scholarly journals Disease risk estimation by combining case-control data with aggregated information on the population at risk

Biometrics ◽  
2014 ◽  
Vol 71 (1) ◽  
pp. 114-121 ◽  
Author(s):  
Xiaohui Chang ◽  
Rasmus Waagepetersen ◽  
Herbert Yu ◽  
Xiaomei Ma ◽  
Theodore R. Holford ◽  
...  
Keyword(s):  
At Risk ◽  
Disease Risk ◽  
Risk Estimation ◽  
Case Control ◽  
Control Data ◽  
Population At Risk

Penyelidikan KLB keracunan makanan acara ruwahan di desa Mulo, Gunung Kidul provinsi DIY

2018 ◽  
pp. 1
Author(s):  
Mur Prasetyaningrum ◽  
Z. Chomariyah ◽  
Trisno Agung Wibowo
Keyword(s):  
At Risk ◽  
Total Population ◽  
Total Coliform ◽  
Case Control ◽  
P Value ◽  
Common Source ◽  
Population At Risk ◽  
E Coli

Tujuan: Studi ini untuk mengetahui gambaran KLB keracunan pangan yang terjadi di desa Mulo menurut deskripsi epidemiologi, faktor risiko dan penyebab KLB keracunan makanan. Metode: Studi ini menggunakan studi analitik case control, dimana kasus adalah orang yang mengalami sakit pada tanggal 7 - 8 Mei 2017, tinggal di desa Mulo dan mengkonsumsi makanan olahan dari bapak S dan K. Instrument menggunakan kuesioner. Hasil: KLB terjadi di Desa Mulo RT 5 dan 6 dengan jumlah kasus sebanyak 18 orang dari total population at risk 112 orang dengan gejala utama diare (100%), mual (72,2%), demam (66,6%), pusing (66,6%) dan muntah (50%). Dari diagnosa banding menurut gejala, masa inkubasi dan agent penyebab keracunan, kecurigaan kontaminasi bakteri mengarah pada E. Coli (ETEC). Masa inkubasi 1-16 jam (rata-rata 9 jam) dan common source curve. Penyaji makanan ada dua (pak K dan pak S). Dari perhitungan AR, berdasarkan sumber makanan mengarah pada makanan dari pak S (AR=42,8%). Bedasarkan menu, perhitungan OR dan CI 95 % jenis makanan yang dicurigai sebagai penyebab KLB adalah urap/gudangan (OR=4,33; p value0,0071) dan sayur lombok (OR=6,31; p value 0,0071). Sampel yang didapatkan adalah sampel air bersih, feses, dan muntahan penderita, sampel makanan tidak didapatkan karena keterlambatan informasi dari masyarakat. Hasil laboratorium, Total Coliform sampel air bersih melebihi ambang batas, sampel feses dan muntahan mengandung bakteri Klebsiella pneumonia.Simpulan: Terdapat 3 (tiga) faktor yang diduga sebagai penyebab keracunan pada warga Desa Mulo yaitu air bersih untuk mengolah makanan tercemar bakteri patogen, pengolahan makanan tidak hygienis dan penyajian makanan pada suhu ruang lebih dari 1 jam.

scholarly journals Genomic Prediction of Complex Disease Risk

2018 ◽  
Author(s):  
Louis Lello ◽  
Timothy G. Raben ◽  
Soke Yuen Yong ◽  
Laurent CAM Tellier ◽  
Stephen D.H. Hsu
Keyword(s):  
Genomic Prediction ◽  
Complex Disease ◽  
Disease Risk ◽  
Penalized Regression ◽  
Case Control ◽  
Nucleotide Polymorphisms ◽  
Uk Biobank ◽  
Rapid Improvement ◽  
Control Data ◽  
Polygenic Scores

AbstractWe construct risk predictors using polygenic scores (PGS) computed from common Single Nucleotide Polymorphisms (SNPs) for a number of complex disease conditions, using L1-penalized regression (also known as LASSO) on case-control data from UK Biobank. Among the disease conditions studied are Hypothyroidism, (Resistant) Hypertension, Type 1 and 2 Diabetes, Breast Cancer, Prostate Cancer, Testicular Cancer, Gallstones, Glaucoma, Gout, Atrial Fibrillation, High Cholesterol, Asthma, Basal Cell Carcinoma, Malignant Melanoma, and Heart Attack. We obtain values for the area under the receiver operating characteristic curves (AUC) in the range ~ 0.58 – 0.71 using SNP data alone. Substantially higher predictor AUCs are obtained when incorporating additional variables such as age and sex. Some SNP predictors alone are sufficient to identify outliers (e.g., in the 99th percentile of PGS) with 3 – 8 times higher risk than typical individuals. We validate predictors out-of-sample using the eMERGE dataset, and also with different ancestry subgroups within the UK Biobank population. Our results indicate that substantial improvements in predictive power are attainable using training sets with larger case populations. We anticipate rapid improvement in genomic prediction as more case-control data become available for analysis.

Case–control studies

2021 ◽  
pp. 75-84
Author(s):  
Noel S. Weiss
Keyword(s):  
At Risk ◽  
Cohort Study ◽  
Case Control Study ◽  
Case Control ◽  
Case Control Studies ◽  
Population At Risk ◽  
Control Study

Case–control studies compare ill or injured individuals (cases) with those at risk of the illness or injury (controls) with regard to prior exposures or characteristics, and so appear to proceed backwards, from consequence to potential cause. They have the potential to identify associations that are not causal, either because of chance, or because of the influence of some other factor associated with both the exposure and outcome. However, if a case–control study is able to enrol cases and controls from the same underlying population at risk of the outcome, and can measure exposure status of these persons in a valid manner, the results obtained will closely resemble those of a properly performed cohort study.

Attributable Risk Estimation from Matched Case-Control Data

Biometrics ◽  
1988 ◽  
Vol 44 (2) ◽  
pp. 355 ◽  
Author(s):  
Stephen J. Kuritz ◽  
J. Richard Landis
Keyword(s):  
Risk Estimation ◽  
Case Control ◽  
Attributable Risk ◽  
Control Data ◽  
Matched Case

Summary attributable risk estimation from unmatched case-control data

1988 ◽  
Vol 7 (4) ◽  
pp. 507-517 ◽  
Author(s):  
Stephen J. Kuritz ◽  
J. Richard Landis
Keyword(s):  
Risk Estimation ◽  
Case Control ◽  
Attributable Risk ◽  
Control Data

An approach using random forest methodology for disease risk prediction using imbalanced case–control data in GWAS

2014 ◽  
Vol 4 (6) ◽  
pp. 289-294
Author(s):  
Prabina Kumar Meher ◽  
Atmakuri Ramakrishna Rao ◽  
Sant Dass Wahi ◽  
B.K. Thelma
Keyword(s):  
Random Forest ◽  
Risk Prediction ◽  
Disease Risk ◽  
Case Control ◽  
Control Data

scholarly journals Outlining a Population “at Risk” of Parkinson’s Disease: Evidence from a Case-Control Study

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Tommaso Schirinzi ◽  
Giuseppina Martella ◽  
Alessio D’Elia ◽  
Giulia Di Lazzaro ◽  
Paola Imbriani ◽  
...  
Keyword(s):  
Risk Factors ◽  
Parkinson’S Disease ◽  
At Risk ◽  
Parkinson's Disease ◽  
Family History ◽  
Case Control Study ◽  
Case Control ◽  
Italian Population ◽  
Population At Risk ◽  
Control Study

The multifactorial pathogenesis of Parkinson’s Disease (PD) requires a careful identification of populations “at risk” of developing the disease. In this case-control study we analyzed a large Italian population, in an attempt to outline general criteria to define a population “at risk” of PD. We enrolled 300 PD patients and 300 controls, gender and age matched, from the same urban geographical area. All subjects were interviewed on demographics, family history of PD, occupational and environmental toxicants exposure, smoking status, and alcohol consumption. A sample of 65 patients and 65 controls also underwent serum dosing of iron, copper, mercury, and manganese by means of Inductively Coupled-Plasma-Mass-Spectrometry (ICP-MS). Positive family history, toxicants exposure, non-current-smoker, and alcohol nonconsumer status occurred as significant risk factors in our population. The number of concurring risk factors overlapping in the same subject impressively increased the overall risk. No significant differences were measured in the metal serum levels. Our findings indicate that combination of three to four concurrent PD-risk factors defines a condition “at risk” of PD. A simple stratification, based on these questionnaires, might be of help in identifying subjects suitable for neuroprotective strategies.

Attributable Risk Estimation from Case-Control Data via Logistic Regression

Biometrics ◽  
1991 ◽  
Vol 47 (4) ◽  
pp. 1247 ◽  
Author(s):  
Karsten Drescher ◽  
Walter Schill
Keyword(s):  
Logistic Regression ◽  
Risk Estimation ◽  
Case Control ◽  
Attributable Risk ◽  
Control Data
Sign in / Sign up

Export Citation Format

Share Document