OUR SOUTHEAST ASIA POLICY

Political Economy,

Living alone and mental health: parallel analyses in UK longitudinal population surveys and electronic health records prior to and during the COVID-19 pandemic

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

Study protocol: Comparison of different risk prediction modelling approaches for COVID-19 related death using the OpenSAFELY platform

On March 11th 2020, the World Health Organization characterised COVID-19 as a pandemic. Responses to containing the spread of the virus have relied heavily on policies involving restricting contact between people. Evolving policies regarding shielding and individual choices about restricting social contact will rely heavily on perceived risk of poor outcomes from COVID-19. In order to make informed decisions, both individual and collective, good predictive models are required.   For outcomes related to an infectious disease, the performance of any risk prediction model will depend heavily on the underlying prevalence of infection in the population of interest. Incorporating measures of how this changes over time may result in important improvements in prediction model performance.  This protocol reports details of a planned study to explore the extent to which incorporating time-varying measures of infection burden over time improves the quality of risk prediction models for COVID-19 death in a large population of adult patients in England. To achieve this aim, we will compare the performance of different modelling approaches to risk prediction, including static cohort approaches typically used in chronic disease settings and landmarking approaches incorporating time-varying measures of infection prevalence and policy change, using COVID-19 related deaths data linked to longitudinal primary care electronic health records data within the OpenSAFELY secure analytics platform.</ns4:p

Comparative effectiveness of nirmatrelvir/ritonavir versus sotrovimab and molnupiravir for preventing severe COVID-19 outcomes in non-hospitalised high-risk patients during Omicron waves: observational cohort study using the OpenSAFELY platform

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

Clinical coding of long COVID in primary care 2020-2023 in a cohort of 19 million adults: an OpenSAFELY analysis

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)

The need for exercise sciences and an integrated response to COVID-19: A position statement from the international HL-PIVOT network

COVID-19 is one of the biggest health crises that the world has seen. Whilst measures to abate transmission and infection are ongoing, there continues to be growing numbers of patients requiring chronic support, which is already putting a strain on health care systems around the world and which may do so for years to come. A legacy of COVID-19 will be a long-term requirement to support patients with dedicated rehabilitation and support services. With many clinical settings characterized by a lack of funding and resources, the need to provide these additional services could overwhelm clinical capacity. This position statement from the Healthy Living for Pandemic Event Protection (HL-PIVOT) Network provides a collaborative blueprint focused on leading research and developing clinical guidelines, bringing together professionals with expertise in clinical services and the exercise sciences to develop the evidence base needed to improve outcomes for patients infected by COVID-19

Impact of long COVID on health-related quality-of-life: an OpenSAFELY population cohort study using patient-reported outcome measures (OpenPROMPT).

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)

Psychology and aggression

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68264/2/10.1177_002200275900300301.pd

Green tea extract inhibits chemokine production, but up-regulates chemokine receptor expression, in rheumatoid arthritis synovial fibroblasts and rat adjuvant-induced arthritis

Objective. Evaluation of the efficacy of green tea extract (GTE) in regulating chemokine production and chemokine receptor expression in human RA synovial fibroblasts and rat adjuvant-induced arthritis (AIA). Methods. Fibroblasts isolated from human RA synovium were used in the study. Regulated upon activation normal T cell expressed and secreted (RANTES)/CCL5, monocyte chemoattractant protein (MCP)-1/CCL2, growth-regulated oncogene (GRO)a/CXCL1 and IL-8/CXCL8 production was measured by ELISA. Western blotting was used to study the phosphorylation of protein kinase C (PKC)d and c-Jun N-terminal kinases (JNK). Chemokine and chemokine receptor expression was determined by quantitative RT–PCR. The benefit of GTE administration in rat AIA was determined. Results. GTE (2.5–40 mg/ml) inhibited IL-1b-induced MCP-1/CCL2 (10 ng/ml), RANTES/CCL5, GROa/CXCL1 and IL-8/CXCL8 production in human RA synovial fibroblasts (P<0.05). However, GTE inhibited MCP-1/CCL2 and GROa/CXCL1 mRNA synthesis in RA synovial fibroblasts. Furthermore, GTE also inhibited IL-1b-induced phosphorylation of PKCd, the signalling pathway mediating IL-1b-induced chemokine production. Interestingly, GTE preincubation enhanced constitutive and IL-1b-induced CCR1, CCR2b, CCR5, CXCR1 and CXCR2 receptor expression. GTE administration (200 mg/kg/day p.o.) modestly ameliorated rat AIA, which was accompanied by a decrease in MCP-1/CCL2 and GROa/CXCL1 levels and enhanced CCR-1, -2, -5 and CXCR1 receptor expression in the joints of GTE administered rats. Conclusions. Chemokine receptor overexpression with reduced chemokine production by GTE may be one potential mechanism to limit the overall inflammation and joint destruction in RA.NIHPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77485/1/Marotte, et al. Green tea extract...Rheumatology 2010.pdf-