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1.
PLoS One ; 14(7): e0218438, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31269042

RESUMO

INTRODUCTION: Verbal autopsy (VA) is a useful tool for evaluating causes of death, especially in places with limited or no vital registration systems. The Population Health Metrics Research Consortium (PHMRC) developed a validated questionnaire and a set of automated methods to determine the cause of death from a VA. However, the application of these methods needs to be tested in a community environment. OBJECTIVE: To estimate cause-specific mortality fractions (CSMFs) using VAs and compare them against those obtained in the vital statistics of the state of Hidalgo, Mexico. METHODS: A random sample of deaths occurred in 2009 was selected from vital statistics in the state of Hidalgo. The full PHMRC validated VA instrument was applied to the relatives of the deceased, and the cause of death was determined using Tariff's automated method. The causes of death were grouped into 34 causes for adults, 21 for children and 6 for newborns. Results were compared with cause of death on death certificates for all deaths. RESULTS: A total of 1,198 VAs were analyzed. The Tariff method was not able to assign a cause of death in only 9% of adults, 2% of children and 7% of neonatal deaths. The CSMFs obtained from the Tariff method were similar in some cases to those of vital statistics (e.g. cirrhosis), but different in others (e.g. sepsis). CONCLUSION: The application of VAs in a community sample, analyzed with the Tariff method, allowed assigning a cause of death to most of the cases, with results similar to those of vital statistics for most conditions. This tool can be useful to strengthen the quality of vital statistics.


Assuntos
Causas de Morte , Atestado de Óbito , Inquéritos e Questionários , Estatísticas Vitais , Adolescente , Adulto , Idoso , Autopsia , Criança , Pré-Escolar , Feminino , Humanos , Lactente , Recém-Nascido , Masculino , México , Pessoa de Meia-Idade
2.
BMC Med ; 13: 15, 2015 Jan 26.
Artigo em Inglês | MEDLINE | ID: mdl-25620318

RESUMO

BACKGROUND: Easy-to-collect epidemiological information is critical for the more accurate estimation of the prevalence and burden of different non-communicable diseases around the world. Current measurement is restricted by limitations in existing measurement systems in the developing world and the lack of biometry tests for non-communicable diseases. Diagnosis based on self-reported signs and symptoms ("Symptomatic Diagnosis," or SD) analyzed with computer-based algorithms may be a promising method for collecting timely and reliable information on non-communicable disease prevalence. The objective of this study was to develop and assess the performance of a symptom-based questionnaire to estimate prevalence of non-communicable diseases in low-resource areas. METHODS: As part of the Population Health Metrics Research Consortium study, we collected 1,379 questionnaires in Mexico from individuals who suffered from a non-communicable disease that had been diagnosed with gold standard diagnostic criteria or individuals who did not suffer from any of the 10 target conditions. To make the diagnosis of non-communicable diseases, we selected the Tariff method, a technique developed for verbal autopsy cause of death calculation. We assessed the performance of this instrument and analytical techniques at the individual and population levels. RESULTS: The questionnaire revealed that the information on health care experience retrieved achieved 66.1% (95% uncertainty interval [UI], 65.6-66.5%) chance corrected concordance with true diagnosis of non-communicable diseases using health care experience and 0.826 (95% UI, 0.818-0.834) accuracy in its ability to calculate fractions of different causes. SD is also capable of outperforming the current estimation techniques for conditions estimated by questionnaire-based methods. CONCLUSIONS: SD is a viable method for producing estimates of the prevalence of non-communicable diseases in areas with low health information infrastructure. This technology can provide higher-resolution prevalence data, more flexible data collection, and potentially individual diagnoses for certain conditions.


Assuntos
Inteligência Artificial , Métodos Epidemiológicos , Prevalência , Inquéritos e Questionários , Adulto , Idoso , Algoritmos , Causas de Morte , Doença Crônica/epidemiologia , Mineração de Dados , Feminino , Inquéritos Epidemiológicos , Humanos , Masculino , México/epidemiologia , Pessoa de Meia-Idade , Processamento de Linguagem Natural
3.
Popul Health Metr ; 9: 38, 2011 Aug 04.
Artigo em Inglês | MEDLINE | ID: mdl-21816103

RESUMO

BACKGROUND: In Mexico, the vital registration system relies on information collected from death certificates to generate official mortality figures. Although the death certificate has high coverage across the country, there is little information regarding its validity. The objective of this study was to assess the concordance between the underlying cause of death in official statistics obtained from death certificates and a gold standard diagnosis of the same deaths derived from medical records of hospitals. METHODS: The study sample consisted of 1,589 deaths that occurred in 34 public hospitals in the Federal District and the state of Morelos, Mexico in 2009. Neonatal, child, and adult cases were selected for causes of death that included infectious diseases, noncommunicable diseases, and injuries. We compared the underlying cause of death, obtained from medical death certificates, against a gold standard diagnosis derived from a review of medical records developed by the Population Health Metrics Research Consortium. We used chance-corrected concordance and accuracy as metrics to evaluate the quality of performance of the death certificate. RESULTS: Analysis considering only the underlying cause of death resulted in a median chance-corrected concordance between the cause of death in medical death certificates versus the gold standard of 54.3% (95% uncertainty interval [UI]: 52.2, 55.6) for neonates, 38.5% (37.0, 40.0) for children, and 66.5% (65.9, 66.9) for adults. The accuracy resulting from the same analysis was 0.756 (0.747, 0.769) for neonates, 0.683 (0.663, 0.701) for children, and 0.780 (0.774, 0.785) for adults. Median chance-corrected concordance and accuracy increased when considering the mention of any cause of death in the death certificate, not just the underlying cause. Concordance varied substantially depending on cause of death, and accuracy varied depending on the true cause-specific mortality fraction composition. CONCLUSIONS: Although we cannot generalize our conclusions to Mexico as a whole, the results demonstrate important problems with the quality of the main source of information for causes of death used by decision-makers in settings with highly technological vital registration systems. It is necessary to improve death certification procedures, especially in the case of child and neonatal deaths. This requires an important commitment from the health system and health institutions.

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