describes the prevalence and demographics of psoriasis patients within the THIN database, and compares them to the THIN population as a whole. Prevalence rates are shown overall, by decade, and by sex. Overall prevalence was found to be 1.9% (2.2% in adults 20 years or older) and did not differ substantially by sex. The prevalence is low in the pediatric population, increases sharply in young adulthood, gradually increases until late adulthood and then decreases in older individuals. In a sensitivity analysis, patients who had never received a medical code were removed from the calculation (534,875 patients). Prevalence rates increased only slightly, with overall prevalence becoming 2.0%. The frequency of various subtypes of psoriasis based on Read Codes is shown in Table 5
(available online). Psoriasis unspecified (55.5%) or NOS (58.7%) was most common (note patients could receive more than one code and therefore the sum is greater than 100%). Variants of psoriasis were coded less frequently and include guttate (7.4%), scalp (6.8%), pustular (2.8%) palmer or plantar (0.8% and 0.5% respectively) and erythrodermic (0.3%).
Prevalence and epidemiology of psoriasis in the THIN database
At the close of the survey collection period we had received 4,634 of the 4,900 surveys yielding a response rate of 95%. One practice (39 surveys, 1% of the sample) was removed from the survey-based analysis based on a high proportion (52%) of internally inconsistent surveys. The demographics of the sampled patients and survey respondents were nearly identical. Amongst those with returned surveys, 51% (n= 2361) of patients were male and the median age was 55 years (IQR: 49.9–60.9).
To validate the electronic psoriasis Read Codes, the questionnaire asked GPs to confirm or refute the psoriasis diagnosis. The diagnosis of psoriasis was confirmed in 90% (n = 4543, 95% CI 89–91%) of patients who met the criteria for entry into our cohort. compares patients with confirmed and refuted diagnoses, and describes the best models for predicting an accurate psoriasis diagnosis. Patient demographics did not differ significantly between the confirmed and refuted groups. The model producing the highest AUC was the number of electronic psoriasis diagnostic codes (AUC=0.75). On average, patients with a confirmed diagnosis had 6 psoriasis codes while those with a refuted diagnosis had only 2. Patients whose diagnosis was confirmed also received significantly more treatments specific for psoriasis (8 vs 2); however, incorporating psoriasis treatments did not improve the AUC or PPV of the models.
2A: Comparison of patients in our cohort whose psoriasis diagnosis was confirmed vs. refuted by general practitioner questionnaire. Models for discriminating between these groups of patients are shown.
shows descriptive statistics of the best model at several cut points along the ROC. One or more electronic psoriasis codes yields a PPV of 90%. Requiring more than one code increases the PPV to 95% but will exclude 26% of patients with a valid diagnosis.
If the GP confirmed the psoriasis diagnosis s/he was further asked to identify the percent of the patient’s body surface area the psoriasis typically involves. In our cohort, 12% (n = 478, 95%CI = 11–13%) had extensive disease covering more than 10% of their body. compares the extensive and non-extensive psoriasis patients. Patients with extensive disease were slightly younger and more likely to be male.
Table 3 3A: Comparison of patients in our cohort whose psoriasis involves extensive amounts of their body (>10% Body Surface Area) vs those without extensive disease as defined by GP questionnaire. Models for discriminating between these groups of patients (more ...)
We developed models to determine if electronic codes could reliably identify patients with extensive disease. Though the AUCs of the models presented were similar, the model producing the highest AUC was the number of electronic psoriasis codes (treatment or diagnostic) the patient received per year of psoriasis since their prospective data collection began in THIN (marked with a ‘**’ in and shown in ). On average, patients with extensive disease received 16 codes per year while those with less involved disease received only 3.
ROC curve for prediction of extensive psoriasis based on number of psoriasis diagnostic or treatment codes per year with psoriasis in THIN
shows attributes of the model at several cut points. Even with the strictest test criteria we were only able to achieve a PPV of 45%. Though this cut point correctly classified 87% of patients, the sensitivity was only 18.9%. Less strict cut points were able to achieve a substantially higher sensitivity at the expense of a lower PPV.
Models were also developed to determine if disease severity (as defined by skin disease that would require a systemic agent to achieve clearance per the GP’s opinion) could be predicted from the database. Results were similar to the results for extensive disease and are not shown.
If the GP confirmed the psoriasis diagnosis s/he was asked if the diagnosis was also corroborated by a dermatologist. In our cohort, 46% (n = 1,816, 95%CI 44–47%) of psoriasis patients had their diagnosis corroborated. We tested whether a dermatology NHS code in the database could be used as a surrogate marker to indicate that a dermatologist confirmed the psoriasis diagnosis. Results are shown in . More than three-quarters (77%) of psoriasis patients with a dermatology NHS code had their diagnosis corroborated, and 75% of those without a dermatology NHS code did not have their diagnosis corroborated. If we further require the NHS code to be entered in conjunction with a psoriasis code the PPV increases to 91%, but only a third of patients with a dermatologist confirmation are captured.
Dermatology consultation validation models (n = 3973)
Finally, GPs were queried regarding the number of years the patient has had psoriasis. This response was compared to the number of years since their first psoriasis code in the database. When the duration of disease was broken into 10-year increments the linearly weighted Cohen’s kappa was 0.69 (95%CI 0.67–0.71, 0.62 without weighting). The physician survey and database date matched exactly in 76.5% of cases.