RESUMO
Adult death rates are a critical indicator of population health and well-being. Wealthy countries have high-quality vital registration systems, but poor countries lack this infrastructure and must rely on estimates that are often problematic. In this article, we introduce the network survival method, a new approach for estimating adult death rates. We derive the precise conditions under which it produces consistent and unbiased estimates. Further, we develop an analytical framework for sensitivity analysis. To assess the performance of the network survival method in a realistic setting, we conducted a nationally representative survey experiment in Rwanda (n = 4,669). Network survival estimates were similar to estimates from other methods, even though the network survival estimates were made with substantially smaller samples and are based entirely on data from Rwanda, with no need for model life tables or pooling of data from other countries. Our analytic results demonstrate that the network survival method has attractive properties, and our empirical results show that this method can be used in countries where reliable estimates of adult death rates are sorely needed.
Assuntos
Inquéritos Epidemiológicos/métodos , Modelos Estatísticos , Mortalidade/tendências , Apoio Social , Adolescente , Adulto , Feminino , Inquéritos Epidemiológicos/normas , Humanos , Entrevistas como Assunto , Pessoa de Meia-Idade , Reprodutibilidade dos Testes , Ruanda/epidemiologia , Fatores Socioeconômicos , Adulto JovemRESUMO
Respondent-driven sampling (RDS) is a recently introduced, and now widely used, technique for estimating disease prevalence in hidden populations. RDS data are collected through a snowball mechanism, in which current sample members recruit future sample members. In this paper we present RDS as Markov chain Monte Carlo importance sampling, and we examine the effects of community structure and the recruitment procedure on the variance of RDS estimates. Past work has assumed that the variance of RDS estimates is primarily affected by segregation between healthy and infected individuals. We examine an illustrative model to show that this is not necessarily the case, and that bottlenecks anywhere in the networks can substantially affect estimates. We also show that variance is inflated by a common design feature in which the sample members are encouraged to recruit multiple future sample members. The paper concludes with suggestions for implementing and evaluating RDS studies.
Assuntos
Cadeias de Markov , Método de Monte Carlo , Estudos de Amostragem , Algoritmos , Biometria , Métodos Epidemiológicos , Feminino , Infecções por HIV/complicações , Infecções por HIV/epidemiologia , Humanos , Masculino , Modelos Estatísticos , Cidade de Nova Iorque/epidemiologia , Saúde Pública/estatística & dados numéricos , Apoio Social , Transtornos Relacionados ao Uso de Substâncias/complicaçõesRESUMO
Hit songs, books, and movies are many times more successful than average, suggesting that "the best" alternatives are qualitatively different from "the rest"; yet experts routinely fail to predict which products will succeed. We investigated this paradox experimentally, by creating an artificial "music market" in which 14,341 participants downloaded previously unknown songs either with or without knowledge of previous participants' choices. Increasing the strength of social influence increased both inequality and unpredictability of success. Success was also only partly determined by quality: The best songs rarely did poorly, and the worst rarely did well, but any other result was possible.