An Empirical Investigation of Unmet Health Care, Health Care Utilization and Health Outcomes.
Bibliographic record
Abstract
This thesis is comprised of three chapters that empirically examine two important areas in health economics: access to health care and health outcomes. The first chapter explores the impact of health care utilization on unmet health care needs (UHC) using four biennial confidential master files (2001-2010) of the Canadian Community Health Survey and applying an instrumental variables (IV) approach to deal with the endogeneity of health care utilization. The presence of drug insurance and the number of physicians in each health region are used to identify the causal effect. I find a clear and robustly negative relationship between health care use and unmet health care needs; individuals who are more likely to report unmet health care needs are those who use the health care system less frequently. One more visit to a family doctor, specialist or a medical doctor on average, decreases the probability of having unmet health care needs by 7.1, 4.6 and 2.8 percentage points, respectively. Further analysis by sub groups reveals that the impact of health care utilization on UHC is larger for females in comparison to males, rural residents in comparison to urban dwellers and those with low household income rather than high. The second chapter of this thesis examines whether the presence of the unmet health-care (UHC) needs has an adverse effect on health outcomes using the National Population Health Survey, a nationally representative longitudinal data set spanning 18 years. I pay close attention to the potential endogeneity of this problem. Five direct and indirect measures of health-related outcomes are examined. I find clear and robust evidence that the presence of UHC either two-years previously or anytime in the past, affects negatively the current health of the individual – controlling for a host of other influences. For instance, reporting UHC in the previous cycle reduces the probability of being in excellent or very good health and in good mental health, respectively by 8.1 and 1.2 percentage points; it reduces the HUI3 score by 2.9 percentage points and increases the expected number of medications used by 11%. Further analysis by looking at the effect of UHC when it was due to accessibility reasons, reveal that the effect of UHC because of accessibility reasons on health outcomes is larger than the one of the overall UHC, but the difference is small in general. Finally, the third chapter of this thesis examines the link between social networks and access to health care utilization, focusing particularly on the probability of having a regular family doctor. Unlike previous work that uses cross sectional data, I use panel data from the National Population Health survey to control for unobserved heterogeneity. Access to a regular family doctor is modeled using the dynamic random effects probit model, which makes it possible to explore the dynamics of access to a regular family doctor– for instance, the role played by past access status to a family doctor in predicting current access. In particular, I use the dynamic random effects probit model that controls for both unobserved heterogeneity and for initial conditions effects. I find robust evidence of a highly statistically significant relationship between social capital and the probability of having a regular family doctor. Although the marginal effects are modest, the results from all model specifications show that there is clear evidence that individuals with high levels of tangible, affection, emotional, social interaction, who live with spouse only or with spouse and children are more likely to have a regular family doctor, whereas those living alone are less likely to have a regular family doctor. The results also reveal that past access to a family doctor is an important determinant for both current and future access. The predicted probability of having a regular family doctor is about 18 percentage points (or 20%) higher for individuals who had a family doctor in the previous period, relative to those who did not. In addition, I find that unobserved heterogeneity accounts for about 25% of the variation in accessing a regular family doctor and is significantly correlated with the access to a family doctor over my long panel.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".