Exploring the clinical profiles and management of juvenile dermatomyositis in Africa: a survey of African rheumatology care providers
Bibliographic record
Abstract
BACKGROUND: There are limited studies of juvenile dermatomyositis (JDM) in low and middle-income countries (LMIC). Many demonstrate delays to care, high prevalence of severe manifestations, and high mortality. Given the disease-associated damage with JDM, understanding JDM in Africa further is critical. Our objectives are to understand the burden of JDM in Africa and provider access to diagnostic tools and therapy through survey methodology. METHODS: A survey (available in English and French) was distributed via WhatsApp to 363 total members of the African League of Associations for Rheumatology (AFLAR; n = 233) and Paediatric Society of the African League Against Rheumatism (PAFLAR; n = 130) from November 2022-January 2023. Topics included respondent specialty, number of JDM patients followed, severe manifestations, and available diagnostic tools and medications (with and without considering cost). RESULTS: Forty-three (12%) of the 363 providers who received the survey started it. Among the 43 who started the survey, 37 (86%) provided consent and manage JDM patients; of these 37 providers, 4 (11%) partially and 16 (43%) fully completed the survey. Most were adult and/or pediatric rheumatologists (n = 19; 95%). Respondents represented all 5 African regions and described 216 children with JDM within the last 10 years. There was high prevalence of calcinosis (as high as 100%) and interstitial lung disease (ILD) (as high as 32%); mortality rates in Kenya (6/42; 14%) and Zambia (2/7; 29%) exceeded the 1-3% mortality reported in studies of high-income countries. Thirteen of 27 diagnostic tools and medications were accessible to ≤ 50% of respondents after considering cost, mostly in Northern or Southern Africa (9/13; 69%). Despite being cost-free, disease assessment tools and physical exam to assess calcinosis were not reported as universally available or accessible. CONCLUSIONS: This is the first study to explore experiences of providers caring for children with JDM in Africa. Respondents identified 216 children with JDM seen within the last 10 years, exceeding the 196 children with JDM reported within the last 25 years but likely still underestimating prevalence. Our findings align with reports of severe manifestations and poor outcomes in African children with JDM. Access to many diagnostics and medications is limited, and differences in accessibility parallel regional healthcare disparities. The potential differences in JDM severity warrant systematic study and highlight the need to include patients and providers from LMIC in collaborative research efforts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".