Firat Bilgel - Okan University Burhan Can Karahasan - Piri Reis University ERF Workshop on The Economics of Healthcare in the ERF Region Cairo, Egypt - May 23, 2016 www.erf.org.eg

1. Introduction
Empirical Strategy
Results and Inference
Potential Caveats and Future Directions
Healthcare Utilization and Self-assessed Health in
Turkey: Evidence from the 2012 Health Survey
Fırat Bilgel∗ and Burhan Can Karahasan∗∗
∗Okan University, Istanbul Turkey
∗∗Piri Reis University, Istanbul Turkey
F. Bilgel and B.C. Karahasan Healthcare Utilization and SAH 1 / 25

2. Introduction
Empirical Strategy
Results and Inference
Potential Caveats and Future Directions
Objective
Identifying the causal impact of healthcare utilization on
self-assessed health (SAH)
Challenge: Selection into healthcare is not random!
Some unobservable factors may determine healthcare
utilization and SAH simultaneously
F. Bilgel and B.C. Karahasan Healthcare Utilization and SAH 2 / 25

3. Introduction
Empirical Strategy
Results and Inference
Potential Caveats and Future Directions
Contribution
Prior related literature in Turkey is devoted to inequalities in
healthcare utilization and SAH in a univariate framework
Determinants of utilization
Ignores a possible endogenous relationship between utilization
and SAH
Attempts to identify causal links
F. Bilgel and B.C. Karahasan Healthcare Utilization and SAH 3 / 25

4. Introduction
Empirical Strategy
Results and Inference
Potential Caveats and Future Directions
SAH as an Ordinal Outcome
SAH as a Binary Outcome
Data and Sample
Individual-level data from the 2012 Health Survey (TurkStat)
A nationally representative sample of 37,979 respondents
Individuals are drawn from the population using a two-stage
stratified cluster sampling method
External stratification criterion: urban/rural distinction
First stage: the sampling units of blocks were chosen from
clusters in which the average number of households is 100.
Second stage: households are selected from each cluster
Sampling weights represent the inverse probability of being
selected into the sample
The final number of observations used in the analysis: 24,022
F. Bilgel and B.C. Karahasan Healthcare Utilization and SAH 4 / 25

5. Introduction
Empirical Strategy
Results and Inference
Potential Caveats and Future Directions
SAH as an Ordinal Outcome
SAH as a Binary Outcome
Outcome Variables
Two versions of SAH:
an ordinal scale from 1 to 5, 1 representing “very poor” health
and 5 representing “very good” health
as a binary variable taking the value of 1 for “suboptimal”
health and 0 otherwise
F. Bilgel and B.C. Karahasan Healthcare Utilization and SAH 5 / 25

6. Introduction
Empirical Strategy
Results and Inference
Potential Caveats and Future Directions
SAH as an Ordinal Outcome
SAH as a Binary Outcome
Treatment Variable
Five types of healthcare utilization were assessed:
Preventive care: Encompasses measures taken to prevent
diseases as opposed to treatment
GP care: Range of healthcare provided by general practitioner,
first contact with the healthcare system
Specialist care: Services provided by a specialist (e.g.
oncology, cardiology, radiology etc..)
Inpatient care: Healthcare services that require hospital
admission
Emergency care: Healthcare for undifferentiated and
unscheduled patients requiring immediate medical attention
F. Bilgel and B.C. Karahasan Healthcare Utilization and SAH 6 / 25

7. Introduction
Empirical Strategy
Results and Inference
Potential Caveats and Future Directions
SAH as an Ordinal Outcome
SAH as a Binary Outcome
Control Variables
chronic disease history
age
gender
location (urban vs. rural)
educational attainment
obesity
the type of health insurance
income
marital status
consumption of fruit / juice / alcohol / tobacco
frequency of physical exercise
F. Bilgel and B.C. Karahasan Healthcare Utilization and SAH 7 / 25

8. Introduction
Empirical Strategy
Results and Inference
Potential Caveats and Future Directions
SAH as an Ordinal Outcome
SAH as a Binary Outcome
Healthcare Utilization as an Endogenous Treatment
Our outcome of interest is the individual’s SAH, measured on
an ordinal scale with 5 possible ordered outcomes:
Very poor (1)
Poor (2)
Average (3)
Good (4)
Very good (5)
F. Bilgel and B.C. Karahasan Healthcare Utilization and SAH 8 / 25

9. Introduction
Empirical Strategy
Results and Inference
Potential Caveats and Future Directions
SAH as an Ordinal Outcome
SAH as a Binary Outcome
A Recursive Bivariate Ordered Probit Model
Let Ti ∈ {0, 1} be the binary treatment variable that takes the
value of one if the individual utilizes healthcare and zero if the
individual does not. The selection equation is:
Ti =
1
0
if T∗
i = Zi γ + υi > 0
if T∗
i = Zi γ + υi ≤ 0
where Zi is the set of covariates and υi is the error term
The latent outcome variable Y ∗
i is defined as:
Y ∗
i = α + Xi β + δTi + εi
where εi is the idiosyncratic error term and Xi are the covariates
F. Bilgel and B.C. Karahasan Healthcare Utilization and SAH 9 / 25

10. Introduction
Empirical Strategy
Results and Inference
Potential Caveats and Future Directions
SAH as an Ordinal Outcome
SAH as a Binary Outcome
Error Distribution
Latent errors υi and εi should follow a bivariate joint normal
distribution (BiVN) with correlation ρ
If ρ = 0, the first equation can be estimated by generalized
ordered probit
If ρ = 0, then the unobservable determinants of health care
selection are said to be correlated with the unobservable
determinants of SAH, rendering health care utilization
endogenous. Individuals either:
observe health status and choose to resort to receive healthcare
receive healthcare and observe health status
F. Bilgel and B.C. Karahasan Healthcare Utilization and SAH 10 / 25

11. Introduction
Empirical Strategy
Results and Inference
Potential Caveats and Future Directions
SAH as an Ordinal Outcome
SAH as a Binary Outcome
A latent-factor approach
If the BiVN assumption is violated, the estimates will be
inconsistent and biased
Reformulate the error-generating process in the following way:
υi = λT ηi + ςi
εi = λY ηi + ιi
where λT and λY are the loading factors describing the dependence of
the latent errors for the treatment and the outcome respectively and only
the marginal distributions of ς and ι are assumed to be normal.
We simulate the distribution of η by taking random draws form its
chosen distribution and assume that the loading factors follow a
gamma distribution
F. Bilgel and B.C. Karahasan Healthcare Utilization and SAH 11 / 25

12. Introduction
Empirical Strategy
Results and Inference
Potential Caveats and Future Directions
SAH as an Ordinal Outcome
SAH as a Binary Outcome
Instrument Choice
What moves around the covariate of interest that might
plausibly be viewed as random?
The choice of instrument, Wi , for primary healthcare utilization
is the individual’s knowledge of their family physician
Individuals who are acquainted with their family physician are
more likely to utilize preventive and GP care
The physician knowledge has no direct, evident relation to
one’s subjective health status
F. Bilgel and B.C. Karahasan Healthcare Utilization and SAH 12 / 25

13. Introduction
Empirical Strategy
Results and Inference
Potential Caveats and Future Directions
SAH as an Ordinal Outcome
SAH as a Binary Outcome
Average Treatment Effect (ATE)
The ATE shows the expected effect of healthcare utilization on
suboptimal SAH for a randomly drawn individual from the
population for a given level of j:
E [Yi (1) − Yi (0) | j] = E [Yi (1) | T = 1, j] − E [Yi (1) | T = 0, j]
F. Bilgel and B.C. Karahasan Healthcare Utilization and SAH 13 / 25

14. Introduction
Empirical Strategy
Results and Inference
Potential Caveats and Future Directions
SAH as an Ordinal Outcome
SAH as a Binary Outcome
Average Treatment Effect on the Treated (ATT)
The ATT shows the expected effect of healthcare utilization
for a randomly drawn individual only from those individuals
who utilize healthcare for a given level of j:
E [Yi (1) − Yi (0) | T = 1, j] = E [Yi (1) | T = 1]−E [Yi (0) | T = 1, j]
F. Bilgel and B.C. Karahasan Healthcare Utilization and SAH 14 / 25

15. Introduction
Empirical Strategy
Results and Inference
Potential Caveats and Future Directions
SAH as an Ordinal Outcome
SAH as a Binary Outcome
Healthcare Utilization as an Endogenous Treatment
SAH is coded as a binary variable:
“poor” and “very poor” are coded as “suboptimal” health,
taking the value of 1
“very good”, “good” and “average” are coded as otherwise,
taking the value of 0
F. Bilgel and B.C. Karahasan Healthcare Utilization and SAH 15 / 25

16. Introduction
Empirical Strategy
Results and Inference
Potential Caveats and Future Directions
SAH as an Ordinal Outcome
SAH as a Binary Outcome
A Recursive Bivariate Probit Model
Our outcome of interest is the likelihood to report suboptimal SAH,
Yi is determined by the latent index
Yi = 1 Xi β + δTi > εi
where Xi is the set of covariates, Ti is healthcare utilization, εi is error
term and 1 [.] is the indicator function taking the value of 1 if the
statement in the brackets is true and 0 otherwise.
Individuals can “treat” themselves by utilizing healthcare. The
treatment equation is given by the following:
Ti = 1 Zi γ1 + γ0Wi > υi
where Zi is the set of covariates, Wi is the instrumental variable, and υi
is the error term.
F. Bilgel and B.C. Karahasan Healthcare Utilization and SAH 16 / 25

17. Introduction
Empirical Strategy
Results and Inference
Potential Caveats and Future Directions
SAH as an Ordinal Outcome
SAH as a Binary Outcome
Effects of non-utilization variables
Recursive Bivariate Ordered Probit
Individuals suffering from chronic health problems and obesity are
more likely to use healthcare but also more likely to report a
pessimistic SAH
Males tend to be more optimistic in their SAH, but also less likely
to use healthcare
Residents in rural areas are more likely to report a pessimistic SAH
and they are also less likely to utilize GP and emergency care
Individuals tend to be more optimistic about their SAH when
exercised at least thrice a week
Regular smokers report pessimistic SAH and tend to use more
preventive and emergency care
Individuals tend to report an increasingly pessimistic SAH as they
grow older
F. Bilgel and B.C. Karahasan Healthcare Utilization and SAH 17 / 25

18. Introduction
Empirical Strategy
Results and Inference
Potential Caveats and Future Directions
SAH as an Ordinal Outcome
SAH as a Binary Outcome
Effects of non-utilization variables, continued
Recursive Bivariate Ordered Probit
Senior individuals (75+) are more likely to utilize GP care
Singles are less likely to utilize healthcare
Those with an educational attainment below higher education tend
to have pessimistic SAH
No conclusive and strong evidence as to the impact of education on
healthcare utilization
Publicly insured are more likely to use preventive and inpatient
healthcare services
Uninsureds (out-of-pocket) are less likely to use inpatient and
emergency healthcare
F. Bilgel and B.C. Karahasan Healthcare Utilization and SAH 18 / 25

19. Introduction
Empirical Strategy
Results and Inference
Potential Caveats and Future Directions
SAH as an Ordinal Outcome
SAH as a Binary Outcome
Average Treatment Effect (ATE)
Recursive Bivariate Ordered Probit
Preventive care utilization;
decreases the probability for an individual to report very poor,
poor or average health by 0.005 percent, 2.7 percent and 6.1
percent
increases the probability to report very good health by 7.8
percent.
Inpatient care utilization;
decreases the probability for an individual to report very poor,
poor or average health by 0.005 percent, 3.5 percent and 9.2
percent
increases the probability to report very good health by 12.5
percent.
F. Bilgel and B.C. Karahasan Healthcare Utilization and SAH 19 / 25

20. Introduction
Empirical Strategy
Results and Inference
Potential Caveats and Future Directions
SAH as an Ordinal Outcome
SAH as a Binary Outcome
Average Treatment Effect (ATE), continued
Recursive Bivariate Ordered Probit
Specialist care utilization;
increases the probability to report poor, average and good
health by 5.6 percent, 19.8 percent and 13.8 percent
respectively
decreases the probability to report very good health by 40
percent.
GP and Emergency care utilization has no causal impact on SAH
F. Bilgel and B.C. Karahasan Healthcare Utilization and SAH 20 / 25

21. Introduction
Empirical Strategy
Results and Inference
Potential Caveats and Future Directions
SAH as an Ordinal Outcome
SAH as a Binary Outcome
Average Treatment Effect on the Treated (ATT)
Recursive Bivariate Ordered Probit
For those who actually utilize healthcare, preventive care utilization;
decreases the probability for an individual to report very poor,
poor or average health by 2 percent, 6.7 percent and 7.4
percent
increases the probability to report good and very good health
by 11.2 and 4.9 percent
For those who actually utilize healthcare, inpatient care utilization;
decreases the probability for an individual to report very poor,
poor or average health by 9.2 percent, 19.1 percent and 10.3
percent
increases the probability to report good and very good health
by 31.6 and 7 percent.
F. Bilgel and B.C. Karahasan Healthcare Utilization and SAH 21 / 25

22. Introduction
Empirical Strategy
Results and Inference
Potential Caveats and Future Directions
SAH as an Ordinal Outcome
SAH as a Binary Outcome
Effects of non-utilization variables
Recursive Bivariate Probit
Individuals suffering from chronic health problems and obesity as
well as regular alcohol and tobacco consumers are more likely to
report suboptimal SAH and more likely to utilize primary care
Residents in rural areas are more likely to report suboptimal SAH
and less likely to utilize primary care
Males are less likely to report suboptimal SAH and less likely to
utilize primary care
Individuals with a primary or no education are more likely to report
suboptimal SAH
The likelihood of reporting suboptimal SAH increases at a
decreasing rate with higher income brackets
Individual’s knowledge of their family physician has a statistically
significant and positive impact on primary care utilization
F. Bilgel and B.C. Karahasan Healthcare Utilization and SAH 22 / 25

23. Introduction
Empirical Strategy
Results and Inference
Potential Caveats and Future Directions
SAH as an Ordinal Outcome
SAH as a Binary Outcome
Average Treatment Effects
Recursive Bivariate Probit
Preventive care utilization decreases the probability to report
suboptimal SAH;
by 7.8 percent irrespective of utilization (ATE)
for those who actually utilize preventive care by about 22
percent (ATT)
GP care utilization has no causal impact on the probability to
report suboptimal SAH
F. Bilgel and B.C. Karahasan Healthcare Utilization and SAH 23 / 25

24. Introduction
Empirical Strategy
Results and Inference
Potential Caveats and Future Directions
Potential Caveats
Biases related to the use of a survey not originally designed to
study healthcare utilization
General concerns with the accuracy of self-assessment
Self-report biases
Lack of georeferenced data
Lack of data on healthcare access
F. Bilgel and B.C. Karahasan Healthcare Utilization and SAH 24 / 25

25. Introduction
Empirical Strategy
Results and Inference
Potential Caveats and Future Directions
Future Directions
Alternative latent-factor structure specifications
Assessment of average marginal effects under exogeneity
Additional robustness checks
Healthcare utilization among rural residents and males
F. Bilgel and B.C. Karahasan Healthcare Utilization and SAH 25 / 25