Financial Inclusion,Health-Seeking Behavior,and Health Outcomes Among Older Adults in Ghana

Abstract

Purpose:

This study examines the associations between financial inclusion, health-seeking behavior, and health-related outcomes in older persons in Ghana.

Method:

Employing data from a 2016/2017 Aging, Health, Psychological Well-Being and Health-Seeking Behavior Study (N = 1,200; mean age = 66.2 years [standard deviation = 11.9], we estimated regression models of self-rated health (SRH), psychological distress (PD), and health-care use (HCU) on a variable representing compositional characteristics of financial inclusion.

Results:

Multivariate logistic and generalized Poisson models showed that financial inclusion is positively associated with SRH (β = .104, standard error [SE] = .033, p < .001) but inversely related to both PD (β = .038, SE = .032, p < .005) and HCU (β = −.006, SE = .009, p < .05) independent of other factors. However, after adjusting for socioeconomic and health-related factors, the associations were tempered and the effect of SRH decreased by 0.094 and PD increased by 0.065 points but HCU became statistically insignificant (β = −.020, SE = .0114, p > .05)

Conclusions:

Financial services inclusion profoundly appears to buffer against and retard health-related challenges in later life. Social and health policies targeted at improving the health outcomes of older people should include and build on the growing recognition of the importance of inclusive financial services and strategies.

Keywords

bank account financial inclusion health-seeking behavior older adults psychological distress self-rated health

The considerable increase in the absolute numbers and proportion of older people have led to profound public health and social policy concerns over the past few decades (Aboderin & Hoffman, 2015; Phillips & Feng, 2018; World Health Organization [WHO], 2015a). Studies have regularly noted that although demographic aging in itself should be celebrated, it is now the most distinctive demographic process and increasingly presents important socioeconomic and health implications (Domènech-Abella et al., 2017; Phillips & Feng, 2018; United Nations, 2015). As a major risk factor for declining health status, aging is often associated with increasing mental/cognitive impairments and various forms of disabilities including mobility deficiencies and limitations of daily living tasks (Gyasi & Phillips, 2018b; MacKenbach & McKee, 2013; McCracken & Phillips, 2017; WHO, 2015a). While innovative strategies such as effective engagement in and sustained access to financial services and instruments for older persons are required, these remain a major global socioeconomic and political challenge in the aging process, especially in low- and middle-income countries (LMICs) (McCracken & Phillips, 2017; WHO, 2015a).

Gerontological research, especially involving studies in developed countries, has increasingly reported the potential effects of social and economic opportunities such as financial inclusion on health outcomes in later life (Aguila, Angrisani, & Blanco, 2016; Agrigoroaei, Lee-Attardo & Lachman, 2017). The ability to maintain reasonable financial competence such as having access to a bank and chequing account, involvement in a credit union (microfinance), and having a mobile money account and access to credit through loans from banking and nonbanking systems can be a relevant determinant of health among older people (Agrigoroaei et al., 2017; Aguila et al., 2016; Krause & Bastida, 2011; Mensah & Dzokoto, 2011). Some evidence also suggests that individuals in lower income settings who are involved in informal financial instruments, such as membership of co-operative credit union and operating a Susu ¹ rotating savings account (which is less formal account than traditional bank account), have higher odds of reporting better health outcomes (Bank of Ghana, 2018; Goldberg, 2014). Many sub-Saharan African nations have started to evaluate the effects of financial literacy training for young people to empower them with good savings habits for their future life course and also to assist their older parents’ spending and savings patterns (Adam, Boadu, & Frimpong, 2018; Goldberg, 2014). However, policies on this area remain slim although it is acknowledged that developing conscious interests in providing evidence-based policies such as financial inclusion to address age-related health challenges is crucial (WHO, 2015a).

In many richer countries, evidence is building about the effects of financial inclusion and exclusion on older persons’ health outcomes, but, regrettably, the topic remains very underinvestigated in most LMICs and in sub-Saharan Africa in particular (Goldberg, 2014; Mensah & Dzokoto, 2011). Exploring these important relationships in a new and innovative context is crucial due to this region’s distinct socioeconomic characteristics, levels of technological advancement (Aker, Boumnijel, McClelland, & Tierney, 2011), and its imminent considerable demographic aging. Drawing on the capability framework, this study examines the associations among financial inclusion, health services use, and health outcomes among noninstitutionalized older Ghanaians. As well as addressing the knowledge gap in the literature, the findings have the potential to inform policy for older persons’ involvement in financial activities and to safeguard their health outcomes. This will be crucial in rapidly aging environments such as sub-Saharan Africa where the pace of growth of older populations is currently dramatic (WHO, 2015b).

Theoretical Framework

Capability theory, particularly associated with Sen (1999), has contributed to research on financial inclusion and health by guiding specific economic opportunities and empowerment particularly among older populations (Allmark & Machaczek, 2015). The capability perspective provides a context for analyzing financial capability which could provide practical implications for public health and primary care for populations (Nussbaum, 2011; Robeyns, 2005). Financial capability reflects people’s ability to maintain reasonable financial alertness; and having some finances to manage is better to deal with health risks than not having much (Allmark, Baxter, Goyder, Guillaume, & Crofton-Martin, 2013; Atkinson, 2008). Financial capability provides opportunities for older people to take greater control of their finances, external environments, and be able to manage economic resources better and to adopt desired lifestyles and health outcomes (Allmark & Machaczek, 2015; Manor, Matthews, & Power, 2000). Although being financially capable may relate to a wide range of socioeconomic factors, it has a greater influence on mental/physical health and health-seeking behavior (Nussbaum, 2011).

The intersecting subjects of social gerontology and health research address important relationships between growing older and many aspects of health status and change. These include psychological distress (PD; Cheng, Chan, & Lo, 2017; Giné-Garriga, Roqué-Fíguls, Coll-Planas, Sitjà-Rabert, & Salvà, 2014), self-rated health (SRH; Akuamoah-Boateng, 2013; Ameh, Gómez-Olivé, Kahn, Tollman, & Klipstein-Grobusch, 2014; Garatachea et al., 2015), and health services utilization (Ameh et al., 2014; Cameron, Song, Manheim, & Dunlop, 2010; Song, Chang, Manheim, & Dunlop, 2006). It has been noted that population aging can contribute to a rising number of older adults with various types of disability, which adds a greater potential burden to many already strained health- and social-care systems (MacKenbach & McKee, 2013; McCracken & Phillips, 2017; WHO, 2015a). However, how well older persons are equipped socially and financially is very likely to influence their interaction with health services and what their health outcomes might be (Gyasi, 2018). Several empirical studies conducted outside sub-Saharan Africa have documented a positive relationship between financial inclusion and mental health and well-being of populations. Others have noted that safeguarding older adults’ knowledge, access, and the ability to deal with their finances will directly impact on their health outcomes (Agrigoroaei et al., 2017; Aguila et al., 2016; Allmark & Machaczek, 2015). Unfortunately, these important relationships are almost totally noninvestigated in most sub-Saharan African countries due to many institutional, cultural, and socioeconomic challenges.

In their pioneering work on older Hispanics in the United States, Aguila, Angrisani, & Blanco (2016) concluded that increasing financial sector participation for minority and more disadvantaged socioeconomic status groups such as older people may have positive implications for well-being and also help to reduce health disparities. For example, bank account ownership was associated with better mental health although no effect was established on physical health (Aguila et al., 2016). Other studies in the United States (Finkelstein et al., 2012) and Ghana (Gyasi, Phillips, & Buor, 2018) note that some forms of financial inclusion such as ownership of a bank account and access to health insurance are critical to providing individuals and communities with financial protection. This may reduce cognitive stress and improve mental health and the general well-being. Research in the United Kingdom has also noted positive linkages between individuals’ abilities to manage and to take control of their finances and their psychological comfort (Taylor, Jenkins, & Sacker, 2009, 2011). Other similar studies relate bank account ownership and financial inclusiveness to improved financial capability which has clear and important implications for dealing with daily transaction costs, liquidity constraints, saving behavior, and matters such as financial preparedness for retirement and personal security (Carbo, Gardener, & Molyneux, 2005; Clark & d’Ambrosio, 2003; Mullainathan & Shafir, 2011). Having a bank account may also provide the capacity to develop financial awareness and alertness. However, among older populations in particular, with declining scores on instrumental activities of daily living (IADLs) and overall greater health challenges, an individual’s ability to manage finances is often seen as a first area to decline (McCracken & Phillips, 2017). At the community level, various local and mini-savings practice have emerged which are particularly seen in rural communities in Ghana and several other sub-Saharan African settings. Membership of “Susu” operations and mobile money account ownership have provided various financial services for older people, who often receive irregular incomes, to gradually save and accumulate money for their future commitments (Bank of Ghana, 2018; Gyasi, 2018; U.S. Agency for International Development, 2013). This local service model provides an important alternative to the larger formal financial sector and may compensate for the frequent deficiencies in mainstream financial sector coverage and participation in such places.

Nevertheless, the literature is almost devoid of such matters relating to financial inclusiveness and older persons in most LMICs. Therefore, it is unclear how financial inclusion or exclusion may impact subjective health status, stress and psychological health, and health services utilization, especially among vulnerable and disadvantaged groups in the sub-Saharan African context. Exploring these relationships is important as they are groups who may have irregular income sources and encounter barriers to participation in the formal financial sector. This will likely become of ever-increasing importance, given that global financial regulation increasingly makes the opening of bank accounts ever more difficult for those without established financial histories and official proof of identity, by definition excluding many persons in the informal or low-income sectors (McCracken & Phillips, 2017). In particular, in sub-Saharan Africa, the important trajectories and relations between financial inclusion and health remain almost wholly unexamined and the contextual evidence on this topic remains minimal. The present study, therefore, examined the specific associations of overall financial inclusion with older individuals’ SRH, psychological health outcomes, and health services use in Ghana. Understanding these dynamics is likely to be crucial for devising targeted interventions to protect the most vulnerable in the rapidly aging populations in many LMICs.

Method

Sample and Data

This analysis used data from a probability-based sample survey of community-dwelling older Ghanaian adults aged 50 years or older who participated in an Aging, Health, Psychological Well-Being and Health-Seeking Behavior Study (AHPWHB; Gyasi, 2018). The AHPWHB study was conducted between July 2016 and February 2017 in the Ashanti Region of Ghana. The region was chosen for this study for three key factors: First, it is the most populous of Ghana’s 10 regions, and it is also one of the country’s major cosmopolitan and heterogeneous regions, illustrating diverse demographic, cultural, and religious characteristics. Second, being geographically located at the center of the country, the region is considered the main nodal region and has vibrant commercial activities which attract people to settle from other parts of the country and beyond. Third, and importantly, Ghana Statistical Service’s (GSS; 2012) report on population and housing census shows that Ashanti Region is home to the highest proportion (17.5%) of the nation’s older population, making it the ideal setting for the study.

Although the aging process is often perceived as largely biological, it is crucially highly socially influenced, subject to the attitudes, conventions, and support for people at different times in their life course. The categorization of what constitutes being “aged” varies considerably between and among countries and also over time, with important social and workforce implications. In the Ghanaian context, the Government of Ghana specifies 60 as the statutory age of retirement (GSS, 2012), but by far, the greater proportion of older people neither expect nor enter formal retirement and its associated benefits because the labor market is dominated by low-earning subsistence self-employment in the informal sector (GSS, 2013; International Labour Organization, 2015; Vasco & Pierella, 2015). Moreover, employment growth in Ghana has largely occurred in the informal sector, with this sector representing 88% of employment in 2013. These activities often involve arduous nonmechanized or labor-intensive primary economic activities which often make many in the older groups vulnerable to many health challenges, and these often manifest themselves at relatively younger ages.

Indeed, the WHO’s (2018) recent report estimates Ghana’s average life expectancy at 63.4 years (62.5 years for males and 64.4 years for females). Because of this relatively shorter life expectancy and early onset of ill-health, as well as the double burden of noncommunicable and infectious diseases compared with richer countries, we define individuals aged 50 years or older as “older persons.” Indeed, “young olds” aged 50–60 constitute a key contemporary target population to be addressed for possible preventive measures to tackle health-related problems in the coming decades (Poscia, Landi, & Collamati, 2015; WHO, 2015b). This is not an unusual definition, but many recent gerontology studies including the Minimum Data Set project on aging and many other regional studies, including the WHO’s Study on Global Aging and Adult Health in five developing countries, including Ghana, adopted age 50+ to define older persons (see, e.g., Biritwum, Mensah, Yawson, & Minicuci, 2013). While acknowledging it internationally as a relatively low age, this study following these international and low-income regional-based surveys adopted age 50 as the minimum threshold for older persons.

The sample comprised individuals who met the inclusion criteria in six randomly sampled districts which fully represented the socioeconomic and cultural diversities of the country. Details of the sampling procedure have been reported elsewhere (Gyasi, 2019; Gyasi & Phillips, 2018a, 2018b, 2019; Gyasi, Phillips, & Abass, 2018; Gyasi, Phillips, & Amoah, 2018, Gyasi et al., 2018; Gyasi, Phillips, & David, 2019). A multistage stratified sampling procedure was used to reflect the heterogeneous population and socioeconomic characteristics of people who have settled in the different parts of the Ashanti Region. In the initial sampling stage, three subregional areas of the northern, middle, and southern sectors of the region were defined as primary sampling units based on their differences in demographic, cultural, and geographic characteristics. Two districts in each subregion were randomly selected, with all districts having equal chances of selection. Urban and rural sectors were identified in each selected district based on the GSS’s (2012) classification. In total, 24 communities (9 urban and 15 rural) were selected for the survey.

The sample size was estimated using a formula, assuming 5% margin of error, 95% confidence interval, design effect of 1.5, 5% and 15% of type 1 and type 2 errors, respectively, and a conservative prevalence of 50% (because the actual proportion of people aged 50+ years in the selected areas was unknown). The required sample size was, therefore, computed to be 901, but considering a 35% nonresponse, the final proposed sample size for this study was approximately 1,219. Moreover, the statistical power calculation revealed that the sample size had 85% power to detect an odds ratio of ≥2. In the final stage of the selection process, 1,247 older persons were selected by systematic random sampling with the sampling interval varying by the relative size of the study communities. Of 1,247 approached, 1,219 (97.8%) were eligible to participate. Of these eligible participants, 19 declined to participate in the study yielding a study sample of 1,200 representing a response rate of 98.4%. Interviews were via an interviewer-administered questionnaire conducted by trained research assistants with experience in health-related research. The survey questionnaire was developed in English and then translated into Twi (the major local dialect) and back-translated into English to ensure consistency in the meaning of items following WHO translation guidelines for assessment of instruments (Üstun, Chatterji, Mechbal, Murray, & WHS Collaborating Groups, 2005).

Human Subjects’ Protection

In line with the Declaration of Helsinki (Carlson, Boyd, & Webb, 2004; World Medical Association, 1964), the Human Subjects Certification was received ahead of the interview. First, the study protocol was reviewed and approved by the Committee on Human Research Publication and Ethics, School of Medical Sciences, Kwame Nkrumah University of Science and Technology, and Komfo Anokye Teaching Hospital, Kumasi, Ghana (ref.: CHRPE/AP/507/16). Ethics approval was also granted by the Research Ethics Committee of Lingnan University, Hong Kong. The participants were fully briefed on the research objectives and they provided informed written and or oral consent as appropriate, after the assurance of confidentiality and anonymity of the information they provided. Personal identifying items such as names were not taken.

PD Outcome

PD construct was assessed by a composite 10-item question measuring the psychological health and depressive symptomatology of the participants on a range of health complaints adapted from the Kessler Psychological Distress Scale (Kessler, Andrews, et al., 2002; Kessler, Barker, et al., 2003). These items included, “In the last 30 days about how often did you feel…(1) ‘tired out for no good reason?’ (2) ‘nervous or uneasy?’ (3) ‘so nervous that nothing could calm you down?’ (4) ‘hopeless?’ (5) ‘restless or fidget?’ (6) ‘so restless you could not sit still?’ (7) ‘depressed?’ (8) ‘that everything was an effort?’ (9) ‘so sad that nothing could cheer you up?’ and (10) ‘worthless?’” Respondents rated the items with a 5-point response scale from 1 = none of the time, 2 = a little of the time, 3 = some of the time, 4 = most of the time, to 5 = all of the time. Cronbach’s α of the scale was assessed to be .88. Scores of the 10 items were then summed, yielding a minimum score of 10 and a maximum score of 50.

In our analysis, we used a cutoff point of ≥20 to define PD (1) while the range 10–19 was adjudged psychologically not distressed (0) according to a previous validation study by the Victorian Population Health Survey (2001). It has also been argued that the dichotomized PD variable in community-based studies is an effective way to identify people who may need further clinical evaluation for diagnosis and treatment (Herrman et al., 2002).

SRH

SRH was assessed with a single item from the 36-item short-form survey instrument (Rand Health, 2007; Ware & Sherbourne, 1992). The item inquired about the self-reported current general health status of individuals using the item: “In general, how would you rate your health?” on a 5-point response scale, 1 = excellent, 5 = poor. These were later collapsed and dichotomized into 0 = fair/poor, 1 = excellent/very good/good mainly because the data were highly skewed against the extremes. Manor, Matthews, & Power (2000) also note that the dichotomization of SRH does not present problems but rather tends to increase the robustness of the analysis, especially with skewed data.

Health-Seeking Behavior

We obtained data on health services utilization involving the use of health professional or health facility in dealing with health challenges over the last 12 months preceding the survey. This was assessed using the item “how many times did you seek treatment for your health challenges from a health facility (e.g., health center, clinic, or hospital) over the past 12 months?” This included physician/general practitioner/medical specialist visits, outpatient service use, and inpatient hospitalization of one night or longer. The responses were recorded as count data on a ratio/continuous scale.

Financial Inclusion

The participants were asked to give no (0) or yes (1) responses to a 7-item question regarding the most regular financial activities they have been involved in. These included, over the past 12 months: ownership of personal current/savings bank account, recent withdrawal of money from an account, use of automatic teller machines, membership of any credit union activities, ownership of Susu accounts and contribution to savings with local banking operators/managers, opportunities of obtaining a loan from a financial institution, and ownership or operation of a mobile money service account. A possible total score from 0 to 7 was recorded. Higher scores indicated an older person had been involved in a larger number of financial inclusion activities.

Individual Characteristics

We included health-related, lifestyle, and sociodemographic factors as control variables. The prevalence of chronic illnesses (comorbidities) was assessed via stated or self-reported diagnoses often chronic illnesses by a health-care professional: diabetes, respiratory diseases, cancers, stroke, chronic kidney diseases, asthma, arthritis, depression, and insomnia with yes/no responses. The total score ranged from 0 to 5 with a higher score indicating a higher comorbidity level. ADLs decline was assessed via responses to self-reported functioning (WHO, 2012). Older participants were asked to rate their level of difficulty in carrying out six ADLs including eating, bathing, getting dressed/undressed, toileting, getting in/out of bed, and continence. Responses were recorded on a 4-point scale: (1) not limited at all, (2) less limited, (3) somewhat limited, and (4) much limited. The total score ranged from 6 to 24 with a higher score indicating a higher level of ADL decline.

Alcohol intake was assessed by asking respondents to indicate a no/yes response if they consumed any drink that contains alcohol such as beer, wine, or hard spirits over the past 30 days. Physically active level was measured with 3 items adapted from the General Physical Activity Questionnaire (United Kingdom Department of Health & Social Care, 2013): “How many days in the last week did you (1) walk for at 30 min in total; do moderate activities such as dancing for about 30 min in total? Do vigorous activities such as running, gardening”? The responses were recorded on a ratio scale. Sociodemographic variables included were age (years), gender (1 = male, 2 = female), marital status (1 = married/cohabiting, 2 = single/widowed/divorced/separated), residence (1 = rural, 2 = urban), living arrangement (1 = living with others, 2 = living alone), level of schooling (1 = primary/none, 2 = secondary, 3 = tertiary), employment status (1 = not employed/retired, 2 = currently employed), and household monthly income (in Ghana Cedis).

Statistical Analyses

We first conducted descriptive analyses of univariate and correlation matrix to describe the sample. Diagnostics were conducted to check multicollinearity prior to regression analyses and the variance inflation factor (VIF) for all variables was 2.38. We then performed various multivariate analyses taking into consideration the different measurements of the outcome variables. Specifically, while logistic regression models examined the effects of financial inclusion on SRH and PD levels, generalized Poisson regression models were employed to assess the association between financial inclusion status and the frequency of health services use among older persons. Models 1, 3, and 5 were the base models which included financial inclusion status and control variables of age and gender only. Models 2, 4, and 6 for each outcome measure added the socioeconomic, lifestyle, and health-related factors to investigate whether these variables play any role and might temper the effects of financial inclusion on health outcomes and health services use. A sensitivity analysis for the measure of PD was conducted using the log scale transformation of this outcome measure to chiefly address the potential challenge associated with the skewness of the data. However, the results were essentially the same as the originally dichotomized measure in terms of the direction and effect size. In addition, we performed an auxiliary analysis for each of the financial instruments included in the financial inclusion composite measure to ascertain their individual effects on the health outcomes and health-care use (HCU) among the older sample by running series of models isolating each financial instrument per model. A statistical significance threshold of p < .05 was set and we used SPSS Version 21.0 (IBM, Armonk, NY) in all analyses.

Results

The average age of respondents was 66.2 years (standard deviation [SD] = 11.9) and 38.2% were married or cohabiting with their spouses. The majority of respondents were female (63.3%), had had no or up to primary formal schooling (86.2%), were unemployed (55.6%) and lived alone (55.2%), and lived in the urban areas (55.0%). Income levels were generally low with the average monthly income of GH¢308.2 (where US$1.00 = ¢4.80) and with widespread inequality (SD = GH¢338.9, range = GH¢100–GH¢4000; Table 1). In addition to the lower socioeconomic status, respondents reported a wide range of physical and psychological health challenges. Typically, average levels of ADLs (13.7, SD = 5.1, range = 9–36), comorbidities (0.67, SD = 0.79, range = 1–5), and physical activity (8.8, SD = 4.4, range = 1–15) were revealed. Moreover, 31.4% of the participants had consumed alcohol over the past 4 weeks. More importantly, while responses on SRH were very good (19.9%), good (30.8%), fair (29.0%), and poor (20.3%), 45.3% of the respondents revealed depressive symptoms. The average number of visits to a health facility was 3.6 (SD = 1.6) and a financial inclusion score of 1.9 (SD = 1.8, range = 0–7) was also reported.

Table 1.

Univariate Analysis of Selected Outcome and Explanatory Variables (Older Persons in Ghana Survey).

Variable	Valid N (%)	Mean	SD	Range
Frequency of health services utilization		3.56	1.62
Self-rated health
Poor/fair	592 (49.3)
Good/very good/excellent	608 (50.7)
Psychological health
Nondistressed	656 (54.7)
Distressed	544 (45.3)
Financial inclusion		1.91	1.79	0–7
Age (years)		66.15	11.85	50–111
Gender
Women	759 (63.3)
Men	441 (36.8)
Marital status
Single	742 (61.8)
Married or cohabiting	458 (38.2)
Residential status
Rural	540 (45.0)
Urban	660 (55.0)
Living arrangement
Living with others	538 (44.8)
Living alone	662 (55.2)
Level of schooling
Primary or below	1,034 (86.2)
Secondary	104 (8.7)
Tertiary	62 (5.2)
Employment status
Unemployed	667 (55.6)
Employed	533 (44.4)
Average monthly income (in Ghana Cedis)		GH¢308.18	GH¢338.89	GH¢100–GH¢4000
Comorbidities		0.67	0.79	0–5
Activities of daily living decline		13.70	5.09	9–36
Physical activity levels		8.75	4.43	1–15
Alcohol intake
No	823 (68.6)
Yes	377 (31.4)

Table 2 is a matrix showing the Pearson product–moment correlation, point–biserial correlation, and ϕ correlation values for the dependent and independent variables. The analysis found a positive correlation between financial inclusion and SRH, but it is negatively correlated with PD. Apart from residential status, all the control variables showed significant correlations with financial inclusion. Negative associations were observed for higher age, living alone, and having comorbidities with financial inclusion. Also, being male, married, schooling, employed, higher income, physically active, and using alcohol were positively related to financial inclusion. SRH correlated with all the study variables except for residence, educational level, and alcohol intake. Finally, PD did not correlate with alcohol use, income, schooling residence, and age.

Table 2.

Pearson Product–Moment, Point–Biserial, and φ Correlation Matrix for Financial Inclusion, Sociodemographic, and Health-Related Variables.

Variable	M ± SD	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
1. Self-rated health	0.51 (0.50)	1
2. Psychological distress	0.45 (0.50)	−.233**	1
3. Health services use	3.56 (1.62)	−.189**	.134**	1
4. Financial inclusion	1.91 (1.79)	.093**	−.034*	−.023	1
5. Age	66.15 (11.85)	−.159**	.017	.128**	−.245**	1
6. Gender	0.37 (0.48)	.130**	−.118**	−.098**	.178**	−.003	1
7. Marital status	0.38 (0.49)	.175**	−.209**	−.105**	.238**	−.203**	.333**	1
8. Residence	0.55 (0.49)	−.011	−.034	.080*	.015	−.057*	−.044	.111**	1
9. Living arrangement	1.55 (0.49)	−.202**	.400**	.115**	−.191**	.060*	−.112**	−.275**	−.071*	1
10. Schooling	1.19 (0.51)	.041	−.044	.006	.311**	−.095**	.219**	.166**	.009	−.098**	1
11. Employment status	1.44 (0.50)	.265**	−.127**	−.258**	.127**	−.363**	.129**	.254**	−.095	−.138**	.075**	1
12. Income	308.18 (338.89)	.133**	−.057	−.060	.186**	−.168**	.179**	.247**	.114**	−.188**	.223**	.241**	1
13. Comorbidities	0.67 (0.79)	−.223**	.131**	.314**	−.098**	.178**	−.109**	−.081**	.072*	.090**	−.016	−.180**	−.050	1
14. Activities of daily living decline	13.70 (5.09)	.462**	−.239**	−.334**	.218**	−.401**	.173**	.227**	−.019	−.256**	.109**	.408**	.218**	−.388**	1
15. Physical activity	8.75 (4.43)	.256**	−.141**	−.168**	.180**	−.342**	.181**	.186**	−.068*	−.161**	.019	.265**	.180**	−.283**	.492**	1
16. Alcohol use	0.31 (0.46)	.039	−.010	−.061	.133**	−.045	.359**	.063*	−.023	−.065*	.065*	.027	.005	−.099**	.127**	.006

*p < .05. **p < .005.

Results of the logistic and Poisson regression analyses used to examine the associations between financial inclusion and health outcomes are presented in Table 3. Model 1 showed that older people with higher levels of financial inclusion had 0.104 points (standard error [SE] = .033, p < .001) higher than their nonincluded counterparts to reveal excellent/very good SRH, adjusting for age and gender. The inclusion of all socioeconomic and health-related variables in Model 2 slightly reduced the magnitude of the positive association between financial inclusion and SRH by 0.094 points, but the significant positive relationship remained statistically robust (β = .010, SE = .044, p < .005). In Model 3, the logistic regression results showed that financial inclusion reduced PD levels controlling for age and gender (β = −.038, SE = .032, p < .005). Although the magnitude increased by 0.065 points, the inverse effect of financial inclusion on PD persisted after adjusting for the theoretically relevant variables (β = −.103, SE = .044, p < .05; Model 4). For health services utilization, the Poisson regression results in Model 5 showed that financial inclusion decreased the likelihood to use health care (β = −.006, SE = .009, p < .05). In Model 6, however, while health variables of comorbidities and ADLs significantly increased frequency of HCU, the association between financial inclusion and frequency of HCU remained negative but lost its significance (β = −.020, SE = .011, p > .05). This implies that the frequency of HCU among older people is largely explained by physical health status.

Table 3.

Multivariate Analyses of Associations Between Self-Rated Health, Psychological Distress, and Frequency of Health Services Utilization Among Older Respondents.

Variable	Self-Reported Health: Coefficients for Logit Models		Psychological Distress: Coefficients for Logit Models		Health Services Use: Coefficients for Poisson Models
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
	β (SE)	β (SE)	β (SE)	β (SE)	β (SE)	β (SE)
Financial inclusion	.104 (.033)***	0.010 (.044)**	−.038 (.032)**	−0.103 (.044)*	−.006 (.0093)*	−0.020 (.0114)
Age (in years)	−.021 (.009)*	−0.012 (.007)	−.033 (.009)***	−0.023 (.007)***	.009 (.0071)	0.008 (.0019)
Gender (ref: male)	−.128 (.155)	−0.131 (.176)	.310 (.195)	0.259 (.174)	.008 (.0550)**	0.007 (.0460)
Marital status (ref: single)		0.192 (.166)		−0.469 (.164)***		−0.031 (.0425)
Residence (ref.: urban)		−0.180 (.153)		−0.076 (.149)		0.047 (.0387)
Living alone		−0.289 (.153)		1.602 (.154)***		0.015 (.0396)
Schooling
Primary/none (ref.)		1.00		1.00		1.00
Secondary		0.046 (.275)		0.170 (.274)		0.006 (.0715)
Tertiary		−0.303 (.341)		−0.236 (.341)		0.007 (.0835)
Employed		0.322 (.162)*		−0.125 (.165)		−0.134 (.0437)
Income (in Cedis)		0.019 (.0121)		0.015 (.0113)*		0.021 (.0012)
Comorbidities		0.178 (.103)		0.063 (.097)		0.098 (.0232)***
Activities of daily living decline		0.195 (.020)***		−0.077 (.019)***		0.019 (.0047)***
Physically active		0.003 (.019)		−0.012 (.019)		0.002 (.0050)
Alcohol intake		−0.077 (.170)		0.271 (.170)		−0.045 (.0442)
Two log pseudolikelihood	−1,653.006	−1,152.290	−1,651.713	−1,187.383	−1,939.611	−1,607.359
Model significance (Wild χ²)	32.118 (1)***	58.025 (14)***	26.587 (1)***	72.910 (14)***	4,826.232 (1)***	65.684 (14)***
Sample size	1,200	1,200	1,200	1,200	1,200	1,200

Note. Coefficients (β) are adjusted for clustering with robust standard errors (SE) in parentheses.

*p < .05. **p < .005. ***p < .001.

A sensitivity analyses were conducted (results not shown). The results showed that the ownership of bank account and Susu account significantly improved both mental and SRH outcomes, but their relationships with health services use were not robust. Also, being a member of credit union and ownership of Mobile Money account were associated with increased health services use and reduced PD among older people.

Discussion and Implications

Most nation states including the majority of those in sub-Saharan Africa generally claim to seek to achieve the aging well paradigm, encompassing not only an absence of ill-health but also an enhancement of health-related quality of life and well-being in later life (Jivraj, Nazroo, Vanhoutte, & Chandola, 2014). By employing a financial capability perspective within the capability concept, this study sought to evaluate the associations between financial inclusion and health and health care for community-dwelling older people in Ghana. This study addresses an important but regionally very much neglected topic in a local setting and provides empirical information and a policy relevant contribution to the gerontological literature, given that it appears to be the first study to investigate these relationships in a sub-Saharan African context.

Adjusting for age and gender, the findings suggest that older people with some level of financial engagement were more likely to report better SRH and lower levels of PD but were, perhaps surprisingly, less likely to use health services. These results suggest that in Ghana, financial capability and security likely have profound implications for health and well-being outcomes of older people. Again, one might suggest that healthy people use fewer health care services and that having money or monetary knowledge and resources are strongly associated with better health and other socioeconomic resources. The findings partly reinforce certain previous findings although these are predominantly from high-income countries such as the United States (Aguila et al., 2016) and the United Kingdom (Finkelstein et al., 2012; Taylor et al., 2009), which show that financial inclusion positively affects the health status of older people. For example, social health insurance protection and ownership of a bank account had the tendency to reduce mental ill-health among older people in the United States and some emerging economies (Aguila et al., 2016) despite sociocultural differences. Other studies note that gains in financial services inclusion potentially enabled older persons to maintain independence, reduced stigma, and stereotyping (Adams, White, Moffatt, Howel, & Mackintosh, 2006).

Moreover, being engaged in financial markets can reduce stress, anxiety, and worries which may enhance psychological health and well-being of older adults, irrespective of their income statuses (Abbott, Hobby, & Cotter, 2005; Aguila, Kapteyn, & Smith, 2015; Aguila et al., 2016). Better psychological well-being could also potentially improve a myriad of physical health problems which intend to reduce the rate of medical consultations and health service consumption, as noted in the popular perspective of the biopsychosocial model (Engel, 1977, 1980; Hajek et al., 2017). Our findings, therefore, support the view that financial inclusion is a relevant pathway to empower older people toward improving their general health-related quality of life and well-being.

Although the significant positive associations between financial inclusion and health outcomes persisted after adjusting for all socioeconomic, lifestyle, and health-related factors, the findings showed a reduced magnitude for SRH and an increased effect size of PD. This suggests that, while paid employment and absence of ADLs contribute to better SRH (Gyasi & Phillips, 2018b), factors such as marriage, living with meaningful others, and lack of NCDs and ADLs also play an important role in reducing PD among older people. Typically, marital cohabitation and wider social support have strong linkages with older persons’ capacities to achieve and to maintain both better physical and mental health outcomes (Gyasi & Phillips, 2019; Gyasi et al., 2018; Hajek et al., 2017; Kauppi et al., 2017; Kemp, Arias, & Fisher, 2017; Stoeckel & Litwin, 2016). Marriage may also provide a sense of cohesion, security, be a coping resource and potentially offset stress among older persons. Further, those who are embedded in larger, stronger or denser constellations of supportive social networks also tend to live healthier and longer lives with less evidence of PD compared with the socially isolated (Gyasi, Phillips, & Abass, 2018; Holt-Lunstad, Smith, & Layton, 2010; Smith & Christakis, 2008; Yang et al., 2016).

Supplementary findings of our analyses showed that the ownership of bank, Susu, and mobile money accounts, as well as membership of credit union, were significantly important financial instruments relating to health and health care of older people. While to a larger extent, the findings are not surprising because of the view that the identified financial services are most commonly patronized among the Ghanaian general population (Bank of Ghana, 2018), these observations may present key implications for policy. Efforts to improve financial inclusion among older people should be cognizance of the specific financial instruments.

Moreover, while not our focus here, higher socioeconomic and better health status as a whole accounted for some portion of the effects of financial inclusion on SRH and psychological health outcomes in older Ghanaians. The analysis also showed an insignificant relationship between financial inclusion and HCU with the introduction of theoretically relevant covariates. This indicates that the frequency of health services use is more a function of health-related variables (Gyasi et al., 2018) rather than financial inclusion instruments per se. Importantly, the finding that financial inclusion does not predict health care use in the presence of other variables is an interesting one. Further exploratory analysis pursuing this relationship may be instructive.

Some evidence suggests that countries such as India and South Africa among other LMICs have started operating universal opening of checking bank accounts for their general populations as a means to reduce cash handling and transfers in cash (Adam et al., 2018; Aker et al., 2011; Goldberg, 2014). Engaging in electronic payments may indeed reduce the costs of economic transactions and increase financial inclusion of poor and vulnerable groups including older people. In the context of LMICs, for example, South Africa has transitioned to electronic payments including shifting from cash distribution to mobile money distribution to aid payments. Other countries like Kenya and Ghana are introducing similar practices through “M-Pesa” and “MoMo” respectively (Aker et al., 2011; Goldberg, 2014; Mensah & Dzokoto, 2011). Although these arrangements may vary with the quality of the local banking infrastructure, especially in terms of saving capacities, opportunities to consume a wider variety of goods and to incorporate welfare of beneficiaries, stakeholders in various sub-Saharan African countries including Ghana are increasingly expected which may potentially safeguard the general well-being in later life.

Despite the novelty and strengths of the current study, we need to consider a number of limitations associated with these findings. The cross-sectional data used mean that findings are restricted as they are unable to account for unobserved individual characteristics. This does not allow the establishment of causal relationships between variables of interest. While our measure for financial inclusion, a composite of seven individual financial activities, appears unique, a self-created indicator that has not been previously tested in the literature could be limited in strength and incomparability with previous findings. However, it provides a new insight which may be replicated in future research. Most studies break down the components and often use ownership of a bank account as the most basic level of financial inclusion; though as mentioned, a bank account is often an unrealistic expectation especially in the informal economies of most rural areas in this group of countries. In addition, as financial inclusion and HCU variables were collected retrospectively through self-reporting, recall and reporting biases become almost inevitable. Nevertheless, most epidemiological studies have used self-reported data, and these biases are generally not deemed substantial enough to severely undermine the value of the findings. We also acknowledge a two-way causality limitation regarding health outcomes and financial inclusion since health indicators of SRH and PD could also potentially influence financial inclusion. However, this was not included in the objectives of the current study. Therefore, we suggest that future studies consider an investigation into the two-way causal relationship between health outcomes and financial inclusion in later life.

Conclusions

The findings of this study provide some compelling evidence to support the general premise that financial services inclusion and engagement enhance health-related outcomes and may help to protect older adults from many catastrophic consequences of health shocks. However, certain socioeconomic and health factors contribute to the associations between financial inclusion and SRH, mental health, and health-seeking behavior. Providing opportunities for older persons’ involvement in financial inclusion as a means to improve health outcomes appears likely to be a strategic and viable approach within a smart-aging agenda. Social policies and microlevel efforts targeted at maintaining and restoring the good health of older persons should include expanding financial inclusion options as a way to improve well-being and health-related quality of life among community-dwelling older people in sub-Saharan Africa and Ghana in particular.

Footnotes

Authors’ Note

Razak M. Gyasi conceived and designed the study under the PhD supervision of David R. Phillips, supervised the fieldwork, analyzed the data, and wrote the initial version of the manuscript. David R. Phillips and Anokye M. Adam undertook critical review and revision of the manuscript. All authors read and approved the final manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Lingnan University, Hong Kong, through its Studentship Package for Research Postgraduates (RPG-1129310). The funders, however, played no role in designing the study, collecting and analyzing data, manuscript preparation, and the decision to publish the manuscript.

ORCID iD

Razak M. Gyasi

Note

References

Abbott

Hobby

Cotter

(2005). What is the impact on the individual health of services in general practice setting which offer welfare benefits advice? Health and Social Care in the Community, 14, 1–8.

Aboderin

Hoffman

(2015). Families, intergenerational bonds and aging in sub-Saharan Africa. Canadian Journal on Aging [La Revue canadienne du vieillissement], 34, 282–289. doi:10.1017/S0714980815000239

Adam

A. M.

Boadu

M. O.

Frimpong

. (2018). Does gender disparity in financial literacy still persist after retirement? Evidence from Ghana. International Journal of Social Economics. doi:10.1108/IJSE-06-2016-0159

Adams

White

Moffatt

Howel

Mackintosh

(2006). A systematic review of health, social and financial impacts of welfare rights advice delivered in healthcare settings. BMC Public Health, 6, 81.

Agrigoroaei

Lee-Attardo

Lachman

M. E.

(2017). Stress and subjective age: Those with greater financial stress look older. Research on Aging, 39, 1075–1099. doi:10.1177/0164027516658502

Aguila

Angrisani

Blanco

L. R.

(2016). Ownership of a bank account and health of older Hispanics. Economics Letters, 144, 41–44.

Aguila

Kapteyn

Smith

J. P.

(2015). Effects of income supplementation on health of the poor elderly: The case of Mexico. PNAS, 112, 70–75.

Aker

J. C.

Boumnijel

McClelland

Tierney

(2011). Zap it to me: The impacts of a mobile cash transfer program (Working Paper No. 268). Washington, DC: Center for Global Development. Center for Global Development 1800 Massachusetts Ave., NW 20036.

Akuamoah-Boateng

(2013). Self-reported vision health status among older people in the Kassena-Nankana District, Ghana. Global Health Action, 6, 19012. doi:10.3402/gha.v6i0.19012

10.

Alabi

Ahiawodzi

(2007). Effects of “Susu”: A traditional micro-finance mechanism on organized and unorganized micro and small enterprises (MSEs) in Ghana. African Journal of Business Management, 1, 201–208. Retrieved from http://www.academicjournals.org/AJBM

11.

Allmark

Baxter

Goyder

Guillaume

Crofton-Martin

(2013). Assessing the health benefits of advice services: Using research evidence and logic model methods to explore complex pathways. Health & Social Care in the Community, 21, 59–68. doi:10.1111/j.1365-2524.2012.01087.x

12.

Allmark

Machaczek

(2015). Financial capability, health and disability. BMC Public Health, 15, 243. doi:10.1186/s12889-015-1589-5

13.

Ameh

Gómez-Olivé

F. X.

Kahn

Tollman

S. M.

Klipstein-Grobusch

(2014). Predictors of health care use by adults 50 years and over in a rural South African setting. Global Health Action, 7, 24771. doi:10.3402/gha.v7.24771

14.

Atkinson

(2008). Evidence of impact: An overview of financial education evaluations. London, England: Financial Services Authority.

15.

Bank of Ghana. (2018). Guidelines for e-Money issuers in Ghana. Retrieved from https://www.bog.gov.gh/privatecontent/Banking/E-MONEY%20GUIDELINES-29-06-2015-UPDATED5.pdf

16.

Biritwum

R. B.

Mensah

Yawson

Minicuci

(2013). Study on Global Ageing and adult health (SAGE) Wave 1: The Ghana national report. Geneva, Switzerland: World Health Organization.

17.

Cameron

K. A.

Song

Manheim

L. M.

Dunlop

D. D

. (2010). Gender disparities in health and healthcare use among older adults. Journal of Women’s Health, 19. doi:10.1089=jwh.2009.1701

18.

Carbo

Gardener

E. P. M.

Molyneux

(2005). Financial exclusion. New York, NY: Palgrave Macmillan.

19.

Carlson

R. V.

Boyd

K. M.

Webb

D. J

. (2004). The revision of the Declaration of Helsinki: Past, present and future. British Journal of Clinical Pharmacology. doi:10.1111/j.1365-2125.2004.02103.x

20.

Cheng

G. H.-L.

Chan

J. C

. (2017). Importance of social relationships in the association between sleep duration and cognitive function: Data from community-dwelling older Singaporeans. International Psychogeriatrics. doi:10.1017/S1041610217001041

21.

Clark

R. L.

d’Ambrosio

. (2003). Ignorance is not bliss: The importance of financial education. TIAA-CREF institute. Research Dialogue, 78. Retrieved from tiaa-crefinstitute.org.

22.

Domènech-Abella

Perales

Lara

Moneta

M. V.

Izquierdo

Alejandra Rico-Uribe

… Haro

J. M.

(2017). Sociodemographic factors associated with changes in successful aging in Spain: A follow up study. Journal of Aging and Health, 1–19. doi:10.1177/0898264317714327

23.

Engel

G. L.

(1977). The need for a new medical model: A challenge for biomedicine. Science, 196, 129–136. doi:10.1126/science.847460

24.

Engel

G. L.

(1980). The clinical application of the biopsychosocial model. The American Journal of Psychiatry, 137, 535–544.

25.

Finkelstein

Taubman

Wright

Bernstein

Gruber

Newhouse

J. P.

,… Oregon Health Study Group. (2012). The Oregon health insurance experiment: Evidence from the first year. Quarterly Journal of Economics, 127, 1057–1106.

26.

Garatachea

Pareja-Galeano

Sanchis-Gomar

Santos-Lozano

Fiuza-Luces

Morán

… Lucia

(2015). Exercise attenuates the major hallmarks of aging. Rejuvenation Research, 18, 57–89.

27.

Giné-Garriga

Roqué-Fíguls

Coll-Planas

Sitjà-Rabert

Salvà

(2014). Physical exercise interventions for improving performance-based measures of physical function in community dwelling, frail older adults: A systematic review and meta-analysis. Archives of Physical Medicine and Rehabilitation, 95, 753–769.e3.

28.

Goldberg

(2014). Products and policies to promote saving in developing countries: Combine behavioral insights with good products to increase formal savings in developing countries. IZA World of Labor, 74. doi:10.15185/izawol.74. wol.iza.org

29.

Ghana Statistical Service. (2012). Population and housing census, 2010 summary of report of final results. Accra, Ghana: Ghana Statistical Service, Ministry of Finance and Economic Planning, Ghana.

30.

Ghana Statistical Service. (2013). Ghana living standards survey 6 with Labour Force Module (Glss6/LFS) 2012/2013: Three-cycle labour force report (Fourth to Sixth Cycle Report). Accra, Ghana.

31.

Gyasi

R. M.

(2018). Ageing, health and health-seeking behaviour in Ghana (Doctor’s thesis). Lingnan University, Hong Kong. Retrieved December 9, 2018, from https://commons.ln.edu.hk/otd/41/

32.

Gyasi

R. M.

(2019). Social support, physical activity and psychological distress among community-dwelling older Ghanaians. Archives of Gerontology and Geriatrics, 81, 142–148. doi:10.1016/j.archger.2018.11.016

33.

Gyasi

R. M.

Phillips

D. R

. (2018a). Demography, socioeconomic status and health services utilisation among older Ghanaians: Implications for health policy. Ageing International. doi:10.1007/s12126-018-9343-9

34.

Gyasi

R. M.

Phillips

D. R.

(2018b). Gender, self-rated health and functional decline among community-dwelling older persons. Archives of Gerontology and Geriatrics, 77, 174–183. doi:10.1016/j.archger.2018.05.010

35.

Gyasi

R. M.

Phillips

D. R.

(2019). Risk of psychological distress among community-dwelling older adults experiencing spousal loss in Ghana. The Gerontologist. doi: 10.1093/geront/gnz052

36.

Gyasi

R. M.

Phillips

D. R.

Abass

. (2018). Social support networks and psychological wellbeing in community-dwelling older Ghanaian cohorts. International Psychogeriatrics. doi:10.1017/S1041610218001539

37.

Gyasi

R. M.

Phillips

D. R.

Amoah

P. A

. (2018). Multidimensional social support and health services utilization among noninstitutionalized older persons in Ghana. Journal of Aging and Health. doi:10.1177/0898264318816217

38.

Gyasi

R. M.

Phillips

D. R.

Buor

. (2018). The role of a health protection scheme in health services utilisation among community-dwelling older persons in Ghana. The Journals of Gerontology: Series B. doi:10.1093/geronb/gby082

39.

Gyasi

R. M.

Phillips

D. R.

David

. (2019). Explaining the gender gap in health services use in Ghanaian community-dwelling older cohorts. Women & Health, doi:10.1080/03630242.2019.1587666

40.

Hajek

Brettschneider

Mallon

van der Leeden

Mamone

Wiese

… König

H. H

. (2017). How does social support affect functional impairment in late life? Findings of a multicenter prospective cohort study in Germany. Age and Ageing, 46, 813–820. doi:10.1093/ageing/afx012

41.

Herrman

Patrick

D. L.

Diehr

Martin

M. L.

Fleck

Simon

G. E.

Buesching

D. P.

(2002). Longitudinal investigation of depression outcomes in primary care in six countries: The LIDO study. Functional status, health service use and treatment of people with depressive symptoms. Psychological Medicine, 32, 889–902. doi:10.1017/S003329170200586X

42.

Holt-Lunstad

Smith

T. B.

Layton

J. B.

(2010). Social relationships and mortality risk: A meta-analytic review. PLoS Medicine, 7, e1000316.

43.

International Labour Organization. (2015). The transition from the informal to the formal economy. International Labour Conference 104th Session, 2015, Geneva, Switzerland.

44.

Jivraj

Nazroo

Vanhoutte

Chandola

. (2014). Aging and subjective wellbeing in later life. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences. doi:10.1093/geronb/gbu006

45.

Kauppi

Kawachi

Batty

G. D.

Oksanen

Elovainio

Pentti

… Kivimäki

. (2017). Characteristics of social networks and mortality risk: Evidence from two prospective cohort studies. American Journal of Epidemiology. doi:10.1093/aje/kwx301

46.

Kemp

A. H.

Arias

J. A.

Fisher

. (2017). Social ties, health and wellbeing: A literature review and model. In Ibáñez

Sedeño

García

A. M.

(Eds.), Neuroscience and social science. doi:10.1007/978-3-319-68421-5_17

47.

Kessler

R. C.

Andrews

Colpe

L. J.

Hiripi

Mroczek

D. K.

Normand

S-L. T.

… Zaslavsky

A. M

. (2002). Short screening scales to monitor population prevalence and trends in non-specific psychological distress. Psychological Medicine, 32, 959–956. doi:10.1017/S0033291702006074

48.

Kessler

R. C.

Barker

P. R.

Colpe

L. J.

Epstein

J. F.

Gfroerer

J. C.

Hiripi

… Zaslavsky

A. M.

(2003). Screening for serious mental illness in the general population. Archives of General Psychiatry, 60, 184–189.

49.

Krause

Bastida

(2011). Financial strain, religious involvement, and life satisfaction among older Mexican Americans. Research on Aging, 33, 403–425. doi:10.1177/0164027511400433

50.

MacKenbach

J. P.

McKee

(2013). A comparative analysis of health policy performance in 43 European countries. European journal of public health, 23, 195–201. doi:10.1093/eurpub/cks192. Retrieved from http://researchonline.lshtm.ac.uk/768566/

51.

Manor

Matthews

Power

(2000). Dichotomous or categorical response? Analysing self-rated health and lifetime social class. Int J Epidemiol, 29, 149–157.

52.

McCracken

Phillips

D. R.

(2017). Global health. (2nd ed.). London, England: Routledge.

53.

Mensah

E. C.

Dzokoto

V. A.

(2011). Post redenomination and Money management among Ghana’s urban poor (Technical Report). Institute for Money, Technology, & Financial Inclusion (IMTFI), Irvine: University of California.

54.

Mullainathan

Shafir

(2011). Savings policy and decision making in low-income households. In Barr

Blank

(Eds.), Insufficient funds: Savings, assets, credit and banking among low-income households (pp. 121–145). New York, NY: Russell Sage Foundation Press.

55.

Nussbaum

(2011). Creating capabilities: The human development approach. Belknap, TX: Harvard University Press.

56.

Phillips

D. R.

Feng

(2018). Global ageing. In Skinner

Andrews

Cutchin

(Eds.), Geographical gerontology: Perspectives, concepts, approaches. London, England: Routledge.

57.

Poscia

Landi

Collamati

(2015). Public health gerontology and active aging. In Boccia

Villari

Ricciardi

(Eds.), A systematic review of key issues in public health. 2015. Switzerland: Springer International.

58.

Rand Health. (2007). Scoring instructions for MOS 36-Item Short Form Survey Instrument (SF-36). Retrieved from http://www.rand.org/health/surveys_tools/mos/mos_core_36item_scoring.pdf

59.

Robeyns

(2005). The capability approach: A theoretical survey. Journal of Human Development and Capabilities, 6, 93–117. doi:10.1080/146498805200034266

60.

Sen

(1999). Development as freedom. Oxford, England: Oxford University Press.

61.

Smith

K. P.

Christakis

N. A.

(2008). Social networks and health. Annual Review of Sociology, 34, 405–429.

62.

Song

Chang

R. W.

Manheim

L. M.

Dunlop

D. D.

(2006). Gender differences across race/ethnicity in use of health care among Medicare-aged Americans. Journal of Women’s Health, 15, 1205–1213.

63.

Stoeckel

K. J.

Litwin

(2016). The impact of social networks on the relationship between functional impairment and depressive symptoms in older adults. International Psychogeriatrics, 28, 39–47.

64.

Taylor

Jenkins

Sacker

. (2009). Financial capability and wellbeing: Evidence from the BHPS (Occasional Papers Series 34). Financial Services Authority, UK.

65.

Taylor

Jenkins

Sacker

(2011). Financial capability and psychological health. Journal of Economic Psychology, 32, 710–723.

66.

United Kingdom Department of Health and Social Care. (2013). General physical activity questionnaire. Retrieved from https://www.gov.uk/government/publications/general-practice-physical-activity-questionnaire-gppaq

67.

United Nations. (2015). World population ageing. Retrieved from http://www.un.org/en/development/desa/population/publications/pdf/ageing/WPA2015_Highlights.Pdf

68.

U.S. Agency for International Development. (2013). Mobile money for health. The health finance and governance project. AID-OAA-A-12-00080. Retrieved from https://www.hfgproject.org/wp-content/uploads/2013/12/Mobile-Money-for-Health-Case-Study.pdf

69.

Üstun

T. B.

Chatterji

Mechbal

Murray

C. J. L

., & WHS Collaborating Groups. (2005). WHO translation guidelines Quality assurance in surveys: Standards, guidelines and procedures. Chapter X. Geneva: World Health Organization.

70.

Vasco

Pierella

. (2015). Poverty reduction in Ghana: Progress and challenges. World Bank. Washington, DC. World Bank. License: CC by 3.0 IGO. Retrieved form https://openknowledge.worldbank.org/handle/10986/22732

71.

Victorian Population Health Survey. (2001). Melbourne: Department of Human Services. Victoria, Melbourne: Victorian Government.

72.

Ware

J. E.

Sherbourne

C. D.

(1992). The MOS 36-item short-form health survey (SF-36): 1. Conceptual framework and item selection. Medical Care, 30, 473–483. doi:10.1097/00005650-199206000-00002

73.

World Health Organization. (2012). The World Health Organization Disability Assessment Schedule II (WHODAS II). Retrieved from http://www.who.int/icidh/whodas/index.html

74.

World Health Organization. (2015a). World report on ageing and health. Geneva, Switzerland: Author. Retrieved October 18, 2016, from http://www.who.int/ageing/events/world-report-2015-launch/en/

75.

World Health Organization. (2015b). Country statistics and global health estimates by WHO and UN partner. Geneva, Switzerland: Author.

76.

World Health Organization. (2018). World health ranking: Live longer, live better. Retrieved from https://www.worldlifeexpectancy.com/ghana-life-expectancy

77.

World Medical Association. (1964). Declaration of Helsinki. Helsinki, Finland: General Assembly.

78.

Yang

Y. C.

Boen

Gerken

Schorpp

Harris

K. M.

(2016). Social relationships and physiological determinants of longevity across the human life span. Proceedings of the National Academy of Sciences of the United States of America, 113, 578–583.