003e \u003cp\u003eAnother factor that has a significant influence on health outcomes in the South is sex. The current study showed that women in the South have poorer health outcomes than men. While older women face the same health conditions that affect older men, such as fractures and cardiovascular diseases, they also tend to experience multiple chronic health conditions, such as osteoporosis, hypertension, arthritis, and depression (Harvard Health Publishing, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Crimmins, Shim, Zhang, \u0026amp; Kim, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gerberding, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). In addition to these biological processes, gender and sex combined to negatively impact the health of women and girls in Ghana and in most developing countries. In these countries, several women lack decision-making powers, have a high illiteracy rate, lack access to health information and are limited by multiple gender norms that make them vulnerable to illnesses (WHO, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile healthcare use and sex were significant only for health outcomes in the South region, exercise and age were significant across both regions. We should note that the survey question for vigorous exercise was framed within the context of work. The question was \u0026ldquo;Does your work involve vigorous-intensity activity that causes large increases in breathing or heart rate, [such as heavy lifting, digging or chopping wood] for at least 10 minutes continuously?\u0026rdquo;. This means the work that most respondents do provides them with opportunities for physical activities that have a positive impact on their health. The majority of Ghanaians work in the primary sectors of the economy\u0026mdash;agriculture, forestry, fishing, mining, and quarrying. The agricultural sector alone employs approximately 45 percent of the population (Ministry of Food and Agriculture (2024). Several women are employed in trading, small-scale manufacturing, and food processing (Amu, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In addition to their economic roles, traditional gender roles assign home and most house chores to women (gender roles, work, and structural transformation \u0026hellip; n.d.). The findings of the present research suggest that the physical and manual labor associated with primary economic activities and housework have moderating effects on the health outcomes of respondents across regions.\u003c/p\u003e \u003cp\u003eWith an optimal health index score of 86 percent in the North and 82 percent in the South, we can argue that a vast majority of our respondents across both regions are currently in good health. Nonetheless, natural aging processes and associated health conditions are inevitable. The current study showed that health outcomes decline as respondents age. As people age, they are more likely to encounter multiple health conditions (e.g., frailty, osteoarthritis, urinary incontinence, dementia, etc.) that negatively impact their health (WHO, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Aging processes and health experiences differ for individuals, but generally, decreasing health often leads to decreasing physical and mental capacity among older people, particularly in advanced age (WHO, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study explored how Ghana’s North‒South regional divide manifests in the health outcomes of older people. The literature on healthcare suggests that the North lacks healthcare infrastructure and personnel and is more likely to carry a heavier health burden and poorer health outcomes. The results of the present study showed the opposite results. Compared to those in the southern region, health outcomes in the northern region were comparatively better. In both regions, optimal health was substantially influenced by work-related physical activities. Older adults whose work involved vigorous activities had better health outcomes. However, with increasing age, the health outcome of older adults in both regions decreased. The current study showed that healthcare use and sex influence health outcomes, particularly in the southern region. The study suggested that older adult women in the South had poorer health outcomes than did their male counterparts. It was also found that older adults in the South who were in poor health used available healthcare services.\u003c/p\u003e "},{"header":"Limitations","content":"\u003cp\u003eThe data used for the current study were harmonized across different countries to meet the needs of another study. Data harmonization sometimes requires the exclusion of some data, a situation that may affect how variables relate to each other. Nonetheless, the optimal health index variable used in the current study included broad and multiple dimensions of health indicators that were appropriate and applicable to the current study. Another limitation of the study is the use of cross-sectional data. Cross-sectional data provide a snapshot in time, which limits the ability of these methods to establish causal associations between variables. Several of the survey items required respondents to provide subjective interpretations of life situations and recall past activities. Furthermore, the North-South regions are artificial geographic creations. The cultural and life experiences captured by the survey data cut across the regions; therefore, the artificial North‒South regions likely affected the spatial distribution of the survey data. Thus, the cross-sectional and recalled data and the artificial regional demarcation may introduce bias and could explain the presence and/or lack of effects of some variables.\u003c/p\u003e"},{"header":"Recommendations and Policy Implications","content":"\u003cp\u003eDespite the limitations of the current study, it provides useful information on health outcomes for older people in Ghana. The current study identified several socioeconomic determinants that can influence health outcomes across geographic regions. The study also revealed the nuances of the concept of healthcare determinants, resources, and health outcomes. It is logical to presume that the South, which has relatively better health resources, would have better health outcomes than the North. However, the current study has shown that the link between healthcare resources and healthiness has subtle nuances that warrant attention from policymakers and healthcare providers. The findings of the current study can help policymakers and healthcare providers account for factors such as age, sex, health use and exercise, as they seek to establish healthcare policies and provide healthcare services to older adults.\u003c/p\u003e\u003cp\u003eThe current study reemphasized the positive impact of physical exercise on health outcomes. Participation in vigorous work-related exercises is found to lead to positive health outcomes for older adults. While the physical exercise studied in the current research was within the context of older people’s work life, work exercise routines can be harnessed to keep older people active and healthy as they age. For instance, policymakers can design programs that can encourage older people to take up gardening and other activities that require mild physical activity, such as hobbies and/or part-time work, as they “retire” from their physically demanding work that is currently keeping them healthy. Working in the garden or on hobbies would mimic some of the physical activities they currently perform. Gardening can result in a level of physical exercise that can help older adults maintain their health status as they age.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbalo, E. M., Mensah, C. M., Agyemang-Duah, W., Peprah, P., Budu, H. I., Gyasi, R. M., et al., (2018). Geographical differences in perceived health status among older adults in Ghana: Do gender and educational status matter? \u003cem\u003eGerontology \u0026amp; Geriatric Medicine, 4\u003c/em\u003e. Retrieved from: https://www.ncbi.nlm.nih.gov/pubmed/30202775 \u003c/li\u003e\n\u003cli\u003eAkum, L. A. Offei, E. A., Kpordoxah, M. R. Yeboah, D. Issah, A. N. \u0026amp; Boah, M. (2023). Compliance with the World Health Organization\u0026apos;s 2016 prenatal care contact recommendation reduces the incidence rate of adverse birth outcomes among pregnant women in northern Ghana. \u003cem\u003ePLoS One. 18\u003c/em\u003e(6):e0285621. doi: 10.1371/journal.pone.0285621. PMID: 37289811; PMCID: PMC10249792.\u003c/li\u003e\n\u003cli\u003eAmu, N. J. (2005). The role of women in Ghana\u0026rsquo;s economy. https://library.fes.de/pdf-files/bueros/ghana/02990.pdf \u003c/li\u003e\n\u003cli\u003eAshiagbor, G. Ofori-Asenso, R., Forkuo, E. K. \u0026amp; Agyei-Frimpong, S. (2020). Measures of geographic accessibility to health care in the Ashanti Region of Ghana. Scientific African, (9), e00453\u003c/li\u003e\n\u003cli\u003eBlue Cross Blue Shield (2019). \u003cem\u003eBCBS Health Index\u003c/em\u003e. Retrieved from: https://www.bcbs.com/the-health-of-america/health-index \u003c/li\u003e\n\u003cli\u003eBuor, D. (2004). 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Women and infectious diseases. \u003cem\u003eEmerging Infectious Diseases, 10\u003c/em\u003e(11), 1965-1967. doi: 10.3201/eid1011.040800. PMID: 16010725; PMCID: PMC3329060.\u003c/li\u003e\n\u003cli\u003eGhana Statistical Service (2012). 2010 Population and Housing Census: Summary report of final results. Retrieved from: http://countrystat.org/country/GHA/contents/docs/2010_Population_Report.pdf\u003c/li\u003e\n\u003cli\u003eHarvard Health Publishing (2019). Mars vs. Venus: The gender gap in health. https://www.health.harvard.edu/newsletter_article/mars-vs-venus-the-gender-gap-in-health#:~:text=The%20health%20gap,likely%20to%20have%20aortic%20aneurysms.\u003c/li\u003e\n\u003cli\u003eKuupiel, D., Adu, K. M., Bawontuo, V. \u0026amp; Mashamba-Thompson, T. P. (2019). Geographical accessibility to district hospitals/medical laboratories for comprehensive antenatal point-of-care diagnostic services in the Upper East Region, Ghana. DOI:https://doi.org/10.1016/j.eclinm.2019.06.015\u003c/li\u003e\n\u003cli\u003eMancini, L. (2009). Comparative Trends in Ethno-Regional Inequalities in Ghana and Nigeria: Evidence from Demographic and Health Surveys. https://assets.publishing.service.gov.uk/media/57a08b69e5274a31e0000b30/workingpaper72.pdf \u003c/li\u003e\n\u003cli\u003eMcEniry, M. (2009). \u003cem\u003eMortality decline in the Twentieth Century, early life conditions and the \u003c/em\u003e\u003cem\u003ehealth of aging populations in the developing world\u003c/em\u003e. Working paper #2009-04, Center for Demography \u0026amp; Ecology, University of Wisconsin-Madison.\u003c/li\u003e\n\u003cli\u003eMcEniry, M. Moen, S. \u0026amp; McDermott, J. (2013). \u003cem\u003eMethods report on the compilation of the \u003c/em\u003e\u003cem\u003eRELATE cross-national dataset on older adults from 20 low-, middle- and high-income countries\u003c/em\u003e. Ann Arbor, MI: Interuniversity Consortium for Political and Social Research [distributor].\u003c/li\u003e\n\u003cli\u003eMcEniry, M. (2015). \u003cem\u003eResearch on Early Life and Aging Trends and Effects (RELATE): A Cross-\u003c/em\u003e\u003cem\u003enational study\u003c/em\u003e. Ann Arbor, MI: Interuniversity Consortium for Political and Social Research [distributor]. Retrieved from: https://doi.org/10.3886/ICPSR34241.v2\u003c/li\u003e\n\u003cli\u003eMinistry of Food and Agriculture (MOFA). Ministry of Agriculture (2024), 45 percent of Ghanaians are employed in the agricultural sector. https://mofa.gov.gh/site/components-eugap/65-agribusiness/investment-areas/354-agric-investment-guide \u003c/li\u003e\n\u003cli\u003eMinistry of Health (2007). Service availability mapping (SAM). Retrieved from: https://www.who.int/healthinfo/systems/sara_reports/en/index1.html \u003c/li\u003e\n\u003cli\u003eMolini, V. \u0026amp; Pierella, P. (2015). \u003cem\u003ePoverty Reduction in Ghana \u0026ndash; Progress and Challenges: \u003c/em\u003e\u003cem\u003eOverview\u003c/em\u003e. Ghana in Brief. World Bank, Washington, DC. Author. https://openknowledge.worldbank.org/bitstream/handle/10986/22733/K8480.pdf?sequence=4\u0026amp;isAllowed=y\u003c/li\u003e\n\u003cli\u003eMusah, A., Ibrahim, M. \u0026amp; Adam, I. O. (2016). Poverty, income diversification and welfare in Northern Ghana. \u003cem\u003eJournal of African Political Economy \u0026amp; Development, 1\u003c/em\u003e. Retrieved from: http://afec-japed.com/wp-content/uploads/2017/05/Poverty-and-income-diversification.pdf \u003c/li\u003e\n\u003cli\u003ePeters, E., Baker, D. P., Dieckmann, N. F., Leon, J. \u0026amp; Collins, J. (2010). Explaining the effect of education on health: A field study in Ghana. \u003cem\u003ePsychological Science, 21\u003c/em\u003e(10). 1369-1376. Retrieved from https://www.jstor.org/stable/pdf/41062491.pdf?refreqid=excelsior%3A1a9517653063888a2469982705d2907c \u003c/li\u003e\n\u003cli\u003eRoberts, M., Mogan, C. \u0026amp; Asare, J. B. (2014). An overview of Ghana\u0026apos;s mental health system: Results from an assessment using the World Health Organization\u0026apos;s Assessment Instrument for Mental Health Systems (WHO-AIMS). International Journal of Mental Health System, (4) 8-16. doi: 10.1186/1752-4458-8-16. PMID: 24817908; PMCID: PMC4016652.\u003c/li\u003e\n\u003cli\u003eShi, L. (2012). The impact of primary care: A focused review. \u003cem\u003eScientifica\u003c/em\u003e, 2012.\u003c/li\u003e\n\u003cli\u003eSiaw-Frimpong, M., Touray, S. \u0026amp; Sefa, N (2021). Capacity of intensive care units in Ghana. \u003cem\u003eJournal of Critical Care, (61) \u003c/em\u003e76-81. DOI: 10.1016/j.jcrc.2020.10.009. PMID: 33099204; PMCID: PMC7560159.\u003c/li\u003e\n\u003cli\u003eStarfield, B., Shi, L., \u0026amp; Macinko, J. (2005). Contribution of primary care to health systems and health. \u003cem\u003eMilbank Quarterly, 83\u003c/em\u003e(3), 457\u0026ndash;502. doi: 10.1111/j.1468-0009.2005.00409.x\u003c/li\u003e\n\u003cli\u003eTsikata, D. \u0026amp; Seini, W. (2004). Identities, Inequalities and Conflicts in Ghana. CRISE Working Paper 5. Oxford: Centre for Research on Inequality, Human Security and Ethnicity, Department of International Development, University of Oxford\u003c/li\u003e\n\u003cli\u003eUNICEF Ghana (n.d.). Reaching out to those missing out on school. Retrieve from https://www.unicef.org/ghana/education.html \u003c/li\u003e\n\u003cli\u003eWHO (n.d.). \u003cem\u003eDeterminants of health\u003c/em\u003e. https://www.who.int/hia/evidence/doh/en/ \u003c/li\u003e\n\u003cli\u003eWHO (2021). Gender and health. https://www.who.int/news-room/questions-and-answers/item/gender-and-health \u003c/li\u003e\n\u003cli\u003eWHO (2022). Aging and health. https://www.who.int/news-room/fact-sheets/detail/ageing-and-health#:~:text=Common%20conditions%20in%20older%20age,conditions%20at%20the%20same%20time.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Optimal Health, Ghana, Health Determinants, Regional Disparities, Health Outcomes","lastPublishedDoi":"10.21203/rs.3.rs-3971583/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3971583/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHealthcare services and outcomes are often not evenly distributed across geographic regions. This study used the harmonized Research on Early Life and Aging Trends and Effects (RELATE) dataset to compare health outcomes across the North‒South divide of Ghana and determine the factors underlying the difference in health outcomes. Although the literature indicates that the South has more health resources and better health indicators, the study found that health outcomes in the North were comparatively better than those in the South. According to the optimal health index scores, people in the North are living at 86 percent of their optimal health, while 82 percent are living at their optimal health in the South. In both regions, optimal health was substantially influenced by work-related physical activity and age. Older adults whose work involved vigorous activities had better health outcomes, but health outcomes decreased as people aged. The study also revealed that healthcare use and sex influence health outcomes, particularly in the southern region. The results showed that older adult women in the South have poorer health outcomes than their male counterparts. It has also been shown that older adults in the South are more likely to contact health professionals for medical conditions.\u003c/p\u003e","manuscriptTitle":"Geographic region and health outcomes: Examining health determinants and outcomes of older adults across Ghana’s North‒South divide","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-21 05:11:40","doi":"10.21203/rs.3.rs-3971583/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7add7a25-22a2-4906-a821-2c5ae63f4cb3","owner":[],"postedDate":"February 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28855110,"name":"Health Economics \u0026 Outcomes Research"},{"id":28855111,"name":"Social Work"},{"id":28855112,"name":"Social Policy"},{"id":28855113,"name":"Nursing"}],"tags":[],"updatedAt":"2024-02-21T05:11:40+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-21 05:11:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3971583","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3971583","identity":"rs-3971583","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}