Long-term Cardiovascular, Cerebrovascular, and Other Thrombotic Complications in COVID-19 Survivors: A Retrospective Cohort Study.

BACKGROUND: Growing evidence suggests that some coronavirus disease 2019 (COVID-19) survivors experience a wide range of long-term postacute sequelae. We examined the postacute risk and burden of new-incident cardiovascular, cerebrovascular, and other thrombotic complications after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in a highly vaccinated multiethnic Southeast Asian population, during Delta predominance. METHODS: This cohort study used national testing and healthcare claims databases in Singapore to build a cohort of individuals who had a positive SARS-CoV-2 test between 1 September and 30 November 2021 when Delta predominated community transmission. Concurrently, we constructed a test-negative control group by enrolling individuals between 13 April 2020 and 31 December 2022 with no evidence of SARS-CoV-2 infection. Participants in both groups were followed up for a median of 300 days. We estimated risks of new-incident cardiovascular, cerebrovascular, and other thrombotic complications using doubly robust competing-risks survival analysis. Risks were reported using 2 measures: hazard ratio (HR) and excess burden (EB) with 95% confidence intervals. RESULTS: We included 106 012 infected cases and 1 684 085 test-negative controls. Compared with the control group, individuals with COVID-19 exhibited increased risk (HR, 1.157 [1.069-1.252]) and excess burden (EB, 0.70 [.53-.88]) of new-incident cardiovascular and cerebrovascular complications. Risks decreased in a graded fashion for fully vaccinated (HR, 1.11 [1.02-1.22]) and boosted (HR, 1.10 [.92-1.32]) individuals. Conversely, risks and burdens of subsequent cardiovascular/cerebrovascular complications increased for hospitalized and severe COVID-19 cases (compared to nonhospitalized cases). CONCLUSIONS: Increased risks and excess burdens of new-incident cardiovascular/cerebrovascular complications were reported among infected individuals; risks can be attenuated with vaccination and boosting.

Intravenous fluid therapy in hospitalized adult dengue patients without shock: Impact on subsequent severe dengue and potential adverse effects.

There is a lack of evidence on the optimal administration of intravenous (IV) fluids in hospitalized adult dengue patients without compensated and hypotensive shock. This study utilized a well-established cohort of dengue patients to compare risks of progressing to severe dengue (SD) over time for patients who were administered IV fluid versus others who were not. We included adult patients (n = 4781) who were hospitalized for dengue infection from 2005 to 2008. Cases were patients who developed SD (n = 689) and controls were patients who did not up until discharge (n = 4092). We estimated the hazard ratios (HRs) and risk of SD over time between groups administered different volumes of IV fluids versus the no IV fluid comparison group using Cox models with time-dependent covariates. The doubly-robust estimation approach was used to control for the propensity of fluid administration given clinical characteristics of patients. Subgroup analyses by age, sex, and dengue warning signs before IV fluid administration were conducted. High (>2000 mL/day) IV fluids volume was associated with a higher risk of development of SD for those who had warning signs (HR: 1.77 [1.05-2.97], p: 0.0713) and for those below 55 years old (HR: 1.53 [1.04-2.25], p: 0.0713). Low (<1000 mL/day) IV fluids volume was protective against SD for patients without warning signs (HR: 0.757 [0.578-0.990], p: 0.0883), no lethargy (HR: 0.770 [0.600-0.998], p: 0.0847), and females (HR: 0.711 [0.516-0.980], p: 0.0804). Over the course of hospitalization, there were no significant differences in IV fluid administration and SD risk in most subgroups, except in those who experienced lethargy and were administered IV fluid volume or quantity. Administering high volumes of IV fluids may be associated with an increased risk of SD during hospitalization for adult dengue patients without shock. Judicious use of IV fluids as supportive therapy is warranted.

Receptiveness to monkeypox vaccines and public health communication strategies among gay, bisexual and other men who have sex with men in Singapore: cross-sectional quantitative and qualitative insights.

OBJECTIVES: Monkeypox (mpox) was declared as a global health emergency by the WHO, with most reported cases disproportionately involving gay, bisexual and other men who have sex with men (GBMSM). This study explored the willingness of Singaporean GBMSM to receive mpox vaccines and engage in changes to sexual behaviour, and analysed the factors influencing both decisions. METHODS: An online cross-sectional study was disseminated through community groups and Grindr from September to October 2022 among GBMSM in Singapore, where we surveyed respondents' demographics, stigma associated with mpox, perceived risks of sexually transmitted infections (STIs) and changes to sexual behaviours in response to mpox. Descriptive statistics and multivariable linear and logistics regression analyses, as well as thematic analysis of data, were also conducted. RESULTS: 237 GBMSM community members responded to the survey, with the majority being receptive to vaccine and 67.5% indicating changes to sexual behaviour in view of rising mpox infections. Predictors of vaccine receptivity among GBMSM included self-perceived mpox risk (adjusted OR (aOR)=0.44, 95% CI 0.07, 0.82) and self-perceived STI risk (aOR=0.39, 95% CI 0.03, 0.76). Predictors for changes to sexual behaviour included self-perceived mpox stigma (aOR=1.17, 95% CI 1.08, 1.27), self-perceived mpox risk (aOR=1.22, 95% CI 1.03,1.44), age (aOR=0.96, 95% CI 0.93, 1.00) and race (aOR=0.31, 95% CI 0.10, 0.93). In the thematic analysis, respondents raised concerns about vaccine effectiveness, side effects, cost and privacy. CONCLUSIONS: Our findings suggest that the rise in mpox infections have prompted changes to GBMSM's sexual practices. In general, GBMSM are willing to receive the mpox vaccine but are concerned about the physical and social consequences of uptake. These concerns should be addressed when vaccines are released.

Forecasting upper respiratory tract infection burden using high-dimensional time series data and forecast combinations.

Upper respiratory tract infections (URTIs) represent a large strain on primary health resources. To mitigate URTI transmission and public health burdens, it is important to pre-empt and provide forward guidance on URTI burden, while taking into account various facets which influence URTI transmission. This is so that appropriate public health measures can be taken to mitigate strain on primary care resources. This study describes a new approach to forecasting URTIs which can be used for national public health resource planning. Specifically, using environmental and disease data comprising more than 1000 dimensions, we developed sub-models which optimizes model explainability, in-sample model fit, predictive accuracy and combines many weaker predictors over a 2-month time horizon to generate direct, point forecasts over a 1-8 week ahead forecast horizon. Predictive performance was evaluated using rolling out-of-sample forecast assessment within both periods with/without structural breaks in transmission over the period of 2012-2022. We showed that forecast combinations of 5 other forecasting models had better and more consistent predictive performance than other modelling approaches, over periods with and without structural breaks in transmission dynamics. Furthermore, epidemiological analysis on high dimensional data was enabled using post-selection inference, to show the dynamic association between lower temperature, increases in past relative humidity and absolute humidity and increased URTIs attendance. The methods proposed can be used for outbreak preparedness and guide healthcare resource planning, in both stable periods of transmission and periods where structural breaks in data occur.

Evaluating quasi-experimental approaches for estimating epidemiological efficacy of non-randomised field trials: applications in Wolbachia interventions for dengue.

BACKGROUND: Wolbachia symbiosis in Aedes aegypti is an emerging biocontrol measure against dengue. However, assessing its real-world efficacy is challenging due to the non-randomised, field-based nature of most intervention studies. This research re-evaluates the spatial-temporal impact of Wolbachia interventions on dengue incidence using a large battery of quasi-experimental methods and assesses each method's validity. METHODS: A systematic search for Wolbachia intervention data was conducted via PUBMED. Efficacy was reassessed using commonly-used quasi-experimental approaches with extensive robustness checks, including geospatial placebo tests and a simulation study. Intervention efficacies across multiple study sites were computed using high-resolution aggregations to examine heterogeneities across sites and study periods. We further designed a stochastic simulation framework to assess the methods' ability to estimate intervention efficacies (IE). RESULTS: Wolbachia interventions in Singapore, Malaysia, and Brazil significantly decreased dengue incidence, with reductions ranging from 48.17% to 69.19%. IEs varied with location and duration. Malaysia showed increasing efficacy over time, while Brazil exhibited initial success with subsequent decline, hinting at operational challenges. Singapore's strategy was highly effective despite partial saturation. Simulations identified Synthetic Control Methods (SCM) and its variant, count Synthetic Control Method (cSCM), as superior in precision, with the smallest percentage errors in efficacy estimation. These methods also demonstrated robustness in placebo tests. CONCLUSIONS: Wolbachia interventions exhibit consistent protective effects against dengue. SCM and cSCM provided the most precise and robust estimates of IEs, validated across simulated and real-world settings.

Fine-scale estimation of effective reproduction numbers for dengue surveillance.

The effective reproduction number Rt is an epidemiological quantity that provides an instantaneous measure of transmission potential of an infectious disease. While dengue is an increasingly important vector-borne disease, few have used Rt as a measure to inform public health operations and policy for dengue. This study demonstrates the utility of Rt for real time dengue surveillance. Using nationally representative, geo-located dengue case data from Singapore over 2010-2020, we estimated Rt by modifying methods from Bayesian (EpiEstim) and filtering (EpiFilter) approaches, at both the national and local levels. We conducted model assessment of Rt from each proposed method and determined exogenous temporal and spatial drivers for Rt in relation to a wide range of environmental and anthropogenic factors. At the national level, both methods achieved satisfactory model performance (R2EpiEstim = 0.95, R2EpiFilter = 0.97), but disparities in performance were large at finer spatial scales when case counts are low (MASE EpiEstim = 1.23, MASEEpiFilter = 0.59). Impervious surfaces and vegetation with structure dominated by human management (without tree canopy) were positively associated with increased transmission intensity. Vegetation with structure dominated by human management (with tree canopy), on the other hand, was associated with lower dengue transmission intensity. We showed that dengue outbreaks were preceded by sustained periods of high transmissibility, demonstrating the potential of Rt as a dengue surveillance tool for detecting large rises in dengue cases. Real time estimation of Rt at the fine scale can assist public health agencies in identifying high transmission risk areas and facilitating localised outbreak preparedness and response.

Association between ambient air pollutants and upper respiratory tract infection and pneumonia disease burden in Thailand from 2000 to 2022: a high frequency ecological analysis.

BACKGROUND: A pertinent risk factor of upper respiratory tract infections (URTIs) and pneumonia is the exposure to major ambient air pollutants, with short term exposures to different air pollutants being shown to exacerbate several respiratory conditions. METHODS: Here, using disease surveillance data comprising of reported disease case counts at the province level, high frequency ambient air pollutant and climate data in Thailand, we delineated the association between ambient air pollution and URTI/Pneumonia burden in Thailand from 2000 - 2022. We developed mixed-data sampling methods and estimation strategies to account for the high frequency nature of ambient air pollutant concentration data. This was used to evaluate the effects past concentrations of fine particulate matter (PM2.5), sulphur dioxide (SO2), and carbon monoxide (CO) and the number of disease case count, after controlling for the confounding meteorological and disease factors. RESULTS: Across provinces, we found that past increases in CO, SO2, and PM2.5 concentration were associated to changes in URTI and pneumonia case counts, but the direction of their association mixed. The contributive burden of past ambient air pollutants on contemporaneous disease burden was also found to be larger than meteorological factors, and comparable to that of disease related factors. CONCLUSIONS: By developing a novel statistical methodology, we prevented subjective variable selection and discretization bias to detect associations, and provided a robust estimate on the effect of ambient air pollutants on URTI and pneumonia burden over a large spatial scale.

Estimating direct and spill-over impacts of political elections on COVID-19 transmission using synthetic control methods.

Mass gathering events have been identified as high-risk environments for community transmission of coronavirus disease 2019 (COVID-19). Empirical estimates of their direct and spill-over effects however remain challenging to identify. In this study, we propose the use of a novel synthetic control framework to obtain causal estimates for direct and spill-over impacts of these events. The Sabah state elections in Malaysia were used as an example for our proposed methodology and we investigate the event's spatial and temporal impacts on COVID-19 transmission. Results indicate an estimated (i) 70.0% of COVID-19 case counts within Sabah post-state election were attributable to the election's direct effect; (ii) 64.4% of COVID-19 cases in the rest of Malaysia post-state election were attributable to the election's spill-over effects. Sensitivity analysis was further conducted by examining epidemiological pre-trends, surveillance efforts, varying synthetic control matching characteristics and spill-over specifications. We demonstrate that our estimates are not due to pre-existing epidemiological trends, surveillance efforts, and/or preventive policies. These estimates highlight the potential of mass gatherings in one region to spill-over into an outbreak of national scale. Relaxations of mass gathering restrictions must therefore be carefully considered, even in the context of low community transmission and enforcement of safe distancing guidelines.

Increased Dengue Transmissions in Singapore Attributable to SARS-CoV-2 Social Distancing Measures.

Social distancing (SD) measures aimed at curbing the spread of SARS-CoV-2 remain an important public health intervention. Little is known about the collateral impact of reduced mobility on the risk of other communicable diseases. We used differences in dengue case counts pre- and post implementation of SD measures and exploited heterogeneity in SD treatment effects among different age groups in Singapore to identify the spillover effects of SD measures. SD policy caused an increase of over 37.2% in dengue cases from baseline. Additional measures to preemptively mitigate the risk of other communicable diseases must be considered before the implementation/reimplementation of SARS-CoV-2 SD measures.

Time varying methods to infer extremes in dengue transmission dynamics.

Dengue is an arbovirus affecting global populations. Frequent outbreaks occur, especially in equatorial cities such as Singapore, where year-round tropical climate, large daily influx of travelers and population density provide the ideal conditions for dengue to transmit. Little work has, however, quantified the peaks of dengue outbreaks, when health systems are likely to be most stretched. Nor have methods been developed to infer differences in exogenous factors which lead to the rise and fall of dengue case counts across extreme and non-extreme periods. In this paper, we developed time varying extreme mixture (tvEM) methods to account for the temporal dependence of dengue case counts across extreme and non-extreme periods. This approach permits inference of differences in climatic forcing across non-extreme and extreme periods of dengue case counts, quantification of their temporal dependence as well as estimation of thresholds with associated uncertainties to determine dengue case count extremities. Using tvEM, we found no evidence that weather affects dengue case counts in the near term for non-extreme periods, but that it has non-linear and mixed signals in influencing dengue through tvEM parameters in the extreme periods. Using the most appropriate tvEM specification, we found that a threshold at the 70th (95% credible interval 41.1, 83.8) quantile is optimal, with extreme events of 526.6, 1052.2 and 1183.6 weekly case counts expected at return periods of 5, 50 and 75 years. Weather parameters at a 1% scaled increase was found to decrease the long-run expected case counts, but larger increases would lead to a drastic expected rise from the baseline correspondingly. The tvEM approach can provide valuable inference on the extremes of time series, which in the case of infectious disease notifications, allows public health officials to understand the likely scale of outbreaks in the long run.

Inference on dengue epidemics with Bayesian regime switching models.

Dengue, a mosquito-borne infectious disease caused by the dengue viruses, is present in many parts of the tropical and subtropical regions of the world. All four serotypes of dengue viruses are endemic in Singapore, an equatorial city-state. Frequent outbreaks occur, sometimes leading to national epidemics. However, few studies have attempted to characterize breakpoints which precede large rises in dengue case counts. In this paper, Bayesian regime switching (BRS) models were employed to infer epidemic and endemic regimes of dengue transmissions, each containing regime specific autoregressive processes which drive the growth and decline of dengue cases, estimated using a custom built multi-move Gibbs sampling algorithm. Posterior predictive checks indicate that BRS replicates temporal trends in Dengue transmissions well and nowcast accuracy assessed using a post-hoc classification scheme showed that BRS classification accuracy is robust even under limited data with the AUC-ROC at 0.935. LASSO-based regression and bootstrapping was used to account for plausibly high dimensions of climatic factors affecting Dengue transmissions, which was then estimated using cross-validation to conduct statistical inference on long-run climatic effects on the estimated regimes. BRS estimates epidemic and endemic regimes of dengue in Singapore which are characterized by persistence across time, lasting an average of 20 weeks and 66 weeks respectively, with a low probability of transitioning away from their regimes. Climate analysis with LASSO indicates that long-run climatic effects up to 20 weeks ago do not differentiate epidemic and endemic regimes. Lastly, by fitting BRS to simulated disease data generated from a stochastic Susceptible-Infected-Recovered model, mechanistic links between infectivity and regimes classified using BRS were provided. The model proposed could be applied to other localities and diseases under minimal data requirements where transmission counts over time are collected.

Identifying COVID-19 cases in outpatient settings.

Case identification is an ongoing issue for the COVID-19 epidemic, in particular for outpatient care where physicians must decide which patients to prioritise for further testing. This paper reports tools to classify patients based on symptom profiles based on 236 severe acute respiratory syndrome coronavirus 2 positive cases and 564 controls, accounting for the time course of illness using generalised multivariate logistic regression. Significant symptoms included abdominal pain, cough, diarrhoea, fever, headache, muscle ache, runny nose, sore throat, temperature between 37.5 and 37.9 °C and temperature above 38 °C, but their importance varied by day of illness at assessment. With a high percentile threshold for specificity at 0.95, the baseline model had reasonable sensitivity at 0.67. To further evaluate accuracy of model predictions, leave-one-out cross-validation confirmed high classification accuracy with an area under the receiver operating characteristic curve of 0.92. For the baseline model, sensitivity decreased to 0.56. External validation datasets reported similar result. Our study provides a tool to discern COVID-19 patients from controls using symptoms and day from illness onset with good predictive performance. It could be considered as a framework to complement laboratory testing in order to differentiate COVID-19 from other patients presenting with acute symptoms in outpatient care.

Revealing regional disparities in the transmission potential of SARS-CoV-2 from interventions in Southeast Asia.

SARS-CoV-2 is a new pathogen responsible for the coronavirus disease 2019 (COVID-19) outbreak. Southeast Asia was the first region to be affected outside China, and although COVID-19 cases have been reported in all countries of Southeast Asia, both the policies and epidemic trajectories differ substantially, potentially due to marked differences in social distancing measures that have been implemented by governments in the region. This paper studies the across-country relationships between social distancing and each population's response to policy, the subsequent effects of these responses to the transmissibility and epidemic trajectories of SARS-CoV-2. The analysis couples COVID-19 case counts with real-time mobility data across Southeast Asia to estimate the effects of host population response to social distancing policy and the subsequent effects on the transmissibility and epidemic trajectories of SARS-CoV-2. A novel inference strategy for the time-varying reproduction number is developed to allow explicit inference of the effects of social distancing on the transmissibility of SARS-CoV-2 through a regression structure. This framework replicates the observed epidemic trajectories across most Southeast Asian countries, provides estimates of the effects of social distancing on the transmissibility of disease and can simulate epidemic histories conditional on changes in the degree of intervention scenarios and compliance within Southeast Asia.

Revealing two dynamic dengue epidemic clusters in Thailand.

BACKGROUND: Thailand is home to around 69 million individuals. Dengue is hyper-endemic and all 4 serotypes are in active circulation in the country. Dengue outbreaks occur almost annually within Thailand in at least one province but the spatio-temporal and environmental interface of these outbreaks has not been studied. METHODS: We develop Bayesian regime switching (BRS) models to characterize outbreaks, their persistence and infer their likelihood of occurrence across time for each administrative province where dengue case counts are collected. BRS was compared against two other classification tools and their agreement is assessed. We further examine how these spatio-temporal clusters of outbreak clusters arise by comparing reported dengue case counts, urban population, urban land cover, climate and flight volumes on the province level. RESULTS: Two dynamic dengue epidemic clusters were found nationally. One cluster consists of 47 provinces and is highly outbreak prone. Provinces with a large number of case counts, urban population, urban land cover and incoming flight passengers are associated to the epidemic prone cluster of dengue. Climate has an effect on determining the probability of outbreaks over time within provinces, but have less influence on whether provinces belong to the epidemic prone cluster. BRS found high agreement with other classification tools. CONCLUSIONS: Importation and urbanization drives the risk of outbreaks across regions strongly. In provinces estimated to have high epidemic persistence, more resource allocation to vector control should be applied to those localities as heightened transmission counts are likely to occur over a longer period of time. Clustering of epidemic and non-epidemic prone areas also highlights the need for prioritization of resource allocation for disease mitigation over provinces in Thailand.

The need for pre-emptive control strategies for mpox in Asia and Oceania.

Introduction: The transmission dynamics of the recent mpox outbreak highlights the lack of infrastructure available to rapidly respond to novel STI outbreaks, of which Asia and Oceania remains particularly susceptible. Here, we simulate outbreaks in this setting and propose the use of pre-emptive vaccination within the men who have sex with men (MSM) community before the arrival and establishment of the virus. Materials and methods: Using data driven heterogeneous sexual contact networks, we simulated outbreaks of mpox in Singapore, Hong Kong, and Sydney. An individual based SEIR compartmental model was used to simulate epidemic trajectories and the impact of different vaccination uptakes was assessed in their ability to avert or suppress outbreaks upon the arrival of mpox within the MSM populations. Results: The highly dense sexual networks of Singapore and Sydney experience rapid outbreaks, with infection peaks occurring at day 41 and 23 respectively, compared to Hong Kong which occurs at day 77. Across the simulations with no vaccination, 68.2%-89.7% of the MSM community will become infected with mpox across the different cities, over a simulation period of 1 year. By implementing vaccination strategies, the infection rate across the cities can be reduced to as low as 3.1% of the population (range: 3.1%-82.2%) depending on the implementation and uptake of the vaccine. Vaccination is also extremely effective in slowing the start of the epidemic, delaying the epidemic peak by 36-50 days in Hong Kong, or even preventing the outbreak of mpox. Discussion: With extremely dense and well-connected sexual contact networks, where 65.2%-83.2% of the population are connected to a super-spreader in the different contact networks, pre-emptive or immediate vaccination upon identification of the first case is strongly recommended to help better manage the outbreak of mpox and prevent potential straining of healthcare systems.

Autoimmune Sequelae After Delta or Omicron Variant SARS-CoV-2 Infection in a Highly Vaccinated Cohort.

Importance: Studies have reported increased risk of autoimmune sequelae after SARS-CoV-2 infection. However, risk may potentially be attenuated by milder Omicron (B.1.1.529) variant infection and availability of booster vaccination. Objective: To estimate the 300-day risk of new-incident autoimmune sequelae after SARS-CoV-2 Delta or Omicron BA.1 or BA.2 variant infection in adults who received COVID-19 vaccines and boosters, compared with a contemporary control group without infection. Design, Setting, and Participants: This cohort study in Singapore enrolled adults from September 1, 2021, to March 7, 2022, and followed up for 300 days. Participants were adults aged 18 years or older with SARS-CoV-2 infection during the predominance of the Delta and Omicron BA.1 or BA.2 variants and were still alive at 30 days after COVID-19 diagnosis. Exposure: The national SARS-CoV-2 testing registry was used to construct cohorts of adults with SARS-CoV-2 Delta or Omicron BA.1 or BA.2 variant infection (hereafter, cases) and a contemporaneous group with negative polymerase chain reaction or rapid antigen test results (hereafter, controls). Main Outcomes and Measures: New-incident autoimmune diagnoses after SARS-CoV-2 infection. This information was recorded in the MediClaims national health care claims database and identified 31 to 300 days after index date of infection. Risks and excess burdens were estimated using Cox proportional hazards regression model with overlap weights applied. Results: In total, 1 766 036 adults (915 096 females [51.9%]; mean [SD] age, 49 [18] years) were included in the study population, with 480 082 (27.2%) categorized as cases and 1 285 954 (72.8%) as controls. Of these adults, 73.1% had Chinese, 13.7% Malay, and 9.9% Indian ethnicity. There were 104 179 cases and 666 575 controls included during the Delta variant-predominance transmission, while 375 903 cases and 619 379 controls were included during the Omicron variant-predominance transmission. During the Delta variant period, 81.1% of cases had completed primary vaccination; during the Omicron variant period, 74.6% of cases received boosters. No significantly elevated risk of 12 prespecified autoimmune sequelae was recorded across the Omicron and Delta variant cohorts. Elevated risks of inflammatory bowel disease (adjusted hazard ratio [AHR], 2.23; 95% CI, 1.45-3.46; P < .001) and bullous skin disorders (AHR, 4.88; 95% CI, 2.47-9.66; P < .001) were observed only in the subset of COVID-19 cases requiring hospitalization during the predominance of the Omicron variant. While elevated risk of vasculitis (AHR, 5.74; 95% CI, 1.48-22.23; P = .01) was observed in vaccine-breakthrough Omicron variant infections, no increased risk of vasculitis was observed in the corresponding subgroup who received boosters. Conclusions and Relevance: This cohort study observed no significantly elevated long-term risk of autoimmune sequelae after SARS-CoV-2 Delta and Omicron BA.1 or BA.2 variant infection, except for a modestly increased risk of inflammatory bowel disease and bullous skin disorders in the hospitalized subgroup during the predominance of the Omicron variant. Booster vaccination appeared to mitigate the risk of long-term autoimmune sequelae.

Long-term neuropsychiatric sequelae of Delta versus Omicron SARS-CoV-2 infection.

OBJECTIVES: Studies have reported increased rates of long-term neuropsychiatric sequelae after SARS-CoV-2 infection using electronic health-record (EHR) data; however, the majority were conducted before Omicron and booster rollout. We estimated the long-term risks and excess burdens of pre-specified new-incident neuropsychiatric diagnoses after Delta versus Omicron BA.1/2 infection in a highly-vaccinated and boosted cohort of adult Singaporeans. METHODS: The national SARS-CoV-2 testing registry was used to construct cohorts of Singaporean adults infected during periods of Delta and Omicron BA.1/2 predominance and a contemporaneous test-negative control group. New-incident neuropsychiatric diagnoses recorded in the national health care claims database were identified up to 300 days postinfection. Risks and excess burden were estimated using a doubly robust competing-risks survival analysis. RESULTS: 104 179 and 375 903 infected cases were assigned to Delta and Omicron cohorts and compared against test-negative controls (Delta: N = 666 575 and Omicron: N = 619 379). Elevated risk of cognition or memory disorders was consistently reported across Omicron (Adjusted hazards ratio [aHR], 1.24; 95% CI, 1.12-1.38) and Delta cohorts (aHR, 1.63; 95% CI, 1.39-1.92). Delta-variant infection was associated with an increased risk of anosmia or dysgeusia (aHR, 4.53; 95% CI, 2.78-7.41) and psychosis (aHR, 1.65; 95% CI, 1.22-2.22). By contrast, Omicron-variant infection was associated with a risk of abnormal involuntary movements (aHR, 1.93; 95% CI, 1.32-2.83). Risks of neuropsychiatric sequelae predominantly accrued in hospitalized individuals. DISCUSSIONS: A modestly increased risk of cognition and memory disorders at 300 days after SARS-CoV-2 infection was observed among adult Singaporeans infected during the Delta/Omicron BA.1/2 transmission. There was no overall increased risk of neuropsychiatric sequelae observed across other domains. Variant-specific differences were also observed in individual neuropsychiatric sequelae, including an elevated risk of anosmia or dysgeusia after Delta-variant infection.

Nirmatrelvir/ritonavir treatment and risk for post-acute sequelae of COVID-19 in older Singaporeans.

OBJECTIVES: Significant heterogeneity has been reported in cohort studies evaluating the impact of early oral antiviral treatment on preventing post-acute sequelae after COVID-19 (PASC). We evaluated the impact of early nirmatrelvir/ritonavir on risk of post-acute cardiovascular, neurological, respiratory and autoimmune diagnoses, as well as post-acute symptoms amongst older Singaporeans. METHODS: National COVID-19 registries and healthcare claims databases were utilized to construct a retrospective population-based cohort enrolling all Singaporeans aged≥60 years diagnosed with SARS-CoV-2 infection at primary care during Omicron transmission (18th March 2022-4th August 2023). The cohort was divided into nirmatrelvir/ritonavir-treated and untreated groups. Between-group differences in baseline characteristics were adjusted using overlap weighting. Risks of post-acute cardiovascular, neurological, respiratory and autoimmune diagnoses and post-acute symptoms (31-180 days) following SARS-CoV-2 infection were contrasted in treated/untreated groups using competing-risks regressions (adjusted for demographics/vaccination status/comorbidities). RESULTS: 188,532 older Singaporeans were included; 5.8% (10,905/188,532) received nirmatrelvir/ritonavir. No significantly decreased risk of post-acute sequelae (any sequelae: adjusted-hazards-ratio, aHR=1.06[0.94-1.19]; cardiovascular sequelae: aHR=1.01 [0.83-1.24]; neurological sequelae: aHR=1.09 [0.95-1.27]; respiratory sequelae: aHR=1.14[0.84-1.55]; autoimmune sequelae: aHR=0.76[0.53-1.09] or any post-acute symptom: aHR=0.97[0.80-1.18]) was observed up to 180 days post-infection in nirmatrelvir/ritonavir-treated individuals, versus untreated cases. Across all vaccination and age subgroups, no significantly decreased risk of any post-acute diagnosis/symptom or any cardiovascular, neurological, respiratory and autoimmune complications up to 180 days post-infection was observed. CONCLUSION: Early outpatient receipt of nirmatrelvir/ritonavir did not significantly reduce risk of post-acute cardiovascular, neurological, respiratory and autoimmune sequelae or risk of post-acute symptoms in a boosted cohort of older Singaporeans.