scholarly journals EARLIER BIRTH IS A KEY FACTOR IN EDUCATIONAL DISADVANTAGE FACED BY TWINS: A POPULATION‐BASED DATA LINKAGE STUDY

2019 ◽  
Vol 55 (S1) ◽  
pp. 39-40 ◽  
Keyword(s):  
Data Linkage ◽  
Population Based ◽  
Linkage Study ◽  
Educational Disadvantage ◽  
Key Factor

scholarly journals Early birth is a key factor in educational disadvantage of twins: A data linkage study

2019 ◽  
Vol 109 (3) ◽  
pp. 534-540
Author(s):  
Justin Zeltzer ◽  
Antonia W. Shand ◽  
Patrick Kelly ◽  
John L. Hopper ◽  
Katrina J. Scurrah ◽  
...  
Keyword(s):  
Data Linkage ◽  
Linkage Study ◽  
Educational Disadvantage ◽  
Key Factor

The Link between Male Genital Anomalies and Adult Male Reproductive Disorders: A Population-Based Data Linkage Study Spanning Over 40 Years

2018 ◽  
Author(s):  
Francisco Schneuer ◽  
Elizabeth Milne ◽  
Sarra E. Jamieson ◽  
Gavin Pereira ◽  
Michele Hansen ◽  
...  
Keyword(s):  
Adult Male ◽  
Data Linkage ◽  
Population Based ◽  
Linkage Study ◽  
Reproductive Disorders ◽  
Over 40 Years ◽  
Male Genital

scholarly journals Frailty Assessed by Administrative Tools and Mortality in Patients With Pneumonia Admitted to the Hospital and ICU: a Population-based Data-linkage Study

2021 ◽  
Author(s):  
Tamas Szakmany ◽  
Joe Hollinghurst ◽  
Richard Pugh ◽  
Ashley Akbari ◽  
Rowena Griffiths ◽  
...  
Keyword(s):  
Data Linkage ◽  
Social Deprivation ◽  
Frailty Index ◽  
Significant Risk ◽  
Population Based ◽  
Linkage Study ◽  
Increased Risk ◽  
Comorbidity Index ◽  
Population Scale

Abstract Background: The ideal method of identifying frailty is uncertain, and data on long-term outcomes is relatively limited. We examined frailty indices derived from population-scale linked data on Intensive Care Unit (ICU) and hospitalised non-ICU patients with pneumonia to elucidate the influence of frailty on mortality.Methods: Longitudinal cohort study between 2010-2018 using population-scale anonymised data linkage of healthcare records for adults admitted to hospital with pneumonia in Wales. Primary outcome was in-patient mortality. Age, hospital frailty risk score (HFRS), electronic frailty index (eFI), Charlson comorbidity index (CCI), and social deprivation index were entered in the multivariate regression models.Results: Of the 107,188 patients, mean (SD) age was 72.6 (16.6) years, 50% were men. The two frailty indices and the comorbidity index had an increased risk of mortality for individuals with an ICU admission. Advancing age, increased frailty and comorbidity affected short- and long-term mortality. For predicting inpatient deaths, the CCI and HFRS based models were similar, however for longer term outcomes the CCI based model was superior. Discussion: Frailty and comorbidity are significant risk factors for patients admitted to hospital with pneumonia. Frailty and comorbidity scores based on administrative data have only moderate ability to predict outcome.

scholarly journals The Australian Injury Comorbidity Indices (AICIs) to predict in-hospital complications: A population-based data linkage study

PLoS ONE ◽  
2020 ◽  
Vol 15 (9) ◽  
pp. e0238182
Author(s):  
Dasamal Tharanga Fernando ◽  
Janneke Berecki-Gisolf ◽  
Stuart Newstead ◽  
Zahid Ansari
Keyword(s):  
Data Linkage ◽  
Population Based ◽  
Linkage Study

scholarly journals Effect of socioeconomic disadvantage, remoteness and Indigenous status on hospital usage for Western Australian preterm infants under 12 months of age: a population-based data linkage study

BMJ Open ◽  
2017 ◽  
Vol 7 (1) ◽  
pp. e013492 ◽  
Author(s):  
Natalie A Strobel ◽  
Sue Peter ◽  
Kimberley E McAuley ◽  
Daniel R McAullay ◽  
Rhonda Marriott ◽  
...  
Keyword(s):  
Preterm Infants ◽  
Data Linkage ◽  
Population Based ◽  
Linkage Study ◽  
Socioeconomic Disadvantage ◽  
Indigenous Status ◽  
Western Australian

scholarly journals Parity: A key measure of confounding in data-linkage studies of outcomes after medically assisted reproduction

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Georgina M Chambers ◽  
Christos A Venetis ◽  
Louisa R Jorm ◽  
Claire M Vajdic
Keyword(s):  
Assisted Reproduction ◽  
Data Linkage ◽  
Population Based ◽  
Linkage Study ◽  
Design Element ◽  
Medically Assisted Reproduction ◽  
Critical Design ◽  
First Child ◽  
Hormone Sensitive ◽  
Universal Health

Parity is a potential confounder of the association between medically assisted reproduction (MAR) and health outcomes. This concept paper describes a population-based record linkage study design for selecting MAR-unexposed women matched to the parity of MAR-exposed women, at the time of the first exposure to MAR. Women exposed to MAR were identified from claims for government subsidies for relevant procedures and prescription medicines, linked to perinatal records. Women unexposed to MAR were identified from linked perinatal and death records, matched to exposed women by age, rurality, age of first child (if any) and parity at the date of first MAR. The availability of a longitudinal, whole-of-population dataset (“population spine”) based on enrolments in Australia’s universal health insurance scheme was a critical design element. The example application examines cancer risk in women after exposure to MAR. Parity is a confounder in this setting because it is associated with MAR and hormone-sensitive cancers.

The cost of in-patient care in Western Australia in the last years of life: A population-based data linkage study

Health Policy ◽  
2008 ◽  
Vol 85 (3) ◽  
pp. 380-390 ◽  
Author(s):  
Rachael Elizabeth Moorin ◽  
Cashel D’Arcy James Holman
Keyword(s):  
Patient Care ◽  
Western Australia ◽  
Data Linkage ◽  
Population Based ◽  
Linkage Study ◽  
The Cost

Dental Hospital Admissions in the Children of Mothers with an Alcohol-Related Diagnosis: A Population-Based, Data-Linkage Study

2013 ◽  
Vol 163 (2) ◽  
pp. 515-520.e1 ◽  
Author(s):  
Colleen M. O'Leary ◽  
Linda M. Slack-Smith
Keyword(s):  
Data Linkage ◽  
Hospital Admissions ◽  
Population Based ◽  
Linkage Study ◽  
Dental Hospital

105: HEALTHCARE USE BEFORE AND AFTER CRITICAL CARE: A POPULATION-BASED DATA LINKAGE STUDY

2020 ◽  
Vol 48 (1) ◽  
pp. 35-35
Author(s):  
Tamas Szakmany ◽  
Mohammad Al Sallakh ◽  
Ashley Akbari ◽  
Richard Pugh ◽  
Ronan Lyons
Keyword(s):  
Critical Care ◽  
Data Linkage ◽  
Population Based ◽  
Linkage Study ◽  
Healthcare Use ◽  
Before And After
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