Healthcare Utilization and Self-assessed Health in Turkey: Evidence from the 2012 Health Survey

IntroductionEmpirical Strategy

Results and InferencePotential Caveats and Future Directions

Healthcare Utilization and Self-assessed Health inTurkey: 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



Objective

Identifying the causal impact of healthcare utilization onself-assessed health (SAH)Challenge: Selection into healthcare is not random!

Some unobservable factors may determine healthcareutilization and SAH simultaneously




Contribution

Prior related literature in Turkey is devoted to inequalities inhealthcare utilization and SAH in a univariate framework

Determinants of utilizationIgnores a possible endogenous relationship between utilizationand SAH

Attempts to identify causal links




SAH as an Ordinal OutcomeSAH as a Binary Outcome

Data and Sample

Individual-level data from the 2012 Health Survey (TurkStat)A nationally representative sample of 37,979 respondentsIndividuals are drawn from the population using a two-stagestratified cluster sampling method

External stratification criterion: urban/rural distinctionFirst stage: the sampling units of blocks were chosen fromclusters in which the average number of households is 100.Second stage: households are selected from each clusterSampling weights represent the inverse probability of beingselected into the sample

The final number of observations used in the analysis: 24,022





Outcome Variables

Two versions of SAH:

an ordinal scale from 1 to 5, 1 representing “very poor” healthand 5 representing “very good” healthas a binary variable taking the value of 1 for “suboptimal”health and 0 otherwise





Treatment Variable

Five types of healthcare utilization were assessed:

Preventive care: Encompasses measures taken to preventdiseases as opposed to treatmentGP care: Range of healthcare provided by general practitioner,first contact with the healthcare systemSpecialist care: Services provided by a specialist (e.g.oncology, cardiology, radiology etc..)Inpatient care: Healthcare services that require hospitaladmissionEmergency care: Healthcare for undifferentiated andunscheduled patients requiring immediate medical attention





Control Variables

chronic disease historyagegenderlocation (urban vs. rural)educational attainmentobesitythe type of health insuranceincomemarital statusconsumption of fruit / juice / alcohol / tobaccofrequency of physical exercise





Healthcare Utilization as an Endogenous Treatment

Our outcome of interest is the individual’s SAH, measured onan ordinal scale with 5 possible ordered outcomes:

Very poor (1)Poor (2)Average (3)Good (4)Very good (5)





A Recursive Bivariate Ordered Probit Model

Let Ti ∈ {0, 1} be the binary treatment variable that takes thevalue of one if the individual utilizes healthcare and zero if theindividual does not. The selection equation is:

Ti =

{10

if T ∗i = Ziγ + υi > 0

if T ∗i = Ziγ + υi ≤ 0

where Zi is the set of covariates and υi is the error termThe latent outcome variable Y ∗

i is defined as:Y ∗i = α+ Xiβ + δTi + εi

where εi is the idiosyncratic error term and Xi are the covariates





Error Distribution

Latent errors υi and εi should follow a bivariate joint normaldistribution (BiVN) with correlation ρIf ρ = 0, the first equation can be estimated by generalizedordered probitIf ρ 6= 0, then the unobservable determinants of health careselection are said to be correlated with the unobservabledeterminants of SAH, rendering health care utilizationendogenous. Individuals either:

observe health status and choose to resort to receive healthcarereceive healthcare and observe health status





A latent-factor approach

If the BiVN assumption is violated, the estimates will beinconsistent and biasedReformulate the error-generating process in the following way:

υi = λTηi + ςiεi = λY ηi + ιi

where λT and λY are the loading factors describing the dependence ofthe latent errors for the treatment and the outcome respectively and onlythe marginal distributions of ς and ι are assumed to be normal.

We simulate the distribution of η by taking random draws form itschosen distribution and assume that the loading factors follow agamma distribution





Instrument Choice

What moves around the covariate of interest that mightplausibly be viewed as random?The choice of instrument, Wi , for primary healthcare utilizationis the individual’s knowledge of their family physician

Individuals who are acquainted with their family physician aremore likely to utilize preventive and GP careThe physician knowledge has no direct, evident relation toone’s subjective health status





Average Treatment Effect (ATE)

The ATE shows the expected effect of healthcare utilization onsuboptimal SAH for a randomly drawn individual from thepopulation for a given level of j :

E [Yi (1)− Yi (0) | j ] = E [Yi (1) | T = 1, j ]− E [Yi (1) | T = 0, j ]





Average Treatment Effect on the Treated (ATT)

The ATT shows the expected effect of healthcare utilizationfor a randomly drawn individual only from those individualswho 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 ]





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





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[X

′

i β + δTi > εi

]where Xi is the set of covariates, Ti is healthcare utilization, εi is errorterm and 1 [.] is the indicator function taking the value of 1 if thestatement in the brackets is true and 0 otherwise.

Individuals can “treat” themselves by utilizing healthcare. Thetreatment equation is given by the following:

Ti = 1[Z

′

i γ1 + γ0Wi > υi

]where Zi is the set of covariates, Wi is the instrumental variable, and υiis the error term.





Effects of non-utilization variablesRecursive Bivariate Ordered Probit

Individuals suffering from chronic health problems and obesity aremore likely to use healthcare but also more likely to report apessimistic SAHMales tend to be more optimistic in their SAH, but also less likelyto use healthcareResidents in rural areas are more likely to report a pessimistic SAHand they are also less likely to utilize GP and emergency careIndividuals tend to be more optimistic about their SAH whenexercised at least thrice a weekRegular smokers report pessimistic SAH and tend to use morepreventive and emergency careIndividuals tend to report an increasingly pessimistic SAH as theygrow older





Effects of non-utilization variables, continuedRecursive Bivariate Ordered Probit

Senior individuals (75+) are more likely to utilize GP careSingles are less likely to utilize healthcareThose with an educational attainment below higher education tendto have pessimistic SAHNo conclusive and strong evidence as to the impact of education onhealthcare utilizationPublicly insured are more likely to use preventive and inpatienthealthcare servicesUninsureds (out-of-pocket) are less likely to use inpatient andemergency healthcare





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 6.1percentincreases the probability to report very good health by 7.8percent.

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 9.2percentincreases the probability to report very good health by 12.5percent.





Average Treatment Effect (ATE), continuedRecursive Bivariate Ordered Probit

Specialist care utilization;

increases the probability to report poor, average and goodhealth by 5.6 percent, 19.8 percent and 13.8 percentrespectivelydecreases the probability to report very good health by 40percent.

GP and Emergency care utilization has no causal impact on SAH





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 7.4percentincreases the probability to report good and very good healthby 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 10.3percentincreases the probability to report good and very good healthby 31.6 and 7 percent.





Effects of non-utilization variablesRecursive Bivariate Probit

Individuals suffering from chronic health problems and obesity aswell as regular alcohol and tobacco consumers are more likely toreport suboptimal SAH and more likely to utilize primary careResidents in rural areas are more likely to report suboptimal SAHand less likely to utilize primary careMales are less likely to report suboptimal SAH and less likely toutilize primary careIndividuals with a primary or no education are more likely to reportsuboptimal SAHThe likelihood of reporting suboptimal SAH increases at adecreasing rate with higher income bracketsIndividual’s knowledge of their family physician has a statisticallysignificant and positive impact on primary care utilization





Average Treatment EffectsRecursive Bivariate Probit

Preventive care utilization decreases the probability to reportsuboptimal SAH;

by 7.8 percent irrespective of utilization (ATE)for those who actually utilize preventive care by about 22percent (ATT)

GP care utilization has no causal impact on the probability toreport suboptimal SAH




Potential Caveats

Biases related to the use of a survey not originally designed tostudy healthcare utilizationGeneral concerns with the accuracy of self-assessment

Self-report biases

Lack of georeferenced dataLack of data on healthcare access




Future Directions

Alternative latent-factor structure specificationsAssessment of average marginal effects under exogeneityAdditional robustness checksHealthcare utilization among rural residents and males


Healthcare Utilization and Self-assessed Health in Turkey: Evidence from the 2012 Health Survey

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