Shifting chronic disease management from hospitals to primary care in Estonian health system: analysis of national panel data
Bibliographic record
Abstract
BACKGROUND: Following independence from the Soviet Union in 1991, Estonia introduced a national insurance system, consolidated the number of health care providers, and introduced family medicine centred primary health care (PHC) to strengthen the health system. METHODS: Using routinely collected health billing records for 2005-2012, we examine health system utilisation for seven ambulatory care sensitive conditions (ACSCs) (asthma, chronic obstructive pulmonary disease [COPD], depression, Type 2 diabetes, heart failure, hypertension, and ischemic heart disease [IHD]), and by patient characteristics (gender, age, and number of co-morbidities). The data set contained 552 822 individuals. We use patient level data to test the significance of trends, and employ multivariate regression analysis to evaluate the probability of inpatient admission while controlling for patient characteristics, health system supply-side variables, and PHC use. FINDINGS: Over the study period, utilisation of PHC increased, whilst inpatient admissions fell. Service mix in PHC changed with increases in phone, email, nurse, and follow-up (vs initial) consultations. Healthcare utilisation for diabetes, depression, IHD and hypertension shifted to PHC, whilst for COPD, heart failure and asthma utilisation in outpatient and inpatient settings increased. Multivariate regression indicates higher probability of inpatient admission for males, older patient and especially those with multimorbidity, but protective effect for PHC, with significantly lower hospital admission for those utilising PHC services. INTERPRETATION: Our findings suggest health system reforms in Estonia have influenced the shift of ACSCs from secondary to primary care, with PHC having a protective effect in reducing hospital admissions.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".