Pathogen exposure misclassification can bias association signals in GWAS of infectious diseases when using population-based common control subjects.

Genome-wide association studies (GWASs) have been performed to identify host genetic factors for a range of phenotypes, including for infectious diseases. The use of population-based common control subjects from biobanks and extensive consortia is a valuable resource to increase sample sizes in the identification of associated loci with minimal additional expense. Non-differential misclassification of the outcome has been reported when the control subjects are not well characterized, which often attenuates the true effect size. However, for infectious diseases the comparison of affected subjects to population-based common control subjects regardless of pathogen exposure can also result in selection bias. Through simulated comparisons of pathogen-exposed cases and population-based common control subjects, we demonstrate that not accounting for pathogen exposure can result in biased effect estimates and spurious genome-wide significant signals. Further, the observed association can be distorted depending upon strength of the association between a locus and pathogen exposure and the prevalence of pathogen exposure. We also used a real data example from the hepatitis C virus (HCV) genetic consortium comparing HCV spontaneous clearance to persistent infection with both well-characterized control subjects and population-based common control subjects from the UK Biobank. We find biased effect estimates for known HCV clearance-associated loci and potentially spurious HCV clearance associations. These findings suggest that the choice of control subjects is especially important for infectious diseases or outcomes that are conditional upon environmental exposures.

Development of the multivariate administrative data cystectomy model and its impact on misclassification bias.

BACKGROUND: Misclassification bias (MB) is the deviation of measured from true values due to incorrect case assignment. This study compared MB when cystectomy status was determined using administrative database codes vs. predicted cystectomy probability. METHODS: We identified every primary cystectomy-diversion type at a single hospital 2009-2019. We linked to claims data to measure true association of cystectomy with 30 patient and hospitalization factors. Associations were also measured when cystectomy status was assigned using billing codes and by cystectomy probability from multivariate logistic regression model with covariates from administrative data. MB was the difference between measured and true associations. RESULTS: 500 people underwent cystectomy (0.12% of 428 677 hospitalizations). Sensitivity and positive predictive values for cystectomy codes were 97.1% and 58.6% for incontinent diversions and 100.0% and 48.4% for continent diversions, respectively. The model accurately predicted cystectomy-incontinent diversion (c-statistic [C] 0.999, Integrated Calibration Index [ICI] 0.000) and cystectomy-continent diversion (C:1.000, ICI 0.000) probabilities. MB was significantly lower when model-based predictions was used to impute cystectomy-diversion type status using for both incontinent cystectomy (F = 12.75; p < .0001) and continent cystectomy (F = 11.25; p < .0001). CONCLUSIONS: A model using administrative data accurately returned the probability that cystectomy by diversion type occurred during a hospitalization. Using this model to impute cystectomy status minimized MB. Accuracy of administrative database research can be increased by using probabilistic imputation to determine case status instead of individual codes.

Does misclassification of former tobacco smokers explain the 'smoker's paradox' in the risk of COVID-19? Insights from the Stockholm Public Health Cohort.

BACKGROUND: The association between tobacco smoking and the risk of COVID-19 and its adverse outcomes is controversial, as studies reported contrasting findings. Bias due to misclassification of the exposure in the analyses of current versus non-current smoking could be a possible explanation because former smokers may have higher background risks of the disease due to co-morbidity. The aim of the study was to investigate the extent of this potential bias by separating non-, former, and current smokers when assessing the risk or prognosis of diseases. METHODS: We analysed data from 43,400 participants in the Stockholm Public Health Cohort, Sweden, with information on smoking obtained prior to the pandemic. We estimated the risk of COVID-19, hospital admissions and death for (a) former and current smokers relative to non-smokers, (b) current smokers relative to non-current smokers, that is, including former smokers; adjusting for potential confounders (aRR). RESULTS: The aRR of a COVID-19 diagnosis was elevated for former smokers compared with non-smokers (1.07; 95% confidence interval (CI) =1.00-1.15); including hospital admission with any COVID-19 diagnosis (aRR= 1.23; 95% CI = 1.03-1.48); or with COVID-19 as the main diagnosis (aRR=1.23, 95% CI= 1.01-1.49); and death within 30 days with COVID-19 as the main or a contributory cause (aRR=1.40; 95% CI=1.00-1.95). Current smoking was negatively associated with risk of COVID-19 (aRR=0.79; 95% CI=0.68-0.91). CONCLUSIONS: Separating non-smokers from former smokers when assessing the disease risk or prognosis is essential to avoid bias. However, the negative association between current smoking and the risk of COVID-19 could not be entirely explained by misclassification.

Alcohol and early mortality (before 65 years) in the 'Seguimiento Universidad de Navarra' (SUN) cohort: does any level reduce mortality?

The aim of this study was to assess the association between alcohol intake and premature mortality (younger than 65 years) and to explore the effect of potential alcohol underreporting by heavy drinkers. We followed-up 20 272 university graduates. Four categories of alcohol intake were considered (abstainer, light, moderate and heavy consumption). Repeated measurements of alcohol intake and updated information on confounders were used in time-dependent Cox models. Potential underreporting of alcohol intake by some heavy drinkers (likely misclassified as light or moderate drinkers) was explicitly addressed in an attempt to correct potential underreporting by using indirect information. During 12·3 years of median follow-up (interquartile range: 6·8-15·0), 226 participants died before their 65th birthday. A higher risk of early mortality was found for the highest category of alcohol intake (≥50 g/d) in comparison with abstention (multivariable-adjusted hazard ratio (HR) = 2·82, 95 % CI 1·38, 5·79). In analyses of alcohol as a continuous variable, the multivariable-adjusted HR was 1·17 (95 % CI 1·08, 1·26), for each 10 g/d of alcohol. This harmful linear association was present both in uncorrected models and in models corrected for potential underreporting. No significant inverse association between light or moderate alcohol intake and premature mortality was observed, even after correcting for potential misclassification. Alcohol intake exhibited a harmful linear dose-response association with premature mortality (<65 years) in this young and highly educated Mediterranean cohort. Our attempts to correct for potential misclassification did not substantially change these results.

Bias correction methods for test-negative designs in the presence of misclassification.

The test-negative design (TND) has become a standard approach for vaccine effectiveness (VE) studies. However, previous studies suggested that it may be more vulnerable than other designs to misclassification of disease outcome caused by imperfect diagnostic tests. This could be a particular limitation in VE studies where simple tests (e.g. rapid influenza diagnostic tests) are used for logistical convenience. To address this issue, we derived a mathematical representation of the TND with imperfect tests, then developed a bias correction framework for possible misclassification. TND studies usually include multiple covariates other than vaccine history to adjust for potential confounders; our methods can also address multivariate analyses and be easily coupled with existing estimation tools. We validated the performance of these methods using simulations of common scenarios for vaccine efficacy and were able to obtain unbiased estimates in a variety of parameter settings.

Conditional validation sampling for consistent risk estimation with binary outcome data subject to misclassification.

PURPOSE: Misclassification of a binary outcome can introduce bias in estimation of the odds-ratio associated with an exposure of interest in pharmacoepidemiology research. It has been previously demonstrated that utilizing information from an internal randomly selected validation sample can help mitigate this bias. METHODS: Using a Monte Carlo simulation-based approach, we study the properties of misclassification bias-adjusted odds-ratio estimators in a contingency table setting. We consider two methods of internal validation sampling; namely, simple random sampling and sampling conditional on the original (possibly incorrect) outcome status. Additional simulation studies are conducted to investigate these sampling approaches in a multi-table setting. RESULTS: We demonstrate that conditional validation sampling, across a range of subsampling fractions, can produce better estimates than those based on an unconditional simple random sample. This approach allows for greater flexibility in the chosen categorical composition of the validation data, as well as the potential for obtaining a more efficient estimator of the odds-ratio. We further demonstrate that this relationship holds for the Mantel-Haenszel misclassification bias-adjusted odds-ratio in stratified samples. Recommendations for the choice of validation subsampling fraction are also provided. CONCLUSIONS: Careful consideration when choosing the sampling scheme used to draw internal validation samples can improve the properties of the outcome misclassification bias-adjusted odds-ratio estimator in a (multiple) contingency table.

Combining population-based administrative health records and electronic medical records for disease surveillance.

BACKGROUND: Administrative health records (AHRs) and electronic medical records (EMRs) are two key sources of population-based data for disease surveillance, but misclassification errors in the data can bias disease estimates. Methods that combine information from error-prone data sources can build on the strengths of AHRs and EMRs. We compared bias and error for four data-combining methods and applied them to estimate hypertension prevalence. METHODS: Our study included rule-based OR and AND methods that identify disease cases from either or both data sources, respectively, rule-based sensitivity-specificity adjusted (RSSA) method that corrects for inaccuracies using a deterministic rule, and probabilistic-based sensitivity-specificity adjusted (PSSA) method that corrects for error using a statistical model. Computer simulation was used to estimate relative bias (RB) and mean square error (MSE) under varying conditions of population disease prevalence, correlation amongst data sources, and amount of misclassification error. AHRs and EMRs for Manitoba, Canada were used to estimate hypertension prevalence using validated case definitions and multiple disease markers. RESULTS: The OR method had the lowest RB and MSE when population disease prevalence was 10%, and the RSSA method had the lowest RB and MSE when population prevalence increased to 20%. As the correlation between data sources increased, the OR method resulted in the lowest RB and MSE. Estimates of hypertension prevalence for AHRs and EMRs alone were 30.9% (95% CI: 30.6-31.2) and 24.9% (95% CI: 24.6-25.2), respectively. The estimates were 21.4% (95% CI: 21.1-21.7), for the AND method, 34.4% (95% CI: 34.1-34.8) for the OR method, 32.2% (95% CI: 31.8-32.6) for the RSSA method, and ranged from 34.3% (95% CI: 34.1-34.5) to 35.9% (95% CI, 35.7-36.1) for the PSSA method, depending on the statistical model. CONCLUSIONS: The OR and AND methods are influenced by correlation amongst the data sources, while the RSSA method is dependent on the accuracy of prior sensitivity and specificity estimates. The PSSA method performed well when population prevalence was high and average correlations amongst disease markers was low. This study will guide researchers to select a data-combining method that best suits their data characteristics.

Weighted estimation for confounded binary outcomes subject to misclassification.

In the presence of confounding, the consistency assumption required for identification of causal effects may be violated due to misclassification of the outcome variable. We introduce an inverse probability weighted approach to rebalance covariates across treatment groups while mitigating the influence of differential misclassification bias. First, using a simplified example taken from an administrative health care dataset, we introduce the approach for estimation of the marginal causal odds ratio in a simple setting with the use of internal validation information. We then extend this to the presence of additional covariates and use simulated data to investigate the finite sample properties of the proposed weighted estimators. Estimation of the weights is done using logistic regression with misclassified outcomes, and a bootstrap approach is used for variance estimation.

Short communication: Model for metritis severity predicts that disease misclassification underestimates projected milk production losses.

The objective of this research was to determine the effect of disease misclassification on the estimated effect of metritis on milk production. Misclassification introduces bias that usually results in an underestimation of the association between exposure (disease) and the outcome of interest (milk production). This distorted measure of association results from the comparison of an affected population (some of which may not truly be affected) to a nonaffected population (which often includes affected subjects that are unidentified). A convenience sample of DairyComp305 (Valley Agricultural Software, Tulare, CA) data representing 1 yr of calvings (n = 3,277) from 1 Midwestern Holstein herd was used. This herd was chosen because of its ongoing efforts to consistently and completely record all clinical diseases, including the incidence of both mild and severe metritis cases. Metritis was defined as the presence of a flaccid uterus containing fetid fluids or a foul watery discharge within 14 d of calving. Cows that appeared clinically normal other than the discharge were considered mild and those with systemic signs of disease were classified as severe. The original data set included metritis recorded as mild, severe, or not recorded (NR), where no metritis was observed, and was considered to contain the metritis true severity (TrS). First, to evaluate the effect of misclassification bias, we retrospectively randomized 45% of mild metritis to be classified as NR to simulate inconsistent disease recording (IR); then, in a separate model, all mild metritis cases were changed to NR to simulate a situation of very poor disease recording (PR), where only the most severe cases are recorded. The TrS, IR, and PR data sets were analyzed separately in JMP (SAS Institute Inc., Cary, NC). An ANOVA was conducted for second test 305-d mature-equivalent milk projection (2nd305ME), and nonsignificant variables were removed, but the variable metritis was forced into all models. Based upon the TrS model, adjusting for effects of lactation group, month of calving, dystocia, twins, retained placenta, early-lactation mastitis, displaced abomasum, and significant interactions, a case of mild metritis was associated with 384 kg less 2nd305ME and a case of severe metritis was associated with 847 kg less 2nd305ME compared with no metritis. For the IR model, a case of mild metritis was associated with 315 kg less 2nd305ME and a case of severe metritis was associated with 758 kg less 2nd305ME compared with no metritis. For the PR model, severe metritis was associated with 680 kg less 2nd305ME compared with NR. The IR and PR models underestimated 2nd305ME loss for severe metritis cases by 89 and 166 kg/cow, and resulted in 180,441 and 330,256 kg of total milk loss unaccounted for at the herd level, respectively, compared with TrS. Overall, misclassification of metritis cases results in greater bias and largely underestimates the true association between metritis and the consequence costs of the disease.

Original Findings and Updated Meta-Analysis for the Association Between Maternal Diabetes and Risk for Congenital Heart Disease Phenotypes.

Maternal diabetes is associated with congenital heart defects (CHDs) as a group, but few studies have assessed risk for specific CHD phenotypes. We analyzed these relationships using data from the Texas Birth Defects Registry and statewide vital records for deliveries taking place in 1999-2009 (n = 48,249 cases). We used Poisson regression to calculate prevalence ratios for the associations between maternal diabetes (pregestational or gestational) and each CHD phenotype, adjusting for potential confounders. Analyses were repeated by type of diabetes. To address the potential for misclassification bias, we performed logistic regression, using malformed controls. We also conducted meta-analyses, combining our estimates of the association between pregestational diabetes and each CHD phenotype with previous estimates. The prevalence of every CHD phenotype was greater among women with pregestational diabetes than among nondiabetic women. Most of these differences were statistically significant (adjusted prevalence ratios = 2.47-13.20). Associations were slightly attenuated for many CHD phenotypes among women with gestational diabetes. The observed associations did not appear to be the result of misclassification bias. In our meta-analysis, pregestational diabetes was significantly associated with each CHD phenotype. These findings contribute to a better understanding of the teratogenic effects of maternal diabetes and improved counseling for risk of specific CHD phenotypes.

Invited Commentary: Harnessing Housing Natural Experiments Is Important, but Beware Differential Misclassification of Difference in Difference.

In this issue of the Journal, Reeves et al. (Am J Epidemiol. 2016;184(6):421-429) present the findings of a natural experiment analyzing the association between reduced housing affordability and mental ill health. Their difference-in-difference analysis of cross-sectional, quarterly population health surveys administered before and after implementation of a policy to reduce Housing Benefit payments in the United Kingdom in April 2011 represents an important way to assess the impact of a national housing policy shift on public health. It is a well-conducted study harnessing a natural experiment and adds to the weight of evidence supporting an association between housing costs and mental health. However, quantitative bias analysis based on the reported findings suggests that a small amount of differential (by unblinded Housing Benefit status) misclassification bias in the outcome may be enough to explain the observed association. Our analysis of possible misclassification bias in the outcome used in the study highlights the need for caution when a difference-in-difference estimate is small, the population is not blinded to its postintervention exposure status, and the outcome measure is subjective and prone to differential (by unblinded exposure or treatment status) misclassification.

The problem of controlling for imperfectly measured confounders on dissimilar populations: a database simulation study.

OBJECTIVE(S): Observational database research frequently relies on imperfect administrative markers to determine comorbid status, and it is difficult to infer to what extent the associated misclassification impacts validity in multivariable analyses. The effect that imperfect markers of disease will have on validity in situations in which researchers attempt to match populations that have strong baseline health differences is underemphasized as a limitation in some otherwise high-quality observational studies. The present simulations were designed as a quantitative demonstration of the importance of this common and underappreciated issue. DESIGN: Two groups of Monte Carlo simulations were performed. The first demonstrated the degree to which controlling for a series of imperfect markers of disease between different populations taking 2 hypothetically harmless drugs would lead to spurious associations between drug assignment and mortality. The second Monte Carlo simulation applied this principle to a recent study in the field of anesthesiology that purported to show increased perioperative mortality in patients taking metoprolol versus atenolol. SETTING/PARTICIPANTS/INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Simulation 1: High type-1 error (ie, false positive findings of an independent association between drug assignment and mortality) was observed as sensitivity and specificity declined and as systematic differences in disease prevalence increased. Simulation 2: Propensity score matching across several imperfect markers was unlikely to eliminate important baseline health disparities in the referenced study. CONCLUSIONS: In situations in which large baseline health disparities exist between populations, matching on imperfect markers of disease may result in strong bias away from the null hypothesis.

Validation and impact of algorithms for identifying variables in observational studies of routinely collected data.

BACKGROUND AND OBJECTIVES: Among observational studies of routinely collected health data (RCD) for exploring treatment effects, algorithms are used to identify study variables. However, the extent to which algorithms are reliable and impact the credibility of effect estimates is far from clear. This study aimed to investigate the validation of algorithms for identifying study variables from RCD, and examine the impact of alternative algorithms on treatment effects. METHODS: We searched PubMed for observational studies published in 2018 that used RCD to explore drug treatment effects. Information regarding the reporting, validation, and interpretation of algorithms was extracted. We summarized the reporting and methodological characteristics of algorithms and validation. We also assessed the divergence in effect estimates given alternative algorithms by calculating the ratio of estimates of the primary vs. alternative analyses. RESULTS: A total of 222 studies were included, of which 93 (41.9%) provided a complete list of algorithms for identifying participants, 36 (16.2%) for exposure, and 132 (59.5%) for outcomes, and 15 (6.8%) for all study variables including population, exposure, and outcomes. Fifty-nine (26.6%) studies stated that the algorithms were validated, and 54 (24.3%) studies reported methodological characteristics of 66 validations, among which 61 validations in 49 studies were from the cross-referenced validation studies. Of those 66 validations, 22 (33.3%) reported sensitivity and 16 (24.2%) reported specificity. A total of 63.6% of studies reporting sensitivity and 56.3% reporting specificity used test-result-based sampling, an approach that potentially biases effect estimates. Twenty-eight (12.6%) studies used alternative algorithms to identify study variables, and 24 reported the effects estimated by primary analyses and sensitivity analyses. Of these, 20% had differential effect estimates when using alternative algorithms for identifying population, 18.2% for identifying exposure, and 45.5% for classifying outcomes. Only 32 (14.4%) studies discussed how the algorithms may affect treatment estimates. CONCLUSION: In observational studies of RCD, the algorithms for variable identification were not regularly validated, and-even if validated-the methodological approach and performance of the validation were often poor. More seriously, different algorithms may yield differential treatment effects, but their impact is often ignored by researchers. Strong efforts, including recommendations, are warranted to improve good practice.

Quantitative bias analysis of prevalence under misclassification: evaluation indicators, calculation method and case analysis.

Prevalence estimates are fundamental to epidemiological studies. Although they are highly vulnerable to misclassification bias, the risk of bias assessment of prevalence estimates is often neglected. Quantitative bias analysis (QBA) can effectively estimate misclassification bias in epidemiological studies; however, relatively few applications are identified. One reason for its low usage is the lack of knowledge and tools for these methods among researchers. To expand existing evaluation methods, based on the QBA principles, three indicators are proposed. One is the relative bias that quantifies the bias direction through its signs and the bias magnitude through its quantity. The second is the critical point of positive test proportion in case of a misclassification bias that is equal to zero. The third is the bound of positive test proportion equal to adjusted prevalence at misclassification bias level α. These indicators express the magnitude, direction and uncertainty of the misclassification bias of prevalence estimates, respectively. Using these indicators, it was found that slight oscillations of the positive test proportion within a certain range can lead to substantial increases in the misclassification bias. Hence, researchers should account for misclassification error analytically when interpreting the significance of adjusted prevalence for epidemiological decision making. This highlights the importance of applying QBA to these analyses. In this article, we have used three real-world cases to illustrate the characteristics and calculation methods of presented indicators. To facilitate application, an Excel-based calculation tool is provided.

Exploring the impact of selection bias in observational studies of COVID-19: a simulation study.

BACKGROUND: Non-random selection of analytic subsamples could introduce selection bias in observational studies. We explored the potential presence and impact of selection in studies of SARS-CoV-2 infection and COVID-19 prognosis. METHODS: We tested the association of a broad range of characteristics with selection into COVID-19 analytic subsamples in the Avon Longitudinal Study of Parents and Children (ALSPAC) and UK Biobank (UKB). We then conducted empirical analyses and simulations to explore the potential presence, direction and magnitude of bias due to this selection (relative to our defined UK-based adult target populations) when estimating the association of body mass index (BMI) with SARS-CoV-2 infection and death-with-COVID-19. RESULTS: In both cohorts, a broad range of characteristics was related to selection, sometimes in opposite directions (e.g. more-educated people were more likely to have data on SARS-CoV-2 infection in ALSPAC, but less likely in UKB). Higher BMI was associated with higher odds of SARS-CoV-2 infection and death-with-COVID-19. We found non-negligible bias in many simulated scenarios. CONCLUSIONS: Analyses using COVID-19 self-reported or national registry data may be biased due to selection. The magnitude and direction of this bias depend on the outcome definition, the true effect of the risk factor and the assumed selection mechanism; these are likely to differ between studies with different target populations. Bias due to sample selection is a key concern in COVID-19 research based on national registry data, especially as countries end free mass testing. The framework we have used can be applied by other researchers assessing the extent to which their results may be biased for their research question of interest.

Determining prescriptions in electronic healthcare record data: methods for development of standardized, reproducible drug codelists.

Objective: To develop a standardizable, reproducible method for creating drug codelists that incorporates clinical expertise and is adaptable to other studies and databases. Materials and Methods: We developed methods to generate drug codelists and tested this using the Clinical Practice Research Datalink (CPRD) Aurum database, accounting for missing data in the database. We generated codelists for: (1) cardiovascular disease and (2) inhaled Chronic Obstructive Pulmonary Disease (COPD) therapies, applying them to a sample cohort of 335 931 COPD patients. We compared searching all drug dictionary variables (A) against searching only (B) chemical or (C) ontological variables. Results: In Search A, we identified 165 150 patients prescribed cardiovascular drugs (49.2% of cohort), and 317 963 prescribed COPD inhalers (94.7% of cohort). Evaluating output per search strategy, Search C missed numerous prescriptions, including vasodilator anti-hypertensives (A and B:19 696 prescriptions; C:1145) and SAMA inhalers (A and B:35 310; C:564). Discussion: We recommend the full search (A) for comprehensiveness. There are special considerations when generating adaptable and generalizable drug codelists, including fluctuating status, cohort-specific drug indications, underlying hierarchical ontology, and statistical analyses. Conclusions: Methods must have end-to-end clinical input, and be standardizable, reproducible, and understandable to all researchers across data contexts.

Replicate Testing of Clinical Endpoints Can Prevent No-Go Decisions for Beneficial Vaccines.

In vaccine efficacy trials, inaccurate counting of infection cases leads to systematic under-estimation-or "dilution"-of vaccine efficacy. In particular, if a sufficient fraction of observed cases are false positives, apparent efficacy will be greatly reduced, leading to unwarranted no-go decisions in vaccine development. Here, we propose a range of replicate testing strategies to address this problem, considering the additional challenge of uncertainty in both infection incidence and diagnostic assay specificity/sensitivity. A strategy that counts an infection case only if a majority of replicate assays return a positive result can substantially reduce efficacy dilution for assays with non-systematic (i.e., "random") errors. We also find that a cost-effective variant of this strategy, using confirmatory assays only if an initial assay is positive, yields a comparable benefit. In clinical trials, where frequent longitudinal samples are needed to detect short-lived infections, this "confirmatory majority rule" strategy can prevent the accumulation of false positives from magnifying efficacy dilution. When widespread public health screening is used for viruses, such as SARS-CoV-2, that have non-differentiating features or may be asymptomatic, these strategies can also serve to reduce unneeded isolations caused by false positives.

Accuracy and precision of using partial-mouth recordings to study the prevalence, extent and risk associations of untreated periodontitis.

To study the accuracy and precision of estimating the prevalence, extent and associated risks of untreated periodontitis using partial-mouth recording protocols (PRPs) Methods: A purposive sample of 431 individuals who had never been treated for periodontal disease was recruited from screening clinics at the King Saud bin Abdul-Aziz University for Health Sciences. Data were collected using questionnaires and clinical examinations. The prevalence, extent and risk associations of periodontitis were evaluated. Three PRPs were compared to full-mouth recordings (FRPs) in terms of the sensitivity, specificity, predictive values, and absolute bias. Results: The prevalence of periodontitis was estimated with the highest accuracy and precision by examinations of the full mouth at the mesiobuccal and distolingual sites (FM)MB-DL, followed by random half-mouth (RHM) recordings. The extent of periodontitis was estimated with high precision using all the PRPs, and the absolute bias ranged from -0.6 to -2.3. The absolute bias indicated by OR for risk associations was small for the three PRPs and ranged from -0.8 to 0.8. Conclusion: (FM)MB-DL and RHM were the PRPs with moderate to high levels of accuracy and precision for estimating the prevalence and risk associations of periodontitis. The extent of periodontitis was estimated with high precision using all three PRPs. The results of this study showed that the magnitude and direction of bias were associated with the severity of periodontitis, the selected PRPs and the magnitude of the risk associations.

Infertility treatment and the risk of small for gestational age births: a population-based study in the United States.

OBJECTIVE: To evaluate the association between infertility treatments and small for gestational age (SGA) births. DESIGN: Cross-sectional study. SETTING: United States, 2015-2019. PATIENTS: Women (n = 16,836,228) who delivered nonmalformed, singleton live births (24-44 weeks' gestation). INTERVENTIONS: Any infertility treatment, including assisted reproductive technology (ART) and prescribed fertility-enhancing medications. MAIN OUTCOME MEASURES: Small for gestational age birth, defined as sex-specific birth weight <10% for gestational age. Associations between SGA and infertility treatment were derived from Poisson regression with robust variance. Risk ratios (RR) and 95% confidence intervals (CI) were derived after adjusting for confounders. In a sensitivity analysis, we corrected for nondifferential exposure misclassification and unmeasured confounding biases. RESULTS: Subsequently, 1.4% (n = 231,177) of pregnancies resulted from infertility treatments (0.8% ART and 0.6% fertility-enhancing medications). Of these, SGA births occurred in 9.4% (n = 21,771) and 11.9% (n = 1,755,925) of pregnancies conceived with infertility treatment and naturally conceived pregnancies, respectively (adjusted RR, 1.07; 95% CI, 1.06, 1.08). However, after correction for misclassification bias and unmeasured confounding, infertility treatment was associated with a 27% reduced risk of SGA (bias-corrected RR, 0.73; 95% CI, 0.53, 0.85). Similar trends were seen for analyses stratified by exposure to ART and fertility-enhancing medications, as well as for SGA <5th and <3rd percentiles. CONCLUSIONS: Exposure to infertility treatment is associated with a reduced risk of SGA births. These findings, which are contrary to some published reports, may reflect changes in the modern practice of infertility care, maternal lifestyle, and compliance with prenatal care within the infertile population. Until these findings are corroborated, the associations must be cautiously interpreted.