An evaluation of the adequacy of Indian national and state Essential Medicines Lists (EMLs) for palliative care medical needs - a comparative analysis
Bibliographic record
Abstract
Abstract Objectives Essential Medicines Lists (EMLs) guide the public sector procurement and supply of medications to impact access to adequate and appropriate palliative care drugs. This study evaluates the adequacy of India’s national and sub-national EMLs that can directly impact palliative care for 5.4 million patients. Methods In this qualitative document review, we compared Indian national, and state EMLs acquired from official government websites with the International Association for Hospice & Palliative Care (IAHPC) EML recommendations. We analysed data on the indication and formulation of drugs under the different categories of formulations present (all, some, and no), and drugs absent. Literature review and inputs from palliative care experts provided alternatives of absent medications to assess the adequacy of lists in managing the symptoms listed by IAPHC. Results We analysed 3 national and 25 state lists for 33 recommended drugs. The Central Government Health Services list had the maximum availability of all formulations of drugs (16 [48%]) nationally. Among states and union territories, the Delhi EML was the closest to IAHPC with 17 (52%) drugs with all formulations present. Nagaland had the most incomplete EML with only 3 (9%) drugs with all formulations present. No EML had all the recommended formulations of morphine. In one national and sixteen state EMLs, oral morphine was absent. Conclusion While Indian EMLs lack drugs for palliative care when compared with the IAHPC EML, symptom management is adequate. There is a need for countries with limited resources to modify the IAPHC list for their settings. What is already known on this topic Essential Medicines Lists (EMLs) are instrumental in guiding public sector procurement of drugs. The implementation of EMLs is known to improve drug availability and prescription practices. The rising burden of people requiring end-of-life care globally necessitates the availability of appropriate drugs for the medical management of symptoms, which can be achieved through their inclusion in local EMLs. What this study adds The national and sub-national EMLs of India do not fully adhere to the International Association for Hospice and Palliative Care (IAHPC) recommendations. However, they contain adequate drugs for the management of the listed symptoms. Additionally, the inclusion of various formulations of morphine remains a challenge to be addressed. How this study might affect research, practice or policy This study highlights the need to develop a fit-for-purpose EML for palliative care, taking into account the geographical variations in palliative care needs, and resource constraints in healthcare delivery at the state and country level.
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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 | 0.047 | 0.125 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 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".