The document provides an overview of the fundamentals of health economics through a series of lectures delivered by Mohsin Hassan Alvi at the Pakistan Institute of Living and Learning. It discusses key topics including the structure of Pakistan's healthcare system, budget reforms for primary care units, popular health economics terms, types of economic evaluation, valuing healthcare benefits, and global burden of disease. The document aims to educate about health economics principles and issues relevant to the Pakistani healthcare context.

1. Fundamentals of Health Economics
Mr. Mohsin Hassan Alvi
Pakistan Institute of Living and Learning
The University of Manchester

2. HealthCare Industry of Pakistan

3. Lecture 1

4. Structure of HealthCare
• Healthcare system of Pakistan consists of private and public sector.
• The private sector serves nearly 70% of the population and 30% by
the public sector.
• As per Pakistan constitution provision of health is the responsibility of
provincial governments except in federally administered areas.

9. Budget to Primary Care Units Reforms
Issues of care units
1. On estimate, current health budget 20-21 only
covers 5-10% of entire burden of mental illness in
Pakistan.
2. Primary care units (PCU) are Underutilized and
Secondary and Tertiary are over burdened.
3. 5395 BHUs and 4813 Dispensaries across
Pakistan which are insufficient for rapidly
growing population.
4. BHUs are facing infrastructural deficiencies.
5. Ratio of doctors to nurses is 3:1 in Pakistan- that
should be 1:3
6. Ad Hoc Policies to recruit temporary staff
7. Unavailability of medicines and diagnostic tools.
8. Remote Area are highly neglected (600 across
Pakistan). 14.5 physicians per 1000 are working
in urban areas while only 3.6 in rural areas.
Reforms
1. Budget for Mental Health should be allocated like
neighboring countries (Such as China and India).
2. More BHUs should be established as they are
considered first level are facilities.
3. Moderate proportion of the health sector surplus
should be granted to BHUs. Yearly mean recurrent
cost per BHU was PKR 9.5 Lacs in (2006) and
26.5 Lacs in (2020)
4. Recruitment of paramedic staff and introduce
appropriate incentives to ensure their retention.
5. Interest and incentives of ad hoc based staff
should be secured.
6. PCUs should be standardized to reduce the burden
on other setups.
7. Certified physicians should be appointed
All Parliamentary Mental Health Summit 2020-21
[1] Usman et al., 2015; [2] Malik et al., 2015; [3] 11th five year plan of health; [4] Akram et al.,, 2007; [5] Zaidi et al., 2017

10. Thank You

11. Lecture 2

12. Fundamentals of Health Economics
Facilitator:
Mohsin Hassan Alvi
Research Fellow – Health Economics
Pakistan Institute of Living and Learning

13. Popular Health Economics Terms
1. Morbidity (Point, Limited Period)
2. Mortality (Point, Unlimited Period)
3. H.A.L.E. (Health-Adjusted Life Expectancy)
4. Y.L.D. (Years of Life lived with Disability)
5. Y.L.L. (Years of Life Lost)
6. P.Y.L.L. (Potential Years of Life Lost) OR Y.P.L.L.
7. Q.A.L.Y. (Quality Adjusted Life Years)
8. D.A.L.Y. (Disability Adjusted Life Years)

14. 1. Morbidity (Point, Limited Period)
• The condition of being diseased.
• The rate of disease in a population.

15. Mental Health Morbidity in Pakistan
• 6% prevalence of depression
• 1.5% schizophrenia,
• 1 to 2% epilepsy
• 1% from Alzheimer's disease
These mental morbidities culminate in high suicide
rate.

16. 2. Mortality (Point, Unlimited Period)
• The state of being subject to death.
• Death, especially on a large scale.

17. 3. H.A.L.E. (Health-Adjusted Life Expectancy)
HALE is a measure of
population
health that takes into
account mortality and
morbidity. It adjusts
overall life
expectancy by the
amount of time lived
in less than
perfect health.

18. 4. Y.L.D. (Years of Life lived with Disability)
Years Lived with Disability (YLD) is a component of
DALY, and measures the burden of living with a
disease or disability in the amount of years.

19. 5. Y.L.L. (Years of Life Lost)
Years of life are lost (YLL) take into account the age at
which deaths occur by giving greater weight to deaths
at younger age and lower weight to deaths at older
age.

20. 6. P.Y.L.L. (Potential Years of Life Lost) OR Y.P.L.L.
Years of potential life lost (YPLL) or potential years of life lost (PYLL), is
an estimate of the average years a person would have lived if he or
she had not died prematurely. It is, therefore, a measure of
premature mortality.

21. 7. QALY (Quality Adjusted life year)
• Quality Adjusted life year is a generic measure of
disease burden including both the quality and the
quantity of life lived.
• QALY scores range from 1 (perfect health) to 0 (dead).
• It is used in economic evaluation to assess the value
of medical interventions.

25. 8. D.A.L.Y. (Disability Adjusted Life Years)
The disability-adjusted life year is a measure of overall disease
burden, expressed as the number of years lost due to ill-health,
disability or early death.

27. Thank You

28. Lecture 3

29. Science
It is a systematic study about structure and behavior of an object
through observations and experiments.

30. Technology
Applications of scientific knowledge for practical purpose.

31. Health Technology
A Health Technology is the application of organized knowledge and
skills in the form of devices, medicines, vaccines, procedures and
systems developed to solve a health problem and improve quality of
lives.

32. Health Technology Assessment
• Applying scientific knowledge in order to asses the health of an object
of subject.
• Health Technology Assessment (HTA) is the systematic evaluation of
the properties and effects of a health technology, addressing the
direct and intended effects of this technology, as well as its indirect
and unintended consequences, and aimed mainly at informing
decision making regarding health technologies.

35. Types of Economic Evaluation

36. Types of Economic Evaluation

37. Types of Economic Evaluation

40. Thank You

41. Lecture 4

54. Thank You

55. Lecture 5

62. Thank You

63. Lecture 6

72. Thank You

73. Lecture 7

74. Numerical

75. Cost Benefit Analysis
Q. Select the best option using Cost Benefit
Analysis

76. Q. Calculate QALY of Alternate A & B
Compute QALY

77. Cost Minimization Analysis (CMA)
Alternate A
Injury prevention by building a (Speed Breaker)
Cost per injury prevented = 500 Rupees
Number of injury prevented = 200 Drivers
Alternate B
Injury prevention by building a (Foot Over-Bridge)
Cost per injury prevented = 1800 Rupees
Number of injury prevented = 200 Drivers
The outcomes (number of injury prevented) are identical for alternatives ‘A’ and
‘B’. Alternative ‘A’ has lower cost of intervention. Using CMA, we can choose
(Alternative A), i.e. building a (Speed Breaker).

78. Cost Effectiveness Analysis (CEA)

79. Cost Effectiveness Ratio (CER)

80. Cost Effectiveness Ratio (CER)

81. Cost Effectiveness Ratio (CER)

82. Incremental Cost Effectiveness Ratio (ICEA)
Suppose that the standard treatment for a particular
Chemotherapy cost Rs.100,000/- and on average leads to 1
additional QALY for those receiving treatments.
An alternate treatment is discovered that cost Rs.200,000/-
and on average leads to 3 additional QALY for those receiving
treatments.
What is the ICER for the alternate treatment relative to the
standard treatment.
Options
A. Rs.1,666.67/QALY
B. Rs.50,000/QALY
C. 2
D. 1.2

83. Thank You

84. Lecture 8

85. Valuing the benefits in healthcare
• Time trade-off (TTO): where respondents are asked to trade health
improvement for length of life.
• Standard Gamble (SG): where respondents are asked to trade health
improvement for a risk of death.

92. Global Burden of Disease
• Who bear the burden of disease if a person(s) in family, country
and world have physically and mentally disability?
• The Individual level whole family suffers with the disability of a
person, because family income decreases and expenses increase,
a relatively independent person becomes a dependent person.
• National level impact on the remaining person bears the burden
of disable persons, if they don’t bear so yearly GDP target not
attains. For this country GDP drop rapidly due to disable persons.
• Global burden of disease control the UNO according to its policy,
develop country give aid to developing country to get rid of
disease in the whole world.

93. Thank You

94. Lecture 9

95. Concept of Intervention

98. Cost-effectiveness of Learning Through Play Plus intervention on maternal depression in Pakistan
Design
- A cluster RCT
Intervention
- Parental psycho education on child development
- Cognitive behavioural therapy (Learning Through Play Plus – LTP Plus)
Outcomes
-Primary economic outcome was health service care cost per QALY gained
- Maternal Depression, Anxiety, self-esteem, Perceived Social Support, Health
Related Quality of Life, Resource Used, Home Observation for Measurement of
the Environment, child social and emotional development
Procedure
- (QALYs) was measured using the EQ-5D-3L
- Thailand Value Sets
- Individual resource use were collected
- Unit costs were obtained
- Baseline and 6 months
Study Sample
- Community (For sample selection)
- Hospitals in the catchment area (For Cost)
- Sample size is 402 and 372 participants into LTP Plus and TAU groups
Statistical Analysis
- We evaluate its cost-effectiveness
- ICER per QALY gained of Intervention versus TAU group
- ICER = (Cost1 – Cost2) / (QALY1 – QALY2)
Conclusions
- LTP+ is a cost-effective intervention strategy.

99. NICE: £20,000 to £30,000
per (QALY)
WHO: US$ 80,000 per
(QALY)
CADTH: CA$ 50,000 per
(QALY)

101. Thank You

102. Lecture 10

103. Health Economics Research

104. Variables
• All research projects are based around variables.
• In this section, we decide what is our variables in research, some dependent
and some independent & sub categories of variables.
• Variables define anything in the research paper because in research, we see
the effect of independent to the dependent variable.
• Variables can be straightforward and easy to measure.
• There are many types of variables in research like, dependent, independent,
intervening, moderator, control, extraneous variables.

105. Types of Variables
There are Six types of Variables in Research
• Dependent Variables
• Independent Variables.
• Intervening Variables.
• Moderator Variables.
• Control Variables.
• Extraneous Variables.

106. Cont…
• Dependent Variables
a variable (often denoted by y ) whose value depends on that of
another.
• Independent Variables.
a variable (often denoted by x ) whose variation does not depend on
that of another.
• Intervening Variables.
An intervening variable is a hypothetical variable used to explain
causal links between other variables. Intervening variables cannot be
observed in an experiment (that's why they are hypothetical).

107. Cont…
• Moderator Variables.
A moderator variable, commonly denoted as just M, is a third variable that
affects the strength of the relationship between a dependent and independent
variable.
• Control Variables.
The control variable is not part of an experiment (not the independent or
dependent variable), but it is important because it can have an effect on the
results.
• Extraneous Variables.
Extraneous Variables are undesirable variables that influence the
relationship between the variables that an experimenter is examining. Extraneous
variables are any variables that you are not intentionally studying in your
experiment or test.

108. Ignored Variables
There are some ignored variable in model all of these variable are add
in error term. Ignored variable is we use in equation as an ET symbol.

109. Sustainable
Development
Goals
• SDG 1: No Poverty
• SDG 2: Zero Hunger
• SDG 8: Decent Work and
Economic Growth
• SDG 9: Industry, Innovation,
and Infrastructure
• SDG 10: Reduced Inequalities
Review Questions:
What are the economic risk factors associated with mental health
disorders/distress/conditions in Pakistani Young adults?
What type of interventions, focused on economic factors, are potentially beneficial
to reduce mental health disorders/distress/conditions among Young adults in
Pakistan?

110. Assumptions
Mental Health Outcomes
Common Psychological
Disorders in Pakistani Youth
• Suicides
• Depression
• Bipolar Disorders
• PTSD
• Substance Misuse and
Addiction
• Schizophrenia
By: Fatima Hassani
Key Economic Factors
• Food Insecurity
• Household Expenses
• Financial Debt
• Unemployment
• Financial Strain
• Income Inequality
• Economic Recessions
Micro
Level
Macro
Level

112. Thank You

113. Lecture 11

114. Health Econometrics

115. Descriptive Statistics
In descriptive statistics describe the basic features of the data in this
we use charts, graph, central tendency and dispersion.

116. Inferential Statistics
Statistical inference is the process of using data analysis to deduce
properties of an underlying probability distribution. Inferential
statistical analysis infers properties of a population, for example by
testing hypotheses and deriving estimates

117. Difference Between Descriptive and Inferential Statistics
• Descriptive Statistics describes data (for example, a chart or graph)
and inferential statistics allows you to make predictions (“inferences”) from
that data.
• The reporting methods of descriptive statistics are measures of central
tendency, range, quartiles, absolute deviation, variance and standard
deviation whereas, inferential statistics are (1) the estimation of
parameter(s) and (2) testing of statistical hypotheses.

118. Skewness
• Skewness is asymmetry in a statistical distribution, in
which the curve appears distorted or skewed either to
the left or to the right.
• Skewness can be quantified to define the extent to
which a distribution differs from a normal
distribution.
• Symmetry means no skewness and asymmetry means
skewness is present.

119. Kurtosis
The sharpness of the peak of the data it is kurtosis. If data is flattened
so this is a issue of kurtosis.

120. Central Tendencies
• There are three central tendencies mean, median and
mode these three define in the separate head. Mean is
the average of observation, like sum of all observations
and divided by number of numbers.
• Median is middle value of the observation, to find median
firstly we set data in ascending order. It is applicable for
odd observations and for even observations adding up
middle two values and it divided by 2.
• The mode is the values that occur most often in the data
or in other words most repeated value in the data. If in
data there are no observations that are repeated so that
is no mode in data.

121. Dispersion
• In statistics dispersion is variability, scatter or spread of
the data. Common example of measure dispersion is
variance and standard deviation.
• Standard deviation tells how spread out the observations
from the mean value.
• Variance is also a measure of dispersion and it is square of
standard deviation, this is use in vast dimension but
advantage in variance is there is less chances of error if
we gathered large amount data.
• We use both concepts in health economics in the context
of risk in-term expected life loss.

122. Normality Checking
• For checking normality in data, firstly find the central
tendencies (mean=median=mode) shows data is normal.
• Another one is skewness of the data its mean all observation
between Z value so the data is normal if the skewness is left
or right so data is not normal.
• Thirdly QQ plot data gathered with line of mean so data is
normal otherwise data spread in graph that is mean data is
not normal.
• Last one is testing for data normality checking 2 test are
apply Kolmogrov-Simrov and Shapiro Wilk if in results
probabilities is > 0.05 so data is normal, or probabilities
<0.05 so data is not normal.

123. Endogenity
It is measurable. Endogenous for parameter and endogenity is
parametric test. If variable is ignored and it’s measurable so, its
endogenity. If that ignored variable is co-linear with any independent
variable so due to this error term will also co-linear with that
independent variable. Import of pesticides is measurable so it is
endogenity.

124. Exogenity
It is not measurable. Exogenous for parameter and exogenity is non-
parametric test. If variables is ignored and its not measurable so, it is
exogenity. If that ignored variable is co-linear with any independent
variable so due to this error term will also co-linear with that
independent variable. Sunlight is not measurable so it is exogenety.

125. Homoscedasticity
This word has a complimentary notion of homogeneity of variance.
We define homoscedasticity as all data value in sequence distances
with its mean value or same finite variance.

126. Graph shows Homoscedasticity

127. Heteroscedasticity
This is the violation of homoscedasticity. All data value has not equal
distance with its mean value so this is called heteroscedasticity.
Furthermore, data with unequal scatter across a set of predictor
variable.

128. Graph shows Heteroscedasticity

129. Econometrical Model
• Straight Line Equation
• Y=mx+c
• In Research it is written as:
• DV = α + β (IV) + ε
Here,
DV = Dependent Variable
IV = Independent Variable
α is constant and intercept.
β is slope.
ε is error term (It comprised of all the ignored
variables)

130. OLS (Ordinary Least Square) Model
• OLS (Ordinary Least Square) model is the stochastic model
it has chances of error not 100% accurate result.
• Stochastic model uses in social sciences, managerial
science and behavioral science research.
• Some models are deterministic no chances of error 100%
accurate result. Those are uses in the disciplines like
Chemistry, Mathematics and etc
• Example:
1. H2 + O = H2O
2. 2 + 2 = 4

131. Co-Linearity
• X is independent variable, within model there are more
than one independent variable, two independent variable
impact each other so it is co-linearity.
• In one model two independent are not use if co-linearity
exists.
• If more than two independent variables impact/effect
each other in the model so it is called multi co-linearity.
• Independent variable can’t explain dependent variable
completely due to co-linearity so its an issue.

132. ARLD Model
• In OLS model if lag variables of dependent variable is introduced so model become
ARLD model.
• In Auto-regressive lag distribution model has a lag variables that mean we predict
current data with the help of past analysis.
• Furthermore, dependent variables regress with its lag that is mean auto regression
is present.
• Auto regression is measuring test and coefficient is Rho, it show the association
between current and previous lag.
• = Rho = Zero than ARLD model can not apply. Its mean that there is no coefficient
of previous lags variable.
• DV = α + β1(IV) + β2(DV)t-1 + ε
• In this ARLD model small (a) is constant, β1 and β2 is the slope and x and yt-1 is the
independent variable associated with lag values. Every lag associated all of this
include ARLD model.

133. Logit and Probit Model
• Logit and Probit model are attempting where the
models are having dichotomous dependent variable.
Example, yes/no, agree/disagree, etc.
• why we use logit and Probit because in OLS model has
the linear regression line creates the problem of
hetroskedasticity.
• Logit and Probit solve the problem by fitting the data
nonlinear regression. So, the data looks like the
following:

135. • This S shape curve respects the boundaries of the
dependent variable and this is assuming the proper
specification of independent variables.
• This nonlinear curve does away with heteroskedasticy.
• Logit and Probit predictors can be written as:

136. • Cumulative distribution function of the logistic distribution uses in
Logit model and the cumulative distribution function of the standard
normal distribution uses in Probit model.
• Both functions use numbers between 0 and 1.

137. Difference Between Logit and Probit
• The logit and Probit are symmetric around the
proportion of 0.5, where t both Logit and Probit are 0.
• The Probit is based on the standard Normal
distribution while the Logit is based on the standard
logistic distribution.
• For the Logit model, the errors (in the error term) are
assumed to follow the standard logistic distribution
while for the Probit, the errors are assumed to follow
a Normal distribution.
• But; In principle for general practice the model
formalism both work fine and often lead to the same
conclusions regardless of the problem complexity.

138. mohsin.h.alvi@pill.org.pk
mohsin.alvi@manchester.ac.uk