Risk Factors for Inflammatory Bowel Disease in the General Population

L. A. García Rodríguez; A. González-Pérez; S. Johansson; M.-A. Wallander

Disclosures

Aliment Pharmacol Ther. 2005;22(4):309-315. 

In This Article

Methods

The GPRD contains computerized information entered by selected general practitioners (GPs) in the UK. Data on about 2 million patients are systematically recorded and sent anonymously to the Medicines and Healthcare Products Regulatory Agency (MHPRA) that collects and organizes this information in order to be used for research projects. The information recorded includes demographics, medical diagnoses, referrals and hospitalizations. Also, prescriptions are automatically produced from the computer and recorded on the patient's computerized file. A previous study utilizing this computerized data source has documented that over 90% of all referrals are entered on the GP's computers with a code that reflects the clinical diagnosis.[2]

We identified all patients 20–84 years old between January 1995 and December 1997 registered in the GPRD. Patients became members of the study cohort (start date) on the first day of the study period when they meet the criteria of at least 2 years enrollment with the GP and 1 year of computerized prescription history. Patients with a code for IBD or cancer before start date were omitted. Also, patients aged 70 years and older with a follow-up longer than 1 year and no single health contacts during their complete period of follow-up were removed from the source population. Our final study cohort comprised 977 457 patients.

All study members were followed from the start date until the earliest occurrence of one of the following endpoints: recorded code of IBD, age of 85 years, death, or end of study period (December 1997). Total follow-up time in the study cohort summed 2 157 528 py, averaging 2.2 years of follow-up per patient.

From the study cohort, we identified 806 patients with a recorded code for IBD and manually reviewed their computerized patient profiles. Information included demographic data and all clinical information (with data on personal identifiers removed). Following the review of the patient profiles, we excluded 215 patients: the main reasons were prevalent IBD (61%), and diagnosis initially suspected but ruled out in subsequent visits (28%). We sent questionnaires for validation of the remaining 591 potential cases to the corresponding GPs. In these questionnaires the GPs were asked to confirm the IBD diagnosis based on all the information held in patients' written records. If the diagnosis was confirmed, we asked them to provide the exact date when the diagnosis was originally made and the specific type of IBD (UC, CD, or indeterminate colitis).

We either received no information or the information was incomplete in 79 patients (13%). Out of 512 valid questionnaires, the IBD diagnosis was confirmed in a total of 489. Forty-five of them turned out to be prevalent cases (the diagnosis had been first made prior to start date), leaving 444 confirmed incident cases of IBD: 222 corresponded to UC, 171 to CD, and 51 were classified as indeterminate colitis.

Incidence rates of IBD in our study population were calculated by dividing the number of incident cases over the total follow-up in the study cohort. Incidence rates of specific IBD diagnoses stratified by age and sex were also calculated. For the nested case–control study, we used all 444 confirmed incident cases of IBD and considered the date of first diagnosis as the index date. A date during the study period was generated at random for every member of the study cohort. If the random date of a study member was included in his/her eligible person-time, we used this random date as the index date and marked that patient as an eligible control. This mechanism (i.e. incidence density sampling) allows that the likelihood of being selected as a control is proportional to the person-time at risk. Ten thousand controls free of IBD were frequency-matched by sex, age (interval of 1 year), and calendar year from the list of all eligible controls.

Estimates of odds ratios (ORs), assumed to be valid estimates of the relative risk, and 95% confidence intervals (CI) associated with different risk factors were computed using unconditional logistic regression.

We ascertained patients with prior comorbidity including osteoarthritis, rheumatoid arthritis (excluding ankylosing spondylitis), depression, anxiety, stress-related disorders, diabetes mellitus, chronic obstructive pulmonary disease (COPD), asthma, irritable bowel syndrome (more than 1 year before the index date), and appendectomy.

We also elicited subjects' smoking status and prior use of aspirin, non-aspirin non-steroidal anti-inflammatory drugs (NA-NSAIDs), and paracetamol. Among women, we also ascertained use of oral contraceptives (OCs) and hormone replacement therapy (HRT). We defined three time windows of exposure for each class of drugs: current use, past use and non-use. Current use was categorized as use that lasted until the index date or ended in the month prior to the index date based on the supply of drug therapy as prescribed by the GP. Past use was use that ended more than 1 month before the index date. Finally, the time window of non-use was defined as non-use of each respective drug group at any time before the index date. Current users were subdivided according to treatment duration into less than 1 month, between 1 and 12 months, and more than 1 year.

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