scholarly journals Association of Infrastructure and Route Environment Factors with Cycling Injury Risk at Intersection and Non-Intersection Locations: A Case-Crossover Study of Britain

2021 ◽  
Vol 18 (6) ◽  
pp. 3060
Author(s):  
Rachel Aldred ◽  
Georgios Kapousizis ◽  
Anna Goodman
Keyword(s):  
Injury Risk ◽  
Conditional Logistic Regression ◽  
Traffic Signals ◽  
Control Groups ◽  
Class A ◽  
Case Crossover ◽  
Main Class ◽  
Road Intersections ◽  
Matched Case ◽  
Safety In Numbers

Objective: This paper examines infrastructural and route environment correlates of cycling injury risk in Britain for commuters riding in the morning peak. Methods: The study uses a case-crossover design which controls for exposure. Control sites from modelled cyclist routes (matched on intersection status) were compared with sites where cyclists were injured. Conditional logistic regression for matched case–control groups was used to compare characteristics of control and injury sites. Results: High streets (defined by clustering of retail premises) raised injury odds by 32%. Main (Class A or primary) roads were riskier than other road types, with injury odds twice that for residential roads. Wider roads, and those with lower gradients increased injury odds. Guard railing raised injury odds by 18%, and petrol stations or car parks by 43%. Bus lanes raised injury odds by 84%. As in other studies, there was a ‘safety in numbers’ effect from more cyclists. Contrary to other analysis, including two recent studies in London, we did not find a protective effect from cycle infrastructure and the presence of painted cycle lanes raised injury odds by 54%. At intersections, both standard and mini roundabouts were associated with injury odds several times higher than other intersections. Presence of traffic signals, with or without an Advanced Stop Line (‘bike box’), had no impact on injury odds. For a cyclist on a main road, intersections with minor roads were riskier than intersections with other main roads. Conclusions: Typical cycling environments in Britain put cyclists at risk, and infrastructure must be improved, particularly on busy main roads, high streets, and bus routes.

Flexible derivative time-varying model in matched case-crossover studies for a small number of geographical locations among the participants

2020 ◽  
pp. 096228022096817
Author(s):  
Ana M Ortega-Villa ◽  
Inyoung Kim
Keyword(s):  
Logistic Regression ◽  
Conditional Logistic Regression ◽  
Effect Modification ◽  
Fixed Number ◽  
Time Varying ◽  
Model Framework ◽  
Conditional Logistic Regression Model ◽  
Case Crossover ◽  
Crossover Studies ◽  
Matched Case

In matched case-crossover studies, any stratum effect is removed by conditioning on the fixed number of case–control sets in the stratum, and hence, the conditional logistic regression model is not able to detect any effects associated with matching covariates. However, some matching covariates such as time and location often modify the effect of covariates, making the estimations obtained by conditional logistic regression incorrect. Therefore, in this paper, we propose a flexible derivative time-varying coefficient model to evaluate effect modification by time and location, in order to make correct statistical inference, when the number of locations is small. Our proposed model is developed under the Bayesian hierarchical model framework and allows us to simultaneously detect relationships between the predictor and binary outcome and between the predictor and time. Inference is proposed based on the derivative function of the estimated function to determine whether there is an effect modification due to time and/or location, for a small number of locations among the participants. We demonstrate the accuracy of the estimation using a simulation study and an epidemiological example of a 1–4 bidirectional case-crossover study of childhood aseptic meningitis with drinking water turbidity.

Flexible omnibus test in 1:M matched case-crossover study with measurement error in covariate

2020 ◽  
Vol 29 (10) ◽  
pp. 3019-3031
Author(s):  
Byung-Jun Kim ◽  
Inyoung Kim
Keyword(s):  
Measurement Error ◽  
Crossover Study ◽  
Conditional Logistic Regression ◽  
Statistical Testing ◽  
Binary Outcomes ◽  
Omnibus Test ◽  
Case Crossover ◽  
Functional Forms ◽  
Matched Case ◽  
Matched Data

The matched case-crossover study design is used in public health, biomedical, and epidemiological research with clustered binary outcomes. Conditional logistic regression is commonly used for analysis because any effects associated with the matching covariates by stratum can be removed. However, some matching covariates often play an important role as effect modifications, causing incorrect statistical testing. The covariates in such studies are often measured with error, so that not accounting for this error can also lead to incorrect inferences for all covariates in the model. However, the methods available for simultaneously evaluating effect modification by matching covariates as well as assessing and characterizing error-in-covariates are limited. In this paper, we propose a flexible omnibus test for testing (1) the significance of a functional association between the clustered binary outcomes and covariates with the measurement error, (2) the existence of effect modifications by matching covariates, and (3) the significance of an interaction effect between the measurement error covariate and other covariates, without specifying the functional forms for these testings. The proposed omnibus test has the flexibility to allow inferences on various hypothesis settings. The advantages of the proposed flexible omnibus test are demonstrated through simulation studies and 1:4 bidirectional matched data analyses from an epidemiology study.

scholarly journals 717. Antibiotic treatment of Shiga toxin-producing Escherichia coli related gastroenteritis and the risk of hemolytic uremic syndrome: a population based matched case-control study in Japan

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S409-S410
Author(s):  
Shota Myojin ◽  
Kyongsun Pak ◽  
Mayumi Sako ◽  
Tohru Kobayashi ◽  
Takuri Takahashi ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Shiga Toxin ◽  
Case Control Study ◽  
Conditional Logistic Regression ◽  
Population Based ◽  
Case Control ◽  
Age Group ◽  
Matched Case ◽  
Matched Case Control Study ◽  
Control Study

Abstract Background The role of therapeutic intervention, particularly antibiotics, for Shiga toxin-producing Escherichia coli (STEC) related infection is controversial. Methods We performed a population based matched case-control study to assess the association between treatment (antibiotics, antidiarrheal agents and probiotics) for STEC related infections and HUS development. We identified all STEC HUS patients as cases and matched five non-HUS patients as controls using the data from the National Epidemiological Surveillance of Infectious Diseases (NESID) between January 1, 2017, and December 31, 2018. Further medical information was obtained by standardized questionnaires answered by physicians who registered each patient. We used multivariate conditional logistic regression model to evaluate the association between exposures (use of antibiotics, use of antidiarrheal agents, days between disease onset and fosfomycin administration [within two or three days]) and the development of HUS, by matched odds ratios (OR) and 95% confidence intervals (CI). Covariates we used were sex, age group, area code, presence of diarrhea and other factors. We also performed subgroup analyses using age (adults and children) as a stratification factor. Results 7,760 STEC related patients were registered in the NESID. We selected patients who had a record of HUS diagnosis (n=182) and matched controls without HUS (n=910). After collecting standardized paper-based questionnaires, we enrolled 90 HUS patients and 371 non-HUS patients for analysis. In the main analysis, matched OR of fosfomycin was 0.75(0.47-1.20) in all ages, 1.41(0.51-3.88) in adults and 0.58(0.34-1.01) in children. Matched OR of antidiarrheal agents was 2.07(1.07-4.03) in all ages, 1.84(0.32-10.53) in adults, 2.65(1.21-5.82) in children. Matched OR of probiotics was 0.86(0.46-1.61) in all ages, 0.76(0.21-2.71) in adults, 1.00(0.48-2.09) in children. There was no significant association between the timing of fosfomycin use in the first two or five days of illness and HUS development in any age group. Conclusion Our results suggest that fosfomycin might decrease the risk of HUS in children younger than 15 years of age with STEC confirmed bacterial gastroenteritis. Disclosures All Authors: No reported disclosures

Alcohol consumption and injury risk: A case-crossover study in Sydney, Australia

2010 ◽  
Vol 30 (4) ◽  
pp. 344-354 ◽  
Author(s):  
MANDY WILLIAMS ◽  
MOHAMMED MOHSIN ◽  
DANIELLE WEBER ◽  
BIN JALALUDIN ◽  
JOHN CROZIER
Keyword(s):  
Alcohol Consumption ◽  
Crossover Study ◽  
Injury Risk ◽  
Case Crossover ◽  
Sydney Australia

scholarly journals Hospital Readmission in Patients With COVID-19

2021 ◽  
Author(s):  
Neda Rahimian ◽  
Mahshid Heidari ◽  
Nahid Hashemi-Madani ◽  
Nader Tavakoli ◽  
Moammad E Khamseh
Keyword(s):  
Risk Factors ◽  
Hospital Readmission ◽  
Underlying Disease ◽  
Control Groups ◽  
Factors Associated ◽  
Infection Age ◽  
Discharge Plan ◽  
Matched Case ◽  
And Control ◽  
Control Study

Abstract Objective: During the COVID-19 pandemic, the demand for hospital beds has exceeded substantially. Thus, we aimed to conduct this study to identify factors associated with the risk of readmission in order to introduce the best discharge plan for patients with high risk of hospital readmission. Method: This is a multicenter, case-control study included 1357 patients hospitalized with COVID-19 infection. Age-sex-matched case and control groups were paired at 1:2 ratios. COVID-19 readmission rate was assessed. Moreover, Logistic regression analysis was applied to determine the factors associated with readmission. Results: Of the 1357 patients, 99 (7.29%) subjects were readmitted. The most common cause of readmission was respiratory distress. The median (IQR) of the interval between hospital discharge and the second admission was 5 (2-16) days. Upon adjusting with the main risk factors, having at least one underlying disease and being treated with the corticosteroid (GC) were significantly associated with a higher rate of readmission (OR: 2.76, 95% CI :1.30- 5.87) and (OR:8.24, 95% CI :3.72- 18.22), respectively.Conclusion: Identification of Risk factors of COVID 19 readmission will improve resource utilization and patient care.

scholarly journals A novel weighting method to remove bias from within-subject exposure dependency in case-crossover studies

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Kiyoshi Kubota ◽  
Thu-Lan Kelly ◽  
Tsugumichi Sato ◽  
Nicole Pratt ◽  
Elizabeth Roughead ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Crossover Study ◽  
Odds Ratio ◽  
Time Trends ◽  
Rate Ratio ◽  
Conditional Logistic Regression ◽  
Time Varying ◽  
Weighting Method ◽  
Case Crossover ◽  
Crossover Studies

Abstract Background Case-crossover studies have been widely used in various fields including pharmacoepidemiology. Vines and Farrington indicated in 2001 that when within-subject exposure dependency exists, conditional logistic regression can be biased. However, this bias has not been well studied. Methods We have extended findings by Vines and Farrington to develop a weighting method for the case-crossover study which removes bias from within-subject exposure dependency. Our method calculates the exposure probability at the case period in the case-crossover study which is used to weight the likelihood formulae presented by Greenland in 1999. We simulated data for the population with a disease where most patients receive a cyclic treatment pattern with within-subject exposure dependency but no time trends while some patients stop and start treatment. Finally, the method was applied to real-world data from Japan to study the association between celecoxib and peripheral edema and to study the association between selective serotonin reuptake inhibitor (SSRI) and hip fracture in Australia. Results When the simulated rate ratio of the outcome was 4.0 in a case-crossover study with no time-varying confounder, the proposed weighting method and the Mantel-Haenszel odds ratio reproduced the true rate ratio. When a time-varying confounder existed, the Mantel-Haenszel method was biased but the weighting method was not. When more than one control period was used, standard conditional logistic regression was biased either with or without time-varying confounding and the bias increased (up to 8.7) when the study period was extended. In real-world analysis with a binary exposure variable in Japan and Australia, the point estimate of the odds ratio (around 2.5 for the association between celecoxib and peripheral edema and around 1.6 between SSRI and hip fracture) by our weighting method was equal to the Mantel-Haenszel odds ratio and stable compared with standard conditional logistic regression. Conclusion Case-crossover studies may be biased from within-subject exposure dependency, even without exposure time trends. This bias can be identified by comparing the odds ratio by the Mantel-Haenszel method and that by standard conditional logistic regression. We recommend using our proposed method which removes bias from within-subject exposure dependency and can account for time-varying confounders.

scholarly journals Fatal opioid overdoses during and shortly after hospital admissions in England: A case-crossover study

PLoS Medicine ◽  
2021 ◽  
Vol 18 (10) ◽  
pp. e1003759
Author(s):  
Dan Lewer ◽  
Brian Eastwood ◽  
Martin White ◽  
Thomas D. Brothers ◽  
Martin McCusker ◽  
...  
Keyword(s):  
Drug Use ◽  
Hospital Admission ◽  
Crossover Study ◽  
Hospital Admissions ◽  
Conditional Logistic Regression ◽  
Early Discharge ◽  
Odds Ratios ◽  
Drug Poisoning ◽  
Case Crossover ◽  
Increased Risk

Background Hospital patients who use illicit opioids such as heroin may use drugs during an admission or leave the hospital in order to use drugs. There have been reports of patients found dead from drug poisoning on the hospital premises or shortly after leaving the hospital. This study examines whether hospital admission and discharge are associated with increased risk of opioid-related death. Methods and findings We conducted a case-crossover study of opioid-related deaths in England. Our study included 13,609 deaths between January 1, 2010 and December 31, 2019 among individuals aged 18 to 64. For each death, we sampled 5 control days from the period 730 to 28 days before death. We used data from the national Hospital Episode Statistics database to determine the time proximity of deaths and control days to hospital admissions. We estimated the association between hospital admission and opioid-related death using conditional logistic regression, with a reference category of time neither admitted to the hospital nor within 14 days of discharge. A total of 236/13,609 deaths (1.7%) occurred following drug use while admitted to the hospital. The risk during hospital admissions was similar or lower than periods neither admitted to the hospital nor recently discharged, with odds ratios 1.03 (95% CI 0.87 to 1.21; p = 0.75) for the first 14 days of an admission and 0.41 (95% CI 0.30 to 0.56; p < 0.001) for days 15 onwards. 1,088/13,609 deaths (8.0%) occurred in the 14 days after discharge. The risk of opioid-related death increased in this period, with odds ratios of 4.39 (95% CI 3.75 to 5.14; p < 0.001) on days 1 to 2 after discharge and 2.09 (95% CI 1.92 to 2.28; p < 0.001) on days 3 to 14. 11,629/13,609 deaths (85.5%) did not occur close to a hospital admission, and the remaining 656/13,609 deaths (4.8%) occurred in hospital following admission due to drug poisoning. Risk was greater for patients discharged from psychiatric admissions, those who left the hospital against medical advice, and those leaving the hospital after admissions of 7 days or more. The main limitation of the method is that it does not control for time-varying health or drug use within individuals; therefore, hospital admissions coinciding with high-risk periods may in part explain the results. Conclusions Discharge from the hospital is associated with an acute increase in the risk of opioid-related death, and 1 in 14 opioid-related deaths in England happens in the 2 weeks after the hospital discharge. This supports interventions that prevent early discharge and improve linkage with community drug treatment and harm reduction services.

scholarly journals Nested Case-Control Studies With Multiple Measurements: Estimating Marginal Relationships

2021 ◽  
Author(s):  
Joshua N. Sampson ◽  
Paul S. Albert ◽  
Mark P. Purdue
Keyword(s):  
Conditional Logistic Regression ◽  
Case Control ◽  
Case Control Studies ◽  
Robust Variance ◽  
Non Hodgkin Lymphoma ◽  
Multiple Measurements ◽  
Matched Case ◽  
Nested Case Control ◽  
Standard Software ◽  
Single Time Point

Abstract Background: We consider the analysis of nested, matched, case-control studies that have multiple biomarker measurements per individual. We propose a simple approach for estimating the marginal relationship between a biomarker measured at a single time point and the risk of an event. We know of no other standard software package that can perform such analyses while explicitly accounting for the matching. Results: We propose an application of conditional logistic regression (CLR) that can include all measurements and uses a robust variance estimator. We compare our approach to other methods such as performing CLR with only the first measurement, CLR with an average of all measurements, and Generalized Estimating Equations. In simulations, our approach is significantly more powerful than CLR with one measurement or an average of all measurements, and has similar to power to GEE but correctly accounts for the matching. We then apply our approach to the CLUE cohort to show that an increased level of the immune marker sCD27 is associated with non‐Hodgkin lymphoma (NHL) and, by evaluating the strength of the association as a function of time until diagnosis, that the an increased level is likely an effect of the disease as opposed to a cause of the disease. The approach can be implemented by the R function clogitRV available at https://github.com/sampsonj74/clogitRV.Conclusion: We offered an approach and software for analyzing matched case-control studies with multiple measurements. We demonstrated that these methods are accurate, precise, and statistically powerful.

scholarly journals Risk of Infective Endocarditis After Invasive Dental Treatments

Circulation ◽  
2018 ◽  
Vol 138 (4) ◽  
pp. 356-363 ◽  
Author(s):  
Tzu-Ting Chen ◽  
Yi-Chun Yeh ◽  
Kuo-Liong Chien ◽  
Mei-Shu Lai ◽  
Yu-Kang Tu
Keyword(s):  
Infective Endocarditis ◽  
Confidence Interval ◽  
Regression Model ◽  
Conditional Logistic Regression ◽  
Case Series ◽  
Crossover Design ◽  
The Self ◽  
Series Design ◽  
Case Crossover ◽  
Study Designs

Background: Invasive dental treatments (IDTs) can yield temporary bacteremia and have therefore been considered a potential risk factor of infective endocarditis (IE). It is hypothesized that, through the trauma caused by IDTs, bacteria gain entry to the bloodstream and may attach to abnormal heart valves or damaged heart tissue, giving rise to IE. However, the association between IDTs and IE remains controversial. The aim of this study is to estimate the association between IDTs and IE. Methods: The data in this study were obtained from the Health Insurance Database in Taiwan. We selected 2 case-only study designs, case-crossover and self-controlled case series, to analyze the data. The advantage of these methods is that confounding factors that do not vary with time are adjusted for implicitly. In the case-crossover design, a conditional logistic regression model with exposure to IDTs was used to estimate the risks of IE following an IDT with 4, 8, 12, and 16 weeks delay, respectively. In the self-controlled case series design, a conditional Poisson regression model was used to estimate the risk of IE for the risk periods of 1 to 4, 5 to 8, 9 to 12, and 13 to 16 weeks following an IDT. Results: In total, 9120 and 8181 patients with IE were included in case-crossover design and self-controlled case series design, respectively. In the case-crossover design, 277 cases and 249 controls received IDTs during the exposure period, and the odds ratio was 1.12 (95% confidence interval, 0.94–1.34) for 4 weeks. In the self-controlled case series design, we observed that 407 IEs occurred during the first 4 weeks after IDTs, and the age-adjusted incidence rate ratio was 1.14 (95% confidence interval, 1.02–1.26) for 1 to 4 weeks after IDTs. Conclusions: In both study designs, we did not observe a clinically larger risk for IE in the short periods after IDTs. We also found no association between IDTs and IE among patients with a high risk of IE. Therefore, antibiotic prophylaxis for the prevention of IE is not required for the Taiwanese population.

scholarly journals The Value of Local Heatwave Impact Assessment: A Case-Crossover Analysis of Hospital Emergency Department Presentations in Tasmania, Australia

2019 ◽  
Vol 16 (19) ◽  
pp. 3715 ◽  
Author(s):  
Sharon L. Campbell ◽  
Tomas A. Remenyi ◽  
Grant J. Williamson ◽  
Christopher J. White ◽  
Fay H. Johnston
Keyword(s):  
Emergency Department ◽  
Capacity Planning ◽  
Conditional Logistic Regression ◽  
Hospital Emergency Department ◽  
Hospital Emergency ◽  
Case Crossover ◽  
Crossover Analysis ◽  
The Impact ◽  
The Relationship ◽  
Local Research

Heatwaves have been identified as a threat to human health, with this impact projected to rise in a warming climate. Gaps in local knowledge can potentially undermine appropriate policy and preparedness actions. Using a case-crossover methodology, we examined the impact of heatwave events on hospital emergency department (ED) presentations in the two most populous regions of Tasmania, Australia, from 2008–2016. Using conditional logistic regression, we analyzed the relationship between ED presentations and severe/extreme heatwaves for the whole population, specific demographics including age, gender and socio-economic advantage, and diagnostic conditions that are known to be impacted in high temperatures. ED presentations increased by 5% (OR 1.05, 95% CI 1.01–1.09) across the whole population, by 13% (OR 1.13, 95% CI 1.03–1.24) for children 15 years and under, and by 19% (OR 1.19, 95% CI 1.04–1.36) for children 5 years and under. A less precise association in the same direction was found for those over 65 years. For diagnostic subgroups, non-significant increases in ED presentations were observed for asthma, diabetes, hypertension, and atrial fibrillation. These findings may assist ED surge capacity planning and public health preparedness and response activities for heatwave events in Tasmania, highlighting the importance of using local research to inform local practice.

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