The impact of COVID-19 lockdowns on primary care contact among vulnerable populations in England: a controlled interrupted time-series study
Bibliographic record
Abstract
Background UK COVID-19 lockdowns significantly affected primary care access and delivery. Little is known about whether lockdowns disproportionally impacted vulnerable groups, including people who misuse substances, people who have experienced domestic violence or abuse, those with intellectual disability, and children with safeguarding concerns. Aim To evaluate the impact of UK COVID-19 lockdowns on primary care contact rates among vulnerable groups. Design & setting Natural experimental design using all registered patients in the OpenSAFELY platform. Method With approval from NHS England, we conducted controlled interrupted time-series analyses on records from 24 million patients in England between September 2019 and September 2021. Results Pre-pandemic, primary care contact rates were 110.1 per 1000 patients per week. Following the initiation of the first lockdown (23 March 2020), there was a large reduction of 29–61 contacts per 1000 patients per week among vulnerable and general population groups. For patients with alcohol misuse, those aged ≥14 years with intellectual disability, and children with safeguarding concerns, this reduction was significantly more extreme than corresponding general populations (relative rate difference -23.8 [95% confidence interval {CI} = -39.8 to -7.7, P = 0.003], -24.6 [95% CI = -38.8 to -10.5, P <0.001], and -15.4 [95% CI = -26.9 to -3.8, P = 0.009], respectively). Following the final lockdown (29 March 2021), all groups had contact rates exceeding pre-pandemic rates (with increases more marked in vulnerable populations), except those only including children. Conclusion Our results suggested a larger short-term impact of the first COVID-19 lockdown on primary care contact for some vulnerable groups, compared with the general population, and differential impacts persisted through subsequent lockdowns and beyond for some vulnerable groups. There is a need to examine drivers of these differences to enable more equitable primary care access and provision.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".