# Factors Influencing Nonrenewal of Health Insurance Membership in Ejisu-Juaben Municipality of Ashanti Region, Ghana

Authors: Kwawukume Mawumenyo Aku; Kofi Akohene Mensah; Peter Twum; Peter Agyei-Baffour; Daniel Opoku; Joseph Kwasi Brenyah
Journal: Advances in Public Health
 (2021)

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## Abstract

Background. In the quest to prevent households from making catastrophic expenditures at the point of seeking healthcare, the government of Ghana introduced the National Health Insurance in 2003. However, people are reluctant to renew their membership. This study was, therefore, conducted to identify factors influencing the nonrenewal of National Health Insurance membership in the Ejisu-Juaben Municipality. Methods. A cross-sectional study was conducted among 427 respondents in the Ejisu-Juaben Municipality to ascertain factors influencing the nonrenewal of health insurance membership status. Data were entered and analyzed using Stata version 14. Univariate and multivariate analyses were performed to determine sociodemographic factors, household factors, and systemic factors influencing the nonrenewal of health insurance status. Statistical significance for all testing was set as p≤0.05. Results. Sociodemographic factors such as gender (AOR = 0.531; CI = 0.287–0.985) and educational level (AOR = 5.268; CI = 1.130–24.551)) were associated with the nonrenewal of health insurance membership. Income levels in Ghana Cedis were 500–1000 (AOR = 0.216; CI = 0.075–0.617) and 1001–2000 (AOR = 0.085; CI = 0.019–0.383). Systemic decision on factors such as clients’ satisfaction (AOR = 0.149; CI = 0.035–0.640), making copayment (AOR = 0.152; CI = 0,068–0.344), acquiring all prescribed drugs (AOR = 4.191; CI = 2.027–8.668), and awareness of mobile renewal (AOR = 3.139; CI = 1.462–6.739) was associated with nonrenewal of membership. Conclusions. The nonrenewal of health insurance membership was influenced by sociodemographic, household, and systemic factors. Therefore, the Municipal Health Directorate and the National Health Insurance Authority have to work on these factors to reach the target of 100% active coverage in the municipality.


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## Body

## 1. Background

Health insurance coverage is an imperative aspect of achieving Universal Health Coverage (UHC) [1, 2]. In Canada, every citizen and legal residents of the country have basic health insurance coverage which is provided by the government [3]. The public programme, therefore, account for only 37.1% of residents. This shows that public-funded health insurance as practiced by Canada is the way to go to ensure equity and financial protection.Learning from the developed countries, some low- and middle-income countries have begun introducing health insurance to their populations. A study by Escobar et al (2010) in Costa Rica, Peru, Indonesia, Rural China, Columbia as well as Ghana on the impact of health insurance in low- and middle-income countries ascertained a positive correlation between health insurance status and good health outcomes [4, 5].In 2012, the Ghana Parliament made the membership of National Health Insurance Scheme (NHIS) mandatory for all residents in the country through an Act of Parliament [6]. However, out of the 18.5 million people who are registered into the scheme, only 10 million (40%) are active [7, 8]. This, if not addressed, could hamper the country’s effort to attain 100% health insurance coverage for the Ghanaian population by the year 2030.This calls for pragmatic measures in retaining the existing members in addition to enrolling the noninsured in order not to deter Ghana’s effort to achieve UHC. Some studies have been conducted in the Eastern, Central, and Volta Regions of Ghana to ascertain factors that influence enrolment and retention into the health insurance scheme [9, 10]. However, Ashanti Region, the second largest and the most populated region in Ghana, had not benefited from such studies. In addition, for those regions, which have benefited from such studies, the investigations were facility-based, scheme-based, and based on individual factors influencing the nonrenewability of NHIS. How sociodemographic factors, household factors, and health system factors influenced the nonrenewability of NHIS membership had not been adequately explored in Ghana. Therefore, there was a need for such a study to be conducted in the Ejisu-Juaben Municipality as a lesson from the region could be used in other regions in the country and even in other countries in the subregion. This will help put measures in place to propel the country toward achieving its aim of 100% health insurance coverage.


## 2. Methods

### 2.1. Design

A cross-sectional study was conducted from 1 June 2019 to 31 December 2019 among 427 people in Ejisu-Juaben Municipality who have ever registered with NHIS to find out the factors that influence the nonrenewal of health insurance membership.


### 2.2. Sampling Method

Multistage sampling, involving cluster and simple random sampling methods, was used in this study. Ejisu-Juaben Municipality has five submunicipal health administrative areas. Each health administration was taken as a cluster. One community was selected from each cluster using a simple random sampling method. Here, the names of the communities in each submunicipality were written on pieces of a paper and put into a box and thoroughly shaken. One community was selected in each submunicipality resulting in a total of five communities sampled for the study. In each of these selected communities, 85 household heads were interviewed except in Ejisu, the capital town where 87 were interviewed.In reaching each of the selected communities, the researcher stood at the center and span a pen. Houses the tip of the pen pointed at were chosen and the household heads were engaged once they were qualified to take part in the study. On leaving each house, the opposite houses were selected and household heads who met the inclusion criteria were interviewed. In a house where there was no opposite house, the researcher span the pen again and repeated the same procedure to engage household heads. These processes were replicated across all selected communities till the required sample size for each community was attained.


### 2.3. Sample Size

Since the number of households in the Ejisu-Juaben Municipality from the 2010 census is 33,078, which is a relatively large population, the sample size was calculated using Cochran’s formula:(1)No=Z2pqe2,where No is the sample size, p is the assumed prevalence of renewal of NHIS membership in the Ejisu-Juaben district, q is (1-p), Z is the significance level at 95% confidence interval read from the z table to be 1.96, and e is the margin of error, which is assumed to be 5%.For this sample calculation, a 50% prevalence rate of renewal was assumed. Therefore, our sample size was calculated as(2)No=1.9620.50.50.052=384.Adjusting for a nonresponse rate of 10%, the final sample size was(3)final sample size=effective sample size1−nonresponse rate anticipated,final sample size=3841−0.1=427households heads.


### 2.4. Data Collection Techniques and Tools

Data were collected using a semistructured questionnaire designed on the Kobo Collect application. Kobo Collect is Kobo Toolbox’s data collection application that is used to collect survey data and can be used on any android phone. The questionnaires were administered by four trained research assistants who communicated in English and Twi (the local language of study participants).Data on sociodemographic characteristics, such as age, educational level, ethnicity, marital status, occupation, religion, place of residence, and income, were collected. Data collected on the household level comprised of household income, the health status of a household, household size, the number of dependents in a household, the presence of a person with chronic disease in the household, and the wealth status of a household. Systematic factors assessed include distance to a health facility, road network, quality of care, copayments, and availability of medicines.


### 2.5. Data Handling and Analysis

Data were exported from the Kobo Collect application into Microsoft Excel for coding and further imported into Stata version 14.1 for analysis. Data validation was done to ensure data quality. Descriptive data were presented using frequencies and percentages. Univariate and multivariate analyses were performed to establish associations between sociodemographic factors, household factors, and systematic factors influencing the nonrenewal of NHIS membership. Statistical significance for all testing was set asp≤0.05.


## 2.1. Design

A cross-sectional study was conducted from 1 June 2019 to 31 December 2019 among 427 people in Ejisu-Juaben Municipality who have ever registered with NHIS to find out the factors that influence the nonrenewal of health insurance membership.


## 2.2. Sampling Method

Multistage sampling, involving cluster and simple random sampling methods, was used in this study. Ejisu-Juaben Municipality has five submunicipal health administrative areas. Each health administration was taken as a cluster. One community was selected from each cluster using a simple random sampling method. Here, the names of the communities in each submunicipality were written on pieces of a paper and put into a box and thoroughly shaken. One community was selected in each submunicipality resulting in a total of five communities sampled for the study. In each of these selected communities, 85 household heads were interviewed except in Ejisu, the capital town where 87 were interviewed.In reaching each of the selected communities, the researcher stood at the center and span a pen. Houses the tip of the pen pointed at were chosen and the household heads were engaged once they were qualified to take part in the study. On leaving each house, the opposite houses were selected and household heads who met the inclusion criteria were interviewed. In a house where there was no opposite house, the researcher span the pen again and repeated the same procedure to engage household heads. These processes were replicated across all selected communities till the required sample size for each community was attained.


## 2.3. Sample Size

Since the number of households in the Ejisu-Juaben Municipality from the 2010 census is 33,078, which is a relatively large population, the sample size was calculated using Cochran’s formula:(1)No=Z2pqe2,where No is the sample size, p is the assumed prevalence of renewal of NHIS membership in the Ejisu-Juaben district, q is (1-p), Z is the significance level at 95% confidence interval read from the z table to be 1.96, and e is the margin of error, which is assumed to be 5%.For this sample calculation, a 50% prevalence rate of renewal was assumed. Therefore, our sample size was calculated as(2)No=1.9620.50.50.052=384.Adjusting for a nonresponse rate of 10%, the final sample size was(3)final sample size=effective sample size1−nonresponse rate anticipated,final sample size=3841−0.1=427households heads.


## 2.4. Data Collection Techniques and Tools

Data were collected using a semistructured questionnaire designed on the Kobo Collect application. Kobo Collect is Kobo Toolbox’s data collection application that is used to collect survey data and can be used on any android phone. The questionnaires were administered by four trained research assistants who communicated in English and Twi (the local language of study participants).Data on sociodemographic characteristics, such as age, educational level, ethnicity, marital status, occupation, religion, place of residence, and income, were collected. Data collected on the household level comprised of household income, the health status of a household, household size, the number of dependents in a household, the presence of a person with chronic disease in the household, and the wealth status of a household. Systematic factors assessed include distance to a health facility, road network, quality of care, copayments, and availability of medicines.


## 2.5. Data Handling and Analysis

Data were exported from the Kobo Collect application into Microsoft Excel for coding and further imported into Stata version 14.1 for analysis. Data validation was done to ensure data quality. Descriptive data were presented using frequencies and percentages. Univariate and multivariate analyses were performed to establish associations between sociodemographic factors, household factors, and systematic factors influencing the nonrenewal of NHIS membership. Statistical significance for all testing was set asp≤0.05.


## 3. Results

### 3.1. Background Characteristics of Respondents

The mean age of the respondents was 36.3 ± 0.51 years, and a little over half (57.79%) were females. Those who completed Junior High School were 30.48% and traders constituted 26.14%. Vast majority (82.25%) of the respondents were Akan and Christians constituted 89.93% while 70.26% lived in urban areas as shown in Table1.Table 1
Background characteristics of the respondents.
CharacteristicFrequency (n = 417)PercentageAge (mean ± SD)36.3 ± 0.51Age18–277618.2328–3719145.8038–479021.5848–57399.35>58215.04GenderMale17642.21Female24157.79Level of educationNone419.83Primary5312.71JHS12730.46SHS9021.58Tertiary8720.86MSLC194.56EthnicityAkan34382.25Ewe133.12Northern ethnic groups368.63Ga61.44Others194.56Marital statusSingle9623.02Married23957.31Divorced368.63Widowed235.52Cohabiting153.60Separated81.92EmploymentEmployed37088.73Unemployed4711.27OccupationNone174.08Trading10926.14Farming6916.55Civil/public servant7618.23Hairdresser194.56Seamstress153.60Businessperson204.80Others9222.06Place of residenceRural12429.74Urban29370.26ReligionChristianity37589.93Islam419.83Traditional10.24


### 3.2. Sociodemographic Factors Influencing the Nonrenewal of Health Insurance Membership

In univariate and multivariate regression analyses in Table2, sociodemographic factors such as gender and level of education influenced a person’s decisions not to renew their health insurance membership. Females were 0.531 less likely not to renew their health insurance membership compared to males (AOR = 0.531; 95% CI = 0.287–0.985). In addition, people with MSLC, as their highest level of education, were five times more likely not to renew their NHIS membership as compared to those with other levels of education (AOR = 5.268; 95% CI = 1.130–24.551).Table 2
Sociodemographic factors influencing the nonrenewal of health insurance membership.
CharacteristicUnivariate logistic regressionMultivariate logistic regressionOdds ratio (OR)95% confidence intervalP valueAdjusted odds ratio (AOR)95% confidence intervalP valueAge0.900.3418–27RefRef28–370.9120.465–1.7890.791.3870.585–3.2870.4638–471.2370.587–2.6110.581.7330.606–4.9580.3148–570.8890.329–2.4030.820.7430.199–2.7680.67>580.9570.281–3.2630.940.6210.120–3.2310.57GenderRef0.01RefMale——————Female0.4960.304–0.8080.010.5310.287–0.9850.04Level of education0.32NoneRefRefPrimary2.3400.759–0.7210.142.3880.704–8.1040.16JHS1.6780.596–4.7250.331.3490.435–4.1870.61SHS1.8000.618–5.2400.281.5710.483–5.1170.45Tertiary1.5000.505–4.4530.471.8370.483–6.9930.37MSLC4.2001.121–15.7310.035.2681.130–24.5510.03Ethnicity0.76AkanRefRefNorthern ethnic groups0.7660.307–1.9120.570.9710.322–2.9280.96Ga1.9150.344–10.6690.462.1100.332–13.3960.43Others0.7180.203–2.5330.600.5150.133–1.9930.34Marital status0.19SingleRefRefMarried0.7180.397–1.2970.270.6340.285–1.4100.26Divorced0.8620.331–2.2440.760.7940.232–2.7120.71Widowed0.9920.329–2.9880.990.9030.221–3.6930.89Cohabiting3.1251.016–9.6150.042.0270.544–7.5500.29Separated1.1900.224–6.3360.841.5920.254–9.9840.62Employment0.29EmployedRefRefUnemployed1.4690.726–0.9750.291.8360.742–4.5420.19Occupation0.24NoneRefRefTrading0.4750.149–1.5130.210.8090.198–2.3110.77Farming0.5570.167–1.8590.340.8700.197–3.8450.86Civil/public servant0.4500.134–1.5120.200.6720.127–3.5640.64Hairdresser0.2820.047–1.7060.170.6130.082–4.5950.63Seamstress0.3690.060–2.2740.280.7840.096–6.4200.82Businessperson0.4240.085–2.1210.300.8240.121–5.6060.84Others0.9970.320–3.1041.001.1730.282–4.8700.83Place of residence0.01RuralRefRefUrban0.5190.314–0.8560.010.7510.394–1.4290.38Religion0.64ChristianityRefRefIslam0.8240.371–1.9200.660.8910.320–2.4850.83


### 3.3. Logistics Regression of Household Factors That Influence the Nonrenewal of Health Insurance Membership

In univariate and multivariate analyses, as shown in Table3, household factors such as household income and household expenditure influenced a person’s decision not to renew their health insurance membership. For instance, those with an average household income of GH¢500–1000 were 0.216 times less likely not to renew compared to those who earn less than GH¢500 (AOR = 0.216; 95% CI = 0.075–0.675) and those earning GH¢1001–2000 were 0.085 times less likely not to renew compared to those who earn less than GH¢500 (AOR = 0.085; 95% CI = 0.019–0.389).Table 3
Logistics regression of household factors that influence the nonrenewal of health insurance membership.
CharacteristicUnivariate logistic regressionMultivariate logistic regressionOdds ratio95% confidence intervalP valueOdds ratio95% confidence intervalP valueIncome0.050.01Below 500RefRef500–10000.6550.337–1.2700.210.2160.075–0.6170.011001–20001.3230.673–2.6000.420.0850.019–0.3820.012001–30002.4260.704–8.3680.160.1970.027–1.4330.11Over 30013.8820.231–65.3030.340.3690.010–13.2410.58Expenditure0.01Below 100RefRef100–2001.1500.235–6.6080.861.6620.284–9.7210.57201–5002.7320.602–12.390.197.3441.094–49.2630.04501–7002.8240.594–13.4240.1920.7202.398–179.0660.01701–10008.6251.796–41.4080.0146.2784.686–457.0040.01Above 10005.3671.108–29.0030.0423.8902.219–257.1560.01Health statusRef0.01RefExcellentVery good0.4880.115–2.0530.330.8520.189–3.8490.84Good0.1340.032–0.5640.010.2660.057–1.2500.09Fair0.3530.066–1.8730.220.4390.062–3.0970.42Poor0.5000.0313–7.9940.630.2220.006–8.7360.42Presence of chronic disease in household0.07YesRefRefNo1.8680.916–3.8080.092.0730.852–5.0440.11Number of individuals in a household0.011–3RefRef4–60.4620.276–0.7750.010.6250.258–1.5140.307 and above0.8730.351–2.1700.770.7960.140–4.5150.80Number of children in a household0.010RefRef10.9370.460–1.9100.851.0080.442–2.2970.9720.3180.156–0.6520.010.6440.236–1.7580.3930.4190.196–0.8930.020.7820.250–2.4480.6740.5180.190–1.1400.200.9190.229–3.6960.9150.8060.152–4.2780.800.9810.105–9.1990.9961.4500.321–6.5530.631.1560.097–13.7910.91Number of aged in household0.010RefRef10.3530.177–0.7040.010.7180.312–1.6530.4420.2390.111–0.6520.010.9010.294–2.7600.863 and above4.2340.691–25.9350.129.4150.704–125.870.90Wealth status of household0.19PoorRefRefMiddle class0.4780.216–1.0590.070.4880.1556–1.5330.22Rich1.0500.084–12.9950.971.1460.039–33.2460.94


### 3.4. Systematic Factors Influencing the Nonrenewal of Health Insurance Membership

Table4 shows that the majority of the respondents (60.67%) lived 1–5 km away from a health facility, 83.45% had good road network to the health facility, 85.85% had a good experience on their last hospital visit, and 65.47% made some form of copayment though they were insured. The majority of the respondents (65.47%) were aware of the use of mobile phones to renew their national health insurance cards, but 75.06% of those aware did not know the code used to renew their membership.Table 4
Systematic factors influencing the nonrenewal of health insurance membership.
CharacteristicFrequency (n = 417)PercentageDistance from health facility>1 km13632.611 km–5 km25360.676 km–10 km112.64More than 10 km174.08Nature of roadExcellent20.48Very good6315.11Good34883.45Poor40.96Experience at hospitalExcellent10.24Very good337.91Good35885.85Poor256.00Made copaymentYes27365.47No14434.53Received all drugs prescribedYes20649.40No21150.60Close to the health insurance office<1 km266.241 km–5 km21250.846 km–10 km15737.65More than 10 km225.28Awareness of mobile renewalYes28468.11No13331.89Knowledge of mobile renewal codeYes10424.94No31375.06A little over half of the respondents (50.84%) lived 1–5 km from the health insurance office, with 6.24% living less than 1 km, 37.65% lived 6–10 km away from the health insurance office, and 5.28% lived more than 10 km away from the health insurance office.Only 49.40% of the respondents received all the drugs prescribed while 50.60% not received all the prescribed drugs.


### 3.5. Logistic Regression Analysis of Systemic Factors Influencing Health Insurance Renewal

The univariate and multivariate logistic regression analyses in Table5 show that the level of clients’ satisfaction, making copayment, receiving all prescribed drugs, proximity to the health insurance office, and awareness of the mobile phone for the renewal of membership influenced a person’s decision not to renew their health insurance membership. For example, those who had a good and very good level of satisfaction in their last hospital visit were 0.149 times (AOR = 0.149; 95% CI = 0.035–0.640) and 0.290 times (AOR = 0.107;95% CI = 0.107–0.785), respectively, less likely not to renew their health insurance membership as compared to those who had a poor experience. In addition, those who made no copayments in their last hospital visit were 0.152 times less likely (AOR = 0.152; 95% CI = 0.068–0.344) not to renew compared to those who made copayments. Those who did not receive all their drugs were 4 times likely (AOR = 4.191; 95% CI = 2.027–8.668) not to renew compared to those who received all their drugs. In addition, those who were not aware of the mobile renewal were 3 times more likely (AOR = 3.139; 95% CI = 1.462–6.739) not to renew compared to those who were aware.Table 5
Logistic regression analysis on systemic factors influencing health insurance renewal.
CharacteristicUnivariate logistic regressionMultivariate logistic regressionOdds ratio95% confidence intervalP valueOdds ratio95% confidence intervalP valueDistance from health facility0.100.01>1 kmRefRef1 km–5 km1.6710.945–2.9560.080.5760.747–3.2120.246 km–10 km2.3090.582–9.4840.250.1860.012–2.7740.22More than 10 km3.3591.111–10.1570.030.3400.021–5.5940.45Nature of road0.01PoorRefRefVery good1.7250.169–17.5630.653.180.109–93.190.50Good0.6000.061–5.8700.660.850.031–23.440.92Satisfaction level0.01PoorRefRefVery good0.1480.045–0.4880.010.1490.035–0.6400.01Good0.1360.058–0.3190.010.2900.107–0.7850.02Made copayment0.01YesRefRefNo0.3560.196–0.6520.010.1520.068–0.3440.01Received all drugs prescribed0.01YesRefRefNo2.331.406–3.8850.014.1912.027–8.6680.01Closeness to the health insurance office0.07>1 kmRefRef1 km–5 km0.8310.293–2.3510.730.5580.159–1.9580.366 km–10 km1.1180.392–3.1880.842.3730.672–8.3790.17More than 10 km2.9080.797–10.6010.1153.8283.097–937.4100.01Awareness of mobile renewal0.63YesRefRefNo1.1360.681–1.8940.633.1391.462–6.7390.01Knowledge of mobile renewal code0.01YesRefRefNo2.2091.144–4.2630.021.4580.675–3.1510.33


## 3.1. Background Characteristics of Respondents

The mean age of the respondents was 36.3 ± 0.51 years, and a little over half (57.79%) were females. Those who completed Junior High School were 30.48% and traders constituted 26.14%. Vast majority (82.25%) of the respondents were Akan and Christians constituted 89.93% while 70.26% lived in urban areas as shown in Table1.Table 1
Background characteristics of the respondents.
CharacteristicFrequency (n = 417)PercentageAge (mean ± SD)36.3 ± 0.51Age18–277618.2328–3719145.8038–479021.5848–57399.35>58215.04GenderMale17642.21Female24157.79Level of educationNone419.83Primary5312.71JHS12730.46SHS9021.58Tertiary8720.86MSLC194.56EthnicityAkan34382.25Ewe133.12Northern ethnic groups368.63Ga61.44Others194.56Marital statusSingle9623.02Married23957.31Divorced368.63Widowed235.52Cohabiting153.60Separated81.92EmploymentEmployed37088.73Unemployed4711.27OccupationNone174.08Trading10926.14Farming6916.55Civil/public servant7618.23Hairdresser194.56Seamstress153.60Businessperson204.80Others9222.06Place of residenceRural12429.74Urban29370.26ReligionChristianity37589.93Islam419.83Traditional10.24


## 3.2. Sociodemographic Factors Influencing the Nonrenewal of Health Insurance Membership

In univariate and multivariate regression analyses in Table2, sociodemographic factors such as gender and level of education influenced a person’s decisions not to renew their health insurance membership. Females were 0.531 less likely not to renew their health insurance membership compared to males (AOR = 0.531; 95% CI = 0.287–0.985). In addition, people with MSLC, as their highest level of education, were five times more likely not to renew their NHIS membership as compared to those with other levels of education (AOR = 5.268; 95% CI = 1.130–24.551).Table 2
Sociodemographic factors influencing the nonrenewal of health insurance membership.
CharacteristicUnivariate logistic regressionMultivariate logistic regressionOdds ratio (OR)95% confidence intervalP valueAdjusted odds ratio (AOR)95% confidence intervalP valueAge0.900.3418–27RefRef28–370.9120.465–1.7890.791.3870.585–3.2870.4638–471.2370.587–2.6110.581.7330.606–4.9580.3148–570.8890.329–2.4030.820.7430.199–2.7680.67>580.9570.281–3.2630.940.6210.120–3.2310.57GenderRef0.01RefMale——————Female0.4960.304–0.8080.010.5310.287–0.9850.04Level of education0.32NoneRefRefPrimary2.3400.759–0.7210.142.3880.704–8.1040.16JHS1.6780.596–4.7250.331.3490.435–4.1870.61SHS1.8000.618–5.2400.281.5710.483–5.1170.45Tertiary1.5000.505–4.4530.471.8370.483–6.9930.37MSLC4.2001.121–15.7310.035.2681.130–24.5510.03Ethnicity0.76AkanRefRefNorthern ethnic groups0.7660.307–1.9120.570.9710.322–2.9280.96Ga1.9150.344–10.6690.462.1100.332–13.3960.43Others0.7180.203–2.5330.600.5150.133–1.9930.34Marital status0.19SingleRefRefMarried0.7180.397–1.2970.270.6340.285–1.4100.26Divorced0.8620.331–2.2440.760.7940.232–2.7120.71Widowed0.9920.329–2.9880.990.9030.221–3.6930.89Cohabiting3.1251.016–9.6150.042.0270.544–7.5500.29Separated1.1900.224–6.3360.841.5920.254–9.9840.62Employment0.29EmployedRefRefUnemployed1.4690.726–0.9750.291.8360.742–4.5420.19Occupation0.24NoneRefRefTrading0.4750.149–1.5130.210.8090.198–2.3110.77Farming0.5570.167–1.8590.340.8700.197–3.8450.86Civil/public servant0.4500.134–1.5120.200.6720.127–3.5640.64Hairdresser0.2820.047–1.7060.170.6130.082–4.5950.63Seamstress0.3690.060–2.2740.280.7840.096–6.4200.82Businessperson0.4240.085–2.1210.300.8240.121–5.6060.84Others0.9970.320–3.1041.001.1730.282–4.8700.83Place of residence0.01RuralRefRefUrban0.5190.314–0.8560.010.7510.394–1.4290.38Religion0.64ChristianityRefRefIslam0.8240.371–1.9200.660.8910.320–2.4850.83


## 3.3. Logistics Regression of Household Factors That Influence the Nonrenewal of Health Insurance Membership

In univariate and multivariate analyses, as shown in Table3, household factors such as household income and household expenditure influenced a person’s decision not to renew their health insurance membership. For instance, those with an average household income of GH¢500–1000 were 0.216 times less likely not to renew compared to those who earn less than GH¢500 (AOR = 0.216; 95% CI = 0.075–0.675) and those earning GH¢1001–2000 were 0.085 times less likely not to renew compared to those who earn less than GH¢500 (AOR = 0.085; 95% CI = 0.019–0.389).Table 3
Logistics regression of household factors that influence the nonrenewal of health insurance membership.
CharacteristicUnivariate logistic regressionMultivariate logistic regressionOdds ratio95% confidence intervalP valueOdds ratio95% confidence intervalP valueIncome0.050.01Below 500RefRef500–10000.6550.337–1.2700.210.2160.075–0.6170.011001–20001.3230.673–2.6000.420.0850.019–0.3820.012001–30002.4260.704–8.3680.160.1970.027–1.4330.11Over 30013.8820.231–65.3030.340.3690.010–13.2410.58Expenditure0.01Below 100RefRef100–2001.1500.235–6.6080.861.6620.284–9.7210.57201–5002.7320.602–12.390.197.3441.094–49.2630.04501–7002.8240.594–13.4240.1920.7202.398–179.0660.01701–10008.6251.796–41.4080.0146.2784.686–457.0040.01Above 10005.3671.108–29.0030.0423.8902.219–257.1560.01Health statusRef0.01RefExcellentVery good0.4880.115–2.0530.330.8520.189–3.8490.84Good0.1340.032–0.5640.010.2660.057–1.2500.09Fair0.3530.066–1.8730.220.4390.062–3.0970.42Poor0.5000.0313–7.9940.630.2220.006–8.7360.42Presence of chronic disease in household0.07YesRefRefNo1.8680.916–3.8080.092.0730.852–5.0440.11Number of individuals in a household0.011–3RefRef4–60.4620.276–0.7750.010.6250.258–1.5140.307 and above0.8730.351–2.1700.770.7960.140–4.5150.80Number of children in a household0.010RefRef10.9370.460–1.9100.851.0080.442–2.2970.9720.3180.156–0.6520.010.6440.236–1.7580.3930.4190.196–0.8930.020.7820.250–2.4480.6740.5180.190–1.1400.200.9190.229–3.6960.9150.8060.152–4.2780.800.9810.105–9.1990.9961.4500.321–6.5530.631.1560.097–13.7910.91Number of aged in household0.010RefRef10.3530.177–0.7040.010.7180.312–1.6530.4420.2390.111–0.6520.010.9010.294–2.7600.863 and above4.2340.691–25.9350.129.4150.704–125.870.90Wealth status of household0.19PoorRefRefMiddle class0.4780.216–1.0590.070.4880.1556–1.5330.22Rich1.0500.084–12.9950.971.1460.039–33.2460.94


## 3.4. Systematic Factors Influencing the Nonrenewal of Health Insurance Membership

Table4 shows that the majority of the respondents (60.67%) lived 1–5 km away from a health facility, 83.45% had good road network to the health facility, 85.85% had a good experience on their last hospital visit, and 65.47% made some form of copayment though they were insured. The majority of the respondents (65.47%) were aware of the use of mobile phones to renew their national health insurance cards, but 75.06% of those aware did not know the code used to renew their membership.Table 4
Systematic factors influencing the nonrenewal of health insurance membership.
CharacteristicFrequency (n = 417)PercentageDistance from health facility>1 km13632.611 km–5 km25360.676 km–10 km112.64More than 10 km174.08Nature of roadExcellent20.48Very good6315.11Good34883.45Poor40.96Experience at hospitalExcellent10.24Very good337.91Good35885.85Poor256.00Made copaymentYes27365.47No14434.53Received all drugs prescribedYes20649.40No21150.60Close to the health insurance office<1 km266.241 km–5 km21250.846 km–10 km15737.65More than 10 km225.28Awareness of mobile renewalYes28468.11No13331.89Knowledge of mobile renewal codeYes10424.94No31375.06A little over half of the respondents (50.84%) lived 1–5 km from the health insurance office, with 6.24% living less than 1 km, 37.65% lived 6–10 km away from the health insurance office, and 5.28% lived more than 10 km away from the health insurance office.Only 49.40% of the respondents received all the drugs prescribed while 50.60% not received all the prescribed drugs.


## 3.5. Logistic Regression Analysis of Systemic Factors Influencing Health Insurance Renewal

The univariate and multivariate logistic regression analyses in Table5 show that the level of clients’ satisfaction, making copayment, receiving all prescribed drugs, proximity to the health insurance office, and awareness of the mobile phone for the renewal of membership influenced a person’s decision not to renew their health insurance membership. For example, those who had a good and very good level of satisfaction in their last hospital visit were 0.149 times (AOR = 0.149; 95% CI = 0.035–0.640) and 0.290 times (AOR = 0.107;95% CI = 0.107–0.785), respectively, less likely not to renew their health insurance membership as compared to those who had a poor experience. In addition, those who made no copayments in their last hospital visit were 0.152 times less likely (AOR = 0.152; 95% CI = 0.068–0.344) not to renew compared to those who made copayments. Those who did not receive all their drugs were 4 times likely (AOR = 4.191; 95% CI = 2.027–8.668) not to renew compared to those who received all their drugs. In addition, those who were not aware of the mobile renewal were 3 times more likely (AOR = 3.139; 95% CI = 1.462–6.739) not to renew compared to those who were aware.Table 5
Logistic regression analysis on systemic factors influencing health insurance renewal.
CharacteristicUnivariate logistic regressionMultivariate logistic regressionOdds ratio95% confidence intervalP valueOdds ratio95% confidence intervalP valueDistance from health facility0.100.01>1 kmRefRef1 km–5 km1.6710.945–2.9560.080.5760.747–3.2120.246 km–10 km2.3090.582–9.4840.250.1860.012–2.7740.22More than 10 km3.3591.111–10.1570.030.3400.021–5.5940.45Nature of road0.01PoorRefRefVery good1.7250.169–17.5630.653.180.109–93.190.50Good0.6000.061–5.8700.660.850.031–23.440.92Satisfaction level0.01PoorRefRefVery good0.1480.045–0.4880.010.1490.035–0.6400.01Good0.1360.058–0.3190.010.2900.107–0.7850.02Made copayment0.01YesRefRefNo0.3560.196–0.6520.010.1520.068–0.3440.01Received all drugs prescribed0.01YesRefRefNo2.331.406–3.8850.014.1912.027–8.6680.01Closeness to the health insurance office0.07>1 kmRefRef1 km–5 km0.8310.293–2.3510.730.5580.159–1.9580.366 km–10 km1.1180.392–3.1880.842.3730.672–8.3790.17More than 10 km2.9080.797–10.6010.1153.8283.097–937.4100.01Awareness of mobile renewal0.63YesRefRefNo1.1360.681–1.8940.633.1391.462–6.7390.01Knowledge of mobile renewal code0.01YesRefRefNo2.2091.144–4.2630.021.4580.675–3.1510.33


## 4. Discussion

The results of the study show gender and level of education as sociodemographic factors that influence the renewal of health insurance membership. Females were more likely to renew their insurance compared to their male counterparts. This could be attributed to the fact that females use healthcare services frequently due to reproductive health issues. Another reason could be assigned to the free maternal health policy under the national health insurance. Under this policy, pregnant women are registered with the health insurance without paying for registration and premium; hence, women appear to be renewing their health insurance membership more than men. This finding is consistent with findings from previous studies which reported that females were more likely to have active health insurance compared to males [11, 12].In addition, in this study, there was no association between age and renewal of health insurance membership. This is inconsistent with previous studies that report that those who were older were more likely to renew their health insurance membership [13, 14]. On the other hand, educational level showed a significant relationship between renewing health insurance status and educational attainment. This is in consistence with previous studies which suggest that those with some form of formal education were more likely to renew their health insurance membership [11]. Our study, however, showed that those with MSLC were more likely not to renew their insurance status contrary to a finding by a previous study which reports that there was no significant relationship between education and individual’s decision to renew insurance membership [9].The study showed no significant relationship between ethnicity and health insurance status contrary to a previous study [11]. Marital status also did not show any statistically significant association with the health insurance status. This is similar to a finding by a previous study [15] but inconsistent with other studies [9, 11] which suggest that females who are not married are less likely to have active health insurance compared to the married. This study also did not find any statistically significant relationship between employment and the renewal of health insurance membership. This is similar to a finding reported in a previous study [9]. However, findings in our study were not in line with a study which reported that there is an association between employment and the renewal of health insurance membership [12].Religion did not have an association with the renewal of health insurance membership. This is inconsistent with other studies that show that Christians are more likely to renew their insurance compared to Muslims and people from other religions [11]. The results of the study did not show statistically significant relationship between the place of residence and the health insurance status. This is inconsistent with a study conducted in Kenya [16] as well as in Ghana [11].Household income was found to influence a person’s decision to renew or not to renew their health insurance. Those who earned more were more likely to renew their health insurance compared to those who earned less. This result affirms a previous study conducted in Ethiopia which found that high-income earners are more likely to renew their health insurance than low-income earners [17]. A similar finding is reported in a study carried out in Ghana [11, 14]. However, a study carried out in the Dormaa Municipality in Ghana reported findings contrary to those in our study [18].The study shows a statistically significant relationship between household expenditure and renewal of health insurance status. The multivariate analysis indicates that those who spend more have higher odds of not renewing their health insurance. This could be attributed to the fact that they spend their money on other things which they consider to be more important to them than renewing their health insurance. This is contrary to a previous study which reported that household with higher expenditure have a higher likelihood to renew their health insurance [12].Health system factors found to influence health insurance nonrenewal were the quality of care at the health facility, making copayments, acquiring all drugs prescribed, closeness to the insurance office, and awareness of mobile renewal. The distance to a health facility did not show any statistical significance. This is contrary to previous studies which showed that those who lived far away from a health facility were less likely to subscribe to the health insurance. If the health facility is far from the individuals, it might cause the person to seek other sources of healthcare, thereby deterring the person from renewing the health insurance [17, 19].The quality of care was found to be statistically significant with the health insurance status. The adjusted odds ratio shows that those who felt they had good and very good quality of care were more likely to renew their health insurance status compared to those who had poor quality of care. The quality of care has been identified as a major factor that affects health insurance use [10, 19, 20].In this study, it was found that availability of the prescribed drugs influenced people’s decision on the renewal of the health insurance status. Those who did not receive all the prescribed drugs were 4 times more likely not to renew their health insurance compared to those who received. This affirms a previous study [9].


## 5. Conclusion

The health insurance nonrenewal rate was low and was influenced by sociodemographic, household, and systemic factors. Therefore, the Municipal Health Directorate and the National Health Insurance Authority need to work on these factors to reach the target of 100% active coverage in the municipality.



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*Source: 5575822-2021-07-10.xml*