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FACTORS INFLUENCING THE CHOICE OF HEALTHCARE PROVIDING FACILITY AMONG
HOUSEHOLDS IN SOUTH AFRICA
University of Johannesburg
Orcid ID: orcid.org/0000-0002-8512-2124
University of Johannesburg
Orcid ID: org/0000-0002-5362-7099
The study sought to investigate factors that influence households to choose a health-care
facility from among the three groups of health-care facilities in South Africa, which include public
health-care facilities, private healthcare facilities and traditional healthcare centers. Multinomial
logistic regression was employed in the analysis of. The study found that the most widely used
facilities were public health-care facilities, followed by private health-care facilities and, lastly,
traditional health facilities. Using the traditional health facilities as a reference category, the
results from the multinomial logit model indicate that sex of household head, net household
income per month in Rand and grants as a source of income were the significant variables
influencing households to choose public health institutions over other healthcare facilities. On
the other hand, the variables that were significant in influencing households to choose private
health-care institutions were the age of household head, net household income per month in
Rand, access to grants, access to income from salaries/wages/commission, and access to
pensions. Based on the findings of this study, it is recommended that there should be more
investment in the public health institutions in South Africa due to the high figures of households
using public healthcare, which was approximately above 70 per cent.
Factors, multinomial logit model, healthcare providing facilities, South Africa
JEL classification: I13, I15, O10
1. INTRODUCTION AND BACKGROUND
Access to comprehensive, quality health care services is essential for the promotion and
maintenance of health, prevention and management of various diseases, reducing unnecessary
disability and premature death as well as achieving health equity for all as articulated by the
Department of Health (DOH) (Klemick et al., 2009; DOH, 2011; DOH, 2018). Different scholars
also argue that people should conveniently and confidently have access to health services like
primary care, dental care, behavioural health, emergency care, and public health services
because access to health-care is crucial for overall physical, social, and mental health status,
disease prevention, detection, diagnosis, and treatment of illness, improving the quality of life,
preventing preventable death and improving life expectancy (Levesque et al., 2013; Kuenburg
et al., 2016; Godbole and Lamb, 2018). Malik and Sharma (2017) state that medical centers
such as public and private hospitals help ensure that people receive quality and comprehensive
health-care services. They add that health centers help address socio-economic issues by
ensuring that people get access to health care and health insurance.
The World Health Organization (WHO) argues that many people are pushed into extreme
poverty by bearing the excessive burdens of health spending when they try to have access to
health-care services (Mhlanga & Dunga, 2020; WBG, 2018; WBG, 2016). The South African
government has identified poverty, unemployment, and inequality as the three principal evils
that the country must deal with to bring prosperity. However, poverty is not dealt with in
isolation; most policies target poor people from a multidimensional understanding, one of which
is access to healthcare (Davis, 2014; Davis & Sanchez-Martinez, 2015). In South Africa, the
private and public health systems exist in parallel to provide a broad range of acute,
convalescent, and terminal care required by the people.
Malik and Sharma (2017) argue that patients using health facilities, except for those referred to
by other centers or emergency cases, do not select a health center by chance. Dixon-Woods et
al. (2006) posit that in the past patients used to depend more on the advice received from the
physician, consultant, family members, and friends when choosing a health-care provider. Malik
and Sharma (2017) argue that the lack of knowledge and awareness was the reason people
had to depend on other interested groups for advice. However, according to Phillimore et al.
(2019), the coming in of digital technology changed the old set up, which altered the market and
the individual in terms of how they think. Digital technology helped much in media penetration
and increased the level of awareness; as a result, patients are now demanding quality
information, therapeutic services, and personalisation of services (Phillimore et al., 2019; Ansari
et al., 2020).
Ansari et al. (2020) state that the internet of things (IoT) -based health-care monitoring systems
that use smart gateways between sensor networks and the internet to facilitate household
access to healthcare have emerged. This has given the edge for patients to be more cautious in
choosing health-care services than before. The information that patients get from the diagnosis
about their disease puts pressure on them to make sure that their treatment is appropriate
(Ansari et al., 2020; Malik & Sharma, 2017). Technology, on the other hand, also ensures that
patients can get their medical records electronically and can also schedule appointments and
order prescriptions online (Malik and Sharma, 2017). The online patient portals help in this
regard, and they even assist patients to effectively communicate with doctors through instant
messaging (Ansari et al., 2020). All this is improving the ability of a patient to choose among a
pool of health care providers (Malik and Sharma, 2017; Phillimore et al., 2019).
Given the importance of health care, and how critical access to healthcare is, the WHO
consistently encourages countries to achieve universal health coverage (UHC) through
sustainable health financing mechanisms. In addition, access to health care is spelt out clearly
in the Sustainable Development Goals (SDGs) because of its importance in the achievement of
all the goals. Goal 3.8 of the sustainable development goals explains clearly the issue of UHC,
including financial risk protection, access to quality essential health-care services, and access
to safe, effective, quality and affordable essential medicines and vaccines for all (Squires,
2019). The South African Government is working on establishing a National Health Insurance
(NHI) system due to concerns of disparities within the national health care system such as
unequal access to healthcare amongst different socio-economic groups (Mhlanga & Garidzirai,
2020; DOH, 2011). The central philosophy of implementation of NHI is to bring into the fold
those people who are not insured, specifically those who are unable to afford medical scheme
cover (DOH, 2018; DOH, 2011). The NHI seeks to find ways to make health care more
available to those who currently cannot afford it or whose situation prevents them from attaining
the services they need (DOH, 2018). The other reason is that there is a discrepancy between
money spent in the private sector and that spent in the public sector, which serves about 84 per
cent of the population.
The Presidential Health Summit (PHS) held in 2018 indicated that quality healthcare in South
Africa is fully enjoyed by those that can afford. South Africa spends about 8.7 per cent of Gross
Domestic Product (GDP) in health but almost half of that amount services only 16 per cent of
the population (PHS, 2018). Apart from that, the 2018 national general household survey data
indicate that 71,5 per cent of households prefer public clinics, hospitals, or other public
institutions whenever they need treatment, whether its injury or sickness (STATSSA, 2018).
Therefore, it is essential to understand the factors that influence households to choose between
the available health facilities, those in the public sector, those in the private sector, and
traditional sectors. Knowledge of these factors is essential to influence policy, especially on
resource allocation, as well as address the hindrances that may prevent a household from
choosing other health care centers from the other.
2. HEALTH CARE FACILITY CONSULTATION IN SOUTH AFRICA
Figure 1 below shows, by province, the 2018 percentage distribution of the type of health-care facility
consulted first by households when members fall ill, or they are injured.
Figure 1: Percentage distribution of the type of health-care facility consulted first by the households
when members fall ill, or they are injured by the province in 2018
Source: STATSSA (2018)
Figure 1 above shows the type of health-care facility that is consulted first by households when
household members fall ill or have accidents (STATSSA, 2018). Nationally, 71.5 per cent of
households first go to public clinics, hospitals, or other public institutions when they fall ill or
have accidents, while 27.1 per cent of households first consult a private doctor, private clinic, or
hospital when danger befalls them. Only 0.7 per cent of households did indicate that they first
go to a traditional healer when they fall sick or have an accident. Provincially, the use of public
health facilities was least common in Western Cape with 56,1 per cent, Free State with 63,5 per
cent and Gauteng with 63,9 per cent. It was most common in Limpopo with 86,1 per cent,
Eastern Cape with 79,8 per cent and KwaZulu-Natal with 79,0 per cent. The high number of
households that are using public health care centers is a concern and has implications for the
government and policymakers (STATSSA, 2018). From the objectives of the study, there was a
need to understand the factors that influence households to choose more public healthcare
centers compared to other health-care facilities. This is important, as it will influence policy on
resource allocation as well as address hindrances that may prevent households from choosing
one health care center from the other.
3. THE THEORETICAL AND CONCEPTUAL FRAMEWORK
A health facility is, in general, any location where healthcare is provided (Goodrich and
Goodrich, 1987; Buchbinder et al., 2019). Health facilities range from small clinics and doctors’
offices to urgent care centers and large hospitals with elaborate emergency rooms and trauma
centers (Goodrich and Goodrich, 1987). The number and quality of health facilities in a country
or region is one standard measure of that area's prosperity and quality of life (Hartholt et al.,
2011). In many countries, health facilities are regulated to some extent by law; licensing by a
regulatory agency is often required before a facility may open for business. Health facilities may
be owned and operated by for-profit businesses, non-profit organisations, governments, and in
some cases, by individuals, with proportions varying by country. Access to healthcare is
generally defined as the timely use of personal health services to achieve the best possible
health outcomes (Lambrew et al., 1996; Fortney et al., 2011).
On the other hand, seminal work by Michael Grossman first published in 1972 introduced a new
theoretical model for the determination of the health status of the people (Goodrich and
Goodrich, 1987; Lambrew et al., 1996). The work by Grossman was based on Gary S. Becker’s
household production function model and the theory of investment in human capital. Michael
Grossman describes health as illness-free days in each period or life expectancy (Goodrich and
Goodrich, 1987). Grossman posits that consumers demand health defined as the number of
health days produced by the input of medical care services, diet, and other market goods and
services; time included. In this model, health and knowledge are treated as equal parts of the
durable stock of human capital. As a result, consumers demand health to raise their total
earnings in the future (Grossman, 1972, Grossman, 2017). The theory by Grossman
contributed more to the debate of the link between poverty, education, and structural constraints
(Grossman, 1972; Grossman, 2017).
4. EMPIRICAL LITERATURE REVIEW
There are various health-care providers in South Africa, ranging from public, private, and
informal (traditional healers, medicine store dealers, or hawkers). Most patients’ choices of
health-care providers depend on various characteristics of potential providers and the patients
themselves. Such factors can influence the accessibility of healthcare, even when facilities exist
around them. Some of the factors include the price of care, quality of care, perception of
consumers, attitude, type of illness and severity, socioeconomic and demographic conditions of
consumers. In Nigeria, Okumagba (2011) investigated the determinants of the choice of the
health care provider in the Delta State. The study established that factors like distance from the
health facility, quality of healthcare, and the age of the household head were the significant
factors influencing the choice of a health-care provider. The distance was the variable with a
negative influence on the choice of a health facility, while quality had a positive influence on the
choice of health care facility.
In another study in Kenya, Muriithi (2013) investigated the factors that influence households’
choice of a health-care provider. Using the multinomial logit model with health care providers
categorised into five groups, namely a public clinic, public hospital, self-treatment, private
hospital, and private clinics, the study discovered that distance to the nearest health care
facility, gender, waiting time and education level were significant variables. The study by
Kirdruang (2011) in Thailand discovered that the level of income, amount of co-payment,
household size, and age of the household head were the significant factors in influencing the
choice of the health-care facility.
In addition, Kamgnia et al. (2007) also assessed factors influencing the choice of a health care
provider in Cameroon, and the study found that the choice was influenced by consultation fees,
age and gender of the household head. On the other hand, Brown et al. (2002) investigated
factors that influence university students to choose a health care institution and found that the
severity of illness, consultation fee, and religion were the major factors influencing students to
choose a health facility. In Zimbabwe, Mugweni et al. (2008) analysed factors influencing
pregnant women’s’ choice of a health provider in Marondera district. The study concluded that
the choice of a health-care provider was influenced by the occupation level, number of children,
and distance from the nearest health-care facility.
The brief literature review above shows that many factors influence households to choose
specific health-care facilities. Some of these are the distance from the health facility, quality of
healthcare, age of household head, consultation fees, level of income, household size, the
severity of illness, waiting time, and the education level. The methodology used in this study is
explained in the section below.
Relevant data for the study were extracted from the South Africa 2018 General Household
Survey (GHS) dataset (STATSSA, 2018). Some variables were recorded to fulfil the objectives
of the study.
5.1 Multinomial Logit model
The multinomial logit was used to investigate factors that influence the choice of health-care
providers in South Africa. This tool is necessary because it is a qualitative response regression
model, where the dependent variable Y, the health care providers, is qualitative. The objective,
which is to find the probability of making a specific choice, is influenced by a specific
explanatory variable. The tool is also relevant where there are polychotomous or multiple
category response variables, and one assumes that all the alternatives are mutually exclusive.
In this case, each choice variable stands as an unordered category, and the self-medication
category is chosen as the baseline. This tool was used to generate the marginal effect of the
variables falling into a given category.
5.2 The model
The multinomial logit model is best understood, first, in terms of the probability that a
respondent will fall in one of the j = 1, …, J category of the dependent variable. Let
be a set
of j = 1… J, Bernoulli random variable such that = ,
. With distributed as a Bernoulli random variable, E () = P ( =
1). Letting ≡ P ( =1) for convenience, the multinomial logit can be represented as:
Where and . When the numerator and denominator
grow large without bound while the ratio remains constant, the Bj becomes unidentified.
Therefore, the restriction is applied. When the restriction is applied Bj = 0 giving
for categories j =1…j-1 .......................................................... (2)
for category J ......................................................................... (3)
Interpreting the parameter estimate in terms of the probability that fall in category j giving a
change in is given as:
With the identifying restrictions given for category J, the log odd is given as:
The maximum likelihood estimator, which is log-likelihood maximised, was used because it
guarantees consistent parameter estimates and corrects large sample statistics (Scott et al.,
2001). This and Chi-square (X²) distribution were used to test overall model adequacy at 95 %
significant level. The marginal effects are interpreted as the change in probability of using a kind
of health service facilities as the one-unit change in the explanatory variable occur (Wright
1994). Having “k” health providers, we consider the effect of changing by one unit a regressor
on the probability as indicated below. The formula for marginal effect estimation is given by:
Where is the probability that a respondent used a healthcare facility
Where j = serves as the dependent variable1, 2, and 3 standing for public providers, private
providers, and traditional providers. (1 is assigned for belonging to a category, 0 otherwise)
K = 1… hm (total number of respondents), Bo = intercepts, Bi = coefficient and X = value of
explanatory or independent variable for the i-th individual.
Where: X1 = Gender. This variable is a dummy variable where 1= male and 0 otherwise. The
variable is expected to be + influence on the choice of a public health care center for women
and - for private health care institutions. X2 = Age. This variable is a continuous variable that
explains the age of the individual. The variable is expected to be a positive influence on access
to public and private health facilities. Age (years). X3 = Household size. explains the number of
people in the household. The variable is expected to have a positive influence on the choice for
public institutions and traditional institutions while negative on the private institution. X4 = Net
household income per month in Rand. This variable is described as the amount of money
(income) earned by a household per month. The variable can have a positive or negative
X5 = Own house (1 for yes, 0 - otherwise). The variable is a dummy variable where 1 is
described as owning a house while 0 is otherwise. The variable can have a negative or positive
influence on the choice of a health-care provider. X6 = Grants is a dummy variable that takes
the value of 1 when a household receives grants and 0 if otherwise. X7 =
Salaries/wages/commission is a dummy variable where the variable takes the value of 1 if a
household receives salaries/wages while 0 is if otherwise. The variable is expected to have a
positive or negative influence. X8 = Remittances is a dummy variable that takes the value of 1 if
a household receives remittances and 0 if otherwise. The variable is expected to have a
positive or negative influence. X11 = Pensions is a dummy variable that assumes the value of 1
if a household receives pension and 0 if otherwise. The variable is expected to have a positive
or negative influence.
X9 = Income from a business is the total income a household receives from a business. In this
study, the variable is described as a dummy variable where the variable assumes the value of 1
if a household has income from a business and zero if otherwise. The variable is expected to
have a positive or negative influence. The multinomial logit result for this study was analyzed
using Stata 9 package, and results are reported in the section below.
6. RESULTS AND DISCUSSION
6.1 Multinomial logit results
Table 2 below presents results from the multinomial regression, in which the dependent
variables were categorised into three. These are public health facilities, private health facilities
and traditional health facilities.
Table 1: Multinomial Logit Results
Age of household head
Sex of household head (1)
Net household income per
month in Rand
House ownership (1)
Income from a business
Age of household head
Sex of household head (1)
Net household income per
month in Rand
House ownership (1)
Income from a business
Reference category: traditional health facilities
Pseudo R-Square Cox and Snell .189, Nagelkerke .273, McFadden .177. Model Fitting
Information, Model Fitting Criteria, Likelihood Ratio Tests, -2 Log Likelihood, Intercept Only,
23123.272, Chi-Square 4327.914, df 20, Sig .000.
Source: Author’s computation
The analysis tried to include many different independent variables to understand factors that
influence households to choose among the different health facilities in South Africa. Traditional
health facilities were taken as the reference category in the multinomial logistic regression
analysis to investigate factors that motivate a household to choose one type of health care
facility and not the other. Among the ten variables that were included in the model, only sex of
household head, net household income per month in Rand and grants were the significant
variables influencing variables to choose public health institutions
The gender of the household head had a significant favourable influence on a household’s
choice of public health-care facilities. The results indicate that being female increases the
probability of a household to choose public health care institutions like public clinics and public
hospitals when they become ill. The variable was significant at 5 per cent level of significance
(P-value, 0.19). The odds ratio of choosing public health institutions was higher for females than
for males. Females had an odds ratio of 1.520, which means that the probability of females
choosing public health institutions increases by 1.520 when they become ill compared to males.
This means that in South Africa, more females use public health institutions compared to private
and traditional health centers. These results resonate with results of the study done in Kenya by
Muriithi (2013), who also investigated the factors that influence households’ choice of a health-
care provider using the multinomial logit model.
Furthermore, net household income per month in Rand had a significant favourable influence
on households’ choice of public healthcare centers compared to private and traditional health
centers. The variable was significant at 1 percent (P < 0.004). The odds of choosing a public
health care institution was 1.000. The probability of choosing public health-care institutions
increases by 1.000 when a household becomes ill or when they require health care. In addition,
the variable grants had a negative influence on the choice of public health institutions by
households when they become ill or require health care. The meaning of the variable was that
the probability of choosing public health institutions declines for households who were not
receiving grants (households without income from grants) compared to households that were
receiving grants (households with income from grants). The variable was significant at 10 per
cent (P< 0.055). The odds ratio was 0.665. This means that the probability of choosing public
health-care institutions declines by 0.665 for households that were not receiving grants
compared to households that were receiving grants. The variable grant was an essential factor
in the South African context since we have households who depend on grants for survival in
their day to day living.
On the other hand, the variables that were significant in influencing households to choose
private health-care institutions were the age of the household head, net household income per
month in Rand, access to grants, access to income from salaries/wages/commission, and
access to pensions. The variable age of the household head was significant in influencing
households to choose private health-care institutions at 1 per cent level of significance, with a p-
value of 0.000 and an odds ratio of 1.034. The probability of choosing a private health care
institution increases by 1.034 when the age of an individual increase by a unit. This can be
because of an increase in the income of an individual with a change in age. As the age of the
household head increases, holding all other factors constant, income can also increase due to
an increase in experience at work. This can influence households to be able to pay for private
healthcare, hence the positive influence on the choice for private health institutions. These
findings are in line with Okumagba’s (2011) study, which investigated the determinants of the
choice of the health care provider in the Delta State of Nigeria.
In addition, net income per month in Rand was significant at 5 per cent level of significance,
with a p-value of 0.015 and an odds ratio of 1.000. The variable was positive and significant.
The probability of choosing private healthcare institutions increases by approximately 1.000 if
the income of a household increases by a unit. An increase in income encourages households
to demand more private health care institutions, which are typically associated with high-quality
health care. Moreover, access to grants was also significant at 1 per cent level of significance,
with a p-value of .000 and odds ratio of 4.973. In this case, households that were not receiving
grants had 4.973 higher odds of choosing private health care institutions compared to
households that were receiving grants as a source of income. This may be because households
that are receiving grants as a source of income generally have low levels of income, which can
influence their decision on the form of a healthcare institution to choose when they become ill or
require health care.
The variable salaries/wages/commission had a significant negative influence on households to
choose private health-care institutions. In this variable, households with income from
salaries/wages had a higher probability of choosing private health institutions compared to
households without income from salaries. The variable was significant at 5 per cent level of
significance, with a p-value of 0.000 and odds ratio of 0.314. Lacking income from
salaries/wages reduces the probability of households to demand the use of private health
facilities. Households with no income coming from salaries and wages had 0.314 odds of
choosing private health-care institutions. The negative influence on households without income
coming from salaries/wages can be because of a high number of households that depend on
income from their jobs for survival. Apart from employment, households in South Africa have
limited options for other sources of income.
Lastly, the variable pension had a significant negative influence on households to choose
private health care facilities. In this variable, households that were not receiving pensions had
less probability of choosing private health care compared to households that were receiving
pensions. The variable was significant at five per cent level of significance, with a p-value of
0.003 and an odds ratio of 0.243. The meaning of the odds ratio is that households with no
income coming from pensions had less probability of choosing private health facilities compared
to those with income from pensions. Households without insurance had 0.243 odds of choosing
private institutions compared to households with income that comes from pensions.
7. CONCLUSION AND POLICY RECOMMENDATIONS
The results indicated that the pattern of choice of healthcare by households in South Africa is
such that most households widely use public healthcare facilities, followed by private health-
care facilities and with traditional health facilities being the least chosen. The study sought to
investigate factors that influence households to choose one health-care facility over another.
The study used the multinomial logit model, with traditional health facilities taken as a reference
category. Among the ten variables that were included in the model, sex of household head, net
household income per month in Rand and grants as a source of income were the significant
variables influencing households to choose public health institutions over other health-care
facilities, inclusive of the private sector and the traditional sector. On the other hand, the
variables that were significant in influencing households to choose private health-care
institutions were the age of household head, net household income per month in Rand, access
to grants, access to income from salaries/wages/commission, and access to pensions.
Based on the findings of this study, it is recommended that there should be more investment in
public health institutions in South Africa. The high figure of approximately 75 per cent of
households that are using public health facilities shows that more investment is required in this
sector since public facilities are the most used facilities compared to private and traditional
facilities. The government of South Africa should also review the grant policy because the
probability of choosing private health institutions was lower for households who were receiving
grants compared to non-grant recipients. As a result, the government needs to revisit and
review the amount of money received by households as grants to ensure that the money can
allow them to go for private health facilities when they need medical assistance.
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