Accuracy of administrative claims data for polypectomy

Background:

The frequency of polypectomy is an important indicator of quality assurance for population-based colorectal cancer screening programs. Although administrative databases of physician claims provide population-level data on the performance of polypectomy, the accuracy of the procedure codes has not been examined. We determined the level of agreement between physician claims for polypectomy and documentation of the procedure in endoscopy reports.

Methods:

We conducted a retrospective cohort study involving patients aged 50–80 years who underwent colonoscopy at seven study sites in Montréal, Que., between January and March 2007. We obtained data on physician claims for polypectomy from the Régie de l’Assurance Maladie du Québec (RAMQ) database. We evaluated the accuracy of the RAMQ data against information in the endoscopy reports.

Results:

We collected data on 689 patients who underwent colonoscopy during the study period. The sensitivity of physician claims for polypectomy in the administrative database was 84.7% (95% confidence interval [CI] 78.6%–89.4%), the specificity was 99.0% (95% CI 97.5%–99.6%), concordance was 95.1% (95% CI 93.1%–96.5%), and the kappa value was 0.87 (95% CI 0.83–0.91).

Interpretation:

Despite providing a reasonably accurate estimate of the frequency of polypectomy, physician claims underestimated the number of procedures performed by more than 15%. Such differences could affect conclusions regarding quality assurance if used to evaluate population-based screening programs for colorectal cancer. Even when a high level of accuracy is anticipated, validating physician claims data from administrative databases is recommended.Population-based screening programs for colorectal cancer rely heavily on the performance of colonoscopy as either the initial examination or as the follow-up to a positive screening by virtual colonography, double-contrast barium enema or fecal occult blood testing. Colonoscopy is the only screening examination accepted at 10-year intervals among people at average risk without significant polyps found. It allows direct visualization of the entire colon and rectum and permits removal of adenomatous polyps, precursors of colorectal cancer. The frequency of polypectomy is an important indicator of quality assurance for colorectal cancer screening programs.In the province of Quebec, physicians are reimbursed for medical services by the Régie de l’Assurance Maladie du Québec (RAMQ), the government agency responsible for administering the provincial health insurance plan. Physicians receive additional remuneration for performing a polypectomy if they include the procedure code in their claim.Data from physician claims databases are commonly used in health services research,^1–7 even though the data are collected for administrative purposes and physician reimbursement. Procedure codes in physician claims databases are presumed to have a very high level of agreement with data in medical charts.⁸ A physician making a claim will need to submit the diagnostic code and, when applicable, the procedure code. Studies that rely on physician claims databases can be divided into those that examine the diagnostic codes entered and those that examine the procedure codes entered. Few studies have attempted to validate procedure codes, and often not as the primary study objective.^9–14We conducted a study to determine the level of agreement between physician claims for polypectomy and documentation of the procedure in endoscopy reports.     

Systematic Review and Meta-Analysis of Validation Studies on a Diabetes Case Definition from Health Administrative Records

Objectives

Health administrative data are frequently used for diabetes surveillance. We aimed to determine the sensitivity and specificity of a commonly-used diabetes case definition (two physician claims or one hospital discharge abstract record within a two-year period) and their potential effect on prevalence estimation.

Methods

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched Medline (from 1950) and Embase (from 1980) databases for validation studies through August 2012 (keywords: “diabetes mellitus”; “administrative databases”; “validation studies”). Reviewers abstracted data with standardized forms and assessed quality using Quality Assessment of Diagnostic Accuracy Studies (QUADAS) criteria. A generalized linear model approach to random-effects bivariate regression meta-analysis was used to pool sensitivity and specificity estimates. We applied correction factors derived from pooled sensitivity and specificity estimates to prevalence estimates from national surveillance reports and projected prevalence estimates over 10 years (to 2018).

Results

The search strategy identified 1423 abstracts among which 11 studies were deemed relevant and reviewed; 6 of these reported sensitivity and specificity allowing pooling in a meta-analysis. Compared to surveys or medical records, sensitivity was 82.3% (95%CI 75.8, 87.4) and specificity was 97.9% (95%CI 96.5, 98.8). The diabetes case definition underestimated prevalence when it was ≤10.6% and overestimated prevalence otherwise.

Conclusion

The diabetes case definition examined misses up to one fifth of diabetes cases and wrongly identifies diabetes in approximately 2% of the population. This may be sufficiently sensitive and specific for surveillance purposes, in particular monitoring prevalence trends. Applying correction factors to adjust prevalence estimates from this definition may be helpful to increase accuracy of estimates.     

Use of information on disease diagnoses from databases for animal health economic,welfare and food safety purposes: strengths and limitations of recordings

Many animal health, welfare and food safety databases include data on clinical and test-based disease diagnoses. However, the circumstances and constraints for establishing the diagnoses vary considerably among databases. Therefore results based on different databases are difficult to compare and compilation of data in order to perform meta-analysis is almost impossible. Nevertheless, diagnostic information collected either routinely or in research projects is valuable in cross comparisons between databases, but there is a need for improved transparency and documentation of the data and the performance characteristics of tests used to establish diagnoses. The objective of this paper is to outline the circumstances and constraints for recording of disease diagnoses in different types of databases, and to discuss these in the context of disease diagnoses when using them for additional purposes, including research. Finally some limitations and recommendations for use of data and for recording of diagnostic information in the future are given. It is concluded that many research questions have such a specific objective that investigators need to collect their own data. However, there are also examples, where a minimal amount of extra information or continued validation could make sufficient improvement of secondary data to be used for other purposes. Regardless, researchers should always carefully evaluate the opportunities and constraints when they decide to use secondary data. If the data in the existing databases are not sufficiently valid, researchers may have to collect their own data, but improved recording of diagnostic data may improve the usefulness of secondary diagnostic data in the future.     

Estimating prevalence and test accuracy in disease ecology: How Bayesian latent class analysis can boost or bias imperfect test results

1. Obtaining accurate estimates of disease prevalence is crucial for the monitoring and management of wildlife populations but can be difficult if different diagnostic tests yield conflicting results and if the accuracy of each diagnostic test is unknown. Bayesian latent class analysis (BLCA) modeling offers a potential solution, providing estimates of prevalence levels and diagnostic test accuracy under the realistic assumption that no diagnostic test is perfect.
2. In typical applications of this approach, the specificity of one test is fixed at or close to 100%, allowing the model to simultaneously estimate the sensitivity and specificity of all other tests, in addition to infection prevalence. In wildlife systems, a test with near‐perfect specificity is not always available, so we simulated data to investigate how decreasing this fixed specificity value affects the accuracy of model estimates.
3. We used simulations to explore how the trade‐off between diagnostic test specificity and sensitivity impacts prevalence estimates and found that directional biases depend on pathogen prevalence. Both the precision and accuracy of results depend on the sample size, the diagnostic tests used, and the true infection prevalence, so these factors should be considered when applying BLCA to estimate disease prevalence and diagnostic test accuracy in wildlife systems. A wildlife disease case study, focusing on leptospirosis in California sea lions, demonstrated the potential for Bayesian latent class methods to provide reliable estimates under real‐world conditions.
4. We delineate conditions under which BLCA improves upon the results from a single diagnostic across a range of prevalence levels and sample sizes, demonstrating when this method is preferable for disease ecologists working in a wide variety of pathogen systems.

     

Can Italian Healthcare Administrative Databases Be Used to Compare Regions with Respect to Compliance with Standards of Care for Chronic Diseases?

Background

Italy has a population of 60 million and a universal coverage single-payer healthcare system, which mandates collection of healthcare administrative data in a uniform fashion throughout the country. On the other hand, organization of the health system takes place at the regional level, and local initiatives generate natural experiments. This is happening in particular in primary care, due to the need to face the growing burden of chronic diseases. Health services research can compare and evaluate local initiatives on the basis of the common healthcare administrative data.However reliability of such data in this context needs to be assessed, especially when comparing different regions of the country. In this paper we investigated the validity of healthcare administrative databases to compute indicators of compliance with standards of care for diabetes, ischaemic heart disease (IHD) and heart failure (HF).

Methods

We compared indicators estimated from healthcare administrative data collected by Local Health Authorities in five Italian regions with corresponding estimates from clinical data collected by General Practitioners (GPs). Four indicators of diagnostic follow-up (two for diabetes, one for IHD and one for HF) and four indicators of appropriate therapy (two each for IHD and HF) were considered.

Results

Agreement between the two data sources was very good, except for indicators of laboratory diagnostic follow-up in one region and for the indicator of bioimaging diagnostic follow-up in all regions, where measurement with administrative data underestimated quality.

Conclusion

According to evidence presented in this study, estimating compliance with standards of care for diabetes, ischaemic heart disease and heart failure from healthcare databases is likely to produce reliable results, even though completeness of data on diagnostic procedures should be assessed first. Performing studies comparing regions using such indicators as outcomes is a promising development with potential to improve quality governance in the Italian healthcare system.     

Confidentiality,ownership and transfer of medical records (update)

The Canadian Medical Association (CMA) regards medical records as confidential documents, owned by the physician/institution/clinic that compiled them or had them compiled. Patients have a right to medical information contained in their records but not to the documents themselves. The first consideration of the physician is the well-being of the patient, and discretion must be used when conveying information contained in a medical record to a patient. This medical information often requires interpretation by a physician or other health care professional. Other disclosures of information contained in medical records to third parties (eg. physician-to-physician transfer for administrative purposes, lawyer, insurance adjuster) require written patient consent or a court order. CMA is opposed to legislation at any level which threatens the confidentiality of medical records.     

Validity of Diagnostic Codes and Prevalence of Physician-Diagnosed Psoriasis and Psoriatic Arthritis in Southern Sweden – A Population-Based Register Study

Objective

To validate diagnostic codes for psoriasis and psoriatic arthritis (PsA) and estimate physician-diagnosed prevalence of psoriasis and PsA in the Skåne region, Sweden.

Methods

In the Skåne Healthcare Register (SHR), all healthcare consultations are continuously collected for all inhabitants in the Skåne region (population 1.2 million). During 2005–2010 we identified individuals with ≥1 physician-consultations consistent with psoriasis (ICD-10). Within this group we also identified those diagnosed with PsA. We performed a validation by reviewing medical records in 100 randomly selected cases for psoriasis and psoriasis with PsA, respectively. Further, we estimated the pre- and post-validation point prevalence by December 31, 2010.

Results

We identified 16 171 individuals (psoriasis alone: n = 13 185, psoriasis with PsA n = 2 986). The proportion of ICD-10 codes that could be confirmed by review of medical records was 81% for psoriasis and 63% for psoriasis with PsA with highest percentage of confirmed codes for cases diagnosed ≥2 occasions in specialized care. For 19% and 29% of the cases respectively it was not possible to determine diagnosis due to insufficient information. Thus, the positive predicted value (PPV) of one ICD-10 code for psoriasis and psoriasis with PsA ranged between 81–100% and 63–92%, respectively. Assuming the most conservative PPV, the post-validation prevalence was 1.23% (95% CI: 1.21–1.25) for psoriasis (with or without PsA), 1.02% (95% CI: 1.00–1.03) for psoriasis alone and 0.21% (95% CI: 0.20–0.22) for psoriasis with PsA. The post-validation prevalence of PsA in the psoriasis cohort was 17.3% (95% CI: 16.65–17.96).

Conclusions

The proportion of diagnostic codes in SHR that could be verified varied with frequency of diagnostic codes and level of care highlighting the importance of sensitivity analyses using different case ascertainment criteria. The prevalence of physician-diagnosed psoriasis and PsA confirm other population-based studies, also after adjustment due to misclassification of disease.     

Estimating infection prevalence: Best practices and their theoretical underpinnings

Accurately estimating infection prevalence is fundamental to the study of population health, disease dynamics, and infection risk factors. Prevalence is estimated as the proportion of infected individuals (“individual‐based estimation”), but is also estimated as the proportion of samples in which evidence of infection is detected (“anonymous estimation”). The latter method is often used when researchers lack information on individual host identity, which can occur during noninvasive sampling of wild populations or when the individual that produced a fecal sample is unknown. The goal of this study was to investigate biases in individual‐based versus anonymous prevalence estimation theoretically and to test whether mathematically derived predictions are evident in a comparative dataset of gastrointestinal helminth infections in nonhuman primates. Using a mathematical model, we predict that anonymous estimates of prevalence will be lower than individual‐based estimates when (a) samples from infected individuals do not always contain evidence of infection and/or (b) when false negatives occur. The mathematical model further predicts that no difference in bias should exist between anonymous estimation and individual‐based estimation when one sample is collected from each individual. Using data on helminth parasites of primates, we find that anonymous estimates of prevalence are significantly and substantially (12.17%) lower than individual‐based estimates of prevalence. We also observed that individual‐based estimates of prevalence from studies employing single sampling are on average 6.4% higher than anonymous estimates, suggesting a bias toward sampling infected individuals. We recommend that researchers use individual‐based study designs with repeated sampling of individuals to obtain the most accurate estimate of infection prevalence. Moreover, to ensure accurate interpretation of their results and to allow for prevalence estimates to be compared among studies, it is essential that authors explicitly describe their sampling designs and prevalence calculations in publications.     

Refining Hypertension Surveillance to Account for Potentially Misclassified Cases

Administrative health data have been used in hypertension surveillance using the 1H2P method: the International Classification of Disease (ICD) hypertension diagnosis codes were recorded in at least 1 hospitalization or 2 physician claims within 2 year-period. Accumulation of false positive cases over time using the 1H2P method could result in the overestimation of hypertension prevalence. In this study, we developed and validated a new reclassification method to define hypertension cases using regularized logistic regression with the age, sex, hypertension and comorbidities in physician claims, and diagnosis of hypertension in hospital discharge data as independent variables. A Bayesian method was then used to adjust the prevalence estimated from the reclassification method. We evaluated the hypertension prevalence in data from Alberta, Canada using the currently accepted 1H2P method and these newly developed methods. The reclassification method with Bayesian adjustment produced similar prevalence estimates as the 1H2P method. This supports the continued use of the 1H2P method as a simple and practical way to conduct hypertension surveillance using administrative health data.     

SaudiVeg ecoinformatics: Aims,current status and perspectives

During the last decade many electronic databases of vegetation plots were established in many countries around the world. These databases contain valuable phytosociological information assisting both governmental and NGO (Non-governmental organizations) agencies to formulate strategies and on-ground plans to manage and protect nature resources. This paper provides an account on aims, current status and perspectives of building of a vegetation database for the Central Region (Najd) of Saudi Arabia – the founding element of the Saudi Vegetation Database (SVD). The data stored by the database are sample plots (vegetation relevés) collected according to the field techniques of the Braun-Blanquet approach (lists of taxa accompanied by semi-quantitative cover assessment), and are accompanied by general vegetation characteristics such as vegetation layering and cover, information on life-form of the recorded species, geographical coordinates, altitude, soil typology, topography and many more. More than 2900 vegetation-plot records (relevés) have so far been collected in the Najd region; of these more than 2000 have already been stored using the Turboveg database platform. These field records cover many habitats such as depressions, wadis (dry river beds), agricultural lands, sand dunes, sabkhas, and ruderal habitats. The ecological information collected in the database is currently the largest set of vegetation data collated into a database in the Middle East. These data are of great importance for biodiversity studies in Saudi Arabia, since the region is recording a loss of biodiversity at a fast rate due to environmental problems such as global warming and land-use changes. We envisage that this database would catalyze further data collection on vegetation of the entire Arabian Peninsula, and shall serve as one of the most important datasets for classification and mapping of the vegetation of the Kingdom of Saudi Arabia.     

Cross sectional survey of multicentre clinical databases in the United Kingdom

Objectives To describe the multicentre clinical databases that exist in the United Kingdom, to report on their quality, to explore which organisational and managerial features are associated with high quality, and to make recommendations for improvements.Design Cross sectional survey, with interviews with database custodians and search of electronic bibliographic database (PubMed).Studies reviewed 105 clinical databases across the United Kingdom.Results Clinical databases existed in all areas of health care, but their distribution was uneven—cancer and surgery were better covered than mental health and obstetrics. They varied greatly in age, size, growth rate, and geographical areas covered. Their scope (and thus their potential uses) and the quality of the data collected also varied. The latter was not associated with any organisational characteristics. Despite impressive achievements, many faced substantial financial uncertainty. Considerable scope existed for improvements: greater use of nationally approved codes; more support from relevant professional organisations; greater involvement by nurses, allied health professionals, managers, and laypeople in database management teams; and more attention to data security and ensuring patient confidentiality. With some notable exceptions, the audit and research potential of most databases had not been realised: half the databases had each produced only four or fewer peer reviewed research articles.Conclusions At least one clinical database support unit is needed in the United Kingdom to provide assistance in organisation and management, information technology, epidemiology, and statistics. Without such an initiative, the variable picture of databases reported here is likely to persist and their potential not be realised.     

Modelling Conditions and Health Care Processes in Electronic Health Records: An Application to Severe Mental Illness with the Clinical Practice Research Datalink

Background

The use of Electronic Health Records databases for medical research has become mainstream. In the UK, increasing use of Primary Care Databases is largely driven by almost complete computerisation and uniform standards within the National Health Service. Electronic Health Records research often begins with the development of a list of clinical codes with which to identify cases with a specific condition. We present a methodology and accompanying Stata and R commands (pcdsearch/Rpcdsearch) to help researchers in this task. We present severe mental illness as an example.

Methods

We used the Clinical Practice Research Datalink, a UK Primary Care Database in which clinical information is largely organised using Read codes, a hierarchical clinical coding system. Pcdsearch is used to identify potentially relevant clinical codes and/or product codes from word-stubs and code-stubs suggested by clinicians. The returned code-lists are reviewed and codes relevant to the condition of interest are selected. The final code-list is then used to identify patients.

Results

We identified 270 Read codes linked to SMI and used them to identify cases in the database. We observed that our approach identified cases that would have been missed with a simpler approach using SMI registers defined within the UK Quality and Outcomes Framework.

Conclusion

We described a framework for researchers of Electronic Health Records databases, for identifying patients with a particular condition or matching certain clinical criteria. The method is invariant to coding system or database and can be used with SNOMED CT, ICD or other medical classification code-lists.     

RDIS: The Rabies Disease Information System

Rabies is a deadly viral disease causing acute inflammation or encephalitis of the brain in human beings and other mammals. Therefore, it is of interest to collect information related to the disease from several sources including known literature databases for further analysis and interpretation. Hence, we describe the development of a database called the Rabies Disease Information System (RDIS) for this purpose. The online database describes the etiology, epidemiology, pathogenesis and pathology of the disease using diagrammatic representations. It provides information on several carriers of the rabies viruses like dog, bat, fox and civet, and their distributions around the world. Information related to the urban and sylvatic cycles of transmission of the virus is also made available. The database also contains information related to available diagnostic methods and vaccines for human and other animals. This information is of use to medical, veterinary and paramedical practitioners, students, researchers, pet owners, animal lovers, livestock handlers, travelers and many others.

Availability

The database is available for free http://rabies.mscwbif.org/home.html     

Protein Information and Knowledge Extractor: Discovering biological information from proteomics data

One of the main goals in proteomics is to solve biological and molecular questions regarding a set of identified proteins. In order to achieve this goal, one has to extract and collect the existing biological data from public repositories for every protein and afterward, analyze and organize the collected data. Due to the complexity of this task and the huge amount of data available, it is not possible to gather this information by hand, making it necessary to find automatic methods of data collection. Within a proteomic context, we have developed Protein Information and Knowledge Extractor (PIKE) which solves this problem by automatically accessing several public information systems and databases across the Internet. PIKE bioinformatics tool starts with a set of identified proteins, listed as the most common protein databases accession codes, and retrieves all relevant and updated information from the most relevant databases. Once the search is complete, PIKE summarizes the information for every single protein using several file formats that share and exchange the information with other software tools. It is our opinion that PIKE represents a great step forward for information procurement and drastically reduces manual database validation for large proteomic studies. It is available at http://proteo.cnb.csic.es/pike .     

A regression-based method for estimating mean treatment cost in the presence of right-censoring

In many clinical trials and evaluations using medical care administrative databases it is of interest to estimate not only the survival time of a given treatment modality but also the total associated cost. The most widely used estimator for data subject to censoring is the Kaplan-Meier (KM) or product-limit (PL) estimator. The optimality properties of this estimator applied to time-to-event data (consistency, etc.) under the assumptions of random censorship have been established. However, whenever the relationship between cost and survival time includes an error term to account for random differences among patients' costs, the dependency between cumulative treatment cost at the time of censoring and at the survival time results in KM giving biased estimates. A similar phenomenon has previously been noted in the context of estimating quality-adjusted survival time. We propose an estimator for mean cost which exploits the underlying relationship between total treatment cost and survival time. The proposed method utilizes either parametric or nonparametric regression to estimate this relationship and is consistent when this relationship is consistently estimated. We then present simulation results which illustrate the gain in finite-sample efficiency when compared with another recently proposed estimator. The methods are then applied to the estimation of mean cost for two studies where right-censoring was present. The first is the heart failure clinical trial Studies of Left Ventricular Dysfunction (SOLVD). The second is a Health Maintenance Organization (HMO) database study of the cost of ulcer treatment.     

Epidemiological studies based on medical and administrative databases : a potential strength in France

Population-based epidemiological cohorts may include nowadays hundreds of thousands of subjects, followed-up during decades. France has a major potential strength: nationwide medical and social databases set up for administrative purposes. The main databases useful for epidemiology are the social security database which contains individual medical data from different sources, and the retirement fund database on employment and social benefits. These databases have several advantages: they cover the whole French population, with no lost to follow-up, data are often of good quality and it is possible to link them with individual surveys. However medical data are not always ascertained and an important methodological and practical work has to be done, and some legal and practical problems have to be solved for an optimal use.     

2004 update of dosimetry for the Utah Thyroid Cohort Study

In the 1980s, individual thyroid doses and uncertainties were estimated for members of a cohort of children identified in 1965 in Utah and Nevada who had potentially been exposed to fallout from the Nevada Test Site. That reconstruction represented the first comprehensive assessment of doses received by the cohort and was the first large effort to assess the uncertainty of dose on an individual person basis. The data on dose and thyroid disease prevalence during different periods were subsequently used in an analysis to determine risks of radiogenic thyroid disease. This cohort has received periodic medical follow-up to observe changes in disease frequency and to reassess the previously reported radiation-related risks, most recently after a Congressional mandate in 1998. In a recent effort to restore the databases and computer codes used to estimate doses in the 1980s, various deficiencies were found in the estimated doses due to improperly operating computer codes, corruption of secondary data files, and lack of quality control procedures. From 2001 through 2004, the dosimetry system was restored and corrected and all doses were recalculated. In addition, two parameter values were updated. While the mean of all doses has not changed significantly, many individual doses have changed by more than an order of magnitude.     

Probe Region Expression Estimation for RNA-Seq Data for Improved Microarray Comparability

Rapidly growing public gene expression databases contain a wealth of data for building an unprecedentedly detailed picture of human biology and disease. This data comes from many diverse measurement platforms that make integrating it all difficult. Although RNA-sequencing (RNA-seq) is attracting the most attention, at present, the rate of new microarray studies submitted to public databases far exceeds the rate of new RNA-seq studies. There is clearly a need for methods that make it easier to combine data from different technologies. In this paper, we propose a new method for processing RNA-seq data that yields gene expression estimates that are much more similar to corresponding estimates from microarray data, hence greatly improving cross-platform comparability. The method we call PREBS is based on estimating the expression from RNA-seq reads overlapping the microarray probe regions, and processing these estimates with standard microarray summarisation algorithms. Using paired microarray and RNA-seq samples from TCGA LAML data set we show that PREBS expression estimates derived from RNA-seq are more similar to microarray-based expression estimates than those from other RNA-seq processing methods. In an experiment to retrieve paired microarray samples from a database using an RNA-seq query sample, gene signatures defined based on PREBS expression estimates were found to be much more accurate than those from other methods. PREBS also allows new ways of using RNA-seq data, such as expression estimation for microarray probe sets. An implementation of the proposed method is available in the Bioconductor package “prebs.”