Indian dermatology online journal
August 21, 2026 · via Europe PMC
Quackery in Dermatology - A Multidimensional Burden: Cross-sectional Evidence by an Exploratory Survey from a Dermatology Camp.
Samagani A, Pandey A, Damisetty R, +5 more, Swaroop MR.
AI summary — for doctors, generated from the abstract only
Among dermatology camp patients in Bengaluru who had used unqualified practitioners, three-quarters experienced adverse effects and two-thirds reported worsening.
What was studied
Cross-sectional questionnaire study of 81 patients with prior exposure to dermatological quackery at a Bengaluru dermatology camp (January 2026).
Key findings
- Mostly young, female, low-income and rural
- Influencers (100%) and unqualified 'specialists' (93.8%) were key sources
- Combination creams used by 66.7%; steroid creams confirmed in 13.6%, possible in 44.4%
- Adverse effects 76.5%; worsening 65.4%; delayed care >1 month 59.3%
Clinical relevance
This supports asking patients about prior non-medical treatments and advocating public awareness, though camp-based sampling limits generalisability.
Who should care
Limitations
Camp-based convenience sample and self-reported data.
Skin 'quackery' commonly harms patients and delays proper care.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
Background
Dermatological quackery is an increasing public health concern in India, driven by poor regulation, easy access to medications, and the growing influence of social media and unqualified practitioners.
Aim and objectives
To assess the patterns, outcomes, economic burden, awareness, and psychosocial impact of dermatological quackery among patients attending a dermatology camp.
Patients and methods
A cross-sectional study was conducted during a dermatology camp in Bengaluru, India (January 2026). Patients with prior exposure to unqualified practitioners were included. Data were collected using a structured, validated questionnaire. Descriptive statistics were computed, and associations were analyzed using Chi-square/Fisher's exact tests with odds ratios (OR) and 95% confidence intervals.
Results
Eighty-one patients were analyzed; most were young (55.6%), female (59.3%), from low-income households (79%), and from rural areas (60.5%). Common indications reported included skin lightening (96.3%), aesthetic concerns (91.3%), fungal infections (88.8%), and hair loss (83.9%) at various instances. Social media influencers (100%) and self-proclaimed skin/hair specialists without national medical commission-recognized postgraduate qualifications (93.8%) were major contributors. Topical treatments were used by 84.0% of participants; combination creams were used by 66.7%, while steroid-containing cream use was confirmed by 13.6% and considered possible by 44.4%. Adverse effects occurred in 76.5%, and 65.4% reported worsening. Most patients (90.1%) required dermatologist consultation, with delayed care (>1 month) in 59.3%. Side effects were strongly associated with worsening (OR 23.21, P < 0.001) and additional expenses (OR 15.50, P < 0.001). Awareness of legal aspects was low (27.2%), and reporting rates were poor (13.6%). Psychosocial effects included loss of confidence (66.7%) and emotional distress (16%).
Limitations
Camp-based sampling and self-reported data may limit generalizability.
Conclusions
Dermatological quackery leads to significant clinical harm, delayed care, and psychosocial burden, highlighting the need for stricter regulation and public awareness.
DOI
10.4103/idoj.idoj_468_26
PMID
42627937
PMCID
PMC13588435
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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