Unveiling the impact: Health insurance effects on healthcare accessibility in
Tanzania.
Author. Gloriana E Mateves
(+255) 0623263166/0716612277
Email matevesglory150@gmail.com
Abstract.
The global target of achieving Universal health coverage (UHC) by 2030 is a challenging task
especially in developing countries. To search for detailed reasons, this study examines the effects
of health insurance on healthcare accessibility. The study used Non experimental research design
and data from Demographic and Health Survey (DHS) 2022. Simple logistic regression model
was used to estimate the effects of health insurance on healthcare accessibility. The results
revealed
that age (0.042,p<0.000) , marital status (0.010, p < 0.034) ,wealth (0.016,p<0.032)
,employment status (0.019,p<0.001) and education status (0.017,p<0.001) ,are
main
determinants of demand for health insurance in Tanzania .The results also revealed that marital
status (0.037,p<0.007) and income (0.049,p<0.002) are the key factors affecting an access to
health care. The study therefore recommends the government to invest more in education and
create more employment opportunities to alleviate poverty in Tanzania in order to increase the
demand for health insurance and healthcare accessibility.
Keywords
Healthcare; Health insurance; Accessibility; Demand; Determinants.
Introduction
At all stages of life, access to health care services has a considerable effect on overall health (Gu
et al., 2009). Globally, most of health care system emphasize minimizing barriers to health care
access for citizen’s (Ahrq, n.d.) Sustainable development goal number 3 aims at ensuring lives
and promote wellbeing for all at all ages. Apart from aiming at reducing the rate of diseases, it
also emphasizes access to essential health services, safe and affordable medicines. Sufficient
healthcare services utilization allows for earlier detection and diagnosis of health problems and
hence becoming addressed more proactively (Papastergiou et al., 2020)
In 2021 global spending on health reached a new high of US$ 9.8 trillion or 10.3% of global
gross domestic product (GDP). However, the distribution of spending remained grossly unequal.
In 2021, about 11% of the world's population lived in countries that spent less than US$50 per
person per year, while the average per capita spending on health was around US$ 4000 in highincome countries. Low-income countries accounted for only 0.24% of the global health
expenditure, despite having an 8% share of the world’s population (WHO 2023) (Kitole et al.,
2023), Proclaimed that the question of whether health insurance has a role to play in facilitating
access to health care is highly debatable in most developing countries. The debate is louder in
these countries because of poverty, which increases pressure on the households' decision to seek
medical services that are accompanied with unfriendly user fees and out-of-pocket expenditures,
which have been threatening households' incomes.
Health insurance coverage is still low in Tanzania thus contributing to low health
accessibility(Kitole et al., 2023). In the year 2019, 32% of Tanzanians had health insurance
coverage, of which 8% have subscribed to NHIF, 23% are members of Community Health Fund
(CHF), and 1% are members of private health insurance companies. Beneficiaries of NHIF
includes the contributing members, spouse and up to four dependents (NHIF ACT 2015). The
CHF beneficiaries include head of household, spouse and all children below 18 years. CHF
mainly focuses its coverage in rural population while private health insurance schemes target
urban population (Community Health Fund Act No 1 of 2001)
As a strategy to low access of health care accessibility, Tanzania government has recently
introduced a policy of health insurance for all citizens. The aim of this policy is to ensure that
every citizen in the country has access to health care by providing health insurance coverage.
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This policy is expected to improve the health outcomes of citizens by increasing access to
healthcare services and reducing the financial burden of healthcare costs. Despite the strategy
taken, the problem is still yet not solved. It is from this ground this study aims to give some
insights on effects of health insurance on health care accessibility in Tanzania.
Empirical literature review.
Kitole et al (2023) Conducted study in Tanzania on “Does health insurance solve health care
utilization puzzle in Tanzania?” Tanzania Panel Survey data of 2020/21 was used and Probit
model, negative binomial regression, and instrumental variable Poisson with generalized method
of moments were used in the study. The study revealed that, geographical location, Income,
education, household size, health status and health care cost are the key determinants of demand
for health insurance. The study also revealed that geographical location, income ,education,
household size, health insurance ,distance from home steady to health facility and membership
to social groups are main factors which influencing in access to health care. Therefore the study
recommended that the government should expand its share on health sector and also there should
be
interventions that ensure the affordability of health services without compromising the
quality of services offered.
Kitole et al (2022) conducted a study on analysis on the equity differential on household
healthcare financing in developing
countries where the study used empirical evidence from
Tanzania. The non-experimental research design has been used to explore the Tanzania Panel
Survey (NPS) data 2019/2020, to investigate equity differential in household healthcare
financing in Tanzania by the use of conventional instrumental variable methods of Two-stage
and Three-stage least square methods. The study revealed that household’s income, education,
health care waivers, out-of-pocket expenditure, and user fees have been found to have a
significant impact on household equity in healthcare financing. Then the results of this study,
recommended
that health insurance is an important component in promoting equity in health
care financing among households therefore the government should put more efforts to improve
the national and community health insurances that are easily affordable to a large group of the
society.
Tungu et al. (2020) conducted a study on “does health insurance contribute to improve utilization
of health services for the eldery in rural Tanzania?” The study used data which obtained from a
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household survey conducted in the Nzega and Igunga districts. A standardized survey instrument
from the World Health Organization Study on global Ageing and adult health was used. This
comprised of questions regarding demo-graphic and socio-economic characteristics, health and
insurance status, health seeking behaviors, sickness history (three months and one year prior to
the survey), and the receipt of health care. A multistage sampling method was used to select
wards, villages and respondents in each district. Local ward and hamlet officers guided the
researchers in identifying households with older people. Crude and adjusted logistic regression
methods were used to explore associations between health insurance and outpatient and inpatient
health care use. The study revealed that there were positive relationship between health insurance
and health care utilization where the study involved independent variables such as education,
income, health status, sex, age, and marital status to examine the relationship.
Isabella Kiplagat (2011) conducted a study on the determinants of health insurance choice in
Kenya. While utilizing the 2008-2009 Kenya Demographic and Health Survey (KDHS) data, a
multinomial logistic regression model was used to examine the determinants of health insurance
in Kenya. The study revealed that age, gender, wealth index, income, employment status,
education ,household size have been found to have significant impact on choice of health
insurance. The study recommended that the most important factors to target when designing
policy instruments for health insurance uptake in Kenya are: size of household, wealth index,
education/awareness and employment because to increase uptake of the insurance scheme will
require policies that facilitate schooling and a raise in the living standard of Kenyans.
But also Sandra Hopkins (2014) conducted study in Australia and found that, low awareness
stops many people from enrolling in any arrangement of health insurance scheme. She used
binary logistic model also to examine demand for health insurance and highlighted various
social-economic characteristics such as age ,income ,health status and geographical location as
the determinants of health insurance .At the end she found that the demand for health insurance
declined insurance.
Awiti (2014) carried out a study on poverty and healthcare demand in Kenya. He used a
multinomial probit model and used data from a survey that was carried out in 2005-2006 on the
effects of poverty on healthcare demand. Their results indicated that for all age groups, the
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predictors of poverty include large household sizes and longer distances to the nearest health
facility. The result further revealed that poverty reduces the probability of visiting a modern
health care provider amongst all age groups. The study recommended that to encourage the use
of modern health care facilities, therefore, requires the pursuit of poverty–reduction strategies.
Some of the ways this could be done include lowering the household sizes and reducing the
average distance to modern health care facilities.
Theoretical review
State dependent utility theory.
This theory was founded by Jeremy Bentham’s (1748-1832), which declares that a consumer’s
utility level and taste are influenced by the state of their health or socioeconomic status. Thus,
individuals have different degrees of risk aversion, which influence their decision to purchase
insurance, as the result it affects their access to health care services. For instance, individuals
who perceive their health status as good are less likely to purchase insurance than individuals
who perceive their health status as poor. Also, individuals in households with higher
socioeconomic status, such as education, wealth, and employment, are more likely to demand
health insurance because they can afford it. The theory also explains that having a better
understanding of the benefits of being insured increases the demand for health insurance and
hence even increasing the accessibility to health care services. However, the poor have liquidity
constraints that cause them to remain uninsured even when they may be better off with
insurance.
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Conceptual framework.
Figure 1.0
Demographic factors
Age
sex
Marital status
Demand for
health insurance.
Access to health care.
Social economic factors
Wealth
Employment
Education
Health status
Income
Authors’ design
Methodology
The study employs a non-experimental research design to provide a realistic depiction of the
factors that influence the demand for health insurance and its impact on healthcare accessibility
in Tanzania. Non-experimental research design is used to analyze the existing conditions and
variables to gain a deeper understanding of the determinants of demand for health insurance and
the effects of health insurance on healthcare accessibility .Demographic and Health Survey
(DHS) of 2022 are used in this study. The 2022 Tanzania Demographic and Health Survey
5
(DHS) were implemented by the Tanzania National Bureau of Statistics (NBS) and the Office of
the Chief Government Statistician (OCGS) in collaboration with the Ministry of Health,
Tanzania Mainland and the Ministry of Health, Zanzibar.
Analytical modeling
A simple logistic model will be used to analyze the data collected to examine determinants of
demand for health insurance.
Logit (Y) = ) β0 + βX1+ βX1 + βX2 + βX3 + βX4 + βX5 + βX6 + βX7 + βX8 + µ
Where by
Y= Demand for health insurance.
Where Xi are explanatory variables and is defined as follows?
β= constant parameter
X1 = Age
X2 = Sex
X3 =Marital status
X4 = Wealth status
X5 = Health status
X6=Income of respondent.
X7=Employment status
X8=Education status.
The factors influencing access to health care also are premeditated by using simple logistic
regression model.
Logit(Y) = )= β0 + βX1+ βX1 + βX2 + βX3 + βX4 + βX5 + βX6 + βX7 + βX8 + βX9 + µ
Y= Demand for health insurance.
Where Xi are explanatory variables and is defined as follows?
β= constant parameter
X1 = Age
X2 = Sex
X3 =Marital status
X4 = Wealth status
6
X5 = Health status
X6=Income of respondent.
X7=Employment status
X8=Education status.
X9=Health insurance.
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Table 1.Description of variables.
Variable name
Operational definition
Expected
relationship
Dependent variable
Health insurance
Respondents who demand for health insurance ( demand for
health insurance =1, 0 do not demand for health insurance.)
Independent variables
Age
Age of respondent
Positive/negative
Sex
Dummy, 1=male , 0 otherwise
Positive/negative
Marital status
Dummy,1=married,0 otherwise
Positive/negative
Employment status
Dummy,1= employed,0 otherwise
Positive
Education status
Dummy, 1= educated,0 otherwise
Positive
Wealth status
Dummy, 1=Rich, 0 otherwise, 1=middle, 0 otherwise, 0=poor.
Positive/Negative
Health status
Dummy,1=good
health,0
Positive
income,0=therwise,1=middle
Positive.
health,0
otherwise,1=
moderate
otherwise,0= bad health.
Income
Dummy,1=high
income,0=otherwise,0=low income.
Dependent variable
Health care
Respondents access to health care (1= get access to health care
,0=do not get access to health care )
Independent variables
Age
Sex
Age of respondent
Positive/Negative
Dummy,1=Male,0 otherwise
Positive/Negative
Marital status
Dummy, 1=married , 0 otherwise
Positive/negative
Employment status
Dummy,1= employed,0 otherwise
Positive
Education status
Dummy, 1= educated,0 otherwise
Positive
Wealth status
Dummy, 1=Rich, 0 otherwise, 1=middle, 0 otherwise, 0=poor.
Positive
Health status
Dummy,1=bad health,0 otherwise,1= moderate health,0
Positive.
otherwise,0= bad health.
Income
Dummy,1=high income,0=otherwise,1=middle
Positive
income,0=otherwise,0=low income
Health insurance
Dummy,1=yes,0 otherwise
Positive.
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Results
Descriptive statistics
Table 1 :Age of respondents.
Variable
Obs
Mean
Std. Dev.
Min
Max
Age
2531
30.132
7.531
15
49
As shown by the table above, households heads were aged 30 years old on average, the eldest
household head was aged 49 years old and the youngest household head was aged 15 years
old.
Table 3:Education level of respondents.
educational level
Freq.
Percent
Cum.
no education, preschool/early childhood education
322
12.72
12.72
Primary
1533
60.57
73.29
Secondary
607
23.98
97.27
Higher
69
2.73
100.00
Total
2531
100.00
The table above show that most of the respondents surveyed were having primary education
60.57% followed by the respondents having secondary education 23.98% and then followed by
respondents with no education, preschool, early childhood education 12.72%and few of them
they had higher education 2.73%
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Table 5: Marital Status
Freq.
Percent
Cum.
Married
2227
87.99
87.99
living with partner
304
12.01
100.00
Total
2531
100.00
Table above show that most of the respondents
surveyed were married thus 87.99% of all
respondents were married and 12.01% of respondents were living with partners.
Table 5: Sex of respondents
sex of household head
Freq.
Percent
Cum.
Male
2350
92.85
92.85
Female
181
7.15
100.00
Total
2531
100.00
Table above show that most of the respondents surveyed were males, thus 92.85% of all
respondents were male and 7.15%. of respondents were female.
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Empirical results
Marginal effects after logit
y = Pr(Health insurance ) (predict)
= .02111068
Table 6: Simple logistic regression on determinants of demand for health insurance in Tanzania
Simple logistic
Variable
Marginal effects
p
Standard
Coefficient
dy/dx
error
Standard
error
P
Age
2.009
0.478
0.000
0.042***
0.101
0.000
Sex
-0.023
0.41
0.954
-0.000
0.008
0.955
Marital status
0.602
0.319
0.059
0.010***
0.005
0.034
Rich class
0.713
0.303
0.018
0.016***
0.007
0.032
Middle class
-1.129
0.518
0.029
-0.018***
0.006
0.004
Good health
0.312
0.259
0.227
0.006
0.005
0.232
Moderate
0.460
0.290
0.113
0.010
0.008
0.160
of 0.423
0.220
0.054
0.009
0.005
0.082
0.932
0.286
0.001
1.074
0.442
0.015
health
Income
respondent
Employment
status
Education
status
Number of observations
2531
R squared
0.1329
Chi square
113.64
***p<0.01, **p<0.05, *p<0.1
11
0.019 ***
0.006
0.001
0.017 ***
0.005
0.001
As shown by the table above age of the respondents show a positive relationship with demand
for health insurance and this show that as an individual’s age increased by one year, the more
likely they demanded for an health insurance by 0.042.
Marital status is significant at 5% having positive relationship with demand for health insurance
which implies that people who are married are 0.010 times more likely to demand for health
insurance compared to ones not married.
Wealth was also found to have positive relationship with demand for health insurance where by
rich people are 0.016 times more likely to demand for health insurance than poor and middleclass people are 0.018 times less likely to demand for health insurance than poor people.
Employment was found to have a positive significance on demand for health insurance at a 5%
significance which implies that people who are employed are 0.019 times more likely to seek for
health insurance than ones not employed.
Education found to have positively
significantly affected demand for health insurance which
describe that educated people are 0.017 times more likely to seek for health insurance than noneducated people.
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Marginal effects after logit
y = Pr (Heath care) (predict)
= .1157494
Table 7 Simple logistic regression on factors influencing on accessibility to healthcare.
Simple logistic
Variable
Marginal effects
p
Standard
Coefficient
dy/dx
error
Standard
error
P
Age
-0.092
0.252
0.714
-0.009
0.026
0.714
Sex
0.042
0.239
0.858
0.004
0.024
0.858
Marital status
0.391
0.158
0.013
0.037
0.014
0.007
Rich class
0.005
0.165
0.978
0.000
0.017
0.978
Middle class
-0.070
0.179
0.695
-0.007***
0.018
0.690
Good health
-0.072
0.152
0.638
-0.007
0.016
0.639
Moderate
-0.202
0.179
0.261
-0.019
0.017
0242
of 0.444
0.138
0.001
0.049***
0.016
0.002
-0.108
0.136
0.428
-0.011
0.014
0.431
-0.210
0.162
0.163
0.385
0.925
0.163
health
Income
respondent
Employment
status
Education
status
Health
insurance
Number of observations
2531
R squared
0.0131
Chi square
24.18
*p<0.01, **p<0.05, ***p<0.1
13
0.022
0.018
0.045
0.037
0.215
0.217
From the table above marital status is significant at 5% having positive relationship with access
to health care which implies that people who are married are 0.402 times more likely on access
to health care compared to ones not married.
It was found from this study that there is positive relationship between income and access to
health care, which entails that high income earners are 0.049 times more likely on access to
health care than low-income earners.
Discussion.
The findings revealed that employed people are more likely to demand for health insurance
compare to unemployed ones. These results are similar to the study of (Duchon et al 2000) who
surveyed on health insurance provided for employees by their employers. He found that
employed people are more likely to demand for health insurance than the unemployed this could
be because employers contribute a certain amount to the premiums of their employees but also
this could be because employed people receive income which may help them to pay required
payment of their insurers.
The findings revealed that educated people are more likely to seek for health insurance than
ones not educated .These results are similar to the study of (Grossman, 2015) who searched on
relationship between formal education and use of health –related information where he found
that people with formal education are more likely use different health facilities including health
insurance this may be due to that formal education helps to know the important of being insured
and the risks of not being insured but also the results are the same to the results of (Diop 2005)
and Franco et al (2008) which described that the individual from household headed by the people
who had education were more likely to enroll in a voluntary health insurance scheme than
households headed by people with no education this may also due to that, education create
awareness to the people on different issues including health relating issues, therefore through
education people knows the benefits of health insurance.
The findings revealed that rich people are more likely to demand for health insurance than poor.
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This results are similar to the results obtained by (Jaime Pinilla& Beatriz G .Lopez 2008) when
they studied about ”Income and wealth as determinants of voluntary private health insurance
,the study found that rich people are more likely to demand for health insurance compare to the
middle and poor people. This could be that rich people can afford premiums charged by
insurance providers that could not be afforded by poor and middle people.
The findings also revealed that as an individual’s age increased by one year, the more likely they
demanded for an health insurance. These results are the same to the results of (Richard Ssempala
2015) who researched on factors influencing d for health insurance in Uganda. He found that as
the age of respondents increases the demand for health insurance also increases and this could be
because at lower age the individuals are still under their parents or in other words at lower age
individuals are depend on their parents who could pay for them so their age does not affect the
demand for health insurance but as they tend to grow now they depending on themselves so they
can pay for health insurance. Positive relationship between age and health insurance demand is
also supported by broader studies on insurance behavior and preferences. In a study by Pauly et
al. (2003), the researchers found that older individuals tend to place a higher value on insurance
coverage, recognizing the increasing healthcare needs that come with age. This aligns with the
notion that as individuals grow older, they become more cognizant of potential health risks and
the importance of having adequate insurance coverage.
The findings revealed that people who are married are more likely to have access in health care
compared to ones not married. These results are the similar to the results obtained by (M Jong et
al 1995) who surveyed on marital status and health care utilization. The study found that
married people are more likely to seek for health care and this could be because of several
factors example married people are likely to face reproductive health problems which force them
to seek for health care than unmarried people but also married people can get moral and financial
supports from their partners in term of seeking for health care but also married people may have
children who face health problems and force them to seek to health care compared to unmarried
ones.
But also study by Han, Liu, and Lu (2009) in the United States found that married
individuals were more likely to have health insurance coverage compared to their unmarried
counterparts. This could be due to that the presence of a spouse might contribute to a sense of
15
shared responsibility for health and financial matters, influencing the decision to seek health
insurance.
On other hand the study have found that, there is positive relationship between income and
healthcare accessibility. This results are similar to the results obtained by( Kitole et al 2022) on
the study of Analysis on the equity differential on household healthcare financing in developing
countries, the study found that household income increases healthcare financing .Income plays a
significant role in determining the accessibility of healthcare. People need a certain level of
income to afford the basics for a healthy life, such as food and quality housing. Higher incomes
enable people to have more choice , and this often means they have access to healthier options.
Conclusion
Health insurance plays a crucial role in improving healthcare accessibility for individuals and
communities. The purpose of this study was to investigate the factors influencing the demand for
health insurance and healthcare accessibility. The key findings revealed significant correlations
between employment status, education level, income, age, marital status, and the likelihood of
seeking health insurance and healthcare services..
Furthermore the study recommends on the need of government to enhance health education
programs, particularly those that focus on health insurance and financial literacy, to significantly
impact individuals’ ability to make informed decisions about their health insurance options and
encourage them to seek necessary healthcare services. Therefore, comprehensive health
education programs that encompass health insurance and financial literacy can play a vital role in
empowering individuals to navigate the healthcare system effectively and make informed
choices about their health insurance and healthcare services.
In addition, the study recommends that government needs to promote income-based subsidies for
health insurance and should be improved to increase access to healthcare services for lowincome individuals and families. These subsidies make health insurance more affordable by
reducing premiums and out-of-pocket costs for eligible individuals and families. By
implementing subsidies based on income levels, low-income individuals and families can access
necessary healthcare services, leading to better health outcomes and reduced financial strain.
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These subsidies can also encourage preventative care, which can help prevent or manage chronic
conditions.
However on promoting employer-sponsored health insurance there is need of government to
increase coverage rates and ensure that more people have access to healthcare services.
Encouraging employers to offer health insurance plans to their employees can improve health
outcomes and increase access to healthcare services. Public health education has been found to
significantly increase people’s demand for commercial health insurance, highlighting the
positive impact of education on insurance decision-making and healthcare utilization. Therefore,
policymakers and employers should prioritize promoting employer-sponsored health insurance
and investing in public health education to increase the demand for health insurance and improve
healthcare accessibility.
On the other hand, developing countries should promote health insurance campaigns tailored to
different age groups implementing this can increase demand for health insurance and improve
healthcare accessibility. Young adults are a key target for insurers because they can help balance
spending on older enrollees and stabilize risk pools in the health insurance marketplaces.
Limitation of the study.
The study on the determinants of demand for health insurance and the effects of health insurance
on health care accessibility in Tanzania has several limitations that need to be taken into
consideration .Firstly the study used secondary data, which means that the researchers had no
control over how the data were collected or how variables were measured. This lack of control
over data collection can lead to biasedness in the data which can be made intentionally or
unintentionally. Additionally, the secondary data used in this study failed to address specific
research questions, as most of the variables that were highly needed to increase insight were not
found .Therefore the study’s results are limited in scope.
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