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215 results for “case-control study”
Inter-Chemical Correlation results for the study: HHEARx2016-1534 (A Nested Case-Control Study of Prenatal Exposure to Phthalates and Psychosocial Stress: Adverse Pregnancy Outcomes and the Mediating Role of Placental Function)
Title: A Nested Case-Control Study of Prenatal Exposure to Phthalates and Psychosocial Stress: Adverse Pregnancy Outcomes and the Mediating Role of Placental Function <br>Species: Homo sapiens <br>Number of samples: 5789 <br>Number of named analytes: 17 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=14 <br>
A Case-Control Study to Measure Behavioral Risks of Malware Encounters in Organizations
<p>The behavior of enterprise users (e.g. browsing at night or visiting gambling sites) is a potential factor that might increase the chances of malware encounters (e.g. coinminers vs ransomware) on the field. This dataset report the aggregated results of a case-control study on telemetry data collected by Trend Micro, a global cybersecurity vendor, to identify users’ behavioral characteristics that can be used to differentiate cybersecurity risks profiles.</p>
Data in support of "An exploration of linkage fine-mapping on sequences from case-control studies"
<p>These data were simulated for an exploration of linkage fine-mapping on sequences from case-control studies. The code to generate and analyze the data is available on GitHub in the scripts at <a href="https://github.com/SFUStatgen/PBJ0">https://github.com/SFUStatgen/PBJ0</a>. Queries may be directed to Payman Nickchi at <a href="mailto:pnickchi@sfu.ca">pnickchi@sfu.ca</a> or Charith (Bhagya) Karunarathna at <a href="mailto:ch757276@dal.ca">ch757276@dal.ca</a>.</p>
Hyperglycemia and steroid use increase the risk of rhino-orbito-cerebral mucormycosis regardless of COVID-19 hospitalization: Case-control study, India
<p><strong>Abstract</strong></p> <p><strong><em>BACKGROUND</em></strong></p> <p>In the context of the ongoing COVID-19 pandemic increased incidence of ROCM was noted in India, among those infected with COVID. We determined risk factors for rhino-orbito-cerebral mucormycosis (ROCM) post Coronavirus disease 2019 (COVID-19) among those never and ever hospitalized for COVID-19 separately through a multi-centric, hospital-based, unmatched case-control study across India.</p> <p><strong><em>METHODS</em></strong></p> <p>We defined cases and controls as those with and without post-COVID ROCM, respectively. We compared their socio-demographics, comorbidities, steroid use, glycaemic status, and practices. We calculated crude and adjusted odds ratio (AOR) with 95% confidence intervals (CI) through logistic regression. The covariates with p-value for crude OR of less 0·20 were considered for the regression model.</p> <p><strong><em>RESULTS</em></strong></p> <p>Among hospitalised, we recruited 267 cases and 256 controls and 116 cases and 231 controls among never hospitalised. Risk factors (AOR; 95% CI) for post-COVID ROCM among the hospitalised were age 45-59 years (2·1; 1·4 to 3·1), having diabetes mellitus (4·9; 3·4 to 7·1), elevated plasma glucose (6·4; 2·4 to 17·2), steroid use (3·2; 2 to 5·2) and frequent nasal washing (4·8; 1·4 to 17). Among those never hospitalised, age ≥ 60 years (6·6; 3·3 to 13·3), having diabetes mellitus (6·7; 3·8 to 11·6), elevated plasma glucose (13·7; 2·2 to 84), steroid use (9·8; 5·8 to 16·6), and cloth facemask use (2·6; 1·5 to 4·5) were associated with increased risk of post-COVID ROCM.</p> <p><strong><em>CONCLUSIONS</em></strong></p> <p>Hyperglycemia irrespective of having diabetes mellitus and steroid use was associated with increased risk of ROCM independent of COVID-19 hospitalisation. Rational steroid usage and glucose monitoring may reduce the risk of post-COVID.</p>
Data in support of "An exploration of linkage fine-mapping on sequences from case-control studies"
<p>These data were simulated for an exploration of linkage fine-mapping on sequences from case-control studies. The scripts to generate and analyze the data are available at <a href="https://github.com/SFUStatgen/PBJ0">https://github.com/SFUStatgen/PBJ0</a>. Queries may be directed to Payman Nickchi at <a href="mailto:pnickchi@sfu.ca">pnickchi@sfu.ca</a> or Charith (Bhagya) Karunarathna at <a href="mailto:ch757276@dal.ca">ch757276@dal.ca</a>.</p> <p><strong>README file for All_data directory</strong></p> <p><strong>Directory structure</strong></p> <p>The All_data directory consists of this README file and 500 sub-directories named DatasetX, for X=1 to 500. Within each DatasetX sub-directory are further sub-directories named alt and null containing files named pop_data.RData and sample_data.RData.</p> <p><strong>alt <em>versus</em> null directories</strong></p> <p>The files in the alt and null directories contain the same variant data but different phenotype data. In particular, under the null hypothesis, disease status is simulated at random according to a 5% prevalence in the population, whereas under the alternative hypothesis disease status is simulated according to a penetrance model that depends on causal SNVs. The R script to simulate data<br> under the alternative hypothesis is in the file 1_SimulateData.R in the Github repository <a href="https://github.com/SFUStatgen/PBJ0">https://github.com/SFUStatgen/PBJ0</a>.</p> <p><strong>pop_data.RData and sample_data.RData files</strong></p> <p>The data structures contained in the pop_data.RData and sample_data.RData files are described below. The structure is the same under both the null and alternative hypothesis.</p> <p><strong>pop_data.RData</strong></p> <p>From R, load("pop_data.RData") loads a list named pop_data whose elements describe the population’s haplotype and phenotype data. The list elements are as follows.</p> <ul> <li>Variants: a matrix of variants for the population of 6200 haplotypes <ul> <li>rows are SNVs,</li> <li>columns are sequences</li> </ul> </li> <li>Positions: a data frame of SNV positions <ul> <li>rows are SNVs,</li> <li>column 1 is the SNV name and column 2 is the SNV position in base pairs</li> </ul> </li> <li>Population.Mapping: a data frame telling us how the sequences are paired into individuals <ul> <li>rows are individuals</li> <li>First column 1 is an individual ID from 1,…,3100; columns 2 and 3 are the sequence IDs of the first and second sequence for that individual where the sequence IDs are the column names of the Variants matrix.</li> </ul> </li> <li>Genotype.Matrix: a matrix of genotypes (i.e. variant counts) for the 3100 individuals <ul> <li>rows are SNVs</li> <li>columns are the individuals</li> </ul> </li> <li>causal_region: a vector containing the lower- and upper-limit of the causal region in base pairs.</li> <li>cSNV: a vector containing the IDs of the causal SNVs, where the SNV IDs are the row names of the Variants matrix.</li> <li>DISCRETE: a list with the following elements. <ul> <li>CaseIndividuals: vector of IDs of the affected individuals in the population.</li> <li>ControlIndividuals: vector of IDs of the unaffected in the population.</li> <li>BinaryTrait: a vector of trait status (0=unaffected, 1=affected) for each individual.</li> </ul> </li> </ul> <p><strong>Note:</strong> Within the same DatasetX directory, the only difference between the pop_data data structures under the null and alternative hypothesis is the phenotype information contained in their respective DISCRETE list elements. Both the null and alternative pop_data data structure share list elements: Variants, Positions, Population.Mapping, Genotype.Matrix, causal_region and cSNV.</p> <p><strong>sample_data.RData</strong></p> <p>From R, load("sample_data.RData") loads a list whose elements describe the sequences and phenotypes of the sample of 50 affected individuals (cases) and 50 unaffected individuals (controls) from the population.</p> <ul> <li>Haps: a list with two elements. <ul> <li>sample_haps: a matrix of 200 sequences for the 50 cases and 50 controls. Rows are SNVs and columns are sequences, with the sequences of sampled cases appearing first (i.e. first 100 columns), followed by the sequences of sampled controls (i.e. last 100 columns). Sequences include only those SNVs that are polymorphic in the sample.</li> <li>ccStatus: a vector indicating the case/control status of the individual to which the sequence belongs, with case=1 and control=0.</li> </ul> </li> <li>Genos: a list with two elements. <ul> <li>sample_genos: a matrix of 100 genotypes for the 50 cases and 50 controls. Rows are SNVs and columns are genotypes, with genotypes of cases appearing first, followed by genotypes of controls.</li> <li>ccStatus: a vector indicating the case/control status of each individual, with case=1 and control=0.</li> </ul> </li> <li>Posn: a data frame of SNV positions for each SNV that is polymorphic in the sample. The first column is the SNV name and the second is the SNV position in base pairs. Posn is a subset of pop_data$Positions.</li> <li>poly_cSNV: a vector of IDs for causal SNVs that are polymorphic in the sample.</li> <li>CaseIND: a vector of individual IDs for the case individuals (see pop_data$Population.Mapping).</li> <li>ControlIND: a vector of individual IDs for the control individuals (see pop_data$Population.Mapping).</li> <li>CaseHapID: a vector of IDs for the sequences that belong to cases (see the sequence IDs in the column names of the matrix pop_data$Variants).</li> <li>ControlHapID: a vector of IDs for the sequences that belong to controls (see the sequence IDs in the column names of the matrix pop_data$Variants).</li> </ul> <p> </p>
Dataset from: "Assessment of depression, anxiety, and psychological symptoms in parents of pediatric palliative care patients: A single-center case-control study"
<p>The dataset comprises raw data from a case-control study that compared levels of depression, anxiety, and general psychological symptoms between parents of pediatric palliative care (PPC) patients and parents of healthy controls.</p> <p>Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), and Symptom Checklist-90 Revised (SCL-90-R) were employed to provide a comprehensive and nuanced understanding of the mental health challenges faced by parents.</p> <p>The designation "case" in the first column (A) represents the parents of PPC patients, while the designation "control" represents the control group. The columns labeled B to S present demographic characteristics data, while columns T to AR present inventory scores. Columns BB to GC, in turn, present the responses to the inventory items.</p> <p> </p> <p> </p>
Dataset Case-control study: Risk factors for sporadic non-pregnancy related listeriosis in Germany, 2012-2013
<p>The dataset contains data of a nationwide age-frequency matched case-control study in Germany, 2012-2013, which was performed to identify underlying conditions and foods asscociated with sporadic listeriosis. It covers anonymous sociodemografic information, information on underlying conditions, and >60 food items. Data for control subjects were obtained from a population-based random telephone sample, which was generated according to the method by Gabler and Häder and considered telephone numbers not registered in telephone books.</p>
Headache in workers. A matched case-control study
<p>Data utilized for the study to be published</p>
Effectiveness of seasonal malaria chemoprevention administered in a mass campaign in the Kedougou region of Senegal in 2016: a Case-control study
<p><strong><span>Context</span></strong></p> <p><span>Seasonal malaria chemoprevention (SMC) with Sulfadoxine-Pyrimethamine plus Amodiaquine (SPAQ) is a malaria prevention strategy recommended since 2012 by the World Health Organization (WHO) for children under 5 years of age. In Senegal, the scaling up of the SMC has started since 2013 in the south-eastern regions of the country with an extension of the target to 10 years old children. The scaling up of SMC requires a regular evaluation of the strategy as recommended by the WHO. This study was conducted to evaluate the effectiveness of SMC. </span></p> <p><strong><span>Methodology </span></strong></p> <p><span>A case-control study was conducted in some villages of the health districts of Saraya and Kedougou in the Kedougou region from July to December 2016. A "case" was a sick child, aged 3 months to 10 years, seen in consultation and with a positive RDT. The "control" was a child of the same age group with a negative RDT and living in the same compound as the case or in a neighbouring compound. Each case was matched with two controls. Exposure to SMC was assessed by interviewing the mothers/caretakers and by checking the SMC administration card.</span></p> <p><strong><span>Results</span></strong></p> <p><span>A total of 492 children, including 164 cases and 328 controls, were recruited for our study. Their mean ages were 5.32 (+/- 2.15) and 4.44 (+/-2.25) years for the cases and the controls respectively. Male children predominated in both cases (55.49%) and controls (51.22%) (p=</span><span>0.18)</span><span>. Net ownership was 85.80% among cases and 90.85% among controls (p=0.053). The proportion of controls who received SMC was higher than that of cases (98.17% vs 85.98%; (p=1.10<sup>-7</sup>)). The protective effectiveness of SMC was 89% </span><span>(IC 95% = 84–93%)</span><span> </span><span>(OR=0.11).</span></p> <p><strong><span>Conclusion </span></strong></p> <p><span>SMC is therefore an effective strategy in the control of malaria in children. Case-control studies are a good approach for monitoring the efficacy of drugs administered during SMC.</span></p>
Data from: Non-inheritable risk factors during pregnancy for congenital heart defects in offspring: a matched case-control study
<p>Data analyzed in "Non-inheritable risk factors during pregnancy for congenital heart defects in offspring: a matched case-control study". The data provided by the authors to benefit other researchers. The posted materials are not copyedited and are the sole responsibility of the authors, so questions should be addressed to the corresponding author.</p>
Salivary and Serum Leptin Levels in Oral Lichen Planus Patients: A Case-control Study.
ClinicalTrials.gov study NCT06078579. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Myeloproliferative Neoplasms: an In-depth Case-control Study
ClinicalTrials.gov study NCT01831635. IPD Sharing: YES. Countries: 1. Publications: 3.
Melanoma of the Skin and Exposure to Solar Ultraviolet Radiation at Work in Modena Territory: a Case-control Study to Promote an Active Search and Prevention of Occupational Diseases Based on Recent I
ClinicalTrials.gov study NCT07251335. IPD Sharing: NO. Countries: 1. Publications: 7.
OZONE_EXO: Comparative Analysis of Protocols for Dental Exactions in Patients at Risk of MRONJ: Case-control Study
ClinicalTrials.gov study NCT06072404. IPD Sharing: NO. Countries: 1. Publications: 4.
Causal inference and risk prediction of gestational diabetes mellitus based on case-control study and Mendel randomization
Open the record for dataset details and reuse information.
Effectiveness of seasonal malaria chemoprevention administered in a mass campaign in the Kedougou region of Senegal in 2016: a Case-control study
Open the record for dataset details and reuse information.
Computerized analysis of brain MRI parameters dynamics in young patients with Cushing Syndrome – a case-control study
Open the record for dataset details and reuse information.
Could dementia be detected from UK primary care patients' records by simple automated methods earlier than by the treating physician? A retrospective case-control study - Extended Data
<p>Extended data for Article published in Wellcome Open Research (Appendices 1,2 & 3). </p> <p>Abstract of Article: </p> <p><strong>Background:</strong> Timely diagnosis of dementia is a policy priority in the United Kingdom (UK). Primary care physicians receive incentives to diagnose dementia; however, 33% of patients are still not receiving a diagnosis. We explored automating early detection of dementia using data from patients’ electronic health records (EHRs). We investigated: a) how early a machine-learning model could accurately identify dementia before the physician; b) if models could be tuned for dementia subtype; and c) what the best clinical features were for achieving detection.</p> <p><strong>Methods:</strong> Using EHRs from Clinical Practice Research Datalink in a case-control design, we selected patients aged >65y with a diagnosis of dementia recorded 2000-2012 (cases) and matched them 1:1 to controls; we also identified subsets of Alzheimer’s and vascular dementia patients. Using 77 coded concepts recorded in the 5 years before diagnosis, we trained random forest classifiers, and evaluated models using Area Under the Receiver Operating Characteristic Curve (AUC). We examined models by year prior to diagnosis, subtype, and the most important features contributing to classification.</p> <p><strong>Results:</strong> 95,202 patients (median age 83y; 64.8% female) were included (50% dementia cases). Classification of dementia cases and controls was poor 2-5 years prior to physician-recorded diagnosis (AUC range 0.55-0.65) but good in the year before (AUC: 0.84). Features indicating increasing cognitive and physical frailty dominated models 2-5 years before diagnosis; in the final year, initiation of the dementia diagnostic pathway (symptoms, screening and referral) explained the sudden increase in accuracy. No substantial differences were seen between all-cause dementia and subtypes.</p> <p><strong>Conclusions:</strong> Automated detection of dementia earlier than the treating physician may be problematic, if using only primary care data. Future work should investigate more complex modelling, benefits of linking multiple sources of healthcare data and monitoring devices, or contextualising the algorithm to those cases that the GP would need to investigate.</p>
Data from: Skin autofluorescence and subclinical atherosclerosis in mild to moderate chronic kidney disease: a case-control study
Advanced glycation end-products (AGEs) are increased and predict mortality in patients with chronic kidney disease (CKD) who are undergoing hemodialysis, irrespective of the presence of type 2 diabetes. However, little information exits about the relationship between AGEs and subclinical atherosclerosis at the early stages of CKD. A case-control study was performed including 87 patients with mild-to-moderate stages of CKD (glomerular filtration rate from 89 to 30 ml/min/per 1.73m2) and 87 non-diabetic non-CKD subjects matched by age, gender, body mass index, and waist circumference. Skin autofluorescence (AF), a non-invasive assessment of AGEs, was measured. The presence of atheromatous disease in carotid and femoral arteries was evaluated using vascular ultrasound, and vascular age and SCORE risk were estimated. Patients with mild-to-moderate stages of CKD showed an increase in skin AF compared with control subjects (2.5±0.6 vs. 2.2±0.4 AU, p<0.001). A skin AF value >2.0 AU was accompanied by a 3-fold increased risk of detecting the presence of an atheromathous plaque (OR 3.0, 95% CI 1.4–6.5, p = 0.006). When vascular age was assessed through skin AF, subjects with CKD were almost 12 years older than control subjects (70.3±25.5 vs. 58.5±20.2 years, p = 0.001). Skin AF was negatively correlated with glomerular filtration rate (r = -0.354, p<0.001) and LDL-cholesterol (r = -0.269, p = 0.001), and positively correlated with age (r = 0.472, p<0.001), pulse pressure (r = 0.238, p = 0.002), and SCORE risk (r = 0.451, p<0.001). A stepwise multivariate regression analysis showed that age and glomerular filtration rate independently predicted skin AF (R2 = 0.289, p<0.001). Skin AF is elevated in patients with mild-to-moderate CKD compared with control subjects. This finding may be independently associated with the glomerular filtration rate and the presence of subclinical atheromatous disease. Therefore, the use of skin AF may help to accurately evaluate the real cardiovascular risk at the early stages of CKD.
Data set from "Impact of the Free-Pelvis innovation in very rigid braces for adolescents with idiopathic scoliosis: short-term results of a matched case-control study"
<p>Dataset from a still non-published study titled "Impact of the Free-Pelvis innovation in very rigid braces for adolescents with idiopathic scoliosis: short-term results of a matched case-control study"</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.