Journal of Medical and Clinical Studies

The world is gradually ageing, both at the individual and population levels [1-5]. As a concept, ageing has suffered from one universal definition mainly because population ageing varies across space and time. Despite its multidimensional nature, ageing is universally understood to involve biological, cultural, and social transformation [1,6,7]. The United Nations (UN) however, defines an older person as someone 60 years and over for developing countries and 65 years for developed countries [4]. The United Nations (UN) in its 2019 report projects the population of older persons 65 years and over to rise from 0.7 billion (9%) in 2019 to exceed 2 billion (16%) by 2050 [1-4]. The increasing pressure on healthcare infrastructure would require reforms in health systems to meet the growing need of the aged particularly in lower and middle-income countries [1-3]. Current demographics reflect decreasing fertility rates and increasing life expectancies in many parts of the world [4,7]. It is projected that developing countries would be greatly impacted by this demographic tide [7-9]. For the first time in 2018, older people aged 65 years and above outnumbered children under five years of age worldwide [4]. Statistics indicate that this trend of population growth could double between 2019 and 2050. The proportion of older people will reach nearly 12% and 23% in 2030 and 2100 [1-4]. This transition is closely linked to the epidemiological transition and health transition [7]. Given the emergence of triple burden of disease in developing countries, older people are at risk of suffering from chronic communicable and infectious illnesses.

Older people 65 years and above constitute 18% and 16% of the total population in (Europe and northern America) and Australia/New Zealand respectively. It is estimated that one in every four persons could be 65 years and over by 2050. Africa’s aged population was 3.5% of 1.2 billion in 2015 [5]. By 2050, this figure will rise to 6% of 2.5 billion and it is projected to reach 14.6% by the end of the twenty-first century [5]. The question is, are developing countries prepared to handle the challenges of a rapidly ageing population? Longevity alone is not enough; human dignity and good health must be prioritized [5]. Population ageing will exacerbate the plight of ill-health, disability, and dependence in society and the economy. Evidence suggests that health systems could face an annual increase in costs due to the rising ageing population.

In the context of Ghana and other lower and middle-income countries, “ageing in place” is an entirely new phenomenon. Institutional support for older people such as retirement/nursing homes in Ghana is limited. The family remain the primary source of care for older adults. Children, grandchildren, nieces and nephews perform caregiving tasks such as cooking, cleaning, mobility support, healthcare financing among others for older family members. However, and more recently, the role of the family in the care and wellbeing of older people is dwindling. The weakening social ties, changing household structure, economic challenges, and family nuclearization weakens the support for older people [1,4,10,11]. Kpessa-Whyte and Tsekpo (2020) noticed that the idea of retirement homes is being considered in some parts of Ghana. Recent studies by Avuviry-Newton et al [12] re-emphasized the need for long-term care for older people in Ghana. Old age is generally characterized by social and health challenges [13]. Older adults are at risk of non-communicable diseases such as hypertension, anemias, kidney diseases, diabetes, cardiovascular diseases [1,4], obesity, and cancer. Poor diet, lack of physical exercise, alcoholism, poor sanitary conditions, and over-reliance on orthodox medicine result in health crises among older cohorts [13]. These health conditions are mostly exacerbated by lack of social security and financial problems [13]. Poverty remains an important determinant of health-seeking behavior [1]. To alleviate the plight of older adults, many governments have instituted programmes and policies to safeguard the health and dignity of seniors. The current study looked at healthcare utilization among older adults under the LEAP in the Daffiama-Bussie-Issa District of the Upper West region of Ghana.

Healthcare financing in Ghana since independence

Achieving Universal Health Coverage (UHC) has been a challenge for many governments. At independence, Ghana’s first president, Dr. Kwame Nkrumah introduced ‘‘Free Health Care for All’’ policy. Although private health facilities were excluded from the benefit package [14], the number of health centers increased from 10 in 1957, to 41 by 1963 [15,16]. Also, rural folks could not access health care under the policy due to distance decay [17]. Essential health commodities later run short in health facilities making the policy less efficient. As a solution to this, successive governments discontinued the “Free Health Care for All” policy and introduced a cost-sharing intervention policy known as the “Nominal User Fee” system in 1969. This resulted in the inability of the poor and vulnerable to afford health services. As a result, life expectancy fell from an average of 55 years in 1970 to 53 years in 1979 [14]. Following the global economic depression of the 1970s and early 1980s, compounded by droughts and bushfires and coupled with mismanagement of state resources, huge budget deficits, and high inflation which was further exacerbated by the repatriation of Ghanaians from Nigeria, the supply of drugs and other essential health products in health facilities was drastically affected [15]. The Provisional National Defense Council (PNDC) government at the time sought assistance under the Structural Adjustment Program (SAP) and implemented Economic Recovery Reforms (ERP) by the IMF and World Bank which led to a cut in expenditure on the provision of social services. The government subsequently withdrew all subsidies on healthcare provision and social intervention programmes.

By 1985, the user fee system was replaced with the “Cash and Carry System”. This system required that people must pay before they could access essential health services. This resulted in poor health outcomes among most Ghanaians who could not afford out-of-pocket payment. Although, the cash-and-carry system improved the supply of essential medicines, it forced vulnerable households to resort to delaying treatment, self-medication, and reliance on informal healthcare [10]. Others are of the view that the user-fee system encouraged people to be conscious of their health [15].

This cost-sharing approach helped the government to raise more revenue for the health sector [18]. Domapielle and colleagues argued that though the policy increased enrolment and the uptake of health services, it failed to deliver on vertical equity [19]. As a remedy to address the growing inequities in the health system, the Catholic Church later introduced the Community Based Health Insurance Scheme. The program was first piloted in Nkoranza in 1992 and extended to the Damongo hospital in 1996. A major challenge with this program was the geographic limitation to the policy as most Ghanaians were left out [14]. As a comprehensive plan to achieving UHC, the National Health Insurance Scheme was introduced in 2003 and implemented in 2004 by the government of Ghana.

Subsequently, the LEAP programme was instituted in 2008 as part of the National Social Protection Strategy, which now covers all the districts in Ghana, including the Daffiama Bussie Issa District. LEAP supports extremely poor households with conditional and unconditional cash transfers and empowers targeted populations by offering them their basic needs and improving their access to existing government social protection interventions to enable them to contribute to the socio-economic development of the country. More explicitly, LEAP seeks to improve basic household consumption and nutrition, and access to health care services, among children under two years old, older persons, and people with severe disability; increase basic school enrolment, attendance, and retention of beneficiary children between the ages of 5–15; and facilitate access to complementary services to enhance productive capacity (Ministry of Employment and Social Welfare, 2012). The National Health Insurance Scheme (NHIS) provides exemptions for selected groups of the population. Among these groups are children under eighteen years, pregnant women, and persons 70 years and above, and indigents (the extremely poor).

By eliminating financial barriers through the NHIS exemptions, it is envisaged that equity in healthcare accessibility and utilization will be guaranteed [1,20,21]. Enrolling older people under the NHIS is aimed at ameliorating the financial burden of the aged in Ghana by eliminating the cash and carry system [20,21,22]. A major disadvantage of this exemption policy is the problem of identifying the poorest of the poor. The Livelihood Empowerment Against Poverty programme, therefore, became the mechanism for selecting the very poor for exemption [1,22]. According to Anderson’s (1972) behavioral model of health service usage, the presence of enabling resources such as the NHIS subscription increases the odds of utilizing healthcare. The LEAP program, therefore, aims to bolster older people’s access to healthcare. However, the persistent surge in morbidities is a testimony that the healthcare delivery system needs reinforcement [4,21].

Theoretical Underpinning of the Study

The study is underpinned by the Health Care Utilization Model by Anderson and Newman (1973). According to the model, three key elements: predisposing, enabling, and need-for-care dictate the utilization of services by individuals. Predisposing characteristics such as demographic (age, gender, marital status), social structure (religion, education, occupation), and belief systems (values and knowledge on health services) of individuals influence health seeking behavior. Enabling factors are the resources at the disposal of the individual or at the community level.

Figure 2: health care utilization Model.

Source: Adapted from Anderson and Newman, 1973.

Enabling resources include financial resources such as income, health insurance and accessibility to health services facilitates the utilization of health care services [18]. Need factors are the illness levels (perceived and evaluated) requiring the urgency for health care use. Specifically, it has been used to examine health care services utilization. Need factors hold the principle that there may be the presence of predisposing and enabling conditions, but how one perceives the severity of an illness or ailment will inform utilization of health care [23]. Despite its wider application in different disciplines, the model seldom pays attention to cultural dimensions and social interactions. Andersen (1973) however, argued that need in itself is a social construct.

Study Context

The Daffiama-Bussie-Issa (DBI) District is 57 kilometers away from the regional capital of Wa. The district is located between latitudes 11'300 and 10'200 North and 3'100 and 2'100 West. The district is bordered by Sissala West District to the North, Wa East District to the east, Wa Municipal to the south, and Nadowli-Kaleo to the West. The district has the least population in the Upper West of Ghana. According to the 2021 population and housing census, the district has a total population of about 38,754. This corresponds to 4.7% of the region's total population in the Upper West. Females represent 19,831 (51.2%) and males, 18,923 (48.8%) in the district (GSS, 2021).

The district contains thirteen (13) health facilities, including eight (8) CHPS compounds at Owlo, Wogu, Kamahegu, Challa, Tabiasi, Samambo, Jimpensi, and Konzokala, five (4) health centers located at Daffiama, Bussie, Fian, and Kojokpere. Access to healthcare is difficult due to bad roads, and a lack of motor transportation services. The current healthcare facilities are understaffed and under-resourced. The Issa District hospital is home to the only medical doctor in the area. In contrast, to the national average of 1:10,450 and the WHO-recommended ratio of 1:1,320, the doctor-patient ratio stood at 1:38,754.

The local economy is predominantly agrarian with commence and industry poorly developed. The agriculture sector employs about 78% of the total population. Food crop production is on a subsistence basis interspersed with animal rearing while cash crop production is limited. The second-largest employer is the commerce/service sector. It is composed of petty trading activities and transport business. The sector is dominated by petty trading, mainly agricultural goods, and consumer goods. The major markets in the district are in Bussie, Daffiama, Issa, Kojokperi Tabiasi, Wogu, Owlo, and Sazie. These markets operate periodically on market days. The periodic market is a major source of revenue for the district assembly. Bussie has the largest market center in the whole district serving most of the settlements.

Figure 1: Map Of The Daffiama Bussie Issa District.

Source: District Developing Planning Unit, DBI (2020)

Sampling Techniques And Procedures

Multiple sample techniques such as cluster sampling, simple random sampling, and convenience were employed to select both research communities and respondents. In the first place, the study district was clustered into three geographical areas: North (Bussie traditional area), East (Daffiama traditional area), and West (Issa traditional area). It is worth noting that these clusters are unique and pre-existing in the study district. The clusters reflected the heterogeneity of the total population as a mini version of the whole population is reflected on each cluster; hence the researchers were capable of getting a representative pattern to produce findings that reflect the views of the sampled population.

The clustered geographical areas were numbered based on North, East, and West delineation. The study communities in the Western, Eastern, and Northern delineations were chosen based on the availability of a Community-Based Health Planning and Services (CHPS) compound and those without CHPS compounds. Communities in the West included Daffiama, Dankyelle, Owlo, and Buoyiri whilst communities in the North comprised Bussie, Fian, Pulbaa, Kamahegu, and the Eastern communities included Issa, Kojokpere, Tabiasi, and Duang.

Table 1: Sampled communities and sub-sample.

Geographical area	Sample allocation	Sampled communities
West	28	Daffiama, Dankyelle, Owlo, and Buoyiri
North	26	Bussie, Fian, Pulbaa, Kamahegu
East	50	Issa, Kojokpere, Tabiasi and Duang.

Source: Authors construct, 2022

In all, twelve (12) study communities out of the thirty-five (36) communities that are enrolled in the LEAP programme in the DBI district were selected based on simple random sampling technique. Because of the different population sizes for the poor older people in the three geographical areas an estimate of 50, 28, and 26 of the sampled was allocated to the East, West and Northern demarcations respectively based on proportion. The sampling technique used to select the poor older people was simple random. The use of simple random sampling in the study helped to ensure fairness and transparent sampling procedure which ensured that every respondent has an equal chance of taking part in the study. Furthermore, simple random sampling is employed in the study because the list of the participants was available [25]. Added that a simple random sampling is appropriate for geographical delineation as adopted in the study. This reinforced the need to employ simple random sampling to recruit poor older persons as far as this study is concerned.

The actual sampling and selection procedure followed eight main stages. First, the list of all beneficiaries of the LEAP programme from the Department of Social Welfare, at the DBI District assembly was obtained. Second, the names/list of older persons who were 65 years or above was extracted. Third, a required number of study respondents were estimated using Miller & Brewer's (2003) formula for sample size calculation.

Where n (sample size), N (sample frame), is margin of error (0.05)

Mathematically, this can be illustrated as

This was estimated to be 104 poor older people. This method of determining sample size is scientific, representative, and generalizable and has been used in a lot of ageing studies in Ghana and elsewhere. Fourth, a blindfolded person was made to select the required sample size allotted for each study community without replacement until the assigned sample for each community was obtained [26]. Five, the sample was distributed among the randomly selected communities based on proportion.

Table 2: Sample allocation to sample communities.

Geographical location	Sampled communities	Sub-sample
West	Daffiama	14
	Dankyelle	3
	Owlo	7
	Buoyiri	4
North	Bussie	15
	Pulbaa	3
	Fian	6
	Kamahegu	2
East	Issa	23
	Kojokepere	14
	Tabiasi	9
	Duang	4
Total	12	104

Source: Authors construct 2022

Step six, the sampled list was taken to the various chosen communities for the data collection exercise to commence. Respondents were selected randomly to take part in the study. Provision was made such that if the randomly selected respondents were not available or declined to participate, in the study, the process could be repeated to get a replacement.

Data Collection and Analysis

Questionnaire format featured closed-ended questions to ensure that respondents provided direct answers to questions. The questions were translated from English to Dagaare (the participants' native tongue) to prevent ambiguity. To assist in the data gathering process, graduate students from the Department of Governance and Development Management at UDS were enlisted and trained. However, the close-ended questionnaire gave little opportunity to verify the veracity of the research participants' responses [25]. The reliability of the data gathering tools went through rigorous examination by experts in the field of public health. Language clarity, simplicity, and the significance of the study instrument were evaluated. Using SPSS software, the original data were statistically evaluated (Version 20.0). The data were first described using descriptive statistics like frequencies and percentages. A 95% confidence level chi-square test was performed to assess how socio-demographic, economic, perceived health status, and behavioral variables of older people was associated with their use of health care in study communities.

Ethical considerations

Ethical considerations under the declaration of Helsinki (1964) were adhered to (World Medical Association, 2001). The research recognized that the well-being of human beings takes precedence over the interests of science and society hence adhered to the principles of informed consent, confidentiality, and anonymity. Before the data collection, introductory letters were obtained from the Department of Governance and Development Management and sent to the Department of Social Welfare and the District Health Directorate of the Daffiama-Bussie-Issa District to obtain the needed consent and permission. Informed consent was sought before the administration of data collection instruments. Consent forms were read out to participants and those who agreed to participate in the study consented by thumbprinting or signing the consent forms before the commencement of actual data collection. Participants were provided with adequate information and made aware that their participation was voluntary. To ensure confidentiality, participants’ identities were kept anonymous. Participants were identified numerically as their names were not required during questionnaire administration. All necessary protocols were observed before data collection commenced.

According to the study's findings, 48 of the respondents were men and 56 were women, making up 46.2% of the population whose demographics were studied. This indicates that a higher percentage of women (54.8%) than the national average made up the LEAP program's beneficiaries. Regarding age, the majority (80.8%) of respondents fell into the 65–69 age range, while 9.6% and 9.6%, respectively, of respondents were in the 70–74 and 75–79 age ranges. Due to their extreme poverty and hardship, this qualifies them as LEAP recipients. The LEAP has consequently arrived in order to alleviate their poverty through its empowerment programs.

The study's findings indicated that the two main ethnic groups in the Daffiama Bussie Issa District are the Dagaabas and the Sissalas. The Dagaaba ethnic group made up more study participants (53.8%), while the Sissala ethnic group came close behind. In terms of religion, most respondents (63.5%) identify as Christians, while 36.5% identified as Muslims. This illustrates that the two main religions in modern Ghana are Christianity and Islam. Christianity and Islam account for around 71.1 and 17.6 % of the country's population, respectively.

Table 3: Socio-economic characteristics of poor older people.

Socio-economic variable	Category	N=104	(%)
Gender	Male	48	46.2
Gender	Female	56	53.8
Age	65-69	84	80.8
	70-74	10	9.6
	75-79	10	9.6
Ethnic Group	Dagaaba	56	53.8
Ethnic Group	Sissaala	48	46.2
Religion	Christianity	66	63.5
Religion	Islam	33	36.5
Marital Status	Married	55	52.9
Marital Status	Widowed	49	47.1
Educational Background	No formal education	97	93.3
Educational Background	Primary/JHS	7	6.7
Occupation	Farming	71	68.3
	Artisanship	8	7.7
	Civil/Public Service	6	2.9
	Others	16	5.8
Monthly income	100 or less	57	54.8
	101-200	17	16.3
	201-300	6	5.8
	301-400	21	20.2
	≥400	3	2.9

Source: Field survey (2022).

The survey also showed that the majority of respondents (52.9%) were married, while 47.1% were widowed. Given that the majority of individuals their age had lost their relationships, this is not unexpected as that was a criterion of selecting LEAP beneficiaries. Most respondents had no formal education, while the remaining respondents, or 6.7%, had only completed elementary and junior high school.

Factors associated with healthcare use among older adults.

The study examined the relationship between socio-demographic and economic characteristics of older people and healthcare utilization. A 95% confidence level chi-square test was performed to assess how socio-demographic, economic, perceived health status, and behavioral factors of older people predict their use of health care in study communities.

The study examined the relationship between socio-demographic and economic characteristics of older people and healthcare utilization. A 95% confidence level chi-square test was performed to assess how socio-demographic, economic, perceived health status, and behavioral factors of older people predict their use of health care in study communities. More women (59.5%) than men (40.5%) possessed valid NHIS cards. The study found that older people's gender was associated with having an active NHIS for access to health care (p-value = 0.003). In terms of ethnicity, it found that more Dagaaba (73.8%) were active NHIS members than Sissala (66.1%), whose NHIS cards had expired at the time of the survey.

Table 4: Chi-square results of the factors influencing formal healthcare use among older adults.

Socio-Demographic Characteristics	Category	Health Insurance Status		Sought Formal Healthcare		Rate of Seeking Formal Healthcare
		Health Insurance Status		Within the last six months		Rate of Seeking Formal Healthcare
		Active	Expired	Yes	No	None	Daily	Monthly	Quarterly	Yearly
Gender	Male	17(40.5%)	31(50.0%)	48(49.5%)	0 (0.0%)	11(100.0%)	0 (0.0%)	10 (35.7%)	17(35.4%)	10(100.0%)
	Female	25(59.5%)	31(50.0%)	49(50.5%)	7(100.0%)	0(0.0%)	7(100.0%)	18 (64.3%)	31(64.6%)	0(0.0%)
	Chi-Square	0.914 (0.339)		9.098 (0.003)		44.661 (0.001)
	(P-Value)	0.914 (0.339)		9.098 (0.003)		44.661 (0.001)
Ethnic Group	Dagaaba	31(73.8%)	21(33.9%)	45(46.4%)	7(100.0%)	0(0.0%)	7(100.0%)	28(100.0%)	17(35.4%)	0(0.0%)
	Sissala	11(26.2%)	41(66.1%)	52(53.6%)	0(0.0%)	11(100.0%)	0(0.0%)	0(0.0%)	31(64.6%)	10(100.0%)
	Chi-Square	15.975 (0.001)		10.210 (0.001)		81.776 (0.001)
	(P-Value)	15.975 (0.001)		10.210 (0.001)		81.776 (0.001)
Religion	Christianity	24(57.1%)	42(67.7%)	59(60.8%)	7(100.0%)	11(100.0%)	7(100.0%)	28(100.0%)	10(20.8%)	10(100.0%)
	Islam	18(42.9%)	20(32.3%)	38(39.2%)	0(0.0%)	0(0.0%)	0(0.0%)	0(0.0%)	38(79.2%)	0(0.0%)
	Chi-Square	1.213 (0.271)		6.654 (0.010)		87.415 (0.000)
	(P-Value)	1.213 (0.271)		6.654 (0.010)		87.415 (0.000)
Marital Status	Widowed	28(66.7%)	21(33.9%)	49(50.5%)	0(0.0%)	0(0.0%)	0(0.0%)	28(100.0%)	21(43.8%)	0(0.0%)
	Married	14(33.3%)	41(66.1%)	48(49.5%)	7(100.0%)	11(100.0%)	7(100.0%)	0(0.0%)	27(56.3%)	10(100.0%)
	Chi-Square	10.808 (0.001)		9.368 (0.002)		78.038 (0.000)
	(P-Value)	10.808 (0.001)		9.368 (0.002)		78.038 (0.000)
Educational Background	No Formal	35(83.3%)	62(100.0%)	90(92.8%)	7(100.0%)	11(100.0%)	7(100.0%)	28(100.0%)	41(85.4%)	10(100.0%)
	Primary/JHS	7(16.7%)	0(0.0%)	7(7.2%)	0(0.0%)	0(0.0%)	0(0.0%)	0(0.0%)	7(14.6%)	0(0.0%)
	Chi-Square	13.449(0.000)		1.011 (0.315)		11.417(0.022)
	(P-Value)	13.449(0.000)		1.011 (0.315)		11.417(0.022)
Occupation	Farming	16(38.1%)	55(88.7%)	64(66.0%)	7(100.0%)	11(100.0%)	7(100.0%)	6(21.4%)	37(77.1%)	10(100.0%)
	Artisanal Work	6(14.3%)	2(3.2%)	8(8.2%)	0(0.0%)	0(0.0%)	0(0.0%)	8(28.6%)	0(0.0%)	0(0.0%)
	Industrial Work	1(2.4%)	2(3.2%)	3(3.1%)	0(0.0%)	0(0.0%)	0(0.0%)	3(10.7%)	0(0.0%)	0(0.0%)
	Civil/Public Service	6(14.3%)	0(0.0%)	6(6.2%)	0(0.0%)	0(0.0%)	0(0.0%)	6(21.4%)	0(0.0%)	0(0.0%)
	Others	13(31.0%)	3(4.8%)	16(16.5%)	0(0.0%)	0(0.0%)	0(0.0%)	5(17.9%)	11(22.9%)	0(0.0%)
	Chi-Square (p-Value)	36.274(0.001)		5.576 (0.233)		71.328 (0.001)

Source: Field Survey, 2022

Statistically, the results show a significant association between older people's ethnicity and having an active NHIS card for access to healthcare (p-value 0.001). Similarly, the results showed a statistically significant association between older people's ethnicity and the manner in which they accessed formal health care (p-value = 0.001) and their frequency of health care use (p- value = 0.001). For example, more than half (53.6%) of the elderly in Sissala sought formal health care, compared with less than half (46.4 %) of the elderly in Dagaaba, at different times.

Regarding religion, the results indicate that many Christians (67.7 %) had not renewed their NHIS, compared to Muslims (42.9 %) who possesses active NHIS cards at the time of the survey. Results suggest that older people's religion is not significantly associated with possession of an active NHIS card for access to health care in the event of illness (p-value = 0.271). Despite the small sample size, it is Clear that Christianity and Islam are supplanting indigenous African religions in the study district. However, there was a significant association between older people's religion and their use of formal health care, even when they were ill (p-value = 0.001). Widows and widowers (66.7 %) possessed valid NHIS cards than their counterparts who were married (33.3 %). As a result, marital status of older people was significantly associated with possession of an active NHIS cards for access to health care (p-value = 0.001). In addition, the marital status of older people was significantly associated with the utilization rate of formal health care (p-value = 0.002) and the frequency of use (p-value = 0.001).

The study examined the factors affecting healthcare utilization among older people under the LEAP program in DBI District. The study found that gender, ethnicity, religion, and marital status were significant variables influencing the utilization of healthcare by older adults in the Daffiama Bussie Issa area. Regarding gender, the findings supported earlier research that found a substantial positive correlation between impoverished older people's gender and their use of healthcare [28,29]. However, it conflicts findings of other studies by Barreto et al. (2006) and Liu (2014), which indicated that older men and women use healthcare equally regardless of their healthcare-seeking behaviors [30,31]. As reported by Kim & Lee (2016) and Rieker & Bird (2005), older women suffer higher disease burden, as a result, use outpatient services more frequently than older men. Song & Bian's (2014) research also suggests that women are more proactive in seeking out medical attention than their counterparts. However, the study did not clearly measure the gender differences in health care access and utilization among older adults in the study area [31,32].

In the Daffiama-Bussie-Issa district, it was ascertained that ethnicity has a strong and significant correlation with older individuals' usage of healthcare. This supports research by Dhingra

et al. (2010). Yunus (2015) also found a statistically significant link between ethnicity and elderly Malaysians' usage of healthcare. However, research findings diverged with Exavery (2010) and found no statistically significant link between ethnicity and older persons' use of healthcare in rural Ghana. The dominant ethnic groups in the district are Dagaaba and Sissaalas with sharp differences in language and cultural practices. Also, the geographical delineation of the district among the two ethnic groups is also worth noting. The Dagaabas occupy the North, and Western portions making them the dominant group in the district. Also, the majority of the health centers are concentrated in these areas.

Marital status was shown to be substantially related to older persons' health-seeking behavior. This supports findings by Gyasi et al. (2019), Kim & Lee (2016), and Badu et al. (2018) that people's preferences for using healthcare often correlate with their socio-demographic traits, including marital status [33]. Gyasi (2019) further reported that elderly married persons are more likely to use healthcare services than their single counterparts. The study diverged from findings by McNamara et al. (2013), which showed no connection between marital status and older people's usage of healthcare services in Ireland [34]. Spousal support is crucial at old age as they serve as a source of emotional and psychological support during times of illness. Spousal support also helps absorb some financial burden of hospitalization and cost of drugs.

In contrast to previous research, the study found no statistically significant correlation between age, education, income, health insurance, occupation, and the use of medical services among older persons from low-income families. These findings went counter to those of researchers who found that characteristics such as age, education, money, health insurance, and occupation are statistically related with impoverished older people's use of healthcare [1,4,18,35]. The researchers argued that educated persons tend to use more health services than those who lack education since they are more conscious of their health and lifestyle. However, the findings did not corroborate this, probably because of the socio-demographic and economic contexts in which the study was conducted.

Previous studies identified income as a predictor for health care use among older people, but this study did not maybe because of how an older person is defined in the current study [18,35,36,37]. For instance, some research classified an older person as someone who is 50 years or older, but this study used age of 65 as the baseline to identify an older person. In Ghana, this category of people are over the retirement age of 60 and do not qualify for employment and as a result cannot generate income. Therefore, they are entirely dependent on the LEAP subvention and family assistance to pay for their medical expenses and drug use. The study indicates the dominance of predisposing factors (gender, ethnic group, religion, marital status and household size) as predictive variables in health care utilization against enabling and need factors [38-45].

Strengths and Limitations of the Study

The study was underpinned by Anderson and Newman’s model of health care utilization model [46]. Utilizing the model provided a broader framework for unpacking systematically, the individual determinants and behavioral factors of health use among older adults in the study district. Despite the validity of the results, there are a few limitations to the study that should be mentioned [47,48]. The case study was exclusively conducted on a small elderly population under the LEAP program in the Daffiama Bussie District. Because of this, it may be difficult to generalize the findings to all Ghana's elderly poor [47,49-56]. Therefore, it is recommended that the study be repeated in other Districts to confirm the results [57,58]. Also, the study did not measure the impact of the LEAP on the health care use of older adults [ 59,60].

The study contributed to the discourse on ageing and health in resource poor settings using quantitative measurements such as chi-square to analyze the factors influencing health care utilization among impoverished older people. The study added to our understanding of the literature, methodology, theoretical and conceptual frameworks relating to healthcare utilization. The study partially endorses the use of Anderson and Newman's framework for healthcare utilization. This highlighted the research's policy ramifications on the variables affecting older adults in the usage of healthcare. The findings brought to light the dominance of micro level variables (gender, ethnicity, religion, and marital status) as significant determinants of healthcare use in the study area. It is important for government and policy makers to take into consideration the predisposing characteristics and other social determinants of health in the formulation of policies geared towards the long-term care needs of older adults. A key policy recommendation is an expansion of the in LEAP coverage and benefit packages to older people to enable them to meet the nutritional and health care needs.

Funding Statement

The study is part of a master’s research funded by the German Academic Exchange Service (DAAD) under the West African Center for Sustainable Rural Transformation (WAC-SRT) at the Simon Diedong University for Business and Integrated Development Studies, Ghana.

Journal of Medical and Clinical Studies

Healthcare Utilization among Older Indigents under Social Protection in Rural Ghana

Abstract

Keywords

Introduction

Materials and Methods

Results

Discussion

Conclusion and Policy Implications

References