A Test-Negative Design with Additional Population Controls Can Be Used to Rapidly Study Causes of the SARS-CoV-2 Epidemic.

Testing of symptomatic persons for infection with severe acute respiratory syndrome coronavirus-2 is occurring worldwide. We propose two types of case-control studies that can be carried out jointly in test settings for symptomatic persons. The first, the test-negative case-control design (TND) is the easiest to implement; it only requires collecting information about potential risk factors for Coronavirus Disease 2019 (COVID-19) from the tested symptomatic persons. The second, standard case-control studies with population controls, requires the collection of data on one or more population controls for each person who is tested in the test facilities, so that test-positives and test-negatives can each be compared with population controls. The TND will detect differences in risk factors between symptomatic persons who have COVID-19 (test-positives) and those who have other respiratory infections (test-negatives). However, risk factors with effect sizes of equal magnitude for both COVID-19 and other respiratory infections will not be identified by the TND. Therefore, we discuss how to add population controls to compare with the test-positives and the test-negatives, yielding two additional case-control studies. We describe two options for population control groups: one composed of accompanying persons to the test facilities, the other drawn from existing country-wide healthcare databases. We also describe other possibilities for population controls. Combining the TND with population controls yields a triangulation approach that distinguishes between exposures that are risk factors for both COVID-19 and other respiratory infections, and exposures that are risk factors for just COVID-19. This combined design can be applied to future epidemics, but also to study causes of nonepidemic disease

Variance estimation for a low-income proportion

Proportions below a given fraction of a quantile of an income distribution are often estimated from survey data in poverty comparisons. We consider the estimation of the variance of such a proportion, estimated from Family Expenditure Survey data. We show how a linearization method of variance estimation may be applied to this proportion, allowing for the effects of both a complex sampling design and weighting by a raking method to population controls. We show that, for 1998-99 data, the estimated variances are always increased when allowance is made for the design and raking weights, the principal effect arising from the design. We also study the properties of a simplified variance estimator and discuss extensions to a wider class of poverty measures

Health-related quality of life in patients waiting for major joint replacement. A comparison between patients and population controls

BACKGROUND: Several quality-of-life studies in patients awaiting major joint replacement have focused on the outcomes of surgery. Interest in examining patients on the elective waiting list has increased since the beginning of 2000. We assessed health-related quality of life (HRQoL) in patients waiting for total hip (THR) or knee (TKR) replacement in three Finnish hospitals, and compared patients' HRQoL with that of population controls. METHODS: A total of 133 patients awaiting major joint replacement due to osteoarthritis (OA) of the hip or knee joint were prospectively followed from the time the patient was placed on the waiting list to hospital admission. A sample of controls matched by age, gender, housing and home municipality was drawn from the computerised population register. HRQoL was measured by the generic 15D instrument. Differences between patients and the population controls were tested by the independent samples t-test and between the measurement points by the paired samples t-test. A linear regression model was used to explain the variance in the 15D score at admission. RESULTS: At baseline, 15D scores were significantly different between patients and the population controls. Compared with the population controls, patients were worse off on the dimensions of moving (P < 0.001), sleeping (P < 0.001), sexual activity (P < 0.001), vitality (P < 0.001), usual activities (P < 0.001) and discomfort and symptoms (P < 0.001). Further, psychological factors – depression (P < 0.001) and distress (P = 0.004) – were worse among patients than population controls. The patients showed statistically significantly improved average scores at admission on the dimensions of moving (P = 0.026), sleeping (P = 0.004) and discomfort and symptoms (P = 0.041), but not in the overall 15D score compared with the baseline. In patients, 15D score at baseline (P < 0.001) and body mass index (BMI) (P = 0.020) had an independent effect on patients' 15D score at hospital admission. CONCLUSION: Although patients' HRQoL did not deteriorate while waiting, a consistently worse HRQoL was observed in patients waiting for major joint replacement compared with population controls

Maternal and paternal carriage of the annexin A5 M2 haplotype: a possible risk factor for recurrent implantation failure (RIF)

OBJECTIVE This study was carried out to determine the potential role of the M2/ANXA5 haplotype as a risk factor for recurrent implantation failure (RIF). Carriage of the M2/ANXA5 haplotype that induces prothrombotic changes has been implicated in failure of early pregnancies and placenta-mediated complications (preeclampsia, IUGR, preterm birth). MATERIAL AND METHODS In the present case control study, 63 couples (females and males) with RIF presenting for IVF/ICSI to the Fertility Center of masked were analyzed. RIF was defined as ≥ 4 consecutive failed ART-transfers of ≥ 4 blastocysts or ≥ 8 cleavage-stage embryos of optimal quality and maternal age ≤ 41. Fertile female controls (n = 90) were recruited from the same center. Population controls (n = 533) were drafted from the PopGen biobank, UKSH Kiel. RESULTS Couples carrying the M2/ANXA5 haplotype turned out to have a significantly increased relative risk (RR) for RIF. Compared with female fertile controls, RR was 1.81 with p = 0.037 (OR 2.1, 95{\%}CI 1.0-4.3) and RR was 1.70, with p = 0.004 (OR 2.0, 95{\%}CI 1.2-3.1) compared with population controls (15.4{\%} M2 carriers). Male partners were comparable with RIF females for M2/ANXA5 haplotypes (28.6{\%} vs. 23.8{\%}, p = 0.54). RIF females compared with population controls had a RR of 1.55 (p = 0.09) and RIF males compared with population controls had a RR of 1.9 (p = 0.01). Couples with ≥ 7 failed transfers showed a RR of 1.82 (p = 0.02) compared with population controls. CONCLUSION Our findings suggest that maternal as well as paternal M2/ANXA5 haplotype carriages are risk factors for RIF. These results allow new insights into the pathogenesis of RIF and might help to identify relevant risk groups

Analysis of Rare, Exonic Variation amongst Subjects with Autism Spectrum Disorders and Population Controls

We report on results from whole-exome sequencing (WES) of 1,039 subjects diagnosed with autism spectrum disorders (ASD) and 870 controls selected from the NIMH repository to be of similar ancestry to cases. The WES data came from two centers using different methods to produce sequence and to call variants from it. Therefore, an initial goal was to ensure the distribution of rare variation was similar for data from different centers. This proved straightforward by filtering called variants by fraction of missing data, read depth, and balance of alternative to reference reads. Results were evaluated using seven samples sequenced at both centers and by results from the association study. Next we addressed how the data and/or results from the centers should be combined. Gene-based analyses of association was an obvious choice, but should statistics for association be combined across centers (meta-analysis) or should data be combined and then analyzed (mega-analysis)? Because of the nature of many gene-based tests, we showed by theory and simulations that mega-analysis has better power than meta-analysis. Finally, before analyzing the data for association, we explored the impact of population structure on rare variant analysis in these data. Like other recent studies, we found evidence that population structure can confound case-control studies by the clustering of rare variants in ancestry space; yet, unlike some recent studies, for these data we found that principal component-based analyses were sufficient to control for ancestry and produce test statistics with appropriate distributions. After using a variety of gene-based tests and both meta- and mega-analysis, we found no new risk genes for ASD in this sample. Our results suggest that standard gene-based tests will require much larger samples of cases and controls before being effective for gene discovery, even for a disorder like ASD. © 2013 Liu et al

Risk of mortality (including sudden cardiac death) and major cardiovascular events in atypical and typical antipsychotic users: a study with the general practice research database.

Objective. Antipsychotics have been associated with increased cardiac events including mortality. This study assessed cardiac events including mortality among antipsychotic users relative to nonusers. Methods. The General Practice Research Database (GPRD) was used to identify antipsychotic users, matched general population controls, and psychiatric diseased nonusers. Outcomes included cardiac mortality, sudden cardiac death (SCD), all-cause mortality (excluding suicide), coronary heart disease (CHD), and ventricular arrhythmias (VA). Sensitivity analyses were conducted for age, dose, duration, antipsychotic type, and psychiatric disease. Results. 183,392 antipsychotic users (115,491 typical and 67,901 atypical), 544,726 general population controls, and 193,920 psychiatric nonusers were identified. Nonusers with schizophrenia, dementia, or bipolar disorder had increased risks of all-cause mortality compared to general population controls, while nonusers with major depression had comparable risks. Relative to psychiatric nonusers, the adjusted relative ratios (aRR) of all-cause mortality in antipsychotic users was 1.75 (95% CI: 1.64-1.87); cardiac mortality 1.72 (95% CI: 1.42-2.07); SCD primary definition 5.76 (95% CI: 2.90-11.45); SCD secondary definition 2.15 (95% CI: 1.64-2.81); CHD 1.16 (95% CI: 0.94-1.44); and VA 1.16 (95% CI: 1.02-1.31). aRRs of the various outcomes were lower for atypical versus typical antipsychotics (all-cause mortality 0.83 (95% CI: 0.80-0.85); cardiac mortality 0.89 (95% CI: 0.82-0.97); and SCD secondary definition 0.76 (95% CI: 0.55-1.04). Conclusions. Antipsychotic users had an increased risk of cardiac mortality, all-cause mortality, and SCD compared to a psychiatric nonuser cohort

Autoantibodies against MDA-LDL in subjects with severe and minor atherosclerosis and healthy population controls

Autoantibodies against oxidized low-density lipoprotein (LDL) have been reported to be associated with atherosclerosis. However, data are not consistent. We compared the titres of autoantibodies to malondialdehyde-modified LDL in three groups, a case group with angiographically documented severe coronary stenosis (> 80% stenosis in at least 1 vessel, n = 47), a hospital control group with minor stenosis on the coronary angiography (< 50% stenosis in all three major vessels, n = 47) and a healthy population control group with no history of coronary heart disease (n = 49). Age ranged from 26 to 68 years. Subjects were frequency-matched for gender distribution and storage time of the blood samples. No relevant differences in autoantibody titre between case and control groups were found. The mean autoantibody titres (± S.D.) were 1.44 ± 1.82, 1.46 ± 1.40 and 1.62 ± 1.95 for cases, hospital controls and population controls, respectively. No correlations were found between autoantibody titre and age, number of cigarettes smoked and LDL or total cholesterol. Autoantibody titres were correlated wit

Stratified Meta-Analysis to Examine Data Biases in Lung Cancer Studies of Refinery Workers

Petroleum refineries employ a variety of workers who historically experienced different potentials for asbestos exposure depending on job tasks. Associations between petroleum refinery work and lung cancer related to occupational asbestos exposure have been quantified among various locations, corporations, and time periods. To combine the data from several individual refinery studies and examine an overall effect, a systematic review and stratified meta-analysis was employed. Using set search terms among four databases, 112 potential publications were identified, of which 29 qualified for meta-analysis. Risk estimates and confidence intervals were extracted from these publications to construct four separate datasets. Inverse variance weighting assuming random effects was used to combine Standardized Mortality and Incidence data separately for all male and female refinery workers, as well as both standardized and relative risk measurements for the subset of male maintenance workers, who may have been exposed to higher levels. Males in cohorts consisting of all refinery workers, which included both blue and white collar workers, had a meta-Risk Ratio (mRR) of 0.80 (95% CI: 0.75-0.85) when compared to population controls, all female refinery workers had an mRR of 1.27 (95% CI: 0.86-1.87) when compared to population controls, a statistically significant difference. Male maintenance workers exhibited an mRR of 0.88 (95% CI: 0.74-1.04) with population controls, and an mRR of 1.62 (95% CI: 1.30-2.02) when internally compared to other refinery workers. This large differential in risk estimates for the same population could be related to sampling biases in opposite directions: population controls are subject to the “healthy worker effect”, while internal comparisons may differ from maintenance workers in both socio-economics and smoking rates. Due these potentially confounded and conflicting results, no conclusion could be drawn regarding lung cancer risk for refinery workers. Accurate quantification of lung cancer risk for refinery workers will depend on addressing these issues. Keywords: Biostatistics, Meta-analysis, Risk, Epidemiology, Review, Database, Cancer, Asbestos, Bias, Confoundin