Do interviewer attitudes to data linkage influence respondents’ consent to linkage? Analysis of Understanding Society

2020 ◽  
Author(s):  
Lynsey Patterson ◽  
Sharon M Cruise ◽  
Chris R Cardwell ◽  
Dermot O’Reilly
Keyword(s):  
Logistic Regression ◽  
Data Security ◽  
Record Linkage ◽  
Data Linkage ◽  
Perceived Usefulness ◽  
Logistic Regression Models ◽  
The Us ◽  
Linkage Information ◽  
Multi Level ◽  
Interviewer Training

Abstract Background Variable consent rates threaten the validity of linked datasets. One modifiable element is the interviewer–respondent relationship. We examine interviewer attitudes to consent to linkage and the effect on respondent consent. Methods Subjects were 27 380 respondents from the Wave 1 Understanding Society (US) survey in Great Britain and 449 interviewers who completed the US Interviewer Survey. Two types of consent were considered: (i) whether the interviewer would hypothetically agree to having their data linked if he/she was an US respondent and (ii) whether the respondent consented to have their data linked. Factors influencing the interviewer’s propensity to link data were examined using logistic regression. The association between interviewer consent and respondent consent to health record linkage was assessed using multi-level logistic regression models. Results The interviewer’s propensity to consent to data linkage was strongly positively associated with its perceived usefulness: those that found it somewhat useful were 57% less likely to consent [adjusted odds ratio (AOR) 0.43, 95% CI: 0.22–0.82] compared to those who thought it was very useful. Positive beliefs about data security and their ability to understand the data linkage information were also associated. Respondents were 17% less likely to consent when interviewed by an interviewer who would not consent to record linkage (AOR 0.83, 95% CI: 0.71–0.97). Conclusions The interviewer’s propensity to consent was influenced by their beliefs about data linkage, which in turn influenced respondent consent. We recommend using interviewer training to emphasize the usefulness of data linkage and the measures around data security.

scholarly journals POS0023 RACIAL/ETHNIC AND REGIONAL DIFFERENCES IN MULTIMORBIDITY BETWEEN PATIENTS WITH RHEUMATOID ARTHRITIS AND COMPARATORS IN A LARGE NATIONWIDE US STUDY

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 213.1-214
Author(s):  
H. J. Dykhoff ◽  
E. Myasoedova ◽  
M. Peterson ◽  
J. M. Davis ◽  
V. Kronzer ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Logistic Regression ◽  
Regression Models ◽  
Index Date ◽  
Ethnic Disparities ◽  
Logistic Regression Models ◽  
The Us ◽  
Race Ethnicity ◽  
Geographic Regions ◽  
Racial Ethnic

Background:Patients with rheumatoid arthritis (RA) have an increased burden of multimorbidity. Racial/ethnic disparities have also been associated with an increased burden of multimorbidity.Objectives:We aimed to compare multimorbidity among different racial/ethnic groups and geographic regions of the US in patients with RA and comparators without RA.Methods:We used a large longitudinal, real-world data warehouse with de-identified administrative claims for commercial and Medicare Advantage enrollees, to identify cases of RA and matched controls. Cases were defined as patients aged ≥18 years with ≥2 diagnoses of RA in January 1, 2010 - June 30, 2019 and ≥1 prescription fill for methotrexate in the year after the first RA diagnosis. Controls were persons without RA matched 1:1 to RA cases on age, sex, census region, calendar year of index date (corresponding to the date of second diagnosis code for RA), and length of prior medical/pharmacy coverage. Race was classified as non-Hispanic White (White), non-Hispanic Black (Black), Asian, Hispanic, or other/unknown, based on self-report or derived rule sets. Multimorbidity (2 or more comorbidities) was defined using 25 chronic comorbidities from a combination of the Charlson and Elixhauser Comorbidity Indices assessed during the year prior to index date. Rheumatic comorbidities were not included. Logistic regression models were used to estimate odds ratios (OR) with 95% confidence intervals (CI).Results:The study included 16,363 cases with RA and 16,363 matched non-RA comparators (mean age 58.2 years, 70.7% female for both cohorts). Geographic regions were the same in both cohorts: 50% South, 26% Midwest, 13% West, and 11% Northeast. Race/ethnicity was not part of the matching criteria and varied slightly between the cohorts: among RA (non-RA) patients, 74% (74%) were White, 11% (9%) Hispanic, 10% (9%) Black, 3% (4%) Asian, and 3% (4%) other/unknown. Patients with RA had more multimorbidity than non-RA subjects (51.3% vs 44.8%). Multimorbidity comparisons across US geographic regions were similar in both cohorts, with comparable multimorbidity levels for patients in the West and Midwest and higher levels for those in the Northeast and South (Figure 1). Among the non-RA patients, 43.5% of Whites experienced multimorbidity, compared to 33.9% of Asians, 46.1% of Hispanics, and 58.4% of Blacks. These associations remained after adjustment for age, sex, and geographic region, with significantly lower multimorbidity among Asians (OR: 0.81; 95%CI: 0.67-0.99) and significantly higher multimorbidity among Hispanics (OR: 1.21; 95%CI: 1.07-1.37) and Blacks (OR: 1.74; 95%CI: 1.54-1.97), compared to Whites in the non-RA cohort. Among the RA patients, racial/ethnic differences were less pronounced; 50.6% of Whites, 42.8% of Asians, 48.8% of Hispanics, and 58.4% of Blacks experienced multimorbidity. Adjusted analyses revealed no significant differences in multimorbidity for Asians (OR: 0.88; 95%CI: 0.70-1.08) and Hispanics (OR: 1.06; 95%CI: 0.95-1.19) and a less pronounced increase in multimorbidity among Blacks (OR: 1.32; 95%CI: 1.17-1.49) compared to Whites in the RA cohort.Conclusion:This large nationwide study showed increased occurrence of multimorbidity in RA versus non-RA patients and in both cohorts for residents of the Northeast and South regions of the US. Racial/ethnic disparities in multimorbidity were more pronounced among patients without RA compared to RA patients. This indicates the effects of RA and race/ethnicity on multimorbidity do not aggregate. The underlying mechanisms for these associations require further investigation.Figure 1.Logistic regression models comparing multimorbidity levels in RA and non-RA cohorts.Disclosure of Interests:Hayley J. Dykhoff: None declared, Elena Myasoedova: None declared, Madeline Peterson: None declared, John M Davis III Grant/research support from: Research grant from Pfizer, Vanessa Kronzer: None declared, Caitrin Coffey: None declared, Tina Gunderson: None declared, Cynthia S. Crowson: None declared.

scholarly journals The Presence of Gut Microbial Genes Encoding Bacterial Genotoxins or Pro-Inflammatory Factors in Stool Samples from Individuals with Colorectal Neoplasia

Diseases ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 16 ◽  
Author(s):  
Ramón Gómez-Moreno ◽  
María González-Pons ◽  
Marievelisse Soto-Salgado ◽  
Marcia Cruz-Correa ◽  
Abel Baerga-Ortiz
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Colorectal Neoplasia ◽  
Bacterial Toxin ◽  
Inflammatory Factors ◽  
Logistic Regression Models ◽  
The Us ◽  
Genes Encoding ◽  
Stool Samples ◽  
Bacterial Genes

Gut bacterial toxins are thought to contribute to the development of colorectal cancer (CRC). This study examines the presence of specific gut bacterial toxin genes in stool samples from individuals with colorectal neoplasia (adenomas and/or CRC). The presence of bacterial genes encoding genotoxic or pro-inflammatory factors (pks, tcpC, gelE, cnf-1, AMmurB, and usp) was established by PCR of stool samples from individuals from mainland US (n = 30; controls = 10, adenoma = 10, CRC = 10) and from Puerto Rico (PR) (n = 33; controls = 13; adenomas = 8; CRC = 12). Logistic regression models and multinomial logistic regression models were used to estimate the magnitude of association. Distinct bacterial gene profiles were observed in each sample cohort. In individuals with CRC, AMmurB was detected more frequently in samples from the US and gelE in samples from PR. In samples from PR, individuals with ≥2 gut bacterial toxin genes in stool had higher odds of having colorectal neoplasia (OR = 11.0, 95%: CI 1.0–637.1): however, no significant association between bacterial genes and colorectal neoplasia was observed in the US cohort. Further analyses are warranted in a larger cohort to validate these preliminary findings, but these encouraging results highlight the importance of developing bacterial markers as tools for CRC diagnosis or risk stratification.

scholarly journals Data linkage for public health research – the Fforestfach tyre fire

2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Leon May ◽  
Lloyd Evans
Keyword(s):  
General Population ◽  
Cardiovascular Health ◽  
Data Linkage ◽  
Public Health Research ◽  
Environmental Data ◽  
Logistic Regression Models ◽  
The People ◽  
Linkage Information ◽  
Using Data ◽  
Respiratory Conditions

Background The Fforestfach tyre fire started on the 16th of June 2011 and continued to burn for 22 days. Smoke from tyre fires contain a number of chemicals that might cause health problems, especially for people who already have long-term health conditions. This research investigated whether people living close to the Fforestfach fire contacted their General Practice (GP) more often during the fire than they might have done otherwise. This is important both for the people living in the Fforestfach area and also for those living near similar fires in the future. Aim To use advances in mapping and data linkage techniques to assess associations between the Fforestfach fire incident and respiratory and cardiovascular health outcomes. The report focusses on the occurrence of respiratory and cardiovascular Read codes in patient’s GP records. Methods Using data linkage, information provided by the Met office was used to identify households likely to have been exposed to above threshold levels of pollution. Residents from these households were linked to their GP records via the Secure Anonymised Information Linkage (SAIL) databank. Logistic regression models tested associations between above-threshold exposure to a specific type of pollution (PM10) and an increase in GP contact. Results Regression modelling demonstrated a small but significant increase in GP contact for respiratory conditions in patients with pre-existing asthma. The models did not demonstrate any affect in the general population. Conclusion The study demonstrated the value of linking health and environmental data using advanced data linkage techniques. Findings support current health advice used in environmental incidents such as this, that individuals with certain chronic conditions may be more likely to experience symptoms when exposed to 24-hour mean concentrations of PM10 exceeding 50µg/m3; but the risk of significant symptoms as a result of such exposure in the general population is likely to be minimal.

The impact of SPY angiography on intraoperative decision making and outcomes for post-mastectomy reconstruction

2019 ◽  
pp. 1-5
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Skin Necrosis ◽  
Regression Models ◽  
Multivariable Analysis ◽  
P Value ◽  
Flap Necrosis ◽  
Single Institution ◽  
Logistic Regression Models ◽  
The Impact

Objective: While the use of intraoperative laser angiography (SPY) is increasing in mastectomy patients, its impact in the operating room to change the type of reconstruction performed has not been well described. The purpose of this study is to investigate whether SPY angiography influences post-mastectomy reconstruction decisions and outcomes. Methods and materials: A retrospective analysis of mastectomy patients with reconstruction at a single institution was performed from 2015-2017.All patients underwent intraoperative SPY after mastectomy but prior to reconstruction. SPY results were defined as ‘good’, ‘questionable’, ‘bad’, or ‘had skin excised’. Complications within 60 days of surgery were compared between those whose SPY results did not change the type of reconstruction done versus those who did. Preoperative and intraoperative variables were entered into multivariable logistic regression models if significant at the univariate level. A p-value <0.05 was considered significant. Results: 267 mastectomies were identified, 42 underwent a change in the type of planned reconstruction due to intraoperative SPY results. Of the 42 breasts that underwent a change in reconstruction, 6 had a ‘good’ SPY result, 10 ‘questionable’, 25 ‘bad’, and 2 ‘had areas excised’ (p<0.01). After multivariable analysis, predictors of skin necrosis included patients with ‘questionable’ SPY results (p<0.01, OR: 8.1, 95%CI: 2.06 – 32.2) and smokers (p<0.01, OR:5.7, 95%CI: 1.5 – 21.2). Predictors of any complication included a change in reconstruction (p<0.05, OR:4.5, 95%CI: 1.4-14.9) and ‘questionable’ SPY result (p<0.01, OR: 4.4, 95%CI: 1.6-14.9). Conclusion: SPY angiography results strongly influence intraoperative surgical decisions regarding the type of reconstruction performed. Patients most at risk for flap necrosis and complication post-mastectomy are those with questionable SPY results.

Utilizing Twitter Data Analysis and Deep Learning to Identify Drug Use (Preprint)

2019 ◽  
Author(s):  
Joseph Tassone ◽  
Peizhi Yan ◽  
Mackenzie Simpson ◽  
Chetan Mendhe ◽  
Vijay Mago ◽  
...  
Keyword(s):  
Social Media ◽  
Logistic Regression ◽  
Deep Learning ◽  
Decision Tree ◽  
Semantic Meaning ◽  
Predictive Capability ◽  
Logistic Regression Models ◽  
Twitter Data ◽  
Data Points ◽  
Positive Classification

BACKGROUND The collection and examination of social media has become a useful mechanism for studying the mental activity and behavior tendencies of users. OBJECTIVE Through the analysis of a collected set of Twitter data, a model will be developed for predicting positively referenced, drug-related tweets. From this, trends and correlations can be determined. METHODS Twitter social media tweets and attribute data were collected and processed using topic pertaining keywords, such as drug slang and use-conditions (methods of drug consumption). Potential candidates were preprocessed resulting in a dataset 3,696,150 rows. The predictive classification power of multiple methods was compared including regression, decision trees, and CNN-based classifiers. For the latter, a deep learning approach was implemented to screen and analyze the semantic meaning of the tweets. RESULTS The logistic regression and decision tree models utilized 12,142 data points for training and 1041 data points for testing. The results calculated from the logistic regression models respectively displayed an accuracy of 54.56% and 57.44%, and an AUC of 0.58. While an improvement, the decision tree concluded with an accuracy of 63.40% and an AUC of 0.68. All these values implied a low predictive capability with little to no discrimination. Conversely, the CNN-based classifiers presented a heavy improvement, between the two models tested. The first was trained with 2,661 manually labeled samples, while the other included synthetically generated tweets culminating in 12,142 samples. The accuracy scores were 76.35% and 82.31%, with an AUC of 0.90 and 0.91. Using association rule mining in conjunction with the CNN-based classifier showed a high likelihood for keywords such as “smoke”, “cocaine”, and “marijuana” triggering a drug-positive classification. CONCLUSIONS Predictive analysis without a CNN is limited and possibly fruitless. Attribute-based models presented little predictive capability and were not suitable for analyzing this type of data. The semantic meaning of the tweets needed to be utilized, giving the CNN-based classifier an advantage over other solutions. Additionally, commonly mentioned drugs had a level of correspondence with frequently used illicit substances, proving the practical usefulness of this system. Lastly, the synthetically generated set provided increased scores, improving the predictive capability. CLINICALTRIAL None

scholarly journals Behavioral and Attitudinal Correlates of Trusted Sources of COVID-19 Vaccine Information in the US

2021 ◽  
Vol 11 (4) ◽  
pp. 56
Author(s):  
Carl A. Latkin ◽  
Lauren Dayton ◽  
Jacob R. Miller ◽  
Grace Yi ◽  
Afareen Jaleel ◽  
...  
Keyword(s):  
Regression Models ◽  
Logistic Regression Models ◽  
The Public ◽  
Public Health Systems ◽  
Online Study ◽  
News Sources ◽  
The Us ◽  
Vulnerable People ◽  
Two Factors ◽  
Vaccine Information

There is a critical need for the public to have trusted sources of vaccine information. A longitudinal online study assessed trust in COVID-19 vaccine information from 10 sources. A factor analysis for data reduction revealed two factors. The first factor contained politically conservative sources (PCS) of information. The second factor included eight news sources representing mainstream sources (MS). Multivariable logistic regression models were used. Trust in Dr. Fauci was also examined. High trust in MS was associated with intention to encourage family members to get COVID-19 vaccines, altruistic beliefs that more vulnerable people should have vaccine priority, and belief that racial minorities with higher rates of COVID-19 deaths should have priority. High trust in PCS was associated with intention to discourage friends from getting vaccinated. Higher trust in PCS was also associated with participants more likely to disagree that minorities with higher rates of COVID-19 deaths should have priority for a vaccine. High trust in Dr. Fauci as a source of COVID-19 vaccine information was associated with factors similar to high trust in MS. Fair, equitable, and transparent access and distribution are essential to ensure trust in public health systems’ abilities to serve the population.

scholarly journals Effect of prostatic apex shape (Lee types) and urethral sphincter length in preoperative MRI on very early continence rates after radical prostatectomy

2021 ◽  
Author(s):  
Mike Wenzel ◽  
Felix Preisser ◽  
Matthias Mueller ◽  
Lena H. Theissen ◽  
Maria N. Welte ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Radical Prostatectomy ◽  
Regression Models ◽  
Urethral Sphincter ◽  
Anatomical Characteristics ◽  
Logistic Regression Models ◽  
Pad Test ◽  
Type D ◽  
Multivariable Logistic Regression ◽  
Type C

Abstract Purpose To test the effect of anatomic variants of the prostatic apex overlapping the membranous urethra (Lee type classification), as well as median urethral sphincter length (USL) in preoperative multiparametric magnetic resonance imaging (mpMRI) on the very early continence in open (ORP) and robotic-assisted radical prostatectomy (RARP) patients. Methods In 128 consecutive patients (01/2018–12/2019), USL and the prostatic apex classified according to Lee types A–D in mpMRI prior to ORP or RARP were retrospectively analyzed. Uni- and multivariable logistic regression models were used to identify anatomic characteristics for very early continence rates, defined as urine loss of ≤ 1 g in the PAD-test. Results Of 128 patients with mpMRI prior to surgery, 76 (59.4%) underwent RARP vs. 52 (40.6%) ORP. In total, median USL was 15, 15 and 10 mm in the sagittal, coronal and axial dimensions. After stratification according to very early continence in the PAD-test (≤ 1 g vs. > 1 g), continent patients had significantly more frequently Lee type D (71.4 vs. 54.4%) and C (14.3 vs. 7.6%, p = 0.03). In multivariable logistic regression models, the sagittal median USL (odds ratio [OR] 1.03) and Lee type C (OR: 7.0) and D (OR: 4.9) were independent predictors for achieving very early continence in the PAD-test. Conclusion Patients’ individual anatomical characteristics in mpMRI prior to radical prostatectomy can be used to predict very early continence. Lee type C and D suggest being the most favorable anatomical characteristics. Moreover, longer sagittal median USL in mpMRI seems to improve very early continence rates.

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