Potentials, challenges, and limitations of the analysis of administrative data on vascular limb amputations in health care
Abstract
Summary. Although more and more data on lower limb amputations are becoming available by leveraging the widening access to health care administrative databases, the applicability of these data for public health decisions is still limited. Problems can be traced back to methodological issues, how data are generated and to conceptual issues, namely, how data are interpreted in a multidimensional environment. The present review summarised all of the steps from converting the claims data of administrative databases into the analytical data and reviewed the wide array of sources of potential biases in the analysis of such data. The origins of uncertainty of administrative data analysis include uncontrolled confounding due to a lack of clinical data, the left- and right-censored nature of data collection, the non-standardized diagnosis/procedure-based data extraction methods (i.e., numerator/denominator problems) and additional methodological problems associated with temporal and spatial analyses. The existence of these methodological challenges in the administrative data-based analysis should not deter the analysts from using these data as a powerful tool in the armamentarium of clinical research. However, it must be done with caution and a thorough understanding and respect of the methodological limitations. In addition to this requirement, there is a profound need for pursuing further research on methodology and widening the search for other indicators (structural, process or outcome) that allow a deeper insight how the quality of vascular care may be assessed. Effective research using administrative data is based on strong collaboration in three domains, namely expertise in claims data handling and processing, the clinical field, and statistical analysis. The final interpretations of results and the countermeasures on the level of vascular care ought to be grounded on the integrity of research, open discussions and institutionalized mechanisms of science arbitration and honest brokering.
References
1 . Registry and health insurance claims data in vascular research and quality improvement. Vasa – Zeitschrift fur Gefässkrankheiten. 2017;46(1):11–5.
2 . Analysis of large databases in vascular surgery. J Vasc Surg. 2010;52(3):768–74.
3 Comparison of global estimates of prevalence and risk factors for peripheral artery disease in 2000 and 2010: a systematic review and analysis. Lancet. 2013;382(9901):1329–40.
4 . Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants. Lancet. 2016;387(10027):1513–30.
5 . Peripheral artery disease: epidemiology and global perspectives. Nat Rev Cardiol. 2017;14(3):156–70.
6 . A systematic literature review of quality of life in lower limb amputees. Disabil Rehabil. 2011;33(11):883–99.
7 . The quality of care. How can it be assessed? JAMA. 1988;260(12):1743–8.
8 [Guideline recommendations and quality indicators for invasive treatment of peripheral arterial disease in Germany: The IDOMENEO study for quality improvement and research in vascular medicine]. Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz. 2018;61(2):218–23.
9 . Assessing the quality of surgical care in vascular surgery; moving from outcome towards structural and process measures. Eur J Vasc Endovasc Surg. 2010;40(6):696–707.
10 Indicators of outcome quality in peripheral arterial disease revascularisations – a Delphi expert consensus. Vasa – Zeitschrift fur Gefässkrankheiten. 2018;47(6):491–7.
11 . Healthcare quality indicators of peripheral artery disease based on systematic reviews. Eur J Vasc Endovasc Surg. 2014;48(1):60–9.
12 Towards actionable international comparisons of health system performance: expert revision of the OECD framework and quality indicators. Int J Qual Health Care. 2015;27(2):137–46.
13 “No more amputations”: a complex scientific problem and a challenge for effective preventive strategy implementation on vascular field. Int Angiol. 2017;36(2):107–15.
14 International Consortium of Vascular Registries Consensus Recommendations for Peripheral Revascularisation Registry Data Collection. Eur J Vasc Endovasc Surg. 2018;56(2):217–37.
15 . Amputation in the diabetic: ten years experience in a district general hospital. Annals of the Royal College of Surgeons of England. 1987;69(3):127–9.
16 . Amputations in diabetic patients in Gotland and Umea counties 1971–1980. Acta medica Scandinavica Supplementum. 1984;687:89–93.
17 . Bias in amputation research; impact of subjects missed from a prospective study. PLoS One. 2012;7(8):e43629.
18 . Diabetes-related amputations of lower extremities in the Medicare population – Minnesota, 1993–1995. MMWR Morbidity and mortality weekly report. 1998;47(31):649–52.
19 Epidemiology of nontraumatic lower-extremity amputation in area 7, Madrid, between 1989 and 1999: a population-based study. Diabetes Care. 2001;24(9):1686–9.
20 . Fifteen-year trends in lower limb amputation, revascularization, and preventive measures among medicare patients. JAMA Surg. 2015;150(1):84–6.
21 . Norwegian trends in numbers of lower extremity revascularisations and amputations including regional trends in endovascular treatments for peripheral arterial disease: a retrospective cross-sectional registry study from 2001 to 2014. BMJ Open. 2017;7(11):e016210.
22 . Guide to Health Informatics. CRC Press, Taylor & Francis Group; 2015.
23 . Considerations of Analysis of Healthcare Claims Data SAS Global Forum 2018. [Available from: https://www.sas.com/content/dam/SAS/support/en/sas-global-forum-proceedings/2018/2630-2018.pdf. Accessed on 13th of August 2019].
24 Feasibility of using administrative data to compare hospital performance in the EU. Int J Qual Health Care. 2014;26(Suppl 1):108–15.
25 Administrative record linkage as a tool for public health research. Annual Review of Public Health. 2011;32:91–108.
26 Challenges in administrative data linkage for research. Big Data & Society. 2017;4(2):2053951717745678.
27 . Danish trends in major amputation after vascular reconstruction in patients with peripheral arterial disease 2002–2014. Eur J Vasc Endovasc Surg. 2019;57(1):111–20.
28 . Amputations and socioeconomic position among persons with diabetes mellitus, a population-based register study. BMJ Open. 2013;3(4).
29 . Socioeconomic and hospital-related predictors of amputation for critical limb ischemia. J Vasc Surg. 2011;53(2):330–9.e1.
30 . The influence of socio-economic deprivation on rates of major lower limb amputation secondary to peripheral arterial disease. Eur J Vasc Endovasc Surg. 2010;40(1):76–80.
31 . Differentiating incident from recurrent stroke using administrative data: the impact of varying lengths of look-back periods on the risk of misclassification. Neuroepidemiology. 2017;48(3–4):111–8.
32 Effect of the lookback period’s length used to identify incident acute myocardial infarction on the observed trends on incidence rates and survival: cardiovascular disease in Norway project. Circ Cardiovasc Qual Outcomes. 2015;8(4):376–82.
33 Influence of using different databases and “look back” intervals to define comorbidity profiles for patients with newly diagnosed hypertension: implications for health services researchers. PLoS One. 2016;11(9):e0162074.
34 Estimation using all available covariate information versus a fixed look-back window for dichotomous covariates. Pharmacoepidemiology and Drug Safety. 2013;22(5):542–50.
35 . Measuring prevalence and incidence of chronic conditions in claims and electronic health record databases. Clinical epidemiology. 2019;11:1–15.
36 . The Current Procedural Terminology (CPT) system the American Medical Association; [Available from: https://www.ama-assn.org/amaone/cpt-current-procedural-terminology. Accessed on 13th of August 2019].
37 . The International Classification of Health Interventions (ICHI) World Health Organization (WHO); [Available from: https://www.who.int/classifications/ichi/en/. Accessed on 13th of August 2019].
38 . The Nomesco Classification of Surgical Procedures (NCPS): The Nordic Medico-Statistical Committee (NOMESCO); [Available from: http://nowbase.org/publications/ncsp-classification-surgical-procedures. Accessed on 13th of August 2019].
39 . A Guide to Health Insurance Billing. 4th ed: Delmare Cengage Learning; 2014.
40 Systematic review of discharge coding accuracy. Journal of Public Health (Oxford, England). 2012;34(1):138–48.
41 Validity of administrative database code algorithms to identify vascular access placement, surgical revisions, and secondary patency. The Journal of Vascular Access. 2018;19(6):561–8.
42 Development of administrative data algorithms to identify patients with critical limb ischemia. Vasc Med. 2014;19(6):483–90.
43 Validation of carotid artery revascularization coding in Ontario health administrative databases. Clinical and Investigative Medicine Medecine Clinique et Experimentale. 2016;39(2):E73–8.
44 Systematic review of validated case definitions for diabetes in ICD-9-coded and ICD-10-coded data in adult populations. BMJ Open. 2016;6(8):e009952.
45 . A systematic review identifies valid comorbidity indices derived from administrative health data. J Clin Epidemiol. 2015;68(1):3–14.
46 . What are the key conditions associated with lower limb amputations in a major Australian teaching hospital? Journal of Foot and Ankle Research. 2012;5(1):12.
47 . Are we underestimating diabetes-related lower-extremity amputation rates? Results and benefits of the first prospective study. Diabetes Care. 2004;27(8):1892–6.
48 Trends in major lower limb amputation related to peripheral arterial disease in Hungary: A nationwide study (2004–2012). Eur J Vasc Endovasc Surg. 2015;50(1):78–85.
49 Incidence of lower extremity amputations in the diabetic compared with the non-diabetic population: A systematic review. PLoS One. 2017;12(8):e0182081.
50 . Pain and pain-related interference in adults with lower-limb amputation: comparison of knee-disarticulation, transtibial, and transfemoral surgical sites. Journal of Rehabilitation Research and Development. 2009;46(7):963–72.
51 . Systematic review and meta-analysis of validation studies on a diabetes case definition from health administrative records. PLoS One. 2013;8(10):e75256.
52 . Incidence and characteristics of lower limb amputations in people with diabetes. Diabet Med. 2009;26(4):391–6.
53 . Lower extremity amputations in persons with and without diabetes in Italy: 2001–2010. PLoS One. 2014;9(1):e86405.
54 . Trends in rates of lower extremity amputation among patients with end-stage renal disease who receive dialysis. JAMA Intern Med. 2018;178(8):1025–32.
55 Validation of a case definition to define chronic dialysis using outpatient administrative data. BMC Med Res Methodol. 2011;11:25.
56 . Diabetes registries: where we are and where are we headed? Diabetes Technology & Therapeutics. 2009;11(4):255–62.
57 . Effects of diabetes definition on global surveillance of diabetes prevalence and diagnosis: a pooled analysis of 96 population-based studies with 331,288 participants. Lancet Diabetes Endocrinol. 2015;3(8):624–37.
58 . Advancing measurement of diabetes at the population level. Current Diabetes Reports. 2018;18(11):108.
59 . Declines in the Incidence of Diabetes in the U.S. – Real Progress or Artifact? Diabetes Care. 2017;40(9):1139–43.
60 . Fewer major amputations among individuals with diabetes in Finland in 1997–2007: a population-based study. Diabetes Care. 2010;33(12):2598–603.
61 . Indicators for comparing the incidence of diabetic amputations: a nationwide population-based register study. Eur J Vasc Endovasc Surg. 2013;46(5):569–74.
62 . Modern epidemiology. London: Wolters Kluwer/Lippincont Wiliams and Wilkins; 2008.
63 . A comparison of direct adjustment and regression adjustment of epidemiologic measures. Journal of Chronic Diseases. 1985;38(10):849–56.
64 . Standardisation of rates using logistic regression: a comparison with the direct method. BMC Health Serv Res. 2008;8:275.
65 . Difficulties with regression analyses of age-adjusted rates. Biometrics. 1984;40(2):437–43.
66 . The cost of dichotomising continuous variables. BMJ. 2006;332(7549):1080.
67 . Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis. Springer; 2015.
68 . Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC. 2017.
69 . Statistical significance versus clinical importance of observed effect sizes: What do P values and confidence intervals really represent? Anesth Analg. 2018;126(3):1068–72.
70 . Geographic variation of lower-extremity major amputation in individuals with and without diabetes in the Medicare population. Diabetes Care. 2001;24(5):860–4.
71 Evaluation of regional variation in total, major, and minor amputation rates in a national health-care system. Int J Qual Health Care. 2007;19(6):368–76.
72 Location, location, location: geographic clustering of lower-extremity amputation among Medicare beneficiaries with diabetes. Diabetes Care. 2011;34(11):2363–7.
73 Temporal trends and geographic variation of lower-extremity amputation in patients with peripheral artery disease: results from U.S. Medicare 2000–2008. J Am Coll Cardiol. 2012;60(21):2230–6.
74 Regional intensity of vascular care and lower extremity amputation rates. J Vasc Surg. 2013;57(6):1471–79, 80 e1–3.; discussion 9–80.
75 . National review of factors influencing disparities and types of major lower extremity amputations. Ann Vasc Surg. 2014;28(5):1157–65.
76 . Variation in the recorded incidence of amputation of the lower limb in England. Diabetologia. 2012;55(7):1919–25.
77 . Lower limb amputation in England: prevalence, regional variation and relationship with revascularisation, deprivation and risk factors. A retrospective review of hospital data. J R Soc Med. 2014;107(12):483–9.
78 . Prevalence and regional distribution of lower limb amputations from 2006 to 2012 in Germany: a population based study. Eur J Vasc Endovasc Surg. 2015;50(6):761–6.
79 . Regional variation in the incidence of diabetes-related amputations in The Netherlands. Diabetes Res Clin Pract. 1996;31(1–3):125–32.
80 . Geographic variation of the incidence rate of lower limb amputation in Australia from 2007–12. PLoS One. 2017;12(1):e0170705.
81 . Applied Spatial Statistics for Public Health Data: John Wiley & Sons; 2004.
82 . Administrative database research has unique characteristics that can risk biased results. J Clin Epidemiol. 2012;65(2):126–31.
83 . Use of administrative medical databases in population-based research. J Epidemiol Community Health. 2014;68(3):283–7.
84 . Lower extremity amputations – a review of global variability in incidence. Diabet Med. 2011;28(10):1144–53.
85 . Process versus outcome indicators in the assessment of quality of health care. Int J Qual Health Care. 2001;13(6):475–80.
86 The All-Party Parliamentary Group on Vascular and Venous Disease [Available from: https://www.vvappg.com/new-page-1. Accessed on 13th of August 2019].
87 . The honest broker. Making sense of Science in Policy and Politics: Cambridge University Press; 2007.