An Empirical Investigation of Unmet Health Care, Health Care Utilization and Health Outcomes.
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».