Lupus and the Bottom Line: Why we Need to Talk About the Economic Impact
Notice bibliographique
Résumé
Systemic lupus erythematosus (SLE) results in premature morbidity in close to 10% of the patients within 5 years of diagnosis.[1] While morbidity and mortality are being evaluated closely by various groups, attempts to understand the impact of SLE at personal, societal, and financial levels are lagging, especially from an Indian perspective, where insurance coverage is poor and the majority of the expenditure is out of pocket. A recently published systematic review on the economic burden in lupus nephritis (LN) showed that in the 22 studies included in the analysis, LN was associated with substantially higher direct costs (e.g., total annual, hospitalization, and end-stage kidney disease-related direct costs), total indirect costs, and health-care resource utilization cost (e.g., hospitalization, outpatient services, and medication use) compared with patients without SLE or nonrenal SLE controls. However, the majority of the studies (n = 13) were from North America.[2] The problem in evaluating the costs involved does not end there. Even within the USA, the mean cost per 12 months differed by more than $6000 across studies. The difference was further exaggerated when the mean annual cost was compared between countries. While the mean cost per 12 months in Canada was $12,597[3] and $14,190 in Sweden,[4] it was $ 33,472 in the USA.[5] All these data indirectly reflect on the complexity in assessing economic burden and that the cost analysis needs to be specific for each geographic location. To add to the complexities, there are different terms often used by experts in health economics. To simplify the jargon in health economics, the economic burden can broadly be classified into direct, indirect, and intangible costs. Direct costs comprise all the expenditures undertaken for caring the patient. They are mainly viewed under two subgroups: Direct medical costs (DMCs) (medication, procedures, hospital charges, investigations, interventions, etc.) and direct nonmedical costs (travel expenses, food, and so on). Indirect costs are essentially the losses incurred by the patient and his family as a result of the illness and during the treatment. Indirect costs include the loss of employment, loss of productivity at the workplace, domestic responsibilities, and social and leisure activities.[6] Intangible costs relate to issues such as anxieties and the impact on quality of life due to the illness. These are generally difficult to measure and are often left out during the construction of the cost profile for a disease.[7] In a study published in the Indian Journal of Rheumatology, Sumeir et al.[8] have done a comprehensive cost analysis of Indian patients with SLE. They first determined the financial burden, from a patient perspective, both at the time of index admission (IA) and during follow-up visits for a period of 1 year. They also addressed an important aspect by identifying the proportion of patients, having a catastrophic health expenditure (CHE). The authors defined CHE as spending of ≥40% of declared household income in one admission. The authors analyzed the data of 73 patients with SLE who were admitted in the hospital between January 2019 and October 2020. The mean ± SD SLE Disease Activity Index score (SLEDAI) was 16 ± 8. More than half (59%) had high disease activity >12 and 41% had mild–moderate disease (SLEDAI ≤12). The most common major organ manifestation was observed in the renal (53%) domain, followed by neuropsychiatric (27%). Eight (11%) patients required intensive care unit (ICU) admission. The median duration of hospital stay for all patients was 13.6 days (interquartile range [IQR]: 10.5–17.5) and for those requiring ICU admission was 14.5 days (IQR: 9.7–17.7). There were 7 inhospital deaths, leaving 66 patients available for follow-up. All had a minimum follow-up of 6 months, and the majority (n = 36) had a follow-up of 1 year. For the IA, the median (IQR) cost of care was Rs. 135,768 (94,053–223,954) which was higher in severe (Rs. 167,362 [111,409–250,045]) than in the mildmoderate (Rs 102,983 [78,391–185,023]) disease category. This amount is roughly $1,900. Moreover, the authors found that the DMC means compromised 83% of the total costs, and investigations were the highest component of DMC 36%). The DMC was significantly higher (P = 0.02) in the severe group due to a higher proportion requiring ICU stay and longer hospital stay. Direct nonmedical costs constituted 10% of the total IA costs, of which travel was the largest (41%) component. The cost of outpatient care during follow-up among patients with severe disease was Rs. 43,428 (17,269–72,044) for a median of seven visits, which was comparable to the cost for patients with mild–moderate disease (Rs. 43,780 [24,954–83,536]). However, when further hospitalizations were required, the cost was higher in the severe disease group Rs. 26,949 (18,276–113,236) versus Rs. 15,692 (8364–60,840). All this resulted in an annualized cost of Rs. 245,579 (156,485–363,157) in the severe category and Rs 174,649 (119,093–299,029) in the mild–moderate category. One might argue that all these analyses were done on patients from the lower socioeconomic strata and may not be universally applicable. However, we must bear in mind that these costs only represent the cost of care and not the actual economic burden of the disease. I consider this an under estimation because, this was a retrospective study, which could lead to recall bias caregivers. In addition, the cost estimate does not take into account important factors such as replacement costs, loss of productivity at work, willingness to pay, and other relevant factors that determine the true economic burden. The authors estimated the impact of the disease on quality of life using the EQ5D-5 L and they found that those who had severe SLE had similar EQ5D-5 L scores as those with mild disease. When it comes to discussion on CHE, 86% of the patients had to spend >40% of their declared income during the IA. The proportion rose to 94% when the annualized costs were considered. It must also be noted that approximately 3.5% of hospitalization and 9.9% of all ambulatory care text are due to rheumatological diseases in India. There is a huge variation in the cost of care between private and governmental institutes. Over a quarter of families borrow or sell household assets to meet the hospitalization expenditure in India.[9] Moreover, rheumatology as a specialty is an emerging specialty in India. The lack of awareness about the field is compounded by a lack of an adequate number of qualified rheumatologists, specialist nurses, occupational therapists, physiotherapists, and counselors. The scarcity results in further increasing demand which drives cost and limits accessibility, thus entering a vicious cycle.[10] Given the complexity of cost estimation, it is clear that we as rheumatologists need serious help. Help from experts who understand these concepts better. Bringing out representative data for each rheumatic disease from across all social strata and geographical areas will be the first step in highlighting the economic burden of rheumatic diseases. This is urgently needed because insurance cover is slowly expanding to all sections of society with the central government PMJAY scheme.[11] Unfortunately, the reimbursement under this scheme and those offered by other insurance providers is inadequate and limited only to inhospital admissions. Most insurance agencies do not cover the cost of expensive biological agents which are essential for the patients. Patients with rheumatic diseases such as SLE have a high burden of disease and may require lifelong medications. In addition, programs such as social security and special public transport passes could greatly benefit these patients. However, to implement such programs, there is a need to accurately quantify the economic burden of these diseases, and this should be done at the earliest. Besides this, facilitating early diagnosis and early initiation of appropriate treatment by increasing awareness about these diseases among the general public and primary care physicians can result in early control of the disease. Utilizing telemedicine for follow-up care also reduces the direct costs for patients by decreasing the need for in-person consultations.[12] Sumeir et al. has finally exposed the “tip of the economic burden iceberg” in Indian patients with SLE. The onus is on us to generate more data that will convince both the government and private insurance providers to relook into the reimbursement policies for rheumatic diseases. Although there are significant advancements in the scientific front to aid patients with rheumatic diseases, more efforts are needed to address the economic and humanistic fronts.
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.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».