EFFECT OF COEXISTING DISEASES ON THE TREATMENT OF UNRELATED DISEASE NEEDS MORE STUDIES
Notice bibliographique
Résumé
To the Editor: Older people often have multiple chronic diseases1 and take many medications.2,3 Multiple coexisting diseases and multiple medications are associated with more adverse effects2 and poor adherence to taking medications.3 Nevertheless, how one disease affects treatment of another unrelated disease has rarely been studied. Using Medical Expenditure Panel Surveys, Dr. Harman et al. have shown that hypertension and diabetes mellitus, but not heart disease or arthritis, were associated with a greater likelihood of receiving adequate depression care in older people.4 The reason could be due to frequent contacts with primary care providers.4 In contrast to their findings, Dr. Redelmeier et al., using the Ontario Drug Benefit program data, have shown that elderly patients with one disease are undertreated for another unrelated disease.5 For example, a patient with pulmonary emphysema received less treatment for lipid-lowering agents than a patient without pulmonary emphysema, and patients with psychotic syndromes were consistently unlikely to receive lipid-lowering or medical arthritis treatments, but patients with breast cancer were just as likely to receive glaucoma treatment as patients without breast cancer.5 The free medications for older people in the drug program indicated that cost was not a factor. Several alternative explanations were explored.5 First, chronic diseases associated with shorter life expectancy make long-term preventive therapy, such as taking lipid-lowing agents, unwanted. Second, adding more medications increases the risk of potential drug interaction and adverse events. Third, time constraints, communication problems, patient's preferences, and priorities of the specialist may limit time to address more than one disease effectively. Fourth, elderly patients with chronic disease may be exhausted and reluctant to accept multiple interventions. Last, it is often sensible to postpone minor treatment until major diseases are resolved. It was recently found that adhering to disease-based clinical practice guidelines in older people with multiple coexisting diseases may have undesirable effects, including adverse drug effects, cost burden, and multiple medications.6 Disease-based clinical practice guidelines might not work well for elderly patients with multiple coexisting diseases.6,7 Some healthcare providers feel that a typical trial patient is not necessarily the typical patient in their practice and question the applicability of the guidelines based on the trials.8 Therefore, healthcare providers might not be willing to follow the guidelines that were based on the trials.8 This could be another factor for undertreatment of one or more diseases. The true answer for almost opposite findings from these two groups4,5 is still unknown. Alternatively, drug-related morbidity and mortality was estimated to cost $76.6 billion in the ambulatory setting alone in the United States.9 Undertreatment of one disease because of one unrelated or multiple coexisting diseases might not be a bad thing. We have to ask ourselves how many medications are enough for older people with multiple coexisting diseases.10 Studying the influence of one chronic condition on the treatment of another chronic condition in older people4,5 is important and complex. More research is warranted. I have no conflicts of interest and no sponsors and am the sole author of this letter.
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 | 0,011 | 0,077 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,004 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,010 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,002 |
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 ».