The burden of out-of-pocket payment (OOP) for health services cannot be underestimated. Out-of-pocket payment for health services continues to dominate in the health system of most countries especially those in low and middle income settings [
Consequently, the World Health Organization (WHO) in 2005 responded to this by tasking member states to ensure universal health financing through the removal of OOP for health services [
Subsequently, countries in low and middle income settings are gradually closing the gap in addressing financial risk protection for the population. Several forms of prepayment systems including national health and private health insurance have been implemented in the last two decades [
Ghana’s response to universal health coverage (UHC) underscores the passage of the NHI bill into law in 2003 and subsequent operation in March 2005 [
Notwithstanding this, most households in Ghana continue to experience unmet needs to health services due to OOP at the health facility [
The religious affiliations of households have showed to influence their NHIS policy ownership. In some settings, including the Eastern and Central Regions of Ghana, previous studies have showed that households affiliated to some religion including Christianity, Islam, and Tradition were more likely to have NHIS active membership compared with those who were not affiliated to any religion [
Furthermore, the age of household members has been extensively demonstrated as a predictor of NHIS policy ownership [
In the Ghanaian setting, the ability of households to enrol and renew their NHIS policy is associated with the wealth quintiles mostly linked to income. It is previously reported that households with higher socioeconomic status have higher odds to enrol and renew their NHIS policy compared with those in poor socioeconomic standings [
Across different settings, the increase in the education level of households increases the odds of enrolling and renewing NHIS policy [
Additionally, the health status of individuals influences their decision to continuously enrol and renew their NHIS policy. In previous studies, individuals with poor health status were about 1.13–1.9 times more likely to have NHIS active membership compared with those in good health [
The responsible person to head a family influences their ability to enrol and renew their NHIS policy. Households headed by a male have showed to have increased odds (OR = 2.5; 95% CI; 1.2, 5.1) of enrolling and renewing their NHIS policy. The employment status of these household heads influences their economic ability to enrol and renew the NHIS policy. In instances where the individual household heads are engaged in a formal sector employment, they stand a higher propensity (OR = 3.7) to enrol and renew their NHIS policy [
The study was conducted in the Upper Denkyira East Municipality. The Municipality covers a total land area of 1,700 square kilometers, which is about 17% of total land of Central Region, one of the four most deprived regions in Ghana. The municipality accommodates an estimated population of 79,793 people in 2013 [
The study used a cross-sectional design with quantitative methods of data collection. The design was used to collect data from all prospective respondents over three-month period (3 months, February, 2015–April, 2015). The cross-sectional design is relevant to measure the sociodemographic predictors of NHIS active membership and healthcare utilization. This was applicable as the researchers aimed to examine factors that influence active membership and service utilization. The quantitative data helped to make inferences about the situation of NHIS active membership within the municipality.
The sample size was estimated using Cochran’s [
The households for the study were identified through a multistage cluster and simple random sampling approaches. The first stage sampling identified communities in the Upper Denkyira Municipality. The communities sampled were Zongo, Dunkwa-Soro, Atechem, Mfuom, Kadadwen, and Compound [
Data were obtained from respondents through administration of structured questions on face-to-face basis. Questions that were presented to the respondents were related to the sociodemographic information, current status of NHIS, and access and use of healthcare. The sociodemographic variables included gender, age, marital status, highest education, years of schooling, and religion. Questions on NHIS were related to the current status of clients on the scheme.
The study used descriptive and inferential statistics to present results. The analysis first computed percentage distribution of the household profile of respondents. These frequencies and percentage distribution were grouped according to the type of variable-continuous and categorical. The continuous variables were age, monthly income, household size, and the number of dependents. The categorical variables were gender, education, marital status, occupation, place of residence, ethnic background, religion, NHIS status, and healthcare utilization. The analysis further used bivariate and multivariate logistics regression to examine the influence of sociodemographic factors on NHIS active membership. Odds ratio (OR) and adjusted odds ratio (AOR) were used to report the strength of influence of the sociodemographic factors on NHIS active membership. The main outcome variable for the analysis was the current NHIS status of respondents. Healthcare utilization was measured using individuals who had visited the health facility for care with the health insurance in the last twelve (12) months. The analysis was presented at 95% significance level at
A total of 380 respondents were recruited for the study. The average age of respondents was 34 years, and about 47.1% were between 18 and 27 years. Males (57.9%) dominated females in the study. More than a third (46.9%) of respondents had tertiary level education, whilst less than a fifth each had secondary, primary, and no formal education (Table
Sociodemographic characteristics of respondents.
Variable | Frequency | Percentage (%) |
---|---|---|
|
||
Age | ||
18–27 | 179 | 47.11 |
28–37 | 80 | 21.05 |
38–47 | 60 | 15.79 |
48–57 | 33 | 8.68 |
58+ | 28 | 7.37 |
|
|
|
Monthly income (GHC)1 | ||
>200 | 130 | 51.18 |
200–500 | 45 | 17.72 |
500–1000 | 64 | 25.20 |
1000–1500 | 9 | 3.54 |
1500+ | 6 | 2.36 |
|
|
|
Household size | ||
1–3 | 64 | 17.07 |
4–6 | 195 | 52.00 |
7–9 | 116 | 30.93 |
|
|
|
Number of dependents | ||
1–3 | 147 | 61.51 |
4–6 | 70 | 29.29 |
7–9 | 13 | 5.44 |
10+ | 9 | 3.77 |
|
|
|
|
||
Gender | ||
Male | 219 | 57.94 |
Female | 159 | 42.06 |
Education | ||
No formal education | 49 | 12.93 |
Primary | 59 | 15.57 |
Secondary | 79 | 20.84 |
Tertiary | 178 | 46.97 |
Other | 14 | 3.69 |
Marital status | ||
Single | 199 | 52.37 |
Married | 139 | 36.58 |
Divorce | 27 | 7.11 |
Widow | 15 | 3.95 |
Occupation | ||
Skilled | 136 | 36.96 |
Semiskilled | 161 | 43.75 |
Unemployed | 71 | 19.29 |
Place of residence | ||
Slum | 22 | 5.33 |
Zongo | 67 | 18.16 |
Old Town | 127 | 34.42 |
Peri-Urban | 94 | 25.47 |
New-Site | 59 | 15.99 |
Ethnic background | ||
Denkyira | 185 | 53.94 |
Other | 158 | 46.06 |
Religion | ||
Christianity | 296 | 80.00 |
Islam | 74 | 20.00 |
NHIS active | ||
Yes | 277 | 74.26 |
No | 96 | 25.74 |
Use of Healthcare | ||
Yes | 215 | 63.61 |
No | 123 | 36.39 |
More than a third of respondents (43.75%) were engaged in semiskilled employment, 36.9% as skilled workers, and 19.29% were not engaged in any employment. A little over a third, 34.4%, of respondents disclosed their places of residence as old town, 25.47% as peri-urban, and 18.16% as
The median and average monthly income of respondents were GHC 200 (USD 52.35) (using 2016 exchange rate of GHC 3.82 = 1 USD equivalent) and GHC 412.94 (USD 108.12), respectively (Table
Table
Logistic regression analysis of households’ profile and NHIS uptake.
Variable | NHIS active membership | |||||
---|---|---|---|---|---|---|
Model 1 OR | 95% CI |
|
Model 2 AOR | 95% CI |
|
|
|
||||||
Age | ||||||
18–27 | 1.00 | 1.00 | ||||
28–37 | 3.15 | 1.88, 5.29 |
|
0.47 | 0.06, 3.51 | 0.46 |
38–47 | 2.41 | 1.37, 4.24 |
|
0.06 | 0.00, 0.77 |
|
48–57 | 3.13 | 1.40, 6.93 |
|
0.24 | 0.02, 3.19 | 0.28 |
58+ | 3.00 | 1.27, 7.05 |
|
0.01 | 0.00, 0.25 |
|
Monthly income (GHC)1 | ||||||
Below 200 | 1.0 | 1.00 | ||||
200–500 | 2.67 | 1.37, 5.17 |
|
0.96 | 0.17, 5.35 | 0.96 |
500–1000 | 5.3 | 2.69, 10.41 |
|
1.06 | 0.26, 4.36 | 0.92 |
1000–1500 | 3.5 | 0.72, 16.8 | 0.11 | 0.12 | 0.01, 1.47 | |
1500+ | 0.5 | 0.09, 2.72 | 0.42 | 0.23 | 0.02, 3.53 | |
Household size | ||||||
1–3 | 1.0 | 1.0 | ||||
4–6 | 2.82 | 2.04, 3.89 |
|
0.28 | 0.04, 1.64 | 0.16 |
7–9 | 3.07 | 2.00, 4.70 |
|
0.81 | 0.10, 6.35 | 0.84 |
Number of dependents | ||||||
1–3 | 1.0 | 1.0 | ||||
4–6 | 1.07 | 0.55, 2.10 | 0.84 | 3.83 | 0.93, 15.75 | 0.06 |
7–9 | 0.64 | 0.18, 2.24 | 0.48 | 2.84 | 0.17, 45.95 | 0.46 |
10+ | 2.54 | 0.31, 21.06 | 0.39 | 3.30 | 0.07, 155.4 | 0.54 |
|
||||||
Gender | ||||||
Male | 1.0 | 1.0 | ||||
Female | 3.86 | 0.98, 2.61 |
|
3.92 | 1.21, 12.67 |
|
Marital status | ||||||
Single | 1.0 | 1.0 | ||||
Married | 4.27 | 2.78, 6.54 |
|
48.9 | 4.46, 537 |
|
Divorce | 2.0 | 0.89, 4.45 | 0.09 | 97.0 | 5.54, 1697 |
|
Widow | 6.5 | 1.46, 28.80 | 0.14 | 2683 | 32.20, 2235 |
|
Education | ||||||
No formal education | 1.0 | 1.0 | ||||
Primary | 2.22 | 1.27, 3.87 |
|
9.87 | 1.52, 64.07 |
|
Secondary | 2.71 | 1.65, 4.47 |
|
7.80 | 1.24, 49.10 |
|
Tertiary | 4.0 | 2.76, 5.79 |
|
9.68 | 1.00, 92.92 |
|
Other | 1.8 | 0.60, 5.37 | 0.29 | 15.31 | 0.58, 402.7 | 0.10 |
Occupation | ||||||
None | 1.00 | 1.0 | ||||
Public sector | 4.50 | 2.09, 9.68 |
|
1.85 | 0.14, 23.52 | 0.63 |
Farming | 1.88 | 1.04, 3.38 |
|
0.69 | 0.05, 9.72 | 0.79 |
Trading | 3.00 | 1.60, 5.61 |
|
1.38 | 0.09, 20.07 | 0.81 |
Apprenticeship | 2.50 | 0.96, 6.44 | 0.06 | 2.66 | 0.09, 74.81 | 0.56 |
Self-employed | 4.70 | 2.37, 9.30 |
|
1.77 | 0.08, 35.44 | 0.70 |
Other | 3.31 | 1.89, 5.79 |
|
7.41 | 0.24, 220.6 | 0.24 |
Place of residence | ||||||
Slum | 1.00 | 1.0 | ||||
Zongo | 2.67 | 1.55, 4.58 |
|
5.04 | 0.46, 55.38 | 0.18 |
Old Town | 3.37 | 2.39, 5.88 |
|
3.57 | 0.41, 30.56 | 0.24 |
New site | 2.57 | 1.63, 4.05 |
|
7.07 | 0.49, 102.1 | 0.15 |
Peri-urban | 5.33 | 2.61, 10.86 |
|
1.92 | 0.16, 22.77 | 0.60 |
Other | 1.5 | 0.42, 5.31 | 0.53 | 1 | ||
Ethnic background | ||||||
Denkyira | 1.00 | 1.0 | ||||
Other | 0.69 | 0.41, 1.13 | 0.14 | 0.17 | 0.03, 0.78 |
|
Religion | ||||||
Christianity | 1.0 | 1.0 | ||||
Islam | 0.47 | 0.27, 0.80 |
|
0.12 | 0.03, 0.52 |
|
The odds of enrolling and renewing NHIS subscription increased with the level of education. Individuals who had some educational credentials up to primary (OR = 2.22; 95% CI; 1.27, 3.87), secondary (OR = 2.71; 95% CI; 1.65, 4.47), and tertiary level (OR = 4.0; 95% CI; 2.76, 5.79) were more likely to have NHIS active membership compared with those with no education. Again, there was increase in the trends of the odds of using NHIS with the sector of employment. Individuals with public sector employment (OR = 4.5; 95% CI; 2.09, 9.68), Trading (OR = 3.0; 95% CI; 1.60, 5.61), and self-employment (OR = 4.70; 95% CI; 2.37, 9.30) were more likely to have active membership compared with those with no employment. Individuals who described their residency as
The income level of respondents increases the odds of enrolling and renewing NHIS policy. Individuals who earned above GHC 500–GHC 1000 (OR = 5.3; 95% CI; 2.69, 10.41) and 200–500 (OR = 2.63; 95% CI; 1.37, 5.17) were more likely to have their NHIS active compared with those who earned below GHC 200 (USD 52.37). Individuals who had more than 4–6 household size were 2.82 times more likely to have their NHIS status active compared with those who had less 1–3 household size. Individuals who mentioned their religious affiliation as Islam were 0.47 times (95% CI; 0.27, 0.80) less likely to have their NHIS status active.
Sociodemographic factors such as age, gender, marital status, education, ethnicity, and education consistently increased the odds of having active membership in the NHIS after the inclusion of other co-covariates. Individuals who were 38–47 years (AOR 0.06) and 58 years and above (AOR = 0.01) were, respectively, less likely to have their NHIS active after adjusting for other covariates. Consistently, being a female had a higher likelihood of having NHIS status as active AOR = 3.92 (95% CI; 1.21, 12.67) after accounting for the effect of other confounding variables. Different educational levels were consistently associated with active NHIS status after adjusting for other covariates. Respondents who were married consistently had higher odds (AOR = 48.9) of having their NHIS status active after adjusting for other covariates. Similar to the univariate analysis, the odds of having NHIS status active decreased with religious background; those with Islam religious background were less likely to have their NHIS active at AOR 0.12 (95% CI; 0.03, 0.52) compared with Christians after accounting for other covariates.
Table
Logistic regression analysis of households’ profile and healthcare utilization.
Variable | Use of healthcare with NHIS | |||||
---|---|---|---|---|---|---|
Model 1 OR | 95% CI |
|
Model 2 AOR | 95% CI |
|
|
|
||||||
Age | ||||||
18–27 | 1.00 | 1.00 | ||||
28–37 | 0.64 | 0.34, 1.18 | 0.15 | 0.51 | 0.05, 4.95 | 0.56 |
38–47 | 0.39 | 0.18, 0.84 |
|
0.18 | 0.12, 2.73 | 0.22 |
48–57 | 0.84 | 0.31, 2.23 | 0.73 | 0.28 | 0.02, 2.86 | 0.34 |
58+ | 2.81 | 0.79, 9.95 | 0.10 | 12.53 | 0.31, 493.09 | 0.17 |
Monthly income (GHC)1 | ||||||
Below 200 | 1.00 | 1.00 | ||||
200–500 | 2.00 | 1.09, 3.64 |
|
1.37 | 0.29, 8.49 | 0.39 |
500–1000 | 1.10 | 0.66, 1.82 | 0.70 | 0.39 | 0.07, 2.20 | 0.29 |
1000–1500 | 2.25 | 0.69, 7.30 | 0.17 | 1.01 | 0.04, 21.07 | 0.99 |
1500+ | 2.5 | 0.48, 12.88 | 0.27 | 1.00 | - | |
Household size | ||||||
1–3 | 1.00 | 1.00 | ||||
4–6 | 0.72 | 0.38, 1.37 | 0.38 | 1.20 | 0.31, 4.39 | 0.78 |
7–9 | 0.65 | 0.32, 1.32 | 0.24 | 0.23 | 0.04, 1.36 | 0.11 |
Number of dependents | ||||||
1–3 | 1.00 | 1.00 | ||||
4–6 | 0.82 | 0.43, 1.53 | 0.53 | 0.73 | 0.20, 2.64 | 0.63 |
7–9 | 1.40 | 0.34, 5.68 | 0.63 | 0,09 | 0.003, 2.92 | 0.18 |
10+ | 1.00 | 0.22, 4.38 | 1.00 | 2.85 | 0.03, 162.01 | 0.61 |
Gender | ||||||
Male | 1.00 | 1.00 | ||||
Female | 1.16 | 1.17, 2.35 |
|
0.48 | 0.14, 1.61 | 0.23 |
Education | ||||||
No formal education | 1.00 | 1.00 | ||||
Primary | 1.76 | 0.73, 4.27 | 0.19 | 1.00 | ||
Secondary | 1.82 | 0.82, 4.02 | 0.13 | 2.39 | 0.44, 12.80 | 0.30 |
Tertiary | 2.44 | 1.20, 4.97 | 0.01 | 6.46 | 0.89, 46.41 |
|
Other | 0.95 | 0.17, 5.28 | 0.95 | 4.92 | 0.63, 37.98 | 0.12 |
Marital status | ||||||
Single | 1.00 | 1.00 | ||||
Married | 1.47 | 1.00, 2.16 |
|
10.32 | 1.24, 85.94 |
|
Divorce | 1.66 | 0.72, 3.80 | 0.22 | 6.89 | 0.86, 129.89 | 0.19 |
Widow | 12.99 | 1.70, 99.37 |
|
20.49 | 0.63, 37.98 |
|
Occupation | ||||||
None | 1.00 | 1.00 | ||||
Public sector | 1.13 | 0.64, 1.98 | 0.66 | 0.42 | 0.04, 4.23 | 0.46 |
Farming | 1.40 | 0.72, 2.71 | 0.32 | 1.20 | 0.09, 14.76 | 0.88 |
Trading | 1.18 | 0.66, 2.08 | 0.56 | 0.47 | 0.03, 5.86 | 0.56 |
Apprenticeship | 1.22 | 0.50, 2.94 | 0.65 | 3.24 | 0.18, 78.29 | 0.46 |
Self employed | 4.00 | 2.06, 7.74 |
|
1.82 | 0.05, 65.81 | 0.74 |
Other | 2.11 | 1.20, 3.69 |
|
1.00 | ||
Place of residence | ||||||
Slum | 1.00 | 1.00 | ||||
Zongo | 1.65 | 0.61, 4.41 | 0.31 | 10.32 | 1.24, 85.94 |
|
Old town | 1.61 | 0.67, 3.88 | 0.28 | 6.89 | 0.36, 129.89 |
|
Peri-urban | 3.20 | 1.13, 9.07 |
|
20.49 | 0.57, 736.36 |
|
Other | 2.75 | 0.24, 30.51 | 0.41 | 1.00 | ||
Use NHIS [active] | ||||||
No | 1.00 | 1.00 | ||||
Yes | 1.67 | 0.95, 2.92 |
|
2.19 | 1.18, 4.07 |
|
Ethnic background | ||||||
Denkyira | 1.00 | 1.00 | ||||
Other | 0.75 | 0.45, 1.24 | 0.27 | 0.38 | 0.10, 1.46 | 0.16 |
Religion | ||||||
Christianity | 1.00 | 1.00 | ||||
Islam | 0.80 | 0.45, 1.45 | 0.466 | 1.18 | 0.23, 5.44 | 0.82 |
Consistently, having tertiary level education had a higher likelihood of using healthcare AOR = 6.46 (95% CI; 0.89, 46.41) after accounting for the addition of other covariates like age, gender, and marital status. Also, respondents who were married and widowed consistently had higher odds (AOR = 10.32; AOR = 20.49) of using healthcare after adjusting for other covariates. Also, the place of residency of respondents was consistently associated with the use of healthcare after adjusting for the inclusion of other covariates.
The study was conducted to examine households’ profile that predicts NHIS active membership and subsequent utilization of healthcare in the Upper Denkyira East Municipality. The household profiles were categorized according to sociocultural, social class, economics, and spatial or geographical location.
The median and average ages of household members were 28.5 years and 34 years, respectively. The age characteristics demonstrate an active adult population in the study setting. This category of the population has the ability to contribute to the active labour force in both formal and informal sector. The trend of age distribution presents an inverse relationship between population growth and aging; an increase in the ages of the population presents a decreased population. The finding confirms similar trend of age distribution in the 2010 national population census [
The study showed that the median and average monthly incomes of households were GHC 200 (USD 52.35) and GHC 412.94 (USD 108.12), respectively. The average monthly income in particular was about 1.9 times higher than 2015 national monthly minimum wage of GHC 210 (USD 54.97) [
The study showed that 74% of household members were insured under the NHIS. This finding implies that most households were using the NHIS as their primary source of seeking healthcare. This number is about 2 times higher than the 2012 and 2013 national level active membership which stands at 37% and 38%, respectively. Again, the 74% insured clients were about 1.2 times higher than a similar study in Barekese district in the Ashanti Region of Ghana [
The households profile such as age, gender, education, marital status, and ethnic and religious background influenced the NHIS active membership. These factors significantly influenced NHIS status, either likely increase or reduce active membership. The marital status of household members significantly influenced NHIS active membership. Married household members were more likely to have their NHIS active compared with singles. This finding could be attributed to the fact that married couples might fear the burden of out-of-pocket payment of healthcare particularly when there is a dependant. This finding confirms earlier studies in Ghana, which found that married individuals have higher odds of enrolling and renewing NHIS policy [
In this study, females were more likely to have active NHIS policy. This finding could be attributed to the differences in health seeking behaviour between females and males. Females seem more responsive to sickness and will report promptly to health facilities than men who would usually wait till their condition deteriorates due to masculinity. Again, females are perceived as vulnerable especially during pregnancy and more prone to seeking care at the health facility. This may probably influence their decision to continually enrol and renew NHIS policy. This could also be linked to the free maternal health policy that grants exemption to payment of NHIS among pregnant women. This finding corroborate with previous studies across different settings of Ghana [
The age of individual household members influenced their decision to enrol and renew NHIS policy. The elderly people of age 58 years and above were less likely to have active membership to the NHIS though these groups frequently sought healthcare. This finding could be attributed to a number of factors including households financing sources for the scheme. For instance, the NHIS in Ghana has exemption for the aged 70 years and above. This exemption is an effort to cover the indigents otherwise classified as vulnerable including aged, children, pregnant women, and people with disabilities. However, the existing criteria peg exemption at 70 or more years and may have significant implication on payment sources for clients between 58 and 70 years. In Ghana, the compulsory retiring and retiring ages for the formal sector are 55 and 60 years, respectively. With the current exemption age pegged at 70 years and above, people who compulsorily retire from the public service may have to battle with the premium payment which invariably would restrict access to healthcare. Pension payments are so low that pensioners find it difficult to make ends meet let alone think about health expenditure. Individuals who are not resourced financially may not be able to enrol or renew their insurance policy. This is particularly true among elderly persons in the informal sector within the Upper Denkyira East Municipality who are predominantly peasant farmers with no regular pension scheme. This finding reinforces previous studies which suggest that the inability to pay for insurance premium constitutes the most frequently cited reason for limited enrolment [
The ethnic and religious affiliation of households plays a significant role in enrolling and renewing NHIS policy. The minority ethnic groups and Muslims were less likely to enrol and renew their NHIS policy. This finding could be explained by varying factors including limited funds, religious-cultural issues, and health seeking behaviour. This finding confirms previous evidence on the role of religion and ethnicity in determining enrolment and renewal of NHIS policy in Ghana. The role of religion in determining NHIS policy ownership varies according to geographical location and gendered identity. In the Upper West Region, previous finding showed that males who were Muslims were more likely to never enrol or drop out of their NHIS policy compared with Christians [
The study also found that the level of education consistently influenced households’ decision to enrol and renew their NHIS policy. The odds of enrolling and renewing NHIS policy increased with the level of educational attainment. Individuals educated at secondary and tertiary level had higher odds of enrolling and renewing their NHIS policy. The finding implies that education plays a significant role in educating households about the important of enrolling and renewing NHIS policy. This finding confirms previous studies in Ghana [
In this study, we examined the influence of households’ profile on the use of NHIS and healthcare in the Upper Denkyira East Municipality. The factors influencing NHIS and use of healthcare were limited to only sociodemographic information compared with previous studies where management and health authorities’ related factors are considered. Despite these, this study has provided insightful evidence to inform policy decision.
The study concludes that household’s profile such as age, gender, income, education, and marital status influences individual’s decision to renew their NHIS policy. Individuals who are married, females, and those with some educational credentials up to primary, secondary, and tertiary level were more likely to renew their NHIS policy. However, vulnerable population such as the elderly of 57–69 years and minority ethnic and religious groups including Muslims were less likely to renew their NHIS policy. The NHIS policy should revise the exemption bracket to wholly cover vulnerable groups such as minority ethnic and religious groups and elderly people at retiring age of 60 years. This would help to remove out-of-pocket payment for healthcare for these vulnerable populations.
The raw data (the administered questionnaires) informing the findings and conclusion of this article is freely available upon request from the authors and Institutional Review Board of KNUST. The raw data has been entered into STATA version 14 and right-protected with a password. All other materials such as copy of questionnaires and consent form are equally available upon request from the authors.
Ethical clearance was obtained from the Committee for Human Research Publication and Ethics at Kwame Nkrumah University of Science and Technology (KNUST) before the fieldwork. The researchers respected the rights of the respondents and ensured that informed consent was completed before the administration of the questionnaires. Again, a written permission was obtained from the Municipal NHIS Co-Coordinator and Health Directorate of Upper Denkyira East Municipal Assembly prior to the implementation of study methods.
This is to certify that this paper is a finding from original work. The authors have duly acknowledged the work(s) of others they used in writing this article/manuscript and have duly cited all such work(s) in the text as well as in the list of the references and they have presented within quotes all the original sentences and phrases.
The authors declare that they have no financial or personal relationships which may have inappropriately influenced them in writing this article.
Eric Badu wrote the first draft of the manuscript. Eric Badu, Peter Agyei-Baffour, Isaac Ofori Acheampong, Kwasi Addai-Donkor, and Maxwell Preprah Opoku performed the data analysis and interpretation of results. All authors reviewed and made inputs into the intellectual content and agreed on its submission for publication.
The authors wish to thank the Municipal NHIS Authorities and Health Directorate of Upper Denkyira East Municipal Assembly. They again wish to thank the Committee on Human Research, Publications and Ethics, Kwame Nkrumah University of Science and Technology (KNUST), for approving the study protocol prior to its implementation. They are grateful to all the study participants for their support during the data collection.