Computational protocol: Predicting admissions and time spent in hospital over a decade in a population-based record linkage study: the EPIC-Norfolk cohort

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Protocol publication

[…] We examined the distribution of hospital admissions by baseline descriptive data. ORs for each of the main outcomes: ≥7 hospital admissions; bed days ≥20 and no hospital admissions were calculated using unmatched logistic regression with independent variables age, smoking, BMI >30, manual social class and no educational qualifications. We then created a summary risk score, defined as the sum of five baseline risk factors dichotomised as binary categories each coded one or zero. The categories, each contributing one point were male sex, manual social class, low education level (those with no qualifications), current smoker and BMI >30 kg/m². Those with scores four and five were combined into a single category as the number with score equal to five was very low.We used logistic regression rather than survival analysis to prevent the censoring of participants who had died, since we wished to make no distinction between non-attendance of hospital due to good health and non-attendance because of death. The number of missing values were: 53 BMI, 218 smoking status, 545 social class, 18 level of education. We examined mortality rates in the cohort by risk score stratified by age over three periods of follow-up time: 1993–1998, 1999–2004; and 1999–2009 to explore the possibility of differential mortality and therefore attrition of the population in the different risk groups which might explain some of the patterns observed. In addition, to explore the possibility of the effect of participant migration during the period under examination a sensitivity analysis was conducted on the subset of the cohort whose postcode area was Norfolk (‘NR’) at both the start and end of the period. All analyses were performed using the R statistical language (R Foundation for Statistical Computing, Vienna, Austria V.3.1.2 with packages knitr, Gmisc and IRanges) and Stata statistical software V.12 (Stata Corporation, College Station, Texas, USA). […]

Pipeline specifications

Software tools Knitr, IRanges
Application Miscellaneous
Organisms Homo sapiens