[ad_1]
RESULTS
Data was available for 2,059,453 patients across 110 practices in England and Wales, utilising the Read 2 classification.
Mean valid ethnicity recording was 48.97% (n =1,008,667) across the sample population. Of those identified with a valid ethnicity recording, 96.14% (n = 969,740) were recorded to have a definite code. 3.71% (n = 37,443) and 0.15% (n = 1,487) had possible and probable codes, respectively.
Just under 1% (0.97%) were noted to have multiple ethnicities recorded over time. Of those with multiple ethnicity codes, 35.26% had a clearly identifiable majority ethnic group recorded and were classified accordingly. Of those who had no clear determined majority ethnicity code recorded, 82.83% were classified according to a valid single most recent ethnicity code within the dataset. 0.11% of the population had an underterminable ethnicity coded and were thus excluded from the dataset. Of the patient population, 52.09% had no definitive ethnicity code mapped and were excluded from the analysis. Figure 2 describes the logical data model used for ethnicity classification.
The median ethnicity identification rate across the dataset was 55.20% (inter quartile range: 35.6%). Considerable variations were noted between practices, with minimum and maximum ethnicity identification rates being 1.5% and 91.6%, respectively (Figure 3). Six practices were seen to have ethnicity identification proportions greater than 80%, two of which had a greater than 90% recording.Ten practices had less than 10% of the patient population coded with a recognisable ethnicity identifier.
Geographical variations exist within the dataset. Ethnicity identification was greatest in London-based practices (n = 30), mean 58.59%, with the identification lowest in those practices based in the South of the country (n = 25), mean 39.14%. Ethnicity identification was highest in practices with the highest proportions of non-white people; a 0.16% increase in the total ethnicity identified per percent increase in the proportion of non-white people. However, the correlation was poor (R2= 0.016).
Ethnicity identification was highest in children aged between 5 and 9 years (n = 64,432/99,232, 64.93%). In the adult population, ethnicity recording was consistent between age bands, with higher recording levels in young adults, 54.67% (n = 97,587/178,493) for 25–29 years and 53.53% (n = 106,335/198,654) for 30–34 years. For those greater than 40 years, ethnicity recording was 46.51% (n = 463,203/996,013), and for those greater than 65 years, this was 46.33% (n =175,286/378,323). Ethnicity recording in females was greater than that observed in males, 50.81% (n = 537,293/1,057,559) and 47.05% (n = 471,374/1,001,874) respectively.
The QOF highlights the use of the ‘9i…’ coding hierarchy for ethnicity within primary care datases. Utilising iterated proxy markers for ethnicity, inclusive of language spoken and read, and requirement for an interpreter, identification was increased across each respective ethnic group (Figure 4), compared to using the QOF codes. Across the whole database, the detection of white ethnicity increased from 30.61% (CI:30.55%–30.67%) to 40.24% (CI:40.17%–40.30%); for black, from 1.65% (CI:1.63%–1.67%) to 2.29% (CI:2.27%–2.32%); for Asian, from 3.08% (CI:3.06%–3.11%) to 4.11% (CI:4.08%–4.14%); for those of a mixed ethnicity, from 0.89% (CI:0.88%–0.90%) to 1.01% (CI: 0.99%–1.02%); and those from other ethnic groups from 0.04% (CI:0.03%–0.04%) to 0.92% (0.91%–0.93%).
[ad_2]
Source link
