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Record W4378904070 · doi:10.4103/injr.injr_71_23

Lupus and the Bottom Line: Why we Need to Talk About the Economic Impact

2023· article· en· W4378904070 on OpenAlexaboutno aff
Chengappa Kavadichanda

Bibliographic record

VenueIndian Journal of Rheumatology · 2023
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLupus nephritisIndirect costsSystemic lupus erythematosusTotal costDemographyHealth economicsEconomic impact analysisHealth careDiseaseDisease burdenEnvironmental healthIntensive care medicinePublic healthInternal medicineEconomic growthPathology

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.951
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.315
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

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".

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Citations1
Published2023
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