Factors Affecting out of Pocket Health Care Expenditure among Sudanese Households

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Factors Affecting out of Pocket Health Care Expenditure

among Sudanese Households

Aamer Basheir Mohammed Ghaleb*

Abstract

Health is a human right yet out of pocket Health expenditure seems to be a factor that

impoverishes people in Sudan. OBJECTIVE: This study was conducted to identify factors affecting

individual OOP health expenditure for different types of health care and total health care expenditure.

This study using Secondary data from Sudan Household health utilizations and expenditure survey 2010.a

total of 15000 households and 75184 individuals were included in two types of regressions. OLS, seeming

uncorrelated regression and Tobit were used for all related type of care (non-chronic care, chronic care,

preventive care, dental care, and health expenditure abroad). Variables that usually positively impact

OOP spending include age groups, education level, widowed, land capacity, hospital rate, bed rate.

Variables that usually negatively impact OOP spending include divorce, type of medical personnel as well

as some of state dummies. Recall that the OOP variables come from the summation of treatment cost,

cost of food, and accommodation for the co patient and transportation cost. Transportation costs seem

to be very high for all type of care. This suggests that the distribution of medical personnel and medical

facilities is unequal.

Key Word: Sudan, Out of Pocket, Health Expenditure, Factors, Household

Introduction

Sudan as one of the low income countries tries achieving its Millennium Development Goals

(MDGs) in reducing poverty by controlling the factors that lead to it. the poor need to divide their low

income among basic necessities, including food, shelter and health care and it is possible that health care

could lead to catastrophic expenditure for the household (Mustafa; & Alsiddiq;, 2007). The financing of

health care is a complex issue for policy makers. The Millennium Development Goals (MDGs) may be

difficult to attain. This is an issue of serious concern and highlights the need for this kind of study. Many

scholars, decision makers and politicians have started to doubt whether they can reach the level that

covers the needs of their citizen or not.

* Master’s student, Master of Science in Health Economics and Health Care Management Program, Chulalongkorn University;

E-mail: [email protected]




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In 2012, the poor people about 44.8% of the population of Sudan North. The unemployment

rate stood at 17% for the overall population, and it was 25.4%. For age group (15-24), the nutrition

situation in Sudan is also poor, characterized by a high number of underweight children and children with

chronic malnutrition, as well as persistently increasing levels of acute malnutrition. Nationally, one third

(32.2%) of children under five years old in Sudan was severely underweight (WHO, 2012).

The out-of-pocket health expenditure (OOPHE) made by individuals had been increased as

percentage from total health expenditure in the past 25 years (You and Kobayashi 2011). Many factors

affect Out of Pocket health care expenditure (OOPHE) of individuals and households. Whether people are

healthy or not is determined by their environment. To a large extent, factors such as where we live, age,

gender, the status of our environment, genetics, income, education level, and relationships with friends

and family all have considerable impact on health, as well as in OOPHE whereas the more commonly

considered factors such as access and use of health care services often have less of an impact (Mustafa; &

Alsiddiq; 2007).

Life expectancy rate was 56.6 years old at birth; it was very low. The crude death rate was 11.5

per 1000 people, which was not high at all. Total fertility rate was 5.6 per woman. So there was decrease

in the life expectancy. Also for children under five years old mortality rate was 112 per 1000 population,

which was very high (WHO 2008; WHO 2009)

Out Of Pocket health expenditure made up a very high percentage of private health care

expenditure in Sudan (around 96%). However, the income share held by the richest 20% of the

population was 42%. This suggests that the richest 20% had almost half of the nation’s wealth. The Gini

coefficient was 35.29, which seems to indicate a relatively equal society. The Gini coefficient was

relatively low not because there was equality but because most of the population in Sudan was poor;

according to the national poverty line, 44.8% of the population was poor (in 2009). For rural residents it

was about 50%, and, it was over 26% for urban residents. The high level of OOPHE (96.17%) lead us to

say that for those who live under the poverty line in urban and rural areas are likely to suffer from high

OOPE (World Data Bank, 2012).

. Table 1: Sudan health expenditure as a percentage of GDP in 1995 – 2009




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Source: World Health Organization National Health Account database 2013

Table 1 shows that public health expenditures increased when compared with Total Health

Expenditures (THE) during the period 1999-2008 but decreased in the same period when compared with

Government Expenditures (GE).It possible that no additional resource was allocated to the health sector

during this period. The contribution of public sector was also less than the private sector as percentage of

GDP; consistent with the fact that out of pocket health expenditure (OOPHE) was the main source of

health expenditure. And according to the Millennium Development Goals (MDGs) health was one of the

human rights and the government should prevent its citizen from the effects of the health status that

caused poverty.

Objective and Scope

Objective

The overall objective for this study is to

1. Determine the socioeconomic factors that affect Out of Pocket health expenditure of

individuals in Sudan.

2. And it’s a specific objective is to identify factors that affect Out Of Pocket health

expenditure for various types of health care at the individual level

Scope

This study will be based on the Sudan household Health Utilization and Expenditure Survey

(SHHUES). This survey was conducted as part of Knowledge Attitude and Practice (KAP) in 2010 and took

place in 15 states. The sample taken from each state was equal to 1000 households, and the total

number of individuals in this survey was equal to 75184 persons. The unit of analysis is the individual.




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Conceptual Framework

This study includes urban and rural residents and tries to assess factors that affect health

expenditure according to different types of diseases and services as well as other related expenditure like

transportation, the expense for the co-patient (the care taker) and other indirect health expenditure.

First of all a regression of OLS well be used to determine factors that affect OOP spending at individual

health factors level, community health factors level and as well as socio-economics factors level. The

OLS model is very common used in determine the effect of independent variable in the dependent

variable to conduct the parameters and its sign to show the direction of the effect. The Tobit model also,

will be used to see if there was any difference in the results if we mentioned the data as pay or not.

I use secondary data from Sudan Household Health Utilizations and Expenditures Survey 2010.

The Household survey was conducted in three rounds to see the effect of seasonality on the impact of

disease and how people utilize the health facilities in the same year. Note that because the data were

collected in the same year and information of the same person does not vary too much, no panel data

analysis will be conducted in this study.

The survey has been conducted as part of Knowledge Attitude and Practice (KAP) to assess the

situation of expenditure on health care. Survey tools are based on the models and standards developed

by the global MICS project, to collect information on the situation of utilizing and spending in different

type of care in 15 states.

The survey contains healthcare expenditure data for various categories of treatment like

hospitalization care, outpatient care, birth delivery and chronic illness etc. The reference period however

is different for each of the cases, i.e., the recall period of a year is for both hospitalization care and

childbirth; three months for outpatient care and a period of one-month for chronic illness. All information

is based on the last episode of illnesses (reported morbidity). Household health care expenditure is

defined as the out-of-pocket expenditures on drug and medicines, consultation fees, hospital bed

charges, transport charges to the treatment site and daily leaving cost, including food and lodging for the

escorts of the ailing household member.

Variables used in the analysis:

The dependent variables are out of pocket health expenditures (OOPHE) on different type of

care. Different regressions will be run on 1) total OOP expenditure, 2) inpatient care OOP expenditure

(hospitalization), 3) chronic care OOP expenditure, 4) non-chronic care OOP expenditure, 5) preventive

care OOP expenditure and 6) dental care OOP expenditure

. And independent variable as the table below

Table 2: independent variable




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Literature Review

The literature review contains two parts first one in health expenditure and the second is health

care subsidies. The health expenditure was studied in many articles at different levels (national versus

state versus individuals). The main conclusion from this part is Hypertension and diabetes were the

highest type’s chronic diseases among population in the high wealth quintile, and malnutrition was high

among the lowest quintiles. Age, gender, education, and residence are the main social factors that have

impact on OOP health expenditure.

I reviewed many studies for the subsidies for health sector. I find almost all the subsidies

concentrate only for the public sector. Sometimes the government gives subsidy to individuals according




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to their health situation. According to the literature main effects of health subsidies are increasing health

expenditure.

Health expenditure is sensitive to change in income; the poor uninsured people had to pay more

out of pocket than richest and insured people (Parker and Wong 1997)

The current food consumption, and children’s education, chronic illness, hospitalizations, and

institutional birth deliveries were main factors leading to catastrophic expenditure (Swadhin Monda, Barun

Kanjila et al. June 2010).

Access to health facilities was identified as a factor affecting health expenditure due to increase

in cost of transportation. (Ke Xu, Chris James et al. 2006)

In another study from Botswana, gender and education status of household head were found to

influence the probability of facing catastrophic health expenditure. (Akinkugbe, Chama-Chiliba. et al. 2012)

Studies on the impact of new drug discount card and prescription benefits on health care expenditures

over low income individuals in Northern Virginia found a decrease in medication expenditures for those

enrolled in all programs for all income categories more than those without pharmaceutical assistance.

(Havrda, Omundsen et al. 2005).

Yardim, M. S Cilingiroglu, N. Yardim, N (2010), in their study identified household factors that led

to catastrophic health expenditure. They illustrated that the socioeconomic factors that were related with

high health expenditures were the head’s insurance status, rural residence, having preschool children,

and those elderly people and disabled all increasing the risky catastrophic expenditure (Yardim,

Cilingiroglu et al. 2010)

Research Methods

Econometric models

Two models will be used in analyzing the data. The first regression is OLS, the second one is

Tobit. The same set of independent variable will be used in the two models

Ordinary Least Squares (OLS) model: The first model is Ordinary Least Squares (OLS) model. This

model has classical assumptions. In this model we assume that the error term is distributed randomly

and the standard error is not a function of the observed variables. The error term has mean 0 and

variance σ2. The dependent and independent variable should have the property of linearity in the

parameters (Gujarati 2003). The specification as follows:




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Where HOOPHEij is a continuous measure of household out of pocket health expenditure, where

i indicate the individual and j indicates the type of healthcare 1) non-chronic care, 2) chronic, 3) hospital

care, 4) preventive care, 5) health care abroad. Independent variables as in table (2) and u is the error

term

Tobit model: According to Gujarati (2004) Tobit takes the following form

Y*i = β1 + β2Xi + ui if Y*i > 0

= 0 otherwise

Where Y is household out of pocket health expenditure, where i indicate the individual and j

indicates the type of healthcare 1) non-chronic care, 2) chronic care 3) hospital care, 4) prevention care,

5) health care abroad. Independent variables as in table (2) and δ is the error term. Here the

independent variable is non-negative and the specification is:-

Where HOOPHE is household health expenditure and the independent variables as in table (2) and u is

the error term.




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Research Results

According to the research methodology, 75184 persons were interviewed. Data were collected

about their health expenditure for different types of care in 2010. This chapter answers the research

question “What are household and individual characteristics that affect individual out -of-pocket health

care expenditure?” Two different types of regression were to find out each factor that affects out of

pocket health care expenditures. The first one is OLS model and the second is Tobit model. In both of

them, log values of the dependent variables are used to capture the relationship between the

dependent and the independent variables. The results between the two models are quite similar.

The Ordinary lest squire OLS results: For OLS I also run a seemingly uncorrelated analysis between

different types of OOP to see if the error terms of different types of OOP are correlated. But I found that

there is no change in the results, so I can say there is no correlation between the error terms. Individuals

seem to choose to spend OOP on different types of care independently.

Table 3: The Ordinary lest squire OLS results

variable TOOP ACOOP CROOP PROOP HSOOP DNOOP

Age group

(05-15)

0.28***

(0.04)

0.05***

(0.02)

0.04***

(0.02)

-0.07***

(0.01)

0.02

(0.03)

0.05***

(0.02)

Age group

(16-39)

0.84***

(0.05)

0.26***

(0.03)

0.20***

(0.03)

-0.04***

(0.02)

0.22***

(0.03)

0.26***

(0.03)

Age group

(40-59)

0.72***

(0.06)

0.33***

(0.03)

0.61***

(0.03)

-0.44***

(0.02)

0.07

(0.04)

0.33***

(0.03)

Age group (60+)0.81***

(0.07)

0.12***

(0.03)

0.88***

(0.04)

-0.43***

(0.02)

0.17***

(0.04)

0.12***

(0.03)

Sex0.06***

(0.02)

0.05

(0.01)

-0.01

(0.01)

0.12***

(0.01)

-0.01

(0.02)

0.05***

(0.01)

Primary education0.28***

(0.03)

0.13***

(0.02)

0.06***

(0.02)

0.04***

(0.01)

0.00

(0.02)

0.13***

(0.02)

Secondary education0.34***

(0.05)

0.28

(0.03)

0.03

(0.03)

0.07***

(0.02)

-0.02

(0.03)

0.28***

(0.03)

University education0.35***

(0.07)

0.45*

(0.04)

-0.01

(0.04)

0.16***

(0.02)

-0.05

(0.05)

0.45***

(0.04)

Urban/rural area0.10***

(0.03)

0.02***

(0.01)

0.03***

(0.01)

0.01

(0.01)

-0.02

(0.02)

0.02

(0.01)

Married0.36***

(0.04)

0.11

(0.02)

0.00

(0.02)

0.43***

(0.02)

0.05*

(0.03)

0.11***

(0.02)

Divorced 0.00 0.06 -0.02 0.21*** -0.11 0.06




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(0.12) (0.06) (0.06) (0.04) (0.08) (0.06)

Widowed0.27***

(0.08)

0.09

(0.04)

0.00

(0.04)

0.34***

(0.03)

0.00

(0.05)

0.09***

(0.04)

Land capacity0.00***

(0.00)

0.00***

(0.00)

0.00

(0.00)

0.00***

(0.00)

0.00***

(0.00)

0.00***

(0.00)

Hospital rate0.46***

(0.05)

0.07***

(0.03)

-0.02

(0.03)

0.13***

(0.02)

0.14***

(0.03)

0.07***

(0.03)

Bed rate0.00***

(0.00)

0.00

(0.00)

0.00***

(0.00)

0.00***

(0.00)

0.00***

(0.00)

0.00

(0.00)

Consult doctor1.50***

(0.05)

-0.08***

(0.03)

-0.19***

(0.03)

-0.04***

(0.02)

-0.13***

(0.03)

-0.08***

(0.03)

Consult medical

assistant

1.44***

(0.09)

-0.09***

(0.05)

-0.10***

(0.05)

-0.06**

(0.03)

-0.14***

(0.06)

-0.09***

(0.05)

Consult other person2.39***

(0.12)

-0.10***

(0.06)

-0.24***

(0.07)

-0.12***

(0.04)

-0.07

(0.08)

-0.10

(0.06)

Live in River Nile state-0.02

(0.07)

-0.09***

(0.03)

0.16***

(0.04)

-0.06***

(0.02)

-0.01

(0.04)

-0.09***

(0.03)

live in Red Sea state-0.10

(0.06)

-0.03

(0.03)

-0.10***

(0.03)

0.05***

(0.02)

0.01

(0.04)

-0.03

(0.03)

Live in Kassala state0.26***

(0.06)

0.02***

(0.03)

0.06*

(0.03)

0.05***

(0.02)

0.07

(0.04)

0.02

(0.03)

Live in Gadareif state-0.35***

(0.07)

-0.03

(0.03)

-0.19***

(0.04)

-0.09***

(0.02)

-0.06

(0.04)

-0.03

(0.03)

Live in White Nile state0.33***

(0.07)

0.09***

(0.04)

-0.06

(0.04)

-0.02

(0.03)

0.05

(0.05)

0.09***

(0.04)

Live in Sinnar state0.10

(0.06)

-0.05***

(0.03)

-0.16***

(0.03)

0.09***

(0.02)

0.14***

(0.04)

-0.05

(0.03)

Live in Blue Nile state-0.43***

(0.06)

-0.08***

(0.03)

-0.17***

(0.03)

-0.09***

(0.02)

-0.03

(0.04)

-0.08***

(0.03)

Live in North Kordofan

state

0.11

(0.06)

-0.05***

(0.03)

-0.07***

(0.03)

0.07***

(0.02)

0.06

(0.04)

-0.05

(0.03)

Live in South Kordofan

state

-0.23***

(0.06)

0.01

(0.03)

-0.10***

(0.03)

-0.08***

(0.02)

-0.04

(0.04)

0.01

(0.03)

Live in North Darfor 0.20*** 0.06*** 0.07* -0.04 0.01 0.06*




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state (0.07) (0.03) (0.04) (0.02) (0.04) (0.03)

Live in South Darfor

state

0.10

(0.07)

0.08

(0.04)

0.04

(0.04)

-0.04

(0.03)

0.02

(0.05)

0.08***

(0.04)

Constant -.0119234 .0589287 -.0845668 -.0702697 .1342322 -.1262843

Number of observation 27078 27078 27078 27078 27078 27078

R-squared 0.1562 0.1797 0.0936 0.0940 0.0116 0.0669

Adj R-squared 0.1553 0.1789 0.0927 0.0930 0.0105 0.0659

The coefficients, standard errors and the significance related to the independent variables from

OLS run on the log value of the dependent variables. The dependent variable include total OOP health

expenditure, non-chronic care OOP expenditure, chronic care OOP expenditure, hospitalization care OOP

expenditure, preventive care OOP expenditure, OOP health expenditure abroad, and dental care OOP

expenditure.

The equations were run only on those reported to sick. Therefore, there were 27078 observations

(out of 75184 observations) i.e. 36% of the sample. This means one in three people sought care for one

or more type of diseases during the identified period in the survey. This could imply that there was a very

bad health situation overall in the country. If we connect this with situation in Sudan where OOP reached

64.3% from THE, and poverty was high at 44.8% with per capita health expenditure of US$111, the results

suggest that there were be catastrophic health expenditure among Sudanese households especially in

rural areas which represent 69% of the population in Sudan, of who 57.6 % were poor.

The age groups in general have significant effect on different types of care. OOP health

expenditure increase when age increases. Only in preventive care is the relationship negative. This means

the preventive care will decrease when age increases. This is consistent with the real situation that

immunization is for children less than five years old. Gender seems to not have any with chronic and

non-chronic care OOP but it has a high correlation with total health OOP expenditure and dental care

OOP expenditure. Education level increases health expenditure in total. But it does not have a significant

impact on chronic or non-chronic care. For preventive care there is a positive relationship. In urban areas

there is a high OOP for every type of health care. There is a high OOP spending on preventive care, dental

care and total OOP health expenditure for married and widowed persons. Divorced people seem to have

a high OOP spending on preventive care only. Land capacity has an effect on all types of diseases except

chronic care but the effect is very small. The number of hospitals in the area has a high positive and

significant effect on all kinds of care except chronic care. This means hospitals play a very good role to

provide care for the population around their areas, especially chronic care. If individuals consulted a

medical doctor, OOP expenditure would increase. Living in any state far away from Khartoum state seems




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to decrease total OOP expenditure and increase the burden of acute and chronic care.

The result of the Tobit model:

variable TOOP ACOOP CROOP PROOP HSOOP DNOOP

Age group (05-15)1.09***

(0.10)

1.04***

(0.13)

3.72***

(0.44)

-5.49***

(0.59)

0.49

(0.46)

6.32***

(0.54)

Age group (16-39)2.39***

(0.12)

1.03***

(0.17)

6.55***

(0.46)

-2.33***

(0.45)

3.26***

(0.54)

9.07***

(0.55)

Age group (40-59)2.10***

(0.14)

0.66***

(0.20)

10.08***

(0.51)

-7.88***

(0.55)

1.04

(0.63)

9.49***

(0.58)

Age group (60+)2.23***

(0.16)

0.30

(0.23)

11.44***

(0.52)

-8.65***

(0.66)

2.30***

(0.68)

7.74***

(0.60)

Sex0.17

(0.06)

-0.10

(0.08)

-0.11

(0.18)

2.74***

(0.22)

-0.04

(0.27)

0.58***

(0.16)

Primary education0.70***

(0.08)

0.47***

(0.11)

0.47***

(0.20)

0.70***

(0.23)

-0.17

(0.33)

1.31***

(0.18)

Secondary education0.71***

(0.12)

0.24

(0.17)

0.16

(0.30)

0.57***

(0.33)

-0.58

(0.51)

2.09***

(0.26)

University education0.65***

(0.16)

-0.34

(0.24)

-0.29

(0.42)

1.47***

(0.43)

-1.13

(0.74)

2.71***

(0.34)

Urban/rural area0.45***

(0.06)

0.53***

(0.09)

0.71***

(0.18)

0.38***

(0.19)

-0.13

(0.28)

0.43***

(0.16)

Married0.78***

(0.10)

-0.28

(0.14)

-0.02

(0.28)

6.96***

(0.41)

0.87

(0.44)

0.90***

(0.22)

Divorced-0.01

(0.28)

-0.62

(0.40)

-0.01

(0.66)

2.43***

(1.06)

-1.44

(1.33)

0.48

(0.60)

Widowed0.62***

(0.19)

-0.09

(0.27)

-0.03

(0.43)

5.37***

(0.73)

0.32

(0.82)

0.84***

(0.42)

Land capacity0.01***

(0.00)

0.01***

(0.00)

0.00

(0.00)

0.02***

(0.00)

0.02***

(0.00)

0.01***

(0.00)

Hospital rate1.18***

(0.12)

0.77***

(0.17)

-0.25

(0.33)

2.90***

(0.36)

2.09***

(0.54)

0.88***

(0.30)

Bed rate0.00*

(0.00)

0.00***

(0.00)

0.02***

(0.01)

-0.02***

(0.01)

-0.02

(0.01)

0.00

(0.01)

Consult doctor 3.93*** 6.86*** -4.72*** -0.92*** -2.49*** -1.00***




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(0.12) (0.15) (0.67) (0.44) (0.68) (0.39)

Consult medical

assistant

4.30***

(0.19)

7.09***

(0.23)

-3.30***

(1.06)

-2.51***

(1.02)

-3.94***

(1.30)

-1.55***

(0.74)

Consult other person4.39***

(0.26)

7.97***

(0.30)

-3.06***

(0.99)

-3.18***

(1.22)

-1.35

(1.37)

-1.34

(0.76)

Live in River Nile state0.18

(0.16)

-0.21***

(0.22)

1.69***

(0.36)

-0.96

(0.54)

-0.14

(0.72)

-0.41

(0.39)

live in Red Sea state0.00

(0.16)

0.11

(0.23)

-1.41***

(0.48)

2.28***

(0.60)

0.89

(0.74)

0.03

(0.44)

Live in Kassala state1.01***

(0.15)

1.47***

(0.20)

0.79***

(0.40)

2.72***

(0.53)

2.15***

(0.65)

1.01***

(0.38)

Live in Gadareif state-0.51***

(0.16)

0.37

(0.22)

-2.99***

(0.56)

-0.28

(0.50)

-0.32

(0.72)

0.18

(0.42)

Live in White Nile state1.03***

(0.17)

1.48***

(0.23)

-0.82

(0.50)

1.35***

(0.53)

1.33

(0.75)

1.42***

(0.43)

Live in Sinnar state0.65***

(0.13)

1.06***

(0.18)

-1.61***

(0.40)

3.11***

(0.41)

2.72***

(0.57)

0.06***

(0.35)

Live in Blue Nile state-0.59***

(0.14)

-0.07

(0.19)

-2.17***

(0.44)

-0.39

(0.48)

0.69

(0.61)

-0.08

(0.37)

Live in North Kordofan

state

0.40

(0.17)

0.67

(0.23)

-1.20***

(0.49)

3.65***

(0.59)

1.64***

(0.74)

-0.89

(0.50)

Live in South Kordofan

state

-0.37***

(0.15)

-0.15

(0.21)

-1.52***

(0.44)

0.00

(0.55)

-0.07

(0.68)

0.52

(0.38)

Live in North Darfor

state

0.66***

(0.17)

0.40

(0.24)

0.58

(0.47)

0.92

(0.68)

0.98

(0.79)

1.25***

(0.46)

Live in South Darfor

state

0.29

(0.19)

0.02

(0.27)

0.11

(0.53)

1.41***

(0.70)

1.19

(0.85)

1.33***

(0.49)

Constant -5.3265 -7.1945 -17.712 -17.070 -20.476 -20.080

Number of observation 27078 27078 27078 27078 27078 27078

R-squared 0.0629 0.0873 0.1184 0.1630 0.0146 0.0951

From the result of the Tobit model of the log value of dependent variables on the explanatory

variables I find that: Age groups are significant and it has an impact on different types of health care with

a positive sign. The gender effect is not significant. The level of education statistically increases total OOP

health expenditure and hospitalization care OOP spending. For non-chronic care, almost all independent

variables have high effect. Marital status has no significance on chronic care OOP expenditure. The type of




[248]

medical personnel, state dummies and urban residency play a main role in determining chronic care OOP

expenditure.

Conclusion

In the regressions, there are some statistically significant explanatory variables. Variables that

usually positively impact OOP spending include age groups, education level, widowed, land capacity,

hospital rate, bed rate. Variables that usually negatively impact OOP spending include divorce, type of

medical personnel as well as some of state dummies. Recall that the OOP variables come from the

summation of treatment cost, cost of food, and accommodation for the co patient and transportation

cost. Transportation costs seem to be very high for all type of care. This suggests that the distribution of

medical personnel and medical facilities is unequal.

Recommendation :

Health is a right for all people and to prevent people from having catastrophic health expenditure and to

be impoverished the Government should make plans to reform the health sector in general and the

public sector of health in particularly. I suggest many points for the government to reform health sector:

1) To increase the number of medical doctors.2) To increase the salary of medical staff to stop the

migration to outside the country.3) To open up the country for investment in health sector 4) To upgrade

the young medical assistants to became a general practitioner, a dentist or a pharmacist within their

specialties. 5) To create new medical facilities. And 6) to work with health care providers and consumers

and find ways to control the rising health care prices.

Limitation of the study

The OLS, seeming unrelated regression and Tobit models produce similar results. This study is

not without limitation. First, I do not have information about health of individuals in the study. Second,

Sudan is very big country with different cultures which could affect care seeking behavior. However, I do

not have the statistics or data that represent each culture for each individual.

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Documents

Factors Affecting out of Pocket Health Care Expenditure among Sudanese Households