Unmatched Case-control Studies Research Articles

Measles outbreak investigation in Tocha district, southwestern Ethiopia: an unmatched case-control study.

Measles continues to be a public health challenge in Ethiopia. Rumors of suspected measles were notified on April 8, 2023 from Tocha district. We conducted an assessment to describe measles outbreak and determine risk factors for measles infection in the Tocha district of the Dawuro zone, Southwest Ethiopia. We conducted a 1:2 unmatched case-control studies from April to May 2023. We took all 147 cases registered on line list for descriptive analyses. We used a total of 74 randomly selected cases and 147 controls for case-control part. Any person in Tocha district with laboratory-confirmed measles IgM antibody; or any suspected person epidemiologically linked to confirmed measles cases from March 23 to April 26 2023, were included in the case. Neighborhood who did not fulfill this standard case definition were included in controls. Data were collected using standardized questionnaires deployed on Kobo Collect. Descriptive analyses were conducted using Epi info version 7.2.5.0. The analyses were performed using Statistical Package for Social Science (SPSS) version 26. Binary logistic regression analyses were utilized to select candidate variables. We conducted multiple logistic regression analysis to identify determinants of measles infection at a p value ≤0.05 with 95% confidence interval. The overall attack rate of 22.64/10,000 for general population and 104.59/10,000 among under-five children were attributed to the outbreak with a case fatality rate of 2.72%. Vaccine coverage in the last year and this year were 73.52 and 53.88%, respectively, while vaccine effectiveness in the district was 79%. Poor house ventilation (AOR = 3.540, 95% CI: 1.663-7.535) and having contact history with the case (AOR = 2.528, 95% CI: 1.180-4.557) were positively related to measles infection while being previously vaccinated for measles (AOR = 0.209, 95% CI: 0.180-4.577) reduce risk of measles infections. The highest attack rate was observed among children under 5 years of age, with a case fatality rate of 2.72%. Vaccination coverage was less than what expected to develop herd immunity. Strategies to increase vaccination coverage and strengthening surveillance systems for rumor identification and early responses to prevent person to person transmission are recommended.

Factors associated with use of community-based, peer-led sexual and reproductive health services by adolescent boys and young men aged 18-24 in Lusaka, Zambia: A case control study nested in the Yathu Yathu trial.

Adolescents and young people (AYP) aged 15-24 years have the least access to facility-based sexual and reproductive health (SRH) services, including HIV services. The Yathu-Yathu cluster-randomized trial (CRT) in Zambia tested whether a novel peer-led community-based approach increased knowledge of HIV status amongst AYP. In this nested case-control study, we aimed to identify factors associated with non-attendance to the Yathu Yathu hubs by adolescent boys and young men (ABYM) aged 18-24-years. Yathu Yathu was a CRT conducted in two communities in Lusaka, Zambia, with 10 intervention and 10 control zones. AYP in all zones were offered prevention points cards (PPC), which incentivized and tracked service use at the hubs and health facility. In intervention zones, services were provided to AYP through community-based spaces (hubs) led by peer support workers. In these zones, cases were defined as those not having accessed any service at a hub and controls as those that accessed at least one service. Data were collected from October 2020 to January 2021 and analysed using methods appropriate for unmatched case-control studies. 161 cases and 160 controls consented to participate in the study. Participants aged 20-24 years (adjOR 1.99, 95%CI 1.26-3.12, p = 0.003), who were educated up to college level (adjOR 8.47,95%CI 2.08-34.53, p = 0.001) or who reported being employed in the last 12 months (adjOR 2.15, 95%CI 1.31-3.53, p = 0.002) were more likely to not attend the hubs. ABYM who had a friend with a PPC were more likely to attend the hubs (adjOR 0.18 95%CI 0.09-0.35, p<0.001). Most cases reported having their last HIV test at the local government health facility (58%) while most controls reported HIV-testing at a Yathu Yathu hub (82%). Among the controls, 84% (134/160) rated the hub experience as excellent. Among cases, 65% (104/161) stated they didn't visit the hubs "due to employment". Despite Yathu Yathu services being community-based and more accessible compared to health facilities, we found age, education and employment were associated with not attending hubs. Strategies are needed to reach employed young men who may not have access to SRH/HIV services during conventional working hours and to better utilise peer networks to increase service use.

Outbreak of ceftriaxone-resistant Salmonella enterica serotype Typhi-Tiruchirappalli, Tamil Nadu, India, June 2018

ObjectivesIn May 2018, a laboratory network for antimicrobial resistance (AMR) surveillance in Tamil Nadu, India, detected a cluster of Salmonella enterica serotype Typhi (S. Typhi) isolates resistant to ceftriaxone. We investigated to describe the epidemiology and identify risk factors for the outbreak. MethodsWe conducted unmatched case-control studies. We defined a case as illness (fever with abdominal pain, diarrhea or vomiting) in a person with blood culture-confirmed ceftriaxone-resistant S. Typhi isolated between January 1 and July 4, 2018 in Tiruchirappalli, Tamil Nadu. We interviewed cases using a semi-structured questionnaire to identify common exposures to food, water and places visited. ResultsWe identified 7 cases (5 men) during March 25–June 8, 2018, median age 23 years (range: 12–42); all were hospitalized, none died. Eating at Restaurant A (odds ratio [OR]=22) and chicken gravy (OR=16) was associated with illness. Of the 10 workers at Restaurant A, stool culture from 8 did not detect S. Typhi; 2 did not consent to provide samples. Five water samples around the restaurant showed low or no residual chlorine content. ConclusionsThe investigation highlights the value of AMR surveillance in detecting emerging pathogens and the need for timely investigations, along with strengthening food safety.

Power and sample size for multivariate logistic modeling of unmatched case-control studies.

Sample size calculations are needed to design and assess the feasibility of case-control studies. Although such calculations are readily available for simple case-control designs and univariate analyses, there is limited theory and software for multivariate unconditional logistic analysis of case-control data. Here we outline the theory needed to detect scalar exposure effects or scalar interactions while controlling for other covariates in logistic regression. Both analytical and simulation methods are presented, together with links to the corresponding software.

Efficient Sampling in Unmatched Case-Control Studies When the Total Number of Cases and Controls Is Fixed.

In the case-control design, statistical efficiency is balanced with practical efficiency. In an unmatched case-control study of a binary exposure, in which the expected odds ratio deviates from the null (i.e., no effect) and the sum of the number of cases and controls is fixed, 1:1 sampling of cases and controls may yield suboptimal statistical efficiency. This holds especially for situations where the exposure is rare and for stronger relationships between the exposure and the outcome under study. The equations presented here may provide researchers with a simple and efficient strategy for sampling cases and controls when designing an unmatched case-control study for such a situation.

Environmental arsenic exposure and risk of diabetes type 2 in Ron Phibun subdistrict, Nakhon Si Thammarat Province, Thailand: unmatched and matched case-control studies.

BackgroundThere are inconsistent findings on associations between low-to-moderate level of arsenic in water and diabetes risk from previous epidemiological reports. In Ron Phibun subdistrict, Nakhon Si Thammarat Province, Thailand, a low level of arsenic exposure among population was observed and increased diabetes mellitus (DM) rate was identified.ObjectivesWe aimed to investigate the association between determinants (including low-level water arsenic exposure) of DM type 2 risk among residents of three villages of Ron Phibun subdistrict, Nakhon Si Thammarat Province.Materials and methodsSecondary data from two previous community based-studies, conducted in 2000 and 2008, were utilized. Data on independent variables relating to arsenic exposure and sociodemographic characteristics were taken from questionnaires and worksheets for health-risk screening. Water samples collected during household visit were sent for analysis of arsenic level at certified laboratories. Diabetes cases (N=185) were those who had been diagnosed with DM type 2. Two groups of controls, one unmatched to cases (n=200) and one pair matched on age and gender (n=200), were selected for analysis as unmatched and matched case–control studies, respectively. A multiple imputation technique was used to impute missing values of independent variables. Multivariable logistic regression models, with independent variables for arsenic exposure and sociodemographic characteristics, were constructed. The unmatched and matched data sets were analyzed using unconditional and conditional logistic analyses, respectively.ResultsOlder age, body mass index (BMI), having a history of illness in siblings and parents, and drinking were associated with increased DM type 2 risk. We found no convincing association between DM type 2 risk and water arsenic concentration in either study.ConclusionWe did not observe meaningful association between diabetes risk and the low-to-moderate arsenic levels observed in this study. Further research is needed to confirm this finding in the study area and elsewhere in Thailand.

Statistical methods for studying disease subtype heterogeneity.

A fundamental goal of epidemiologic research is to investigate the relationship between exposures and disease risk. Cases of the disease are often considered a single outcome and assumed to share a common etiology. However, evidence indicates that many human diseases arise and evolve through a range of heterogeneous molecular pathologic processes, influenced by diverse exposures. Pathogenic heterogeneity has been considered in various neoplasms such as colorectal, lung, prostate, and breast cancers, leukemia and lymphoma, and non-neoplastic diseases, including obesity, type II diabetes, glaucoma, stroke, cardiovascular disease, autism, and autoimmune disease. In this article, we discuss analytic options for studying disease subtype heterogeneity, emphasizing methods for evaluating whether the association of a potential risk factor with disease varies by disease subtype. Methods are described for scenarios where disease subtypes are categorical and ordinal and for cohort studies, matched and unmatched case-control studies, and case-case study designs. For illustration, we apply the methods to a molecular pathological epidemiology study of alcohol intake and colon cancer risk by tumor LINE-1 methylation subtypes. User-friendly software to implement the methods is publicly available.

A concordance index for matched case-control studies with applications in cancer risk.

In unmatched case-control studies, the area under the receiver operating characteristic (ROC) curve (AUC) may be used to measure how well a variable discriminates between cases and controls. The AUC is sometimes used in matched case-control studies by ignoring matching, but it lacks interpretation because it is not based on an estimate of the ROC for the population of interest. We introduce an alternative measure of discrimination that is the concordance of risk factors conditional on the matching factors. Parametric and non-parametric estimators are given for different matching scenarios, and applied to real data from breast and lung cancer case-control studies. Diagnostic plots to verify the constancy of discrimination over matching factors are demonstrated. The proposed simple measure is easy to use, interpret, more efficient than unmatched AUC statistics and may be applied to compare the conditional discrimination performance of risk factors.

126. Outcomes after laparoscopic versus open wedge resection for suspected gastric gastrointestinal stromal tumours: A matched-pair case-control study

Background: Laparoscopic resection of gastric gastrointestinal stromal tumors (GISTs) has been shown to be technically feasible and associated with favorable outcomes when compared to the open approach. However, most previous studies demonstrating the superior results of laparoscopy were retrospective unmatched case control studies which raised the concern that this may be due to selection bias in favor of the laparoscopic approach. This study aims to mitigate this bias by performing a case-control study of laparoscopic (LWR) versus open wedge resection (OWR) matched by resection type, location and tumor size.

Increased Slope of the Lateral Tibial Plateau Subchondral Bone Is Associated With Greater Risk of Noncontact ACL Injury in Females but Not in Males

Background: There is an emerging consensus that increased posterior-inferior directed slope of the subchondral bone portion of the tibial plateau is associated with increased risk of suffering an anterior cruciate ligament (ACL) injury; however, most of what is known about this relationship has come from unmatched case-control studies. These observations need to be confirmed in more rigorously designed investigations. Hypothesis: Increased posterior-inferior directed slope of the medial and lateral tibial plateaus are associated with increased risk of suffering a noncontact ACL injury. Study Design: Case-control study; Level of evidence, 3. Methods: In sum, 176 athletes competing in organized sports at the college and high school levels participated in the study: 88 suffering their first noncontact ACL injury and 88 matched controls. Magnetic resonance images were acquired, and geometry of the subchondral bone portion of the tibial plateau was characterized on each athlete bilaterally by measuring the medial and lateral tibial plateau slopes, coronal tibial slope, and the depth of the medial tibial plateau. Comparisons between knees of the same person were made with paired t tests, and associations with injury risk were assessed by conditional logistic regression analysis of ACL-injured and control participants. Results: Controls exhibited side-to-side symmetry of subchondral bone geometry, while the ACL-injured athletes did not, suggesting that the ACL injury may have changed the subchondral bone geometry. Therefore, the uninjured knees of the ACL-injured athletes and the corresponding limbs of their matched controls were used to assess associations with injury risk. Analyses of males and females as a combined group and females as a separate group showed a significant association between ACL injury risk and increased posterior-inferior directed slope of the lateral tibial plateau slope. This relationship was not apparent when males were analyzed as a group. Multivariate analyses indicated that these results were independent of the medial tibial plateau slope, coronal tibial slope, and depth of the medial tibial plateau, which were not associated with ACL injury. Conclusion: There is a 21.7% increased risk of noncontact ACL injury with each degree increase of the lateral tibial plateau slope among females but not among males. The medial tibial plateau slope, coronal tibial slope, and depth of the medial tibial plateau were not associated with risk of injury for females or males.

Network-based regularization for matched case-control analysis of high-dimensional DNA methylation data.

The matched case-control designs are commonly used to control for potential confounding factors in genetic epidemiology studies especially epigenetic studies with DNA methylation. Compared with unmatched case-control studies with high-dimensional genomic or epigenetic data, there have been few variable selection methods for matched sets. In an earlier paper, we proposed the penalized logistic regression model for the analysis of unmatched DNA methylation data using a network-based penalty. However, for popularly applied matched designs in epigenetic studies that compare DNA methylation between tumor and adjacent non-tumor tissues or between pre-treatment and post-treatment conditions, applying ordinary logistic regression ignoring matching is known to bring serious bias in estimation. In this paper, we developed a penalized conditional logistic model using the network-based penalty that encourages a grouping effect of (1) linked Cytosine-phosphate-Guanine (CpG) sites within a gene or (2) linked genes within a genetic pathway for analysis of matched DNA methylation data. In our simulation studies, we demonstrated the superiority of using conditional logistic model over unconditional logistic model in high-dimensional variable selection problems for matched case-control data. We further investigated the benefits of utilizing biological group or graph information for matched case-control data. We applied the proposed method to a genome-wide DNA methylation study on hepatocellular carcinoma (HCC) where we investigated the DNA methylation levels of tumor and adjacent non-tumor tissues from HCC patients by using the Illumina Infinium HumanMethylation27 Beadchip. Several new CpG sites and genes known to be related to HCC were identified but were missed by the standard method in the original paper.

On the equivalence of posterior inference based on retrospective and prospective likelihoods: application to a case‐control study of colorectal cancer

The paper develops a class of priors that leads to equivalent posterior inference for odds ratio parameters based on prospective and retrospective models for categorical response data. The results are applicable to both unmatched and matched case-control studies. The results hold for a general class of link functions for categorical response. The proposed method can accommodate multiple and possibly ordered disease states. The results are applied to the analysis of discrete subtypes in an ongoing case-control study of colorectal cancer. A simulation study illustrates the need for carefully considering prior choices in Bayesian analysis of data collected under retrospective design.

On the covariance of two correlated log‐odds ratios

In many applications two correlated estimates of an effect size need to be considered simultaneously to be combined or compared. Apparently, there is a need for calculating their covariance, which however requires access to the individual data that may not be available to a researcher performing the analysis. We present a simple and efficient method for calculating the covariance of two correlated log-odds ratios. The method is very simple, is based on the well-known large sample approximations, can be applied using only data that are available in the published reports and more importantly, is very general, because it is shown to encompass several previously derived estimates (multiple outcomes, multiple treatments, dose-response models, mutually exclusive outcomes, genetic association studies) as special cases. By encompassing the previous approaches in a unified framework, the method allows easily deriving estimates for the covariance concerning problems that were not easy to be obtained otherwise. We show that the method can be used to derive the covariance of log-odds ratios from matched and unmatched case-control studies that use the same cases, a situation that has been addressed in the past only using individual data. Future applications of the method are discussed.

Pooled Exposure Assessment for Matched Case-control Studies

Exposure assessment using biologic specimens is important for epidemiology but may become impracticable if assays are expensive, specimen volumes are marginally adequate, or analyte levels fall below the limit of detection. Pooled exposure assessment can provide an effective remedy for these problems in unmatched case-control studies. We extend pooled exposure strategies to handle specimens collected in a matched case-control study. We show that if a logistic model applies to individuals, then a logistic model also applies to an analysis using pooled exposures. Consequently, the individual-level odds ratio can be estimated while conserving both cost and specimen. We discuss appropriate pooling strategies for a single exposure, with adjustment for multiple, possibly continuous, covariates (confounders) and assessment of effect modification by a categorical variable. We assess the performance of the approach via simulations and conclude that pooled strategies can markedly improve efficiency for matched as well as unmatched case-control studies.

Estimation of odds ratios of genetic variants for the secondary phenotypes associated with primary diseases

Genetic association studies for binary diseases are designed as case-control studies: the cases are those affected with the primary disease and the controls are free of the disease. At the time of case-control collection, information about secondary phenotypes is also collected. Association studies of secondary phenotype and genetic variants have received a great deal of interest recently. To study the secondary phenotypes, investigators use standard regression approaches, where individuals with secondary phenotypes are coded as cases and those without secondary phenotypes are coded as controls. However, using the secondary phenotype as an outcome variable in a case-control study might lead to a biased estimate of odds ratios (ORs) for genetic variants. The secondary phenotype is associated with the primary disease; therefore, individuals with and without the secondary phenotype are not sampled following the principles of a case-control study. In this article, we demonstrate that such analyses will lead to a biased estimate of OR and propose new approaches to provide more accurate OR estimates of genetic variants associated with the secondary phenotype for both unmatched and frequency-matched (with respect to the secondary phenotype) case-control studies. We also propose a bootstrapping method to estimate the empirical confidence intervals for the corrected ORs. Using simulation studies and analysis of lung cancer data for single-nucleotide polymorphism associated with smoking quantity, we compared our new approaches to standard logistic regression and to an extended version of the inverse-probability-of-sampling-weighted regression. The proposed approaches provide more accurate estimation of the true OR.

A weighted Cox model for modelling time‐dependent exposures in the analysis of case–control studies

Many exposures investigated in epidemiological case-control studies may vary over time. The effects of these exposures are usually estimated using logistic regression, which does not directly account for changes in covariate values over time within individuals. By contrast, the Cox model with time-dependent covariates directly accounts for these changes over time. However, the over-sampling of cases in case-control studies, relative to controls, requires manipulating the risk sets in the Cox partial likelihood. A previous study showed that simple inclusion or exclusion of future cases in each risk set induces an under- or over-estimation bias in the regression parameters, respectively. We investigate the performance of a weighted Cox model that weights subjects according to age-conditional probabilities of developing the disease of interest in the source population. In a simulation study, the lifetime experience of a source population is first generated and a case-control study is then simulated within each population. Different characteristics of exposure are generated, including time-varying intensity. The results show that the estimates from the weighted Cox model are much less biased than the Cox models that simply include or exclude future cases, and are superior to logistic regression estimates in terms of bias and mean-squared error. An application to frequency-matched population-based case-control data on lung cancer illustrates similar differences in the estimated effects of different smoking variables. The investigated weighted Cox model is a potential alternative method to analyse matched or unmatched population-based case-control studies with time-dependent exposures.

A polytomous conditional likelihood approach for combining matched and unmatched case–control studies

In genetic association studies it is becoming increasingly imperative to have large sample sizes to identify and replicate genetic effects. To achieve these sample sizes, many research initiatives are encouraging the collaboration and combination of several existing matched and unmatched case-control studies. Thus, it is becoming more common to compare multiple sets of controls with the same case group or multiple case groups to validate or confirm a positive or negative finding. Usually, a naive approach of fitting separate models for each case-control comparison is used to make inference about disease-exposure association. But, this approach does not make use of all the observed data and hence could lead to inconsistent results. The problem is compounded when a common case group is used in each case-control comparison. An alternative to fitting separate models is to use a polytomous logistic model but, this model does not combine matched and unmatched case-control data. Thus, we propose a polytomous logistic regression approach based on a latent group indicator and a conditional likelihood to do a combined analysis of matched and unmatched case-control data. We use simulation studies to evaluate the performance of the proposed method and a case-control study of multiple myeloma and Inter-Leukin-6 as an example. Our results indicate that the proposed method leads to a more efficient homogeneity test and a pooled estimate with smaller standard error.

On the Usage of HWE for Identifying Genotyping Errors

A recent publication by Zou & Donner (2006) discussed the merits of testing Hardy-Weinberg Equilibrium (HWE) in the setting of unmatched case-control association studies. Specifically, the authors highlighted that testing for HWE should not be used as a criterion for identifying single nucleotide polymorphisms (SNPs) with genotyping errors. The authors went on to discuss the potential problems associated with a two-stage analysis procedure, which incorporates checks for adherence to HWE prior to performing the association analyses.

Bayesian modeling for genetic association in case-control studies: accounting for unknown population substructure

A two-stage parametric Bayesian method is proposed to examine the association between a candidate gene and the occurrence of a disease after accounting for population substructure. This procedure, implemented via a Markov chain Monte Carlo numerical integration technique, first estimates the posterior probability of different unknown population substructures and then integrates this information into a disease-gene association model through the technique of Bayesian model averaging. The model relaxes certain assumptions of previous analyses and provides a unified computational framework to obtain an estimate of the log odds ratio parameter corresponding to the genetic factor after allowing for the allele frequencies to vary across subpopulations. The uncertainty in estimating the population substructure is taken into account while providing credible intervals for parameters in the disease-gene association model. Simulations on unmatched case-control studies that mimic an admixed Argentinean population are performed to demonstrate the statistical properties of our model. The method is also applied to a real data set coming from a genetic association study on obesity.

Sample size calculations for population- and family-based case-control association studies on marker genotypes.

Most previous sample size calculations for case-control studies to detect genetic associations with disease assumed that the disease gene locus is known, whereas, in fact, markers are used. We calculated sample sizes for unmatched case-control and sibling case-control studies to detect an association between a biallelic marker and a disease governed by a putative biallelic disease locus. Required sample sizes increase with increasing discrepancy between the marker and disease allele frequencies, and with less-than-maximal linkage disequilibrium between the marker and disease alleles. Qualitatively similar results were found for studies of parent offspring triads based on the transmission disequilibrium test (Abel and Müller-Myhsok, 1998, Am. J. Hum. Genet. 63:664-667; Tu and Whittemore, 1999, Am. J. Hum. Genet. 64:641-649). We also studied other factors affecting required sample size, including attributable risk for the disease allele, inheritance mechanism, disease prevalence, and for sibling case-control designs, extragenetic familial aggregation of disease and recombination. The large sample-size requirements represent a formidable challenge to studies of this type.