bmj open · for peer review only a temporal relationship between clinical visits for hand syndromes...

For peer review only

A temporal relationship between clinical visits for hand syndromes and subsequent diagnosis of diabetes:

Implication for identifying under-diagnosed type 2 diabetes

Journal: BMJ Open

Manuscript ID bmjopen-2016-012071

Article Type: Research

Date Submitted by the Author: 28-Mar-2016

Complete List of Authors: Hou, Wen-Hsuan; Taipei Medical University, Center of Evidence-based Medicine; Taipei Medical University, 2. Master Program in Long-Term Care, College of Nursing

Chen, Lu-Hsuan ; National Cheng Kung University, Department and Graduate Institute of Public Health, College of Medicine Wang, Liang-Yi ; National Cheng Kung University, Department and Graduate Institute of Public Health, College of Medicine Kuo, Li-Chieh; National Cheng Kung University, Department of Occupational Therapy Kuo, Ken; Taipei Medical University, Center of Evidence-based Medicine Shen, Hsiu-Nien; Chi Mei Medical Center, Department of Intensive Care Medicine Li, Chung-Yi; National Cheng Kung University, Department and Graduate Institute of Public Health, College of Medicine; China Medical University, 8. Department of Public Health, College of Public Health

<b>Primary Subject Heading</b>:

Diabetes and endocrinology

Secondary Subject Heading: Epidemiology, Public health, Diagnostics

Keywords: Type 2 diabetes, Nested case-control study, Diabetes related hand syndromes, Logistic regression, Validity

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Title Page

Title: A temporal relationship between clinical visits for hand syndromes and

subsequent diagnosis of diabetes: Implication for identifying under-diagnosed

type 2 diabetes

Running Title:

Hand syndromes in under-diagnosed diabetes

Authors’ names, academic degrees, and affiliations:

Wen-Hsuan Hou, MD, PhD1,2,3,4, Lu-Hsuan Chen, BSc5, Liang-Yi Wang, PhD5,

Li-Chieh Kuo, PhD6, Ken N. Kuo, MD1, Hsiu-Nien Shen, MD, PhD7,* Chung-Yi

Li, PhD5,8,*

*Both authors contributed equally to this paper.

1. Center of Evidence-based Medicine, Taipei Medical University, Taipei,

TAIWAN.

2. Master Program in Long-Term Care, College of Nursing, Taipei Medical

University, Taipei, TAIWAN.

3. School of Gerontology Health Management, College of Nursing, Taipei

Medical University, Taipei, TAIWAN.

4. Department of Physical Medicine and Rehabilitation, Taipei Medical

University Hospital, Taipei, TAIWAN.

5. Department and Graduate Institute of Public Health, College of

Medicine, National Cheng Kung University, Tainan, TAIWAN.

6. Department of Occupational Therapy, College of Medicine, National

Cheng Kung University, Tainan, TAIWAN.

7. Department of Intensive Care Medicine, Chi Mei Medical Center, Tainan,

TAIWAN.

8. Department of Public Health, College of Public Health, China Medical

University, Taichung, TAIWAN.

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Address correspondence to:

Chung-Yi Li, PhD

Department and Graduate Institute of Public Health

College of Medicine, National Cheng Kung University

#1, University Rd., Tainan, Taiwan, 701

E-mail: [email protected]

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Abstract

Objectives: To assess whether there is a temporal relationship between clinical

visits for diabetes related hand syndromes (DHS) and subsequent diagnosis of

type 2 diabetes (T2DM), which implies a potential for identifying

under-diagnosed T2DM at a sign of DHS.

Design: This was a case-control study design nested within a cohort of

1-million general population, followed from 2005 to 2010. Odds of prior clinical

visits for DHS including carpal tunnel syndrome (CTS), flexor tenosynovitis

(FTS), limited joint mobility (LJM) and Dupuytren’s Disease (DD) was

estimated for cases and controls, respectively. We used conditional logistic

regression model to estimate the odds ratio (OR) and 95% confidence interval

(CI) of T2DM in relation to prior history of DHS. Validity and performance of

using history of DHS in predicting T2DM incidence were calculated.

Setting: Taiwan National Health Insurance medical claims.

Participants: Totally 33,571 T2DM (cases) newly diagnosed between 2005 and

2010 were identified. Each T2DM was matched to 5 controls with the same sex

and birth year, who were alive at the date of T2DM incidence.

Primary and secondary outcome measures: The primary outcome measure

was T2DM

Results: The OR of T2DM was significantly elevated in association with prior

clinical visits for all-cause DHS and CTS at 1.15 (95% CI, 1.10-1.20) and 1.22 (95%

CI, 1.16-1.29), respectively. Some 11% of T2DM sought clinical visits for CTS

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within 3 months prior to the T2DM development. History of DHS shows a low

sensitivity (<0.1%-5.2%) and predictive value of positive (9.9%-11.7%) in

predicting the under-diagnosed T2DM.

Conclusions: This study suggested that clinical visits for DHS could be a sign

of under-diagnosed T2DM. Despite unsatisfactory validity and performance of

DHS as a clinical tool to detect under-diagnosed T2DM, both clinicians and the

general population should still be aware that DHS might be a sign of

under-diagnosed T2DM.

Key words：Type 2 diabetes, Nested case-control study, Diabetes related hand

syndromes, Logistic regression, Validity

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Strengths and limitations of this study

� The nested case-control design employed in this study is with little

likelihood of selection bias.

� The sample size employed in this study is large enough to yield sufficient

statistical power.

� The potential disease misclassification resulting from the use of claim

tends to underestimate rather than overestimate the temporal

relationship between clinical visits for DHS and subsequent diagnosis of

type 2 diabetes.

� Interpretation of the study findings should be cautious as not all known

risk factors for type 2 diabetes were considered in the analysis.

� Clinicians should be aware that DHS might be a sign of under-diagnosed

type 2 diabetes

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Introduction

Diabetes is one of the most common prevalent metabolic disorders in the

world and the prevalence of diabetes in adults has been increasing in the last

decades 1. The International Diabetes Federation has estimated that there are

381.8 million people with diabetes in 2013 with a projected increase of 55% to

591.9 million by 2035 2. Additionally, the prolonged asymptomatic phase of type

2 diabetes may last many years 3, during which time unmanaged elevated blood

glucose leads to severe complications including neuropathy, nephropathy,

retinopathy, coronary artery disease, stroke or peripheral vascular disease 4, and

therefore lead to massive healthcare costs and global health burden5.

Previous studies have estimated the large portion of under-diagnosed type

2 diabetes mellitus and diabetes-related complications, which resulted from the

under-performing health systems, low awareness among the general public and

health professionals, slow onset of symptoms or progression of type 2 diabetes

mellitus, and the condition may remain undetected for many years 6 7. Although

blood sugar screen has been considered the best way of early and timely

identifications of patients with type 2 diabetes mellitus, inadequate health care

services, poor health literacy, and lack of active health behavior still impede

success of blood sugar screen campaign, especially in rural areas and in those

poor 8. Therefore, inspecting possible diabetes-related syndromes during

clinical settings should be considered as a mean used to early locate the

under-diagnosed type 2 diabetes mellitus.

Diabetes related hand syndromes (DHS), defined as the certain

musculoskeletal conditions of hands, is a common clinical problem for persons

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with diabetes and almost invariably involves the long standing diabetes,

suboptimal glycemic control and microvascular complications9-13. DHS has

been characterized by limited joint mobility (LJM) 12 14-16, stenosing flexor

tenosynovitis (SFT) 11 17 18, Dupuytern’s disease (DD) 17 19 20, and carpal tunnel

syndrome (CTS) 15 17 21 22. A previous study suggested clinicians should

emphasize the clinical examination of DHS and use DHS as a clinical diagnostic

tool for type 2 diabetes mellitus due to the significant associations between the

duration of type 2 diabetes mellitus and the prevalence of DHS 23. Therefore,

the purpose of this study is to investigate whether the risk of type 2 diabetes

mellitus incidence may increase after clinical visits for DHS. That is, to assess

whether DHS can be considered as an indication of under-diagnosed type 2

diabetes mellitus.

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Methods

Data sources

Data analyzed in this study were retrieved from the medical claims of the

National Health Insurance Research Database (NHIRD) provided by the

National Health Insurance Administration (NHIA), Ministry of Health and

Welfare, Taiwan. The NHIRD provided all inpatient and ambulatory medical

claims for around 99% of Taiwanese people 24. The NHIRD covered all inpatient

and outpatient claims and medical orders, and the information on health care

providers, including medical institutions and health care workers. The personal

identification numbers of all beneficiaries are encrypted to ensure privacy. To

ensure the accuracy of claim files, the NHIA performs quarterly expert reviews

on a random sample for every 50 to 100 ambulatory and inpatient claims 25.

Access to research data has been reviewed and approved by the Review

Committee of the National Health Research Institutes.

Nested case-control design

This study was based on the claims data in 1997-2010 of one million

beneficiaries randomly selected from all beneficiaries registered in 2005. Several

criteria were set to exclude study subjects, including age < 20 years (n=394,377)

on the first day of 2005, histories of type 1 diabetes mellitus (n=2,507) and type 2

diabetes mellitus (n=61,966) in 1997-2004, rending a total of 541,150 people for

the study cohort. By the end of 2010, we identified 33,571 incident cases of type 2

diabetes mellitus (International Classification Disease codes 9th version Clinical

Modification (ICD-9-CM): 250.xx excluding 250.x1 or 250.x3).

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For each type 2 diabetes mellitus case, we randomly selected 5 controls

using the incidence density sampling technique 26. Each type 2 diabetes mellitus

was matched to 5 controls with the same sex and birth year, who were alive at

the date of type 2 diabetes mellitus incidence. By doing so, an individual could

be selected as a control before he/she became a type 2 diabetes mellitus case.

Besides, an individual could serve as a control for multiple cases. The density

sampling finally resulted in a total of 167,777 controls.

Outcome measures

The outcome variable was incidence of type 2 diabetes mellitus. An

individual was classified as a type 2 diabetes mellitus case only if he or she had

an initial type 2 diabetes mellitus diagnosis in 2005-2010 and then experienced

another one or more diagnoses within the subsequent 12 months. Additionally,

the first and last outpatient visits during the 12-month period had to be

separated by at least 30 days to avoid accidental inclusion of miscoded

patients 27.

Prior histories of DHS investigated

The primary independent variable included four DHSs including carpal

tunnel syndrome (CTS) (ICD-9-CM code: 354.0), stenosing flexor tenosynovitis

(SFT) (ICD-9-CM code: 727.03), limited joint mobility (LJM)(ICD-9-CM code:

718.8), and Dupuytren’s Disease (DD)(ICD-9-CM code: 728.6 ). Information on

the prior histories of DHS was retrieved from ambulatory care visits made

between January 1st, 1997 and date of diagnosis of incident type 2 diabetes

mellitus.

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Statistical analysis

We first described the distributions of covariates in cases and controls. We

then analyzed data using conditional logistic regression models and estimated

odds ratios (ORs) and their 95% confidence intervals (CIs) of prior ambulatory

care visits for DHS in association with type 2 diabetes mellitus. Crude odds

ratios were estimated from simple conditional logistic regression that takes into

account the matching variables including age, gender and date of incident type

2 diabetes mellitus diagnosis in the analysis. We then further adjusted for area

of living (North, Central, South, and East), occupation (blue collar, white collar,

and unclassified), and monthly income based insurance premium in the

conditional logistic regression model.

We considered insurance premium and occupation as covariates mainly

because previous studies had reported that lower socioeconomic status and

exposure to certain work hazards may increase the risk of the selected DHS 28-30.

Adjustment for geographic area may help reduce the presence of a geographic

difference in accessibility to medical health services in Taiwan 31.

To assess validity of using the selected hand syndromes in identifying the

cases of type 2 diabetes mellitus, we calculated the sensitivity and specificity for

each of the selected hand syndromes. With respect to the performance of using

history DHS in predicting the type 2 diabetes mellitus incidence, we estimated

the positive predictive value (PPV) and negative predictive value (NPV), based

on the Bayesian approach 32. To estimate PPV and NPV, the prevalence of type 2

diabetes mellitus was set at 9.9% according a recent Taiwan survey 33.

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The statistical analyses were performed with SAS (version 9.4; SAS

Institute, Cary, NC), and a p-value <0.05 was considered statistically

significant.

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Results

Cases and controls differed significantly on area of living, occupation, and

salary-based insurance premium. Cases were less likely to live in the Central,

more likely to be blue collar workers and with lower insurance premium of

dependents (Table 1). Compared to controls, cases were associated with

significantly elevated adjusted OR (1.31, 95% CI, 1.22-1.40) of having prior

diagnosis of CTS, and any type of DHS (adjusted OR, 1.23, 95% CI, 1.16-1.30).

Adjusted ORs associated with hand syndromes other than CTS were

statistically insignificant (Table 2).

Table 3 shows time elapsed from the last ambulatory care visit for DHS to

the incidence of type 2 diabetes mellitus. Some 11% of cases had their last

clinical visits for CTS in 3 months prior to type 2 diabetes mellitus diagnosis,

compared to only 5.8% of controls. No such tendency was observed for the

other selected DHS. Table 4 shows that the selected DHS were associated with

very low figures of sensitivity in identifying patients with type 2 diabetes

mellitus. The highest sensitivity was noted for CTS (5.2%). The PPV for the

selected syndromes were also considered low, ranging from 9.9% for DD and

11.7% for CTS.

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Discussion

This population-based nested case-control study reported that patients

with type 2 diabetes mellitus had a significantly higher likelihood of prior

history of CTS and any type of DHS in comparison with the control group

(adjusted OR=1.31 and 1.23, respectively). In terms of the validity and

performance of DHS as a clinical tool to detect under-diagnosed type 2

diabetes mellitus, both sensitivity (5.2% for CTS was the highest) and PPV

(9.9% for DD and 11.7% for CTS) were not satisfactory. Nonetheless, to the best

of our knowledge, this is the first study that attempts to examine the temporal

relationship between DHS and type 2 diabetes mellitus and to assess whether

DHS can be considered as an indication of under-diagnosed type 2 diabetes

mellitus in clinical settings.

There is no biological sense that DHS may increase the risk of type 2

diabetes mellitus incidence. The temporal sequence of DHS and subsequence

diagnosis of type 2 diabetes mellitus observed in this study may provide

evidence that some patients who sought care for DHS may in fact suffered from

under-diagnosed type 2 diabetes mellitus. There are several mechanisms that

may explain a higher risk of DHS in patients with type 2 diabetes mellitus.

Accumulation of advanced glycosylation end-products (AGEs), the glycated

proteins or lipids after exposure to sugars, may lead to abnormal cross-linkages

of collagen fibers, thereby manifesting as skin thickening, and the formation of

nodules and contractures over hands 34. Other evidences supported the

increase risk of DHS after type 2 diabetes mellitus is strongly associated with

abnormal expression of certain peptides and subclinical activation of specific

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cytokines, such as transforming growth factor-beta (TGF-β), basic fibroblast

growth factor (bFGF), interleukin (IL)-1, IL-6, and tumor necrosis factor-α

(TNF-α), which may also lead to unregulated and abnormal proliferation of

collagen 35 36.

According to our data, more than two third of DHS patients were

diagnosed by the orthopedists, neurologists, or physiatrists, who infrequently

cared patients with type 2 diabetes mellitus at their clinical settings. Because of

that, these specialists might lack of awareness of linking DHS to type 2

diabetes mellitus. Our study results not only present the under-diagnosis of

type 2 diabetes mellitus in Taiwan as other countries 6 7, but also call for the

alert of those non-internists in finding possible under-diagnosed type 2

diabetes mellitus in patients with DHS, especially carpal tunnel syndrome.

This study had the following strengths. First, it was a population-based

study including a highly representative sample of type 2 diabetes mellitus

patients in Taiwan in the year 2005-2010. The results can be applied and

generalized to various persons with diabetes with disease severities or from

different clinical settings. Second, the advantage of using insurance claim data

in clinical research is that doing so provides easy access to longitudinal records

for a large sample of demographically diverse patients37 38.The size of the

dataset made it possible to conduct stratified analyses according to different

time interval between diagnosis of DHS and development of type 2 diabetes

mellitus. Third, the type 2 diabetes mellitus cases and controls of this nested

case-control study were collected from the NHI database, and all the research

information was retrieved from the NHI claims, which minimized the

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likelihood of nonresponse or loss to follow-up of the study subjects. Our study

had also a limitation. Reliance exclusive reliance on claims data may have

resulted in a potential disease misclassification bias. The number of DHS

estimated from the claims data could be biased because some people who

experienced DHS-related symptoms may not seek ambulatory care, which

would in turn lead to underestimation of the relative risk estimates.

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Conclusion

Our study showed that the clinical visits of DHS could be a sign of

under-diagnosed type 2 diabetes mellitus. Despite unsatisfactory validity and

performance of DHS as a clinical tool to detect under-diagnosed type 2

diabetes mellitus, clinicians, especially those non-internists, should also be

aware of the possibility of under-diagnosed type 2 diabetes mellitus while

caring patients with DHS.

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Acknowledgment:

This study was supported by grant from the Ministry of Science and

Technology, (MOST 104-2314-B-006 -020 -MY2). Chung-Yi Li is principal

investigator of the project supported by Ministry of Health and Welfare

(MOHW104-HPA-H-114-114705). The funders have no role in conducting and

submitting this work.

Contributor statement:

WHH, LHC, and CYL designed the study. LHC and LYW analysed the data.

WHH, LCK, and HNS drafted the manuscript. LYW, LCK, KNK, and CYL

reviewed and revised the manuscript.

Funding:

This study was supported by grants from the Ministry of Science and

Technology (MOST 104-2314-B-006 -020 -MY2) and Chi Mei Medical Center

(105-CM-TMU-02). The interpretation and conclusions contained herein do

not represent those of BNHI, Department of Health or NHRI.

Competing interests:

None.

Data sharing statement:

There are no additional unpublished data from this study.

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30. Shiri R. Personal and workplace risk factors for carpal tunnel syndrome.

Occup Environ Med 2014;71:303.

31. Tan HF, Tseng HF, Chang CK, et al. Accessibility assessment of the

Health Care Improvement Program in rural Taiwan. J Rural Health

2005;21:372-7.

32. Gordis L. More on causal inferences: bias, confounding, and interaction.

Epidemiology. 2nd ed. Philadelphia: W.B. Saunders 2000:204-21.

33. Jiang YD, Chang CH, Tai TY, et al. Incidence and prevalence rates of

diabetes mellitus in Taiwan: analysis of the 2000-2009 Nationwide

Health Insurance database. J Formos Med Assoc 2012;111:599-604.

34. Ravindran Rajendran S, Bhansali A, Walia R, et al. Prevalence and

pattern of hand soft-tissue changes in type 2 diabetes mellitus. Diabetes

Metab 2011;37:312-7.

35. Spranger J, Kroke A, Mohlig M, et al. Inflammatory cytokines and the

risk to develop type 2 diabetes: results of the prospective

population-based European Prospective Investigation into Cancer and

Nutrition (EPIC)-Potsdam Study. Diabetes 2003;52:812-7.

36. Miyauchi K, Takiyama Y, Honjyo J, et al. Upregulated IL-18 expression in

type 2 diabetic subjects with nephropathy: TGF-beta1 enhanced IL-18

expression in human renal proximal tubular epithelial cells. Diabetes

Res Clin Pract 2009;83:190-9.

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37. Jollis JG, Ancukiewicz M, DeLong ER, et al. Discordance of databases

designed for claims payment versus clinical information systems.

Implications for outcomes research. Ann Intern Med 1993;119:844-50.

38. Remick RA. Diagnosis and management of depression in primary care: a

clinical update and review. CMAJ 2002;167:1253-60.

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Table 1. Characteristics of cases and controls

NTD = New Taiwan Dollar, 1 USD ≈ 32 NTD

Cases Controls P n (%) n (%)

Age (years) 0.9871 20-34 1007 (3.00) 5015 (2.99) 35-64 22657 (67.49) 113301 (67.53) >=65 9907 (29.51) 49461 (29.48) Sex 0.9847

Men 17904 (53.33) 89488 (53.34) Women 15667 (46.67) 78289 (46.66)

Area of living 0.0152 North 15472 (46.23) 77085 (46.12) Central 7785 (23.26) 39630 (23.71) South 9178 (27.42) 45752 (27.37) East 1034 (3.09) 4687 (2.80)

Occupation <0.0001 White collar 7454 (30.74) 41809 (33.60) Blue collar 11261 (46.44) 55724 (44.78) Unclassified 5533 (22.82) 26909 (21.62)

Insurance premium (NTD) <0.0001 0 (dependents) 9221 (27.55) 42716 (25.55) 1-17280 6363 (19.01) 31717 (18.97) 17281-21000 8610 (25.73) 43714 (26.15) 21001-34800 4140 (12.37) 20069 (12.01)

>34800 5135 (15.34) 28942 (17.31)

Total 33,571 167,777

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Table 2. Crude and adjusted odds ratio of type 2 diabetes in relation to prior history of diabetes related hand syndromes

Control Cases Crude estimates Adjusted estimates n % n % OR 95% CI OR 95% CI

CTS Yes 7271 4.3 1754 5.2 1.22 1.16-1.29 1.31 1.22-1.40 No 160506 95.7 31817 94.8 Ref. Ref.

SFT Yes 4061 2.4 816 2.4 1.01 0.93-1.09 1.05 0.95-1.16 No 163716 97.6 32755 97.6 Ref. Ref.

LJM Yes 337 0.2 69 0.2 1.02 0.79-1.33 1.05 0.75-1.47 No 167440 99.8 32502 99.8 Ref. Ref.

DD Yes 27 0.02 6 0.02 1.11 0.46-2.69 1.13 0.42-3.03 No 167750 99.98 33565 99.98 Ref. Ref.

Any of the above Yes 11610 6.9 2629 7.8 1.15 1.10-1.20 1.23 1.16-1.30 No 156167 93.1 30942 92.2 Ref. Ref.

Total 167,777 33,571

CTS, Carpal tunnel syndrome; SFT, Stenosing flexor tenosynovitis; LJM, limited joint mobility; DD, Dupuytren’s Disease

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Table 3. Distributions of time elapsed from date of the last diagnosis of diabetes related hand syndromes to date of type 2 diabetes diagnosis between cases and controls

Time elapsed (month)

<3 3-<6 6-<12 12-<24 >=24 n % n % n % n % n % p-value a CTS

Case 193 11.0 74 4.2 138 7.9 235 13.4 1114 63.5 <0.001 Control 422 5.8 288 4.0 540 7.4 949 13.1 5072 69.8

SFT Case 52 6.4 39 4.8 90 11.0 129 15.8 506 62.0 0.216 Control 284 7.0 226 5.6 359 8.8 593 14.6 2599 64.0

LJM Case 4 5.8 2 2.9 5 7.3 9 13.0 49 71.0 0.413 Control 11 3.3 8 2.4 22 6.5 27 8.0 269 80.0

DD Case 0 0.0 0 0.0 0 0.0 1 16.7 5 83.3 0.821 Control 2 7.4 0 0.0 3 11.1 5 18.5 17 63.0

CTS, Carpal tunnel syndrome; SFT, Stenosing flexor tenosynovitis; LJM, limited joint mobility; DD, Dupuytren’s Disease

a Based on Pearson χ2 test or Fisher’s exact test

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Table 4. Validity and performance of using diabetes related hand syndromes as a clinical tool for discovering people with under-diagnosed type 2 diabetes.

CTS FST LJM DD

Sensitivity (%) 5.2 2.4 0.2 <0.1 Specificity (%) 95.7 97.6 99.8 99.9 PPV (%) 11.7 9.9 10.3 9.9 NPV (%) 90.2 90.1 90.1 90.1

CTS, Carpal tunnel syndrome; SFT, Stenosing flexor tenosynovitis; LJM, limited joint mobility; DD, Dupuytren’s Disease; PPV, positive predictive value; NPV, negative predictive value.

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A medical claims-based case-control study of temporal relationship between clinical visits for hand syndromes and

subsequent diabetes diagnosis: Implications for identifying patients with undiagnosed type 2 diabetes

Journal: BMJ Open

Manuscript ID bmjopen-2016-012071.R1


Date Submitted by the Author: 15-Aug-2016

Complete List of Authors: Hou, Wen-Hsuan; Taipei Medical University, Center of Evidence-based Medicine; Taipei Medical University, 2. Master Program in Long-Term Care, College of Nursing Chen, Lu-Hsuan ; National Cheng Kung University, Department and Graduate Institute of Public Health, College of Medicine Wang, Liang-Yi ; National Cheng Kung University, Department and Graduate Institute of Public Health, College of Medicine Kuo, Li-Chieh; National Cheng Kung University, Department of Occupational Therapy Kuo, Ken; Taipei Medical University, Center of Evidence-based Medicine

Shen, Hsiu-Nien; Chi Mei Medical Center, Department of Intensive Care Medicine Li, Chung-Yi; National Cheng Kung University, Department and Graduate Institute of Public Health, College of Medicine; China Medical University, 8. Department of Public Health, College of Public Health






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Title Page

Title: A medical claims-based case-control study of temporal relationship

between clinical visits for hand syndromes and subsequent diabetes diagnosis:

Implications for identifying patients with undiagnosed type 2 diabetes

Running Title:

Hand syndromes in patients with undiagnosed diabetes


Wen-Hsuan Hou, MD, PhD1,2,3,4, Lu-Hsuan Chen, BSc5, Liang-Yi Wang, PhD5,

Li-Chieh Kuo, PhD6, Ken N. Kuo, MD1, Hsiu-Nien Shen, MD, PhD7*, Chung-Yi

Li, PhD5,8*

1. Cochrane Taiwan, Taipei Medical University, Taipei, TAIWAN.












TAIWAN.



* Hsiu-Nien Shen and Chung-Yi Li contributed to this article equally.

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Chung-Yi Li, PhD

Department and Graduate Institute of Public Health

College of Medicine, National Cheng Kung University

#1, University Rd., Tainan, Taiwan, 701


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Abstract

Objectives: To investigate whether a temporal relationship is present between

clinical visits for diabetes-related hand syndromes (DHSs) and subsequent

type 2 diabetes (T2DM) diagnosis, and accordingly, whether DHSs can be used

for identifying patients with undiagnosed T2DM.

Design: This study had a case–control design nested within a cohort of one

million people from the general population, which was followed from 2005 to

2010. The odds of prior clinical visits for DHSs, namely carpal tunnel syndrome

(CTS), flexor tenosynovitis, limited joint mobility, and Dupuytren’s disease,

were estimated for cases and controls. We used a conditional logistic regression

model to estimate the odds ratio (OR) and 95% confidence interval (CI) of

T2DM in association with a prior history of DHSs. The validity and predictive

value of using the history of DHSs in predicting T2DM diagnosis were

calculated.


Participants: We identified 33,571 patients receiving a new diagnosis of T2DM

(cases) between 2005 and 2010. Each T2DM case was matched with five

controls who had the same sex and birth year and were alive on the date of

T2DM diagnosis.


was T2DM diagnosis.

Results: The OR of T2DM in association with prior clinical visits was

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significantly increased for overall DHS and CTS, being 1.15 (95% CI, 1.10–1.20)

and 1.22 (95% CI, 1.16–1.29), respectively. Moreover, 11% of patients with T2DM

made clinical visits for CTS within 3 months prior to T2DM diagnosis. The

history of DHSs had low sensitivity (<0.1%–5.2%) and positive predictive value

(9.9%–11.7%) in predicting T2DM.

Conclusions: Despite unsatisfactory validity and performance of DHSs as a

clinical tool for detecting patients with undiagnosed T2DM, this study

provided evidence that clinical visits for DHSs, particularly for CTS, can be a

sign of undiagnosed T2DM.

Keywords: Type 2 diabetes, Nested case-control study, Diabetes-related hand

syndrome, Logistic regression, Validity

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� The nested case–control design used in this study has a low likelihood of

selection bias.

� The sample size of this study is sufficiently large to yield adequate

statistical power.

� Potential disease misclassification resulting from the use of medical

claims tends to underestimate rather than overestimate the temporal

relationship between clinical visits for diabetes-related hand syndromes

and subsequent type 2 diabetes diagnosis.

� The study findings should be interpreted with caution because not all

known risk factors for type 2 diabetes were considered in the analysis.

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INTRODUCTION

Diabetes is one of the most common metabolic disorders worldwide, and

its prevalence in adults has increased in past decades.1 The International

Diabetes Federation estimated that 381.8 million people had diabetes in 2013

and predicted that this number will increase to 591.9 million (projected increase

of 55%) by 2035.2 In addition, the prolonged asymptomatic phase of type 2

diabetes (T2DM) may last for many years.3 The nonmanagement of increased

blood glucose levels during this phase can lead to severe complications

including neuropathy, nephropathy, retinopathy, coronary artery disease,

stroke, or peripheral vascular disease,4 resulting in massive healthcare costs and

a global health burden.5

Studies have indicated that a high proportion of undiagnosed T2DM- and

diabetes-related complications may be attributable to underperforming health

systems, low awareness among the general public and health professionals, and

slow symptom onset or progression of T2DM; hyperglycemic conditions may

remain undetected for many years.6, 7 Although blood sugar screening has been

considered the most effective method for early and timely diagnosis of T2DM,

inadequate healthcare services, poor health literacy, and lack of active health

behavior still impede the success of blood sugar screening campaigns,

particularly in rural areas and those who are poor.8 Therefore, the inspection of

possible diabetes-related syndromes in clinical settings should be considered as

an additional method for the early identification of undiagnosed T2DM.

Diabetes-related hand syndromes (DHSs), defined as certain

musculoskeletal conditions of the hands, constitute a clinical problem in

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patients with diabetes and are almost invariably associated with long-standing

diabetes, suboptimal glycemic control, and microvascular complications.9-13

DHSs include limited joint mobility (LJM),12, 14-16 stenosing flexor tenosynovitis

(SFT),11, 17, 18 Dupuytren’s disease (DD),17, 19, 20 and carpal tunnel syndrome

(CTS).15, 17, 21, 22 A study suggested that clinicians should emphasize the clinical

examination of DHSs and use DHSs as a clinical diagnostic tool for T2DM

because of the significant association of the duration of T2DM with the

prevalence of DHSs.23 However, DHSs have received less attention compared

with other diabetic complications, such as diabetic foot problems and

cardiovascular disease, which both patients with diabetes and healthcare

professionals are familiar with. Therefore, this study investigated whether the

risk of T2DM increases after clinical visits for DHSs. In other words, we

examined whether DHSs can be considered an indicator of undiagnosed T2DM.

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METHODS

Data sources




Welfare, Taiwan. Approximately 92.3% of the residents of Taiwan were enrolled

in the National Health Insurance (NHI) program by the end of 1996, and the

coverage has increased to 99% since 2004.24, 25 The NHIRD contains all

inpatient and outpatient claims data and medical records and information on

healthcare providers, including medical institutions and healthcare workers.

The personal identification numbers of all beneficiaries are encrypted to ensure

privacy. To ensure the accuracy of claim files, the NHIA performs quarterly

expert reviews on a random sample for every 50–100 ambulatory and inpatient

claims.26 Access to research data has been reviewed and approved by the

Review Committee of the National Health Research Institutes (NHRI).

Nested case–control design

This study was based on the claims data from 1997 to 2010 of one million

beneficiaries randomly selected from all beneficiaries registered in 2005. We

excluded patients who were aged <20 years (n = 394,377) on the first day of 2005

and had a history of type 1 diabetes mellitus (n = 2,507) and T2DM (n = 61,966)

from 1997 to 2004. Finally, we included a total of 541,150 patients in the study

cohort. By the end of 2010, we identified 33,571 incident cases of T2DM

(International Classification of Diseases, Ninth Revision, Clinical Modification

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[ICD-9-CM] code 250.xx excluding 250.x1 and 250.x3).

For each T2DM case, we randomly selected five controls by using the

incidence density sampling technique.27 Each T2DM case was matched with five


T2DM diagnosis. Therefore, people could be selected as controls before they

received the diagnosis of T2DM. In addition, a person could serve as a control

for multiple cases. The density sampling finally resulted in a total of 167,777

controls.

Outcome measures

The outcome variable was the diagnosis of T2DM. We included only

those patients who received a diagnosis of T2DM between 2005 and 2010 and

again within the subsequent 12 months; the first and last ambulatory care

visits (including both hospital outpatients and general practice) for T2DM

during the 12-month period had to be separated by at least 30 days. This

prevented accidental inclusion of miscoded patients.28 29 In addition, the first

and last outpatient visits during the 12-month period had to be separated by at

least 30 days to prevent accidental inclusion of miscoded patients.30

Prior history of DHSs

Four DHSs were included as primary independent variables, namely CTS

(ICD-9-CM code 354.0), SFT (ICD-9-CM code 727.03), LJM (ICD-9-CM code

718.8), and DD(ICD-9-CM code 728.6 ). Information on the prior history of

DHSs was retrieved from ambulatory care visits (including both hospital

outpatients and general practice) made between January 1, 1997, and the date of

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diagnosis of incident T2DM. In Taiwan, physicians with various specialties can

diagnose DHSs.


We first examined the distribution of covariates between the cases and

controls. We then analyzed the data by using conditional logistic regression

models and estimated odds ratios (ORs) and their 95% confidence intervals

(CIs) of prior ambulatory care visits for DHSs in association with T2DM. Crude

ORs were estimated using simple conditional logistic regression that accounted

for matching variables such as age, sex, and the date of incident T2DM

diagnosis in the analysis. Moreover, we adjusted for the area of residence (north,

central, south, and east), occupation (blue collar, white collar, and unclassified),

and monthly income based on the insurance premium in the conditional

logistic regression model.

We considered the insurance premium and occupation as covariates

mainly because studies have reported that low socioeconomic status and

exposure to some work hazards may increase the risk of the selected DHSs.31-33

Moreover, adjustment for the geographic area helped in reducing the presence

of a geographic difference in accessibility to medical health services in Taiwan.34

To assess the validity of using the selected hand syndromes in identifying

the cases of T2DM, we calculated the sensitivity and specificity of each selected

hand syndrome. With respect to the performance of using the history of DHSs

in predicting the incidence of T2DM, we estimated the positive predictive value

(PPV) and negative predictive value (NPV)on the basis of the Bayesian

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approach.35 To estimate the PPV and NPV, the prevalence of T2DM was set at

9.9% according to a recent Taiwan survey.36

The statistical analyses were performed using SAS (Version 9.4; SAS

Institute, Cary, NC), and a p value of <0.05 was considered statistically

significant.

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RESULTS

The area of residence, occupation, and salary-based insurance premium

significantly differed between the cases and controls. The case patients were

less likely to live in the central region, were more likely to be blue-collar

workers, and paid lower insurance premiums for dependents (Table 1).

Compared with the control people, the case patient had a significantly higher

adjusted OR (1.31; 95% CI, 1.22–1.40) of having a prior diagnosis of CTS and any

type of DHS (adjusted OR, 1.23; 95% CI, 1.16–1.30). Adjusted ORs associated

with hand syndromes other than CTS were not significant (Table 2).

Table 3 lists the time elapsed from the last ambulatory care visit for DHSs

to the diagnosis of T2DM. In total, 11% of the case patients and only 5.8% of the

controls had their last clinical visits for CTS in 3 months prior to the diagnosis

of T2DM. The proportions of case patients and controls who had their last

clinical visits for CTS between 3 and 6 months, between 6 and 12 months,

between 12 and 24 months, and after 24 months were similar. Such a pattern

was not observed for other selected DHSs. Table 4 illustrates that the selected

DHSs had very low sensitivity in identifying patients with T2DM. The highest

sensitivity was noted for CTS (5.2%). In addition, the PPV for the selected

syndromes was low, ranging from 9.9% for DD to 11.7% for CTS.

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DISCUSSSION

The results of this population-based nested case–control study indicated

that compared with the controls, the patients with T2DM had a significantly

higher likelihood of a prior history of CTS and any type of DHS (adjusted OR =

1.31 and 1.23, respectively). Regarding the validity and performance of DHSs as a

clinical tool for detecting patients with undiagnosed T2DM, both the

sensitivity (5.2% for CTS was the highest) and PPV (9.9% for DD and 11.7% for

CTS) were not satisfactory. Nevertheless, to the best of our knowledge, this is

the first study to examine the temporal relationship between DHSs and T2DM

and to investigate whether DHSs can be considered an indicator of

undiagnosed T2DM in clinical settings.

Although a significant temporal relationship was present between overall

DHS and the subsequent risk of T2DM, only CTS had a significant association

with T2DM in the analyses of specific DHSs. Perkins et al. reported that the

prevalence of clinical CTS was 14% in patients with diabetes and without

diabetic peripheral neuropathy (DPN), 30% in those with DPN, and 2% in the

reference population.37 Furthermore, they indicated that CTS, which is an

entrapment neuropathy, is prevalent in patients with distal sensory peripheral

neuropathy. In addition, Bahrmann et al. reported that the prevalence of CTS

was higher in patients with diabetes and peripheral neuropathy than in

patients with diabetes and without diabetes-related late complications (30% vs.

14%). Moreover, CTS appears to be a risk factor for late manifestation of

diabetes because patients receiving a new diagnosis of diabetes exhibited CTS

manifestation 1.4-fold more often than did an age-matched reference

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population.38 This relative risk estimate is consistent with that in our study (i.e.,

adjusted OR, 1.31; 95% CI, 1.22–1.40). Our study results revealed that T2DM was

associated with prior CTS, but not with other DHSs, suggesting that CTS is

associated with peripheral neuropathy, which is a definite feature of

prediabetes,39 whereas other DHSs are of musculoskeletal origin.

The temporal sequence of overall DHS and the subsequent diagnosis of

T2DM observed in our study indicates that some patients who seek medical

care for DHSs may have undiagnosed T2DM. The higher risk of DHSs in

patients with T2DM may be attributable to several mechanisms. The

accumulation of advanced glycosylation end products and glycated proteins or

lipids after exposure to sugars may lead to abnormal cross-linking of collagen

fibers, which becomes manifest as skin thickening and nodule and contracture

formation on the hands.40 Other studies have supported the strong association

of the increased risk of DHSs after T2DM diagnosis with the abnormal

expression of some peptides and subclinical activation of specific cytokines,

such as transforming growth factor-beta, basic fibroblast growth factor,

interleukin (IL)-1, IL-6, and tumor necrosis factor-alpha, which may also lead

to unregulated and abnormal proliferation of collagen.41, 42

In Taiwan, numerous patients seek medical services directly from

specialists without initially visiting general practitioners. Because DHSs

present on the nerves or soft tissues of the hands, patients with such hand

syndromes usually visit orthopedists, neurologists, or physiatrists. According to

our data, more than two-thirds of the patients with DHSs received the

diagnosis from orthopedists, neurologists, or physiatrists who did not have

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adequate experience in treating patients with T2DM in their clinical settings.

Our data tended to exhibit an underdiagnosis of T2DM in some patients with

DHSs, implying that these specialists might not be aware of the association of

DHSs with T2DM. Although a standard definition is available for the diagnosis

of DHSs, which is mainly dependent on ultrasonographic and functional

assessment,43, 44 the diagnosis of DHSs in ambulatory care settings could be

subject to error because of limited diagnostic resources in some clinics. In

addition, although patients with diabetes and healthcare professionals have

high awareness regarding some diabetic complications other than DHSs, such

as diabetic foot problems and cardiovascular disease, no studies have

investigated the proportion of T2DM diagnosed according to the clinical

appearance of these common diabetic complications. The ability of

diabetes-related foot problems and cardiovascular disease to identify

undiagnosed T2DM should be explored in future studies.

This study has the following strengths. First, it was a population-based

study including a highly representative sample of patients with T2DM in

Taiwan between 2005 and 2010. The results can be applied and generalized to

patients with varying diabetes severities or to those from different clinical

settings. Second, the advantage of using insurance claim data in clinical

research is that it provides easy access to longitudinal records for a large sample

of demographically diverse patients.45, 46 The size of the dataset enabled

conducting stratified analyses according to different time intervals between the

diagnosis of DHSs and development of T2DM. Third, the T2DM cases and

controls in this nested case–control study were collected from the NHI

database, and all the research information was retrieved from the NHI claims,

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which minimized the likelihood of nonresponse or loss to follow-up of the

study patients. Our study has some limitations. First, exclusive reliance on

claims data may have resulted in a disease misclassification bias. The number

of DHSs estimated from the claims data could be biased because some people

who experienced DHS-related symptoms may have not sought ambulatory care,

which would in turn lead to the underestimation of relative risk estimates.

Second, our study might be subject to surveillance bias because patients who

frequently visited clinics for DHSs may have an increased risk of T2DM later.

Nevertheless, the risk of T2DM was significantly associated with CTS, but not

with the other three DHSs, suggesting that the association of T2DM with DHSs

or particularly with CTS may not entirely be explained by the potential

surveillance bias. Third, the hand complications are more likely to be found in

people who have had diabetes for a number of years, which limits their

usefulness as indicators of prediabetes or early-stage diabetes.

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CONCLUSION

Our study results revealed that clinical visits for DHSs can be a sign of

undiagnosed T2DM. Despite unsatisfactory validity and performance of DHSs

as a clinical tool for detecting patients with undiagnosed T2DM, the awareness

regarding the association of DHSs with undiagnosed T2DM is crucial to both

the public and clinicians, particularly to noninternists.

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Acknowledgment:

None.


WHH, LHC, and CYL designed the study. LHC and LYW analyzed the data.

WHH, LCK, and HNS drafted the manuscript. LYW, LCK, KNK, and CYL

reviewed and revised the manuscript.

Funding:




not represent those of the NHIA and NHRI.


None.



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u_id=296&webdata_id=1942&WD_ID=296 (Accessed 2 August 2016)







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diabetes on the risks of lower extremity amputation and peripheral

revascularization procedures: evidence from a cohort of a half million

diabetic patients. Diabetes Care 2006;29:2409-14.

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37. Perkins BA, Olaleye D, Bril V. Carpal tunnel syndrome in patients with

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38. Bahrmann A, Zieschang T, Neumann T, et al. Carpal tunnel syndrome

in diabetes mellitus. Med Klin (Munich) 2010;105:150-4. [Article in

German]

39. Papanas N, Vinik AI, Ziegler D. Neuropathy in prediabetes: does the

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43. Chen SF, Huang CR, Tsai NW, et al. Ultrasonographic assessment of

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44. Hsu HY, Kuo YL, Jou IM et al. Diagnosis from functional perspectives:

usefulness of a manual tactile test for predicting precision pinch

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NTD = New Taiwan Dollar, 1 USD = 32 NTD a Inconsistency between total population and population summed for individual variables was due to missing information.


Age (years) 0.9871 20–34 1007 (3.00) 5,015 (2.99) 35–64 22,657 (67.49) 113,301 (67.53) ≥65 9,907 (29.51) 49,461 (29.48) Sex 0.9847

Men 17,904 (53.33) 89488 (53.34) Women 15,667 (46.67) 78289 (46.66)

Area of residence 0.0152 North 15,472 (46.23) 77,085 (46.12) Central 7,785 (23.26) 39,630 (23.71) South 9,178 (27.42) 45,752 (27.37) East 1,034 (3.09) 4,687 (2.80)

Occupation <0.0001 White collar 7,454 (30.74) 41,809 (33.60) Blue collar 1,1261 (46.44) 55,724 (44.78) Unclassified 5,533 (22.82) 26,909 (21.62)

Insurance premium (NTD) <0.0001 0 (dependents) 9,221 (27.55) 42,716 (25.55) 1–17,280 6,363 (19.01) 31,717 (18.97) 17,281–21,000 8,610 (25.73) 43,714 (26.15) 21,001–34,800 4,140 (12.37) 20,069 (12.01)

>34,800 5,135 (15.34) 28,942 (17.31)

Total a 33,571 167,777

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Table 2. Crude and adjusted odds ratio of type 2 diabetes in association with a prior history of diabetes-related hand syndromes

Controls Cases Crude estimates Adjusted estimates n % n % OR 95% CI OR 95% CI

CTS Yes 7271 4.3 1754 5.2 1.22 1.16–1.29 1.31 1.22–1.40 No 160,506 95.7 31,817 94.8 Ref. Ref.

SFT Yes 4061 2.4 816 2.4 1.01 0.93–1.09 1.05 0.95–1.16 No 163,716 97.6 32,755 97.6 Ref. Ref.

LJM Yes 337 0.2 69 0.2 1.02 0.79–1.33 1.05 0.75–1.47 No 167,440 99.8 32,502 99.8 Ref. Ref.

DD Yes 27 0.02 6 0.02 1.11 0.46–2.69 1.13 0.42–3.03 No 167,750 99.98 33,565 99.98 Ref. Ref.

Any of the above Yes 11610 6.9 2629 7.8 1.15 1.10–1.20 1.23 1.16–1.30 No 156,167 93.1 30,942 92.2 Ref. Ref.

Total 167,777 33,571

CTS, Carpal tunnel syndrome; SFT, Stenosing flexor tenosynovitis; LJM, limited joint mobility; DD, Dupuytren’s disease

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Table 3. Distribution of the time elapsed from the date of the last diagnosis of diabetes-related hand syndromes to the date of the diagnosis of type 2 diabetes between cases and controls

Time elapsed (months)

<3 3 to <6 6 to <12

12 to <24

≥24

n % n % n % n % n % P value a CTS

Cases 193 11.0 74 4.2 138 7.9 235 13.4 1114 63.5 <0.001 Controls 422 5.8 288 4.0 540 7.4 949 13.1 5,072 69.8

SFT Cases 52 6.4 39 4.8 90 11.0 129 15.8 506 62.0 0.216 Controls 284 7.0 226 5.6 359 8.8 593 14.6 2,599 64.0

LJM Cases 4 5.8 2 2.9 5 7.3 9 13.0 49 71.0 0.413 Controls 11 3.3 8 2.4 22 6.5 27 8.0 269 80.0

DD Cases 0 0.0 0 0.0 0 0.0 1 16.7 5 83.3 0.821 Controls 2 7.4 0 0.0 3 11.1 5 18.5 17 63.0

CTS, Carpal tunnel syndrome; SFT, Stenosing flexor tenosynovitis; LJM, limited joint mobility; DD, Dupuytren’s disease a Based on Pearson’s χ2 test or Fisher’s exact test

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Table 4. Validity and performance of using diabetes-related hand syndromes as a clinical tool for identifying people with undiagnosed type 2 diabetes.

CTS FST LJM DD


CTS, Carpal tunnel syndrome; SFT, Stenosing flexor tenosynovitis; LJM, limited joint mobility; DD, Dupuytren’s disease; PPV, positive predictive value; NPV, negative predictive value.

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STROBE Statement Checklist of items that should be included in reports of observational studies

Section/Topic Item

No Recommendation

Reported

on Page No

Title and abstract 1 (a) Indicate the study’s design with a commonly used term in the title or the abstract Page 1

(b) Provide in the abstract an informative and balanced summary of what was done and what was found Page 3-4

Introduction

Background/rationale 2 Explain the scientific background and rationale for the investigation being reported Page 6-7

Objectives 3 State specific objectives, including any prespecified hypotheses Page 7

Methods

Study design 4 Present key elements of study design early in the paper Page 9-10

Setting 5 Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data collection

Page 8-9

Participants 6

(a) Cohort study—Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of

follow-up

Case-control study—Give the eligibility criteria, and the sources and methods of case ascertainment and control selection. Give the

rationale for the choice of cases and controls

Cross-sectional study—Give the eligibility criteria, and the sources and methods of selection of participants

Page 9

(b) Cohort study—For matched studies, give matching criteria and number of exposed and unexposed

Case-control study—For matched studies, give matching criteria and the number of controls per case Page 9

Variables 7 Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if

applicable Page 9

Data sources/measurement 8* For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe comparability of

assessment methods if there is more than one group Page 8

Bias 9 Describe any efforts to address potential sources of bias Page 10

Study size 10 Explain how the study size was arrived at Page 8

Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and why Page 10

Statistical methods 12

(a) Describe all statistical methods, including those used to control for confounding Page 10

(b) Describe any methods used to examine subgroups and interactions N/A

(c) Explain how missing data were addressed N/A

(d) Cohort study—If applicable, explain how loss to follow-up was addressed

Case-control study—If applicable, explain how matching of cases and controls was addressed

Cross-sectional study—If applicable, describe analytical methods taking account of sampling strategy

Page 9

(e) Describe any sensitivity analyses N/A

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Section/Topic Item

No Recommendation

Reported

on Page No

Results

Participants 13*

(a) Report numbers of individuals at each stage of study—eg numbers potentially eligible, examined for eligibility, confirmed

eligible, included in the study, completing follow-up, and analysed Page 8

(b) Give reasons for non-participation at each stage N/A

(c) Consider use of a flow diagram N/A

Descriptive data 14*

(a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential

confounders Page 12

(b) Indicate number of participants with missing data for each variable of interest Page 24

(c) Cohort study—Summarise follow-up time (eg, average and total amount) N/A

Outcome data 15*

Cohort study—Report numbers of outcome events or summary measures over time N/A

Case-control study—Report numbers in each exposure category, or summary measures of exposure Page 12, 24

Cross-sectional study—Report numbers of outcome events or summary measures N/A

Main results 16

(a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, 95% confidence interval).

Make clear which confounders were adjusted for and why they were included Page 12

(b) Report category boundaries when continuous variables were categorized Table 1

(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period N/A

Other analyses 17 Report other analyses done—eg analyses of subgroups and interactions, and sensitivity analyses Table 2-4

Discussion

Key results 18 Summarise key results with reference to study objectives Page 13

Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and magnitude

of any potential bias Page 16

Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar

studies, and other relevant evidence Page 13-14

Generalisability 21 Discuss the generalisability (external validity) of the study results Page 14-15

Other Information

Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the

present article is based Page 18

*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies.

Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE checklist is

best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at http://www.annals.org/, and

Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org.

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A medical claims-based case-control study of temporal relationship between clinical visits for hand syndromes and

subsequent diabetes diagnosis: Implications for identifying patients with undiagnosed type 2 diabetes

Journal: BMJ Open

Manuscript ID bmjopen-2016-012071.R2


Date Submitted by the Author: 03-Oct-2016

Complete List of Authors: Hou, Wen-Hsuan; Taipei Medical University, Center of Evidence-based Medicine; Taipei Medical University, 2. Master Program in Long-Term Care, College of Nursing Li, Chung-Yi; National Cheng Kung University, Department and Graduate Institute of Public Health, College of Medicine; China Medical University, 8. Department of Public Health, College of Public Health Chen, Lu-Hsuan ; National Cheng Kung University, Department and Graduate Institute of Public Health, College of Medicine Wang, Liang-Yi ; National Cheng Kung University, Department and Graduate Institute of Public Health, College of Medicine

Kuo, Li-Chieh; National Cheng Kung University, Department of Occupational Therapy Kuo, Ken; Taipei Medical University, Center of Evidence-based Medicine Shen, Hsiu-Nien; Chi Mei Medical Center, Department of Intensive Care Medicine Chiu, Chang-Ta ; Tainan Municipal An-Nan Hospital, Department of Dentistry






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Title Page

Title: A medical claims-based case-control study of temporal relationship

between clinical visits for hand syndromes and subsequent diabetes diagnosis:

Implications for identifying patients with undiagnosed type 2 diabetes

Running Title:

Hand syndromes in patients with undiagnosed diabetes


Wen-Hsuan Hou, MD, PhD1,2,3,4, Chung-Yi Li, PhD5,6, Lu-Hsuan Chen, MS5,6,

Liang-Yi Wang, PhD5, Li-Chieh Kuo, PhD7, Ken N. Kuo, MD1, Hsiu-Nien Shen,

MD, PhD8*, Chang-Ta Chiu, DDS, MS9*

1. Cochrane Taiwan, Taipei Medical University, Taipei, TAIWAN.














TAIWAN.

9. Department of Dentistry, Tainan Municipal An-Nan Hospital, China

Medical University, Tainan, TAIWAN.

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* Hsiu-Nien Shen and Chang-Ta Chiu contributed to this article equally.


Chang-Ta Chiu, DDS

Department of Dentistry, Tainan Municipal An-Nan Hospital-China Medical

University

No. 66, Sec. 2, Changhe Rd., Annan Dist., Tainan City 709, Taiwan


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Abstract

Objectives: To investigate whether a temporal relationship is present between

clinical visits for diabetes-related hand syndromes (DHSs) and subsequent

type 2 diabetes (T2DM) diagnosis, and accordingly, whether DHSs can be used

for identifying patients with undiagnosed T2DM.

Design: This study had a case–control design nested within a cohort of one

million people from the general population, which was followed from 2005 to

2010. The odds of prior clinical visits for DHSs, namely carpal tunnel syndrome

(CTS), flexor tenosynovitis, limited joint mobility, and Dupuytren’s disease,

were estimated for cases and controls. We used a conditional logistic regression

model to estimate the odds ratio (OR) and 95% confidence interval (CI) of

T2DM in association with a prior history of DHSs. The validity and predictive

value of using the history of DHSs in predicting T2DM diagnosis were

calculated.


Participants: We identified 33,571 patients receiving a new diagnosis of T2DM

(cases) between 2005 and 2010. Each T2DM case was matched with five


T2DM diagnosis.


was T2DM diagnosis.

Results: The OR of T2DM in association with prior clinical visits was

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