Nested case-control sampling without replacement.

Nested case-control design (NCC) is a cost-effective outcome-dependent design in epidemiology that collects all cases and a fixed number of controls at the time of case diagnosis from a large cohort. Due to inefficiency relative to full cohort studies, previous research developed various estimation methodologies but changing designs in the formulation of risk sets was considered only in view of potential bias in the partial likelihood estimation. In this paper, we study a modified design that excludes previously selected controls from risk sets in view of efficiency improvement as well as bias. To this end, we extend the inverse probability weighting method of Samuelsen which was shown to outperform the partial likelihood estimator in the standard setting. We develop its asymptotic theory and a variance estimation of both regression coefficients and the cumulative baseline hazard function that takes account of the complex feature of the modified sampling design. In addition to good finite sample performance of variance estimation, simulation studies show that the modified design with the proposed estimator is more efficient than the standard design. Examples are provided using data from NIH-AARP Diet and Health Cohort Study.

Quantifying the long-term effects of measles infection-a retrospective cohort study.

OBJECTIVES: To assess whether measles infection has an impact on the rate of non-measles infectious diseases over an extended period. METHODS: This retrospective matched cohort study included 532 measles-diagnosed patients who were exactly matched with 2128 individuals without a previous measles diagnosis. Adjusted OR for any all-cause infectious diagnosis and any viral infection diagnosis ≤2 years after measles diagnosis between the measles and control groups was obtained from a conditional logistic regression model. The Cox proportional hazards model was used to estimate the hazard ratio. RESULTS: Previous measles virus (MeV) exposure was associated with an increased risk for all-cause non-measles infectious disease diagnosis (OR: 1.83, 95% CI: 1.26-2.64, p 0.001), with 492 diagnoses in the MeV-exposed group and 1868 diagnoses in the control group. Additionally, previous MeV exposure was linked to a higher risk of viral infection diagnosis (OR: 1.23, 95% CI: 1.01-1.59, p < 0.05), with 302 viral infection diagnoses in the MeV-exposed group and 1107 diagnoses in the control group. The hazard ratio for viral diagnosis in the MeV-exposed group compared with the control group was 1.54 (95% CI: 1.18-2.02, p < 0.001). DISCUSSION: Individuals diagnosed with measles had a moderately increased risk of being diagnosed with all-cause non-measles infectious disease or viral infection. This observational individual-level study supports previous ecological and individual population-level studies.

Random forests for the analysis of matched case-control studies.

BACKGROUND: Conditional logistic regression trees have been proposed as a flexible alternative to the standard method of conditional logistic regression for the analysis of matched case-control studies. While they allow to avoid the strict assumption of linearity and automatically incorporate interactions, conditional logistic regression trees may suffer from a relatively high variability. Further machine learning methods for the analysis of matched case-control studies are missing because conventional machine learning methods cannot handle the matched structure of the data. RESULTS: A random forest method for the analysis of matched case-control studies based on conditional logistic regression trees is proposed, which overcomes the issue of high variability. It provides an accurate estimation of exposure effects while being more flexible in the functional form of covariate effects. The efficacy of the method is illustrated in a simulation study and within an application to real-world data from a matched case-control study on the effect of regular participation in cervical cancer screening on the development of cervical cancer. CONCLUSIONS: The proposed random forest method is a promising add-on to the toolbox for the analysis of matched case-control studies and addresses the need for machine-learning methods in this field. It provides a more flexible approach compared to the standard method of conditional logistic regression, but also compared to conditional logistic regression trees. It allows for non-linearity and the automatic inclusion of interaction effects and is suitable both for exploratory and explanatory analyses.

Association between metabolic obesity phenotypes and the risk of developing prostate cancer: a propensity score matching study based on Xinjiang.

Background: Obesity-induced metabolic dysfunction increases the risk of developing tumors, however, the relationship between metabolic obesity phenotypes and prostate cancer (PCa) remains unclear. Methods: The term metabolic obesity phenotypes was introduced based on metabolic status and BMI categories. Participants were categorized into four groups: metabolically healthy nonobesity (MHNO), metabolically healthy obesity (MHO), metabolically unhealthy nonobesity (MUNO), and metabolically unhealthy obesity (MUO). Propensity score matching was conducted based on age, ethnicity, marriage, etc. Univariate and multivariate conditional logistic regression analyses were used to assess the relationship between metabolic obesity phenotypes, metabolic risk factors, and PCa. Sensitivity analysis was performed to verify the robustness of the results. Results: After propensity score matching among 564 PCa patients and 1418 healthy individuals, 209 were selected for each of the case and control groups. There were no statistically significant differences in the basic characteristics between the two groups. Univariate and multivariate conditional logistic regression suggested that the risk of developing PCa in both MHO and MUO individuals was higher than in MHNO individuals. Specifically, the risk of developing PCa in MHO individuals was 2.166 times higher than in MHNO individuals (OR=2.166, 95%CI: 1.133-4.139), and the risk in MUO individuals was is 2.398 times higher than in MHNO individuals(OR=2.398, 95%CI:1.271-4.523). Individuals with hyperglycemia and elevated triglycerides also had a higher risk of developing PCa (hyperglycemia:OR=1.488, 95%CI: 1.001-2.210; elevated triglycerides: OR=2.292, 95%CI: 1.419-3.702). Those with more than or equal to three metabolic risk factors had an increased risk of PCa (OR=1.990, 95%CI: 1.166-3.396). Sensitivity analysis indicated an increased risk of PCa in MUO individuals compared to MHNO individuals. Conclusion: In this retrospective study, individuals with MHO and MUO had a higher risk of developing PCa.

The UF-5000 Atyp.C parameter is an independent risk factor for bladder cancer.

Bladder carcinoma (BC) accounts for > 90% of all urothelial cancers. Pathological diagnosis through cytoscopic biopsy is the gold standard, whereas non-invasive diagnostic tools remain lacking. The "Atyp.C" parameter of the Sysmex UF-5000 urine particle analyzer represents the ratio of nucleus to cytoplasm and can be employed to detect urinary atypical cells. The present study examined the association between urinary Atyp.C values and BC risk. This two-center, retrospective case-control study identified clinical primary or newly recurrent BC (study period, 2022-2023; n = 473) cases together with controls with urinary tract infection randomly matched by age and sex (1:1). Urinary sediment differences were compared using non-parametric tests. The correlations between urinary Atyp.C levels and BC grade or infiltration were analyzed using Spearman's rank correlation. The BC risk factor odds ratio of Atyp.C was calculated using conditional logistic regression, and potential confounder effects were adjusted using stepwise logistic regression (LR). Primary risk factors were identified by stratified analysis according to pathological histological diagnosis. The mean value of urinary Atyp.C in BC cases (1.30 ± 3.12) was 8.7 times higher than that in the controls (0.15 ± 0.68; P < 0.001). Urinary Atyp.C values were positively correlated with BC pathological grade and invasion (r = 0.360, P < 0.001; r = 0.367, P < 0.001). Urinary Atyp.C was an independent risk factor for BC and closely related with BC pathological grade and invasion. Elevated urinary Atyp.C values was an independent risk factor for BC. Our findings support the use of Atyp.C as a marker that will potentially aid in the early diagnosis and long-term surveillance of new and recurrent BC cases.

penalizedclr: an R package for penalized conditional logistic regression for integration of multiple omics layers.

BACKGROUND: The matched case-control design, up until recently mostly pertinent to epidemiological studies, is becoming customary in biomedical applications as well. For instance, in omics studies, it is quite common to compare cancer and healthy tissue from the same patient. Furthermore, researchers today routinely collect data from various and variable sources that they wish to relate to the case-control status. This highlights the need to develop and implement statistical methods that can take these tendencies into account. RESULTS: We present an R package penalizedclr, that provides an implementation of the penalized conditional logistic regression model for analyzing matched case-control studies. It allows for different penalties for different blocks of covariates, and it is therefore particularly useful in the presence of multi-source omics data. Both L1 and L2 penalties are implemented. Additionally, the package implements stability selection for variable selection in the considered regression model. CONCLUSIONS: The proposed method fills a gap in the available software for fitting high-dimensional conditional logistic regression models accounting for the matched design and block structure of predictors/features. The output consists of a set of selected variables that are significantly associated with case-control status. These variables can then be investigated in terms of functional interpretation or validation in further, more targeted studies.

Associations of neighborhood greenspace, and active living environments with autism spectrum disorders: A matched case-control study in Ontario, Canada.

BACKGROUND: Increasing evidence links early life residential exposure to natural urban environmental attributes and positive health outcomes in children. However, few studies have focused on their protective effects on the risk of autism spectrum disorder (ASD). The aim of this study was to investigate the associations of neighborhood greenspace, and active living environments during pregnancy with ASD in young children (≤6 years). METHODS: We conducted a population-based matched case-control study of singleton term births in Ontario, Canada for 2012-2016. The ASD and environmental data was generated using the Ontario Autism Spectrum Profile, the Better Outcomes Registry & Network Ontario, and Canadian Urban Environmental Health Research Consortium. We employed conditional logistic regressions to estimate the odds ratio (OR) between ASD and environmental factors characterizing selected greenspace metrics and neighborhoods conducive to active living (i.e., green view index (GVI), normalized difference vegetation index (NDVI), tree canopy, park proximity and active living environments index (ALE)). RESULTS: We linked 8643 mother-child pairs, including 1554 cases (18%). NDVI (OR 1.034, 0.944-1.024, per Inter Quartile Range [IQR] = 0.08), GVI (OR 1.025, 95% CI 0.953-1.087, per IQR = 9.45%), tree canopy (OR 0.992, 95% CI 0.903-1.089, per IQR = 6.24%) and the different categories of ALE were not associated with ASD in adjusted models for air pollution. In contrast, living closer to a park was protective (OR 0.888, 0.833-0.948, per 0.06 increase in park proximity index), when adjusted for air pollution. CONCLUSIONS: This study reported mixed findings showing both null and beneficial effects of green spaces and active living environments on ASD. Further investigations are warranted to elucidate the role of exposure to greenspaces and active living environments on the development of ASD.

Determinants of polycystic ovary syndrome: A matched case-control study.

BACKGROUND: Polycystic ovarian syndrome (PCOS) is a multifaceted endocrine disorder of women of reproductive age with a multifactorial aetiology. Despite much research, there is still inconclusive data on the impact of dietary, lifestyle and socio-economic factors on PCOS aetiology. Thus, the present study explored the association of PCOS with diet, eating behaviour, other lifestyle and socio-economic factors. METHODS: A matched-pair case-control study was conducted on 150 women with PCOS and 150 healthy controls. Information on diet, eating behaviour and physical activity, and also anthropometric and socio-economic data were collected through standard questionnaires. The adjusted odds ratios (AmOR) were calculated and reported using conditional multivariable logistic regression. RESULTS: The results showed low education level (AmOR = 8.44; 95% confidence interval [CI] = 1.63-43.68), high sugar consumption (AmOR = 11.61; 95% CI = 2.05-65.72) along with higher body mass index (BMI) and inactivity to be significantly associated with PCOS. Also, a significant protective effect was found for cognitive dietary restraint (AmOR = 0.79; 95% CI = 0.66-0.93), crude fibre (AmOR = 0.61; 95% CI = 0.45-0.82) and protein intake. CONCLUSIONS: Low education status may contribute to higher receptiveness to choosing unhealthy diets and lifestyles, resulting in adiposity and an increased risk of PCOS.

Adjusting for Berkson error in exposure in ordinary and conditional logistic regression and in Poisson regression.

BACKGROUND: INTEROCC is a seven-country cohort study of occupational exposures and brain cancer risk, including occupational exposure to electromagnetic fields (EMF). In the absence of data on individual exposures, a Job Exposure Matrix (JEM) may be used to construct likely exposure scenarios in occupational settings. This tool was constructed using statistical summaries of exposure to EMF for various occupational categories for a comparable group of workers. METHODS: In this study, we use the Canadian data from INTEROCC to determine the best EMF exposure surrogate/estimate from three appropriately chosen surrogates from the JEM, along with a fourth surrogate based on Berkson error adjustments obtained via numerical approximation of the likelihood function. In this article, we examine the case in which exposures are gamma-distributed for each occupation in the JEM, as an alternative to the log-normal exposure distribution considered in a previous study conducted by our research team. We also study using those surrogates and the Berkson error adjustment in Poisson regression and conditional logistic regression. RESULTS: Simulations show that the introduced methods of Berkson error adjustment for non-stratified analyses provide accurate estimates of the risk of developing tumors in case of gamma exposure model. Alternatively, and under some technical assumptions, the arithmetic mean is the best surrogate when a gamma-distribution is used as an exposure model. Simulations also show that none of the present methods could provide an accurate estimate of the risk in case of stratified analyses. CONCLUSION: While our previous study found the geometric mean to be the best exposure surrogate, the present study suggests that the best surrogate is dependent on the exposure model; the arithmetic means in case of gamma-exposure model and the geometric means in case of log-normal exposure model. However, we could present a better method of Berkson error adjustment for each of the two exposure models. Our results provide useful guidance on the application of JEMs for occupational exposure assessments, with adjustment for Berkson error.

Association Between Abnormal Metabolic Parameters and Receiving Subsequent Interventions in Children and Adolescents Initiating Second-Generation Antipsychotics.

Objectives: This study aimed to examine the association between abnormal readings of metabolic parameters detected during second-generation antipsychotic (SGA) treatment and the likelihood of receiving subsequent adverse drug event interventions. Methods: This was a nested case-control study conducted on patients 1-17 years of age with at least two prescriptions of SGAs between January 2010 and January 2019 using TriNetX EMR data. Following an incident density sampling procedure, patients who received the SGA metabolic adverse event intervention (mAEI) (case) were matched with three nonrecipients (controls). The abnormal readings of metabolic parameters within 30 days before the initiation of mAIEs were further identified. These metabolic parameters include body mass index (BMI) and laboratory parameters such as cholesterol, high-density lipoprotein, low-density lipoprotein, triglycerides, blood glucose, HbA1c, thyroid hormones, liver enzymes, and prolactin. The association of abnormal metabolic parameters with subsequent mAEIs was assessed using a conditional logistic regression model, after adjusting for demographic and other clinical risk factors. Results: One thousand eight hundred eighty-four children and adolescents met the inclusion criteria and were prescribed SGA mAEIs. The most common types of mAEIs prescribed were weight management pharmacotherapy (40.6%), switching from a high or medium metabolic risk profile SGA to a low-risk one (30.9%), nonpharmacological treatment (25.4%), and switching from SGA polytherapy to monotherapy (11.7%). The conditional logistic regression analysis on matched mAEI recipients and nonrecipients showed that patients with an abnormal BMI had 43% higher odds of receiving mAEI (odds ratio [95% confidence interval]: 1.43 [1.13-1.79]). However, the presence of an abnormal laboratory reading was not associated with the initiation of mAEIs. Conclusions: The prescribing of mAEIs were associated with the presence of obesity, but not with abnormal readings of other metabolic parameters, suggesting that additional data are needed to clarify the long-term implication of SGA metabolic adverse events other than weight gain and to inform the appropriate timing for interventions.

Accounting for central place foraging constraints in habitat selection studies.

Habitat selection studies contrast actual space use with the expected use under the null hypothesis of no selection (hereafter neutral use). Neutral use is most often equated to the relative frequencies with which environmental features occur. This generates a considerable bias when studying habitat selection by foragers that perform numerous trips back and forth to a central place (CP). Indeed, the increased space use close to the CP with respect to distant places reflects a mechanical effect, rather than a true selection for the closest habitats. Yet, correctly estimating habitat selection by CP foragers is of paramount importance for a better understanding of their ecology and to properly plan conservation actions. We show that including the distance to the CP as a covariate in unconditional Resource Selection Functions, as applied in several studies, is ineffective to correct for the bias. This bias can be eliminated only by contrasting the actual use to an appropriate neutral use that considers the CP forager behavior. We also show that the need to specify an appropriate neutral use overall distribution can be bypassed by relying on a conditional approach, where the neutral use is assessed locally regardless of the distance to the CP.

Sporadic Use of Antibiotics in Older Adults and the Risk of Dementia: A Nested Case-Control Study Based on German Health Claims Data.

BACKGROUND: Antibiotics for systemic use may increase the risk of neurodegeneration, yet antibiotic therapy may be able to halt or mitigate an episode of neurodegenerative decline. OBJECTIVE: To investigate the association of sporadic use of antibiotics and subsequent dementia risk (including Alzheimer's disease). METHODS: We used data from the largest public health insurance fund in Germany, the Allgemeine Ortskrankenkasse (AOK). Each of the 35,072 dementia cases aged 60 years and older with a new dementia diagnosis during the observation period from 2006 to 2018 was matched with two control-patients by age, sex, and time since 2006. We ran conditional logistic regression models for dementia risk in terms of odds ratios (OR) as a function of antibiotic use for the entire antibiotic group and for each antibiotic subgroup. We controlled for comorbidities, need for long-term care, hospitalizations, and nursing home placement. RESULTS: Antibiotic use was positively associated with dementia (OR = 1.18, 95% confidence interval (95% CI):1.14-1.22), which became negative after adjustment for comorbidities, at least one diagnosis of bacterial infection or disease, and covariates (OR = 0.93, 95% CI:0.90-0.96). Subgroups of antibiotics were also negatively associated with dementia after controlling for covariates: tetracyclines (OR = 0.94, 95% CI:0.90-0.98), beta-lactam antibacterials, penicillins (OR = 0.93, 95% CI:0.90-0.97), other beta-lactam antibacterials (OR = 0.92, 95% CI:0.88-0.95), macrolides, lincosamides, and streptogramins (OR = 0.88, 95% CI:0.85-0.92), and quinolone antibacterials (OR = 0.96, 95% CI:0.92-0.99). CONCLUSION: Our results suggest that there was a decreased likelihood of dementia for preceding antibiotic use. The benefits of antibiotics in reducing inflammation and thus the risk of dementia need to be carefully weighed against the increase in antibiotic resistance.

A Description of the Statistical Methods for the Vaccine Impact on Diarrhea in Africa (VIDA) Study.

BACKGROUND: Diarrheal diseases remain a health threat to children in low- and middle-income countries. The Vaccine Impact on Diarrhea in Africa (VIDA) study was a 36-month, prospective, matched case-control study designed to estimate the etiology, incidence, and adverse clinical consequences of moderate-to-severe diarrhea (MSD) in children aged 0-59 months. VIDA was conducted following rotavirus vaccine introduction at 3 censused sites in sub-Saharan Africa that participated in the Global Enteric Multicenter Study (GEMS) ∼10 years earlier. We describe the study design and statistical methods of VIDA and where they differ from GEMS. METHODS: We aimed to enroll 8-9 MSD cases every 2 weeks from sentinel health centers in 3 age strata (0-11, 12-23, 24-59 months) and 1 to 3 controls matched by age, sex, date of case enrollment, and village. Clinical, epidemiological, and anthropometric data were collected at enrollment and ∼60 days later. A stool specimen collected at enrollment was analyzed by both conventional methods and quantitative PCR for enteric pathogens. For the matched case-control study, we estimated the population-based, pathogen-specific attributable fraction (AF) and attributable incidence adjusted for age, site, and other pathogens, and identified episodes attributable to a specific pathogen for additional analyses. A prospective cohort study nested within the original matched case-control study allowed assessment of (1) the association between potential risk factors and outcomes other than MSD status and (2) the impact of MSD on linear growth. CONCLUSIONS: GEMS and VIDA together comprise the largest and most comprehensive assessment of MSD conducted to date in sub-Saharan Africa populations at highest risk for morbidity and mortality from diarrhea. The statistical methods used in VIDA have endeavored to maximize the use of available data to produce more robust estimates of the pathogen-specific disease burden that might be prevented by effective interventions.

Conditional concordance-assisted learning under matched case-control design for combining biomarkers for population screening.

Incorporating promising biomarkers into cancer screening practices for early-detection is increasingly appealing because of the unsatisfactory performance of current cancer screening strategies. The matched case-control design is commonly adopted in biomarker development studies to evaluate the discriminative power of biomarker candidates, with an intention to eliminate confounding effects. Data from matched case-control studies have been routinely analyzed by the conditional logistic regression, although the assumed logit link between biomarker combinations and disease risk may not always hold. We propose a conditional concordance-assisted learning method, which is distribution-free, for identifying an optimal combination of biomarkers to discriminate cases and controls. We are particularly interested in combinations with a clinically and practically meaningful specificity to prevent disease-free subjects from unnecessary and possibly intrusive diagnostic procedures, which is a top priority for cancer population screening. We establish asymptotic properties for the derived combination and confirm its favorable finite sample performance in simulations. We apply the proposed method to the prostate cancer data from the carotene and retinol efficacy trial (CARET).

A tree-based modeling approach for matched case-control studies.

Conditional logistic regression (CLR) is the indisputable standard method for the analysis of matched case-control studies. However, CLR is strongly restricted with respect to the inclusion of non-linear effects and interactions of confounding variables. A novel tree-based modeling method is proposed which accounts for this issue and provides a flexible framework allowing for a more complex confounding structure. The proposed machine learning model is fitted within the framework of CLR and, therefore, allows to account for the matched strata in the data. A simulation study demonstrates the efficacy of the method. Furthermore, for illustration the method is applied to a matched case-control study on cervical cancer.

Bayesian Approach to Disease Risk Evaluation Based on Air Pollution and Weather Conditions.

BACKGROUND: Environmental factors such as meteorological conditions and air pollutants are recognized as important for human health, where mortality and morbidity of certain diseases may be related to abrupt climate change or air pollutant concentration. In the literature, environmental factors have been identified as risk factors for chronic diseases such as ischemic heart disease. However, the likelihood evaluation of the disease occurrence probability due to environmental factors is missing. METHOD: We defined people aged 51-90 years who were free from ischemic heart disease (ICD9: 410-414) in 1996-2002 as the susceptible group. A Bayesian conditional logistic regression model based on a case-crossover design was utilized to construct a risk information system and applied to data from three databases in Taiwan: air quality variables from the Environmental Protection Administration (EPA), meteorological parameters from the Central Weather Bureau (CWB), and subject information from the National Health Insurance Research Database (NHIRD). RESULTS: People living in different geographic regions in Taiwan were found to have different risk factors; thus, disease risk alert intervals varied in the three regions. CONCLUSIONS: Disease risk alert intervals can be a reference for weather bureaus to issue health warnings. With early warnings, susceptible groups can take measures to avoid exacerbation of disease when meteorological conditions and air pollution become hazardous to their health.

Correlation analysis between uranium exposure and renal injury / 中华放射医学与防护杂志

Objective:To analyze the relationship between plasma uranium concentration and renal injury.Methods:A case-control study was conducted in Hunan province, involving 102 renal injury cases and 102 matched controls. The association between plasma uranium concentration and renal injury was analyzed using conditional logistic regression models, and the dose-response relationship was analyzed through restricted cubic spline regression. The linear regression model and Spearman correlation were used to analyze the association between plasma uranium concentration and renal injury indicators.Results:The median of plasma uranium concentration was 8.94 ng/L in all subjects and 10.19 ng/L in the case group. The plasma uranium may be a risk factor for renal injury, with a dose-response relationship between the both representing nonlinear association ( χ2=5.15, P<0.05). The risk of renal injury was 4.21 times higher in the group exposed to highest uranium concentration than that in the group exposed to lowest uranium concentration. Plasma uranium concentration was closely related to glomerular filtration rate, serum creatinine and β 2-microglobulin ( r=0.211, -0.142, 0.195, P<0.05). Conclusions:The plasma uranium concentration is significantly associated with the renal injury, which may provide epidemiology evidence for the prevention of renal injury.

SARS-CoV-2 Vaccine Breakthrough by Omicron and Delta Variants, New York, USA.

Recently emerged SARS-CoV-2 variants have greater potential than earlier variants to cause vaccine breakthrough infections. During emergence of the Delta and Omicron variants, a matched case-control analysis used a viral genomic sequence dataset linked with demographic and vaccination information from New York, USA, to examine associations between virus lineage and patient vaccination status, patient age, vaccine type, and time since vaccination. Case-patients were persons infected with the emerging virus lineage, and controls were persons infected with any other virus lineage. Infections in fully vaccinated and boosted persons were significantly associated with the Omicron lineage. Odds of infection with Omicron relative to Delta generally decreased with increasing patient age. A similar pattern was observed with vaccination status during Delta emergence but was not significant. Vaccines offered less protection against Omicron, thereby increasing the number of potential hosts for emerging variants.

Estimating COVID-19 Vaccine Effectiveness for Skilled Nursing Facility Healthcare Personnel, California, USA.

We estimated real-world vaccine effectiveness among skilled nursing facility healthcare personnel who were regularly tested for SARS-CoV-2 infection in California, USA, during January‒March 2021. Vaccine effectiveness for fully vaccinated healthcare personnel was 73.3% (95% CI 57.5%-83.3%). We observed high real-world vaccine effectiveness in this population.

Predicting the Risk of Microtia From Prenatal Factors: A Hospital-Based Case-Control Study.

Background: Although a wide range of risk factors for microtia were identified, the limitation of these studies, however, is that risk factors were not estimated in comparison with one another or from different domains. Our study aimed to uncover which factors should be prioritized for the prevention and intervention of non-syndromic microtia via tranditonal and meachine-learning statistical methods. Methods: 293 pairs of 1:1 matched non-syndromic microtia cases and controls who visited Shanghai Ninth People's Hospital were enrolled in the current study during 2017-2019. Thirty-nine risk factors across four domains were measured (i.e., parental sociodemographic characteristics, maternal pregnancy history, parental health conditions and lifestyles, and parental environmental and occupational exposures). Lasso regression model and multivariate conditional logistic regression model were performed to identify the leading predictors of microtia across the four domains. The area under the curve (AUC) was used to calculate the predictive probabilities. Results: Eight predictors were identified by the lasso regression, including abnormal pregnancy history, genital system infection, teratogenic drugs usage, folic acid supplementation, paternal chronic conditions history, parental exposure to indoor decoration, paternal occupational exposure to noise and maternal acute respiratory infection. The additional predictors identified by the multivariate conditional logistic regression model were maternal age and maternal occupational exposure to heavy metal. Predictors selected from the conditional logistic regression and lasso regression both yielded AUCs (95% CIs) of 0.83 (0.79-0.86). Conclusion: The findings from this study suggest some factors across multiple domains are key drivers of non-syndromic microtia regardless of the applied statistical methods. These factors could be used to generate hypotheses for further observational and clinical studies on microtia and guide the prevention and intervention strategies for microtia.