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BMC Research Notes

, 12:758 | Cite as

Predictors of enrollment in a health protection scheme among informal sector workers in Kumasi Metropolis of Ghana

  • Dina Adei
  • Williams Agyemang-DuahEmail author
  • Anthony Acquah Mensah
Open Access
Research note
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Abstract

Objective

Informal sector workers are exposed to occupational hazards which could escalate their healthcare expenditures. Thus, enrollment in a health protection scheme among informal sector workers is useful for reducing their catastrophic healthcare expenditures. However, there is scant information on factors predicting their enrollment in the National Health Insurance Scheme (NHIS) in Ghana, a gap this quantitative study aims to fill. A sample of 350 informal sector workers was involved in a cross-sectional survey.

Results

Approximately 17% of the participants were enrolled in NHIS. Respondents who had worked between 5 and 7 years were significantly more likely to enroll in NHIS compared with those who had worked below 2 years (AOR = 13.159, CI 1.135–152.596, p = 0.039). The study further found that apprentices (AOR = 0.72, CI 0.353–1.056, p = 0.005) were less likely to enroll in NHIS compared with their masters. Participants who were exposed to electrical hazards (AOR = 2.93, CI 1.56–5.10, p = 0.013) and suffered from occupational diseases (AOR = 2.75, CI 1.743–5.17, p = 0.001) were significantly more likely to enroll in NHIS. Also, respondents who were non-Christians were significantly less likely to enroll in NHIS compared with their respective counterparts (AOR = 0.726, CI 0.067–2.503, p = 0.011). The findings are useful for increasing the NHIS enrollment rate among informal sector workers in Ghana.

Keywords

National Health Insurance Scheme Enrollment Informal sector workers Kumasi Metropolis 

Abbreviations

AIDS

Acquired Immunodeficiency Syndrome

HIV

Human Immunodeficiency Virus (HIV)

NHIA

National Health Insurance Authority

NHIS

National Health Insurance Scheme

SSNIT

Social Security and National Insurance Trust

SPSS

Statistical Package for Social Sciences

Introduction

After independence, the Government of Ghana introduced free healthcare for its citizens [1], with the rationales of removing all forms of barriers to healthcare accessibility and utilization and, also ensuring universal health coverage [2]. Free healthcare was funded through taxes and donor support [3]. As part of the free healthcare policy, the government provided additional healthcare facilities across the country and strengthened preventive interventions such as immunization and antenatal care. In the 1980s, however, due to the poor economic growth resulting in limited resources, the government abolished the free healthcare policy [2].

Thus, the out-of-pocket payment systems were gradually implemented in Ghana in the mid-1980s [4]. With this policy, there was a total withdrawal of government subsidy with patients taking care of their healthcare expenditures. The justifications of this policy were to increase funds for providers, make fee recovery legal and restrict unnecessary utilization of healthcare [2]. However, the policy was serving as an obstacle to healthcare utilization [5]. For instance, outpatient visits reduced by 66% as a result of the implementation of the cash and carry system [2].

The National Health Insurance Scheme (NHIS) was introduced in 2004 as an alternative healthcare financing mechanism in Ghana [6]. The NHIS provides free enrollment for Social Security and National Insurance Trust (SSNIT) retirees, persons aged 70 years or older, pregnant women, children under 18 years and paupers [7]. It covers about 95% of disease burden conditions of people [8] including malaria, diarrhea, respiratory tract infections, skin diseases, hypertension, diabetes and asthma [9].

The national enrollment rates of NHIS in 2010, 2015 and 2017 were 33% (8.2 million), 41% (11.3 million) and 35% (10.3 million) respectively. With this, the enrollment rate of NHIS among informal sector workers in 2017 was 29.8% [6], which is below the 2017 national enrollment rate. This shows that informal sector workers have a low enrollment rate in NHIS despite their frequent exposure to occupational hazards [10] such as physical, chemical, biological [11] and other occupational injuries [12]. As such, they are likely to incur catastrophic healthcare expenditures due to their frequent exposure to occupational hazards coupled with their low NHIS enrollment. Enrollment in NHIS could help to minimize their healthcare expenditures [13]. Evidence shows that socio-demographic factors [14], such as education, gender, and income are associated with NHIS enrollment [15]. However, there is scant information on factors influencing NHIS enrollment among informal sector workers in Ghana, a gap this quantitative study aims to fill. Understanding this knowledge area would be useful for increasing the NHIS enrollment rate among informal sector workers in Ghana.

Main text

Methods

Study settings

The study was conducted in Kumasi Metropolis of Ghana. Kumasi Metropolis represents the most populous settlement in Ghana with a population of 1,730,249 inhabitants which comprised 47.8% males and 52.2% females. About 60% of the labor force in Kumasi Metropolis predominantly engaged in informal economic activities [16].

Sample and data
This study was a cross-sectional survey. A headcount of all potential participants was undertaken from September to November 2016 to determine the sampling frame because of the absence of data on the population. The headcount covered the masters and apprentices working as welders and fish processors within the study area. The headcount identified a total of 200 fish processors and ten master welders at Asafo. Meanwhile, 180 master welders were identified at Suame magazine adding up to a total of 190 welders. Slovin’s sample size determination method [17], was used to determine the sample size.
$${\text{n}} = {{\text{N}} / {\left[ {1 + {\text{N}}\left( \upalpha \right)^{2} } \right]}}$$
where, n = sample size; α = error margin of 0.05 and a confidence interval of 95%, and N = the sampling frame. The formula was applied separately for fish processors and master welders and a sample size of 262 (129 master welders and 133 fish processors) was obtained. For every master welder selected, his senior apprentice was also selected adding up to a total of 258 welders (129 masters and 129 apprentices) and 133 fish processors yielding a total sample size of 391 participants. The fish processors had no apprentices. The response rate was 97.7% for fishmongers and 85.3% for welders, a total response rate of 91.5%. Therefore, the sample size used for analyses was 350 participants comprising 220 welders and 130 fish processors. The simple random sampling was used to select the respondents because it is mostly used in health research [18]. This was operationalized by writing the numbers on pieces of paper, keeping them in a box and shuffling thoroughly. The papers were drawn one after the other until the required sample size was obtained.

Face-to-face interviewer-administered questionnaires covering information on socio-demographic, medical characteristics and enrollment in NHIS were used to elicit data from the respondents. The questions were in English but were translated to Twi (a local language of the participants) for easy understanding. This was therefore converted back to English for analyses. Written and verbal informed consents were sought from the respondents before data collection. Respondents were further assured of strict confidentiality of the information they provided.

Measures

Outcome variable The outcome variable was enrollment in NHIS and was measured as a dichotomous variable indicating ‘yes = 1’ or ‘no = 0’.

Predictor variables The independent variables were type of enterprise (1 = welding, 2 = fish processing), type of respondents (1 = master, 2 = apprentices, 3 = employee), gender (1 = male, 2 = female), age (years) (1 = 49 or below 2 = 50 or above), education (1 = no formal education, 2 = basic, 3 = secondary, 4 = tertiary), marital status (1 = married, 2 = single), religion (1 = Christians, 2 = non-Christians), work experience (years) (1 = below 2, 2 = 2–4, 3 = 5–7, 4 = 8–10, 5 = 11–13, 6 = above 13), income (1 = GH¢ < 1000, 2 = 1000 or above). Membership of workers association, exposure to welding light, biological hazards, electrical hazards, fire hazards, occupational injury and occupational diseases were measured as ‘yes = 1’ or ‘no = 0’.

Analytical framework
Data were entered into a database and analyzed statistically using Statistical Package for Social Sciences (SPSS) software (version 16.0). Percentages and frequencies were used to interpret the sample characteristics of the respondents. Chi-square and Fisher exact tests were used to estimate differences between independent variables and NHIS enrollment. Simple and multiple regression models were used to estimate the association between independent variables and enrollment in NHIS. Based on Gujarati and Porter [19], the multiple regression model was developed as follows.
$${\text{P}} = \upalpha_{0} + \upalpha_{ 1} {\text{TOR}} + \upalpha_{ 2} {\text{R}} + \upalpha_{ 3} {\text{MS }} + \upalpha {}_{ 4}{\text{WE }} + \upalpha_{ 5} {\text{MOA }} + \upalpha_{ 6} {\text{EEH}} + \upalpha_{ 7} {\text{OD }} + \upepsilon$$
(1)

P = probability of enrolling in NHIS. TOR, R, MS, WE, MOA, EEH, and OD refer to the type of respondents, religion, marital status, work experience, membership of workers association, exposure to electrical hazards and occupational diseases, respectively. Also, α = parameters to be estimated; ε = error term. Due to the use of multiple regression, we were compelled to choose different independent variables. The test results were considered significant at a probability value of 0.05 or less.

Results

Sample characteristics of the respondents

About 63% of the respondents were welders. Approximately 41% were master welders. It was revealed that 62.9% were males. Also, 34.6% were aged 49 years or below, 48.3% had a secondary level of education and 55.1% were married. Approximately 32% had worked for more than 13 years and 92% received a monthly income of less than GH¢1000.00 ($1 was equivalent to GH¢4.4 as of the time of the survey). Approximately 17% were members of workers association, 99% were exposed to biological hazards, 64% were exposed to welding light, 62% were exposed to electrical hazards and 99% were exposed to fire hazards. About 39% were exposed to occupational injuries in 2016. Further, 61% had suffered from occupational diseases in 2016. The study revealed a statistically significant difference between type of respondent (p = 0.027), marital status (p = 0.037), religion (p = 0.010), work experience (p = 0.003), membership of workers association (p = 0.003), exposure to electrical hazards (p = 0.049) and suffering from occupational diseases in 2016 (p = 0.001) in relation to NHIS enrollment (see Table 1).
Table 1

Socio-demographic and medical characteristics of the respondents by NHIS enrollment

Variable

Category

Enrollment in NHIS (n = 350)

p-value

Yes

No

Total

n = 61

%(100)

n = 289

%

N = 350

%

Type of enterprise

Welding

34

55.7

186

64.4

220

62.9

0.205

Fish processing

27

44.3

103

35.6

130

37.1

 

Type of respondent

Master

33

54.1

111

38.4

144

41.1

0.027*

Apprentice

11

18.0

99

34.3

110

31.4

 

Employee

17

27.9

79

27.3

96

27.4

 

Gender

Male

34

55.7

186

64.4

220

62.9

0.205

Female

27

44.3

103

35.6

130

37.1

 

Age (years)

49 or below

14

23.0

107

37.0

121

34.6

0.065

 

50 or above

47

77.0

182

63.0

229

65.4

 

Educational attainment

No formal education

15

24.6

50

17.3

65

18.6

 

Basic education

8

13.1

71

24.6

79

22.6

0.096

Secondary education

34

55.7

135

46.7

169

48.3

 

Tertiary

4

6.6

33

11.4

37

10.6

 

Marital status

Married

41

67.2

152

52.6

193

55.1

0.037*

Single

20

32.8

137

47.4

157

44.9

 

Religion

Christian

53

86.9

271

93.8

324

92.6

0.010*

Non-Christian (Islam/traditionalist/no religion)

8

13.1

18

6.2

26

7.4

 

Work experience (years)

Below 2

6

9.8

14

4.8

20

5.7

 

2–4

6

9.8

55

19

61

17.4

 

5–7

5

8.2

63

21.8

68

19.4

 

8–10

9

14.8

58

20.1

67

19.1

0.003*

11–13

4

6.6

17

5.9

21

6.0

 

Above 13

31

50.8

82

28.4

113

32.3

 

Total income/allowance per month (GH¢)

< 1000

54

88.5

268

92.7

322

92.0

 

1000 or above

7

11.5

21

7.3

28

8.0

0.198

Membership of workers association

Yes

21

34.4

50

17.3

71

20.3

0.003*

No

40

65.6

239

82.7

279

79.7

 

Exposure to biological hazards at the work place

Yes

61

100

287

99.3

348

99.4

0.515

No

0

0.0

2

0.7

2

0.6

 

Exposure to welding light

Yes

32

52.5

184

63.7

216

61.7

0.102

No

29

47.5

105

36.3

134

38.3

 

Exposure to electrical hazards?

Yes

27

79.4

115

61.8

142

64.5

0.049*

No

7

20.6

71

38.2

78

35.5

 

Exposure to fire hazards

Yes

61

100

286

99

347

99.1

0.424

No

0

0.0

3

1.0

3

0.9

 

Did you experience an occupational accident which led to an injury in the year 2016?

Yes

24

39.3

112

38.8

136

38.9

0.932

No

37

60.7

177

61.2

214

61.1

 

Did you suffer from occupational diseases in the year 2016?

Yes

50

82.0

162

56.1

212

60.6

0.001*

No

11

18.0

127

43.9

138

39.4

 

* p-value is significant at 0.05 or less

Enrollment rate of NHIS

The result indicated that 17.4% of the respondents had enrolled in NHIS. This suggests a low enrollment in NHIS among the participants (see Fig. 1).
Fig. 1

Enrollment rate of NHIS among informal sector workers

Factors associated with enrollment in NHIS

The variables that showed a statistically significant difference with NHIS enrollment in the Chi-square and Fisher exact test analyses were further subjected to simple and multiple regression models with the rationale of estimating their likelihood of influencing NHIS enrollment. In a simple regression model, the study found that apprentices were 0.676 times significantly less likely to enroll in NHIS compared with their masters (OR = 0.676, CI 0.284–0.576, p = 0.009). Non-Christian participants were 0.922 times significantly less likely to enroll in NHIS (OR = 0.922, CI 0.451–2.513, p = 0.003). Participants who had worked between 5 and 7 years were 5.4 times significantly more likely to enroll in NHIS (OR = 5.400, CI 1.442–20.226, p = 0.012). Respondents who were members of workers association were 2.509 times significantly more likely to enroll in NHIS (AOR = 2.509, CI 1.364–4.618, p = 0.03). Those who were exposed to electrical hazards were 1.8 times significantly more likely to enroll in NHIS (OR = 1.8, CI 1.102–3.05, p = 0.045). Participants who had suffered from occupational diseases were 2.2 times significantly more likely to enroll in NHIS (OR = 2.2, CI 1.25–4.256, p = 0.001).

In a multiple regression analysis, we found that apprentices were 0.72 times significantly less likely to enroll in NHIS than their masters (AOR = 0.72, CI 0.353–1.056, p = 0.005). Non-Christians were 0.726 times significantly less likely to enroll in NHIS (AOR = 0.726, CI 0.067–2.503, p = 0.011) Respondents who had worked between 5 and 7 years were 13.159 times significantly more likely to enroll in NHIS than their respective counterparts (OR = 13.159, CI 1.135–152.596, p = 0.039). Participants who were exposed to electrical hazards were 2.93 times significantly more likely to enroll in NHIS (OR = 2.93, CI 1.56–5.10, p = 0.013). Respondents who had suffered from occupational diseases were 2.75 times significantly more likely to enroll in NHIS (OR = 2.75, CI 1.743–5.17, p = 0.001) (see Table 2).
Table 2

Factors associated with enrollment in NHIS among informal sector workers

Variables

Simple regression model

OR (95% CI)

p-value

Multiple regression model

AOR (95% CI)

p-value

Type of respondent

 Master (ref)

1.00

 

1.00

 

 Apprentice

0.676 (0.2840.576)*

0.009*

0.72 (0.3531.056)*

0.005*

 Employee

1.382 (0.720–2.653)

0.332

1.42 (1.13–3.201)

0.12

Marital status

 Married (ref)

1.00

 

1.00

 

 Single

1.848 (1.0323.308)

0.039*

1.411 (0.593–4.125)

0.219

Religion

 Christian (ref)

1.00

 

1.00

 

 Non- Christian

0.922 (0.4512.513)

0.003*

0.726 (0.0672.503)

0.011*

Work experience

 Below 2 years (ref.)

1.00

 

1.00

 

 2 to 4 years

3.929 (1.09814.054)

0.035*

11.727 (0.998137.824)

0.05*

 5 to 7 years

5.400 (1.44220.226)

0.012*

13.159 (1.135152.596)

0.039*

 8 to 10 years

2.762 (0.843–9.047)

0.093

8.767 (0.843–91.170)

0.069

 11 to 13 years

1.821 (0.427–7.761)

0.417

4.827 (0.366–63.721)

0.232

 Above 13 years

1.134 (0.400–3.213)

0.813

2.571 (0.274–24.117)

0.408

Membership of workers association

 Yes

2.509 (1.3644.618)

0.03*

4.20 (1.56–7.75)

 

 No (ref)

1.00

 

1.00

0.121

Exposure to electrical hazards

 Yes

1.8 (1.1023.05)

0.045*

2.93 (1.565.10)

0.013*

 No (ref)

1.00

 

1.00

 

Suffering from occupational diseases

 Yes

2.2 (1.254.256)

0.001*

2.75 (1.7435.17)

0.001*

 No (ref.)

1.00

 

1.00

 

Italic values indicate significance of p value

CI confidence interval, OR odd ratio, AOR adjusted odd ratio

* p-value is significant at 0.05 or less

Discussion

Being one of the first studies in Ghana, this study examined factors influencing NHIS enrollment among informal sector workers in Metropolitan Kumasi. We found that 17.4% of the participants had enrolled in NHIS which is similar to a Namibian study that reported a 17.5% coverage rate of NHIS [15]. It is also similar to a 17.4% coverage rate among Ghanaian female migrants [13] but lower than 35% national enrollment rate in 2017 [6]. The low enrollment rate could be attributed to poor quality of services offered to the insured and failure of the National Health Insurance Authority (NHIA) to intensify education on NHIS. The NHIA should, therefore, engage health insurance stakeholders to develop and implement activities to enforce compulsory enrollment to move the scheme towards universal coverage [20].

In this study, apprentices were significantly less likely to enroll in NHIS. The reason is that master welders are more likely to be wealthier than their apprentices and thus are more likely to enroll in the scheme [21]. Also, apprentices may either find it difficult getting money to pay for their enrollment fee or are unable to make time out of their busy schedules to enroll in NHIS. Thus, masters should encourage their apprentices to enroll in NHIS. The study found that non-Christian participants were less likely to enroll in NHIS compared with their Christian counterparts. This result could be due to limited funds, religious-cultural beliefs, and differences in health-seeking behavior [22]. The study found that respondents who had worked for 5–7 years were more likely to enroll in NHIS than those who had worked below 2 years. This difference in the likelihood of enrollment in NHIS could be due to the differences in financial status and the number of times being exposed to occupational hazards. We further observed that respondents who were exposed to electrical hazards and experienced occupational diseases were more likely to enroll in NHIS which may be subject to their perceived poor health status [20, 23].

Conclusion

This study examined factors predicting NHIS enrollment among informal sector workers in Kumasi Metropolis of Ghana. The study found a low enrollment rate in NHIS among informal sector workers. Respondents who had worked between 5 and 7 years were significantly more likely to enroll in NHIS. Also, apprentices were less likely to enroll in NHIS. Participants who were exposed to electrical hazards and occupational diseases were more likely to enroll in NHIS. Non-Christian participants were less likely to enroll in NHIS. These findings are useful for increasing the NHIS enrollment among informal sector workers in Ghana.

Limitation

As a result of the cross-sectional survey adopted, we were not able to establish causality between and among the study variables.

Notes

Acknowledgements

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

Authors’ contributions

DA conceived the study, DA and WA-D contributed to the design, DA and AAM collected the data, DA and WA-D analyzed the data and drafted the manuscript, DA and AAM reviewed the draft. DA, WA-D, and AAM addressed the review comments. All authors read and approved the final manuscript.

Funding

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

Ethics approval and consent to participate

The Department of Planning, Kwame Nkrumah University of Science and Technology gave the approval to conduct the study. Informed written and verbal consents were obtained from the study participants before data were collected. Respondents were also assured of strict confidentiality and anonymity of the data they provided.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References

  1. 1.
    Wedam EA, Sanyare FN. Health care financing and sustainability: a study of current conceptual dialectics in Ghana. World Dev Perspect. 2017;5:47–55.  https://doi.org/10.1016/j.wdp.2017.03.003.CrossRefGoogle Scholar
  2. 2.
    Adisah-Atta I. Financing health care in Ghana: are Ghanaians willing to pay higher taxes for better health care? Findings from Afrobarometer. Soc Sci. 2017;6(3):1–19.CrossRefGoogle Scholar
  3. 3.
    Nyonator F, Kutzin J. Health for some? The effects of user fees in volta region of Ghana. Health Policy Plan. 1999;14:329–41.CrossRefGoogle Scholar
  4. 4.
    Mills A, Ataguba JE, Akazili J, Borghi J, Garshong B, Makawia S, Mtei G, Harris B, Macha J, Meheus F, McIntyre D. Equity in financing and use of health care in Ghana, South Africa, and Tanzania: implications for paths to universal coverage. Lancet. 2012;380(9837):126–33.CrossRefGoogle Scholar
  5. 5.
    Agyemang-Duah W, Peprah C, 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. 2019;18(1):1–12.CrossRefGoogle Scholar
  6. 6.
    Nsiah-Boateng E, Aikins M. Trends and characteristics of enrolment in the National Health Insurance Scheme in Ghana: a quantitative analysis of longitudinal data. Glob Health Res Policy. 2018;3(1):32.CrossRefGoogle Scholar
  7. 7.
    NHIS. National Health Insurance Scheme, membership. 2018. http://www.nhis.gov.gh/membership.aspx. Accessed 10 April 2018.
  8. 8.
    OXFAM International. Achieving a shared goal free universal healthcare in Ghana. 2011. http://www.anglicanhealth.org/Resources/PDF/AHN%20resources/Health%20financing%20and%20Insurance/Oxfam%20NHIS%20report.pdf. Accessed 13 July 2018.
  9. 9.
    National Health Insurance Scheme. Medicines list. 2018. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=MEDICINES+LIST&btnG. Accessed 03 Jan 2018.
  10. 10.
    Rantanen J. Occupational health services for the informal sector. Afr Newsl Occup Health Saf. 2009;19(2):44–6.Google Scholar
  11. 11.
    Olaoye OJ, OdebiyI OC, Abimbola OT. Occupational hazards and injuries associated with fish processing in Nigeria. J Aquat Sci. 2015;3(1):1–5.Google Scholar
  12. 12.
    Kyei S, Owusu-Ansah A, Boadi-Kusi SB, Abbey DN, Abu EK. Occupational hazards correlates of ocular disorders in Ghanaian fisheries. Healthc Low-Resour Settings. 2016.  https://doi.org/10.4081/hls.2016.5482.CrossRefGoogle Scholar
  13. 13.
    Lattof SR. Health insurance and care-seeking behaviours of female migrants in Accra, Ghana. Health Policy Plan. 2018;33(4):505–15.CrossRefGoogle Scholar
  14. 14.
    Aregbeshola BS, Khan SM. Predictors of enrolment in the National Health Insurance Scheme among women of reproductive age in Nigeria. Int J Health Policy Manag. 2018;7(11):1015.CrossRefGoogle Scholar
  15. 15.
    Allcock SH, Young EH, Sandhu MS. Socio-demographic patterns of health insurance coverage in Namibia. Int J Equity Health. 2019;18(1):16.CrossRefGoogle Scholar
  16. 16.
    Ghana Statistical Service. 2010 population and housing census: summary report of final results. Accra: Ghana Statistical Service; 2012.Google Scholar
  17. 17.
    Guilford JP, Fruchter B. Fundamental statistics in psychology and education. 5th ed. New York: McGraw-Hill Book Publishing Company; 1973.Google Scholar
  18. 18.
    Elfil M, Negida A. Sampling methods in clinical research; an educational review. Emergency. 2017;5(1):1–3.Google Scholar
  19. 19.
    Gujarati ND, Porter DC. Basic econometrics. 5th ed. New-York: McGraw-Hill Companies; 2009.Google Scholar
  20. 20.
    Kotoh AM, Aryeetey GC, Van der Geest S. Factors that influence enrolment and retention in Ghana’ National Health Insurance Scheme. Int J Health Policy Manag. 2018;7(5):443–54.CrossRefGoogle Scholar
  21. 21.
    Van Der Wielen N, Falkingham J, Channon AA. Determinants of National Health Insurance enrolment in Ghana across the life course: are the results consistent between surveys? Int J Equity Health. 2018;17(1):1–14.CrossRefGoogle Scholar
  22. 22.
    Badu E, Agyei-Baffour P, Ofori Acheampong I, Preprah Opoku M, Addai-Donkor K. Households sociodemographic profile as predictors of health insurance uptake and service utilization: a cross-sectional study in a municipality of Ghana. Adv Public Health. 2018; 2018:1–13.CrossRefGoogle Scholar
  23. 23.
    Tawiah EK. Factors influencing enrolment in the National Health Insurance Scheme among Slum Dwellers in Agbogbloshie (Masters dissertation, University of Ghana). 2015.Google Scholar

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Authors and Affiliations

  1. 1.Department of PlanningKwame Nkrumah University of Science and TechnologyKumasiGhana

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