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TYPE
Original Research
PAGE NO.
7-27
10.37547/ijmscr/Volume05Issue01-02
OPEN ACCESS
SUBMITED
16 October 2024
ACCEPTED
09 December 2024
PUBLISHED
04 January 2025
VOLUME
Vol.05 Issue01 2025
CITATION
Sajjad FNU, Bakht Biland Khan, Misbah Nosheen, Wasim FNU,
Muhammad Waqar, Wafa Idrees, & Habiba Idree. (2025). The effect of
hepatitis b and c virus related diseases on labor productivity (labor
market) in Pakistan. International Journal of Medical Sciences And
Clinical Research, 5(01), 7
–
27.
https://doi.org/10.37547/ijmscr/Volume05Issue01-02
COPYRIGHT
© 2025 Original content from this work may be used under the terms
of the creative commons attributes 4.0 License.
The effect of hepatitis b
and c virus related
diseases on labor
productivity (labor market)
in Pakistan
Sajjad FNU
Department Of Economics Hazara University, Mansehra, Khyber
Pakhtunkhwa, Pakistan;
Genome Center For Excellence In Molecular Based Diagnostic And
Research, Cl-25 Block-B Abdalian Housing Society, Lahore, Pakistan
Bakht Biland Khan
Associate Professor Of Medicine, Peshawar Medical College, Peshawar
Khyber Pakhtunkhwa, Pakistan
Misbah Nosheen
Department Of Economics Hazara University, Mansehra, Khyber
Pakhtunkhwa, Pakistan
Wasim FNU
Corewell Health William Beaumont University Hospital Royal Oak
Michigan
Muhammad Waqar
Genome Center For Excellence In Molecular Based Diagnostic And
Research, Cl-25 Block-B Abdalian Housing Society, Lahore, Pakistan
Wafa Idrees
Khyber Medical College, Peshawar Khyber Pakhtunkhwa, Pakistan
Habiba Idree
CMH Lahore Medical Collage, Lahore Cantt. Pakistan
Abstract:
Health is the key and significant asset that a
human being has that allow people to exclusively
develop their abilities. If this asset corrodes or not fully
developed, it will cause emotional and physical
weakness that causes hindrances in people lives.
Keeping in view the importance of health in human
capital and human development index, it is important to
conduct a study that highlight the consequence of
Hepatitis in Pakistan. This study is designed to estimate
the effect of viral hepatitis (B & C) on labor productivity,
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family income, morbidity and mortality, estimate the
direct and indirect cost hepatitis (B & C) and total cost
imposed on each patient and their family in Pakistan.
Primary data was collected from 8,388 Hepatitis B and
C patients at district headquarter hospitals, private
hospitals and doctors’ clinic from 77 districts across
Pakistan including Azad Jammu and Kashmir and Gilgit
Baltistan through a well design questioner containing
36 questions based on demographic and economic
indicators. Descriptive, probit logit and OLS
econometric techniques were applied for data
analysis. This study finds significant effect of viral
hepatitis B and C on labor productivity, labor mobility,
absenteeism and presentism at work place and family
income, in Pakistan. This study also found significant
impact of hepatitis on productivity in terms of
absenteeism and presentism and estimated that an
average per patient and their attendant’s absenteeism
and presentism 1.89 days per month and total working
days. Furthermore hepatitis B and C had also found
significant indirect impact on labor mobility
employment and mortality and concluded that 2.07%
visa rejection, 6.2% job rejection, 1.2% job termination
and 5.2% morality caused by hepatitis B and C. similarly
the effect of hepatitis B and C was found indirect and
caused decline in income in term of loss of working
days and sell of assents. The government should form
such policies that encourage long term investment in
human capital by both government and public sector.
Recommendation: Health care expenditure must be
increased up to 5% of GDP to meet the minimum
requirements for the provision basic health facilities to
population.
Keywords:
Hepatitis, Labor productivity, Labor
Mobility, Absenteeism, Job Rejection.
Introduction:
Viral hepatitis also known as hepatitis A,
B, C, D, and E affects millions of people around the
world, leading to acute and chronic liver disease. Viral
hepatitis is the 8th highest cause of death worldwide
and is responsible for an estimated 1.41 million deaths
every year from hepatitis related liver cancer and
cirrhosis, acute infections, a toll comparable to that of
tuberculosis and HIV. Approximately 55% the deaths
are attributed to HBV, 35% to hepatitis C (HCV) and the
remaining to hepatitis E virus (HEV) and hepatitis A
virus (HAV). Hepatitis virus is also a rising cause of
death among HIV infected people, whereas 5
–
20%
with HBV and 5
–
15% is co-infected with HCV. Globally,
about 130-150 million people are living with HCV and
2.40 billion people are chronically infected with HBV.
Without an accelerated and expanded response, the
number of persons living with HBV infection is
estimated to stay at the existing high levels for the next
40 to 50 years, with an aggregate 20 million deaths
happening between 2015 to 2030. A stepped-up
worldwide response can no longer be delayed. Pakistan
has the 2nd highest number of hepatitis C virus (HCV)
infection after Egypt in the world. Both HCV and
hepatitis B virus (HBV) are connected with increasing
mortality and morbidity. It has been recently estimated
that every 13th Pakistani is infected with either HBV or
HCV infection (Government of Punjab, 2017).
National survey was conducted to estimate the
prevalence of HCV and HBV in the Pakistan in 2008. The
survey explored that 2.5% of population was positive for
HBV whereas the prevalence of HCV was estimated at
5% in Pakistan. The frequency of anti HCV in Punjab was
6.7% which is higher than the national average whereas
the prevalence of HBV was comparable (2.4%). At the
general level the prevalence of HCV at 5% explains that
an estimated 8 million people in Pakistan are exposed to
this infection, while 4 million (2.5% prevalence for HBV)
infected with hepatitis B. Recently, University of Bristol,
UK has done a modelling study to estimate the epidemic
of hepatitis C in Pakistan. The study revealed that to
achieve the WHO target for eliminating hepatitis C from
Pakistan by 2030, Pakistan has to overall treat 1.1
million hepatitis C cases annually.
Pakistan has the 2nd highest number of hepatitis C virus
(HCV) infection after Egypt in the world. The risk factors
responsible for relatively high prevalence of Hepatitis B
and C are includes un safe blood transfusions (15%),
history of hospitalization (14%), dental clinic (13%), use
of contaminated injections (12%), and history of surgery
(9%), sexual relationship, vertical transmission, lack of
initial screening facility, old sewerage system and
barber shop.
Pakistan launched its first ever plan on August 29, 2005
with the help of Center for Excellence in Molecular
Biology (CEMB) at University of the Punjab, Lahore to
significantly decrease the prevalence mortality and
morbidity due to viral hepatitis in the general
population by exploiting the prevailing health
infrastructure. The total cost incurred by government of
Pakistan of that program was Rs.2.59 billion for financial
years 2005 to 2010 and (CEMB) University of the Punjab
provide diagnostic services to patients that cost CEMB
Rs. 113.6 million.
In 2005, Pakistan faced a loss of 1 billion dollar in
national income due to premature deaths caused by
chronic diseases which included heart disease, stroke
and diabetes (WHO report (2005)- facing the facts).
By the inception of endogenous growth theory of Romer
(1989) the importance of human capital was recognized.
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It was also used in the production function by Makin et
al. (1980). Human capital is consisted of many factors
like education, health and professional trainings
whereas the education and health found to be major
determinations of human capital (Schlutz, 1961).
Adequate investments in human capital, especially in
improving health and literacy has broadly been
recognized to be an essential element for, improving
employment
opportunities,
reducing
poverty,
accelerating economic growth and improving human
abilities in developing countries like Pakistan. Poverty
alleviation and economic growth found to be positively
associated with the investment in health and
education (Chaudhry and Rehman, 2009).
Health is the most important assets for human being.
It permits us to completely develop our abilities. The
complete or partial absence of this asset causes
physical and emotional flagging, initiating obstacles in
people social and economic lives. The prior association
can be assessed by correlation between health and
income. Life cycle models explained the impact of
health condition on the determination of people future
income, consumption and wealth status (Smith 1999,
Smith 1998 and Lilliard and Weiss 1997). Sorkin (1997)
in an early empirical review of the impact of health on
economic development, established the impact of
health on economic development through reduction in
mortality and concluded significant impact of health on
economic development in the early 20th century.
However, he also concluded that an increase in the
health status of developed countries had little impact
on economic growth whereas the impact in developing
countries are different. He identified many ways in
which health programs effect the economic growth in
the developing countries.
Labor productivity and efficiency increase in the first
way by increasing working hours of worker by good
health. Jack (1999) explored that labor productivity
depends on factors like mental and physical abilities,
investment in human capital and productivity of labor
organization and management he also emphasizes that
changes in health facilities could affect labor
productivity. On the other hand, developments in
health conditions has significantly affected the
experience of the labor force by enhancing life
expectancy and health status.
The impact of health on labor productivity suggests a
direct correlation between aggregate output and
health. Healthy workforces are active and have less
absenteeism from work due to good health conditions
and produce more output during their working time.
Health improvements had significant impact on
widespread economic growth whereas ill and poor
health traps in poverty (World Health Organization,
1999). World health indicators 1993 on health explained
an increasing interest in the association among health
condition and economic growth (World Bank report
1993). Barro (1996) concluded health as a productive
capital asset and a key instrument for economic growth
and development. WHO 2005 report stated that 50% of
differential in economic growth among developed and
under developed countries are attributed to health
condition and life expectancy that are both low in
developing countries.
Developed nations invest a significant proportion of
their budget on provision of health care as they are
committed to their population’s health and are
convinced that these expenditures are key factor for
economic growth and development. As health is wealth,
any amount resources spent on health structure by a
country is not considered too much. For economic
growth and economic development an average 8% to 10
% of GDP must be allocated for health expenditure (The
United Nation (UN) health expenditure report 2019
recommendation).
The investment in education leads to not only reduction
in poverty but it also reduces the crimes and terrorism
along with abridging the income inequalities (Faber and
Augersaud, 2004). Moreover, the poverty status of
households significantly decreases with the increase in
educational and health attainment (Rodriguez et al,
2000). It is a matter of fact, that poverty is main hurdle
in attaining education as parents do not send their
children due to the adverse economic conditions.
Currently, firms throughout the world are facing many
challenges including universal economic crisis, rising
trend in demand for improved productivity, gradually
fast paced businesses environment and more important
an aging and apparently unhealthy labor force. As the
burden of chronic diseases e.g., hepatitis, HIV,
hypertension, heart disease, diabetes and cancer are
rising workers are becoming unhealthy, sicker and less
productive. A British health insurance company
reported a bleak portrait of the future labor force.
Workers will be older with long-term surroundings and
lifestyle conditions, concerned for others, obese with
hepatitis, diabetes and heart problems. The employees’
health is becoming a significant factor of business in
terms of both cost and in the form of an asset. Firms are
not considering in the lowering of health care
expenditures through containment policies due to
daunting demographic characteristics and current
diseases trends. Coupled with rising trend in demand for
productivity in the i
nternational market place, firm’s
managers are considering the fact that current
occupational health care settings are insufficient. They
found sick leave a significant factor for the reduction of
firms output. European Agency for Safety and Health at
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Work (2000) found 600 million working days were lost
due to work-related disease in Europe. Huisman et al.
(2005) significant decrease in labor productivity due to
hepatitis C and concluded that 70% decrease in their
productivity. Younassi et al. (2016) also found loss of
23.7% and 13% in work related activities with chronic
hepatitis C due to absenteeism. Yaseen et al. (2017)
also found lower productivity of workers with chronic
hepatitis as compared to healthy worker in district
Faisalabad
At present hepatitis is an immense health problem and
major cause of mortality and morbidity worldwide and
in developing countries like Pakistan, Egypt and other
south Asian countries this problem is severe (N Glass
et al., 2012) Around 130-150 and 250 million people
were infected with hepatitis C and B, respectively (Issur
M et al., 2014). Globally, viral hepatitis B and C results
in 1.4 million death every year compared to malaria 1.2
million, HIV 1.5 million and tuberculosis 1.2 million
deaths respectively. The key factors of hepatitis B, C
and D transmission are div fluids, contaminated
syringes, barber shops and vertical transmission etc.,
whereas hepatitis A and E are transmitted by
contaminated food items and water. In Pakistan
estimated 325 people die per day due to viral hepatitis
4 mortality rate shows upward trend.
Globally, developed countries United States of America
and European countries systematically recorded and
studied mortality among patients with viral hepatitis,
but the rest of the world, this vital public health and
economic problem has not received its due attention.
The analysis of World Health Organization (WHO)
mortality data explored significant association of
hepatitis B and C with mortality and stated substantial
increase of mortality in people with HBV and HCV. Still,
the long-term trend of mortality associated to viral
hepatitis are rarely reported in East and Southeast
Asian countries (Wu, J et al., 2020).
In developed countries like United States of America
the total deaths and mortality associated with
hepatitis B and C for the period 2003 to 2013 were
estimated 11,051 in 2003 and 19,368 in 2013 with
average increase rate of 865 deaths per year showed
6.2 % annual increase in mortality due to hepatitis B
and C (Kathleen N et al., 2016).
In East and Southeast Asian countries mortality rate of
hepatitis showed upward trend where highest
mortality rate was recorded in China 64.4 % followed
by Japan 36.9% during 1987 to 2015 but after 2015
Japan surpassed China and mortality associated to
hepatitis declined in China, Philippines and Singapore
(Wu, J et al., 2020). Similarly, Sarah M. Hatcher et al
(2020) also found significant high mortality ratio
associated with hepatitis C among American Indian
19.6% and 5.9 in American Asian. According to WHO
report (2015) viral hepatitis B and C were responsible for
about 1.34 million deaths in the world. The mortality
rate of viral hepatitis was higher than mortality caused
by tuberculosis (TB) and HIV. The untreated hepatitis B
and C leads to 720,000 cirrhosis deaths and 470,000
deaths due to hepatocellular carcinoma. Mortality ratio
of viral hepatitis increased by 22% since 2000.
These long-term complications are life-threatening and
accounted for 96% of the deaths due to viral hepatitis.
Mortality from viral hepatitis has increased by 22% since
2000. The mortality of viral hepatitis will remain
increase till public with HCV and HBV infections are
diagnosed and treated.
Grossman’s (1972, 2000 and 2017) model of health
demand explored the relationship among health status,
human capital and their consumption at micro level and
a framework modelling for human capital accumulation
and its correlation with productivity at macro level. The
key aspects of its model are human capital that are
based on health and education of people and its
relationship to labor supply, earnings and productivity.
The Grossman’s model concluded health as a capital
asset and final consumption good. Human capital theory
stated that better health condition and knowledge are
the main determinant of labor productivity in both non-
market and market activities. The Grossman model
stated that health capital and education capital vary
from other forms of capital both in its activity and
productivity. Health capital defines the healthy time
available for economic activity whereas knowledge
capital affects the productivity of the time spent on
them.
Bloom and Canning (2000) identified four ways that
affect labor productivity first healthy workers are more
productive as they are more physical and mentally
active, second people with longer life expectancy invest
in education and thus increase their productivity, third
healthy people save more and accumulate their savings
and last good health status results in high per capita
income and enhanced their standard of life.
Ilias Gountas et al. (2019) projected 19.6% increase in
hepatitis C related mortality, € 2.3 billion, € 1.1 billion
increase indirect and indirect cost of viral hepatitis C in
Greece by 2030. They also concluded an additional
estimated € 3
.2 to 3.4 billion need to treat 90,000
patients and eliminate hepatitis C from Greece by 2030
whereas per adjusted disability life cost was estimated
from € 10,100 to 13,380.
DATA SOURCES AND METHODOLOGY
Sample size and sampling techniques
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The aim of this study was the economic assessment of
viral hepatitis B and C in Pakistan. The prevalence and
severity of viral hepatitis B and C across the country
would probably have varied, therefore it was
imperative that any hepatitis B and C patient would be
the representative of the whole hepatitis B and C in
Pakistan. Simple random sampling techniques could be
used included the patients for data collection.
However, this technique would have formed
representative data only if enough patients were
recruited. Paki
stan’s population were geographically
diverse. Hence, simple random sampling of hepatitis B
and C patients across the country would be impractical
and expensive. Therefore, multistage stratified cluster
sampling techniques were used to ensure that the
sele
cted sample was the illustrative of Pakistan’s
population. The stratified cluster sampling techniques
combined stratified and cluster sampling techniques.
Initially the country was stratified by province and
region, Pakistan consists of four province Punjab,
Sindh, Khyber Pakhtunkhwa and Baluchistan, federal
territory, capital territory, Azad Jammu and Kashmir
and Gilgit Baltistan.
In order to get representative sample size, assuring
quality of sample and ease of management, 77 districts
among 154 districts from Pakistan including Azad
Jammu and Kashmir and Gilgit Baltistan where the
facilities of Government and private hospitals were
available was selected randomly for sampling and
sample was drawn from every third (3rd) hepatitis B
and every fifth (5th) C patients visited to hepatitis clinic
either public or private hospital and aged between 5
and 75 years completed hepatitis B or C treatment or
in the course of treatment for the period March, 2021
to December 2021. Multi-stage stratified cluster
sampling techniques were used for data collection. In
the first stage the districts were selected while in the
second stage the hospitals selected at random and
categorized in private and public hospitals and at the
third stage the respondents were interviewed at
random for data collection. The selected hospitals
were distributed by public and private hospitals and
the patients were further classified by gender,
occupation,
demographic
and
economic
characteristics. The sample was collected by the
researcher and well-trained representatives of
genome center for molecular based diagnostic and
research laboratory (GCMBDR) in their chain of
collection centers, hospitals, doctor’s clinic and
homeopathic clinics. The overall sample compromised
of 8,388 respondents, entailing 6535 hepatitis C
patients and 1853 hepatitis B patients. The 1395
respondents are from government THQ hospitals, 6819
patients responded from private hospitals and doctors’
clinic, whereas 37 patients selected from homeopathic
clinics and 48 patients attached to peer/ spiritual
healers. The sample size was selected based sampling
calculator provided by
n =
2(Zα + Z1 − β)2 σ 2
Δ2
Where n is the required sample size, Z is a constant, σ
is standard deviation and Δ is the difference in effect
(estimated effect size). (bader et al. 2018), (Kadam, and
Bhalerao, S. (2010), (Rahimzadeh, et al. 2013), Sajjad
and Nosheen (2022).
Absenteeism
Absenteeism is measured as average sick leave days per
month during course of illness (Miroslav et al., 2017),
Sajjad and Nosheen (2022).
Productivity loss
Productivity loss due to hepatitis B or C are calculated
in-patients by the period of hospitalization multiplied by
his/her daily average income and for outpatients, it was
assessed by the total of visit days multiplied by daily
average income of the patient and caregivers (Khsoro et
al., 2015, Dahye Baik et al., 2017 and Sajjad and
Nosheen
(2022).
Salary
conversion
method,
introspective method and firm’s level method were
applied for the measurement of productivity loss due to
hepatitis B and C.
Salary conversion method: This method uses the survey
response and salary information of the respondent to
estimate productivity loss.
Introspective method: This method uses survey
response as basis for assumed experiment to provide
businesses an indication of magnitude to estimate their
lost productivity.
Firm-level method: This method used to monetize
output loss based on cost of counter measures to deal
with absenteeism and presentism.
Measurement of the impact of hepatitis B and C foreign
reserve in Pakistan:
This is estimated by the % age of hepatitis B patients out
of total population of Pakistan that is 2.8% and also the
estimated % age of hepatitis C patients out of total
population that is 5 %. (As referenced in chapter 1) *
Average per patient diagnostic cost (imports of
detection kits and other tools used in hepatitis B and C).
(Sajjad and Nosheen (2022)
(Estimated by average cost were calculated from data
borrowed from five reputed labs and two firms
importing these regents).
Thus, the formula developed for estimating foreign
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reserve is as follow:
Estimation of the effect of hepatitis on foreign reserve
FR = HBP*YC + HCP*YC
Foreign reserves = Total number of hepatitis B patients
* average per patient diagnostic cost + total number of
hepatitis c patients * average per patient diagnostic
cost
Limitation of the above model
This model developed to include only per patient
diagnostics expenses incurred. It did not incorporate
with medication and treatment instruments because
of lack of available data.
Labor mobility
In order to observe the effect of the hepatitis on the
labor mobility, we rely on the following question.
Q1: Has the patient ever experienced visa rejection
because of hepatitis infection?
The objective of the analysis here is to compare the
individuals whose visa has been rejected due to the
exposure to hepatitis and their counterparts.
Labor mobility = β
0
+ β
1
(gender) + β
2
(age) + β
3
(marital
status) + β
4
(sources of exposure) + β
5
(employment
status) + β
6
(education) + ε)
In the equation the dependent variable is a dummy
variable. It indicates the visa rejection a proxy of labor
mobil
ity and coded as “1” if the respondent’s visa is
rejected due to hepatitis and “0” if the visa is not
rejected due to hepatitis. Given the dichotomous
dependent variable we estimated the equation (1) by
using logistic regression.
The coefficient (β_1) cap
tures the differences between
male and female respondents. Age represents how
much old is the respondents and its impact is
estimated by coefficient (β_2). The coefficient (β_3)
estimates the difference between unmarried and
married respondents. Sources of exposure to hepatitis
represents the differences among potential sources of
transmission of hepatitis, and its impact is estimated
by coefficient (β_4). Coefficient (β_5) captures the
effect of employment status, it is categorical variables
comprise of different employment categories.
Coefficient (β_6) captures the role of education.
we re-estimated equation by introducing other control
variables in subsequent specification to check the
robustness of results.
Second, the estimation strategy then relies on the
flowing question to ascertain the impact of hepatitis on
labor productivity. Absenteeism (work loss/day) during
infection or treatment (Sajjad and Nosheen (2022)
Absenteeism = β
0
+ β
1
(gender) + β
2
(age) + β
3
(marital
status) + β
4
(sources of expo
sure) + β
5
(employment
status) + β
6
(education) +ε
In the above equation, the dependent variable indicates
the absenteeism during the treatment by the
respondents exposed to the hepatitis. It is a continuous
variable. the estimation was carried out by Ordinary
Least Square (OLS) methodology. The rest of the
variables are same discussed above.
The effect of hepatitis on the employment
The final model is developed to quantify the effect of
hepatitis on the employment.
Has the patient ever experienced rejection during any
job recruitment because of hepatitis?
Employment = β
0
+ β
1
(gender) + β
2
(age) + β
3
(marital
status) + β
4
(sources of exposure) + β
5
(employment
status) + β
6
(education) + ε
The dependent variables in equation employment
indicates that whether the respondent has been
rejected from the job or not due to hepatitis. It is a
dummy variable and coded as “1” if respondent has
experienced rejection and “0” if there was no rejection.
Equation (3) is estimated by logistic regression given the
dummy dependent variable. Other variables are same
as defined above.
RESULTS AND THEIR INTERPRETATION
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Table 1. Gender wise classification of data
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
Female
3383
40.3
40.3
40.3
Male
5005
59.7
59.7
100.0
Total
8388
100.0
100.0
Table 1 shows the frequency of male and female respondents of the current study. The prevalence of hepatitis
exists more in male than female. Out of 8,388 hepatitis patients, the male respondents were 5,005 and female
were 3,383 with their respective ratio of 59.7% and 40.3%.
Table 2. Age wise classification of viral hepatitis B and C patients in Pakistan
Frequency
Percent Valid Percent Cumulative Percent
Valid
5-18 Years
305
3.6
3.6
3.6
19-32 Years
2898
34.5
34.5
38.2
33-46 Years
2721
32.4
32.4
70.6
47-60 Years
1511
18.0
18.0
88.6
>60 Years
953
11.4
11.4
100.0
Total
8388
100.0
100.0
Table 2 indicate the number of patients/respondents
fall in a particular age group with their respective
mean, median age and standard deviation. These
tables show that 3.6% of the patients lie in the age
group 5-18 years and all of them are dependent as they
are minor and not fall in working population, whereas
34.5%, 32.4%, 18.0% and 11.4 % patients fall in age
group 19-32, 33-46, 47 -60 and above 60 years,
respectively. The table also portrait that 84.9% of the
patients belong to working population because
retirement age in Pakistan is 60 years and that 84.9% of
patients lie in age group of 19 to 60 years.
3. Marital status of the patients
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
Married
6709
80.0
80.0
80.0
Unmarried
1679
20.0
20.0
100.0
Total
8388
100.0
100.0
Table 3 determines the marital status of the patients/respondents. The statistics show that 6709 (80%) of the
respondents were married and 1679 (20%) of the respondents were unmarried. The above results showed that
viral hepatitis B and C are predominant among married people and found 80%
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Table 4. Employment status of the patient
Frequency Percent Valid Percent Cumulative Percent
Valid
Full time employee 708
8.4
8.4
8.4
Part time employee
<30 hr/week
127
1.5
1.5
10.0
Self-employee
3380
40.3
40.3
50.3
Dependent /
Un-employed
4116
49.1
49.1
99.3
Retired
57
.7
.7
100.0
Total
8388
100.0
100.0
The profession and employment status of the patients were elaborated in table 4 House wives, labors, students
and farmers were the most infected people with their respective ratio of 33.8%, 13.5%, 16.1% and 12.9%,
respectively. Data was collected from every field of professional and unprofessional peoples. In respect to
employment status 49.1% of the respondents were unemployed and dependent on other family members
income, 40.3% were self-employed and were involved in self-generated employment, 8.4% were full time
employees in government sector, 1.5% were working part time and seeking for full time employment, 0.7% of the
patients are government retired persons and 1.6% respondents were found to be unemployed.
Table 5: Baseline estimation labor mobility of hepatitis patients
Model
1
Mode
l 2
Mod
el 3
Mod
el 4
Mod
el 5
Mod
el 6
Mod
el 7
Mod
el 8
Mod
el 9
Mod
el 10
Male
0.033*
**
(0.008
)
0.032
***
(0.00
8)
0.032
***
(0.00
8)
0.032
***
(0.00
8)
0.030
***
(0.00
8)
0.030
***
(0.00
8)
0.030
***
(0.00
8)
0.033
***
(0.00
8)
0.033
***
(0.00
8)
0.032
***
(0.00
7)
Age of
the
Patient
-
0.016*
**
(0.004
)
-
0.015
***
(0.00
4)
-
0.016
***
(0.00
4)
-
0.017
***
(0.00
4)
-
0.017
***
(0.00
4)
-
0.019
***
(0.00
4)
-
0.019
***
(0.00
4)
-
0.021
***
(0.00
4)
-
0.020
***
(0.00
4)
-
0.018
***
(0.00
4)
Married
0.012
(0.010
)
0.011
(0.01
0)
0.012
(0.01
0)
0.012
(0.01
0)
0.011
(0.01
0)
0.015
(0.00
9)
0.012
(0.00
9)
0.016
*
(0.00
9)
0.017
*
(0.00
9)
0.015
(0.00
9)
Dental
clinic
-0.018
(0.013
)
-
0.022
*
(0.01
-
0.022
*
(0.01
-
0.026
**
(0.01
-
0.030
**
(0.01
-
0.030
**
(0.01
-
0.028
**
(0.01
-
0.021
(0.01
3)
-
0.024
*
(0.01
-
0.030
**
(0.01
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2)
2)
3)
3)
3)
3)
3)
3)
Sexual
relation
0.013
(0.047
)
0.005
(0.04
6)
-
0.000
(0.04
5)
0.001
(0.04
3)
-
0.006
(0.04
0)
-
0.018
(0.04
1)
-
0.019
(0.04
1)
-
0.025
(0.04
1)
-
0.018
(0.04
4)
-
0.016
(0.04
7)
Barber
shop
-
0.055*
*
(0.026
)
-
0.052
*
(0.02
7)
-
0.053
*
(0.02
7)
-
0.055
**
(0.02
5)
-
0.059
**
(0.02
3)
-
0.068
***
(0.02
0)
-
0.070
***
(0.01
9)
-
0.078
***
(0.01
7)
-
0.076
***
(0.01
8)
-
0.077
***
(0.01
9)
Beauty
parlour
0.019
(0.027
)
0.015
(0.02
6)
0.011
(0.02
5)
0.006
(0.02
5)
0.006
(0.02
5)
0.003
(0.02
5)
0.003
(0.02
5)
0.023
(0.02
6)
0.022
(0.02
5)
0.020
(0.02
5)
Surgery
0.011
(0.015
)
0.008
(0.01
5)
0.006
(0.01
5)
-
0.003
(0.01
5)
-
0.002
(0.01
4)
-
0.005
(0.01
4)
-
0.007
(0.01
4)
0.005
(0.01
5)
0.004
(0.01
5)
-
0.008
(0.01
4)
Blood
transfusi
on
-0.016
(0.021
)
-
0.016
(0.02
1)
-
0.019
(0.02
0)
-
0.023
(0.02
0)
-
0.019
(0.02
0)
-
0.015
(0.02
0)
-
0.017
(0.02
0)
-
0.017
(0.02
1)
-
0.016
(0.02
0)
-
0.027
(0.01
9)
Vertical
transmiss
ion
0.013
(0.012
)
0.015
(0.01
2)
-
0.001
(0.01
2)
-
0.010
(0.01
1)
-
0.010
(0.01
1)
-
0.018
*
(0.01
1)
-
0.019
*
(0.01
1)
-
0.024
**
(0.01
0)
-
0.027
***
(0.01
0)
-
0.025
**
(0.01
0)
Part time
employe
e <30
hr/week
0.279*
**
(0.049
)
0.262
***
(0.04
7)
0.253
***
(0.05
2)
0.273
***
(0.04
9)
0.265
***
(0.05
1)
0.226
***
(0.05
0)
0.213
***
(0.05
0)
0.252
***
(0.05
3)
0.269
***
(0.05
6)
0.257
***
(0.05
3)
Self-
employe
e
0.151*
**
(0.012
)
0.156
***
(0.01
1)
0.160
***
(0.01
1)
0.153
***
(0.01
1)
0.165
***
(0.01
1)
0.163
***
(0.01
1)
0.165
***
(0.01
0)
0.162
***
(0.01
1)
0.140
***
(0.01
2)
0.146
***
(0.01
1)
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Depende
nt
-0.016
(0.010
)
-
0.013
(0.01
0)
-
0.011
(0.01
0)
-
0.014
(0.01
0)
0.001
(0.00
9)
0.001
(0.00
9)
0.003
(0.00
9)
-
0.001
(0.00
9)
-
0.018
*
(0.01
1)
-
0.013
(0.01
0)
un-
employe
d
0.747*
**
(0.036
)
0.741
***
(0.03
6)
0.740
***
(0.03
5)
0.686
***
(0.04
3)
0.703
***
(0.04
2)
0.765
***
(0.04
2)
0.745
***
(0.04
5)
0.755
***
(0.05
2)
0.728
***
(0.05
3)
0.674
***
(0.05
9)
Educatio
n and
Awarene
ss
0.058*
**
(0.004
)
0.058
***
(0.00
4)
0.060
***
(0.00
4)
0.059
***
(0.00
4)
0.056
***
(0.00
4)
0.058
***
(0.00
4)
0.058
***
(0.00
4)
0.054
***
(0.00
4)
0.057
***
(0.00
4)
0.062
***
(0.00
4)
Total
number
of
Family
Members
-
0.023
***
(0.00
6)
-
0.028
***
(0.00
6)
-
0.021
***
(0.00
5)
-
0.020
***
(0.00
5)
-
0.016
***
(0.00
5)
-
0.019
***
(0.00
5)
-
0.024
***
(0.00
5)
-
0.025
***
(0.00
5)
-
0.014
***
(0.00
5)
Number
of
Infected
Family
Members
0.038
***
(0.00
5)
0.025
***
(0.00
5)
0.029
***
(0.00
5)
0.030
***
(0.00
5)
0.032
***
(0.00
5)
0.034
***
(0.00
5)
0.035
***
(0.00
5)
0.037
***
(0.00
5)
Death of
a Family
member
0.084
***
(0.00
7)
0.071
***
(0.00
8)
0.071
***
(0.00
7)
0.067
***
(0.00
7)
0.059
***
(0.00
7)
0.055
***
(0.00
7)
0.050
***
(0.00
7)
Complet
e health
insurance
0.116
***
(0.02
6)
0.107
***
(0.02
5)
0.094
***
(0.02
4)
0.091
***
(0.02
4)
0.114
***
(0.02
5)
0.101
***
(0.02
3)
Estimate
d Direct
Medical
Cost per
-
0.089
***
(0.01
-
0.113
***
(0.01
-
0.094
***
(0.01
-
0.084
***
(0.01
-
0.074
***
(0.01
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Month
0)
3)
4)
4)
3)
Estimate
Indirect
Medical
Cost per
Month
0.061
***
(0.01
3)
0.065
***
(0.01
2)
0.075
***
(0.01
1)
0.075
***
(0.01
0)
Total
Cost
Spent on
Treatmen
t
-
0.054
***
(0.00
7)
-
0.051
***
(0.00
7)
-
0.062
***
(0.00
7)
Monthly
Income
-
0.037
***
(0.00
4)
-
0.036
***
(0.00
4)
Total
Cost of
Visit Per
Month
-
0.025
***
(0.00
3)
Observat
ions
8331
8331
8331
8331
8331
8331
8331
8331
8331
8331
Notes: This table presents the marginal effects of Logit regressions examining the effects of hepatitis on labor
mobility. Standard errors (in brackets) are robust to arbitrary heteroskedasticity. *, **, and *** indicate
statistical significance at the 10%, 5% and 1% level, respectively.
The aim of this estimation strategy is to capture the
impact of hepatitis B and C on labor mobility. We have
empirically estimated equation (1) by using logistics
regression econometric approach. Table 6 contains
outcome for equation (1) and presents average
marginal effect from logistics regression for the
probability of labor mobility (visa rejection) conditional
to wide range of independent variables. The average
marginal effects are estimated by taking labor mobility
as dichotomous variable i.e., 1 (if an individual’s visa is
rejected) and “0” (if an individual not rejected).
The “average marginal effects” derived from logistic
regression provide more holistic picture of regression
phenomenon to compare outcomes. On average, for
males there are 3.2 percent significantly more chances
of visa rejection, as compared with female respondent.
Column/specification (10) indicates 1.8 percentage
significantly lower probability (less likely) with an
additional year of age that visa would be rejected.
The result indicates that individuals who exposed to
hepatitis from dental clinic, barber shop and vertical
transmission found to be 3, 7.7 and 2.5 percent less
likely to be rejected for visa, respectively. Moreover,
table 4.39 shows that individuals working as part time
employee, self-employee and unemployed have 2.57,
1.46 and 6.74 percent significantly higher probability
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(more likely) for visa rejection.
The household size and estimated direct medical cost
per month found to be negatively associated with visa
rejection the magnitude of household size and direct
medical cost -0.014 with P-value of 0.005 and -0.074
with P-value of 0.013 indicated that probability of visa
rejection for large family size and estimated direct
medical had 14% and 7.4% less than others while the
probability of Education, number of infected family
members, death of a family member, complete health
insurance and estimate indirect medical cost per month
has positive and significant effect on visa rejection. In
addition, total amount spent on treatment, monthly
income and total cost of visit per month have
significantly high probability of visa rejection.
Table 6: Baseline estimation absenteeism of hepatitis B and C patients during treatment (labor productivity)
Model
1
Model
2
Model
3
Model
4
Model
5
Model
6
Model
7
Model
8
Model
9
Model
10
Male
0.015*
(0.008
)
0.016*
*
(0.008
)
0.016*
*
(0.008
)
0.016*
*
(0.008
)
0.017*
*
(0.008
)
0.017*
*
(0.008
)
0.017*
*
(0.008
)
0.020*
*
(0.008
)
0.020*
*
(0.008
)
0.025*
**
(0.007
)
Age of
the
Patient
0.011*
*
(0.004
)
0.009*
*
(0.004
)
0.008*
(0.004
)
0.008*
(0.004
)
0.008*
(0.004
)
0.007*
(0.004
)
0.007
(0.004
)
0.005
(0.004
)
0.004
(0.004
)
-0.002
(0.004
)
Married
-
0.044*
**
(0.010
)
-
0.041*
**
(0.010
)
-
0.039*
**
(0.010
)
-
0.039*
**
(0.010
)
-
0.038*
**
(0.010
)
-
0.037*
**
(0.010
)
-
0.038*
**
(0.010
)
-
0.034*
**
(0.010
)
-
0.034*
**
(0.010
)
-
0.025*
*
(0.010
)
Dental
clinic
-
0.053*
**
(0.014
)
-
0.048*
**
(0.014
)
-
0.050*
**
(0.014
)
-
0.049*
**
(0.014
)
-
0.048*
**
(0.014
)
-
0.047*
**
(0.014
)
-
0.047*
**
(0.014
)
-
0.034*
*
(0.015
)
-
0.034*
*
(0.015
)
-0.013
(0.014
)
Sexual
relation
0.055*
(0.028
)
0.060*
*
(0.029
)
0.054*
(0.028
)
0.053*
(0.028
)
0.060*
*
(0.029
)
0.057*
*
(0.029
)
0.059*
*
(0.029
)
0.052*
(0.028
)
0.051*
(0.028
)
0.059*
*
(0.029
)
Barber
shop
0.070*
**
(0.025
0.065*
*
(0.025
0.066*
**
(0.025
0.067*
**
(0.025
0.070*
**
(0.024
0.068*
**
(0.025
0.064*
**
(0.025
0.050*
*
(0.025
0.047*
(0.025
)
0.035
(0.025
)
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International Journal of Medical Sciences And Clinical Research
)
)
)
)
)
)
)
)
Beauty
parlour
-0.025
(0.029
)
-0.023
(0.029
)
-0.028
(0.029
)
-0.025
(0.029
)
-0.027
(0.029
)
-0.027
(0.028
)
-0.025
(0.028
)
-0.007
(0.029
)
-0.006
(0.028
)
0.001
(0.029
)
Surgery
-
0.034*
*
(0.016
)
-
0.030*
(0.016
)
-
0.033*
*
(0.016
)
-
0.031*
*
(0.016
)
-
0.031*
*
(0.016
)
-
0.032*
*
(0.016
)
-
0.033*
*
(0.016
)
-0.020
(0.016
)
-0.019
(0.016
)
0.025*
(0.015
)
Blood
transfusio
n
-
0.047*
*
(0.023
)
-
0.049*
*
(0.023
)
-
0.052*
*
(0.023
)
-
0.050*
*
(0.023
)
-
0.054*
*
(0.023
)
-
0.052*
*
(0.022
)
-
0.052*
*
(0.022
)
-
0.049*
*
(0.022
)
-
0.049*
*
(0.022
)
-0.019
(0.021
)
Vertical
transmiss
ion
0.033*
**
(0.010
)
0.030*
**
(0.010
)
0.017
(0.011
)
0.019*
(0.011
)
0.019*
(0.011
)
0.016
(0.011
)
0.015
(0.011
)
0.007
(0.011
)
0.008
(0.011
)
-0.006
(0.010
)
Part time
employee
<30
hr/week
0.204*
**
(0.028
)
0.212*
**
(0.029
)
0.210*
**
(0.025
)
0.207*
**
(0.024
)
0.201*
**
(0.025
)
0.184*
**
(0.024
)
0.180*
**
(0.025
)
0.201*
**
(0.027
)
0.205*
**
(0.027
)
0.200*
**
(0.029
)
Self-
employee
0.098*
**
(0.014
)
0.089*
**
(0.014
)
0.091*
**
(0.014
)
0.093*
**
(0.014
)
0.077*
**
(0.015
)
0.072*
**
(0.015
)
0.074*
**
(0.015
)
0.068*
**
(0.015
)
0.081*
**
(0.015
)
0.047*
**
(0.015
)
Depende
nt
0.017
(0.013
)
0.010
(0.014
)
0.010
(0.014
)
0.010
(0.014
)
-0.007
(0.014
)
-0.012
(0.014
)
-0.009
(0.014
)
-0.018
(0.014
)
-0.006
(0.014
)
-
0.035*
*
(0.015
)
Retired
0.097*
**
(0.014
)
0.082*
**
(0.015
)
0.074*
**
(0.017
)
0.096*
**
(0.017
)
0.071*
**
(0.018
)
0.064*
**
(0.018
)
0.065*
**
(0.018
)
0.086*
**
(0.020
)
0.082*
**
(0.021
)
0.104*
**
(0.021
)
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International Journal of Medical Sciences And Clinical Research
Un-
employed
-
0.103*
*
(0.050
)
-
0.099*
*
(0.051
)
-
0.106*
*
(0.048
)
-
0.084*
(0.049
)
-
0.103*
*
(0.049
)
-
0.097*
(0.050
)
-
0.106*
*
(0.050
)
-
0.127*
*
(0.052
)
-
0.114*
*
(0.052
)
0.017
(0.048
)
Educatio
n and
Awarenes
s
0.045*
**
(0.004
)
0.046*
**
(0.004
)
0.046*
**
(0.004
)
0.046*
**
(0.004
)
0.048*
**
(0.004
)
0.048*
**
(0.004
)
0.047*
**
(0.004
)
0.042*
**
(0.004
)
0.040*
**
(0.004
)
0.023*
**
(0.003
)
Total
number
of Family
Members
0.031*
**
(0.007
)
0.026*
**
(0.007
)
0.025*
**
(0.007
)
0.024*
**
(0.007
)
0.024*
**
(0.007
)
0.020*
**
(0.007
)
0.014*
*
(0.007
)
0.013*
*
(0.007
)
-
0.023*
**
(0.007
)
Number
of
Infected
Family
Members
0.037*
**
(0.007
)
0.042*
**
(0.007
)
0.039*
**
(0.007
)
0.040*
**
(0.007
)
0.041*
**
(0.007
)
0.037*
**
(0.007
)
0.037*
**
(0.007
)
0.027*
**
(0.007
)
Death of
a Family
member
-
0.033*
**
(0.009
)
-
0.024*
*
(0.009
)
-
0.024*
*
(0.009
)
-
0.022*
*
(0.009
)
-
0.025*
**
(0.009
)
-
0.024*
*
(0.009
)
0.005
(0.009
)
Complete
health
insurance
-
0.092*
**
(0.021
)
-
0.093*
**
(0.021
)
-
0.100*
**
(0.021
)
-
0.112*
**
(0.020
)
-
0.117*
**
(0.020
)
-
0.071*
**
(0.020
)
Estimated
Direct
Medical
Cost per
Month
-
0.051*
**
(0.012
)
-
0.070*
**
(0.015
)
-
0.043*
**
(0.015
)
-
0.047*
**
(0.015
)
-
0.049*
**
(0.014
)
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Estimate
Indirect
Medical
Cost per
Month
0.056*
**
(0.019
)
0.055*
**
(0.019
)
0.054*
**
(0.019
)
0.016
(0.017
)
Total
Cost
Spent on
Treatmen
t
-
0.059*
**
(0.008
)
-
0.061*
**
(0.009
)
-0.002
(0.009
)
Monthly
Income
0.016*
**
(0.006
)
0.009
(0.006
)
Total
Cost of
Visit Per
Month
0.100*
**
(0.005
)
Constant
1.753*
**
(0.024
)
1.674*
**
(0.030
)
1.633*
**
(0.030
)
1.632*
**
(0.030
)
1.651*
**
(0.030
)
1.758*
**
(0.040
)
1.697*
**
(0.044
)
1.825*
**
(0.045
)
1.796*
**
(0.046
)
1.624*
**
(0.045
)
Observati
ons
8388
8388
8388
8388
8388
8388
8388
8388
8388
8388
Notes: This table presents the results of OLS regressions examining the effects of hepatitis on labor productivity.
Standard errors (in brackets) are robust to arbitrary heteroscedasticity. *, **, and *** indicate statistical
significance at the 10%, 5% and 1% level, respectively.
Table 6 explored the results of OLS regression models
estimated for the effect of hepatitis B and C on
absenteeism from job based on gender of the patient,
age of the patients, material status, source of exposure,
employment status, family size, direct and indirect cost
and monthly income of the infect person’s family. The
above table 4.40 model 10 showed significant effect of
gender on absenteeism/productivity and found that
male patient significantly high probability of
absenteeism than female. The magnitude of male
patients indicated 25% greater probability of
absenteeism than female whereas on average age of
the patient has also direct impact on absenteeism
(productivity). Material status of the patient and source
of exposure of Hepatitis B and C were found indirectly
related to absenteeism from job of employees with
hepatitis B or C virus, the probability of married
Patients of Hepatitis B or C were found 44% less than
unmarried while patients exposed for hepatitis from
dental clinic, beauty parlor, surgery and blood
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transfusion had on average had significant indirect
impact on absenteeism while the effect sexual relation
and vertical transmission with absenteeism were found
directly. The results of OLS model also indicated the
significate direct effect of employment status of Viral
Hepatitis patients on absenteeism, the probability of
absenteeism among part time employee, self-
employed and retired employees were estimated 29%,
47% and 10.4% respectively while the effect of
unemployed and dependent patients were estimated
significantly indirect to absenteeism. The results also
revealed that family size and number of infect family
members were also significant direct impact of
absenteeism and found on average 22% and 37%
respectively whereas the effect of complete health
insurance was estimated indirect with absenteeism due
to hepatitis infection. The estimated results of direct
medical cost, Indirect medical cost and monthly income
of the patients also explored the direct impact of viral
hepatitis infection on absenteeism from job among
hepatitis patients.
Table 7: Baseline estimation job rejection of hepatitis B and C patients
Model
1
Model
2
Model
3
Model
4
Model
5
Model
6
Model
7
Model
8
Model
9
Model
10
Male
0.015*
**
(0.005
)
0.015*
**
(0.005
)
0.015*
**
(0.005
)
0.013*
*
(0.005
)
0.011*
*
(0.005
)
0.012*
*
(0.005
)
0.012*
*
(0.005
)
0.014*
**
(0.005
)
0.014*
**
(0.005
)
0.013*
**
(0.005
)
Age of
the
Patient
-0.001
(0.003
)
-0.000
(0.003
)
-0.000
(0.003
)
-0.001
(0.003
)
-0.001
(0.003
)
-0.002
(0.003
)
-0.002
(0.003
)
-0.003
(0.003
)
-0.003
(0.003
)
-0.001
(0.003
)
Married
-0.007
(0.008
)
-0.008
(0.008
)
-0.008
(0.008
)
-0.009
(0.008
)
-0.010
(0.008
)
-0.007
(0.007
)
-0.007
(0.007
)
-0.005
(0.007
)
-0.004
(0.007
)
-0.007
(0.007
)
Dental
clinic
-0.011
(0.009
)
-
0.015*
(0.008
)
-
0.015*
(0.008
)
-
0.016*
(0.008
)
-
0.021*
*
(0.008
)
-
0.020*
*
(0.008
)
-
0.021*
*
(0.008
)
-
0.019*
*
(0.008
)
-
0.021*
*
(0.008
)
-
0.022*
**
(0.008
)
Sexual
relation
0.108*
**
(0.040
)
0.099*
*
(0.039
)
0.099*
*
(0.039
)
0.102*
**
(0.037
)
0.081*
**
(0.029
)
0.073*
*
(0.030
)
0.072*
*
(0.028
)
0.064*
*
(0.026
)
0.065*
*
(0.026
)
0.062*
*
(0.025
)
Barber
shop
0.023
(0.029
)
0.025
(0.032
)
0.025
(0.032
)
0.020
(0.028
)
0.011
(0.026
)
0.005
(0.025
)
0.006
(0.026
)
0.000
(0.024
)
0.000
(0.025
)
0.002
(0.028
)
Beauty
-0.012
-0.013
-0.013
-0.020
-0.018
-
-
-0.008
-0.009
-0.012
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parlour
(0.016
)
(0.015
)
(0.015
)
(0.014
)
(0.014
)
0.022*
(0.013
)
0.023*
(0.013
)
(0.017
)
(0.016
)
(0.015
)
Surgery
0.008
(0.012
)
0.006
(0.012
)
0.006
(0.012
)
0.000
(0.011
)
0.002
(0.011
)
0.004
(0.011
)
0.004
(0.011
)
0.009
(0.012
)
0.008
(0.012
)
-0.005
(0.010
)
Blood
transfusio
n
-0.014
(0.012
)
-0.013
(0.013
)
-0.013
(0.013
)
-0.014
(0.012
)
-0.008
(0.013
)
-0.008
(0.013
)
-0.008
(0.013
)
-0.011
(0.013
)
-0.010
(0.013
)
-0.017
(0.012
)
Vertical
transmiss
ion
0.005
(0.008
)
0.007
(0.009
)
0.008
(0.009
)
0.001
(0.008
)
0.001
(0.008
)
-0.003
(0.008
)
-0.003
(0.008
)
-0.007
(0.007
)
-0.008
(0.007
)
-0.006
(0.007
)
Part time
employee
<30
hr/week
0.095*
**
(0.030
)
0.079*
**
(0.028
)
0.079*
**
(0.028
)
0.095*
**
(0.029
)
0.087*
**
(0.029
)
0.072*
**
(0.023
)
0.076*
**
(0.025
)
0.121*
**
(0.030
)
0.123*
**
(0.031
)
0.110*
**
(0.027
)
Self-
employee
0.000
(0.013
)
0.007
(0.012
)
0.007
(0.012
)
-0.002
(0.012
)
0.020*
*
(0.009
)
0.018*
*
(0.009
)
0.017*
(0.009
)
0.016*
(0.009
)
0.005
(0.010
)
0.015*
(0.008
)
Depende
nt
-
0.053*
**
(0.011
)
-
0.048*
**
(0.010
)
-
0.048*
**
(0.010
)
-
0.053*
**
(0.011
)
-
0.028*
**
(0.009
)
-
0.027*
**
(0.009
)
-
0.027*
**
(0.009
)
-
0.028*
**
(0.008
)
-
0.036*
**
(0.010
)
-
0.029*
**
(0.008
)
Un-
employed
0.512*
**
(0.040
)
0.494*
**
(0.045
)
0.495*
**
(0.045
)
0.374*
**
(0.053
)
0.421*
**
(0.049
)
0.466*
**
(0.055
)
0.481*
**
(0.054
)
0.442*
**
(0.068
)
0.424*
**
(0.062
)
0.310*
**
(0.053
)
Educatio
n and
Awarenes
s
0.030*
**
(0.003
)
0.030*
**
(0.003
)
0.030*
**
(0.003
)
0.030*
**
(0.003
)
0.025*
**
(0.003
)
0.026*
**
(0.003
)
0.026*
**
(0.003
)
0.025*
**
(0.003
)
0.026*
**
(0.003
)
0.032*
**
(0.003
)
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Total
number
of Family
Members
-
0.021*
**
(0.005
)
-
0.020*
**
(0.005
)
-
0.013*
**
(0.004
)
-
0.010*
**
(0.004
)
-
0.008*
*
(0.004
)
-
0.007*
*
(0.004
)
-
0.009*
*
(0.004
)
-
0.010*
**
(0.004
)
-0.002
(0.003
)
Number
of
Infected
Family
Members
-0.001
(0.004
)
-
0.010*
*
(0.004
)
-0.003
(0.004
)
-0.001
(0.003
)
-0.001
(0.003
)
0.002
(0.004
)
0.003
(0.004
)
0.005
(0.003
)
Death of
a Family
member
0.053*
**
(0.004
)
0.036*
**
(0.004
)
0.034*
**
(0.004
)
0.035*
**
(0.004
)
0.029*
**
(0.004
)
0.028*
**
(0.004
)
0.023*
**
(0.004
)
Complete
health
insurance
0.134*
**
(0.020
)
0.131*
**
(0.019
)
0.136*
**
(0.020
)
0.135*
**
(0.020
)
0.148*
**
(0.021
)
0.127*
**
(0.018
)
Estimated
Direct
Medical
Cost per
Month
-
0.038*
**
(0.007
)
-
0.032*
**
(0.009
)
-0.018
(0.011
)
-0.013
(0.010
)
-0.009
(0.008
)
Estimate
Indirect
Medical
Cost per
Month
-0.013
(0.010
)
-0.014
(0.010
)
-0.012
(0.010
)
-0.009
(0.008
)
Total
Cost
Spent on
Treatmen
t
-
0.034*
**
(0.006
)
-
0.032*
**
(0.006
)
-
0.035*
**
(0.005
)
Monthly
income
-
0.013*
-
0.010*
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**
(0.003
)
**
(0.003
)
Total
Cost of
Visit Per
Month
-
0.020*
**
(0.002
)
Observati
ons
8331
8331
8331
8331
8331
8331
8331
8331
8331
8331
Notes: This table presents the marginal effects of Logit regressions examining the effects of hepatitis on
employment. Standard errors (in brackets) are robust to arbitrary heteroskedasticity. *, **, and *** indicate
statistical significance at the 10%, 5% and 1% level, respectively.
The objective of this estimation approach is to estimate
the impact of hepatitis B and C on job rejection. We
have empirically estimated equation (3) by using
logistics regression econometric techniques. Table 8
contains outcome for equation (3) and presented the
average marginal effects estimated from logistics
regression for the probability of job rejection restricted
to wide range of independent variables included in the
current study. The average marginal effects were
estimated by taking job rejection because of hepatitis
infection as dichotomous variable i.e., 1 (if an individual
experienced job rejection caused by hepatitis B or C)
and “0” (if an individual not experienced job rejection
caused by hepatitis B or C).
The results of “average marginal effects” derived from
logistic regression provide more complete picture of
regression phenomenon to compare outcomes. On
average, male has 1.5 percent significantly more
chances of experienced job rejection caused by
hepatitis B or C, as compared with female respondent.
Column (10) of table 4.41 indicates 1% significantly
lower probability (less likely) with an additional year of
age that job be rejected due hepatitis B or C.
The result indicates that respondents who exposed to
hepatitis B or C from dental clinic, barber shop and
blood transfusion found to be 3, 7.7 and 2.7 percent
less likely to be rejected for job respectively.
Furthermore, table 4.41 showed that respondents
working as part time employee, self-employee and
unemployed have 9.5, 1.5 and 5.12 percent significantly
higher probability (more likely) for experienced job
rejection caused by hepatitis B or C.
The household size and estimated direct medical cost
and indirect medical costs per month were estimated
to be significant negative effect on experienced job
rejection caused by hepatitis B or C and significantly
lower probability (less likely) with an additional year.
The estimated average probability of household size
and direct medical cost and indirect medical costs -
0.008 with P-value of 0.004 and -0.018 with P-value of
0.011 and -0.012 with P-value of 0.008 indicated that
probability of significantly lower probability (less likely)
with job rejection caused by hepatitis B or C. Monthly
income of the patients and their family was also found
indirect effect and significantly lower probability (less
likely) with job rejection caused by hepatitis B or C.
CONCLUSION AND RECOMMENDATIONS
Keeping in view the importance of health in human
capital and human development index, it is important
to conduct a study that highlight the consequence of
hepatitis in Pakistan. This study is designed to estimate
the effect of viral hepatitis (B & C) on labor productivity,
family income, morbidity and mortality, estimate the
direct and indirect cost of hepatitis (B & C) and total
cost imposed on each patient and their family in
Pakistan. Primary data was collected from 8,388
hepatitis B and C patients at district headquarter
hospitals, private hospitals and doctors’ clinic from 77
districts across Pakistan including Azad Jammu and
Kashmir and Gilgit Baltistan through a well design
questionaire containing 36 questions based on
demographic and economic indicators. Descriptive,
inferential statistical tools, logit and OLS econometric
techniques were applied for data analysis.
This study found significant effect of viral hepatitis B
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International Journal of Medical Sciences And Clinical Research
and C on labor productivity, labor mobility,
absenteeism and presentism at work place, family
income, mortality and life style in Pakistan. This study
also found significant impact of hepatitis on
productivity in terms of absenteeism and presentism
and estimated that an average per patient and their
att
endant’s absenteeism and presentism 1.89 days per
month and total working days lost in Pakistan were
estimated 32,432,400. Furthermore, hepatitis B and C
had also found significant indirect impact on labor
mobility employment and mortality and concluded that
2.07% visa rejection, 12% job rejection and 5.2%
morality caused by hepatitis B and C. The “average
marginal effects” derived from logistic regression
provide
more
holistic
picture
of
regression
phenomenon to compare outcomes. On average, for
male are 3.2 percent significantly more chances of visa
rejection, as compared with female respondent.
Column (10) of table 8 indicates 1.8 percentage
significantly lower probability (less likely) with an
additional year of age that visa would be rejected.
Similarly, the effect of hepatitis B and C was found
indirect and caused decline in income in term of loss of
working days and selling of assets. This study also found
adverse effect of viral hepatitis B and C on foreign
reserves and concluded high 0.18% reduction in foreign
reserves.
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