Ó The Author 2008. Published by Oxford University Press. All rights reserved. For permissions, please e-mail:
[email protected].
doi:10.1093/fampra/cmn028
Family Practice Advance Access published on 17 June 2008
The coming of age of ICPC: celebrating the 21st birthday of the International Classification of Primary Care Jean-Karl Solera, Inge Okkesb, Maurice Woodc and Henk Lambertsb Soler J-K, Okkes I, Wood M and Lamberts H. The coming of age of ICPC: celebrating the 21st birthday of the International Classification of Primary Care. Family Practice 2008; 25: 312–317. The International Classification of Primary Care (ICPC) has, since its introduction in 1987, been quite successful. Now in its second revised version, it has been translated in 22 languages, accepted by the World Health Organization (WHO) as a member of the Family of International Classifications, and is being widely used both in routine daily practice and in research. In this contribution, it is explained that ICPC was designed as a theoretical classification, and that it has especially great potential when used (1) supported by the ICPC2/ICD10 Thesaurus, (2) in sufficiently large studies to allow all classes to be observed often enough to provide reliable data, and (3) in studies based on data on episodes of care, rather than encounter data only. Under these conditions, the likelihood ratios of symptoms given a diagnosis, and of co-morbidity become available, which define the clinical content of family practice. Keywords. ICPC, episode of care, reason for encounter, prior and posterior probabilities.
Malta and Serbia) and also in encounter studies (Australia, Norway, Denmark and The Netherlands), it is now supported by a large empirical database.6 In the index of family practice, the search term ‘ICPC’ refers to over 100 articles (since 1984); in contrast, ‘Read Codes’ as a search term refers to 39 articles. This trend is also seen in PubMed (108 against 26). ICPC reached ‘adulthood’ with ICPC-2-Revised, published in 2005 by Oxford University Press, presenting the latest revision of ICPC with inclusion and exclusion criteria and its mapping to ICD-10.7 This publication also included a companion CD8 containing this version of ICPC in electronic form, an applied epidemiological retrieval programme with data from the Dutch Transition Project ‘Episodes of Care in Family Practice (EFP)’,9 the ICPC2-ICD-10 Thesaurus,10 a Glossary of terms and an ICPC Tutorial. Although this is reason for satisfaction, it appears that on occasions two of the characteristics of ICPC (Box 1) have been misunderstood: namely the frequency requirement for classes in ICPC and the significant additional utility of the use of episodes of care to structure data versus that of encounter-based data.6,11–13
Introduction In 1987, the International Classification of Primary Care (ICPC) was published as a tool to order the domain of family practice. It was empirically designed, from family medicine data, to appropriately classify and define relationships between events across the whole breadth of the discipline by using the concept of episode of care. An episode of care, as distinct from an episode of illness or disease, is a health problem or disease from its first presentation to the health care provider to the last presentation for the same problem.1,2 As a theoretical classification, several of its characteristics (Box 1) were quite distinct from the dominant perspectives on the content of family practice at that time and constituted a true paradigm shift in many ways.2–5 Notwithstanding initial misunderstandings, it grew up handsomely and became quite successful.3 Translated in 22 languages, accepted by the World Health Organization (WHO) as a member of the Family of International Classifications (WHO/ FIC), widely used for the routine collection of data on episodes of care (The Netherlands, Japan, Poland,
Received 9 October 2007; Revised 29 March 2008; Accepted 29 April 2008. a Visiting Professor, Institute of Postgraduate Medicine and Primary Care, Faculty of Life and Health Sciences, University of Ulster, Coleraine BT52 1SA, UK, bformerly Department of Family Medicine, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands and cformerly Department of Family Medicine, Virginia Commonwealth University, Richmond, VA, USA. Correspondence to Jean-Karl Soler, The Family Practice, Bay Street (Triq ir-Rand), Attard ATD 1300, Malta; Email:
[email protected]
312
The coming of age of ICPC BOX 1 The main characteristics of the ICPC1–5 Its purpose is to order the domain of family practice in the format of episodes of care. It provides a single terminology for the patient’s RFE and the family physicians diagnosis, thus representing both sides of the same coin. It captures the changes (transitions) in the content of episodes of care over time. It follows strict taxonomic rules, and so its classes are mutually exclusive. It offers–if possible–one class for common (occurring >1 per 1000 patient years) reasons for encounter and diagnoses. Less common classes are included in ‘ragbags’. Its biaxial structure (chapters for body systems/problem areas and components identical throughout all chapters) results in threedigit mnemonic, alphanumeric codes. Its reliability and validity are supported by its coding rules and a growing comparative international database. In the coding process, localization takes precedence over aetiology. Symptom diagnoses take precedence over disease diagnoses that are uncertain (i.e. do not fulfil the inclusion criteria). It does not cater for mind-body metaphors: ‘psychosomatic’ and ‘somatoform’ disorders are not included.
Frequency of observations In comparison with mortality and hospital data with a large denominator, ICPC data from family practice are often derived from a relatively small number of patients.5,6 ICPC produces an effective reduction of primary care data, as it has only 684 diagnostic classes (a code or rubric in ICPC which defines one concept, a symptom or sign, intervention or disease), mainly selected on the basis of real frequencies of occurrence in daily family practice. Still, problems may arise in the interpretation of such data in comparative studies. Let us consider a practical example. In countries with well-defined practice populations, a family doctor (FD) might care for around 2000 listed patients. In 1 year, she would thus collect data on approximately 1400 complete patient years.14–17 A group of FDs may deal with ten-fold (14 000 patient years), while a large multi-site study might provide data on 140 000 patient years (from, say, 100 practices).18–20 In Table 1, the distribution of prevalences (a table of the annual prevalences of each component 7 (disease label) ICPC class, calculated on the basis of the three populations listed in the previous paragraph) of diagnostic ICPC classes is given for each of these three denominators, together with the width of their 95% confidence intervals (CIs). This is the preferred presentation of rates under the assumption that the data are unbiased.21,22 In reliability studies in family practice, the error rate (including missing data) is reported
313
to vary between 10% and 30%.23–27 In EFP,9 the error rate, including missing data, has repeatedly been established as approximately 5%.28–30 Infrequent diagnoses included in ragbags (a class which includes symptoms or diseases with a low prevalence, not elsewhere classified), however, appear to have a relatively low reliability.29 A prevalence rate which is not larger than the width of its own CI cannot be interpreted, since the observation is lost in the ‘noise’ of its own observation.31,32 Considering that ICPC covers the domain of family medicine, it would be desirable to have prevalences of most classes which can be usefully analysed, i.e. which are larger than their CIs. Consequently, it appears from Table 1 that data from a single practice are insufficient to characterize the wide variation in morbidity seen in family practice. A group practice does significantly better, but still approximately half of the available classes are not sufficiently represented. In a large study, however, practically all the available classes allow for detailed analysis since the diagnoses are observed commonly enough to provide reliable data.33 However, the situation is worse when only encounter data with no structuring based on episodes of care are available. In those data sets, one can only estimate whether encounters for a certain diagnosis represent recurrence of disease, multiple encounters for the same problem or else various combinations of both. In such cases, the rates are calculated with the number of encounters as the denominator, in contrast with data structured using episode of care with a denominator expressed as the number of patient years.6,11,13 A multi-site episode of care study with approximately 140 000 patient years will contain almost 1 000 000 subencounters, that part of an encounter which deals with one single episode of care, since an average patient year approximately includes three episodes of care with two encounters each (Table 1). A similar study based on encounter data thus requires a much larger denominator to estimate incidence rates, while it is practically impossible to calculate reliable prevalences. However, a rough estimate may be obtained by using all patients visiting only once in 1 year as a denominator, under the assumption that differences in the utilization per episode of care do not substantially skew this estimate. Table 2 contains primary care encounter data derived from the US National Ambulatory Medical Care Survey (NAMCS 1995–1999, which includes data from paediatricians, gynaecologists and internists working in the community)18,19 compared with the episode of care data from the Transition Project (1985–2002, FDs in The Netherlands).8,15 This also allows a comparison between ICPC and ICD-9 data. It is evident that the use of ICD-9-CM as a classification (with 2463 diagnostic classes) in ambulatory care will not produce adequately useful clinical or epidemiological
314
Family Practice—an international journal
TABLE 1 Ranking of the prevalences (described as rates per 1000 patient years) of ICPC classes for diagnoses presenting in family practice, with the width of the 95% CI for the observation within 1 practice (1356 patient years), 10 practices (13 560 patient years) and 100 practices (135 600 patient years) ICPC class
Rank in prevalence
Prevalence per 1000 patient years
95% CI width for 1356 patient years
95% CI width for 13 560 patient years
95% CI width for 135 600 patient years
K86/87 (hypertension) R74 (URTI) N89 (migraine) B80 (iron deficiency anaemia) B81 (pernicious anaemia) A20 (euthanasia discussion/request) P77 (suicide attempt) N86 (multiple sclerosis) D76 (pancreatic malignancy) A73/(malaria)
3 8 51 75 241 316 397 434 587 609
80 54 15 11 3 2 1 1 0.2 0.1
31 25 14 12 6 6 4 3 2 1
9 5 4 4 2 2 1 1 0.5 0.5
3 2 1 1 0.6 0.4 0.3 0.3 0.2 0.1
Source: EFP-extended.17
TABLE 2 Frequency of use of classifications in primary care. ICPC (for diagnoses and RFE) is compared to ICD-9-CM (for diagnoses) and the RVC (for RFV). Data from The Netherlands (FDs) and the US (FDs, internists and paediatricians) expressed as rates per 1000 observations (subencounters) Number of classes occurring once or more (>1) per 1000 observations
Available classes
Classes unused, N (%)
205 168 166 135 179 134 168 108
686 2463 2463 2463 686 1898 1898 1898
9 (13) 374 (15) 1113 (45) 1532 (62) 14 (2) 1242 (65) 1424 (75) 1544 (81)
The Netherlands, FD, diagnosis (ICPC) USA, FD, diagnosis (ICD-9-CM) USA, IM, diagnosis (ICD-9-CM) USA, Ped, diagnosis (ICD-9-CM) Netherlands, RFE (ICPC) USA, FD, RFV (RVC) USA, IM, RFV (RVC) USA, Ped, RFV (RVC) Sources: EFP8,32 and NAMCS 1995–1999.15,16 RVC, reason for visit classification.
data. This is because the large majority of available classes will occur less than once per 1000 observations (Table 2) and will consequently have large CIs. It is surprising that, for both common diagnoses and RFE/ RFV, a need for similar levels of detail and differentiation exists in family practice, internal medicine and paediatrics. In ambulatory care, the main workload for doctors consists of approximately 170 diagnoses and 150 RFE. These form the core of this group’s professional frame of reference. It is also apparent that FDs do use a large number of available diagnostic labels infrequently (only once or twice over the period of observation).
Advantages of using episodes of care A core element of the professional identity of FDs is their ability to reliably estimate the probabilities of diagnoses, and to assess the clinical utility of interventions, in their practice populations. The
estimation of the predictive value of symptoms and complaints and of the co-morbidity of any combination of two diseases form the basis of this applied knowledge.1,21,34 Episode of care data are particularly useful to illustrate this. For example, the probability of the diagnosis ‘asthma’ in an episode of care that starts with the RFE ‘shortness of breath’ is very high. The odds ratio is 23.35 with a narrow CI (Table 3), representing a high post-test (posterior) probability. The positive likelihood ratio (LR+) is substantially greater than unity (14.52; 95% CI 13.51–15.60), but the negative likelihood ratio (LR–) (0.62; 95% CI 0.60 to 0.65) really makes the difference. The low LR– supports the FD in considering asthma to be a less likely diagnosis in those patients who do not present with shortness of breath. The familiar ‘abcd’ matrix of test result against disease present/absent, shown in Table 3, results in a straightforward formula for the odds ratio: (ad/bc). Clearly, it is the very substantial (d) of 194 388 patient years—very characteristic for family practice
315
The coming of age of ICPC TABLE 3
Prior (pre-test) and posterior (post-test) probabilities of the diagnosis asthma (R96) for the RFE shortness of breath (R02) in 201 037 patient years
Presented with RFE shortness of breath (R02) Presented with other RFE
Patient years with episode of care R96 (asthma)
Patient years with other episodes of care
518 (a) 793 (c)
5.438 (b) 194.388 (d)
Total patient years
5.956 195.181
Source: EFP.8 Sensitivity of RFE shortness of breath for the diagnosis asthma: 0.40. Specificity: 0.97. LR+ (of RFE present in episodes with asthma against episodes with another diagnosis): 14.52 (95% CI 13.51–15.60). LR– (of RFE absent in episodes with asthma against episodes with other diagnoses): 0.62 (95% CI 0.60–0.65). Positive predictive value (PV+): 0.09 (prior probability). Negative predictive value (PV–): 0.99 (prior probability). Diagnostic odds ratio: 23.35 (95% CI 20.84–26.17) (posterior probability).
TABLE 4
Co-morbidity of ‘acute bronchitis’ (R78) and asthma (R96) with LR+ and LR– and 95% CI
Co-morbidity of:
% of R78 with R96 (PV+)
Incident R78 (n = 7622) with incident R96 (n = 1311) Incident R96 (n = 7622) with incident R78 (n = 1311) Incident R78 (n = 7622) with prevalent R96 (n = 4267) Prevalent R96 (n = 4267) with incident R78 (n = 7622)
4.8
% of R96 with R78 (PV+)
27.8 11.8 20.3
LR+ (95% CI)
LR– (95% CI)
Odds ratio (95% CI)
7.67 (7.01–8.39) 9.80 (8.70–11.03) 5.91 (5.54–6.30) 6.46 (6.01–6.93)
0.75 (0.72–0.77) 0.96 (0.95–0.96) 0.83 (0.81–0.84) 0.90 (0.90–0.91)
10.24 (9.05–11.58) 10.24 (9.05–11.58) 7.15 (6.61–7.74) 7.15 (6.61–7.74)
PV+, positive predictive value.
research—that gives power to the calculations and allows such precise and reliable estimates! With regard to co-morbidity, it is essential to present odds ratios in addition to the LR+ and LR–. This is because the mutual overlap between two episodes of care (shown as percentages in Table 4) is not identical (e.g. 4.8 versus 27.8%; 11.8 versus 20.3%). Thus the LRs, especially the LR–, differ substantially for the same odds ratio (Table 4). Moreover, the calculation of co-morbidity between a chronic disease, such as asthma, and an acute disease, such as acute bronchitis, requires distinguishing between incident and prevalent episodes of care. Tables 3 and 4 show that data from an episode of care database can provide an epidemiological profile of the occurrence of respiratory problems in a family practice population. The incidence of either condition makes the incidence of the other more likely (LR+ is 7.7 and 9.8), but although incident acute bronchitis is made less likely without incident asthma (LR– is 0.75), the converse is not true (LR– is 0.96). Such an insight into these relationships cannot be derived from an encounter-based study.
Final remarks ICPC has certainly grown into early adulthood: it is full of promise and well prepared for a long and productive life. The potential for the routine use of ICPC,
supported by the ICPC2/ICD-10 Thesaurus included with ICPC-2-R which facilitates automatic double coding in electronic patients records, is now increasingly appreciated and understood.35 Several of the theoretical principles underlying ICPC have proven to be sound and resilient, and deserve to remain guiding principles for an international family of classifications in the 21st century. Two elements require further attention: inter-doctor/ practice variation and its impact of data precision and the implementation of computer-assisted data entry. Inter-practice variation (nesting) in coding RFE, diagnoses and prescriptions appears to be substantial and often follows a characteristic and stable pattern.36,37 Part of this variation is a welcome proof of personalized doctoring and patient choice, another part, however, may be less desirable. As a consequence, empirical data are needed to develop models for computer-assisted data entry by FDs.38 It will be a challenge to incorporate new ordering principles of morbidity into the future versions of ICPC (ICPC-3) based on data drawn from international family practice settings. See the website www.transitieproject.nl for more information, including two public domain databases of ICPC data (used for this study) and ICPC tools and information/references. An ICD-10 ICPC-2 English Dutch thesaurus is included. All these are also available in a CD ROM accompanying the ICPC-2-R book, available from Oxford University Press or from
316
Family Practice—an international journal
Wonca (e-mail Alfred Loh, Wonca CEO, ceo@wonca. com.sg).
12
13
Acknowledgements We are greatly indebted to Dr Kerr White for his never-ending support for ICPC. We thank Prof. GE Fryer for the calculation of the NAMCS data in Table 2.
14
15
Declaration 16
Funding: None. Ethical approval: None. Conflicts of interest: None.
References 1
2
3
4
5
6
7
8
9
10
11
Lamberts H, Wood M. ICPC. International Classification of Primary Care. Oxford: Oxford University Press, 1987. Okkes IM, Lamberts H. Classification and the domain of family practice. In Jones R (ed.). The Oxford Textbook of Primary Medical Care, Volume 1. Oxford: Oxford University Press, 2003: 139–152. Lamberts H, Wood M. The birth of The International Classification of Primary Care (ICPC). Serendipity at the border of Lac Le´man. Fam Pract 2002; 19: 433–435. Lamberts H, Meads S, Wood M. Classification of reasons why persons seek primary care: pilot study of a new system. Public Health Rep 1984; 99: 597–605. Lamberts H, Wood M, Hofmans-Okkes IM. The International Classification of Primary Care in the European Community (with a sixteen language disk of ICPC). Oxford: Oxford University Press, 1993. De Lusignan S, Van Weel C. The use of routinely collected computer data for research in primary care: opportunities and challenges. Fam Pract 2006; 23: 253–263. International Classification Committee of Wonca. ICPC-2-R. International Classification of Primary Care. Revised second edition. With a CD-Rom: ICPC in the Amsterdam Transition project. Oxford: Oxford University Press, 2005. Okkes IM, Oskam SK, Lamberts H. ICPC in the Amsterdam Transition Project. CD-Rom. Amsterdam: Academic Medical Center/University of Amsterdam, Department of Family Medicine, 2005. http://www.transitieproject.nl/ (last accessed 15 May 2008). Okkes IM, Oskam SK, Van Boven K, Lamberts H. EFP. Episodes of Care in Family Practice. Epidemiological data based on the routine use of the International Classification of Primary Care (ICPC) in the Transition Project of the Academic Medical Center/University of Amsterdam. (1985–2003). In Okkes IM, Oskam SK, Lamberts H (eds). ICPC in the Amsterdam Transition Project. CD-Rom, Amsterdam: Academic Medical Center/ University of Amsterdam, Department of Family Medicine, 2005. Becker HW, Oskam SK, Okkes IM, Van Boven K, Lamberts H. ICPC2-ICD10 Thesaurus. A diagnostic terminology for semiautomatic double coding in Electronic Patient Records. In Okkes IM, Oskam SK, Lamberts H (eds). ICPC in the Amsterdam Transition Project. CD-Rom, Amsterdam: Academic Medical Center/University of Amsterdam, Department of Family Medicine, 2005. Britt H. A new coding tool for computerised clinical systems in primary care—ICPC plus. Aust Fam Physician 1997; 26 (suppl 2): S79–S82.
17
18
19
20
21
22
23
24
25
26
27
28
29
Lamberts H, Okkes I. Sense and specificity in computer based patient records in general practice—the ICPC-ICD-10 conversion structure as the Holy Grail [editorial]. Aust Fam Physician 1997; 26 (suppl 2): S57–S9. Soler JK, Okkes IM. Outlining errors and inaccuracies in this review. Available at http://fampra.oxfordjournals.org/cgi/eletters/23/2/253 (last accessed on 15 May 2008). Deveugele M, Derese A, Van den Brink-Muinen A, Bensing J, De Maeseneer J. Consultation length in general practice: cross sectional study in six European countries. Br Med J 2002; 325: 472–477. Okkes IM, Polderman GO, Fryer GE et al. The role of family practice in different health care systems. A comparison of reasons for encounter, diagnoses, and interventions in primary care populations in the Netherlands, Japan, Poland, and the United States. J Fam Pract 2002; 51: 72. Jones R, Latinovic R, Charlton J, Gulliford MC. Alarm symptoms in early diagnosis of cancer in primary care: cohort study using General Practice Research Database. Br Med J 2007; 334 (7602): 1040. Fryer GE, Green LA, Dovey SM, Yawn BP, Phillips RL, Lanier D. Variation in the Ecology of Primary Care. Ann Fam Med 2003; 1: 81–9. Binns HJ, Lanier D, Pace WD et al. Describing primary care encounters: the Primary Care Network Survey and the National Ambulatory Medical Care Survey. Ann Fam Med 2007; 5: 39–47. Tierney WM, Oppenheimer CC, Hudson BL et al. A National Survey of Primary Care Practice Based Research Networks. Ann Fam Med 2007; 5: 242–250. Okkes IM, Oskam SK, Van Boven K, Lamberts H. EFP-Extended. Episodes of Care in Family Practice. Epidemiological data based on the routine use of the International Classification of Primary Care (ICPC) in the Transition Project of the Academic Medical Center/University of Amsterdam, extended version: a retrieval program for the Amsterdam Transition Project’s data with TransHis as an Electronic Patient Record. 1995–2004. 2005. Academic Medical Centre, University of Amsterdam; Amsterdam, The Netherlands. Eldridge S. Good practice in statistical reporting for Family Practice. Fam Pract 2007; 24: 93–94. Altman DG, Machin D, Bryant T, Gardner MJ. Statistics with Confidence. Confidence Intervals and Statistical Guidelines. London: Blackwell BMJ Books, 2000. Jordan K, Porcheret M, Croft P. Quality of morbidity coding in general practice computerized medical records: a systematic review. Fam Pract 2004; 21: 396–412. Porcheret M, Hughes R, Litt M et al. Data quality of general practice electronic health records: the impact of a program of assessments, feedback, and training. J Am Med Inform Assoc 2004; 11: 78–86. Peabody JW, Luck J, Jain S, Berthenthal D, Glassman P. Assessing the accuracy of administrative data in health information systems. Med Care 2004; 42: 1066–1072. Thiru K, Hassey A, Sullivan F. Systematic review of scope and quality of electronic patient record data in primary care. Br Med J 2003; 326: 1070–1072. Brown PJB, Warmington V, Laurence M, Prevost AT. Randomised crossover trial comparing the performance of Clinical Terms Version 3 and Read Codes 5 byte set coding schemes in general practice. Br Med J 2003; 326: 1127–1131. Lamberts H, Brouwer HJ, Marinus AMF, Hofmans-Okkes IM. The use of ICPC in the Transition project. Episode oriented epidemiology in general practice. In Lamberts H, Wood M, Hofmans-Okkes IM (eds). The International Classification of Primary Care in the European Community, Oxford: Oxford University Press, 1993: pp. 45–93. Meijer JS. [A perspective on disease. The classification of disease and health problems in general practice] [Dutch, with a summary in English]. PhD Thesis, University of Amsterdam, Amsterdam, 1998.
The coming of age of ICPC 30
31
32
33
34
Veltman MTM. [Episodes of care in general practice. An analysis of seven years’ data] [Dutch]. PhD Thesis, University of Amsterdam, Lelystad, Meditekst, 1995. Burnand B, Kernan WN, Feinstein AR. Indexes and boundaries for ‘quantitative significance’ in statistical decisions. J Clin Epidemiol 1990; 43: 1273–1284. Feinstein AR. P-values and confidence intervals: two sides of the same unsatisfactory coin. J Clin Epidemiol 1998; 51: 355–360. Eimerl TS. Organized curiosity: a practical approach to the problem of keeping records for research purposes in general practice. J R Coll Gen Pract. 3: 246–252. Okkes IM, Oskam SK, Lamberts H. The probability of specific diagnoses for patients presenting with common
35
36
37
38
317
symptoms to Dutch family physicians. J Fam Pract 2002; 51: 31–36. Okkes IM. Banff declaration calls for ICPC in the United States. Wonca News 2004. Dickinson LM, Basu A. Multilevel modelling and practice-based research. Ann Fam Med 2005; 3 (suppl 1): S52–S60. Marinus AMF. [Inter-doctor variation in general practice] [Dutch, with a summary in English]. PhD Thesis, University of Amsterdam, Meppel, Krips, 1993. George (Spenceley) SE, Warren JR. Statistical modelling of general practice medicine for computer assisted data entry in electronic medical record systems. Int J Med Inform 2000; 57: 77–89.