Hospital Admissions and MS: Temporal Trends and Patient Characteristics
Published Online: November 21, 2012
Charity Evans, PhD; Elaine Kingwell, PhD; Feng Zhu, MSc; Joel Oger, MD, FRCPC, FAAN; Yinshan Zhao, PhD; and Helen Tremlett, PhD
Multiple sclerosis (MS) is a chronic disease and the most common cause of non-traumatic neurological disability in young adults,1,2 placing a considerable burden on patients, families, and the healthcare system. In fact, MS patients are more than twice as likely to visit a medical professional or be hospitalized as individuals without MS.3
Hospital admissions are measured outcomes in both clinical trials and observational studies,4-8 and are often considered important surrogate measures for disease worsening and overall demand on healthcare resources.8 Thus, understanding hospitalization patterns is imperative to facilitate appropriate resource allocation, can aid in the evaluation of disease-management strategies, and can provide an indication of drug effectiveness. Despite this, few studies have specifically examined hospital admission patterns in MS.2,9,10 One American study evaluating admissions captured by a large inpatient care database found MS-related hospital stays average 5 days, remaining stable from 1998 to 2006.2 However, much remains unknown; to date, few others have investigated hospital admission rates over time in MS, or done so in a setting where healthcare is universally covered for all residents. The purpose of this study was to examine patterns, temporal trends, and patient characteristics influencing hospital admissions in a large MS cohort in British Columbia, Canada, from 1986 to 2008.
Patients with definite MS (McDonald11 or Poser12 criteria) registered at one of the 4 British Columbia MS clinics by December 31, 2004, were identified through the British Columbia MS (BCMS) database. Established in 1980, the database contains prospectively collected clinical information on approximately 80% of the British Columbia MS population during the study period. We linked this clinical data with the British Columbia Ministry of Health’s Discharge Abstract (hospital separations) database and Registry and Premium Billing Files (to confirm residency in British Columbia13) at the individual level using unique health identification numbers. The latter linkage provided registration dates for all British Columbia residents enrolled in the universal healthcare plan, providing confirmation of a patient’s residence in British Columbia throughout the study period. These databases have served as the basis for many observational studies, and are considered to be of high quality.13-18
Data were extracted for all hospital admissions from 1986 (the first full year both hospital and registry data were available) to 2008 and included admission and discharge dates, length of stay, primary diagnostic classification (ie, reason for admission), and up to 24 additional diagnostic classifications (International Classification of Disease [ICD]-9 or -10 codes). An ICD-9 code of 340 or ICD-10 code of G35 indicated an admission due to MS. Only admissions occurring after onset of MS symptoms (determined by the MS neurologist and recorded in the BCMS database) were included in the analyses.
The main outcome in this descriptive study was annual all-cause hospital admission rates from 1986 to 2008. Hospital admissions were categorized as “inpatient” (acute admission requiring at least 1 overnight stay), “day care surgery” (admitted and discharged on the same day), and “all-cause” (all admissions [inpatient and day care surgery], regardless of reason, level of care, or length of stay). Day care surgery admissions typically involve a medical procedure, but do not include drug infusions (eg, intravenous corticosteroids or natalizumab). Secondary outcomes included admission rates attributed to MS (ie, with a primary or secondary diagnostic code for MS [ICD-9:340 or ICD-10:G35]), and the length of stay.
Annual admission rates (all cause and MS specific) were estimated by dividing the total number of yearly hospital admissions by the number of MS cohort patients who were resident in British Columbia during that year, and reported per 100 patients with MS. Quasi Poisson regression, fitted using the method of generalized estimating equations (GEEs) with an exchangeable working correlation structure, was used to examine changes in all-cause admission rates over time. The following covariates were determined to be clinically relevant or statistically important based on univariate analyses (P <.1) and were therefore included in the models: sex, age at observation year, disease course (relapsing-onset or primary progressive), disease duration, and calendar year. To account for differences in the British Columbia residency time between patients, the logarithm of time registered with the British Columbia Ministry of Health for each year was included as an offset in the model. No meaningful first-order interactions were detected (P <.1). Findings were reported as adjusted incidence rate ratios (IRRs), with 95% confidence intervals (CIs).
Admissions were further examined only in patients with at least 1 hospital admission during the study period to identify potential factors associated with admission with a primary diagnosis of MS. A multivariable logistic regression model fitted by the GEE method, with an exchangeable working correlation structure, was used, with the same covariates as above. An interaction between sex and age at observation year (P <.001) necessitated separate models for males and females. Findings were reported as adjusted odds ratios with corresponding 95% CIs.
The mean length of hospital stay was examined for inpatient admissions only; a separate mean was also estimated for inpatient admissions where the primary admission reason was MS. To identify factors associated with a longer hospital stay for all inpatient admissions, a negative binomial regression model19 fitted by the GEE method, with an exchangeable working correlation structure, with the same covariates as above, was used. No significant interactions were detected (P <.1). Findings were reported as adjusted IRRs with corresponding 95% CIs. Statistical analyses were carried out using SPSS version 18.0 (SPSS Inc, Chicago, Illinois) and R: A Language and Environment for Statistical Computing V.2.13.2 (R Foundation for Statistical Computing, Vienna, Austria; 2011). This study was approved by the University of British Columbia’s Clinical Research Ethics board (study number: H08-01544).
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