Are Insurance Claims Data Valid Measures Of Clinical Diagnosis And Treatment?

• Loading metrics

Open up Access

Peer-reviewed

Inquiry Commodity

Concordance between Patient Cocky-Reports and Claims Information on Clinical Diagnoses, Medication Apply, and Wellness System Utilization in Taiwan

• Chi-Shin Wu,
• Mei-Shu Lai,
• Susan Shur-Fen Gau,
• Sheng-Chang Wang,
• Hui-Ju Tsai

x

• Published: December ii, 2014
• https://doi.org/x.1371/journal.pone.0112257

Figures

Abstract

Purpose

The aim of this study was to evaluate the cyclopedia betwixt claims records in the National Health Insurance Research Database and patient self-reports on clinical diagnoses, medication use, and health system utilization.

Methods

In this study, we used the data of xv,574 participants collected from the 2005 Taiwan National Health Interview Survey. We assessed positive agreement, negative agreement, and Cohen'southward kappa statistics to examine the concordance between claims records and patient self-reports.

Results

Kappa values were 0.43, 0.64, and 0.61 for clinical diagnoses, medication use, and health arrangement utilization, respectively. Using a strict algorithm to identify the clinical diagnoses recorded in claims records could better the negative agreement; all the same, the consequence on positive agreement and kappa was diverse beyond various conditions.

Determination

We establish that the overall cyclopedia betwixt claims records in the National Health Insurance Research Database and patient self-reports in the Taiwan National Wellness Interview Survey was moderate for clinical diagnosis and substantial for both medication use and health system utilization.

Citation: Wu C-S, Lai M-Due south, Gau SS-F, Wang S-C, Tsai H-J (2014) Concordance between Patient Self-Reports and Claims Data on Clinical Diagnoses, Medication Use, and Health Arrangement Utilization in Taiwan. PLoS One nine(12): e112257. https://doi.org/10.1371/periodical.pone.0112257

Editor: Gabriele Multhoff, Technische Universitaet Muenchen, Germany

Received: May 5, 2014; Accepted: October 14, 2014; Published: December 2, 2014

Copyright: © 2014 Wu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in whatever medium, provided the original author and source are credited.

Data Availability: The authors ostend that, for canonical reasons, some access restrictions apply to the data underlying the findings. The information utilized in this study cannot be made available in the manuscript, the supplemental files, or in a public repository due to the "Personal Information Protection Act" executed by Taiwan's government, starting from 2012. Requests for data can exist sent every bit a formal proposal to obtain approval from the ethics review committee of the appropriate governmental department in Taiwan. Specifically, links regarding contact info for which data requests may be sent to are as follows: http://nhird.nhri.org.tw/en/Data_Subsets.html#S3 and http://nhis.nhri.org.tw/betoken.html.

Funding: No potential fiscal conflicts of involvement relevant to this report were reported. Dr. Wu is supported in part by a grant from National Science Council, Taiwan (PI: Wu, NSC 102-2314-B-418 -002). Dr. Tsai is supported in part by grants from the National Health Research Institutes, Taiwan (PI: Tsai, PH-100-PP-14, PH-102-PP-14, PH-101-SP-14, PH-102-SP-5). The funders had no role in study design, data collection and analysis, conclusion to publish, or grooming of the manuscript.

Competing interests: The authors accept alleged that no competing interests exist.

Introduction

The use of automated claims databases is a widely-used method for obtaining information for utilize in epidemiological studies. The advantages of claims-based studies compared with collecting information from patient reports or medical chart reviews include the inexpensiveness, large sample size, and lower risk of non-response [1], [2]. Taiwan's National Health Insurance Research Database (NHIRD), derived from the single-payer, compulsory National Health Insurance (NHI) plan in Taiwan [three], is i of the largest available claims databases. Every bit of 2007, about 22.6 one thousand thousand (98%) Taiwanese were enrolled. The NHI plan covers ambulatory care, hospitalization and dental services, likewise every bit preventive services. Patients' demographic characteristics, diagnoses, prescriptions, hospitalizations, and medical expenditures are recorded in the NHIRD. The number of published scientific papers using the NHIRD has increased dramatically in contempo years [4]. Nonetheless, since claims records are mainly generated to capture information for reimbursement purposes, physicians might record a diagnosis to receive payment when it is really only just existence considered. In contrast, a diagnosis which is not related to reimbursement might be nether-recorded.

Patient self-report is a common method that is used to gather health information and that usually tin can provide other important information, such as the patient's perspective regarding their ain general health conditions, lifestyle, and health behaviors. However, cocky-report has been shown to be afflicted by measurement error as the outcome of recall bias and the social desirability effect [5].

Both measurement methods have strengths and limitations. A linkage between claims information and patient self-reports could complement each other and enhance the utilization of both information sources in epidemiology and health services inquiry. Despite the potential advantages, the concordance betwixt claims data and patient self-report is still under-investigated in Taiwan. To date, only one study has examined the agreement between self-written report and claims data on health organisation utilization, and the findings showed a fairly skillful cyclopedia in the general population [6]. However, the concordance of clinical diagnoses and medication utilise in Taiwan remains unclear.

In this report, nosotros used self-reported data collected from a nationwide representative survey, the 2005 Taiwan National Health Interview Survey (NHIS), to evaluate the cyclopedia of clinical diagnoses, medication use and health organization utilization, separately, between the self-reported information in the NHIS and claims records in the NHIRD. In addition, factors associated with disconcordance betwixt the cocky-reported data and the claims records were likewise explored.

Materials and Methods

Study population

This study utilized data from the 2005 Taiwan National Health Interview Survey (NHIS), which is a nationwide cantankerous-sectional survey used to investigate the health status of not-institutionalized residents in Taiwan. Detailed information related to the written report design of the 2005 Taiwan NHIS tin can be found elsewhere [vii]. In brief, households were randomly selected using a multistage stratified systematic sampling scheme. All members of these households were interviewed by trained interviewers using structured questionnaires. At that place were three versions of the questionnaires, one for each of the three age groups (<12, 12–64, and ≧65 years, separately). The questionnaire independent several domains, including sociodemographic characteristics, personal wellness status, health system utilization, and occupation and economic status. Almost participants in the survey were interviewed from Apr to July 2005. A full of 30,680 residents was selected and 24,726 (80.6%) agreed to participate in the 2005 Taiwan NHIS. In this written report, nosotros included only those anile 12 years and above, yielding 20,826 participants. Of this group, 15,574 (75%) participants gave informed consent to allow us to link their NHIS information with the data of Taiwan's NHIRD for research purposes. After linkage, any information that could be used to identify the participants was anonymized and de-identified prior to assay. The report was canonical by the ethics committee of the National Health Research Institutes.

Self-reported measures

We used data from the NHIS to mensurate self-reports in three domains, including clinical diagnoses, medication use, and health system utilization. Fourteen common clinical diagnoses, including hypertension, diabetes mellitus, dyslipidemia, malignancy, stroke, asthma, chronic pulmonary diseases, gout, osteoporosis, arthritis, renal disease, middle diseases, chronic hepatitis, and psychiatric disorders were explored in the versions of the questionnaires for the 12–64 and ≧65 years age groups. The participants were asked 2 sequential questions, "Do you have this disease?" and "Were you told by a doc or a nurse that you have this disease?" For some diseases with an episodic class, such equally asthma, the participants were asked a third question, "Did y'all have this affliction during the last year?" If the answers to these questions were all 'Yes', the patient was classified as having this disease. If the patient had any negative or uncontained answers for a question, we classified the patient as not having this affliction.

For patients with whatsoever of the above-mentioned diseases, cocky-reported medication use was explored by request, "Take you received any medication for handling in the concluding twelvemonth?" Given that some diseases were treated without specific medications, we analyzed the cyclopedia in medication employ by focusing only on 5 types of drugs, including antihypertensive drugs, antidiabetic drugs, lipid-lowering agents, anti-asthmatic drugs, and anti-gout drugs.

With regard to health organization utilization, we asked, "Were y'all hospitalized during the last year?"; "Did you visit the emergency department during the terminal twelvemonth?"; "Did you lot utilize dental services during the concluding yr?"; and "Did yous take a health examination though the NHI plan during the concluding year?"

Claims-recorded measures

The clinical diagnoses in the claims records were coded using the clinicians' judgment based on clinical history, diagnostic criteria, image, or biochemical data. The present assay used NHIRD claims records for the i year preceding the 2005 Taiwan NHIS. Nosotros set May 15, 2005, which was approximately the median date of the survey, every bit the date for interviewing all participants. Both convalescent and inpatient claims records for the period from May 16, 2004 to May 15, 2005 were extracted from the NHIRD for subsequent analysis. Nosotros used ICD9-CM codes in any diagnostic position, rather than only the master position, to identify xiv clinical diagnoses from the ambulatory and inpatient claims in the NHIRD. Claims records from all types of healthcare settings were included. Amidst outpatient claims records during the written report menstruum, approximately 82.4% were Western Medicine, seven.viii% were dental services, and 9.8% were Chinese Medicine. The target medications were identified using the Anatomical Therapeutic Chemical (ATC) classification system [8]. Given that a medication tin can be used for different indications, we defined a participant equally a medication user merely if the patient had a prescription record for a targeted medication with a corresponding diagnosis. For example, diuretics tin can be prescribed for hypertension or renal diseases; therefore, only those participants who received a prescribed diuretic and had a diagnosis of hypertension were divers as antihypertensive users. Regarding health arrangement utilization, we assessed the type of medical services provided for each claims tape in the NHIRD. The detailed ICD9-CM codes for clinical diagnoses and ATC codes for medication are provided in Tabular array S1.

Statistical analysis

The percentages for each particular, clinical diagnoses, medication use, and health arrangement utilization, based on self-reports or claims records were calculated by dividing the number of participants who had the presence of each detail, separately, by the total number of participants. In terms of the overall percentage of each domain, the percentage was calculated equally the average value for each item inside the same domain. For the cyclopedia betwixt self-reports and claims records, nosotros calculated the positive, negative, and full agreement and Cohen's kappa statistics, a chance-adapted understanding [9], [ten]. The interpretation for kappa was based on Landis and Koch's classifications: <0.ii every bit slight, 0.21–0.40 as fair, 0.41–0.60 as moderate, 0.61–0.80 as substantial, and 0.81–1.00 as about perfect [11].

Given that physicians might initially code a tentative diagnosis and then change information technology later if further testify did not support this initial diagnosis, the utilize of a strict algorithm, via increasing the number of claims records used to identify clinical diagnoses, is a common strategy to improve accuracy [viii]. Therefore, nosotros explored the concordance betwixt self-reports and claims records using three algorithms to ascertain the clinical diagnoses: ≧1 outpatient or ≧1 discharge claims, ≧two outpatient or ≧ane belch claims, and ≧3 outpatient or ≧one discharge claims. The interval between ii claims records was not specified; however, if two or more outpatient records were institute on the same day, only one claims record was calculated.

Furthermore, we explored the factors of disconcordance betwixt self-reports and claims records for diseases, medication use, and health organization utilization. For each comparison, the consequence was dichotomized into agreement and disagreement between self-study and claims tape. Explanatory variables included grossly trisected age groups (<30, xxx–49, and ≧50), sexual activity, educational level (≤6, 7–12, and>12), marital condition, and urbanization level of residence (urban, suburban, and rural). Since the analyses were based on patient-condition pairs, we estimated the odds ratios and 95% confidence intervals using the multivariate generalized estimating equation with an unstructured covariance matrix to eliminate the issue of multiple representations of each patient. In addition to the above-mentioned full general factors, we too explored the effect of disease-specific factors on certain clinical diagnoses. For example, obesity is strongly associated with hypertension, diabetes, and dyslipidemia. Equally such, we extract information on body mass index (BMI) from Taiwan's NHIS and categorized participants into normal (BMI<24), overweight (24≤BMI<27), and obese (BMI≧27). Using a logistic regression model, the effect of overweight and obesity on the agreement between hypertension, diabetes, and dyslipidemia were investigated.

SAS version nine.2 was used for all statistical analyses (SAS Institute, Cary, NC).

Results

A total of xv,574 participants anile 12 years and to a higher place who consented to link their NHIS data with the NHIRD were included in the analysis. The mean age of participants was 41.2±18.7 years, 47.8% were females, 57.six% were married, 76.ix% had more than 6 years of pedagogy, and 44.six% lived in urban areas. Compared with those who did not consent to link their NHIS data with the NHIRD, the consenters tended to be younger, more probable to exist males, more highly educated, and more probable living in an urban area. However, the difference between consenters and nonconsenters was small (Tabular array 1).

The prevalence of well-nigh clinical diagnoses, medication use, and wellness organization utilization based on claims records was higher than that based on self-report measures, except for dyslipidemia, osteoporosis, use of lipid lowering agents, and utilization of dental services (Table 2). The kappa varied across atmospheric condition, ranging from 0.18 for chronic pulmonary diseases to 0.85 for anti-diabetes drug utilise. The positive agreement ranged from 0.20 for chronic pulmonary diseases to 0.85 for anti-diabetes drug utilise. The total and negative agreement was high (all >0.eight).

Using a strict algorithm for identifying clinical diagnoses in the claims records would take decreased the estimated prevalence. However, the effect on positive agreement and kappa was various across diverse conditions (Effigy. 1).

In terms of factors associated with disagreement between self-reports and claims records, we found that sometime age was associated with more disagreement in clinical diagnosis, medication utilize, and wellness system utilization. Subgroup analyses for the 3 different age groups are shown in Tables S2–S4. Briefly, the prevalence, also equally the kappa, of most items increased with age; however, the full agreement decreased with age. Male gender was associated with disconcordance in medication use, but less disconcordance in health system utilization than in their female counterparts. There was less disagreement betwixt self-reports and claims records of patients who were unmarried or who had a high level of teaching. The level of urbanization of residency had little event on disconcordance (Table 3). In terms of the effect of illness-specific factors on the concordance of clinical diagnosis, we constitute that obesity was associated with disagreement in hypertension, diabetes mellitus, and dyslipidemia but non in psychiatric disorders (Table S5).

Discussion

In this study, using a nationwide representative survey to explore the cyclopedia between self-reports and claims records for clinical diagnoses, medication use, and wellness system utilization, the results from kappa statistics showed that the overall concordance was moderate for clinical diagnosis and substantial for medication use and health organization utilization. In add-on, negative agreement, an analogue of specificity, was quite high for nigh weather.

We plant that the concordance of chronic diseases, such equally hypertension and diabetes mellitus, was substantial. These two diseases had clear diagnostic criteria and are well-known in the general population. Thus, the cyclopedia of these two diseases, every bit well equally those of anti-hypertensive and anti-diabetes drug utilise, was quite loftier. Still, the concordance of stroke, malignancy, and gout were just moderate. The cyclopedia for serious diseases, such every bit malignancy or stroke, was lower than our expectation. There are several possible explanations for this. The low prevalence of these serious diseases might affect kappa statistics [9], and the lack of an exact timeframe in the interview questions might too be responsible for the observed depression concordance. Patients might take had these serious diseases in the past and recovered from them in recent years; therefore, they may have non visited clinics in the past year because of these medical conditions. We also observed that the concordance of gout was merely moderate. Gout causes sudden and astringent hurting; however, given the episodic nature of gout, patients might not recall exactly.

Furthermore, we found that the concordance of dyslipidemia, asthma, chronic pulmonary diseases, osteoporosis, arthritis, renal diseases, heart diseases, chronic hepatitis, and psychiatric disorders were only fair. In fact, we constitute that the cyclopedia of dyslipidemia was lower than our expectation. The prevalence of dyslipidemia based on self-reports (eleven.nine%) was like to the finding for the blood lipid exam in the Diet and Wellness Survey in Taiwan, which showed that the prevalence of hypercholesterolemia was 10.2% in men and xi.2% in women [12]. A possible caption is that the NHI program just reimbursed for lipid-lowering agents among patients who had astringent hyperlipidemia or who were comorbid with other cardiometabolic affliction. Physicians might provide health information for those with balmy dyslipidemia but not code the diagnosis if they did non prescribe lipid-lowering agents. Furthermore, merely the first 3 diagnostic codes for ambulatory claims would be included in the NHIRD. Patients with dyslipidemia besides commonly have other serious comorbid weather. Thus, the diagnosis of dyslipidemia might be coded in the original claims only not included in the NHIRD. The concordance of lipid-lowering agents was moderate, which is meliorate than that for the diagnosis of dyslipidemia. Like findings were also noted for osteoporosis, which was diagnosed via routine screening and patients were unremarkably given lifestyle interventions initially. Thus, physicians might not code the diagnosis for most mild cases.

For composite diseases, such every bit renal diseases and center diseases, the concordances were only off-white. Lack of a specific definition of these clinical diagnoses might increase the discrepancy between self-reports and claims records. Of notation, the prevalence of psychiatric illnesses based on self-reports was much lower than that based on claims records. A previous comprehensive survey using the Chinese-Modified Diagnostic Interview Schedule for psychiatric epidemiology found that the prevalence of major depressive disorder, bipolar disorder, and anxiety disorders was 1.14%, 0.17%, and vii.75%, respectively [13]. The estimated prevalence was similar to those based on claims records, rather than self-reports. Patients might lack insight or hesitate to disclose their psychiatric illness during a general survey. Thus, the cyclopedia was only fair and the positive understanding was low.

The cyclopedia of medication apply was substantial, except for anti-asthmatic medications. Given that asthma attacks are episodic in nature, the use of anti-asthmatic medications would also be intermittent rather than regular. Therefore, patients might accept difficulty recalling their exact use. The concordance of prescription records in the NHIRD was more often than not improve and less varied than that of clinical diagnoses. Previous studies using dissimilar health claims databases also found that the concordance of medication use in claims records was meliorate than that of other data sources [5], [xiv], [fifteen].

We found that the concordance of health system utilization was generally substantial, which was compatible with a previous validity written report of wellness organisation utilization [6]. In this study, we further explored the cyclopedia of a preventive service, the routine health exam. We institute that the concordance for a wellness examination was only moderate. Since a routine health examination by the NHI program is very user-friendly and can be performed in a clinical setting, participants might non even exist aware that they received a routine wellness examination.

Individuals using private health insurance might under-gauge the prevalence of sure wellness conditions based on claims records. In Taiwan, approximately 64.8% have individual wellness insurance [16]. Notwithstanding, about all individuals with private health insurance are also enrolled in the NHI programme. The role of private health insurance in Taiwan is just supplemental. Therefore, the upshot of private health insurance on estimating prevalence-based claims records is balmy.

We further conducted subgroup analyses for the cyclopedia stratified past 3 different historic period groups. Mostly, the concordance amidst participants aged xxx–49 and ≧50 years were grossly consequent with the findings among the whole study sample. Even so, we institute that the kappa values of most clinical diagnoses among participants anile between 12–29 years were relatively small. It should be noted that the prevalence of most examined diseases was quite low amid this age group. Since kappa is sensitive to the prevalence of studied items, a low kappa might be attributed to the paradoxical issue of low prevalence.

Strategies for improving the concordance

In this written report, the prevalence of most clinical diagnoses based on self-reports was lower than that based on claims records, which was divers by the occurrence of one ambulatory or one inpatient claim. This could be overestimated considering the physician might code a tentative diagnosis initially and change it later if farther testify did not support this initial diagnosis. Thus, using strict definitions would reduce the estimated prevalence based on claims records. Nonetheless, whether the kappa could provide accurate estimated concordance would depend on the disease of interest. Generally, using strict definitions could ameliorate the concordance in chronic and/or severe diseases such as diabetes, malignancy, and stroke. All the same, for diseases that are episodic in nature such as gout or asthma, using strict definitions would have no overt benefit on the cyclopedia. For potentially underestimated diseases in the claims records, such every bit dyslipidemia and osteoporosis, using a wide definition to reflect both treated and untreated patients with these atmospheric condition might be amend. Equally such, since in that location is no stock-still algorithm which tin exist practical to all diseases, the use of strict or broad definitions should be cautiously considered based on the aims of the study and the trade-off betwixt sensitivity and specificity [17].

Factors associated with disconcordance

In our assay of the participant characteristics associated with disconcordance, we found that age has an important impact on disconcordance in diseases and medication use. Compared with young adults, the elderly have more complex diseases and a greater vulnerability to retentivity harm; therefore, their self-reports are more likely to be discrepant with the claims records. These findings are consistent with previous studies showing that those at an older age or with a complex comorbidity were more than probable to accept discordance between self-reports and claims records [xviii], [19]. In improver, a depression level of didactics was related to poor wellness noesis and physician-patient communication, and thereby had a negative effect on the agreement between self-reports and claims records [20]. Single status was associated with more actual agreement. In Taiwan, patient family members actively participate in medical treatment decision-making. If the patient has a serious affliction, such equally a malignancy, the physician might discuss this with family unit members first, earlier the patient himself/herself. The family members might even determine to muffle this information to avoid possible negative emotional reactions of the patient.

To explore the upshot of disease-specific factors on the concordance in clinical diagnosis, we assessed the effect of obesity on the concordance in hypertension, diabetes mellitus, and dyslipidemia. Obesity is strongly associated with these cardiometabolic disorders just not with psychiatric illness. While we found that obesity was associated with disagreement between claims records and cocky-reports in hypertension, diabetes mellitus, and dyslipidemia, obesity was not related to that in psychiatric affliction. A possible caption is that patients with obesity are more probable to fall in a gray area of definitive diagnosis; therefore, they might be confused nearly whether or not they take obesity-related cardiometabolic disorders. In contrast, the effect of obesity on the concordance in psychiatric disease was non significant. In summary, patients with strong risk factors for cardiometabolic disorders might increase the proportion of disagreement betwixt claims record and self-reports.

Validity of the NHIRD and NHIS

Despite a dramatic increment in the publication of scientific papers using the NHIRD, the validity of NHIRD is nonetheless under-investigated. 1 study conducted a medical nautical chart review of 372 stroke patients and found that the accuracy of the discharge diagnosis for ischemic stroke in the NHIRD was loftier; in particular, the positive predictive value was 97.nine% [21]. Some other study reviewed 354 randomly selected medical charts and found that the accurateness of the belch diagnoses of astute coronary syndrome was 100% [22]. In improver, Lin et al. looked at patient self-report on a mailed-in questionnaire and found that the rate of agreement with a diabetes diagnosis in either ambulatory or infirmary claims was 74.six% [8]. To engagement, only ane study validated the cocky-reported health services utilization of the NHIS by using the claims records equally reference standards [6]. Our written report explored the concordance between the NHIS and NHIRD and provided important data regarding the validity of both data sources. Further validation studies of the NHIRD and NHIS should focus on specific weather and develop optimal definitions based on the nature of the clinical conditions existence assessed and the aims of the report.

Comparison of the concordance of self-reports and claims records in other claims data

We establish that the concordance of the NHIRD was comparable to that of other claims databases throughout the world. One written report explored the concordance of hypertension diagnoses between a patient survey and an insurance claims database in the U.S. and found that the proportion of understanding was 0.96 [23]. In improver, the kappa statistic for hypertension betwixt the Manitoba Center Wellness Survey and dr. service claims files was 0.56 [18]. These findings are like to our findings for hypertension (the total agreement was 0.93; kappa was 0.69). In terms of the concordance of diabetes diagnoses, the kappa statistic between self-reports and claims records was 0.74 in U.Southward Medicare claims [24] and 0.80 in Medicare Australian information [25]. The kappa statistics of medication utilize between self-reports and claims data ranged from 0.69 to 0.80, depending on the blazon of medication use [14], [26], [27].

Limitations and strengths of the study

Several limitations for this written report should be considered. First, the date of interview was not bachelor in the 2005 Taiwan NHIS. As such, we used the approximate median date of the survey every bit the date of interview for all participants. Since the study timeframe could non be more precisely divers. Lack of an exact timeframe would underestimate the degree of concordance and the proportion of agreement, especially for those conditions that are episodic in nature. Second, 25.2% of participants, or those who did consent to link the NHIRD, were not included in this study. Compared with consenters, those excluded from the assay were slightly elder and less educated, and were less likely to be living in an urban area, which were characteristics that were associated with high disconcordance. [6], [18], [20] Thus, the concordance between self-reports and claims records might exist overestimated. 3rd, since affliction-specific factors could provide more than information regarding each examined disease, we further examined the effect of obesity on hypertension, diabetes, dyslipidemia, and psychiatric disorders, respectively. However, we did non have boosted collected data for assessing affliction-specific factors for the other examined diseases. Farther investigation on disease-specific factors for these other examined diseases is warranted.

Despite the above-mentioned limitations, this is the kickoff study to pervasively and comprehensively assess the concordance between NHIRD claims records and a nationwide representative survey on clinical diagnosis and medication utilise. Given that details regarding socio-demographic and lifestyle information in the NHIRD and comprehensive information on medication exposure and procedures in the NHIS were non available, the linkage betwixt the NHIS and NHIRD could complement the forcefulness of each system, and enhance the utilization of both data sources in epidemiology and wellness services research.

Conclusions

The overall concordance between the claims records and self-study survey results was moderate in clinical diagnoses. Nevertheless, the concordance varied markedly across dissimilar diagnoses. In summary, the cyclopedia in common chronic disease was substantial. The concordance in severe diseases was moderate, which might be attributable to low prevalence. Regarding diseases with episodic nature, likewise as composite diseases without clear definitions, the concordance was only fair. In terms of medication use and wellness organisation utilization, the concordance was substantial. In terms of research implications, the linkage between the NHIS and NHIRD could enhance the utilization of both datasets.

Supporting Information

Acknowledgments

Dr. Wu is supported in function by a grant from the National Science Council, Taiwan (PI: Wu, NSC 102-2314-B-418 -002). Dr. Tsai is supported in part by grants from the National Health Research Institutes, Taiwan (PI: Tsai, PH-100-PP-14, PH-102-PP-fourteen, PH-101-SP-14, PH-102-SP-five). Nosotros thank Tami R. Bartell at Ann & Robert H. Lurie Children's Hospital of Chicago, Stanley Manne Children's Enquiry Institute for English language editing.

This study is based, in part, on data from the National Health Insurance Enquiry Database provided by the Bureau of National Health Insurance of the Department of Wellness, Taiwan, and managed by the National Health Research Institutes, Taiwan. The interpretation and conclusions contained in this article do not represent those of the Agency of National Health Insurance, the Department of Health, or the National Health Inquiry Institutes.

Author Contributions

Conceived and designed the experiments: CSW. Analyzed the data: CSW. Contributed reagents/materials/analysis tools: CSW HJT. Wrote the paper: HJT CSW MSL SSFG SCW.

References

1. 1. Schneeweiss S, Avorn J (2005) A review of uses of wellness intendance utilization databases for epidemiologic research on therapeutics. Periodical of clinical epidemiology 58:323–337.
   • View Commodity
   • Google Scholar
2. two. Suissa S, Garbe E (2007) Primer: authoritative health databases in observational studies of drug effects–advantages and disadvantages. Nature clinical exercise Rheumatology three:725–732.
   • View Article
   • Google Scholar
3. 3. Hennessy S, Bilker WB, Knauss JS, Margolis DJ, Kimmel SE, et al. (2002) Cardiac arrest and ventricular arrhythmia in patients taking antipsychotic drugs: accomplice study using administrative data. Bmj 325:1070.
   • View Article
   • Google Scholar
4. 4. Chen YC, Wu JC, Chen TJ, Wetter T (2011) Reduced access to database. A publicly available database accelerates academic production. BMJ 342:d637.
   • View Article
   • Google Scholar
5. 5. Tisnado DM, Adams JL, Liu H, Damberg CL, Chen WP, et al. (2006) What is the concordance betwixt the medical record and patient self-written report as data sources for ambulatory care? Medical care 44:132–140.
   • View Article
   • Google Scholar
6. six. Yu ST, Chang HY, Lin MC, Lin YH (2009) Agreement between self-reported and health insurance claims on utilization of health intendance: A population study. Journal of clinical epidemiology 62:1316–1322.
   • View Commodity
   • Google Scholar
7. vii. Maclure M (1991) The case-crossover design: a method for studying transient furnishings on the risk of astute events. American periodical of epidemiology 133:144–153.
   • View Article
   • Google Scholar
8. eight. Lin CC, Lai MS, Syu CY, Chang SC, Tseng FY (2005) Accuracy of diabetes diagnosis in health insurance claims data in Taiwan. Journal of the Formosan Medical Association 104:157–163.
   • View Commodity
   • Google Scholar
9. 9. Cicchetti DV, Feinstein AR (1990) High understanding but low kappa: Two. Resolving the paradoxes. Journal of clinical epidemiology 43:551–558.
   • View Article
   • Google Scholar
10. x. Chen G, Faris P, Hemmelgarn B, Walker RL, Quan H (2009) Measuring understanding of administrative information with chart data using prevalence unadjusted and adjusted kappa. BMC medical research methodology 9:5.
    • View Article
    • Google Scholar
11. 11. Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33:159–174.
    • View Article
    • Google Scholar
12. 12. Chang HY, Yeh WT, Chang YH, Tsai KS, Pan WH (2002) Prevalence of dyslipidemia and mean blood lipid values in Taiwan: results from the Nutrition and Health Survey in Taiwan (NAHSIT, 1993–1996). The Chinese journal of physiology 45:187–197.
    • View Article
    • Google Scholar
13. 13. Hwu HG, Yeh EK, Chang LY (1989) Prevalence of psychiatric disorders in Taiwan defined by the Chinese Diagnostic Interview Schedule. Acta psychiatrica Scandinavica 79:136–147.
    • View Article
    • Google Scholar
14. 14. Kwon A, Bungay KM, Pei Y, Rogers WH, Wilson IB, et al. (2003) Antidepressant use: cyclopedia between self-study and claims records. Medical intendance 41:368–374.
    • View Article
    • Google Scholar
15. 15. Boudreau DM, Daling JR, Malone KE, Gardner JS, Blough DK, et al. (2004) A validation study of patient interview information and pharmacy records for antihypertensive, statin, and antidepressant medication use among older women. American journal of epidemiology 159:308–317.
    • View Article
    • Google Scholar
16. 16. Liu TC, Chen CS (2002) An analysis of private health insurance purchasing decisions with national wellness insurance in Taiwan. Social scientific discipline & medicine 55:755–774.
    • View Article
    • Google Scholar
17. 17. Tu Chiliad, Campbell NR, Chen ZL, Cauch-Dudek KJ, McAlister FA (2007) Accuracy of administrative databases in identifying patients with hypertension. Open medicine: a peer-reviewed, independent, open-access periodical one:e18–26.
    • View Article
    • Google Scholar
18. 18. Muhajarine N, Mustard C, Roos LL, Young TK, Gelskey DE (1997) Comparing of survey and physician claims data for detecting hypertension. Periodical of clinical epidemiology fifty:711–718.
    • View Commodity
    • Google Scholar
19. nineteen. Rhodes AE, Lin Eastward, Mustard CA (2002) Cocky-reported apply of mental health services versus administrative records: should we care? International journal of methods in psychiatric research 11:125–133.
    • View Article
    • Google Scholar
20. 20. Haapea 1000, Miettunen J, Lindeman S, Joukamaa Grand, Koponen H (2010) Understanding between self-reported and chemist's shop data on medication use in the Northern Finland 1966 Birth Cohort. International journal of methods in psychiatric research 19:88–96.
    • View Article
    • Google Scholar
21. 21. Cheng CL, Kao YH, Lin SJ, Lee CH, Lai ML (2011) Validation of the National Health Insurance Inquiry Database with ischemic stroke cases in Taiwan. Pharmacoepidemiology and drug rubber 20:236–242.
    • View Commodity
    • Google Scholar
22. 22. Wu CY, Chan FK, Wu MS, Kuo KN, Wang CB, et al. (2010) Histamine2-receptor antagonists are an culling to proton pump inhibitor in patients receiving clopidogrel. Gastroenterology 139:1165–1171.
    • View Commodity
    • Google Scholar
23. 23. Quam L, Ellis LB, Venus P, Clouse J, Taylor CG, et al. (1993) Using claims data for epidemiologic research. The concordance of claims-based criteria with the medical tape and patient survey for identifying a hypertensive population. Medical care 31:498–507.
    • View Article
    • Google Scholar
24. 24. Sakshaug JW, Weir DR, Nicholas LH (2014) Identifying diabetics in Medicare claims and survey data: implications for health services research. BMC health services research 14:150.
    • View Commodity
    • Google Scholar
25. 25. Comino EJ, Tran DT, Haas M, Flack J, Jalaludin B, et al. (2013) Validating self-report of diabetes apply by participants in the 45 and Upwards Study: a record linkage study. BMC health services research 13:481.
    • View Article
    • Google Scholar
26. 26. Curtis JR, Westfall AO, Allison J, Freeman A, Kovac SH, et al. (2006) Agreement and validity of pharmacy data versus cocky-report for use of osteoporosis medications among chronic glucocorticoid users. Pharmacoepidemiology and drug safety 15:710–718.
    • View Article
    • Google Scholar
27. 27. Pit SW, Byles JE, Cockburn J (2008) Accuracy of telephone cocky-report of drug apply in older people and agreement with pharmaceutical claims data. Drugs & aging 25:71–fourscore.
    • View Article
    • Google Scholar

Are Insurance Claims Data Valid Measures Of Clinical Diagnosis And Treatment?,

Source: https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0112257

Posted by: martincouseed1937.blogspot.com

Share this post