Background: Long COVID is the patient-coined term for the persistent symptoms of COVID-19 illness for weeks, months or years following the acute infection. There is a large burden of long COVID globally from self-reported data, but the epidemiology, causes and treatments remain poorly understood. Primary care is used to help identify and treat patients with long COVID and therefore Electronic Health Records (EHRs) of past COVID-19 patients could be used to help fill these knowledge gaps. We aimed to describe the incidence and differences in demographic and clinical characteristics in recorded long COVID in primary care records in England. Methods: With the approval of NHS England we used routine clinical data from over 19 million adults in England linked to SARS-COV-2 test result, hospitalisation and vaccination data to describe trends in the recording of 16 clinical codes related to long COVID between November 2020 and January 2023. Using OpenSAFELY, we calculated rates per 100,000 person-years and plotted how these changed over time. We compared crude and adjusted (for age, sex, 9 NHS regions of England, and the dominant variant circulating) rates of recorded long COVID in patient records between different key demographic and vaccination characteristics using negative binomial models. Findings: We identified a total of 55,465 people recorded to have long COVID over the study period, which included 20,025 diagnoses codes and 35,440 codes for further assessment. The incidence of new long COVID records increased steadily over 2021, and declined over 2022. The overall rate per 100,000 person-years was 177.5 cases in women (95% CI: 175.5-179) and 100.5 in men (99.5-102). The majority of those with a long COVID record did not have a recorded positive SARS-COV-2 test 12 or more weeks before the long COVID record. Interpretation: In this descriptive study, EHR recorded long COVID was very low between 2020 and 2023, and incident records of long COVID declined over 2022. Using EHR diagnostic or referral codes unfortunately has major limitations in identifying and ascertaining true cases and timing of long COVID. Funding: This research was supported by the National Institute for Health and Care Research (NIHR) (OpenPROMPT: COV-LT2-0073).
Background: Long COVID is a major problem affecting patient health, the health service, and the workforce. To optimise the design of future interventions against COVID-19, and to better plan and allocate health resources, it is critical to quantify the health and economic burden of this novel condition. We aimed to evaluate and estimate the differences in health impacts of long COVID across sociodemographic categories and quantify this in Quality-Adjusted Life-Years (QALYs), widely used measures across health systems. Methods: With the approval of NHS England, we utilised OpenPROMPT, a UK cohort study measuring the impact of long COVID on health-related quality-of-life (HRQoL). OpenPROMPT invited responses to Patient Reported Outcome Measures (PROMs) using a smartphone application and recruited between November 2022 and October 2023. We used the validated EuroQol EQ-5D questionnaire with the UK Value Set to develop disutility scores (1-utility) for respondents with and without Long COVID using linear mixed models, and we calculated subsequent Quality-Adjusted Life-Months (QALMs) for long COVID. Findings: The total OpenPROMPT cohort consisted of 7575 individuals who consented to data collection, with which we used data from 6070 participants who completed a baseline research questionnaire where 24.6% self-reported long COVID. In multivariable regressions, long COVID had a consistent impact on HRQoL, showing a higher likelihood or odds of reporting loss in quality-of-life (Odds Ratio (OR): 4.7, 95% CI: 3.72-5.93) compared with people who did not report long COVID. Reporting a disability was the largest predictor of losses of HRQoL (OR: 17.7, 95% CI: 10.37-30.33) across survey responses. Self-reported long COVID was associated with an 0.37 QALM loss. Interpretation: We found substantial impacts on quality-of-life due to long COVID, representing a major burden on patients and the health service. We highlight the need for continued support and research for long COVID, as HRQoL scores compared unfavourably to patients with conditions such as multiple sclerosis, heart failure, and renal disease. Funding: This research was supported by the National Institute for Health and Care Research (NIHR) (OpenPROMPT: COV-LT2-0073).
AIMS: The COVID-19 pandemic created unprecedented pressure on healthcare services. This study investigates whether disease-modifying antirheumatic drug (DMARD) safety monitoring was affected during the COVID-19 pandemic. METHODS: A population-based cohort study was conducted using the OpenSAFELY platform to access electronic health record data from 24.2 million patients registered at general practices using TPP's SystmOne software. Patients were included for further analysis if prescribed azathioprine, leflunomide or methotrexate between November 2019 and July 2022. Outcomes were assessed as monthly trends and variation between various sociodemographic and clinical groups for adherence with standard safety monitoring recommendations. RESULTS: An acute increase in the rate of missed monitoring occurred across the study population (+12.4 percentage points) when lockdown measures were implemented in March 2020. This increase was more pronounced for some patient groups (70-79 year-olds: +13.7 percentage points; females: +12.8 percentage points), regions (North West: +17.0 percentage points), medications (leflunomide: +20.7 percentage points) and monitoring tests (blood pressure: +24.5 percentage points). Missed monitoring rates decreased substantially for all groups by July 2022. Consistent differences were observed in overall missed monitoring rates between several groups throughout the study. CONCLUSION: DMARD monitoring rates temporarily deteriorated during the COVID-19 pandemic. Deterioration coincided with the onset of lockdown measures, with monitoring rates recovering rapidly as lockdown measures were eased. Differences observed in monitoring rates between medications, tests, regions and patient groups highlight opportunities to tackle potential inequalities in the provision or uptake of monitoring services. Further research should evaluate the causes of the differences identified between groups.
Background: Timely evidence of the comparative effectiveness between COVID-19 therapies in real-world settings is needed to inform clinical care. This study aimed to compare the effectiveness of nirmatrelvir/ritonavir versus sotrovimab and molnupiravir in preventing severe COVID-19 outcomes in non-hospitalised high-risk COVID-19 adult patients during Omicron waves. Methods: With the approval of NHS England, we conducted a real-world cohort study using the OpenSAFELY-TPP platform. Patient-level primary care data were obtained from 24 million people in England and were securely linked with data on COVID-19 infection and therapeutics, hospital admission, and death, covering a period where both nirmatrelvir/ritonavir and sotrovimab were first-line treatment options in community settings (February 10, 2022-November 27, 2022). Molnupiravir (third-line option) was used as an exploratory comparator to nirmatrelvir/ritonavir, both of which were antivirals. Cox proportional hazards model stratified by area was used to compare the risk of 28-day COVID-19 related hospitalisation/death across treatment groups. Findings: A total of 9026 eligible patients treated with nirmatrelvir/ritonavir (n = 5704) and sotrovimab (n = 3322) were included in the main analysis. The mean age was 52.7 (SD = 14.9) years and 93% (8436/9026) had three or more COVID-19 vaccinations. Within 28 days after treatment initiation, 55/9026 (0.61%) COVID-19 related hospitalisations/deaths were observed (34/5704 [0.60%] treated with nirmatrelvir/ritonavir and 21/3322 [0.63%] with sotrovimab). After adjusting for demographics, high-risk cohort categories, vaccination status, calendar time, body mass index and other comorbidities, we observed no significant difference in outcome risk between nirmatrelvir/ritonavir and sotrovimab users (HR = 0.89, 95% CI: 0.48-1.63; P = 0.698). Results from propensity score weighted model also showed non-significant difference between treatment groups (HR = 0.82, 95% CI: 0.45-1.52; P = 0.535). The exploratory analysis comparing nirmatrelvir/ritonavir users with 1041 molnupiravir users (13/1041 [1.25%] COVID-19 related hospitalisations/deaths) showed an association in favour of nirmatrelvir/ritonavir (HR = 0.45, 95% CI: 0.22-0.94; P = 0.033). Interpretation: In routine care of non-hospitalised high-risk adult patients with COVID-19 in England, no substantial difference in the risk of severe COVID-19 outcomes was observed between those who received nirmatrelvir/ritonavir and sotrovimab between February and November 2022, when Omicron subvariants BA.2, BA.5, or BQ.1 were dominant. Funding: UK Research and Innovation, Wellcome Trust, UK Medical Research Council, National Institute for Health and Care Research, and Health Data Research UK.
BACKGROUND: People who live alone experience greater levels of mental illness; however, it is unclear whether the COVID-19 pandemic had a disproportionately negative impact on this demographic. OBJECTIVE: To describe the mental health gap between those who live alone and with others in the UK prior to and during the COVID-19 pandemic. METHODS: Self-reported psychological distress and life satisfaction in 10 prospective longitudinal population surveys (LPSs) assessed in the nearest pre-pandemic sweep and three periods during the pandemic. Recorded diagnosis of common and severe mental illnesses between March 2018 and January 2022 in electronic healthcare records (EHRs) within the OpenSAFELY-TPP. FINDINGS: In 37 544 LPS participants, pooled models showed greater psychological distress (standardised mean difference (SMD): 0.09 (95% CI: 0.04; 0.14); relative risk: 1.25 (95% CI: 1.12; 1.39)) and lower life satisfaction (SMD: -0.22 (95% CI: -0.30; -0.15)) for those living alone pre-pandemic. This gap did not change during the pandemic. In the EHR analysis of c.16 million records, mental health conditions were more common in those who lived alone (eg, depression 26 (95% CI: 18 to 33) and severe mental illness 58 (95% CI: 54 to 62) more cases more per 100 000). For common mental health disorders, the gap in recorded cases in EHRs narrowed during the pandemic. CONCLUSIONS: People living alone have poorer mental health and lower life satisfaction. During the pandemic, this gap in self-reported distress remained; however, there was a narrowing of the gap in service use. CLINICAL IMPLICATIONS: Greater mental health need and potentially greater barriers to mental healthcare access for those who live alone need to be considered in healthcare planning.
COVID-19 , Humans , COVID-19/epidemiology , Mental Health , Pandemics , Electronic Health Records , Home Environment , Prospective Studies , United Kingdom/epidemiology
Background: The COVID-19 pandemic disrupted healthcare and may have impacted ethnic inequalities in healthcare. We aimed to describe the impact of pandemic-related disruption on ethnic differences in clinical monitoring and hospital admissions for non-COVID conditions in England. Methods: In this population-based, observational cohort study we used primary care electronic health record data with linkage to hospital episode statistics data and mortality data within OpenSAFELY, a data analytics platform created, with approval of NHS England, to address urgent COVID-19 research questions. We included adults aged 18 years and over registered with a TPP practice between March 1, 2018, and April 30, 2022. We excluded those with missing age, sex, geographic region, or Index of Multiple Deprivation. We grouped ethnicity (exposure), into five categories: White, Asian, Black, Other, and Mixed. We used interrupted time-series regression to estimate ethnic differences in clinical monitoring frequency (blood pressure and Hba1c measurements, chronic obstructive pulmonary disease and asthma annual reviews) before and after March 23, 2020. We used multivariable Cox regression to quantify ethnic differences in hospitalisations related to diabetes, cardiovascular disease, respiratory disease, and mental health before and after March 23, 2020. Findings: Of 33,510,937 registered with a GP as of 1st January 2020, 19,064,019 were adults, alive and registered for at least 3 months, 3,010,751 met the exclusion criteria and 1,122,912 were missing ethnicity. This resulted in 14,930,356 adults with known ethnicity (92% of sample): 86.6% were White, 7.3% Asian, 2.6% Black, 1.4% Mixed ethnicity, and 2.2% Other ethnicities. Clinical monitoring did not return to pre-pandemic levels for any ethnic group. Ethnic differences were apparent pre-pandemic, except for diabetes monitoring, and remained unchanged, except for blood pressure monitoring in those with mental health conditions where differences narrowed during the pandemic. For those of Black ethnicity, there were seven additional admissions for diabetic ketoacidosis per month during the pandemic, and relative ethnic differences narrowed during the pandemic compared to the White ethnic group (Pre-pandemic hazard ratio (HR): 0.50, 95% confidence interval (CI) 0.41, 0.60, Pandemic HR: 0.75, 95% CI: 0.65, 0.87). There was increased admissions for heart failure during the pandemic for all ethnic groups, though highest in those of White ethnicity (heart failure risk difference: 5.4). Relatively, ethnic differences narrowed for heart failure admission in those of Asian (Pre-pandemic HR 1.56, 95% CI 1.49, 1.64, Pandemic HR 1.24, 95% CI 1.19, 1.29) and Black ethnicity (Pre-pandemic HR 1.41, 95% CI: 1.30, 1.53, Pandemic HR: 1.16, 95% CI 1.09, 1.25) compared with White ethnicity. For other outcomes the pandemic had minimal impact on ethnic differences. Interpretation: Our study suggests that ethnic differences in clinical monitoring and hospitalisations remained largely unchanged during the pandemic for most conditions. Key exceptions were hospitalisations for diabetic ketoacidosis and heart failure, which warrant further investigation to understand the causes. Funding: LSHTM COVID-19 Response Grant (DONAT15912).
OBJECTIVES: This study aimed to measure (1) the proportion of children who continue to receive specialist care (rheumatology/ophthalmology) as adults, (2) the characteristics associated with continuing specialist care, and (3) the frequency of specialist care appointments in both paediatric and adult services. METHODS: A retrospective cohort of young people with JIA was identified from UK primary care electronic health records (Clinical Practice Research Datalink) between 1 April 2003 and 31 December 2018. To be included in the study, cases needed to have at least 1 year of registration at their general practice beyond age 18 and linkage to Hospital Episodes Statistics data for secondary care information. All specialist care outpatient visits were identified from Hospital Episodes Statistics outpatient data. RESULTS: There were 666 young people included in the study. Of these, 427 (64%) received specialist care beyond age 18, 90 (13%) had their last recorded contact at 16-17 years and 149 (22%) did not continue after 16 years. Older age at diagnosis, female gender, less deprivation and a childhood diagnosis of uveitis were associated with continuing specialist care beyond age 18. Of those continuing beyond 18, 35% (n = 153) were subsequently discharged by the study end date. Of all those discharged, 32% had a missed appointment recorded after the last attended visit, suggesting failure to attend. CONCLUSIONS: Two-thirds of young people with JIA continue to receive specialist care beyond age 18. This is useful information for children and young people with JIA and their families planning for their future, and for clinicians planning health-care services.
Arthritis, Juvenile , Uveitis , Adult , Humans , Child , Female , Adolescent , Arthritis, Juvenile/complications , Retrospective Studies , Electronic Health Records , England
OBJECTIVES: Patients with RA are frequently treated with glucocorticoids (GCs), but evidence is conflicting about whether GCs are associated with hypertension. The aim of this study was to determine whether GCs are associated with incident hypertension in patients with RA. METHODS: A retrospective cohort of patients with incident RA and without hypertension was identified from UK primary care electronic medical records (Clinical Practice Research Datalink). GC prescriptions were used to determine time-varying GC use, dose and cumulative dose, with a 3 month attribution window. Hypertension was identified through either: blood pressure measurements >140/90 mmHg, or antihypertensive prescriptions and a Read code for hypertension. Unadjusted and adjusted Cox proportional hazards regression models were fitted to determine whether there was an association between GC use and incident hypertension. RESULTS: There were 17 760 patients in the cohort. A total of 7421 (42%) were prescribed GCs during follow-up. The incident rate of hypertension was 64.1 per 1000 person years (95% CI: 62.5, 65.7). The Cox proportional hazards model indicated that recent GC use was associated with a 17% increased hazard of hypertension (hazard ratio 1.17; 95% CI: 1.10, 1.24). When categorized by dose, only doses above 7.5 mg were significantly associated with hypertension. Cumulative dose did not indicate a clear pattern. CONCLUSION: Recent GC use was associated with incident hypertension in patients with RA, in particular doses ≥7.5 mg were associated with hypertension. Clinicians need to consider cardiovascular risk when prescribing GCs, and ensure blood pressure is regularly monitored and treated where necessary.
Antihypertensive Agents/therapeutic use , Arthritis, Rheumatoid/drug therapy , Glucocorticoids/adverse effects , Hypertension/chemically induced , Bias , Blood Pressure Determination , Female , Glucocorticoids/administration & dosage , Humans , Hypertension/drug therapy , Hypertension/epidemiology , Incidence , Male , Middle Aged , Proportional Hazards Models , Retrospective Studies , United Kingdom/epidemiology
Antirheumatic Agents/therapeutic use , Arthritis, Rheumatoid/drug therapy , Immunocompromised Host , Pneumococcal Vaccines/administration & dosage , Vaccination/statistics & numerical data , Aged , Cross-Sectional Studies , Female , Humans , Male , Middle Aged , Pneumonia/immunology , Pneumonia/prevention & control
BACKGROUND: Patients with rheumatoid arthritis (RA) have increased cardiovascular (CV) and mortality risk. Patients with RA are also frequently prescribed glucocorticoids (GCs) which have been associated with increased risk of mortality. In addition, for patients who have concomitant diabetes mellitus (DM), GCs are known to worsen glycaemic control and hence may further increase CV and mortality risk. This study aimed to understand the relationship between GCs, DM and mortality in patients with RA. METHODS: This was a retrospective cohort study of patients with incident RA identified from UK primary care electronic medical records. Patients with linkage to Office for National Statistics (ONS) for mortality data (N = 9085) were included. DM was identified through Read codes, prescriptions and blood tests, and GC use was identified through prescriptions. Mortality rate ratios (RR) and rate differences (RD) were calculated across the different exposure groups. Cox proportional hazards regression models were used to estimate interaction on the multiplicative and additive scales. RESULTS: In those without DM GC use had a 4.4-fold increased all-cause mortality RR (95% confidence interval (CI): 3.83 to 5.14) compared to non-use, whilst those with DM had a lower RR for GC use (3.02 (95% CI: 2.34, 3.90)). However, those with DM had a higher RD associated with GC use because of their higher baseline risk. In those with DM, GC use was associated with an additional 46.7 deaths/1000 person-years (pyrs) (95% CI: 34.1 to 59.3) compared to non-use, while in those without DM GC use was associated with an additional 36.2 deaths/1000 pyrs (95% CI: 31.6 to 40.8). A similar pattern was seen for CV mortality. The adjusted Cox proportional hazards model showed no evidence of multiplicative interaction, but additive interaction indicated a non-significant increased risk. For CV mortality there was no interaction on either scale. CONCLUSIONS: GC use was associated with higher mortality rates in people with comorbid DM compared to people without DM, despite apparently reassuring similar relative risks. Clinicians need to be aware of the higher baseline risk in patients with DM, and consider this when prescribing GCs in patients with RA and comorbid DM.
Glucocorticoids (GCs) are widely used to effectively treat inflammatory disease, but GCs have a number of recognized side effects. Patients and clinicians view these side effects differently, with clinicians most concerned with serious side effects such as osteoporosis and diabetes mellitus. Consequently, these side effects are well researched with clinical guidelines and recommendations. A side effect of particular concern to patients is weight gain, but this topic has not been well researched, and consequently clinicians find it difficult to provide patients with accurate information about the potential of weight gain. The aim of this review is to provide an overview of GC use specifically in rheumatic disease, patient views on GC therapy, and GC-induced weight gain. We will discuss the evidence, including the extent and the impact of weight gain on the patient, and highlight areas that warrant further investigation.
Body Weight/drug effects , Glucocorticoids/pharmacology , Rheumatic Diseases/drug therapy , Glucocorticoids/therapeutic use , Humans
BACKGROUND: Participation in online health communities (OHCs) is a popular trend in the United Kingdom. However, so far, no evidence exists to indicate an association between participation in OHCs and improved health outcomes. OBJECTIVE: This study aimed to (1) determine changes in patient activation over 3 months in new users of an OHC, (2) describe patterns of engagement with an OHC, (3) examine whether patients' characteristics at baseline were associated with subsequent patterns of engagement, and (4) determine if patterns of engagement during the 3 months were associated with changes in patient activation, health care utilization, and health status. METHODS: Active new OHC users on HealthUnlocked (HU) were surveyed to measure demographics, levels of patient activation (describing a person's confidence in managing their own health; scale 0-100 with 4 categories), health care utilization, and health status using a Web-based survey at baseline and 3 months. Patient activation at baseline and 3 months was compared (aim 1). Alongside, for a sample of HU users and survey responders, daily OHC website usage data were automatically captured. This was used to identify clusters of engagement with HU (aim 2). For survey responders, baseline characteristics, patient activation, health care utilization, and health status were compared at baseline and 3 months, overall, and between engagement clusters using t tests and chi-square tests (aims 3 and 4). RESULTS: In 329 people who completed both surveys, baseline activation was most frequently level 3, described as taking action but still lacking confidence. At follow-up, a change of 2.6 points was seen, with the greatest change seen in those at lowest baseline activation levels. In addition, 4 clusters of engagement were identified: low, medium, high, and very high, who were active on HU for a mean of 4, 12, 29, and 59 days, respectively. Survey responders were more commonly high or very high engagers. Baseline activation was highest in low and very high engagers. Overall activation increased over time in all engagement groups. Very high engagers had the greatest improvement in activation (5 points), although the average change was not above what is considered clinically meaningful for any group. Fewer accident and emergency visits were seen at follow-up in those with higher engagement, although this trend was not seen for other health care utilization measures. There was no change in health status at 3 months. CONCLUSIONS: This observational study provides some insight into how patterns of engagement with OHCs are associated with changes in patient activation, health care utilization, and health status. Over 3 months, overall, the change in activation was not clinically significant, and there were some indications that OHCs may be of benefit to particular groups. However, the study limitations prevent firm conclusions about causal relationships.
Delivery of Health Care/methods , Patient Participation/methods , Public Health/standards , Adult , Female , Humans , Internet , Male , Middle Aged , Social Support , Surveys and Questionnaires
