Population Prevalence Estimates Research Articles

GILLES DE LA TOURETTE SYNDROME (GTS)- A RARELY DIAGNOSED AND UNDER REPORTED DISORDER IN NIGERIA

Background: Tourette Syndrome (TS) is an uncommon neuropsychiatric disorder and less frequently diagnosed. The population prevalence estimate of TS in children was 0.3% to 0.9%. In Nigeria, this suggests that approximately 100,000 people are affected considering the estimated population of 200,000,000. Objective: To present a case report of a teenager with a diagnosis of Gilles de la Tourette Syndrome. Case Report: This is case report of a 15-year-old female secondary school student who presented to psychiatric department of a Nigerian Teaching Hospital with 3 years history of verbal and physical aggression towards parents and siblings. She was abusive and sometimes beats her siblings and parents for no obvious reason. She talks repeatedly to imaginary person, saying “leave me alone, I am not killing your child, you do not have respect” She sometimes hits the imaginary person. She blinks, hisses, grunts and shouts. These behaviours are repetitive, usually last for about 3 minutes. Tourette syndrome diagnosis was made after comprehensive evaluation using the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria. Conclusion: The appropriate referral of this patient is of great benefit in the course of her illness. She achieved significant improvement to enable her continue education.

Mental health problems and fear of childbirth: A cohort study of women in an inner‐city maternity service

AbstractObjectivesTo estimate the population prevalence of severe fear of childbirth (FOC) during pregnancy and investigate its association with: (a) antenatal common mental disorders (depression and anxiety disorder) and (b) elective cesarean birth.Methods545 participants from an inner‐city London maternity population were interviewed soon after their first antenatal appointment (mean gestation: 14 weeks). Current mental disorders were assessed using the Structured Clinical Interview DSM‐IV. FOC was measured using the Wijma Delivery Expectancy/Experience Questionnaire (WDEQ‐A) at approximately 28 weeks gestation (n = 377), with severe FOC defined using a cutoff of WDEQ‐A ≥ 85. Birth mode information was collected at 3 months post‐delivery using an adapted Adult Service Use Schedule. Linear regressions were used to model associations, adjusting for the effects of covariates (age, parity, relationship status, education, and planned pregnancy). Sampling weights were used to adjust for bias introduced by the stratified sampling. We also accounted for missing data within the analysis.ResultsThe estimated population prevalence of severe FOC was 3% (95% CI: 2%‐6%) (n = 377). Depression and anxiety were significantly associated with severe FOC after adjustment for covariates (45% vs 11%; coefficient: 15.75, 95% CI: 8.08‐23.42, P < .001). There was a weak association between severe FOC and elective cesarean birth.ConclusionsSevere FOC occurs in around 3% of the population. Depression and anxiety are associated with FOC. Pregnant people with depression and anxiety may be at increased risk of experiencing severe FOC. Attitudes toward childbirth should be assessed as part of routine clinical assessment of pregnant people in contact with mental health services.

The rate of COVID-19 and associated mortality after elective hip and knee arthroplasty prior to cessation of elective services in UK.

The primary aim was to assess the rate of postoperative COVID-19 following hip and knee arthroplasty performed in March 2020 in the UK. The secondary aims were to assess whether there were clinical factors associated with COVID-19 status, the mortality rate of patients with COVID-19, and the rate of potential COVID-19 in patients not presenting to healthcare services. A multicentre retrospective study was conducted of patients undergoing hip or knee arthroplasty during the first wave of the COVID-19 pandemic (1 March 2020 to 31 March 2020) with a minimum of 60 days follow-up. Patient demographics, American Society of Anesthesiologists grade, procedure type, primary or revision, length of stay (LOS), COVID-19 test status, and postoperative mortality were recorded. A subgroup of patients (n = 211) who had not presented to healthcare services after discharge were contacted and questioned as to whether they had symptoms of COVID-19. Five (0.5%) of 1,073 patients who underwent hip or knee arthroplasty tested positive for SARS-CoV-2 postoperatively. When adjusting for confounding factors, increasing LOS (p = 0.022) was the only significant factor associated with developing COVID-19 following surgery and a stay greater than three days was a reliable predictor with an area under the curve of 81% (p = 0.018). There were three (0.3%) deaths in the study cohort and the overall mortality rate attributable to COVID-19 was 0.09% (n = 1/1,073), with one (20%) of the five patients with COVID-19 dying postoperatively. Of the 211 patients contacted, two had symptoms within two to 14 days postoperatively with a positive predictive value of 31% and it was therefore estimated that one patient may have had undiagnosed COVID-19. The rate of postoperative COVID-19 was 0.5% and may have been as high as 1% when accounting for those patients not presenting to healthcare services, which was similar to the estimated population prevalence during the study period. The overall mortality rate secondary to COVID-19 was low (0.09%), however the mortality rate for those patients developing COVID-19 was 20%. Cite this article: Bone Joint J2021;103-B(4):681-688.

Relative Frequency and Risk Factors of COVID-19 Related Headache in a Sample of Egyptian Population: A Hospital-Based Study.

ObjectivesHeadache is considered one of the most frequent neurological manifestations of COVID-19. This work aimed to identify the relative frequency of COVID-19 related headache and to clarify the impact of clinical, laboratory findings of COVID-19 infection on headache occurrence and its response to analgesics.DesignCross-sectional studySettingRecovered COVID-19 patientsSubjects782 patients with a confirmed diagnosis of COVID-19 infection.MethodsClinical, laboratory and imaging data were obtained from the hospital medical records. Regarding patients who developed COVID-19 related headache, a trained neurologist performed an analysis of headache and its response to analgesics.ResultsThe relative frequency of COVID-19 related headache among our sample was 55.1% with 95% CI (0.516–0.586) for the estimated population prevalence. Female gender, malignancy, primary headache, fever, dehydration, lower levels of hemoglobin and platelets and higher levels of neutrophil/lymphocyte ratio (NLR) and CRP were significantly associated with COVID-19 related headache. Multivariate analysis revealed that female gender, fever, dehydration, primary headache, high NLR, and decreased platelet count were independent predictors of headache occurrence. By evaluating headache response to analgesics, old age, diabetes, hypertension, primary headache, severe COVID-19, steroid intake, higher CRP and ferritin and lower hemoglobin levels were associated with poor response to analgesics. Multivariate analysis revealed that primary headache, steroids intake, moderate and severe COVID-19 were independent predictors of non-response to analgesics.DiscussionHeadache occurs in 55.1% of patients with COVID-19. Female gender, fever, dehydration, primary headache, high NLR, and decreased platelet count are considered independent predictors of COVID-19 related headache.

Multiple imputation validation study: addressing unmeasured survey data in a longitudinal design

BackgroundQuestionnaires used in longitudinal studies may have questions added or removed over time for numerous reasons. Data missing completely at a follow-up survey is a unique issue for longitudinal studies. While such excluded questions lack information at one follow-up survey, they are collected at other follow-up surveys, and covariances observed at other follow-up surveys may allow for the recovery of the missing data. This study utilized data from a large longitudinal cohort study to assess the efficiency and feasibility of using multiple imputation (MI) to recover this type of information.MethodsMillennium Cohort Study participants completed the 9-item Patient Health Questionnaire (PHQ) depression module at 2 time points (2004, 2007). The suicidal ideation item in the module was set to missing for the 2007 assessment. Several single-level MI models using different sets of predictors and forms of suicidal ideation were used to compare self-reported values and imputed values for this item in 2007. Additionally, associations with sleep duration and smoking status, which are related constructs, were compared between self-reported and imputed values of suicidal ideation.ResultsAmong 63,028 participants eligible for imputation analysis, 4.05% reported suicidal ideation on the 2007 survey. The imputation models successfully identified suicidal ideation, with a sensitivity ranging between 34 and 66% and a positive predictive value between 36 and 42%. Specificity remained above 96% and negative predictive value above 97% for all imputed models. Similar associations were found for all imputation models on related constructs, though the dichotomous suicidal ideation imputed from the model using only PHQ depression items yielded estimates that were closest with the self-reported associations for all adjusted analyses.ConclusionsAlthough sensitivity and positive predictive value were relatively low, applying MI techniques allowed for inclusion of an otherwise missing variable. Additionally, correlations with related constructs were estimated near self-reported values. Therefore, the other 8 depression items can be used to estimate suicidal ideation that was completely missing from a survey using MI. However, these imputed values should not be used to estimate population prevalence.

Changes in depression and suicidal ideation under severe lockdown restrictions during the first wave of the COVID-19 pandemic in Spain: a longitudinal study in the general population.

To assess whether there is a change in the prevalence of depression and suicidal ideation after the strict lockdown measures due to the first wave of the COVID-19 pandemic in Spain; and to assess which are the factors associated with the incidence of a depressive episode or suicidal ideation during the lockdown. Data from a longitudinal adult population-based cohort from the provinces of Madrid and Barcelona were analysed (n = 1103). Structured face-to-face home-based interviews (pre-pandemic) and telephone interviews were performed. Both depression and suicidal ideation were assessed through an adaptation of the Composite International Diagnostic Interview (CIDI 3.0). A variety of validated instruments and sociodemographic variables including age, sex, educational level, occupational status, home quietness, screen time, resilience, loneliness, social support, physical activity, disability, economic situation and COVID-19-related information were also considered. Population prevalence estimates and multivariable logistic regressions were computed. Overall, prevalence rates of depression and suicidal ideation did not change significantly from before to after the COVID-19 outbreak. However, the rates of depression among individuals aged 50+ years showed a significant decrease compared to before the pandemic (from 8.48 to 6.41%; p = 0.01). Younger individuals (odds ratio (OR) = 0.97 per year older; 95% confidence interval (CI) = 0.95-0.99) and those feeling loneliness (OR = 1.96; 95% CI = 1.42-2.70) during the lockdown were at an increased risk of developing depression during the confinement. Resilience showed a protective effect against the risk of depression (OR = 0.46; 95% CI = 0.32-0.66) and suicidal ideation (OR = 0.33; 95% CI = 0.16-0.68), whereas individuals perceiving social support were at a lower risk of developing suicidal thoughts (OR = 0.35; 95% CI = 0.18-0.69). Continuous reinforcement of mental health preventive and intervening measures during and in the aftermath of the crisis is of global importance, particularly among vulnerable groups who are experiencing the most distress. Future research should strive to evaluate the long-term effects of the COVID-19 crisis on mental health.

A Bayesian approach to estimating the population prevalence of mood and anxiety disorders using multiple measures.

There is currently no universally accepted measure for population-based surveillance of mood and anxiety disorders. As such, the use of multiple linked measures could provide a more accurate estimate of population prevalence. Our primary objective was to apply Bayesian methods to two commonly employed population measures of mood and anxiety disorders to make inferences regarding the population prevalence and measurement properties of a combined measure. We used data from the 2012 Canadian Community Health Survey - Mental Health linked to health administrative databases in Ontario, Canada. Structured interview diagnoses were obtained from the survey, and health administrative diagnoses were identified using a standardised algorithm. These two prevalence estimates, in addition to data on the concordance between these measures and prior estimates of their psychometric properties, were used to inform our combined estimate. The marginal posterior densities of all parameters were estimated using Hamiltonian Monte Carlo (HMC), a Markov Chain Monte Carlo technique. Summaries of posterior distributions, including the means and 95% equally tailed posterior credible intervals, were used for interpretation of the results. The combined prevalence mean was 8.6%, with a credible interval of 6.8-10.6%. This combined estimate sits between Bayesian-derived prevalence estimates from administrative data-derived diagnoses (mean = 7.4%) and the survey-derived diagnoses (mean = 13.9%). The results of our sensitivity analysis suggest that varying the specificity of the survey-derived measure has an appreciable impact on the combined posterior prevalence estimate. Our combined posterior prevalence estimate remained stable when varying other prior information. We detected no problematic HMC behaviour, and our posterior predictive checks suggest that our model can reliably recreate our data. Accurate population-based estimates of disease are the cornerstone of health service planning and resource allocation. As a greater number of linked population data sources become available, so too does the opportunity for researchers to fully capitalise on the data. The true population prevalence of mood and anxiety disorders may reside between estimates obtained from survey data and health administrative data. We have demonstrated how the use of Bayesian approaches may provide a more informed and accurate estimate of mood and anxiety disorders in the population. This work provides a blueprint for future population-based estimates of disease using linked health data.

Mental Illness and Mental Health Care Treatment among People with Criminal Justice Involvement in the United States.

This study analyzes the effect of arrest and community supervision on mental health and mental health care using a U.S.-based nationally representative data from National Survey of Drug Use and Health (NSDUH) and Behavioral Model for Vulnerable Populations. It conducts a bivariate analysis to estimate population prevalence rates and generalized structural equation modeling (GSEM) to test the conceptual model. Individuals who were recently arrested or under community supervision (probation/parole) are more likely to use treatment (medication, inpatient, and outpatient care) for mental health problems compared with those with no CJ contact. However, there still remains a large unmet need. Overall, people with mental illness may be accessing mental health treatment through their involvement with the CJ system more broadly. There is the need for multiple societal institutions to work in tandem and communicate with vulnerable individuals who experience CJ contact to further assess appropriate assistance and mental health treatment.

Trends of Seroprevalence of Chagas´s diseases in healthy blood donors, solid organ donors and heart transplant recipients: experience of a single health care center in Colombia.

The prevalence of Chagas disease has decreased in the Americas region due to vector control measures. However, non-vectorial transmission through blood transfusions and organ transplantation has gained importance in recent years. Screening among blood and organ donors are essential to reduce Trypanosoma cruzi transmission and could provide information to estimate population prevalence. We conducted a cross-sectional study on the prevalence of immunoglobulin G (IgG) antibodies against T. cruzi in healthy blood donors, solid organ donors and heart transplant recipients from 2012 to 2019. We found a total of 99 357 IgG T. cruzi results during the study period. The cumulative seroprevalence in healthy blood donors was 0.13% (95% confidence interval (CI) 0.10–0.15), in organ donors was 0.53% (95% CI 0.06–1.92) and in heart transplant recipients was 3.03 (95% CI 0.07–15.75). Seroprevalence trend in healthy blood donors showed annual increase between 2012 and 2015, decreasing in the following years. No trend was seen in organ donors neither heart recipients. Adjusted rates did not show difference by sex and age among blood donors. No significant increases in seroprevalence T. cruzi were found during the study period. T. cruzi transmission remains low.

The influence of age on the diagnostic yield of genetic testing in dilated cardiomyopathy

Abstract Background Dilated cardiomyopathy (DCM) has an estimated population prevalence of 1/250 and is the underlying diagnosis in a third of heart failure patients. A substantial proportion of patients have familial disease caused by dominant mutations in one of more than 50 genes, but clinical practice guidelines recommend genetic testing in young patients with idiopathic DCM. There is an absence of robust data on the influence of age on the diagnostic yield of genetic testing. Methods The study cohort comprised 825 consecutive and unrelated patients (524 male (63.5%)) with DCM who underwent genetic testing from 2015 to 2019. Genetic variants were classified using American College of Medical Genetics (ACMG) criteria. Analyses were stratified by age and sex. Results 173 (20.1%) patients had a positive genetic test (“pathogenic” or “likely pathogenic” variant); 292 (34.4%) had a variant of unknown significance. Mean age at genetic testing was 49.9±14.4 years. Mean age of patients with a positive test was 47.6±13.6 years. 99 (18.9%) men and 67 (22.3%) women had a positive test (p=0.246). Mutations in the TTN gene, encoding for titin, accounted for 46.1% of positive results. 13.8% of mutations were in DSP, 8.4% in RBM20, 6% in FLNC, 4.2% in LMNA, 3.6% in BAG3 and 3.6% in MYH7. There was a trend to declining yield with age (likelihood ratio chi-square p value = 0.047). The yield was 17.2% in the 56–65 year age group and 11.5% above 66 years of age (figure 1). Conclusions Approximately 1 in 5 patients with DCM had a positive genetic test. The yield declined in patients over 66 years but remained above 11%, suggesting that genetic testing should not be confined to younger patients with DCM. Figure 1. Yield of Genetic Testing by Age Funding Acknowledgement Type of funding source: None

Estimated population prevalence of cardiac transthyretin amyloidosis in elderly men derived from incidental cardiac uptake from routine bone scans – “we're going to need a bigger boat”

Abstract Background/Introduction Transthyretin amyloidosis (TTR) is a cause of restrictive cardiomyopathy and heart failure predominantly in elderly men. Two main factors have moved TTR amyloidosis from super-specialist centres into mainstream cardiology: We aimed to determine the potential magnitude of referrals to our embryonic cardiac TTR service from patients having routine bone scans for non-cardiac reasons. We planned to estimate the prevalence of cardiac TTR in our local over 65 male population to plan service provision. Methods All HDP bone scans performed at a teaching and research hospital in the UK from the 2017/18 financial year were reviewed (n=1530). Our hospital is the only provider of these scans locally. Of these, 1399 were for oncological and musculoskeletal (oncology/MSK) indications and 37 were referred to specifically “exclude amyloidosis”. We excluded paediatric and duplicate follow-up imaging. There are approximately 140,000 people over aged 65 living within our catchment region. We have assumed approximately 50% are male. Results Myocardial uptake was present in 7/1399 of the oncology/MSK group and 3/7 (43%) of these already had features of heart failure. In these 7 patients bone scans were performed to investigate bony metastases in 6 (1 oesophageal cancer and 5 for prostate cancer) and 1 following an orthopaedic procedure. Cardiac uptake was present in 10/37 of the “exclude amyloid” group. In those with cardiac uptake across both groups (17) 94% were male with a mean age of 83 (sd ±6.59) and 41% were from the oncology/musculoskeletal group. Incidental cardiac uptake was seen in 1:200 routine HDP scans. When looking at males >65 specifically the uptake rate increases to (6 out of 701 scans) i.e. 1:117. Assuming there is no increased risk of TTR in patients with prostate or oesophageal cancer, then an estimate of cardiac TTR in the 75,000 males over the age of 65 locally would be approximately 640 men. Conclusions Bone scans account for 41% of all HDP scans with incidental cardiac uptake and therefore represents a significant potential referral source for a cardiac amyloid service. Our data suggests a potential prevalence of cardiac amyloidosis in 1:117 men over 65 with 43% already having heart failure symptoms and signs. Our age and sex specific prevalence suggests cardiac TTR is neither a rare nor unusual diagnosis. We will use our prevalence estimate to ensure our cardiac TTR service is resourced appropriately. We suggest that cardiac amyloid and cardio-oncology services should include pathways incorporating rapid access routes for suitable patients with incidental cardiac uptake on bone scans performed by non-cardiologists. Funding Acknowledgement Type of funding source: None

Estimation of Population Prevalence of COVID-19 Using Imperfect Tests

I formulate three basic biomedical/statistical assumptions that should ideally guide well-designed population prevalence studies of the present or past disease including COVID-19. On the basis of these assumptions alone, I compute several probability distributions required for statistical analysis of testing data collected from a sample of individuals drawn from a heterogeneous population. I also construct a consistent asymptotically unbiased estimator of the population prevalence of the disease or infection from the collected data and derive a simple upper bound for its variance. All the results are rigorously proved and valid for any test for COVID-19 or other disease provided that the sum of the test’s sensitivity and specificity is larger than 1. A few recommendations for the design of COVID-19 prevalence studies informed by the results of this work are formulated. The methodology developed in this article may prove applicable to diseases and conditions other than COVID-19 as well as in some non-epidemiological settings.

Covid-19 Estimating the burden of symptomatic disease in the community and the impact of public health measures on physical, mental and social wellbeing: a study protocol

Introduction: Covid-19 was declared a pandemic in March 2020. Since then, governments have implemented unprecedented public health measures to contain the virus. This study will provide evidence to inform responses to the pandemic by: i) estimating population prevalence and trends of self-reported symptoms of Covid-19 and the proportions of symptomatic individuals and household contacts testing positive for Covid-19; ii) describing acceptance and compliance with physical-distancing measures, explore effects of public health measures on physical, mental and social wellbeing; iii) developing a mathematical network model to inform decisions on the optimal levels of physical distancing measures. Methods: Two cross-sectional nationally-representative telephone surveys will be conducted in Ireland using random digit-dialling, with response rates estimates based on proportion of non-operational and non-answering numbers. The first survey with four waves in May and June will address adherence to social distancing measures and whether the respondent or other household members are or have been unwell during the preceding two weeks with one or more symptoms of Covid-19. The second survey with three waves in June, July and September will address knowledge, attitudes, and compliance towards physical-distancing measures and physical, mental and social wellbeing. The mathematical network model will be developed for all-Ireland (on various levels of spatial granularity including the scale of counties and electoral divisions) based on outputs from both cross-sectional surveys and relevant publicly available data to inform decisions on optimal levels and duration of physical distancing measures. Discussion: This study will contribute to our understanding of the impact and sustainability of public health measures of the Covid-19 pandemic. Findings will have long-lasting benefits, informing decision-making on the best levels, and duration of physical-distancing measures, balancing a range of factors including capacity of the health service with the effects on individuals’ wellbeing and economic disruption. Findings will be shared with key policy-makers.

England SimSmoke: the impact of nicotine vaping on smoking prevalence and smoking‐attributable deaths in England

Whereas the use of nicotine vaping products (NVPs) is widespread, their impact on smoking prevalence is controversial. This study considered the potential impact of NVPs on smoking prevalence in England. Indirect simulation model. The England SimSmoke model is validated through 2012, before NVP use became more widely used by smokers. Because information on NVP-related transitions is limited, an indirect method is used; the difference in observed smoking prevalence (reflecting NVPs) is compared with a 2012-2019 counterfactual No-NVP scenario (without NVPs) to estimate the impact of NVPs on smoking and smoking-attributable deaths. England, 2000-2019. Nationally representative sample of population. England's population, mortality rates and smoking prevalence estimates from three national surveys and tobacco control policies. Between 2000 and 2012, SimSmoke projected a decline in age 18+smoking prevalence of 23.5% in men and 27.0% in women. These projections, as well as those by specific age groups, were generally consistent with findings from the three national surveys. Comparing 2012-2019 relative reduction in age 18+prevalence from the Annual Population Survey (males, 27.5%) with the model-predicted No-NVP reduction (males, 7.3%), the implied NVP-attributable relative reduction in adult smoking prevalence was 20.2% (95% CI, 18.8%-22.0%) for males and 20.4% (18.7%-22.2%) for females. The NVP-attributable reduction was 27.2% (22.8%-31.6%) for males and 31.7% (27.4%-36.5%) for females ages 18-24 and 18.6% (15.2%-21.8%) for males and 15.0% (11.1%-18.8%) for females ages 25-34, with similar reductions for ages 35+. The implied reduction in smoking prevalence between 2012 and 2019 equates to 165 660 (132 453-199 501) averted deaths by 2052. Other surveys yielded smaller, but relatively consistent results. An indirect method of simulation modelling indicates that substantial reductions in smoking prevalence occurred in England from 2012-2019 coinciding with the growth in nicotine vaping product use.

Agency notification and retrospective self-reports of childhood maltreatment in a 30-Year cohort: Estimating population prevalence from different data sources

BackgroundThere is little known about how two sources of child maltreatment reports correspond, specifically for emotional, physical, sexual abuse and emotional or physical neglect. ObjectiveTo compare agency and self-reports of child maltreatment and determine whether they are predicted by similar early life course adverse experiences. Participants Data are taken from a long running birth cohort study (Mater-University of Queensland Study of Pregnancy - MUSP). Mothers (N=7223) were recruited in 1981-83 and their children were followed-up 30 years later (2010-14). In 2000 data from the relevant child protection agency were accessed and linked to the survey data. Setting Consecutive women giving birth to a live singleton baby at a major obstetrical service in Brisbane, Australia were recruited and both mother and child were repeatedly follow-up over a 30 year period. Methods Birth cohort study with data linkage of child protection agency records and self-report survey data (using the Childhood Trauma Questionnaire - CTQ) of childhood maltreatment experiences. We compare reports of emotional, physical and sexual abuse and neglect using agency and self-reports (Cramer's V and kappa). ResultsMost children who are notified cases of maltreatment subsequently self-report they experienced little or no maltreatment in childhood. Most children who report experiencing severe maltreatment have not previously been notified to the protection agency. Teenage mothers have children who are notified 2.89 (1.52, 5.52) times, self-report 2.01 (1.31, 3.09) times and both notified and self-report 3.61 (2.26, 6.10) times more than their older comparison mothers. ConclusionsDifferent methods of assessing maltreatment identify different subsets of those children who have experienced maltreatment.

Comparative analysis of three laboratory based serological assays for SARS-CoV-2 in an Australian cohort

Many unanswered questions remain regarding the role of SARS-CoV-2 serological assays in this unfolding COVID-19 pandemic. These include their utility for the diagnosis of acute SARS-CoV-2 infection, past infection or exposure, correlation with immunity and the effective duration of immunity. This study examined the performance of three laboratory based serological assays, EUROIMMUN Anti-SARS-CoV-2 IgA/IgG, MAGLUMI 2000 Plus 2019-nCov IgM/IgG and EDI Novel Coronavirus (COVID-19) IgM/IgG immunoassays.We evaluated 138 samples from a reference non-infected population and 71 samples from a cohort of 37 patients with SARS-CoV-2 confirmed positive by RT-PCR. The samples were collected at various intervals of 0–45 days post symptoms onset (PSO).Specificity and sensitivity of these assays was 60.9%/71.4% (IgA) and 94.2%/63.3% (IgG) for EUROIMMUN; 98.5%/18.4% (IgM) and 97.8%/53.1% (IgG) for MAGLUMI; and 94.9%/22.5% (IgM) and 93.5%/57.1% (IgG) for EDI, respectively. When samples collected ≥14 days PSO were considered, the sensitivities were 100.0 and 100.0%; 31.0 and 82.8%; 34.5 and 57.1%, respectively. Using estimated population prevalence of 0.1, 1, and 10%, the positive predictive value of all assays remained low.The EUROIMMUN Anti-SARS-CoV-2 IgA lacked specificity for acute diagnosis and all IgM assays offered poor diagnostic utility. Seroconversion can be delayed although all patients had seroconverted at 28 days in our cohort with the EUROIMMUN Anti-SARS-CoV-2 IgG. Despite this, with specificity of only 94% this assay would not be satisfactory for seroprevalence studies in the general Australian population given this is likely to be currently <1%.

Covid-19 Estimating the burden of symptomatic disease in the community and the impact of public health measures on physical, mental and social wellbeing: a study protocol

Introduction: Covid-19 was declared a pandemic in March 2020. Since then, governments have implemented unprecedented public health measures to contain the virus. This study will provide evidence to inform responses to the pandemic by: i) estimating population prevalence and trends of self-reported symptoms of Covid-19 and the proportions of symptomatic individuals and household contacts testing positive for Covid-19; ii) describing acceptance and compliance with physical-distancing measures, explore effects of public health measures on physical, mental and social wellbeing; iii) developing a mathematical network model to inform decisions on the optimal levels of physical distancing measures. Methods: Two cross-sectional nationally-representative telephone surveys will be conducted in Ireland using random digit-dialling, with response rates estimates based on proportion of non-operational and non-answering numbers. The first survey with four waves in May and June will address adherence to social distancing measures and whether the respondent or other household members are or have been unwell during the preceding two weeks with one or more symptoms of Covid-19. The second survey with three waves in June, July and September will address knowledge, attitudes, and compliance towards physical-distancing measures and physical, mental and social wellbeing. The mathematical network model will be developed for all-Ireland (on various levels of spatial granularity including the scale of counties and electoral divisions) based on outputs from both cross-sectional surveys and relevant publicly available data to inform decisions on optimal levels and duration of physical distancing measures. Discussion: This study will contribute to our understanding of the impact and sustainability of public health measures of the Covid-19 pandemic. Findings will have long-lasting benefits, informing decision-making on the best levels, and duration of physical-distancing measures, balancing a range of factors including capacity of the health service with the effects on individuals’ wellbeing and economic disruption. Findings will be shared with key policy-makers.

The Emergency Department Trigger Tool: Validation and Testing to Optimize Yield

Recognized as a premier approach for adverse event (AE) detection, trigger tools have been developed for multiple clinical settings outside the emergency department (ED). We recently derived and tested an ED trigger tool (EDTT) with enhanced features for high-yield detection of harm, consisting of 30 triggers associated with AEs. In this study, we validate the EDTT in an independent sample and compare record selection approaches to optimize yield for quality improvement. This is a retrospective observational study using data from 13months of visits to an urban, academic ED by patients aged ≥ 18years (92,859 records). We conducted standard two-tiered trigger tool reviews on an independent validation sample of 3,724 records with at least one of the 30 triggers found associated with AEs in our previous derivation sample (N=1,786). We also tested three new candidate triggers and reviewed 72 records with no triggers for comparison purposes. We compare derivation and validation samples on: 1) triggers showing persistent associations with AEs, 2) AE yield (AEs detected/records reviewed), and 3) representativeness of AE types detected. We use bivariate associations of triggers with AEs as the basis for trigger selection. We then use multivariable modeling in the combined derivation and validation samples to determine AE risk scores using trigger weights. This allows us to predict occurrence of AEs and derive population prevalence estimates. Finally, we compare yield for detection of AEs under three record selection strategies (random selection, trigger counts, weighted trigger counts). Twenty-four of the 30 triggers were confirmed to be associated with AEs on bivariate testing. Three previously marginal triggers and two of three new candidate triggers were also found to be associated with AEs. The presence of any of these 29 triggers was associated with an AE rate of 10% in our selected sample (compared to 1.1% for none, p<0.001). The risk of an AE increased with number of triggers. Combining data from both phases, we identified 461 AEs in 429 unique visits in 5,582 records reviewed. Our multivariable model (which emphasized parsimony) retained 12 triggers with a ROC AUC of 82% in both samples. Selecting records for review based on number of triggers improves yield to 14% for 4+ triggers (top 10% of visits) and to 28% for 8+ (top 1%). A weighted trigger count has corresponding yields of 18 and 38%. The method for selecting records for review did not appear to affect event-type representativeness, with similar distributions of event types and severities detected. In this single-site study of the EDTT we observed high levels of validity in trigger selection, yield, and representativeness of AEs, with yields that are superior to estimates for traditional approaches to AE detection. Record selection using weighted triggers outperforms a trigger count threshold approach and far outperforms random sampling from records with at least one trigger. The EDTT is a promising efficient and high-yield approach for detecting all-cause harm to guide quality improvement efforts in the ED.

Respiratory illness among Gulf War and Gulf War era veterans who use the Department of Veterans Affairs for healthcare.

Veterans of the 1990-1991 Gulf War were exposed to a variety of toxic substances during their service that included several airborne hazards, but only a few small studies have assessed respiratory outcomes in Gulf War veterans. This paper presents population prevalence estimates and prevalence ratios of respiratory disease among Gulf War and Gulf War Era veterans who use VA healthcare. A total of 360,909 Gulf War deployed veterans and 323,638 Gulf War Era non-deployed veterans were included in the analysis. Ten-year period prevalence rates (PRs) for fifteen respiratory diseases were calculated for Gulf War and Gulf War Era veterans and period prevalence ratios comparing Gulf War veterans to Gulf War Era veterans were calculated. The five respiratory conditions with the highest prevalence per 100,000 veterans across both Gulf War deployed and Gulf War Era non-deployed veterans (respectively) were: allergic rhinitis (8,400 and 8,041), chronic obstructive pulmonary disease (4,763 and 4,795), asthma (4,685 and 4,477), chronic airway obstruction (3,983 and 4,059), and chronic sinusitis (2,863 and 2,672). The adjusted PRs showed a small, but significantly increased, elevation in Gulf War-deployed compared to Gulf War Era non-deployed veterans for chronic bronchitis (PR 1.19; 95% CI 1.10, 1.28), emphysema (PR 1.11; 95% CI 1.01, 1.21), chronic airway obstruction (PR 1.09; 95% CI 1.07, 1.12), and chronic obstructive pulmonary disease (PR 1.09; 1.07, 1.11). Gulf War veterans should continue to be monitored in the future to better evaluate the potential long-term consequences on respiratory health.

Covid-19 Estimating the burden of symptomatic disease in the community and the impact of public health measures on physical, mental and social wellbeing: a study protocol.

Introduction: Covid-19 was declared a pandemic in March 2020. Since then, governments have implemented unprecedented public health measures to contain the virus. This study will provide evidence to inform responses to the pandemic by: i) estimating population prevalence and trends of self-reported symptoms of Covid-19 and the proportions of symptomatic individuals and household contacts testing positive for Covid-19; ii) describing acceptance and compliance with physical-distancing measures, explore effects of public health measures on physical, mental and social wellbeing; iii) developing a mathematical network model to inform decisions on the optimal levels of physical distancing measures. Methods: Two cross-sectional nationally-representative telephone surveys will be conducted in Ireland using random digit-dialling, with response rates estimates based on proportion of non-operational and non-answering numbers. The first survey with four waves in May and June will address adherence to social distancing measures and whether the respondent or other household members are or have been unwell during the preceding two weeks with one or more symptoms of Covid-19. The second survey with three waves in June, July and September will address knowledge, attitudes, and compliance towards physical-distancing measures and physical, mental and social wellbeing. The mathematical network model will be developed for all-Ireland (on various levels of spatial granularity including the scale of counties and electoral divisions) based on outputs from both cross-sectional surveys and relevant publicly available data to inform decisions on optimal levels and duration of physical distancing measures. Discussion: This study will contribute to our understanding of the impact and sustainability of public health measures of the Covid-19 pandemic. Findings will have long-lasting benefits, informing decision-making on the best levels, and duration of physical-distancing measures, balancing a range of factors including capacity of the health service with the effects on individuals' wellbeing and economic disruption. Findings will be shared with key policy-makers.