Achieving cross provincial comparisons of osteoporosis screening performance from administrative health data.
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ABSTRACT:
SUBMITTER: Kueper JK
PROVIDER: S-EPMC8144816 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:IntroductionCosts associated with cancer care are increasing. Cancer costs in the context of other common non-cancer diagnoses have not been extensively studied at the population level. Knowledge from such analyses can inform health care resource allocation and highlight strategies to reduce overall costs.MethodsUsing cross-sectional data from publicly available population-level administrative data sources (health insurance claims, physician billing, and hospital discharge abstracts), we calculated incidence-adjusted health care costs (in 2014 Canadian dollars) for cancers and common non-cancer diagnoses in the adult population in a large Canadian province. Subgroup analyses were also performed for various provincial health administrative regions.ResultsTotal costs related to cancer care amounted to $495 million for the province, of which at least $67 million (14%) was attributable to radiation and chemotherapy. Of the various cancer subtypes, hematologic malignancies were most costly at $70 million, accounting for 14% of the total cancer budget. Colon cancer followed at $51 million (10%), and lung cancer, at $44 million (9%). Cancer costs (with and without costs for radiation and chemotherapy) exceeded those for cardiovascular disease, diabetes mellitus, mental health, and trauma (p < 0.001). In addition, the costs of specific cancer subtypes varied by region, but hematologic and lung cancers were typically the most costly no matter the health region.ConclusionsUsing provincial administrative data to establish cost trends can help to inform health care allocation and budget decisions, and can facilitate comparisons between provinces.
| S-EPMC6476450 | biostudies-literature
Project description:BACKGROUND:Certain cancer case ascertainment methods used in Quebec and elsewhere are known to underestimate the burden of cancer, particularly for some subgroups. Algorithms using claims data are a low-cost option to improve the quality of cancer surveillance, but have not frequently been implemented at the population-level. Our objectives were to 1) develop a colorectal cancer (CRC) case ascertainment algorithm using population-level hospitalization and physician billing data, 2) validate the algorithm, and 3) describe the characteristics of cases. METHODS:We linked physician billing, hospitalization, and tumor registry data for 2,013,430 Montreal residents age 20+ (2000-2010). We compared the performance of three algorithms based on diagnosis and treatment codes from different data sources. We described identified cases according to age, sex, socioeconomic status, treatment patterns, site distribution, and time trends. All statistical tests were two-sided. RESULTS:Our algorithm based on diagnosis and treatment codes identified 11,476 of the 12,933 incident CRC cases contained in the tumor registry as well as 2317 newly-captured cases. Our cases share similar overall time trends and site distributions to existing data, which increases our confidence in the algorithm. Our algorithm captured proportionally 35% more individuals age 50 and younger among CRC cases: 8.2% vs. 5.3%. The newly captured cases were also more likely to be living in socioeconomically advantaged areas. CONCLUSIONS:Our algorithm provides a more complete picture of population-wide CRC incidence than existing case ascertainment methods. It could be used to estimate long-term incidence trends, aid in timely surveillance, and to inform interventions, in both Quebec and other jurisdictions.
| S-EPMC5941340 | biostudies-literature
Project description:ObjectivesMedical Regulatory Authorities (MRAs) provide licences to physicians and monitor those physicians once in practice to support their continued competence. In response to physician shortages, many Canadian MRAs developed alternative licensure routes to allow physicians who do not meet traditional licensure criteria to obtain licences to practice. Many physicians have gained licensure through alternative routes, but the performance of these physicians in practice has not been previously examined. This study compared the performance of traditionally and alternatively licenced physicians in Ontario using quality indicators of primary care. The purpose of this study was to examine the practice performance of alternatively licenced physicians and provide evaluative evidence for alternative licensure policies.DesignA cross-sectional retrospective examination of Ontario health administrative data was conducted using Poisson regression analyses to compare the performance of traditionally and alternatively licenced physicians.SettingPrimary care in Ontario, Canada.ParticipantsAll family physicians who were licenced in Ontario between 2000 and 2012 and who had complete medical billing data in 2014 were included (n=11 419).Outcome measuresPrimary care quality indicators were calculated for chronic disease management, preventive paediatric care, cancer screening and hospital readmission rates using Ontario health administrative data.ResultsAlternatively licenced physicians performed similarly to traditionally licenced physicians in many primary care performance measures. Minimal differences were seen across groups in indicators of diabetic care, congestive heart failure care, asthma care and cancer screening rates. Larger differences were found in preventive care for children less than 2 years of age, particularly for alternatively licenced physicians who entered Ontario from another Canadian province.ConclusionsOur findings demonstrate that alternatively licenced physicians perform similarly to traditionally licenced physicians across many indicators of primary care. Our study also demonstrates the utility of administrative data for examining physician performance and evaluating medical regulatory policies and programmes.
| S-EPMC6575712 | biostudies-literature
Project description:Reducing health inequalities requires improved understanding of the causes of variation. Local-level variation reflects differences in local population characteristics and health system performance. Identifying low- and high-performing localities allows investigation into these differences. We used Multilevel Regression with Post-stratification (MRP) to synthesise data from multiple sources, using chlamydia testing as our example. We used national probability survey data to identify individual-level characteristics associated with chlamydia testing and combined this with local-level census data to calculate expected levels of testing in each local authority (LA) in England, allowing us to identify LAs where observed chlamydia testing rates were lower or higher than expected, given population characteristics. Taking account of multiple covariates, including age, sex, ethnicity, student and cohabiting status, 5.4% and 3.5% of LAs had testing rates higher than expected for 95% and 99% posterior credible intervals, respectively; 60.9% and 50.8% had rates lower than expected. Residual differences between observed and MRP expected values were smallest for LAs with large proportions of non-white ethnic populations. London boroughs that were markedly different from expected MRP values (?90% posterior exceedance probability) had actively targeted risk groups. This type of synthesis allows more refined inferences to be made at small-area levels than previously feasible.
| S-EPMC6506589 | biostudies-literature
Project description: Not available
| S-EPMC10621983 | biostudies-literature
Project description:IntroductionThe Canadian Bronchiolitis Epinephrine Steroid Trial (CanBEST) and the Bronchiolitis Severity Cohort (BSC) study enrolled infants with bronchiolitis during the first year of life. The CanBEST trial suggested that treatment of infants with a combined therapy of high-dose corticosteroids and nebulised epinephrine reduced the risk of admission to hospital. Our study aims to-(1) quantify the risk of developing asthma by age 5 and 10 years in children treated with high-dose corticosteroid and epinephrine for bronchiolitis during infancy, (2) identify risk factors associated with development of asthma in children with bronchiolitis during infancy, (3) develop asthma prediction models for children diagnosed with bronchiolitis during infancy.Methods and analysisWe propose a longitudinal cohort study in which we will link data from the CanBEST and BSC study with routinely collected data from provincial health administrative databases. Our outcome is asthma incidence measured using a validated health administrative data algorithm. Primary exposure will be treatment with a combined therapy of high-dose corticosteroids and nebulised epinephrine for bronchiolitis. Covariates will include type of viral pathogen, disease severity, medication use, maternal, prenatal, postnatal and demographic factors and variables related to health service utilisation for acute lower respiratory tract infection. The risk associated with development of asthma in children treated with high-dose corticosteroid and epinephrine for bronchiolitis will be assessed using multivariable Cox proportional hazards regression models. Prediction models will be developed using multivariable logistic regression analysis and internally validated using a bootstrap approach.Ethics and disseminationOur study has been approved by the ethics board of all four participating sites of the CanBEST and BSC study. Finding of the study will be disseminated to the academic community and relevant stakeholders through conferences and peer-reviewed publications.Trial registration numberISRCTN56745572; Post-results.
| S-EPMC8098926 | biostudies-literature
Project description:BackgroundAdministrative data are commonly used to study clinical outcomes in renal disease. Race is an important determinant of renal health delivery and outcomes in Canada but is not validated in most administrative data, and the correlation with census-based definitions of race is unknown.ObjectivesValidation of self-reported race (SRR) in a Canadian provincial renal administrative database (Patient Records and Outcome Management Information System [PROMIS]) and comparison with the Canadian census categories of race.DesignProspective patient survey study to validate SRR in PROMIS.SettingBritish Columbia, Canada.PatientsAdult patients registered in PROMIS.MeasurementsSurvey SRR was used as gold standard to validate SRR in PROMIS. Self-reported race in PROMIS was compared with census race categories.MethodsThis is a cross-sectional telephone survey of a random sample of all adults in PROMIS conducted between February 2016 and November 2016. Responders selected a race category from PROMIS and from the Canadian census. Sensitivity (Sn) and specificity (Sp) were calculated with 95% confidence intervals (CIs).ResultsA total of 21 039 patients met inclusion criteria, 1677 were selected for the survey and 637 participated (38% response rate). There were no differences between the PROMIS, sampled, and responder populations. PROMIS SRR had an accuracy of 95.3% (95% CI: 94.2%-97.0%) when validated against the survey SRR with Sn and Sp ≥90% in all race groups except in Aboriginals (Sn 87.5%). The positive and negative predictive values were ≥95%, except in very low and high-prevalence groups, respectively. The Canadian census had an accuracy of 95.7% (95% CI: 94.4%-97.6%) when validated against PROMIS SRR with Sn and Sp ≥90%. The results did not differ in subgroups based on age, sex, birth outside Canada, or renal group (glomerulonephritis, chronic kidney disease, hemodialysis, peritoneal dialysis, transplant recipients, or live donors).LimitationsAnalysis of minority groups and lower prevalence groups is limited by sample size. Results may not be generalizable to other administrative databases.ConclusionsWe have shown high accuracy of PROMIS SRR that validates its use in the secondary analysis of administrative data for research. There is high correlation between PROMIS and census race categories which allows linkage with other data sources that use census-based definitions of race.
| S-EPMC6604118 | biostudies-literature
Project description:Background and objectivesBilling codes are frequently used to identify AKI events in epidemiologic research. The goals of this study were to validate billing code-identified AKI against the current AKI consensus definition and to ascertain whether sensitivity and specificity vary by patient characteristic or over time.Design, setting, participants, & measurementsThe study population included 10,056 Atherosclerosis Risk in Communities study participants hospitalized between 1996 and 2008. Billing code-identified AKI was compared with the 2012 Kidney Disease Improving Global Outcomes (KDIGO) creatinine-based criteria (AKIcr) and an approximation of the 2012 KDIGO creatinine- and urine output-based criteria (AKIcr_uop) in a subset with available outpatient data. Sensitivity and specificity of billing code-identified AKI were evaluated over time and according to patient age, race, sex, diabetes status, and CKD status in 546 charts selected for review, with estimates adjusted for sampling technique.ResultsA total of 34,179 hospitalizations were identified; 1353 had a billing code for AKI. The sensitivity of billing code-identified AKI was 17.2% (95% confidence interval [95% CI], 13.2% to 21.2%) compared with AKIcr (n=1970 hospitalizations) and 11.7% (95% CI, 8.8% to 14.5%) compared with AKIcr_uop (n=1839 hospitalizations). Specificity was >98% in both cases. Sensitivity was significantly higher in the more recent time period (2002-2008) and among participants aged 65 years and older. Billing code-identified AKI captured a more severe spectrum of disease than did AKIcr and AKIcr_uop, with a larger proportion of patients with stage 3 AKI (34.9%, 19.7%, and 11.5%, respectively) and higher in-hospital mortality (41.2%, 18.7%, and 12.8%, respectively).ConclusionsThe use of billing codes to identify AKI has low sensitivity compared with the current KDIGO consensus definition, especially when the urine output criterion is included, and results in the identification of a more severe phenotype. Epidemiologic studies using billing codes may benefit from a high specificity, but the variation in sensitivity may result in bias, particularly when trends over time are the outcome of interest.
| S-EPMC3974361 | biostudies-literature
Project description:IntroductionThe Western Cape Provincial Health Data Centre (PHDC) consolidates person-level clinical data across government services, leveraging sustained investments in patient registration systems, a unique identifier, and maturation of administrative and clinical digital health systems.ObjectivesThe PHDC supports clinical care directly through tools for clinicians which integrate patient data or identify patients in need of interventions, and indirectly through supporting operational and epidemiological analyses.MethodsThe PHDC is housed entirely within government. Data are processed from a range of source systems, usually daily, through distinct harmonisation and curation, beneficiation, and reporting processes. Linkage is predominantly through the unique identifier which doubles as a pervasive folder number, augmented by other identifiers. Further data processing includes triangulation of multiple data sources for enumerating health conditions, with assignment of certainty levels for each enumeration. Outputs include patient-specific email alerts, a web-based consolidated patient clinical viewing platform, filterable line-listings of patients with specific conditions and associated characteristics and outcomes, management reports and dashboards, and data releases in response to operational and research data requests. Strict architectural, administrative and governance processes ensure privacy protection.ResultsIn the past decade 8 million unique people are recorded as having sought healthcare in the provincial public sector health services, with current utilisation at 15 million attendances or admissions a year. Cross-sectional enumeration of health conditions includes over 430 000 people with HIV, 500 000 with hypertension, 235 000 with diabetes. Annually 110 000 pregnancies and 54 000 patients with tuberculosis are enumerated. Over 50 data requests are processed each year for internal and external requesters in accordance with data request and release governance processes.ConclusionsThe single consolidated environment for person-level health data in the Western Cape has created new opportunities for supporting patient care, while improving the governance around access to and release of sensitive patient data.
| S-EPMC7482518 | biostudies-literature
Project description:BackgroundVarious data sources may be used to document the presence of chronic medical conditions. This study examined the agreement between self-reported and health administrative data.MethodsA randomly selected cohort of participants aged 25-75 years recruited by telephone from the general population of Quebec reported on the presence of 1 or more chronic conditions from a candidate list of 12 conditions: diabetes, hypertension, thyroid disorder, any cardiac disease, cancer diagnosis in the previous 5 years (including melanoma but excluding other skin cancers), asthma, osteoarthritis, rheumatoid arthritis or lupus, osteoporosis, chronic obstructive pulmonary disease, intestinal disease and hypercholesterolemia. We also used health administrative data from Quebec's universal health insurance provider to identify participants' chronic conditions. Unique identifiers allowed linkage of both data sources to the individual participant. The frequencies of the 12 conditions and the prevalence of multimorbidity (≥ 2, ≥ 3 and ≥ 4 conditions) were analyzed for each data source.ResultsWe analyzed data for 1177 participants (mean age 53 [standard deviation 12.4] yr; 684 women [58.1%]). We found low (but varied) agreement between the 2 data sources, with the poorest agreement for hypercholesterolemia (κ = 0.04 [95% confidence interval (CI) 0.01 to 0.07]) and the best for diabetes (κ = 0.82 [95% CI 0.76 to 0.88]). Prevalence estimates of multimorbidity obtained with health administrative data were lower than those obtained with self-reported data regardless of the operational definition used. Most participants with multimorbidity were identified by self-report.InterpretationWe argue for the use of self-reported chronic conditions in the study of multimorbidity, as health administrative data based on the billing system in Quebec seem to underestimate the prevalence of many chronic conditions, which results in biased estimates of multimorbidity.
| S-EPMC5621946 | biostudies-literature