Skip to main content

Predictors of healthcare utilisation among poor older people under the livelihood empowerment against poverty programme in the Atwima Nwabiagya District of Ghana

Abstract

Background

Like many other low- and middle-income countries (LMICs), the Ghanaian healthcare system remains poor which is likely to affect the utilisation of healthcare services, especially among poor older people who are faced with multiple health problems. Yet, factors that explain healthcare use among poor older people in LMICs, particularly Ghana remain largely unexplored. Understanding the predictors of healthcare use among poor older people could have a huge impact on health policies in LMICs including Ghana. This study, therefore, examined factors associated with healthcare use among poor older people under the Livelihood Empowerment Against Poverty (LEAP) programme in the Atwima Nwabiagya District of Ghana.

Methods

Cross-sectional data were obtained from an Ageing, Health, Lifestyle and Health Services (AHLHS) study conducted between 1 and 20 June 2018 (N = 200) in Atwima Nwabiagya District, Ghana. Sequential logistic regression models were performed to estimate the variables that predict healthcare use among poor older people. All test results were considered significant at 0.05 or less.

Results

The fully adjusted model showed that respondents aged 85–89 years (AOR = 0.094, CI: 0.007–1.170), acquired basic education (AOR =0.251, CI: 0.085–0.987), received no family support (AOR = 0.771, CI: 0.120–0.620), with no past illness records (AOR = 0.236, CI: 0.057–0.197) and who were not diagnosed of chronic non-communicable diseases (AOR = 0.418, CI: 0.101–0.723) were significantly less likely to utilise health facility compared with their respective counterparts. Moreover, those with no disability (AOR = 19.245, CI: 2.415–29.921) and who consumed low fruits (AOR = 1.435 = CI: 0.552–8.740) and vegetables (AOR = 1.202 = CI: 0.362–10.20) had a higher likelihood to use healthcare.

Conclusion

The study has outlined multiple factors influencing utilisation of healthcare among poor older people under the LEAP programme in Ghana. The results, therefore, validate the importance of social and behavioural determinants of healthcare use in the Ghanaian poor older population. We highlight the need for health planners and stakeholders to consider demographic, socio-economic, health-related and lifestyle factors when formulating health policy for poor older people in Ghana.

Peer Review reports

Background

Ghana like any other low-and middle-income countries (LMICs) faces population ageing challenges. Ghanaian older population has increased more than seven-fold from 213,472 in 1960 to 1,643,381 in 2010 [1], which is increasing at a faster pace in the sub-Saharan Africa region. However, most older people are trapped in vicious cycles of poverty in Ghana. The prevalence of poverty among older persons is likely to have a negative effect on their healthcare use patterns. To reduce poverty situation among the older people and to improve their economic conditions and health in Ghana, social policies and programmes, such as the National Social Protection Strategy, National Ageing Policy [2] and the Livelihood Empowerment Against Poverty (LEAP) programme, have been introduced.

The LEAP programme is a financial transfer policy funded by the World Bank, the United Nations International Children’s Emergency Fund (UNICEF) and the Government of Ghana. It gives financial protection to people and households that are considered extremely poor [3, 4]. Those who benefit from the LEAP programme comprise older people aged 65 years or more, orphaned and vulnerable children, and persons with disabilities [3, 5]. They get a bi-monthly amount of GH¢ 64 and 106 (US$13.42–22.23)Footnote 1 as a minimum and maximum amounts, respectively [6]. It also provides support for older persons to utilise healthcare services [7].

Studies have shown that with increasing ageing, healthcare utilisation by older people is expected to increase [8, 9]. This is because ageing comes with increased morbidities [10] in a form of functional limitations, cognitive declines and psychological distress [11, 12], which partly result from a weak immune system [13]. In Irbid Governorate of Jordan, Alkhawaldeh et al. [14] found that approximately three in four older people utilised medical care services. Also, in Nigeria, Atchessi et al. [15] discovered that 53% of older people utilised healthcare services. In a cross-sectional survey by Awoke et al. [9] in Ghana, 17.8 and 51.7% of older persons were found to utilise private and public healthcare facilities respectively. According to Dei & San Sebastian [16], 52.41% of older people in Ghana frequently utilise public healthcare services. This demonstrates that the prevalence of healthcare use among poor older people differs from one country to the other. Even in the same country, differences in healthcare utilisation rate seem to exist across spatial locations.

In developing economies, the use of healthcare follows the social and behavioural constructs [17, 18]. A person’s utilisation of healthcare service is measured by predisposing factors (social structure, demographic and health beliefs), enabling factors (income, health insurance enrolment, access to healthcare facilities and transportation) and needs factors (perceived health status and health evaluation), which either facilitate or hinder utilisation of healthcare [17, 18]. The social and behavioural constructs correlate utilisation of healthcare facilities to both individual and structural factors [19]. Also, the Health Belief Model (HBM) postulates that susceptibility, severity, benefits, costs, barriers and cue to action are the main constructs that influence people to take an action [20]. However, people have higher odds to use health commendation, such as consulting healthcare providers, if they have the intention to do so [20, 21]. With the use of the social and behavioural constructs and the HBM, we are able to provide a theoretical context for the study and situate our findings within these frameworks. Besides, the models were used to limit the scope of the relevant data obtained from the field by focusing on specific variables and defining them to facilitate better interpretation and analysis of the findings in a particular framework.

Evidence has shown that socio-demographic factors influence healthcare utilisation [22,23,24,25,26]. Studies further show that smoking [14, 27,28,29,30], physical activity, alcohol consumption [31,32,33], and health status [14, 33,34,35,36,37,38,39,40] influence healthcare utilisation. Despite the growing literature on factors predicting healthcare utilisation among older people, there is a paucity of information on the predictors of healthcare use among poor older people, who are enrolled in the LEAP programme in Ghana. Involving poor older persons under the LEAP programme in the Atwima Nwabiagya District of Ghana, this study examined the proximate factors influencing their healthcare utilisation. Understanding the predictors of healthcare utilisation from the perspective of poor older people in Ghana could provide significant direction for health policies and health services development in later life.

Methods

Sample and data

The study relied on data from a wider Ageing, Health, Lifestyle and Health Services (AHLHS) study, a cross-sectional study conducted between 1 and 20 June 2018. The AHLHS study was to explore the determinants of healthcare utilisation among poor older Ghanaians under the LEAP programme in the Atwima Nwabiagya District of Ghana. In this study, healthcare use was defined as seeking medical treatment from a health professional at a hospital, clinic and other health centres. Unlike other works [9, 40,41,42,43,44], this study defined an older person as an individual who has attained 65 years or above [45, 46] which was in line with the method used to enroll poor older people in the LEAP programme in Ghana. The LEAP programme helped us to identify poor older people to participate in the study.

In all, 16 communities were randomly selected from Atwima Nwabiagya District, and in these communities, 200 poor older people were selected using cluster and simple random sampling techniques. The sample size was achieved based on a power calculation with alpha 0.05. To ensure that the study participants came from a different socioeconomic background, all geographical areas were fully represented. Atwima Nwabiagya District was grouped into three geographical areas based on north, central and south delineation, where 32, 67 and 101 of the total sample size were respectively allocated based on proportion. The selection of the study participants was based on an exclusion and inclusion criteria approach. Poor older people who were sick as at the time of the survey were excluded from the study. Those who had attained 65 years or more and were enrolled in the LEAP programme in the study area were included in this study.

Data were collected through an interviewer-administered questionnaire from the study participants. The questionnaire was developed in English but it was read in Twi for easy understanding by the respondents and also because most of them were illiterate who could not read or write. Three research assistants from the Department of Geography and Rural Development at the Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana, were hired and trained to help in the data collection exercise. They were trained for 2 days and the purpose of the study was explained to them. The data collection process was monitored to ensure that the research assistants were doing the right thing. Each administered questionnaire took between 30 and 40 min. The study approval was granted by the Committee on Human Research Publication and Ethics (CHRPE), KNUST School of Medical Sciences and Komfo Anokye Teaching Hospital, Kumasi, Ghana (CHRPE/AP/311/18). The purpose of the study was fully explained to the study respondents for their verbal and written informed consent.

Measures

Outcome variable

The outcome variable was healthcare utilisation which was keyed as a dichotomous variable showing ‘no utilisation’ or ‘utilisation’ of healthcare over the past 12 months before the study. This 12 months estimation rate of healthcare utilisation outcome is found to be consistent with earlier studies [13, 35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50].

Predictor variables

The predictor variables were grouped in four areas: demographic, socio-economic, health-related and health behaviour variables. The demographic variables, such as gender (1 = male, 2 = female), marital status (1 = single, 2 = married), and ethnicity group (1 = Non- Akan, 2 = Akan), were measured as dichotomous variables, age (years) (1 = 65–69, 2 = 70–74, 3 = 75–79, 4 = 80–84, 5 = 85–89, 6 = 90 or above) was measured as ranked variable and religious group (1 = Christianity, 2 = Islam, 3 = African Traditional Religion) was measured as nominal variable. The socio-economic variables, such as education (1 = no formal education, 2 = basic school education, 3 = high school education) was measured as nominal variable, income (GH¢) (1 = 200 or below, 2 = 201–300, 3 = Above 300) was measured as ranked variable whereas enrollment in the National Health Insurance Scheme (NHIS) (1 = Yes, 0 = No) and family support (1 = Yes, 0 = No), were measured as dichotomous variables. Health-related variables, such as past illness records (1 = Yes, 0 = No), disability status (1 = Yes, 0 = No), chronic Non-Communicable Diseases (NCDs) (1 = Yes, 0 = No) and self-related health (1 = Good health status, 2 = Poor health status) were measured as dichotomous variables [51]. Engagement in physical activity (1 = Yes, 0 = No), consumption of alcohol (1 = Yes, 0 = No), consumption of tobacco (1 = Yes, 0 = No), fruits intake (1 = 5 or more serving fruits per day, 2 = less than 5 serving fruits per day) and vegetables intake (1 = 5 or more serving vegetables per day, 2 = less than 5 serving vegetables per day) were measured as dichotomous variables (see Table 1). In line with other studies, adequate fruits and/ or vegetable intake was defined as consuming 5 or more servings per day [52,53,54].

Table 1 Operationalisation and coding of predictor variables

Data analysis

Data were verified and cautiously checked for inconsistencies. Cross-reference was then done to the original questionnaires to inform corrections and any necessary modifications [51]. The data were entered into a database and analysed statistically using Statistical Package for the Social Sciences (SPSS) software (Version 16.0). Percentages and frequencies were used to describe the background characteristics of the study sample, prevalence of healthcare use, sources of healthcare information, satisfaction with healthcare use and the number of kilometres respondents cover to access healthcare. Furthermore, sequential logistic regression models were used to estimate the variables associated with healthcare use among the study participants. Four different sets of models were developed to estimate the predictors of healthcare use among poor older people. Model 1 comprised demographic variables. Model 2 consisted of socio-economic variables plus all variables in Model 1. Whereas Model 3 comprised all variables in Model 2 plus health-related variables, Model 4 (full Model) consisted of all variables in Model 3 plus health behaviour variables. The test results were considered significant at 0.05 or less.

Results

Sample characteristics of the study participants

Table 2 presents data on the socio-demographic characteristics of the study participants. Most of the study participants were females (78%), aged 65–69 (29%), belonged to the Akan ethnic group (84%) and professed Christian faith and doctrine (83%). It was no wonder that most of the study participants were females. This is because about 75% of the beneficiaries who are enrolled in the LEAP programme are females. In addition, 74% were single and 63% had no formal education. The majority of the study participants were enrolled in NHIS (96%) and 75.5% earned monthly income of GH¢200 or below. Also, 38% of the study participants received family support.

Table 2 Socio-demographic characteristics of the study sample

Prevalence of healthcare use among the study participants

Table 3 shows the prevalence of healthcare use among the study participants. The results showed that most of the study participants (85%) used healthcare services, covered 2 km to access and utilise healthcare (33%), sourced information on healthcare from family members (54%) and were satisfied with healthcare services utilisation (71%).

Table 3 Prevalence healthcare use among the study participants

Predictors of healthcare use among the study participants

A sequential logistic regression analysis was performed to find the factors associated with healthcare use (Table 4). The results in Model 1 indicated that females were 2.694 times more likely to use healthcare compared to their male counterparts (AOR = 2.694, CI: 1.002–7.244). Model 2 showed that uninsured study participants were significantly less likely to use healthcare facilities compared to those who were insured (AOR = 0.096, CI: 0.016–0.585). Furthermore, respondents without family support were significantly less likely to use healthcare compared to their counterparts who received family support (AOR = 0.207; CI: 0.065–0.655). The introduction of socio-economic variables, such as family support and health insurance in Model 2, displaced the significant association between gender and healthcare use. This means that family support and health insurance condensed the relationship between gender and healthcare use. In Model 3, respondents without family support (AOR = 0.125, CI: 0.028–0.552), with no past illness (AOR = 0.088, CI: 0.026–1.292) and reported no chronic NCDs (AOR = 0.159, CI: 0.042–0.602) were significantly less likely to use healthcare compared to their counterparts. The introduction of health-related variables in Model 3 disappeared the relationship between healthcare use and health insurance enrollment entirely insignificant. In Model 4, respondents aged 85–89 years (AOR = 0.094, CI: 0.007–1.170), acquired basic education (AOR =0.251, CI: 0.085–0.987), received no family support (AOR = 0.771, CI: 0.120–0.620), with no past illness records (AOR = 0.236, CI: 0.057–0.197) and who were not diagnosed of chronic NCDs (AOR = 0.418, CI: 0.101–0.723) were significantly less likely to utilise a health facility compared to their respective counterparts. Moreover, those with no disability (AOR = 19.245, CI: 2.415–29.921) and who consumed low fruits (less than five servings per day) (AOR = 1.435 = CI: 0.552–8.740) and vegetables (AOR = 1.202 = CI: 0.362–10.20) were significantly more likely to utilise healthcare facilities.

Table 4 Sequential Logistic Regression results on the determinants of healthcare use among poor older people

Discussion

This study examined the factors associated with healthcare use among poor older people under the LEAP programme in the Atwima Nwabiagya District of Ghana. As found in this study, 17 in 20 of the respondents reported use of healthcare services in the last 12 months preceding the study. This finding showed a higher utilisation rate of healthcare among the respondents compared to 6 and 40% utilisation rate of mental health services both in the United States [46, 55], 49.6% utilisation rate in Ethiopia [48], 45% reported utilisation rate in rural South Africa [50], 73.8% reported rate in Irbid Governorate of Jordan [14] and 76% in Uganda [56]. The finding is relatively similar to a recent Ghanaian study that reported that 81% of older people utilised formal healthcare services [11]. The higher utilisation of formal healthcare utilisation among the respondents could be attributed to the fact that all the participants were enrolled in the LEAP programme which tries to provide support for poor older people to increase their access to healthcare. Even though the LEAP grant is relatively small to pay for all the medical expenditures of the respondents, it plays a key role in their formal healthcare utilisation.

Moreover, while the prevalence of healthcare use reported in this study appears to be high, other studies in German [33], Korea [37] and rural South African setting [33] have reported a significantly higher utilisation rate of healthcare services of 90, 99 and 96%, respectively. The differences in utilisation rate are subject to disparities in the study design, characteristics of the respondents, study setting, operational definition of healthcare and the period of healthcare use preceding the survey. For instance, whereas some studies [10, 11, 40] defined healthcare utilisation to comprise formal and informal healthcare, this study defined healthcare utilisation in the context of formal healthcare.

In our study, age, education, family support, past illness records, chronic NCDs, disability status, fruits and vegetables intake were significantly associated with healthcare utilisation among the respondents. Age was a predictor of healthcare use among poor older persons. The findings of this study confirm the outcome of previous studies indicating an association between the age of a respondent and healthcare utilisation [9, 48, 57,58,59]. However, the findings of this study also contradict the findings of other studies [58, 60]. This difference in results might be as a result of how this current study and the previous studies defined older people. Whereas this study defined an older person as a person who is 65 years or above, others [9,10,11] considered people who have attained 50 years or more as older people. The study found that those between the ages of 85 and 89 years were less likely to utilise healthcare facilities compared to their counterparts who were between 65 and 69 years. Like our study results, Dou et al. [61] also found that inpatient care utilisation reduced with increasing ageing. Although, Awoke et al. [9] reported lower use of healthcare among older people who have attained 80 and above, our finding that respondents aged 85–89 years were less likely to utilise healthcare services is counter-intuitive and appears beyond our expectations. Poor older people aged 85 years or more had a low rate of healthcare use. This is because they have lived beyond the normal survival stage and may tend to care less about health. Besides, most of them are likely to perceive that they can survive without healthcare. Also, the majority of the poor older people aged 85 years or more might have developed dementia and other forms of Alzheimer’s disease which keep their thoughts from use of healthcare services, especially when they do not have any person to take care of them. We strongly admit that the above reasons may not be adequate as there is the possibility of other underlying factors that explain lower odds of utilisation of healthcare services among poor older people who are 85 years or over. Yet, due to the quantitative nature of our study, we were not able to delve into such underlying factors.

Furthermore, the study found a statistically significant association between education and healthcare utilisation among poor older persons. This was in tandem with other empirical studies that have reported an association between education and healthcare use [35, 41]. It is important to note that older persons with primary education have lower odds of using healthcare services compared with those without any educational background [44]. McNamara et al. [39] also reported that in Ireland, people with higher levels of education have fewer visits to a general practitioner (GP) than those with a lower level of education and this, however, conflicts with other studies [22, 62], which could result from differences in the study setting and sample size. These studies suggest that people who are educated are more conscious of their health than the uneducated and thus, they tend to utilise more healthcare services.

Our study also found a statistically significant association between family support and healthcare utilisation among the study participants. It was revealed that study participants who have no family support were less likely to utilise healthcare services compared to those with family support. In the view of Falaha et al. [59], family support during illness acts as a determinant of health service use among older people, and this corroborates our study findings. These findings explain the importance of family support in determining healthcare use among older people. In essence, social support normally received from family members not only provide opportunities for health information delivery but also offer physical and financial assistance for older people in a form of funds for transport and payment of healthcare expenditure as well as providing support for their movement [10]. As pointed by Gyasi et al. [10], older people living with other people are likely to be supported to seek healthcare considering the pressures met at the health facilities and the difficulties involved in travelling.

Again, this study found that past illness influences healthcare utilisation among older people, particularly the poor. Older persons without past illness were less likely to utilise healthcare. This finding supported Andersen & Newman’s [17] framework of healthcare utilisation that persons who experience different past health problems are more likely to demand and make use of medical services. This explained why poor older people with past health problems were having higher odds of using healthcare services in this study. In their framework for the study of access, Aday & Andersen [63] isolated illness level as the most immediate factor that influences people to use healthcare. The finding of this study, therefore, validated the independent frameworks of healthcare utilisation by Andersen & Newman [17] and Aday & Andersen [63]. The higher use of healthcare services among respondents with past illness is as a result of their perceived seriousness of illness and susceptibility as succinctly explained in the HBM. The HBM indicates that people are concerned about whether a disease could lead to death or deteriorate their physical or mental functioning for a long period of time or disable them permanently. As a result, they seek medical care regularly and utilise healthcare services to address their health problems [20, 64, 65].

Poor older persons without chronic NCDs were less likely to utilise healthcare services compared to those with chronic NCDs. In a previous study, older persons with chronic NCDs had significantly higher odds of using healthcare compared to those with acute conditions [36]. Similarly, Kim & Lee [37] reported that people who are suffering from chronic diseases have higher chances of utilising both outpatient and inpatient health services. In line with the finding of Hajek et al. [33], this study reported a significant association between chronic diseases and healthcare utilisation. Interestingly, poor older persons with no disability were significantly more likely to use healthcare facility compared to those with disabilities. This trend is attributed to the various medical and physical accessibility barriers people with disabilities encounter in their quest to use healthcare services which consequently affects their utilisation patterns.

Fruits and vegetables consumption were associated with healthcare utilisation among the study participants. Respondents who consumed low (inadequate) vegetables and fruits in a day were significantly more likely to use healthcare facilities compared to those who consumed high (adequate) fruits and vegetables in a day. This finding is similar to a Ghanaian study that reported that poor older females who had not consumed vegetables in a month were more likely to utilise healthcare services [51]. This may be due to the fact that people who consume adequate fruits and vegetables in a day are likely to perceive their health status as good because of the health benefits associated with fruits and vegetables consumption [66,67,68]. Thus, whereas adequate consumption of fruits and vegetables improves the health of poor older people, it could also reduce their healthcare expenditure since they would not access healthcare frequently.

This study provides several contributions to policy discussions and research literature concerning healthcae use among poor older people in LMICs. To the best of our knowledge, this is one of the first studies that explore the predictors of healthcare utilisation among poor older people, especially those involved in social protection programmes in Africa. This study provides information to health policy makers to have a better understanding of demographic, socio-economic, health-related and health behaviour correlates of healthcare use among poor older people. Despite the contributions of this study, some limitations are notable. Our findings may be exposed to a potential selective survival bias which may partly be attributed to the selection procedure of the sample and criteria for defining or conceptualising poor older people in this study. Also, given the cross-sectional nature of the study, the direction of the causal relationship between healthcare use and factors influencing healthcare utilisation among poor older people cannot be determined. The results only show a significant association between these variables. Future studies should investigate the causality of the associations and the underlying mechanisms for more detail.

Conclusion

This study examined factors associated with healthcare utilisation among poor older people under the LEAP programme in the Atwima Nwabiagya District of Ghana. The results revealed that most of the poor older people utilised healthcare services. However, poor older people’s age, education, family support, past illness records, chronic NCDs, disability status, fruits and vegetable intakes were significantly associated with healthcare use. The results, therefore, validate the importance of social and behavioral determinants of healthcare use in the poor older population. We highlight the need for health professionals to intensify their efforts in providing relevant health information on chronic NCDs to poor older people in order to improve their health conditions. The Ghana Health Service, Ministry of Health, and health institutions should organise disease prevention programmes and health promotion activities to reduce the incidence of chronic NCDs among poor older people in Ghana, particularly Atwima Nwabiagya District through good health behaviour and lifestyle, such as engaging in physical activity regularly and consuming fruits and vegetables frequently. Also, health stakeholders should consider demographic, socio-economic, health-related and lifestyle factors in their formulation of health policy for poor older people.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Notes

  1. 1.

    Exchange rate as at June 2018 (GH¢ 4.77 = US$1.00)

Abbreviations

AHLHS:

Ageing, Health, Lifestyle and Health Services

AOR:

Adjusted Odd Ratio

CHRPE:

Committee on Human Research and Publication Ethics

CI:

Confidence Interval

GP:

General Practitioner

HBM:

Health Belief Model

LEAP:

Livelihood Empowerment against Poverty

LMICs:

Low- and Middle-Income Countries

NCDs :

Non- Communicable Diseases

NHIS:

National Health Insurance Scheme

OR:

Odd Ratio

SPSS:

Statistical Package for the Social Sciences

References

  1. 1.

    World Health Organization. Ghana country assessment report on ageing and health. Geneva; 2014. Accessed at http://apps.who.int/iris/bitstream/106 65/126341/1/9789241507332_eng.pdf on 10/03.17.

  2. 2.

    Kwankye SO. Growing old in Ghana: health and economic implications. Postgrad Med J Ghana. 2013;2(2):88–97.

    Google Scholar 

  3. 3.

    Handa S, Park MJ, Osei RD, Osei-Akoto I, Davis B, Daidone S. Livelihood empowerment against poverty programme impact evaluation report. Chapel Hill: Carolina Population Center, University of North Carolina; 2013.

    Google Scholar 

  4. 4.

    Ministry of Gender, Children and Social Protection. The state of the livelihood empowerment against poverty programme. Accra: Government of Ghana; 2016.

    Google Scholar 

  5. 5.

    Peprah P, Kyiyaga EM, Afful H, Abalo EM, Agyemang-Duah W. Does the Ghanaian livelihood empowerment against poverty programme lead to an increase in household productive livelihood assets? Analysing the Ashanti scenario. Cogent Soc Sci. 2017;3(1):1–12.

    Google Scholar 

  6. 6.

    Agyemang-Duah W, Peprah P, Peprah P. “Let’s talk about money”: how do poor older people finance their healthcare in rural Ghana? A qualitative study. Int J Equity Health. 2019b;18:1–12.

    Article  Google Scholar 

  7. 7.

    Agyemang-Duah W, Peprah C, Peprah P. Factors influencing the use of public and private healthcare facilities among poor older people in rural Ghana. J Public Health. 2019a;28:1–11. https://doi.org/10.1007/s10389-018-01010-y.

    Article  Google Scholar 

  8. 8.

    Guo C, Du W, Hu C, Zheng X. Prevalence and factors associated with healthcare service use among Chinese elderly with disabilities. J Public Health. 2015;29:1–7.

    Google Scholar 

  9. 9.

    Awoke AM, Negin J, Moller J, Farell P, Yawson EA, Biritwum B, Kowal P. Predictors of public and private healthcare utilisation and associated healthcare responsiveness among older adults in Ghana. Glob Health Action. 2017;10(1):1–10.

    Article  Google Scholar 

  10. 10.

    Gyasi RM, Phillips DR, Buor D. The role of a health protection scheme in health services utilisation among community-dwelling older persons in Ghana. J Gerontol. 2018;XX(XX):1–13.

    Google Scholar 

  11. 11.

    Gyasi RM, Phillips DR, Amoah PA. Multidimensional social support and health services utilisation among noninstitutionalized older persons in Ghana. J Aging Health. 2018;00(0):1–28.

    Google Scholar 

  12. 12.

    United Nations. (2015). World population ageing. Retrieved from http://www.un.org/ en/development/desa/population/publications/pdf/ageing/WPA2015_Report.Pdf.

    Google Scholar 

  13. 13.

    Weiskopf D, Weinberger B, Grubeck-Loebenstein B. The aging of the immune system. Eur Soc Organ Transplant. 2009;22:1041–50.

    CAS  Article  Google Scholar 

  14. 14.

    Alkhawaldeh A, Holm MB, Qaddumi J, Petro W, Jaghbir M, AlOmari O. A cross-sectional study to examine factors associated with primary healthcare service utilisation among older adults in the Irbid governorate of Jordan. Curr Gerontol Geriatr Res. 2014;2014:1–7.

    Article  Google Scholar 

  15. 15.

    Atchessi N, Ridde V, Abimbola S, Zunzunegui MV. Factors associated with the healthcare-seeking behaviour of older people in Nigeria. Arch Gerontol Geriatr. 2018;79:1–7.

    PubMed  Article  Google Scholar 

  16. 16.

    Dei V, San Sebastian M. Is healthcare really equal for all? Assessing the horizontal and vertical equity in healthcare utilisation among older Ghanaians. Int J Equity Health. 2018;17(1):86.

    PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Andersen R, Newman JF. Societal and individual determinants of medical care utilisation in the United States. Milbank Memorial Fund Quart Health Soc. 1973;51(1):95–124.

    CAS  Article  Google Scholar 

  18. 18.

    Andersen R. Revisiting the behavioral-model and access to medical-care - does it matter. J Health Soc Behav. 1995;36(1):1–10.

    CAS  PubMed  Article  Google Scholar 

  19. 19.

    Yeboah I, Gyamfuah IA. Determinants of healthcare facilities and services utilisation among the aged: evidence from Yamoransa in Ghana. Am Sci Res J Eng Technol Sci. 2014;8(1):42–55.

    Google Scholar 

  20. 20.

    Rosenstock IM. Historical origins of the health belief model. Health Educ Monog Winter. 1974a;2(4):328–35.

    Article  Google Scholar 

  21. 21.

    Rosenstock IM. Enhancing patient compliance with health recommendations. J Pediatr Health Care. 1988;2:67–72.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  22. 22.

    Almeida APSC, Nunes BP, Duro SMS, Facchini LA. Socioeconomic determinants of access to health services among older adults: a systematic review. Revista de saude publica. 2017;51:1–15.

    Google Scholar 

  23. 23.

    Olah ME, Gaisano G, Hwang SW. The effect of socioeconomic status on access to primary care: an audit study. Can Med Assoc J. 2013;185(6):263–9.

    Article  Google Scholar 

  24. 24.

    Hoebel J, Rattay P, Prütz F, Rommel A, Lampert T. Socioeconomic status and use of outpatient medical care: the case of Germany. PLoS One. 2016;11(5):1–14.

    Article  CAS  Google Scholar 

  25. 25.

    Brinda EM, Attermann J, Gerdtham UG, Enemark U. Socio-economic inequalities in health and health service use among older adults in India: results from the WHO study on global ageing and adult health survey. Public Health. 2016;141:32–41.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  26. 26.

    Allin, S., Masseria, C., & Mossialos, E. (2006). Inequality in healthcare use among older people in the United Kingdom: an analysis of panel data. Working paper no. 1/2006. Available at, http://eprints.lse.ac.uk/19262/1/LSEHWP1.pdf on 14/03/18.

    Google Scholar 

  27. 27.

    Kahende JW, Adhikari B, Maurice E, Rock V, Malarcher A. Disparities in healthcare utilisation by smoking status – NHANES 1999–2004. Int J Environ Res Public Health. 2009;6(3):1095–106.

    PubMed  PubMed Central  Article  Google Scholar 

  28. 28.

    Rocca P, Beckman A, Ekvall Hansson E, Ohlsson H. Is the association between physical activity and healthcare utilisation affected by self-rated health and socio-economic factors? BMC Public Health. 2015;15:737. https://doi.org/10.1186/s12889-015-2079-5.

    Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Denkinger MD, Lukas A, Herbolsheimer F, Peter R, Nikolaus T. Physical activity and other health-related factors predict healthcare utilisation in older adults. Z Gerontol Geriatr. 2012;45(4):290–7.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  30. 30.

    Galán I, Meseguer M, Herruzo R, Rodríguez-Artalejo F. Self-rated health according to amount, intensity and duration of leisure time physical activity. Prev Med. 2010;51:378–83.

    PubMed  Article  PubMed Central  Google Scholar 

  31. 31.

    Balsa AI, French MT, Maclean JC, Norton EC. From pubs to scrubs: alcohol misuse and healthcare use. Health Serv Res. 2009;44(5):1480–503.

    PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    Zarkin GA, Bray JW, Babor TF, Higgins-Biddle JC. Alcohol drinking patterns and healthcare utilisation in a managed care organization. Health Serv Res. 2004;39(3):553–70.

    PubMed  PubMed Central  Article  Google Scholar 

  33. 33.

    Hajek A, Bock J-O, König H-H. Which factors affect healthcare use among older Germans? Results of the German ageing survey. BMC Health Serv Res. 2017;17:1–8.

    Article  Google Scholar 

  34. 34.

    Gong CH, Kendig H, He X. Factors predicting health services use among older people in China: an analysis of the China health and retirement longitudinal study 2013. BMC Health Serv Res. 2016;16(1):1–16.

    Article  Google Scholar 

  35. 35.

    Tamayo-Fonseca N, Nolasco A, Quesada JA, Pereyra-Zamora P, Melchor I. Self-rated health and hospital services use in the Spanish National Health System: a longitudinal study. Bio Med Commun Health Serv Res. 2015;15(1):1–9.

    Google Scholar 

  36. 36.

    Ameh S, Gómez-Olivé FX, Kahn K, Tollman SM, Klipstein-Grobusch K. Predictors of healthcare use by adults 50 years and over in a rural south African setting. Glob Health Action. 2014;7(1):1–11.

    Article  Google Scholar 

  37. 37.

    Kim H-K, Lee M. Factors associated with healthcare utilisation between the yeaqrs 2010 and 2012 in Korea. Using Andersen’s behavioral model. Public Health Res Perspect. 2016;7(1):18–25.

    Article  Google Scholar 

  38. 38.

    Zhou C, Ji C, Chu J, Medina A, Li M, Jiang S, et al. Non-use of healthcare service among empty-nest elderly in Shandong, China: a cross-sectional study. BMC Health Serv Res. 2015;15(294):1–10.

    Google Scholar 

  39. 39.

    McNamara, A., Normand, C. & Whelan, B. (2013). Patterns and determinants of healthcare utilisation in Ireland. Accessed at; https://tilda.tcd.ie/publications/ reports/pdf/Report_HealthcareUtilisation.pdf, on 23/03/1.

    Google Scholar 

  40. 40.

    Saeed BII, Yawson AE, Nguah S, Agyei-Baffour P, Emmanuel N, Ayesu E. Effect of socio-economic factors in utilisation of different healthcare services among older adult men and women in Ghana. BMC Health Serv Res. 2016;16:1–9.

    Article  Google Scholar 

  41. 41.

    Saeed BII, Xicang Z, Yawson AE, Nguah SB, Nsowah-Nuamha NNN. Impact of socio-economic status and medical conditions on health and healthcare utilisation among aging Ghanaians. BMC Public Health. 2015;15:1–9.

    Article  Google Scholar 

  42. 42.

    Zotorvie O. Assessing healthcare utilisation among the elderly with chronic conditions in Ghana [MSc thesis]. Accra: University of Ghana; 2015.

    Google Scholar 

  43. 43.

    Bourne PA, Morris C, Charles CAD, Eldemire-Shearer D, Kerr-Campbell MD, Crawford TV. Health literacy and health seeking behavior among older men in a middle-income nation. Patient Relat Outcome Meas. 2010;1:39–49.

    PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Dogbey LS. Health-related lifestyle and health conditions among the elderly in Ghana. Accra: [MSc thesis], University of Ghana; 2015.

    Google Scholar 

  45. 45.

    Adei D, Anning A, Mireku SI. Basic needs of the aged in selected districts in Ashanti region of Ghana. Res Humanit Soc Sci. 2015;5(2):92–108.

    Google Scholar 

  46. 46.

    Hsu W-C, Hsu Y-P. Patterns of outpatient care utilisation by seniors under the National Health Insurance in Taiwan. J Formos Med Assoc. 2016;115:325–34.

    PubMed  Article  Google Scholar 

  47. 47.

    Miller RL, Brewer JD. A-Z of social research. London: SAGE Publications Ltd; 2003.

    Google Scholar 

  48. 48.

    Amente T, Kebede B. Determinants of health service utilisation among older adults in Bedele town, Illubabor zone, Ethiopia. J Diabet Metab. 2016;7(11):1–7.

    Article  Google Scholar 

  49. 49.

    Suominen-Taipale AL, Martelin T, Koskinen S, Holmen J, Johnsen R. Gender differences in healthcare use among the elderly population in areas of Norway and Finland. A cross-sectional analysis based on the HUNT study and the FINRISK senior survey. BMC Health Serv Res. 2006;6(1):110.

    PubMed  PubMed Central  Article  Google Scholar 

  50. 50.

    Gómez-Olivé FX, Thorogood M, Clark B, Kahn K, Tollman S. Self-reported health and healthcare use in an ageing population in the Agincourt sub-district of rural South Africa. Global Health Action. 2013;6. https://doi.org/10.3402/gha.%20v6i0.19305.

  51. 51.

    Agyemang-Duah W, Owusu-Ansah JK, Peprah C. Factors influencing healthcare use among poor older females under the livelihood empowerment against poverty Programme in rural Ghana: the case of Atwima Nwabiagya District. BMC Res Notes. 2019;12:1–6.

    Article  Google Scholar 

  52. 52.

    WHO. Food based dietary guidelines in the WHO European region, vol. 8. Copenhagen: Nutrition and Food Security Programme WHO Regional Office for Europe Scherfigsvej; 2003. p. 2100. Available at, http://www.euro.who.int/__data/assets/pdf_file/0017/150083/E79832.pdf. Accessed 28 July 2019.

    Google Scholar 

  53. 53.

    Krauss RM, Eckel RH, Howard B, Appel LJ, Daniels SR, Deckelbaum RJ, et al. AHA dietary guidelines: revision 2000: A statement for healthcare professionals from the nutrition Committee of the American Heart Association. Circulation. 2000;102(18):2284–99.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  54. 54.

    Kabwama SN, Bahendeka SK, Wesonga R, Mutungi G, Guwatudde D. Low consumption of fruits and vegetables among adults in Uganda: findings from a countrywide cross-sectional survey. Arch Public Health. 2019;77(1):4.

    PubMed  PubMed Central  Article  Google Scholar 

  55. 55.

    Blais RK, Tsai J, Southwick SM, Pietrzak RH. Barriers and facilitators related to mental healthcare use among older veterans in the United States. Psychiatr Serv. 2015;66(5):500–6.

    PubMed  Article  Google Scholar 

  56. 56.

    Wandera SO, Kwagala B, Ntozi J. Determinants of access to healthcare by older persons in Uganda: a cross-sectional study. Int J Equity Health. 2015;14:1–10.

    Article  Google Scholar 

  57. 57.

    Odaman OM, Ibiezugbe MI. Health seeking behavior among the elderly in Edo. Central Nigeria. Int Rev Soc Scie Hum. 2014;7(1):201–10.

    Google Scholar 

  58. 58.

    Liu LF. The health heterogeneity of and healthcare utilisation by the elderly in Taiwan. Int J Environ Res Public Health. 2014;11(2):1384–97.

    PubMed  PubMed Central  Article  Google Scholar 

  59. 59.

    Falaha T, Worku A, Meskele M. Healthcare seeking behaviour of elderly people in rural part of Wolaita zone, southern Ethiopia. Health Sci J. 2016;10(4):1–6.

    Google Scholar 

  60. 60.

    Werblow A, Felder S, Zweifel P. Population ageing and healthcare expenditure: A school of “red herrings”? Health Econ. 2007;16:1109–26.

    PubMed  Article  PubMed Central  Google Scholar 

  61. 61.

    Dou L, Liu X, Zhang T, Wu Y. Healthcare utilisation in older people with cardiovascular disease in China. Int J Equity Health. 2015;14(1):1–8.

    Article  Google Scholar 

  62. 62.

    Rosenstock IM. Why people use health services. Milbank Q. 2005;83(4). https://doi.org/10.1111/j.1468-0009.2005.00425.x online-only.

    Article  Google Scholar 

  63. 63.

    Aday LA, Andersen MR. A framework for the study of access to medical care. Health Serv Res. 1974;9:208–20.

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Conner M. Chapter2. Cognitive determinants of health behavior. Handbook Behav Med. 2010:19–30. https://doi.org/10.1007/978-0-387-09488-5_2.

    Google Scholar 

  65. 65.

    Rosenstock IM. Why people use health services. Milbank Mem Fund Quart. 1966;44(3):94–127.

    Article  Google Scholar 

  66. 66.

    Pem D, Jeewon R. Fruit and vegetable intake: benefits and Progress of nutrition education interventions- narrative review article. Iran J Public Health. 2015;44(10):1309–21.

    PubMed  PubMed Central  Google Scholar 

  67. 67.

    Nti CA, Hagan J, Bagina F, Seglah M. Knowledge of nutrition and health benefits and frequency of consumption of fruits and vegetables among Ghanaian homemakers. Afr J Food Sci. 2011;5(6):333–9.

    Google Scholar 

  68. 68.

    Yahia EM, García-Solís P, Celis MEM. Contribution of Fruits and Vegetables to Human Nutrition and Health. Postharvest Physiology and Biochemistry of Fruits and Vegetables. 2019;19–45. https://doi.org/10.1016/b978-0-12-813278-4.00002-6.

    Google Scholar 

Download references

Acknowledgements

We acknowledge our study participants for providing the study data and the authors and publishers whose works were consulted.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Affiliations

Authors

Contributions

WA-D and FA-H drafted the paper. WA-D, CP and FA-H designed the study, developed study tools and participated in data analysis. All authors critically reviewed the manuscript before submission. All authors accepted the final responsibility for the paper. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Williams Agyemang-Duah.

Ethics declarations

Ethics approval and consent to participate

Ethics approval for this study was granted by the Committee on Human Research Publication and Ethics (CHRPE), Kwame Nkrumah University of Science and Technology School of Medical Sciences and Komfo Anokye Teaching Hospital, Kumasi, Ghana (Ref: CHRPE/AP/311/18). The purpose of the study was also explained to the study participants before their informed written and verbal consent were obtained. Again, they were assured of the strict confidentiality and anonymity of the data they provided. They were further assured that their participation in the study was voluntary and that they were free to opt out at any time.

Consent for publication

Not Applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Agyemang-Duah, W., Peprah, C. & Arthur-Holmes, F. Predictors of healthcare utilisation among poor older people under the livelihood empowerment against poverty programme in the Atwima Nwabiagya District of Ghana. BMC Geriatr 20, 79 (2020). https://doi.org/10.1186/s12877-020-1473-8

Download citation

Keywords

  • Healthcare use
  • Social and behavioural determinants
  • Poor older people
  • Health policy
  • Ghana