Multimorbidity and Out-of-pocket Expenditure on Medicines: A Systematic Review
Bibliographic record
Abstract
Background Multimorbidity, the presence of two or more non-communicable diseases (NCDs), is a costly and complex challenge for health systems globally. Patients with NCDs incur high levels of out-of-pocket expenditure (OOPE), often on medicines, but the literature on the association between OOPE on medicines and multimorbidity has not been examined systematically. Objective To examine out-of-pocket expenditure on medicines for patients with multimorbidity. Design Systematic Review Data sources Ovid Medline, Embase, EconLit, the Cochrane Library, and the WHO Global Health Library. Eligibility criteria for selecting articles We included primary original articles in English published from the year 2000 to 31 Dec 2016, without any restriction on populations and settings. We defined OOPE as spending on medicines that was not reimbursed. Articles must compare OOPE for medicines for different numbers of multimorbidities, such as reporting OOPE on medicines for zero, one, two, and three or more NCDs. Study quality was assessed using Newcastle-Ottawa Scale. Results 14 articles met inclusion criteria. Findings indicated that multimorbidity was associated with higher OOPE on medicines. When number of NCDs increased from zero to one, two, and ≥3, annual OOPE on medicines increased by an average of 2.7 times, 5.2 times, and 10.1 times, respectively. When number of NCDs increased from zero, one, two, ≥2, and ≥3, individuals spent a median of 0.36%, 1.15%, 1.41%, 2.42%, 2.63%, of mean annual household net adjusted disposable income per capita, respectively, on annual OOPE on medicines. More multimorbidities was associated with higher OOPE on medicines as a proportion of total healthcare expenditures by patients. Some evidence suggested that the elderly and low-income groups were most vulnerable to higher OOPE on medicines. Non-adherence to medicines was a coping strategy for OOPE on medicines. Key messages: NCD multimorbidity is associated with higher OOPE spending for medication Concerted efforts needed to improve health service coverage and financial protection for patients with multimorbidities, particularly for vulnerable groups such as the elderly.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".