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Validated methods for identifying individuals with obesity in health care administrative databases: A systematic review

Summary Background Health care administrative databases are increasingly used for health studies and public health surveillance. Cases of individuals with obesity are selected using case‐identification methods. However, the validity of these methods is fragmentary and particularly challenging for obesity case identification. Objective The objectives of this systematic review are to (1) determine the case‐identification methods used to identify individuals with obesity in health care administrative databases and (2) to summarize the validity of these case‐identification methods when compared with a reference standard. Methods A systematic literature search was conducted in six bibliographic databases for the period January 1980 to June 2019 for all studies evaluating obesity case‐identification methods compared with a reference standard. Results Seventeen articles met the inclusion criteria. International Classification of Diseases (ICD) codes were the only case‐identification method utilized in selected articles. The performance of obesity‐identification methods varied widely across studies, with positive predictive value ranging from 19% to 100% while sensitivity ranged from 3% to 92%. The sensitivity of these methods was usually low while the specificity was higher. Conclusion When obesity is reported in health care administrative databases, it is usually correctly reported; however, obesity tends to be highly underreported in databases. Therefore, case‐identification methods to monitor the prevalence and incidence of obesity within health care administrative databases are not reliable. In contrast, the use of these methods remains relevant for the selection of individuals with obesity for cohort studies, particularly when identifying cohorts of individuals with severe obesity or cohorts where obesity is associated with comorbidities.

epidemiological and health care costing studies, for chronic disease surveillance, as well as for the study of burden of disease, the study of health service effectiveness, compliance and utilization and to examine population health status and its determinants. 1,[3][4][5][6][7][8] Health care administrative databases are comprehensive, inexpensive and time saving. 9 When combined with population survey data, they can establish the prevalence and incidence of various diseases. 2 In general, these databases include information on medical procedures such as medical visits, emergency room visits, hospitalizations, prescription drugs and associated costs. In order to identify specific populations within these databases, case-identification methods are utilized.
These case-identification methods may take the form of identification codes of the International Classification of Diseases (ICD) system, a drug code or a code related to the use of health services.
Given the rising burden of obesity as well as the fact that obesity is an important risk factor for numerous other chronic diseases, it would be particularly useful to rely on validated methods for identifying individuals with obesity in health care administrative databases.
Previous studies have shown, however, that the use of caseidentification methods to identify individuals with obesity in health care administrative databases may lead to an imperfect estimate of the prevalence of obesity. [10][11][12][13] Previous studies have shown that the coding of obese cases in health care administrative databases is inconsistent, incomplete or underreported. 12,[14][15][16][17] In addition, the caseidentification methods used in recent studies have led to variable performance depending on the existence of other comorbidities, the severity of obesity and the type of health care administrative data. 12,[18][19][20][21][22][23] Accurate case-identification methods for health care administrative databases is essential to minimize classification errors, which can represent a significant form of bias related to the use of administrative databases.
One way to assess the accuracy of case-identification methods is to compare this with a reference standard. For example, data on obesity in the health care administrative database could be compared with clinical data to determine their accuracy. If both the clinical data and the health care administrative database report that the patient suffers from obesity, there is high concordance (validity).
The objective of this systematic review is to determine caseidentification methods used to identify individuals with obesity within health care administrative databases and to report the validity of these case-identification methods when compared with a reference standard. No systematic review of this kind has been conducted to the authors' knowledge.

| Search strategy, data source and selection criteria
This review is one of a series of systematic reviews that aims to assess the case-identification methods used to identify chronic diseases in health care administrative data. This series of systematic reviews were conducted by the Quebec Support for People and Patient-Oriented Research and Trials (SUPPORT Unit) as part of its mandate to facilitate access to and use of health care administrative data. The PRISMA-P guidelines as well as production standards of the systematic reviews of the National Institute of Excellence in Health and Social Services were followed in the execution of this review. 24

| Selection criteria
Studies were included if they met all of the following criteria: (1) reported at least one case-identification method for identifying individuals with obesity in health care administrative databases, (2) validated the case-identification method(s) against a reference standard, (3) reported at least one of the following key measures of diagnostic accuracy: sensitivity, specificity, positive predictive value (PPV) or negative predictive value (NPV), (4) were written in English or French.
To ensure comparability, studies were excluded if only kappa values were reported or if regression models were used to identify cases of obesity. Articles that were not associated with an original study or detailed report (e.g., a research protocol and commentary) were also excluded. Combinations of the following three concepts were used: 1. the health condition of interest (e.g., obesity, overweight and body mass index [BMI]); 2. administrative health care data (e.g., administrative database and medico-administrative data); 3. case-identification method validation measures (e.g., sensitivity, specificity, PPV and NPV).
The search strategy for each of the databases is available in Appendix A.

| Data management and extraction
The identified articles were imported into a bibliographic management software. All results were combined, and duplicates were removed.
Data extraction was carried out by two members of the research team (RD and ECC). In order to ensure systematic and standardized collection, the extraction of information was accompanied by a set of specifications that described in detail the collection of each of the variables of interest. In addition, the assistants received prior training on the type of information collected and how to collect it.
Extracted data were entered into a standardized data extraction spreadsheet designed specifically for the objectives of this study. This spreadsheet included the following information: the definition of obesity utilized in the article, the obesity case-identification method used, the reference population and the performance of the caseidentification method used (sensitivity, specificity, PPV, NPV, Cohen's kappa and area under the curve [AUC]). This review followed a similar approach to what has been used to validate other disease caseidentification methods for administrative health care databases. [26][27][28][29] The primary reviewer was responsible for the initial extraction while the secondary reviewer was responsible for validating the information collected in the data extraction spreadsheet. In the event of nonconsensus, a third member of the research team (AMC) was consulted.

| Evaluation of the validity of obesity caseidentification methods
A case-identification method refers to the method utilized for identifying specific cases of a disease or other health marker in a health care administrative database. Different terminologies are used for health diagnosis depending on the data source and country. This may include codes for medical procedures, interventions, drugs prescribed or diagnostic codes.
The accuracy of case-identification methods to identify individuals with obesity in health care administrative databases was evaluated by comparing these methods with a reference sample. A reference sample may be medical records, clinical data, self-reported outcomes or treatment registries. The primary validation statistics of interest was based on sensitivity, specificity, PPV and NPV (Table 1).
However, for completeness, some studies have reported AUC or Cohen's kappa coefficient.

| Risk of bias analysis
An evaluation of the quality of studies was conducted by two authors (OSS and LI) using a modified version of the Quality Assessment of Diagnostic Accuracy Studies 2 evaluation tool for studies validating administrative data to assess risk of bias within and across studies. 30

| Study selection
The PRISMA study selection flow diagram is shown in Figure 1. A total of 1771 articles were identified. Of these, 1447 unique articles remained after deduplication. Each of the titles and abstracts was screened, and 47 articles were selected for full-text review. Of these, 14 met the inclusion criteria. Three additional articles, identified by the snowball method or in the grey literature, were added to these articles. A total of 17 original studies that report validated methods for identifying obesity individuals in health care administrative databases were included in this systematic review ( Table 2).
T A B L E 1 Indicators to assess the validity of case-identification methods for identifying obesity cases in health care administrative data

| Study and population characteristics
The characteristics of the included studies are presented in Table 2.
All studies were carried out in or after 2006. The majority of studies were conducted in the United States (n = 12/17) and the rest were conducted in Canada (n = 5/17). Three studies included only female populations, 14,21,23 one study included only children 11 and one study included only people aged 65 years and older. 15 The remaining studies focused on mixed populations. All studies were conducted according to a retrospective cohort design with the exception of Golinvaux et al., 19 which was a cross-sectional survey.

| Case-identification methods
All studies used the ICD codes to identify individuals with obesity. No study identified individuals with obesity in health care administrative data based on any other codes, such as codes related to the prescription of drugs or the use of health services. The specific ICD codes utilized are outlined in Table 3.

| Reference standard
Obesity case-identification methods (ICD codes) were compared with a reference standard to determine the level of congruence between health care administrative data and other sources of health data. Of the 17 studies reviewed, two compared case-identification methods with clinical measurements (Table 4), 14 compared against medical records (Table 5) and one compared against a treatment registry (

| Comorbidities
In six of the 17 studies, the case-identification methods were designed to select individuals with obesity with other specified chronic conditions, including pain, 19,31 diabetes, 32 fractures, 33 arthroplasty 20 and acute coronary syndromes. 23 In these cases, obesity was assessed as a comorbidity.
These methods had overall high performance in terms of specificity, PPV and NPV. On the other hand, the sensitivity values recorded were very low. Even when the analyses were restricted to individuals with other comorbidities (diabetes, depression, chronic obstructive pulmonary disease, congestive heart failure), the maximum sensitivity observed was only 38.8% for a single BMI group. The only study that compared obesity identification methods with data from a treatment registry (  ≥1 diagnosis code for obesity as a primary or secondary diagnosis from either a physician visit or a hospital stay. Author

| Risk of bias analysis
This review assessed the quality of the included studies using a modified version of the QUADAS-2 tool. This assessment found that most studies did not include enough information to determine whether the risk of bias was low or high. In cases where enough information was presented, the risk of bias was generally reported as being low.

| DISCUSSION
The objectives of this systematic review were to (1) determine the case-identification methods used to identify individuals with obesity in health care administrative databases and (2) to summarize the validity of these case-identification methods when compared with a reference standard. Understanding the validity of case-identification T A B L E 6 Registry as gold-standard (n = 1 study) The specificity of case-identification methods for the identification of individuals with obesity was generally higher than the sensitivity. This means that a large proportion of individuals without a diagnosis of obesity in the reference sample also did not have a diagnosis of obesity in the health care administrative database. This finding confirms the fact that the encoding of obesity in administrative databases is incomplete. Indeed, the weight status of individuals with obesity is rarely entered in administrative databases. This poor completeness of reporting of individuals with obesity leads to a significant bias in the estimation of the negative predictive value. The NPV, which is very poorly reported in these studies, should be interpreted with caution.
The main contribution of this article is the rigorous manner in which the literature has been searched to identify case-identification methods compared with a reference standard using identified measures of performance. The main limitation of this research is that none of the available reference standards are validated population measures of obesity. They also suffer from many of the same reliability issues as health care administrative databases such as issues with data entry and reliability of entry as were discussed above.
Despite these limitations, some conclusions were able to be drawn. Overall, several factors that are likely to positively affect the accuracy of the case-identification methods were identified. Caseidentification methods tended to be more accurate when reported with comorbidities associated with obesity and in cases of severe obesity. 10,15,19,20,22,23,[31][32][33] This may be explained by the fact that subjects with severe obesity or comorbidities are more likely to be coded as such in administrative databases because the patient's weight has direct relevance to the disease of interest. This may lead to a positive bias in the coding of obesity for these groups.