Sociodemographics and epidemiology of serious infections requiring hospitalization among adults with systemic lupus erythematosus and lupus nephritis, 2000 to 2006
Bibliographic record
Abstract
Serious infections are among the leading causes of hospitalization, morbidity, and mortality in systemic lupus erythematosus (SLE) patients. Patients with lupus nephritis (LN) may be especially vulnerable. We investigated the sociodemographics and epidemiology of serious infections requiring hospitalization in a nationwide cohort of SLE and LN patients enrolled in Medicaid, the US federal-state insurance for low-income individuals. We used the Medicaid Analytic eXtract (MAX) data system, with billing claims and demographics for >24 million Medicaid enrollees from 47 states and Washington, DC, 2000 to 2006. We identified patients aged 18 to 65 years with prevalent SLE (≥3 visits ≥30 days apart with ICD-9 codes of 710.0) and prevalent LN (≥ICD-9 codes for nephritis, proteinuria and/or renal failure on or after SLE diagnosis, ≥30 days apart). We defined serious bacterial, viral, fungal and mycobacterial infections resulting in hospitalization using a validated administrative database method. We stratified infection prevalence in SLE and LN by subtype, sociodemographic factors (age, sex, race/ethnicity, region, socioeconomic status (SES)), and by a validated SLE comorbidity index. We used Poisson regression to calculate incidence rates (cases/person-years) of first and overall infection, stratified by age, sex and race/ethnicity. We identified 43,274 patients with SLE and 8,096 with LN. Mean age was 38 (SD 12) for SLE and 34 (SD 12) for LN. In the SLE cohort, 93% were female, 38% were Black, 37% White and 15% Hispanic; and in the LN cohort, 89% were female, 48% were Black, 23% White, and 17% Hispanic. We identified 17,055 episodes of serious infections requiring hospitalization in 7,823 SLE patients (28%) and 7,486 episodes in 3,035 LN patients (38%). Among SLE patients, the highest percentages of infections occurred in 35 to 50 year olds, in females, African Americans, in the South and in the lowest SES group (Table 1 ). The incidence rates of serious infection per 100 person-years were 15.4 for SLE and 34.5 for LN. In both cohorts, the majority (98%) of infections were bacterial - pneumonia and bacteremia; the most common viral infections were herpes zoster and influenza. In this diverse, nationwide cohort of SLE and LN patients, we observed a significant burden of serious infections requiring hospitalization, most pronounced in LN patients. Further research is necessary to examine risk factors, particularly medication use, by sociodemographic groups.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".