Probabilistic Record Linkage Methods Research Articles

Developing an integrated HIV clinical and population database in rural south Africa

BackgroundHIV cohort data are crucial for understanding the effects of HIV infection and treatment. Linkage between HIV clinical and population records unlocks potential to understand the cause and consequences of social-economic factors on HIV-care seeking behaviors and mortality outcomes. We created HIV data infrastructure by linking HIV treatment records with routinely collected records. MethodsWe linked HIV cohort data(n=119,434) to Health and Demographic Surveillance(n=256,425), laboratory tests(n=617,314), clinic visits and hospital admissions(n=170,325) from KwaZulu-Natal, an HIV-endemic setting in rural South Africa. We used graph-based probabilistic record linkage algorithm to link the data for HIV patients accessing care at 17 clinics within the Hlabisa sub-district. Africa Health Research Institute (AHRI) has collected health and demographic surveillance data since 2000. AHRI has set up a hospital information system in the district to capture ICD-10 code admissions and discharges. ResultsFrom the 5% sample of 462,524 linkage clusters, the computed F-score was 0.88, sensitivity was 88% and positive predictive value was 91%. 38.5% of the patient ID clusters contained records linked across multiple databases. 36% of the HIV patient ID clusters contained records linked with laboratory tests. 19.5% of the HIV patient ID clusters contained data on demographic surveillance. 12.5% of the HIV patient ID clusters contained records linked with clinic visits and hospital admissions. 4% of patient IDs contained records across all four databases. ConclusionWe have successfully created a cross-sectorial HIV cohort through probabilistic record linkage methods. The cohort provides a platform to answer policy-relevant questions.

Community and Facility Health Information System Integration in Malawi: A Comparison of Machine Learning and Probabilistic Record Linkage Methods

Community and Facility Health Information System Integration in Malawi: A Comparison of Machine Learning and Probabilistic Record Linkage Methods

Cancer risk in adolescents and young adults living with HIV in South Africa: a nationwide cohort study.

Literature on cancer in adolescents and young adults (AYA; aged 15-24 years) living with HIV is scarce. We studied cancer incidence in AYA living with HIV in South Africa between 2004 and 2014. In this nationwide cohort study, we included individuals between 15 and 24 years old who had at least two HIV-related laboratory measurements on separate days between Jan 1, 2004, and Dec 31, 2014, recorded in the National Health Laboratory Service database. We used privacy-preserving probabilistic record linkage methods to identify HIV-related laboratory records that most likely belonged to the same individual and to then link these individuals to cancer diagnoses from the National Cancer Registry. We computed incidence rates for the most common cancers in AYA living with HIV, and we assessed associations between these cancers and sex, age, calendar year, and CD4 cell count using Cox proportional hazards models and adjusted hazard ratios (aHRs). We included 782 454 AYA living with HIV (698 066 [89·2%] women) with 1 428 114 person-years of follow-up. Of those, 867 developed incident cancer (incidence rate 60·7 per 100 000 person-years), including 429 who developed Kaposi sarcoma (30·0 per 100 000 person-years), 107 non-Hodgkin lymphoma (7·5 per 100 000 person-years), 48 Hodgkin lymphoma (3·4 per 100 000 person-years), 45 cervical cancer (3·4 per 100 000 woman-years), and 32 leukaemia (2·2 per 100 000 person-years). Kaposi sarcoma was more common in the 20-24 year age group than the 15-19 year age group (aHR 1·39, 95% CI 1·03-1·86). Male sex was associated with higher rates of Kaposi sarcoma (2·06, 1·61-2·63), non-Hodgkin lymphoma (3·17, 2·06-4·89), Hodgkin lymphoma (4·83, 2·61-8·93), and leukaemia (unadjusted HR 5·90, 95% CI 2·87-12·12). Cancer rates decreased over the study period, driven by declining Kaposi sarcoma rates. Lower baseline CD4 cell counts were associated with higher rates of Kaposi sarcoma, cervical cancer, non-Hodgkin lymphoma, and Hodgkin lymphoma, but not leukaemia. Infection-related cancers were the most common cancer types in AYA living with HIV in South Africa, and their incidence rates increased with lower CD4 cell counts. Therefore, innovative strategies to maintaining high CD4 cell counts are needed to reduce the cancer burden in this vulnerable population. US National Institutes of Health and Swiss National Science Foundation.

Field Weights Computation for Probabilistic Record Linkage in Presence of Missing Data

Record linkage is the task for identifying which records refer to the same entity. When records in different data sources do not have a common key and they contain typographical errors in their identifier fields, the extended Fellegi–Sunter probabilistic record linkage method with consideration of field similarity proposed by Winkler, is one of the most effective methods to perform record linkage to our knowledge. But this method has a limitation that it cannot efficiently handle the problem of missing value in the fields, an inappropriate weight is assigned to record pair containing missing data. Therefore, to improve the performance of Winkler’s probabilistic record linkage method in presence of missing value, we proposed a solution for adjusting record pair’s weight when missing data occurred, which allows enhancing the accuracy of the Winkler’s record linkage decisions without increasing much more computational time.

The Challenge of Pairing Big Datasets: Probabilistic Record Linkage Methods and Diagnosis of Their Empirical Viability

In this paper, we evaluated the predictive performance of probabilistic record linkage algorithms, discussing the implications of different configurations of blocking keys, string similarity functions and phonetic code on the prediction’s overall performance and computational complexity. Furthermore, we carried out a bibliographical survey of the main deterministic and probabilistic record linkage methods, as well as of recent advances combining machine learning techniques and main packages and implementations available in open-source R language. The results can provide heuristics for problems of administrative records integration at the national level and have potential value for the formulation and evaluation of public policies.

A hybrid approach to record linkage using a combination of deterministic and probabilistic methodology.

The disjointed healthcare system and the nonexistence of a universal patient identifier across systems necessitates accurate record linkage (RL). We aim to describe the implementation and evaluation of a hybrid record linkage method in a statewide surveillance system for congenital heart disease. Clear-text personally identifiable information on individuals in the Colorado Congenital Heart Disease surveillance system was obtained from 5 electronic health record and medical claims data sources. Two deterministic methods and 1 probabilistic RL method using first name, last name, social security number, date of birth, and house number were initially implemented independently and then sequentially in a hybrid approach to assess RL performance. 16480 nonunique individuals with congenital heart disease were ascertained. Deterministic linkage methods, when performed independently, yielded 4505 linked pairs (consisting of 2 records linked together within or across data sources). Probabilistic RL, using 3 initial characters of last name and gender for blocking, yielded 6294 linked pairs when executed independently. Using a hybrid linkage routine resulted in 6451 linkages and an additional 18%-24% correct linked pairs as compared to the independent methods. A hybrid linkage routine resulted in higher recall and F-measure scores compared to probabilistic and deterministic methods performed independently. The hybrid approach resulted in increased linkage accuracy and identified pairs of linked record that would have otherwise been missed when using any independent linkage technique. When performing RL within and across disparate data sources, the hybrid RL routine outperformed independent deterministic and probabilistic methods.

Unlocking First Nations health information through data linkage.

IntroductionThe importance of Indigenous data sovereignty and Indigenous-led research processes is increasingly being recognized in Canada and internationally. For First Nations in Ontario, Canada, access to routinely-collected demographic and health systems data is critical to planning and measuring health status and outcomes in their populations. Linkage of this data with the Indian Register (IR), under First Nations data governance, has unlocked data for use by First Nations organizations and communities. ObjectivesTo describe the linkage of the IR database to the Ontario Registered Persons Database (RPDB) within the context of Indigenous data sovereignty principles.MethodsDeterministic and probabilistic record linkage methods were used to link the IR to the RPDB. There is no established population of First Nations people living in Ontario with which we could establish a linkage rate. Accordingly, several approaches were taken to determine a denominator that would represent the total population of First Nations we would hope to link to the RPDB. ResultsOverall, 201,678 individuals in the national IR database matched to Ontario health records by way of the RPDB, of which 98,562 were female and 103,116 were male. Of those First Nations individuals linked to the RPDB, 90.2% (n=181,915) lived in Ontario when they first registered with IR, or were affiliated with an Ontario First Nation Community. The proportion of registered First Nations people linking to the RPDB improved across time, from 62.8% in the 1960s to 94.5% in 2012.ConclusionThis linkage of the IR and RPDB has resulted in the creation of the largest First Nations health research study cohort in Canada. The linked data are being used by First Nations communities to answer questions that ultimately promote wellbeing, effective policy, and healing

Design and evaluation of probabilistic record linkage methods supporting the Brazilian 100-million cohort initiative.

ABSTRACTBackground and aimsA cooperation Brazil-UK was set in mid-2013 aiming at to build a huge cohort comprised by individuals registered in CadastroÚnico (CADU), a socioeconomic database used in social programmes of the Brazilian government. Epidemiologists and statisticians wish to assess the impact of Bolsa Família (PBF), a conditional cash transfer programme, on the incidence of several diseases (tuberculosis, leprosy, HIV etc). The cohort must contain all individuals who received at least one payment from PBF between 2007 and 2012, which results in a 100-million records according to our preliminary analysis. These individuals must be probabilistically linked with databases from the Unified Health System (SUS), such as hospitalization (SIH), notifiable diseases (SINAN), mortality (SIM), live births (SINASC), to produce data marts (domain-specific data) to the proposed studies. Within this cooperation, our first goal was to design and evaluate probabilistic methods to routine link the cohort, PBF, and SUS outcomes.
 ApproachWe implemented two probabilistic linkage methods: a full probabilistic, based on the Dice similarity (Sorensen index) of Bloom filters; and an hybrid approach, based on rules to deterministic and probabilistic matching. We performed linkages involving CADU (2011 extraction) and SUS outcomes (SIH, SINAN, and SIM) with samples from 3 states (Sergipe, Santa Catarina and Bahia) with an increasing size (from 1,447,512 to 12,036,010).
 ResultsUsing a Dice between 0.90 and 0.92, our methods retrieved more than 95% of true positive pairs amongst the linked pairs. For Sergipe, we obtained as <linked pairs,true positives>: <23,22>, <315,300>, <32,32>, respectively for SIH, SINAN, and SIM. For Bahia: <771,593>, <2677,2626>, <208,207>. Another linkage between CADU (1,447,512 records) and SINAN (624 records), for tuberculosis in Sergipe, returned 397 (full probabilistic) and 311 (hybrid) linked pairs, being 306 and 300 true positives. Another execution considering CADU (1,988,599 records) and SINAN (2,094 records), for tuberculosis in Santa Catarina, returned 791 (full probabilistic) and 500 (hybrid) linked pairs, with 667 and 472 true positives. Linking CADU (1.685,697 records) and SIM, for mortality of children under-4, returned 18 linked pairs, all of them true positives, for a Dice between 0.90 and 0.92 and with 100% of sensitivity, specificity, and positive predictive value.
 ConclusionDue to the absence of gold standards, we use samples with increasing sizes and manual review when adequate. Our results are quite accurate, although obtained with an unique extraction of CADU. We are starting to run linkages with the entire cohort.

Assessing the accuracy of probabilistic record linkage of social and health databases in the 100 million Brazilian cohort

ABSTRACT
 Background and aimsThe Brazilian government has several social protection programmes that select their beneficiaries based on socioeconomic information kept in the CadastroÚnico (CADU) database. The CADU will be used to build a population-based cohort of approximately 100 million individuals. Among the social programmes is the Bolsa Família (PBF), a conditional cash transfer programme that provides extra income to poor families. These two databases must be deterministically linked to individuals who have received payments from PBF between 2004 and 2012. It will be used in epidemiological studies aiming to assess the impact of PBF on the occurrence and severity of several diseases and health problems (tuberculosis, leprosy, HIV, child health etc). This cohort must be probabilistically linked with databases from the Unified Health System (SUS), such as hospitalization, notifiable diseases, mortality, and live births, in order to produce data marts (domain-specific data) to the proposed studies. Our goals comprise the validation of probabilistic record linkage methods to support this cohort setup.
 ApproachThis paper emphasizes the accuracy assessment of our methods based on the linkage of SIH (hospitalization), SINAN (notifications), and SIM (mortality) records to the 2011 extraction of CADU. We focused on hospitalization and notification of tuberculosis, as well infant mortality for all causes in under-4 children, for a small sample with 30,029 records (CADU). Due to the absence of gold standards, we used two approaches to assess accuracy: a clerical review and an automatic (tool-based) search. In the first case, we used different cut-off points as similarity index to calculate sensitivity and specificity, and a ROC curve to separate matched and non-matched pairs. The second approach retrieves from CADU all matched and non-matched pairs for a given individual, serving as a gold standard for validation.
 ResultsWe retrieved 22 linked pairs, from which 18 are true positives for infant mortality (SIM database). From SINAN, our results were 434 linked pairs with 166 true positives, and with SIH, 121 linked pairs with 34 true positives. The sensitivity of manual scan for SIM (children mortality) ranges from 44% (specificity of 100%) to 95% (specificity of 94%), with similarity indices between 0.80 and 0.97, respectively. For automatic search, we obtained a sensitivity of 69.2% and specificity of 91.8%.
 ConclusionOur results show the need for a continuous improvement in our linkage routines and how to consistently evaluate their accuracy in the absence of adequate gold standards.

Record linkage to correct under-ascertainment of cancers in HIV cohorts: The Sinikithemba HIV clinic linkage project.

The surveillance of HIV‐related cancers in South Africa is hampered by the lack of systematic collection of cancer diagnoses in HIV cohorts and the absence of HIV status in cancer registries. To improve cancer ascertainment and estimate cancer incidence, we linked records of adults (aged ≥ 16 years) on antiretroviral treatment (ART) enrolled at Sinikithemba HIV clinic, McCord Hospital in KwaZulu‐Natal (KZN) with the cancer records of public laboratories in KZN province using probabilistic record linkage (PRL) methods. We calculated incidence rates for all cancers, Kaposi sarcoma (KS), cervix, non‐Hodgkin's lymphoma and non‐AIDS defining cancers (NADCs) before and after inclusion of linkage‐identified cancers with 95% confidence intervals (CIs). A total of 8,721 records of HIV‐positive patients were linked with 35,536 cancer records. Between 2004 and 2010, we identified 448 cancers, 82% (n = 367) were recorded in the cancer registry only, 10% (n = 43) in the HIV cohort only and 8% (n = 38) both in the HIV cohort and the cancer registry. The overall cancer incidence rate in patients starting ART increased from 134 (95% CI 91–212) to 877 (95% CI 744–1,041) per 100,000 person‐years after inclusion of linkage‐identified cancers. Incidence rates were highest for KS (432, 95% CI 341–555), followed by cervix (259, 95% CI 179–390) and NADCs (294, 95% CI 223–395) per 100,000 person‐years. Ascertainment of cancer in HIV cohorts is incomplete, PRL is both feasible and essential for cancer ascertainment.

Using probabilistic record linkage methods to identify Australian Indigenous women on the Queensland Pap Smear Register: the National Indigenous Cervical Screening Project

ObjectiveTo evaluate the feasibility and reliability of record linkage of existing population-based data sets to determine Indigenous status among women receiving Pap smears. This method may allow for the first...

Privacy preserving probabilistic record linkage (P3RL): a novel method for linking existing health-related data and maintaining participant confidentiality.

BackgroundRecord linkage of existing individual health care data is an efficient way to answer important epidemiological research questions. Reuse of individual health-related data faces several problems: Either a unique personal identifier, like social security number, is not available or non-unique person identifiable information, like names, are privacy protected and cannot be accessed. A solution to protect privacy in probabilistic record linkages is to encrypt these sensitive information. Unfortunately, encrypted hash codes of two names differ completely if the plain names differ only by a single character. Therefore, standard encryption methods cannot be applied. To overcome these challenges, we developed the Privacy Preserving Probabilistic Record Linkage (P3RL) method.MethodsIn this Privacy Preserving Probabilistic Record Linkage method we apply a three-party protocol, with two sites collecting individual data and an independent trusted linkage center as the third partner. Our method consists of three main steps: pre-processing, encryption and probabilistic record linkage. Data pre-processing and encryption are done at the sites by local personnel. To guarantee similar quality and format of variables and identical encryption procedure at each site, the linkage center generates semi-automated pre-processing and encryption templates. To retrieve information (i.e. data structure) for the creation of templates without ever accessing plain person identifiable information, we introduced a novel method of data masking. Sensitive string variables are encrypted using Bloom filters, which enables calculation of similarity coefficients. For date variables, we developed special encryption procedures to handle the most common date errors. The linkage center performs probabilistic record linkage with encrypted person identifiable information and plain non-sensitive variables.ResultsIn this paper we describe step by step how to link existing health-related data using encryption methods to preserve privacy of persons in the study.ConclusionPrivacy Preserving Probabilistic Record linkage expands record linkage facilities in settings where a unique identifier is unavailable and/or regulations restrict access to the non-unique person identifiable information needed to link existing health-related data sets. Automated pre-processing and encryption fully protect sensitive information ensuring participant confidentiality. This method is suitable not just for epidemiological research but also for any setting with similar challenges.

Deterministic Linkage as a Preceding Filter for Other Record Linkage Methods

Deterministic record linkage (RL) is frequently regarded as a rival to more sophisticated strategies like probabilistic RL. We investigate the effect of combining deterministic linkage with other linkage techniques. For this task, we use a simple deterministic linkage strategy as a preceding filter: a data pair is classified as ‘match' if all values of attributes considered agree exactly, otherwise as ‘nonmatch'. This strategy is separately combined with two probabilistic RL methods based on the Fellegi–Sunter model and with two classification tree methods (CART and Bagging). An empirical comparison was conducted on two real data sets. We used four different partitions into training data and test data to increase the validity of the results. In almost all cases, application of deterministic linkage as a preceding filter leads to better results compared to the omission of such a pre-filter, and overall classification trees exhibited best results. On all data sets, probabilistic RL only profited from deterministic linkage when the underlying probabilities were estimated before applying deterministic linkage. When using a pre-filter for subtracting definite cases, the underlying population of data pairs changes. It is crucial to take this into account for model-based probabilistic RL.

Evaluating Re-Identification Risks of Data Protected by Additive Data Perturbation

Commercial organizations and government agencies that gather, store, share and disseminate data are facing increasing concerns over individual privacy and confidentiality. Confidential data is often masked in the database or prior to release to a third party, through methods such as data perturbation. In this study, re-identification risks of three major additive data perturbation techniques were compared using two different record linkage techniques. The results suggest that re-identification risk of Kim's multivariate noise addition method is similar to that of simple noise addition method. The general additive perturbation method (GADP) has the lowest re-identification risk and therefore provides the highest level of protection. The study also suggests that Fuller's method of assessing re-identification risk may be better suited than the probabilistic record-linkage method of Winkler, for numeric data. The results of this study should be help organizations and government agencies choose an appropriate additive perturbation technique.

Records linkage of electronic databases for the assessment of adverse effects of antiretroviral therapy in sub‐Saharan Africa

In 2009, the Ministry of Health and Social Services in Namibia decided to conduct a confirmatory assessment of the risk of anemia associated with zidovudine (AZT)-based highly active antiretroviral therapy (HAART) using records contained in three electronic databases. These records did not share a unique identifying number. The first step was to apply probabilistic record linkage methods to link records in the three databases. Records of persons, aged 19-65 years, newly initiated on HAART between January 2007 and June 2008, were selected from a pharmacy electronic dispensing tool (EDT) and linked to an electronic medical records database (ePMS) and a laboratory database (MEDITECH). Using the paper-based clinical record as the gold standard, we measured the sensitivity of the starting HAART regimen, that is, proportion of AZT users in the clinical record correctly identified in electronic record, and specificity of severe anemia, that is, proportion of non-cases of severe anemia in the clinical records correctly identified in the electronic record. Kappa and intraclass correlation coefficients were used to determine reliability. A total of 12 358 records were selected from EDT. Seventy-six percent and 58% of EDT records were linked to ePMS and MEDITECH, respectively. The sensitivity of the starting HAART regimen was 98%, whereas specificity of severe anemia was 100%. The reliability scores for variables including weight, hemoglobin, and CD4 counts were moderate to perfect and ranged from 0.59 to 0.99. Probabilistic record linkage methods were effective for records linkage in this sub-Saharan African setting.

Probabilistic Record Linkage of Infection Records and Death Registrations: A Tool to Strengthen Surveillance

An important element for many infectious disease surveillance programmes is their capacity to monitor not only the incidence of infection, but also the associated mortality. The ability to monitor post-infection mortality is dependent on outcome information being collected through the surveillance reports, or on infections being precisely specified on death certificates. For many infectious diseases, neither of these sources provides a reliable source of this information, so a method for linking infection and death registration data is needed. Given that surveillance data often lacks a unique patient identifier, a probabilistic record linkage method was developed to reliably bring together large-scale data sources to identify deaths following infection. The method was developed using Streptococcus pneumonia infection records but with wider applicability to other infectious disease surveillance programmes. Evaluation of the mechanism was undertaken by tracing patients through a central health service database. Results of the evaluation showed a positive predictive value of 97.7-99.8% for correctly identifying deaths following infection, and a negative predictive value of 90.2-98.0%. The successful application of probabilistic matching to link infections and death registrations paves the way for a new era in infectious disease surveillance in the UK, with its potential application to augment a wide array of ongoing surveillance programmes with information on patient outcome.

Extending the Fellegi–Sunter probabilistic record linkage method for approximate field comparators

Probabilistic record linkage is a method commonly used to determine whether demographic records refer to the same person. The Fellegi–Sunter method is a probabilistic approach that uses field weights based on log likelihood ratios to determine record similarity. This paper introduces an extension of the Fellegi–Sunter method that incorporates approximate field comparators in the calculation of field weights. The data warehouse of a large academic medical center was used as a case study. The approximate comparator extension was compared with the Fellegi–Sunter method in its ability to find duplicate records previously identified in the data warehouse using different demographic fields and matching cutoffs. The approximate comparator extension misclassified 25% fewer pairs and had a larger Welch’s T statistic than the Fellegi–Sunter method for all field sets and matching cutoffs. The accuracy gain provided by the approximate comparator extension grew as less information was provided and as the matching cutoff increased. Given the ubiquity of linkage in both clinical and research settings, the incremental improvement of the extension has the potential to make a considerable impact.

Détermination du statut vital par chaînage entre des données hospitalières et les données de mortalité nationales anonymisées

Background A subject's vital status is essential for epidemiological studies. This information may be obtained for large numbers of patients with different methods, but these are often expensive. This study was aimed at assessing the performance of patient vital status determination using a record linkage method between hospital data and national mortality data once the information was made anonymous in compliance with French legislation. Methods All patients hospitalised in the Gustave-Roussy Institute, a cancer center in Villejuif France, were eligible for inclusion if they lived in France (mainland or Overseas Departments). The study cohort included patients admitted for the first time for malignant or suspected malignant-tumor during the period 1998–2000. Nominal data from the Gustave Roussy Institute hospital files as well as from the French National Institute of Statistics and Economic Studies (INSEE) mortality databases were then anonymised using irreversible hash coding. Once anonymised, the Gustave Roussy Institute and INSEE mortality databases were linked using the Jaro probabilistic method. Record linkage involved the following variables: birth name, first given name and birth date, along with the INSEE code of birth place. Results 10,089 patients were included. The linkage record results were very satisfactory for all the patients included; the percentage of those properly classified was 97.2%, sensitivity was 94.8% and specificity 99.5%. The performance of the probabilistic record linkage method on anonymised data was very satisfactory (sensitivity 96.8% and specificity 99.8%) for determining vital status for patients born in France, suffering from cancer and in-patients at the Institute Gustave-Roussy. Results were inferior for patients born abroad (sensitivity 82.8% and specificity 97.7%) but the method achievements may be enhanced by additional manual validation steps. Conclusion Probabilistic linkage on data rendered anonymous enables to obtain information on vital status for a great number of subjects at low cost, in compliance with French legislation.

Mortalidade infantil em uma amostra de nascimentos do município do Rio de Janeiro, 1999-2001: "linkage" com o Sistema de Informação de Mortalidade

OBJETIVOS: estimar as taxas de mortalidade neonatal (TMN) e pós-neonatal (TMPN) em uma amostra de nascimentos do município do Rio de Janeiro, durante o perído de 1999-2001, compará-las segundo o tipo de maternidade e acesso ao parto hospitalar e descrever os óbitos segundo a sua causa básica. MÉTODOS: a identificação dos óbitos infantis no Sistema de Informação sobre Mortalidade (SIM) foi realizada por meio do método de relacionamento probabilístico de registros. A análise foi bivariada, sendo calculados os riscos relativos (RR) e seus respectivos intervalos de confiança 95%. RESULTADOS: as taxas de mortalidade neonatal e pós-neonatal foram sete e seis vezes maiores nas maternidades públicas que nas privadas, respectivamente. Para as puérperas não residentes no município e aquelas que tentaram "parir" em outros estabelecimentos, sem sucesso, a TMN foi duas vezes maior. Observou-se elevada TMN por afecções do período perinatal (APP), mesmo em crianças com mais de 1500 g (2,3 por mil NV). Óbitos neonatais por APP foram seis vezes mais freqüentes nos hospitais públicos que nos privados. CONCLUSÕES: a enorme diferença nas taxas de mortalidade neonatal e pós-neonatal entre os hospitais públicos e privados aponta, como fatores primordiais a sofrerem intervenção para a retomada da redução dessas taxas no município do Rio de Janeiro, a redução das desigualdades sociais e de acesso.

Educational inequalities in life expectancy in German speaking part of Switzerland 1990-1997: Swiss National Cohort.

Switzerland belongs to the group of nations with the highest life expectancy. However, it is unclear to what extent life expectancy varies across socio-economic groups. We used data from a large longitudinal study to quantify differentials in life expectancy across educational groups for men and women of different ages. The Swiss National Cohort linked the records from the December 4th, 1990 census with death certificate data up to 1997, using a probabilistic record linkage method. The current analysis was restricted to Swiss nationals resident in the German speaking part of the country. Life expectancy was calculated for four educational categories ("compulsory schooling or less", "vocational training", "upper secondary education", "university education") by constructing abridged life tables for men and women aged 30 or older. The study was based on 3.06 million persons and 262,552 deaths recorded during 19.01 million person-years of follow up. The educational level was lower in women than in men. In most age groups vocational training was the dominant educational category. At ages 30, 50, 65 and 80 men with university education lived 7.1, 5.4, 3.5 and 1.6 years longer than their counterparts with compulsory education or less. In women the corresponding differences were 3.6, 3.1, 2.7 and 2.2 years. In Switzerland educational gradients in life expectancy are substantial, particularly among young and middle-aged men. Social policies and public health strategies should address this situation.