a B3E, INSERM SC4, Faculté de Médecine Saint-Antoine, France.
b Réseau National de Santé Publique, Saint-Maurice, France.
c The Clinical Epidemiology Group: Scientific Committee: S Alfandari, F Bastides, E Billaud, A Boibieux, F Boué, A Cabié, L Cotte, L Cuzin, F Dabis, J-P Daurès, V Garrait, J-A Gastaud, C Gaud, A Goudeau, C Katlama, D Lacoste, J-M Lang, H Laurichesse, P Leclercq, C Leport, M-E Mars, S Matheron, M-C Meyohas, C Michelet, J Moreau, C Pradier, D Quinsat, C Rabaud, W Rozenbaum,D Salmon-Ceron, M Sobesky, H Tissot-Dupont, R Verson, J-P Viard, A Waldner-Combernaoux. Data co-ordinating centre: INSERM SC4 (D Costagliola, M Mary-Krause, L Lièvre, J Deloumeaux). Centres d'Information et de Soinsde lImmunodéficience Humaine (CISIH): Paris area: CISIH de Bichat-Claude Bernard (G-H Bichat-Claude Bernard: C Leport, S Matheron, A Villemant, E Bouvet), CISIH de Paris-Centre (Hôpital Broussais; Hôpital Cochin), CISIH de Paris-Ouest (Hôpital Necker adultes: J-P Viard; Hôpital Laennec: W Lowenstein; Hôpital Pasteur), CISIH de Paris-Sud (Hôpital Antoine Béclère: F Boué; CHU de Bicêtre: C Goujard; Hôpital Henri Mondor; Hôpital Paul Brousse), CISIH de Paris-Est (Hôpital Rothschild; Hôpital Saint-Antoine; Hôpital Tenon), CISIH de la Pitié-Salpétrière (G-H Pitié-Salpétrière: A Coutellier, T Similowski), CISIH de Saint-Louis (Hôpital Saint-Louis; Hôpital Lariboisière: J-M Salord), CISIH 92 (Hôpital Ambroise Paré; HôpitalLouis Mourier: C Chandemerle), CISIH 93 (Hôpital Avicenne: M Bentata,B Jarousse; Hôpital de Saint-Denis; Hôpital Jean Verdier). Outside Paris area: CISIH Auvergne-Loire (CHU de Clermont-Ferrand: H Laurichesse; Hôpital de Saint-Etienne), CISIH de Bourgogne-Franche Comté (Hôpital de Besançon: C Drobacmeff; CHU de Dijon: M-C Borne), CISIH de Caen (CHU de Caen: C Bazin), CISIH de Grenoble (CHU de Grenoble: P Leclercq), CISIH de Lyon (Hôpital de la Croix-Rousse; Hôpital Edouard Herriot; Hôtel-Dieu; Hôpital de Lyon-Sud), CISIH de Marseille (Hôpital Conception; Hôpital Houphouët-Boigny; Institut Paoli Calmettes: J-A Gastaut; Hôpital de Sainte-Marguerite: J-A Gastaut; Hôtel-Dieu; rattachés au CISIH (CHG d'Aix-En-Provence; Hôpital d'Arles; CH d'Avignon: G Brun; CH de Digne Les Bains:P Granet-Brunello; Hôpital de Gap; Hôpital de Martigues; Hôpital de Toulon)), CISIH de Montpellier (Hôpital de Montpellier; Hôpital de Nîmes), CISIH de Nancy (CHU de Nancy: C Rabaud), CISIH de Nantes (CHU de Nantes: S Perroy), CISIH de Rennes, CISIH de Rouen, CISIH de Strasbourg, CISIH de Toulouse (Hôpital Purpan: L Cuzin), CISIH de Tourcoing-Lille (CH de Tourcoing: M Valette), CISIH de Tours (CHU Bretonneau: J-F Besnier,M-F Maître), rattaché au CISIH de Nice: Hôpital Antibes. Overseas: CISIH de Guadeloupe (CHU de Pointe-à-Pitre: I Lamaury), CISIH de Guyane (CHG de Cayenne: M Sobesky), CISIH de Martinique (CHU de Fort-de-France:G Sobesky), CISIH de La Réunion (CHD de Saint-Denis: C Sautron).
Reprint requests to: L Lièvre, INSERM SC4, Faculté de Médecine Saint-Antoine, 27 rue Chaligny, 75571 Paris Cedex 12, France. E-mail: lievre{at}b3e.jussieu.fr
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Abstract |
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Methods An anonymous record-linkage algorithm was developed to identify those cases common to both anonymous surveillance systems. The linkage was based on sex, date of birth, and infection risk group, all strictly matched, and on the dates of AIDS diagnosis and of death, the places of diagnosis and residence, and the AIDS-defining diseases at diagnosis. The total number of AIDS cases and completeness of both surveillance systems were estimated using a capture-recapture approach, assuming independence of the ascertainment sources.
Results The completeness of the mandatory reporting was estimated at 83.6% (95% CI : 82.984.3), and that of the FHDH at 47.6% (95% CI : 46.948.3) for the surveillance of AIDS cases diagnosed among adults in France between 1990 and 1993. The completeness of the system based on FHDH increased over the study period as more hospitals joined the project, while the completeness of the DO surveillance system remained stable.
Conclusion This approach was useful in estimating the underreporting of AIDS cases in France. Regularly performed, it will allow the impact of underreporting to be monitored over time.
Keywords AIDS, surveillance, underreporting, record-linkage, capture-recapture, France
Accepted 6 July 1999
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Introduction |
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Capture-recapture methods are valuable tools in epidemiology for estimating disease incidences or prevalences211 that require case notification to different surveillance systems. These methods have already been used in the case of various diseases including AIDS.1221 They provide an estimation of the size of the population of interest and the completeness of the surveillance systems can be deduced from that estimation. It is thus an alternative to the costly and almost impossible search for perfect exhaustiveness.19
Capture-recapture methods are based on matching several databases. This aspect has not received much attention in the capture-recapture literature, because in most situations a unique identifier is common to the different databases, or the size of the respective databases allows a manual search for matches. Nonetheless, there is an increasingly large literature on the techniques for record-linkage between medical databases.2227
This paper describes a method for record-linkage between two anonymous databases and provides an estimation of the underreporting of AIDS cases in France by a capture-recapture method. It examines the correspondence between the AIDS cases reported to the Réseau National de Santé Publique (RNSP) as mandatory reports and the AIDS cases included in the French Hospital Database on HIV (FHDH), housed at INSERM SC4. This study was authorized by the Commission Nationale de lInformatique et des Libertés (National Commission for Data Protection and Privacy).
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Methods |
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The record-linkage procedure used the following available information: date of birth, sex, département of domicile, HIV infection risk category (homo/bisexual, intravenous drug user, homo/bisexual and intravenous drug user, haemophilic, blood transfusion, other or unknown risk category), date of AIDS diagnosis, AIDS-defining clinical manifestations (maximum of four), département of declaration. In addition, the date of death was available for 13 000 of the 13 024 subjects known to have died, and the date of the single transfusion for 460 of the 678 patients in the group infected by transfusion.
The French Hospital Database (FHDH) on HIV infection
DMI2 is a software installed in the HIV Information and Care Centres (CISIH) in France. Each CISIH groups together several hospitals involved in the follow-up of HIV-positive patients. Since 1987, the number of French hospitals participating in a CISIH has gradually increased as more hospitals have become active in treating HIV patients. DMI2 software was developed jointly by INSERM SC4 and the Ministry of Health Affairs and is the property of this Ministry. INSERM SC4 is responsible for the administration and analysis of the national database containing the clinical and epidemiological data extracted by this software from the local databases. It contains information about those seropositive patients infected with HIV-1 or HIV-2, or both, who are treated in a CISIH and who provided written consent to participate in the database.28 The data are nominative in each local database (in the hospital where the patient is treated) but are made anonymous before their centralization at INSERM SC4, in accordance with the guidelines of the Commission Nationale de lInformatique et des Libertés (CNIL): an anonymous code generated from the first and last name and day and month of birth is the identifier in the national database.29 This coding is irreversible and cannot be used, even when the attribution procedure is known, to retrace the steps to identify the individual. When gathering the hospital databases into the national one, duplicates of anonymous code are searched. Information from the medical records is used to state whether the two records are related to the same patient (true matches) or not (false positive matches). When the two records are considered as true matches, their information is gathered in one single record. In the other case, the two records are considered as false positive matches, and they are both eliminated from the national database. The data used in the present study were extracted from the national database in December 1995 and came from 29 CISIH, composed of 54 French hospitals. The database included 11 172 HIV-positive patients 18 years of age diagnosed with AIDS between January 1990 and December 1993. The following information was used for the record-linkage: date of birth (month and year), sex, HIV infection risk category, date of AIDS diagnosis, date of last visit to a CISIH, the AIDS-defining clinical manifestations (maximum of eight), the département of declaration, and date of death for the 5963 known to have died. The département of domicile was available for 8829 patients, and the date of transfusion (when there was only one) was available for 123 of the 328 patients infected by a blood transfusion. The département of declaration is the département of the hospital where the patient is treated at the moment of his transition from HIV-positive to AIDS status. It was not available for 4608 patients whose transition towards AIDS arose outside a hospital participating in the FHDH.
The capture-recapture method
The capture-recapture method consists of crossing the informa-tion from the two surveillance systems in order to identify the number of cases common to both lists, which we call matches'.
Let: N be the unknown size of a closed population (adult AIDS cases diagnosed in France between 1990 and 1993);
N1 the number of AIDS cases declared as part of the Mandatory Declaration system (DO database);
N2 the number of AIDS cases reported to the second system (FHDH), among which n11 are also present in the first database;
n22 the number of AIDS cases omitted by both systems.
The completeness pi of database i is the proportion, among all cases, of the cases found in this database; it is thus the ratio between the number of cases declared to surveillance system i and the total number of cases:
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The total number of AIDS cases that occurred in France between 1990 and 1993 was estimated using the model proposed by Wittes,3032 called Bernouilli census:
![]() | (1) |
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These estimations are valid under the following three assumptions: (i) all the identified cases are cases that really occurred during the study period, (ii) the procedure of data crossing of the two databases identifies all the matches but only true matches', (iii) the two surveillance systems are independent, that is, the fact that a patient is reported to one surveillance system has no influence on the probability that he/she is present in the other database.
Questionnaire for evaluating independence
The source of greatest bias for these estimations is deviation from the hypothesis of independence.33 It is, however, not possible to statistically test the independence between two databases. Nevertheless, one way to evaluate the independence consists in investigating more thoroughly the process of case reporting to both surveillance systems. In this perspective, we set up a survey in order to assess whether one of the surveillance systems (DO or FHDH) was used to declare cases to the other system. A short questionnaire was developed and sent to the 54 hospitals participating in the FHDH in December 1995, and the clinical research assistants, who are in charge of the data collection and registration into the DMI2 software, were asked to complete the questionnaire. This investigation elicited information about the person (a physician, the clinical research assistant himself, or someone else [specified]) reporting AIDS cases to the mandatory (DO) surveillance system, and about the origin of the information transmitted to the DO system (medical records, DMI2 software, or something else [specified]).
Cross-matching the two databases: identifying matches
Because the databases were both quite large and shared no common identifier, we developed an algorithm in order to cross-match the two databases automatically.
This algorithm comprised two steps: (1) determination of potential links; (2) identification of the real matches among these potential links (Figure 1).
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At the end of this step, some FHDH records were linked to several records in the DO database, and conversely (65% of FHDH records were involved in at least 10 potential links, versus 53% of DO records).
The detection of the matches among the potential links was performed using the remaining identifying variables: date of AIDS diagnosis, AIDS-related diseases at the time of the diag-nosis, date of transfusion, date of death, place of diagnosis and place of domicile). Since we were expecting that some patients had been recorded in the two databases with slight differences on some fields, and that the discordant fields could vary from one patient to another, it was not possible to achieve that task in a single step. For instance, we wanted matching of two records with different vital status to be possible. Thus, we proceeded iteratively, by setting up different criteria of agreement, each criterion involving different combinations of matching fields. At each iteration, unique potential links that fitted the criterion of interest were selected as matches. Unique means that each record in the potential link was not involved in another potential link fitting the same criterion. This selection of matches led to the elimination of all potential links that became ineligible because one of their constituting records has already been matched. As an illustration, the first criterion required agreement on date of AIDS diagnosis (within 3 months), date of single transfusion (within 3 months) if records belonged to the transfusion risk group, date of death (within one month) if both records related to a deceased patient, and AIDS-defining diseases (the number of diseases in common was the maximum number possible). The second criterion concerned only those potential links that comprised records relating to a deceased patient and required a strict matching on date of death, together with the same agreement on date of AIDS diagnosis, date of transfusion and AIDS-defining diseases as those required on the first criterion. The third criterion was the same as the first one; indeed, there might be two reasons at each step for the remainder of potential links: the first being that the potential link did not fit the criterion required on at least one matching field; but another reason might also be the existence of competing potential links, that is, two potential links having one record in common, and fitting the criterion of interest, so that the criterion of uniqueness stated above was not fitted. A second pass through a previously applied criterion aimed at detecting those non-unique but real matches, which was then possible because of the prior elimination of numerous potential links. The algorithm overall comprised 35 successive criteria. On the whole, the elaboration of successive criteria gave priority to the combinations of variables that had the strongest discriminating power. For example, matching for date of death was ranked higher than matching for place of residence or place of diagnosis. Matching for date of AIDS diagnosis within 3 months was almost always required (this agreement was observed on 77% of the matches). Nevertheless, discrepancies were sometimes allowed on this latter variable (in case of good agreement on other identifying variables) in order to take into account that hospital personnel do not always know the patient's clinical history and might thus report a new AIDS case although it had previously been reported.
We stopped the algorithm only when the discrepancies on the remaining potential links were too substantial to feel confident about their matching.
The data were integrated in a database under ORACLE®, and the algorithm was implemented using SQL relational database language.
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Results |
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Finally, cross-matching the two databases led to the determination of 9337 matches. The total number of separate cases counted by the two databases was thus 21 460.
Overall completeness
The capture-recapture estimation of the total number of AIDS cases diagnosed in France between 1990 and 1993 was 23 482 (95% CI : 23 354-23 608). The completeness of the DO database was estimated at 83.6% (95% CI : 82.9-84.3), and that of the FHDH at 47.6% (95% CI : 46.9-48.3) over 1990-1993. The consolidation of the two surveillance systems resulted in the identification of 91.4% of the total number of cases.
Completeness by year of AIDS diagnosis
Stratification by year of AIDS diagnosis (Table 1) revealed that the completeness of the DO system was similar throughout the 4 years of the study, but with a slight trend toward improvement. It also indicated that the completeness of the FHDH system improved markedly during the study period, going from 39.2% in 1990 to 53.5% in 1993.
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Discussion |
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The difference observed between the estimated completeness of these two systems is explainable by the fact that the DO database stems from a national mandatory reporting whereas the FHDH system is not intended to be exhaustive on a national scale.
The estimates obtained by applying the two-sample capture-recapture method were based on three hypotheses. The first hypothesis of the model was that all cases belonging at least to one of the two databases were actual AIDS cases, that is, they had been accurately diagnosed: this was not a source of concern for this study, since both systems used the same definition.34,35 The increase observed (from 39.2% in 1990 to 53.5% in 1993) in the completeness of the FHDH surveillance system can partly be explained by the gradual increase in the number of participating hospitals.
The validity of the second hypothesis was based on the performance of the record-linkage. The cross-matching of the two databases was a major part of the work, for it required that we develop an algorithm automating this task as much as possible. In the absence of a common identifier, as in the present study, a combination of identifying data, such as sex, date of birth, département of domicile and date of AIDS diagnosis, must be used. The risk of error is undeniably greater in this case, since each piece of information used for the record-linkage may be either wrong or missing (information related to the date of death, disorders at the time of the diagnosis, the département of domicile, the département of diagnosis, or any combination, was sometimes missing).
The main difficulty was linked to the very large number of potential links obtained after the first step of the algorithm (Figure 1). The procedure adopted (successive choices of combinations of matching variables as the selection criterion) can be related to techniques of probabilistic record-linkage,22,36 in which a weight is attributed to each combination of matching variables. In this study, no numeric value was attributed to a given combination, but the order in which the criteria were applied is equivalent to ranking the combinations. In view of the procedure we used, the real matches' that our method might not have identified (false negative matches) are necessarily linked to non-negligible differences for several variables, which raises the question of the quality of the data in each database. To quantitatively evaluate the second hypothesis of the model would require examining the medical records. This was impossible because of confidentiality considerations. The linking performed would not allow us to update, among the matches identified, the data missing from one of the databases with that present in the other (for example, fill in the date of death from the information in the other database). Since the objective of this record-linkage was to determine the overall number of matches (and not to identify absolutely the same individual in the other database), our procedure appeared reasonable. If the real number of matches in the two databases is lower than that determined by our procedure, the completeness estimations that we have obtained would in fact be overestimations of the real completeness.37 Conversely, if the real number of matches is greater than that we found, the completeness of the databases would have been underestimated by our approach.
The assumption of independence is a more sensitive issue in dealing with a capture-recapture method, for two reasons. First, it cannot be tested when there are only two data sources. Second, the validity of the estimations obtained must be re-appraised if the reality is too distant from the assumption. Generally, a positive dependence between the two systems, that is, probability of being listed in one database increasing the likelihood of listing in the other, would tend, with our method, to overestimate completeness. Conversely, a negative dependence would lead to an underestimation of completeness. Here, the results lead us to suspect a slight positive dependence between the two surveillance systems. First, the answers to the questionnaire suggested that the declarations were made independently in slightly fewer than two-thirds of the cases. Next, we noticed that the completeness of the DO database differed when we considered the entire population (Table 1) and when the analysis was limited to the hospitals participating in the FHDH (83.6% compared with 85.8%, P < 10-9).
Nonetheless, Brenner has shown that when one of the two databases is relatively complete (as was the case with the DO database), the overestimation due to a slight positive depend-ence is modest.33 According to Brenner's work, in case of a slight or even moderate dependence (corresponding to a covariance between p1 and p2 less than 40% of its maximum possible value) and when p1 is approximately 80%, the overestimation factor for completeness rates with the capture-recapture method is less than 1/0.92 (i.e. 1.087). One way to evaluate quantitatively the dependence between the two surveillance systems we studied would be to use a third database (for example, a database derived from that of INSERM SC8, which gathers cause-of-death data in France). We could then use log-linear methods to model and estimate the possible interdependence between the three surveillance systems, but these three-source approaches have their own difficulties.38,39
The completeness of the DO surveillance system was stable during 19901993. Because there has been no significant change since in the reporting process, it seems reasonable to think that it has remained stable up to now. On the other hand, the completeness of the FHDH system has probably continued to increase as it did between 1990 and 1993, because more hospitals have joined the FHDH since 1993.
This study has immediate practical utility. Knowledge of the completeness of the DO database provides a more exact knowledge of the number of AIDS cases in France; it therefore allows an improvement in the back-calculation estimates of the number of people who are HIV-positive.40 From estimates of the completeness of the FHDH, we can extrapolate data about AIDS-related diseases and treatments to patients with AIDS throughout the country.
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Acknowledgments |
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