A prediction model to estimate completeness of electronic physician claims databases

Transitions between versions of the International Classification of Diseases and chronic disease prevalence estimates from administrative health data: a population-based study

Background

Diagnosis codes in administrative health data are routinely used to monitor trends in disease prevalence and incidence. The International Classification of Diseases (ICD), which is used to record these diagnoses, have been updated multiple times to reflect advances in health and medical research. Our objective was to examine the impact of transitions between ICD versions on the prevalence of chronic health conditions estimated from administrative health data.

Methods

Study data (i.e., physician billing claims, hospital records) were from the province of Manitoba, Canada, which has a universal healthcare system. ICDA-8 (with adaptations), ICD-9-CM (clinical modification), and ICD-10-CA (Canadian adaptation; hospital records only) codes are captured in the data. Annual study cohorts included all individuals 18 + years of age for 45 years from 1974 to 2018. Negative binomial regression was used to estimate annual age- and sex-adjusted prevalence and model parameters (i.e., slopes and intercepts) for 16 chronic health conditions. Statistical control charts were used to assess the impact of changes in ICD version on model parameter estimates. Hotelling’s T² statistic was used to combine the parameter estimates and provide an out-of-control signal when its value was above a pre-specified control limit.

Results

The annual cohort sizes ranged from 360,341 to 824,816. Hypertension and skin cancer were among the most and least diagnosed health conditions, respectively; their prevalence per 1,000 population increased from 40.5 to 223.6 and from 0.3 to 2.1, respectively, within the study period. The average annual rate of change in prevalence ranged from -1.6% (95% confidence interval [CI]: -1.8, -1.4) for acute myocardial infarction to 14.6% (95% CI: 13.9, 15.2) for hypertension. The control chart indicated out-of-control observations when transitioning from ICDA-8 to ICD-9-CM for 75% of the investigated chronic health conditions but no out-of-control observations when transitioning from ICD-9-CM to ICD-10-CA.

Conclusions

The prevalence of most of the investigated chronic health conditions changed significantly in the transition from ICDA-8 to ICD-9-CM. These results point to the importance of considering changes in ICD coding as a factor that may influence the interpretation of trend estimates for chronic health conditions derived from administrative health data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13118-8.

Association of chronic pain with comorbidities and health care utilization: a retrospective cohort study using health administrative data

ABSTRACT

Health administrative data provide a potentially robust information source regarding the substantial burden chronic pain exerts on individuals and the health care system. This study aimed to use health administrative data to estimate comorbidity prevalence and annual health care utilization associated with chronic pain in Newfoundland and Labrador, Canada. Applying the validated Chronic Pain Algorithm to provincial Fee-for-Service Physician Claims File data (1999-2009) established the Chronic Pain (n = 184,580) and No Chronic Pain (n = 320,113) comparator groups. Applying the Canadian Chronic Disease Surveillance System coding algorithms to Claims File and Provincial Discharge Abstract Data (1999-2009) determined the prevalence of 16 comorbidities. The 2009/2010 risk and person-year rate of physician and diagnostic imaging visits and hospital admissions were calculated and adjusted using the robust Poisson model with log link function (risks) and negative binomial model (rates). Results indicated a significantly higher prevalence of all comorbidities and up to 4 times the odds of multimorbidity in the Chronic Pain Group (P-value < 0.001). Chronic Pain Group members accounted for 58.8% of all physician visits, 57.6% of all diagnostic imaging visits, and 54.2% of all hospital admissions in 2009/2010, but only 12% to 16% of these were for pain-related conditions as per recorded diagnostic codes. The Chronic Pain Group had significantly higher rates of physician visits and high-cost hospital admission/diagnostic imaging visits (P-value < 0.001) when adjusted for demographics and comorbidities. Observations made using this methodology supported that people identified as having chronic pain have higher prevalence of comorbidities and use significantly more publicly funded health services.

Diabetes diagnosis from administrative claims and estimation of the true prevalence of diabetes among 4.2 million individuals of the Veneto region (North East Italy)

BACKGROUND AND AIMS

Diabetes can often remain undiagnosed or unregistered in administrative databases long after its onset, even when laboratory test results meet diagnostic criteria. In the present work, we analyse healthcare data of the Veneto Region, North East Italy, with the aims of: (i) developing an algorithm for the identification of diabetes from administrative claims (4,236,007 citizens), (ii) assessing its reliability by comparing its performance with the gold standard clinical diagnosis from a clinical database (7525 patients), (iii) combining the algorithm and the laboratory data of the regional Health Information Exchange (rHIE) system (543,520 subjects) to identify undiagnosed diabetes, and (iv) providing a credible estimate of the true prevalence of diabetes in Veneto.

METHODS AND RESULTS

The proposed algorithm for the identification of diabetes was fed by administrative data related to drug dispensations, outpatient visits, and hospitalisations. Evaluated against a clinical database, the algorithm achieved 95.7% sensitivity, 87.9% specificity, and 97.6% precision. To identify possible cases of undiagnosed diabetes, we applied standard diagnostic criteria to the laboratory test results of the subjects who, according to the algorithm, had no diabetes-related claims. Using a simplified probabilistic model, we corrected our claims-based estimate of known diabetes (6.17% prevalence; 261,303 cases) to account for undiagnosed cases, yielding an estimated total prevalence of 7.50%.

CONCLUSION

We herein validated an algorithm for the diagnosis of diabetes using administrative claims against the clinical diagnosis. Together with rHIE laboratory data, this allowed to identify possibly undiagnosed diabetes and estimate the true prevalence of diabetes in Veneto.

Trends in chronic disease incidence rates from the Canadian Chronic Disease Surveillance System

INTRODUCTION

The Public Health Agency of Canada's Canadian Chronic Disease Surveillance System (CCDSS) produces population-based estimates of chronic disease prevalence and incidence using administrative health data. Our aim was to assess trends in incidence rates over time, trends are essential to understand changes in population risk and to inform policy development.

METHODS

Incident cases of diagnosed asthma, chronic obstructive pulmonary disease (COPD), diabetes, hypertension, ischemic heart disease (IHD), and stroke were obtained from the CCDSS online infobase for 1999 to 2012. Trends in national and regional incidence estimates were tested using a negative binomial regression model with year as a linear predictor. Subsequently, models with year as a restricted cubic spline were used to test for departures from linearity using the likelihood ratio test. Age and sex were covariates in all models.

RESULTS

Based on the models with year as a linear predictor, national incidence rates were estimated to have decreased over time for all diseases, except diabetes; regional incidence rates for most diseases and regions were also estimated to have decreased. However, likelihood ratio tests revealed statistically significant departures from a linear year effect for many diseases and regions, particularly for hypertension.

CONCLUSION

Chronic disease incidence estimates based on CCDSS data are decreasing over time, but not at a constant rate. Further investigations are needed to assess if this decrease is associated with changes in health status, data quality, or physician practices. As well, population characteristics that may influence changing incidence trends also require exploration.

Public housing and healthcare use: an investigation using linked administrative data

OBJECTIVE

This study investigated whether a move to public housing affects people's use of healthcare services.

METHOD

Using administrative data from Manitoba, the number of hospitalizations, general practitioner (GP), specialist and emergency department (ED) visits, and prescription drugs dispensed in the years before and after the housing move-in date (2012/2013) were measured for a public housing and matched cohort. Generalized linear models with generalized estimating equations tested for differences between the cohorts in utilization trends. The data were modeled using Poisson (rate ratio, RR), negative binomial (incident rate ratio, IRR), and binomial (odds ratio, OR) distributions.

RESULTS

GP visits (IRR = 1.04, 95% CI 1.01-1.06) and prescriptions (IRR = 1.04, 95% CI 1.02-1.05) increased, while ED visits (RR = 0.90, 95% CI 0.82-1.00) and hospitalizations (OR = 0.95, 95% CI 0.93-0.96) decreased over time. The public housing cohort had a significantly higher rate of GP visits (IRR = 1.08, 95% CI 1.04-1.13), ED visits (RR = 1.18, 95% CI 1.01-1.37), and prescriptions (IRR = 1.09, 95% CI 1.05-1.13), and was more likely to be hospitalized (OR = 1.39, 95% CI 1.21-1.61) compared to the matched cohort. The rate of inpatient days significantly decreased for the public housing cohort, but did not change for the matched cohort.

CONCLUSION

Healthcare use changed similarly over time (except inpatient days) for the two cohorts. Public housing provides a basic need to a population who has a high burden of disease and who may not be able to obtain and maintain housing in the private market.

The Canadian Chronic Disease Surveillance System: A model for collaborative surveillance

Chronic diseases have a major impact on populations and healthcare systems worldwide. Administrative health data are an ideal resource for chronic disease surveillance because they are population-based and routinely collected. For multi-jurisdictional surveillance, a distributed model is advantageous because it does not require individual-level data to be shared across jurisdictional boundaries. Our objective is to describe the process, structure, benefits, and challenges of a distributed model for chronic disease surveillance across all Canadian provinces and territories (P/Ts) using linked administrative data. The Public Health Agency of Canada (PHAC) established the Canadian Chronic Disease Surveillance System (CCDSS) in 2009 to facilitate standardized, national estimates of chronic disease prevalence, incidence, and outcomes. The CCDSS primarily relies on linked health insurance registration files, physician billing claims, and hospital discharge abstracts. Standardized case definitions and common analytic protocols are applied to the data for each P/T; aggregate data are shared with PHAC and summarized for reports and open access data initiatives. Advantages of this distributed model include: it uses the rich data resources available in all P/Ts; it supports chronic disease surveillance capacity building in all P/Ts; and changes in surveillance methodology can be easily developed by PHAC and implemented by the P/Ts. However, there are challenges: heterogeneity in administrative databases across jurisdictions and changes in data quality over time threaten the production of standardized disease estimates; a limited set of databases are common to all P/Ts, which hinders potential CCDSS expansion; and there is a need to balance comprehensive reporting with P/T disclosure requirements to protect privacy. The CCDSS distributed model for chronic disease surveillance has been successfully implemented and sustained by PHAC and its P/T partners. Many lessons have been learned about national surveillance involving jurisdictions that are heterogeneous with respect to healthcare databases, expertise and analytical capacity, population characteristics, and priorities.

Changes in healthcare use among individuals who move into public housing: a population-based investigation

Background

Residence in public housing, a subsidized and managed government program, may affect health and healthcare utilization. We compared healthcare use in the year before individuals moved into public housing with usage during their first year of tenancy. We also described trends in use.

Methods

We used linked population-based administrative data housed in the Population Research Data Repository at the Manitoba Centre for Health Policy. The cohort consisted of individuals who moved into public housing in 2009 and 2010. We counted the number of hospitalizations, general practitioner (GP) visits, specialist visits, emergency department visits, and prescriptions drugs dispensed in the twelve 30-day intervals (i.e., months) immediately preceding and following the public housing move-in date. Generalized linear models with generalized estimating equations tested for a period (pre/post-move-in) by month interaction. Odds ratios (ORs), incident rate ratios (IRRs), and means are reported along with 95% confidence intervals (95% CIs).

Results

The cohort included 1942 individuals; the majority were female (73.4%) who lived in low income areas and received government assistance (68.1%). On average, the cohort had more than four health conditions. Over the 24 30-day intervals, the percentage of the cohort that visited a GP, specialist, and an emergency department ranged between 37.0% and 43.0%, 10.0% and 14.0%, and 6.0% and 10.0%, respectively, while the percentage of the cohort hospitalized ranged from 1.0% to 5.0%. Generally, these percentages were highest in the few months before the move-in date and lowest in the few months after the move-in date. The period by month interaction was statistically significant for hospitalizations, GP visits, and prescription drug use. The average change in the odds, rate, or mean was smaller in the post-move-in period than in the pre-move-in period.

Conclusions

Use of some healthcare services declined after people moved into public housing; however, the decrease was only observed in the first few months and utilization rebounded. Knowledge of healthcare trends before individuals move in are informative for ensuring the appropriate supports are available to new public housing residents. Further study is needed to determine if decreased healthcare utilization following a move is attributable to decreased access.

Electronic supplementary material

The online version of this article (10.1186/s12913-018-3109-7) contains supplementary material, which is available to authorized users.

Emergent Challenges in Determining Costs for Economic Evaluations

This paper describes methods of determining costs for economic evaluations of healthcare and considers how cost determination is being affected by recent developments in healthcare. The literature was reviewed to identify the strengths and weaknesses of the four principal methods of cost determination: micro-costing, activity-based costing, charge-based costing, and gross costing. A scoping review was conducted to identify key trends in healthcare delivery and to identify costing issues associated with these changes. Existing guidelines provide information on how to implement various costing methods. Bottom-up costing is needed when accuracy is paramount, but top-down approaches are often the only feasible approach. We describe six healthcare trends that have important implications for costing methodology: (1) reform in payment mechanisms; (2) care delivery in less restrictive settings; (3) the growth of telehealth interventions; (4) the proliferation of new technology; (5) patient privacy concerns; and (6) growing efforts to implement guidelines. Some costs are difficult to measure and have been overlooked. These include physician services for inpatients, facility costs for outpatient services, the cost of developing treatment innovations, patient and caregiver costs, and the indirect costs of organizational interventions. Standardized methods are needed to determine social welfare and productivity costs. In the future, cost determination will be facilitated by technological advances but hindered by the shift to capitated payment, to the provision of care in less restrictive settings, and by heightened concern for medical record privacy.

Estimating the completeness of physician billing claims for diabetes case ascertainment using population-based prescription drug data

INTRODUCTION

Changes in physician reimbursement policies may hinder the collection of billing claims in administrative data; this can result in biased estimates of disease prevalence and incidence. However, the magnitude of data loss is largely unknown. The purpose of this study was to estimate completeness of capture of disease cases for Manitoba physicians paid by fee-for-service (FFS) and non-fee-for-service (NFFS) methods.

METHODS

Manitoba's administrative data were used to identify a cohort (≥ 20 years) with a new diabetes medication between 1 April, 2007, and 31 March, 2009. Cohort members were classified by payment method of the prescribing physician (i.e. FFS vs. NFFS). The cohort was then classified as missing or not missing a diabetes diagnosis using physician claims and hospital records. Then, χ2 statistics were used to test for differences in the characteristics of the two groups.

RESULTS

The cohort consisted of 12 394 individuals; 86.4% had a prescription for a diabetes medication from an FFS physician. A total of 1172 physicians (81.8% FFS) prescribed these medications for the cohort. Cohort members with a prescription from an FFS physician were older and more likely to reside in the urban Winnipeg health region than those with a prescription from a NFFS physician. A greater percentage of NFFS physicians' cases were missing a diabetes diagnosis (18.7%vs. 14.9% for FFS physicians).

CONCLUSION

The results suggest minimal loss of physician claims associated with remuneration policies in Manitoba. This method of assessing data completeness could be applied to other chronic diseases and jurisdictions to estimate completeness.