Administrative Data Algorithm Research Articles

Breast (female), colorectal, and lung cancer survival in people with intellectual or developmental disabilities: A population-based retrospective cohort study.

Cancer is a leading cause of death among people living with intellectual or developmental disabilities (IDD). There is little empirical evidence documenting survival or comparing outcomes to those without IDD. This study investigated the association between IDD and cancer survival among adults with breast(female), colorectal, or lung cancer. A population-based retrospective cohort study was conducted in Ontario, Canada, with routinely collected data. Patients with breast, colorectal, or lung cancer were included (2007‒2019). IDD status before cancer was determined using an established administrative data algorithm. The outcomes of interest included death from any cause and death from cancer. Cox proportional hazards models and competing events analyses using multivariable cause-specific hazards regression were completed. Analyses were stratified by cancer type. Interactions with age, sex, and stage at diagnosis, as well as sensitivity analyses, were completed. The final cohorts included 123,695 breast, 98,809 colorectal, and 116,232 lung cancer patients. Individuals with IDD experienced significantly worse survival than those without IDD. The adjusted hazard ratios of all-cause death were 2.74 (95% CI 2.41‒3.12), 2.42 (95% CI 2.18‒2.68), and 1.49 (95% CI 1.34‒1.66) times higher for breast, colorectal, and lung cancer patients with IDD relative to those without. These findings were consistent for cancer-specific deaths. With few exceptions, worse survival for people with IDD persisted regardless of stage at diagnosis. People with IDD experienced worse cancer survival than those without IDD. Identifying and intervening on the factors and structures responsible for survival disparities is imperative.

Prevalence and Incidence Trends of Attention Deficit/Hyperactivity Disorder in Children and Youth Aged 1-24 Years in Ontario, Canada: A Validation Study of Health Administrative Data Algorithms: Tendances de la prévalence et de l'incidence du trouble de déficit de l'attention/hyperactivité chez les enfants et les jeunes âgés de 1 à 24 ans, en Ontario, Canada: une étude de validation des algorithmes

To estimate prevalence and incidence rates over time in children and youth with attention deficit/hyperactivity disorder from the validation of population-based administrative data algorithms using family physicians' electronic medical records as a reference standard. A retrospective cohort study was conducted in Ontario, Canada to identify attention deficit/hyperactivity disorder among children and youth aged 1-24 years in health administrative data derived from case-finding algorithms using family physicians' electronic medical records. Multiple administrative data algorithms identifying attention deficit/hyperactivity disorder cases were developed and tested from physician-diagnosis of attention deficit/hyperactivity disorder in the electronic medical record to determine their diagnostic accuracy. We calculated algorithm performance using sensitivity, specificity, and predictive values. The most optimal algorithm was used to estimate prevalence and incidence rates of attention deficit/hyperactivity disorder from 2014 to 2021 in Ontario. The optimal performing algorithm was "2 physician visits for attention deficit/hyperactivity disorder in 1 year or 1 attention deficit/hyperactivity disorder-specific prescription" with sensitivity: 83.2% (95% confidence interval [CI], 81.8% to 84.5%), specificity: 98.6% (95% CI, 98.5% to 98.7%), positive predictive value: 78.6% (95% CI, 77.1% to 80.0%) and negative predictive value: 98.9% (95% CI, 98.8% to 99.0%). From 2014, prevalence rates for attention deficit/hyperactivity disorder increased from 5.29 to 7.48 per 100 population in 2021 (N = 281,785). Males had higher prevalence rates (7.49 to 9.59 per 100 population, 1.3-fold increase) than females (2.96-5.26 per 100 population, 1.8-fold increase) from 2014 to 2021. Incidence rates increased from 2014 (0.53 per 100 population) until 2018, decreased in 2020 then rose steeply in 2021 (0.89 per 100 population, N = 34,013). Males also had higher incidence rates than females from 2014 to 2020 with females surpassing males in 2021 (0.70-0.81 per 100 male population,1.2-fold increase versus 0.36-0.97 per 100 female population, 2.7-fold increase). Attention deficit/hyperactivity disorder is increasing in prevalence. We developed an administrative data algorithm that can reliably identify children and youth with attention deficit/hyperactivity disorder with good diagnostic accuracy.

Use of Analog and Human Insulin in a European Hemodialysis Cohort With Type 2 Diabetes: Associations With Mortality, Hospitalization, MACE, and Hypoglycemia

Poor glycemic control may contribute to the high mortality rate in patients with type 2 diabetes receiving hemodialysis. Insulin type may influence glycemic control, and its choice may be an opportunity to improve outcomes. This study assessed whether treatment with analog insulin compared with human insulin is associated with different outcomes in people with type 2 diabetes and kidney failure receiving hemodialysis. Retrospective cohort study. People in the Analyzing Data, Recognizing Excellence and Optimizing Outcomes (AROii) study with kidney failure commencing hemodialysis and type 2 diabetes being treated with insulin within 288 dialysis facilities between 2007 and 2009 across 7 European countries. Study participants were followed for 3 years. People with type 1 diabetes were excluded using an established administrative data algorithm. Treatment with an insulin analog or human insulin. All-cause mortality, major adverse cardiovascular events (MACE), all-cause hospitalization, and confirmed hypoglycemia (blood glucose<3.0mmol/L sampled during hemodialysis). Inverse probability weighted Cox proportional hazards models to estimate hazard ratios for analog insulin compared with human insulin. There were 713 insulin analog and 733 human insulin users. Significant variation in insulin type by country was observed. Comparing analog with human insulin at 3 years, the percentage of patients experiencing end points and adjusted hazard ratios (AHR) were 22.0% versus 31.4% (AHR, 0.808 [95% CI, 0.66-0.99], P=0.04) for all-cause mortality, 26.8% versus 35.9% (AHR, 0.817 [95% CI, 0.68-0.98], P=0.03) for MACE, and 58.2% versus 75.0% (AHR, 0.757 [95% CI, 0.67-0.86], P<0.001) for hospitalization. Hypoglycemia was comparable between insulin types at 14.1% versus 15.0% (AHR, 1.169 [95% CI, 0.80-1.72], P=0.4). Consistent strength and direction of the associations were observed across sensitivity analyses. Residual confounding, lack of more detailed glycemia data. In this large multinational cohort of people with type 2 diabetes and kidney failure receiving maintenance hemodialysis, treatment with analog insulins was associated with better clinical outcomes when compared with human insulin. People with diabetes who are receiving dialysis for kidney failure are at high risk of cardiovascular disease and death. This study uses information from 1,446 people with kidney failure from 7 European countries who are receiving dialysis, have type 2 diabetes, and are prescribed either insulin identical to that made in the body (human insulin) or insulins with engineered extra features (insulin analog). After 3 years, fewer participants receiving analog insulins had died, had been admitted to the hospital, or had a cardiovascular event (heart attack, stroke, heart failure, or peripheral vascular disease). These findings suggest that analog insulins should be further explored as a treatment leading to better outcomes for people with diabetes on dialysis.

Defining major surgical complications using administrative data in Ontario: a validation study.

Although surgical complications are often included as an outcome of surgical research conducted using administrative data, little validation work has been performed. We sought to evaluate the diagnostic performance of an algorithm designed to capture major surgical complications using health administrative data. This retrospective study included patients who underwent high-risk elective general surgery at a single institution in Ontario, Canada, from Sept. 1, 2016, to Sept. 1, 2017. Patients were identified for inclusion using the local operative database. Medical records were reviewed by trained clinicians to abstract postoperative complications. Data were linked to administrative data holdings, and a series of code-based algorithms were applied to capture a composite indicator of major surgical complications. We used sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy to evaluate the performance of our administrative data algorithm, as compared with data abstracted from the institutional charting system. The study included a total of 270 patients. According to the data from the chart audit, 55% of patients experienced at least 1 major surgical complication. Overall sensitivity, specificity, PPV, NPV and accuracy for the composite outcome was 72%, 80%, 82%, 70% and 76%, respectively. Diagnostic performance was poor for several of the individual complications. Our results showed that administrative data holdings can be used to capture a composite indicator of major surgical complications with adequate sensitivity and specificity. Additional work is required to identify suitable algorithms for several specific complications.

The Association of Homelessness With Rates of Diabetes Complications: A Population-Based Cohort Study.

To estimate the rates of diabetes complications and revascularization procedures among people with diabetes who have experienced homelessness compared with a matched cohort of nonhomeless control subjects. A propensity-matched cohort study was conducted using administrative health data from Ontario, Canada. Inclusion criteria included a diagnosis of diabetes and at least one hospital encounter between April 2006 and March 2019. Homeless status was identified using a validated administrative data algorithm. Eligible people with a history of homelessness were matched to nonhomeless control subjects with similar sociodemographic and clinical characteristics. Rate ratios (RRs) for macrovascular complications, revascularization procedures, acute glycemic emergencies, skin/soft tissue infections, and amputation were calculated using generalized linear models with negative binomial distribution and robust SEs. Of 1,076,437 people who were eligible for inclusion in the study, 6,944 were identified as homeless. A suitable nonhomeless match was found for 5,219 individuals. The rate of macrovascular complications was higher for people with a history of homelessness compared with nonhomeless control subjects (RR 1.85, 95% CI 1.64-2.07), as were rates of hospitalization for glycemia (RR 5.64, 95% CI 4.07-7.81) and skin/soft tissue infections (RR 3.78, 95% CI 3.31-4.32). By contrast, the rates of coronary revascularization procedures were lower for people with a history of homelessness (RR 0.76, 95% CI 0.62-0.94). These findings contribute to our understanding of the impact of homelessness on long-term diabetes outcomes. The higher rates of complications among people with a history of homelessness present an opportunity for tailored interventions to mitigate these disparities.

A64 THE UPTAKE AND IMPACT OF AN ELECTRONIC CIRRHOSIS ADMISSION ORDER SET: AN EARLY EXPERIENCE AT A SINGLE CENTRE

Abstract Background Cirrhosis is a chronic disease that confers high morbidity and mortality. It is a leading cause for hospital admissions and leads to significant healthcare resource utilization. Several guidelines outline recommendations to provide best practice to hospitalized patients with cirrhosis. Despite studies supporting a reduction in mortality when guideline based care is followed, this is achieved in less than 50% of hospitalized patients with cirrhosis1. Standardized electronic order sets can be a potential tool to improving clinical outcomes and bridging this gap in care. Purpose Since March 2021, an electronic cirrhosis admission order set has been available for at our hospital site. Using administrative data, we aimed to describe our early experience with: a) order set uptake by various services, b) characteristics of the population in which the order set was used versus not used, and explore c) the impact of order set use on in-hospital mortality. Method In this single centre cohort study, patients with cirrhosis were identified based an administrative data algorithm containing codes for cirrhosis and complications. This data was used to retrieve parameters such as patient age, sex, primary admitting service, resource intensity weight (RIW), Charlson comorbidity index (CCI) and in-hospital mortality. The chi-squared test and independent samples t-test were used to compare characteristics of patients in whom the order set was used versus not used. Multivariable logistic regression was used to determine the impact of order set use on in-hospital mortality. P value significance was established at &amp;lt;0.05. Result(s) A total of 825 patients were included in the analysis. The overall mean age (standard deviation) of patients was 58.5 (14.2) years with 57.5% being male. Average length of stay was 11.3 days with a mean CCI of 3.2 (2.3) and RIW of 3.3 (7.2). The primary admitting service was Gastroenterology in 36.1%, Internal Medicine in 35.6% and other services in 28.3% of cases. Of those admitted, the order set was used in 27.2% of cases. The overall in-hospital mortality of patients was 14.2%. Mean age, sex and CCI were not significantly different in patients admitted with the order set versus without. In patients admitted with the order set compared to without, RIW was significantly lower (2.06 (2.62) versus 3.80 (8.2), p&amp;lt;0.001), as was length of stay (9.5 (11.8) days compared to 12.0 (18.6) days, p =0.03) and in-hospital mortality (8.5% versus 16.3%, p =0.003). On multivariable regression analysis (Table 1), after adjustment for age, RIW and CCI, use of the order set was associated with lower in-hospital mortality (odds ratio 0.53 (95% CI 0.3 to 0.9), p=0.02). Image Conclusion(s) Uptake of the electronic cirrhosis admission order set was modest at only 27% of eligible admissions. Although it appears to be associated with lower in-hospital mortality, a chart review is in process to assess if this association still holds after accounting for the impact of additional confounders. Please acknowledge all funding agencies by checking the applicable boxes below None Disclosure of Interest None Declared

Identification of Early Onset Dementia in Population-Based Health Administrative Data: A Validation Study Using Primary Care Electronic Medical Records.

Early onset dementia (EOD) occurs when symptoms of dementia begin between 45 to 64 years of age. We developed and validated health administrative data algorithms for EOD and compared demographic characteristics and presence of comorbid conditions amongst adults with EOD, late onset dementia (LOD) and adults with no dementia in Ontario, Canada. Patients aged 45 to 64 years identified as having EOD in their primary care electronic medical records had their records linked to provincial health administrative data. We compared several combinations of physician's claims, hospitalizations, emergency department visits and prescriptions. Age-standardized incidence and prevalence rates of EOD were estimated from 1996 to 2016. The prevalence of EOD for adults aged 45 to 64 years in our primary care reference cohort was 0.12%. An algorithm of ≥1 hospitalization or ≥3 physician claims at least 30 days apart in a two-year period or ≥1 dementia medication had a sensitivity of 72.9% (64.5-81.3), specificity of 99.7% (99.7-99.8), positive predictive value (PPV) of 23.7% (19.1-28.3), and negative predictive value of 100.0%. Multivariate logistic regression found adults with EOD had increased odds ratios for several health conditions compared to LOD and no dementia populations. From 1996 to 2016, the age-adjusted incidence rate increased slightly (0.055 to 0.061 per 100 population) and the age-adjusted prevalence rate increased three-fold (0.11 to 0.32 per 100 population). While we developed a health administrative data algorithm for EOD with a reasonable sensitivity, its low PPV limits its ability to be used for population surveillance.

808-P: Association between Analogue Compared with Human Insulin and the Outcomes of Mortality, Hospitalization, MACE, and Hypoglycemia in Hemodialysis Patients with Type 2 Diabetes: The ARO Research Initiative

Aims: Poor glycemic control may contribute to the very high mortality rate in patients with type 2 diabetes (T2D) receiving hemodialysis (HD) . Whether analogue compared to human insulin therapy associates with different outcomes is unknown. Methods: Incident HD patients with T2D on insulin treatment enrolled at one of the 288 Fresenius Medical Care facilities across 7 European countries between 2007-20were identified using an established administrative data algorithm. All patients were censored after 3 years of observation. Following multiple imputation, inverse probably weighting (IPW) and instrumental variable (IV; using country) analyses were used to generate Cox-proportional hazards to estimate analogue compared to human insulin hazard ratios for all-cause mortality (ACM) , MACE, hospitalization, and confirmed hypoglycemia (&amp;lt;3.0 mmol/L during a dialysis session) . Results: About 713 analogue users were compared to 733 subjects treated with human insulin. Significant variation in prescription by country was observed. IPW hazard ratios [95% confidence intervals] for patients on analogue compared to human insulin were 0.8[0.659-0.991] for ACM (p=0.042) , 0.817 [0.679-0.983] for MACE (p=0.033) , 0.757 [0.665-0.861] for hospitalization (p&amp;lt;0.001) , and 1.6[1.1419-2.268] for hypoglycemia (p=0.008) . Consistent strength and direction of the associations were observed in the IV analysis and across sensitivity analyses. Conclusions: In this large, multinational cohort of patients with T2D on HD, compared to human insulins, analogue insulins were associated with better clinical outcomes, although hypoglycemia rates were increased. Whether analogue insulins represent the preferred therapy in this group requires confirmatory evidence from a clinical trial. Disclosure T. Ebert: Consultant; Sanofi, Santis. Research Support; AstraZeneca, Novo Nordisk. N. Sattar: None. M. Greig: None. C. Lamina: None. K. Eckardt: Consultant; Akebia Therapeutics, Inc. Research Support; Bayer AG, Evotec International GmbH, Fresenius Medical Care. Speaker's Bureau; AstraZeneca, Bayer AG, Otsuka Pharmaceutical Co., Ltd. J. Floege: Other Relationship; Novo Nordisk A/S. F. Kronenberg: Advisory Panel; Amgen Inc., Novartis AG. P. Stenvinkel: Advisory Panel; AstraZeneca, Baxter, Fresenius Medical Care, Reata Pharmaceuticals, Inc., Vifor Pharma Management Ltd. Speaker's Bureau; Astellas Pharma Inc., Bayer AG, Novo Nordisk. D.C. Wheeler: Advisory Panel; Bayer AG, Boehringer Ingelheim International GmbH, Gilead Sciences, Inc., GlaxoSmithKline plc., Janssen Global Services, LLC, Merck Sharp &amp; Dohme Corp., Mundipharma, Tricida, Inc., Vifor Pharma Management Ltd. Consultant; AstraZeneca. Speaker's Bureau; Amgen Inc., Astellas Pharma Inc. J. Fotheringham: Advisory Panel; Novartis Pharmaceuticals Corporation. Speaker's Bureau; Fresenius Medical Care, Novartis Pharmaceuticals Corporation, Vifor Pharma Management Ltd. Funding Amgen (Europe) GmbH- Rotkreuz, Switzerland- Swedish Research Council (grant 2009-1068) - Swedish Heart and Lung Foundation (20160384) - Njurfonden- Westmans Foundation- Novo Nordisk (Postdoctoral fellowship program run inpartnership with Karolinska Institutet, Stockholm,Sweden) - Karolinska Institutet Research Foundation - EFSD (EFSD Mentorship Programme supported by AstraZeneca) - German Research Foundation (SFB TRR 219, projects C1 and M1) - National Institute for Health Research (UK) (Clinician Scientist Award)

Evaluation of Real-World Healthcare Resource Utilization and Associated Costs in Children with Juvenile Idiopathic Arthritis: A Canadian Retrospective Cohort Study.

IntroductionJuvenile idiopathic arthritis (JIA) is a chronic rheumatic disease, whose multifaceted care path can lead to significant expenditure for the healthcare system. We aim to assess the real-world healthcare resource use (HCRU) and associated cost for children with JIA in a single center in Canada.MethodsA single-center consecutive cohort of newly diagnosed patients with JIA attending the pediatric rheumatology clinic from 2011 to 2019 was identified using an administrative data algorithm and electronic medical charts. HCRU was estimated from six administrative health databases that included hospital admissions, emergency, outpatient care, practitioners’ visits, medication, and laboratory and imaging tests. Costs were assigned using appropriate sources. We reported the yearly overall and JIA-associated HCRU and costs 5 years prior to and 6 years after the first visit to the pediatric rheumatologist. The Zhao and Tian estimator was used to calculate cumulative mean costs over a 6-year timeframe. Results were stratified by disease subtype.ResultsA total of 389 patients were identified. The yearly total overall mean costs per patient ranged between $804 and $4460 during the 5 years prior to the first visit to the pediatric rheumatologist and $8529 and $10,651 for the 6 years after. Medication cost, driven by use of biologic therapies, and outpatient visits were the greatest contributor to the total cost. The overall cumulative mean cost for 6 years of care was $48,649 per patient, while the JIA-associated cumulative mean cost was $26,820 per patient. During the first year of rheumatology care, systemic onset JIA had the highest cumulative mean overall cost, while oligoarticular JIA had the lowest cumulative mean cost.ConclusionThe care pathway for children with JIA can be expensive, and complex—and varies by JIA subtype. Although the yearly total mean cost per patient was constant, the distribution of costs changes over time with the introduction of biologic therapies later in the care pathway. This study provides a better understanding of the JIA costs profile and can help inform future economic studies.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40744-021-00331-x.

Validation in Alberta of an administrative data algorithm to identify cancer recurrence.

Readily available population-based data about cancer recurrence would improve surveillance and research for women of reproductive age. We randomly selected 200 women from the Alberta Cancer Registry who had received a cancer diagnosis and who ever had a pregnancy between 2003 and 2012. Administrative data were obtained and linked. Several definitions of recurrence were assessed using various minimum lengths of time between the initial diagnosis date and subsequent diagnoses or treatments, or both. Chart review was used as a "gold standard" definition of recurrence. Chart review identified recurrences in 26 women. The definition that best captured "recurrence" was 2 or more cancer diagnosis codes 10 or more months from the diagnosis date [sensitivity: 80.8%; 95% confidence interval (ci): 60.7% to 93.5%; specificity: 81.0%; 95% ci: 74.4% to 86.6%; positive predictive value: 38.9%; 95% ci: 25.9% to 53.1%; negative predictive value: 96.6%; 95% ci: 92.2% to 98.9%; kappa = 0.42; 95% ci: 0.28 to 0.57]. Recurrence in reproductive-aged women can be captured with moderate validity using administrative data, but should be interpreted with caution.

Identifying cases of chronic pain using health administrative data: A validation study

ABSTRACT Background Most prevalence estimates of chronic pain are derived from surveys and vary widely, both globally (2%–54%) and in Canada (6.5%–44%). Health administrative data are increasingly used for chronic disease surveillance, but their validity as a source to ascertain chronic pain cases is understudied. Aim The aim of this study was to derive and validate an algorithm to identify cases of chronic pain as a single chronic disease using provincial health administrative data. Methods A reference standard was developed and applied to the electronic medical records data of a Newfoundland and Labrador general population sample participating in the Canadian Primary Care Sentinel Surveillance Network. Chronic pain algorithms were created from the administrative data of patient populations with chronic pain, and their classification performance was compared to that of the reference standard via statistical tests of selection accuracy. Results The most performant algorithm for chronic pain case ascertainment from the Medical Care Plan Fee-for-Service Physicians Claims File was one anesthesiology encounter ever recording a chronic pain clinic procedure code OR five physician encounter dates recording any pain-related diagnostic code in 5 years with more than 183 days separating at least two encounters. The algorithm demonstrated 0.703 (95% confidence interval [CI], 0.685–0.722) sensitivity, 0.668 (95% CI, 0.657–0.678) specificity, and 0.408 (95% CI, 0.393–0.423) positive predictive value. The chronic pain algorithm selected 37.6% of a Newfoundland and Labrador provincial cohort. Conclusions A health administrative data algorithm was derived and validated to identify chronic pain cases and estimate disease burden in residents attending fee-for-service physician encounters in Newfoundland and Labrador.

Joint impact of dementia and frailty on healthcare utilisation and outcomes: a retrospective cohort study of long-stay home care recipients

ObjectivesTo examine the associations between dementia and 1-year health outcomes (urgent hospitalisation, long-term care (LTC) admission, mortality) among long-stay home care recipients and the extent to which these associations vary...

Identification of Patients with Statin Intolerance in a Managed Care Plan: A Comparison of 2 Claims-Based Algorithms.

While statins are safe and efficacious, some patients may experience statin intolerance or treatment-limiting adverse events. Identifying patients with statin intolerance may allow optimal management of cardiovascular event risk through other strategies. Recently, an administrative claims data (ACD) algorithm was developed to identify patients with statin intolerance and validated against electronic medical records. However, how this algorithm compared with perceptions of statin intolerance by integrated delivery networks remains largely unknown. To determine the concurrent validity of an algorithm developed by a regional integrated delivery network multidisciplinary panel (MP) and a published ACD algorithm in identifying patients with statin intolerance. The MP consisted of 3 physicians and 2 pharmacists with expertise in cardiology, internal medicine, and formulary management. The MP algorithm used pharmacy and medical claims to identify patients with statin intolerance, classifying them as having statin intolerance if they met any of the following criteria: (a) medical claim for rhabdomyolysis, (b) medical claim for muscle weakness, (c) an outpatient medical claim for creatinine kinase assay, (d) fills for ≥ 2 different statins excluding dose increases, (e) decrease in statin dose, or (f) discontinuation of a statin with a subsequent fill for a nonstatin lipid-lowering therapy. The validated ACD algorithm identified statin intolerance as absolute intolerance with rhabdomyolysis; absolute intolerance without rhabdomyolysis (i.e., other adverse events); or as dose titration intolerance. Adult patients (aged ≥ 18 years) from the integrated delivery network with at least 1 prescription fill for a statin between January 1, 2011, and December 31, 2012 (first fill defined the index date) were identified. Patients with ≥ 1 year pre- and ≥ 2 years post-index continuous enrollment and no statin prescription fills in the pre-index period were included. The MP and ACD algorithms were applied to the population, and concordance was examined using individual (i.e., sensitivity, specificity, positive predictive value [PPV], and negative predictive value [NPV]) and overall performance measures (i.e., accuracy, Cohen's kappa coefficient, balanced accuracy, F-1 score, and phi coefficient). After applying the inclusion criteria, 7,490 patients were evaluated for statin intolerance. The mean (SD) age of the population was 51.1 (8.5) years, and 55.7% were male. The MP and ACD algorithms classified 11.3% and 5.4% of patients as having statin intolerance, respectively. The concordance of the MP algorithm was mixed, with negative classification of statin intolerance measures having high concordance (specificity 0.91, NPV 0.97) and positive classification of statin intolerance measures having poor concordance (sensitivity 0.45, PPV 0.21). Overall performance measures showed mixed agreement between the algorithms. Both algorithms used a mix of pharmacy and medical claims and may be useful for organizations interested in identifying patients with statin intolerance. By identifying patients with statin intolerance, organizations may consider a variety of options, including using nonstatin lipid-lowering therapies, to manage cardiovascular event risk in these patients. This study was funded by Regeneron Pharmaceuticals and Sanofi US. Boklage is employed by, and owns stock in, Regeneron, and Charland is employed by Sanofi. Bellows has received fees from Avenir for advisory board membership and grants from Myriad Genetics, Biogen, Janssen, and National Institutes of Health. Brixner reports advisory board and consultancy fees and grants from Sanofi. Mitchell reports consultancy fees from Sanofi. Study concept and design were contributed by Bellows, Boklage, Charland, and Brixner. Bellows, Sainski-Nguyen, and Olsen took the lead in data collection, along with Mitchell. Data interpretation was performed by Mitchell, along with the other authors. The manuscript was written by Bellows, Sainski-Nguyen, and Olsen and revised by all the authors.

Abstract WP195: Home Time by Stroke Type: a Population-based Study to Evaluate Functional Outcome After a Cerebrovascular Event

Introduction: Home time has been proposed as a surrogate for functional outcome in ischemic stroke, and is also a highly valued patient-centered outcome that reflects resource utilization. In prospective cohort studies and clinical trials, higher home time has been correlated with lower disability; however, the requirement for informed consent results in selection bias. Therefore, the population distributions of home time after stroke are currently unknown. Additionally, home time distributions have not been reported in hemorrhagic stroke and transient ischemic attack (TIA). We developed a novel administrative data algorithm to compare home time distributions after hospital admission for cerebrovascular events in the population of Alberta, Canada. Methods: Home time was defined as the number of nights not spent in an institution, including acute-care, inpatient rehabilitation facilities, and long-term care, in the 90 days after admission for a cerebrovascular event. Community-dwelling residents of Alberta, Canada with a valid healthcare number admitted for a cerebrovascular event between April 2012 and June 2015 were included. We used the Kruskal-Wallis test to compare the median home-times according to stroke type: ischemic stroke (IS), TIA, or hemorrhagic stroke (HS), including intracerebral and subarachnoid hemorrhage. We correlated admitting age and home time with Spearman correlations and assessed sex and home time with Wilcoxon Rank Sum. Results: A total of 12520 admissions were identified, the median age was 74 years (IQR 22), and 53% were male. There were 8482 (68%) IS, 2434 (19%) TIA, and 1604 (13%) HS. The median (IQR) home time by stroke type was 72 nights (85) for IS, 87 nights (6) for TIA, and 29 nights (79) for HS (p&lt;0.001). For each stroke type, lower home time was correlated with higher age (IS: r=-0.35; TIA: r=-0.31; HS: r=-0.33; p&lt;0.001 for each comparison) and female sex (p≤0.001). Conclusion: In this population with universal healthcare access, TIA had the highest home time (i.e. best outcomes) whereas hemorrhagic stroke had the lowest, consistent with expected functional outcomes based on prospective cohort studies. Home time may be a useful metric to track patient outcomes and healthcare utilization based on administrative health data.

Development and validation of an administrative data algorithm to identify adults who have endoscopic sinus surgery for chronic rhinosinusitis

BackgroundThis was a diagnostic accuracy study to develop an algorithm based on administrative database codes that identifies patients with Chronic Rhinosinusitis (CRS) who have endoscopic sinus surgery (ESS).MethodsFrom January 1st, 2011 to December 31st, 2012, a chart review was performed for all hospital-identified ESS surgical encounters. The reference standard was developed as follows: cases were assigned to encounters in which ESS was performed for Otolaryngologist-diagnosed CRS; all other chart review encounters, and all other hospital surgical encounters during the timeframe were controls. Algorithm development was based on International Classification of Diseases, version 10 (ICD-10) diagnostic codes and Canadian Classification of Health Interventions (CCI) procedural codes. Internal model validation was performed with a similar chart review for all model-identified cases and 200 randomly selected controls during the following year.ResultsDuring the study period, 347 cases and 185,007 controls were identified. The predictive model assigned cases to all encounters that contained at least one CRS ICD-10 diagnostic code and at least one ESS CCI procedural code. Compared to the reference standard, the algorithm was very accurate: sensitivity 96.0% (95%CI 93.2–97.7), specificity 100% (95% CI 99.9–100), and positive predictive value 95.4% (95%CI 92.5–97.3). Internal validation using chart review for the following year revealed similar accuracy: sensitivity 98.9% (95%CI 95.8–99.8), specificity 97.1% (95%CI 93.4–98.8), and positive predictive value 96.9% (95%CI 93.0–99.8).ConclusionA simple model based on administrative database codes accurately identified ESS-CRS encounters. This model can be used in population-based cohorts to study longitudinal outcomes for the ESS-CRS population.

Changes in diabetes care introduced by a Chronic Care Model-based programme in Tuscany: a 4-year cohort study.

In 2010, Tuscany (Italy) implemented a Chronic Care Model (CCM)-based programme for the management of chronic diseases. The study’s objective was to evaluate its impact on the care of patients with type 2 diabetes. A population-based cohort study was performed on patients with diabetes, identified by an administrative data algorithm, exposed to a CCM-based programme versus patients not exposed (8486 patients in each group). The groups were matched using a propensity score approach and observed from 2011 to 2014. The outcomes measured were: mortality rate and hazard ratio (HR), hospitalisation incidence rate (IR) (all causes and diabetes-related diseases) and incidence rate ratio (IRR), and Guideline Composite Indicator (GCI) as proxy of adherence to guidelines (IR and IRR). Stratified Cox regression analysis and conditional fixed effect Poisson regression analyses were performed to compute HR and IRR. A significant improvement was observed for GCI (IRR 1.58; 95% CI 1.53–1.62) and for cardiovascular long-term complications (IRR 1.11; 95% CI 1.04–1.18). A protective effect was observed for neurological long-term complications (IRR 0.85; 95% CI 0.76–0.95), acute cardio-cerebrovascular long-term complications—stroke and ST segment elevation myocardial infarction—(IRR 0.81; 95% CI 0.71–0.92) and mortality (HR 0.88; 95% CI 0.81–0.96). The implementation of a CCM-based programme was followed by better management and benefits for the health status of patients. The increase in hospitalisations for cardiovascular long-term complications could engender cost-efficacy issues, but a better integrated care (GPs and specialists) and a more appropriate specialist outpatient services organisation could avoid a part of these, while still maintaining the benefits seen.

Accuracy of administrative data for surveillance of healthcare-associated infections: a systematic review

ObjectiveMeasuring the incidence of healthcare-associated infections (HAI) is of increasing importance in current healthcare delivery systems. Administrative data algorithms, including (combinations of) diagnosis codes, are commonly used to determine the...

Development and validation of an administrative data algorithm to estimate the disease burden and epidemiology of multiple sclerosis in Ontario, Canada

Background: Few studies have assessed the accuracy of administrative data for identifying multiple sclerosis (MS) patients. Objectives: To validate administrative data algorithms for MS, and describe the burden and epidemiology over time in Ontario, Canada. Methods: We employed a validated search strategy to identify all MS patients within electronic medical records, to identify patients with and without MS (reference standard). We then developed and validated different combinations of administrative data for algorithms. The most accurate algorithm was used to estimate the burden and epidemiology of MS over time. Results: The accuracy of the algorithm of one hospitalisation or five physician billings over 2 years provided both high sensitivity (84%) and positive predictive value (86%). Application of this algorithm to provincial data demonstrated an increasing cumulative burden of MS, from 13,326 patients (0.14%) in 2000 to 24,647 patients in 2010 (0.22%). Age-and-sex standardised prevalence increased from 133.9 to 207.3 MS patients per 100,000 persons in the population, from 2000 – 2010. During this same period, age-and-sex-standardised incidence varied from 17.9 to 19.4 patients per 100,000 persons. Conclusions: MS patients can be accurately identified from administrative data. Our findings illustrated a rising prevalence of MS over time. MS incidence rates also appear to be rising since 2009.

A Validation Study of Administrative Data Algorithms to Identify Patients with Parkinsonism with Prevalence and Incidence Trends

Background: Epidemiological studies for identifying patients with Parkinson's disease (PD) or Parkinsonism (PKM) have been limited by their nonrandom sampling techniques and mainly veteran populations. This reduces their use for health services planning. The purpose of this study was to validate algorithms for the case ascertainment of PKM from administrative databases using primary care patients as the reference standard. Methods: We conducted a retrospective chart abstraction using a random sample of 73,003 adults aged ≥20 years from a primary care Electronic Medical Record Administrative data Linked Database (EMRALD) in Ontario, Canada. Physician diagnosis in the EMR was used as the reference standard and population-based administrative databases were used to identify patients with PKM from the derivation of algorithms. We calculated algorithm performance using sensitivity, specificity, and predictive values and then determined the population-level prevalence and incidence trends with the most accurate algorithms. Results: We selected, ‘2 physician billing codes in 1 year' as the optimal administrative data algorithm in adults and seniors (≥65 years) due to its sensitivity (70.6-72.3%), specificity (99.9-99.8%), positive predictive value (79.5-82.8%), negative predictive value (99.9-99.7%), and prevalence (0.28-1.20%), respectively. Conclusions: Algorithms using administrative databases can reliably identify patients with PKM with a high degree of accuracy.

An administrative data algorithm to identify traumatic spinal cord injured patients: a validation study

To assess the validity of different administrative data sources available for the identification of traumatic spinal cord injured (TSCI) patients. Retrospective validation study. Ontario, Canada. Adult patients seen in tertiary outpatient spinal cord rehabilitation clinics after 1 April 2002. Sensitivity, specificity, positive and negative predicative values of diagnostic ICD10 codes from Canadian Institutes of Health Discharge Abstracts (CIHI-DAD), Rehabilitation Coding Groups (RCG) from that National Rehabilitation System (NRS), and spinal cord injury fee codes from the Ontario Healthcare Insurance Plan (OHIP). Secondary outcome was the agreement between actual lesion level and RCG/ICD10 coded lesion level. The RCG codes in the NRS have high sensitivity (92%, 95% confidence interval (CI): 87-95%) and specificity (97%, 95% CI: 94-99%) for the identification of true TSCI patients, whereas CIHI-DAD ICD10 codes are highly specific (99%, 95% CI: 95-100) and moderately sensitive (76%, 95% CI: 79-87%). OHIP fee codes had poor sensitivity (64%, 95% CI: 57-71%). Agreement between true lesion level and the NRS and CIHI-DAD coding is good (Kappa of 0.65-0.88 and 0.56-0.70, respectively). This study demonstrated that the NRS is able to accurately discriminate between patients with and without a TSCI. A large population of incident and prevalent TSCI patients are identifiable using administrative data. This study was funded by a grant from the Division of Urology, Western University.