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Background The optimal model method for estimation of benchmark dose (BMD) does not consider the uncertainty of model selection. There is a lack of studies on using Bayesian model averaging (BMA) to estimate BMD. Objective To apply BMA to the exposure assessment of cadmium pollution in China, discuss the role of BMA in estimating BMD based on dose-response models, and to provide methodological support for health risk assessment of hazardous substances. Methods The parameters of five dose-response models (Gamma, Log-logistic, Log-probit, Two-stage, and Weibull models) estimated from the data from a cadmium-contaminated area in Baiyin City of Gansu Province and the urinary cadmium ranges in five cadmium-contaminated areas in China were used to simulate the data of varied correct models with different numbers of dosage groups (5 and 8) and different sample sizes (50, 100, and 200), then the performance of BMA and traditional optimal model were compared. The case analysis used the cadmium exposure data in Baiyin, Gansu Province. All analyses set urinary cadmium as the indicator of cadmium exposure, the abnormal rate of β2-microglobulin as the effect indicator, and the benchmark response to 10%. The correct model (the model used when simulating data), optimal model [the model with smallest Akaike information criterion (AIC)], and BMA were used to estimate BMD and lower confidence limit of benchmark dose (BMDL); the BMDs, BMDLs, and relative deviations from different methods were compared. Results In the simulation study, with increasing sample size or the number of dosage groups, the intervals of the 5th percentile and the 90th percentile of BMD tended to be narrower; when the correct model was a single model, the relative deviation of BMD estimation by BMA was greater than that of the traditional optimal model; when the correct model was an equal weight mixed model, the relative deviation of BMD estimation by BMA was less than that by the traditional optimal model. For the data of cadmium-contaminated areas, the optimal model was a Log-probit model (AIC=1814.46), followed by a Log-logistic model (AIC=1814.57); the BMDs (BMDLs) estimated by the Log-probit model, the Log-logistic model, and BMA were 3.46 (2.68), 3.16 (2.33), and 2.92 (2.07) μg·g−1, respectively. Conclusion The traditional optimal model is still recommended when the correct model is known. However, when the dose-response relationship of a hazardous substance is uncertain or with different sources or exposure grouping, compared with the traditional optimal model, BMA theoretically provides more stable estimation of BMD and BMDL by considering multiple possible alternative models.
ABSTRACT
Resumo Este estudo avaliou o impacto do tempo de adesão ao termo de compromisso de gestão (TCG), no âmbito do programa Pacto pela Saúde, sobre o nível de eficácia da política municipal de atenção básica, no período de 2008 a 2012. O TCG objetivou aprimorar a governança de política de saúde pelos entes federados, com especial atenção à gestão por resultados. O programa vigorou no Brasil entre 2006 e 2012, tendo recebido a adesão de 4.587 municípios (80% do total). Esta pesquisa buscou responder à seguinte questão: "qual foi o efeito causal do tempo de participação (em anos) no programa Pacto pela Saúde sobre o nível da eficácia da política local de atenção básica, para os municípios participantes?". Para tanto, adotou-se um desenho de pesquisa quase experimental, mediante estimação de um modelo de dose-resposta com escore de propensão generalizado. Estimou-se, via análise de componentes principais, um indicador de eficácia da política de atenção básica (IDEAB), tendo como referência as metas preconizadas pelo programa. Os resultados da estimação da função de dose-resposta evidenciaram que o tempo de adesão ao Pacto pela Saúde teve impacto positivo e estatisticamente significativo sobre o nível de eficácia das políticas de atenção básica nos municípios participantes. Para cada ano adicional de permanência da política, o IDEAB aumentou, em média, entre 0,011 e 0,019 unidades. Portanto, os resultados sugerem que as metas importam para a governança de política de saúde municipal brasileira.
Resumen El presente estudio evaluó el impacto del tiempo de membresía al Término de Compromiso de Gestión (TCG) sobre el nivel de efectividad de la política municipal de salud en Brasil, de 2008 a 2012. El TCG fue parte del programa Pacto por la Salud, y tenía como objetivo mejorar la gobernanza de la política de salud por parte de los estados federados, con especial atención a la gestión basada en resultados. El programa se ejecutó en Brasil entre 2006 y 2012, y fue adoptado por 4.587 municipios (80 por ciento del total). Esta investigación buscó responder a la siguiente pregunta: ¿Cuál fue el efecto causal del tiempo de participación en el programa (en número de años) sobre la efectividad de la política de atención primaria para los municipios participantes? Para ello, se adoptó un diseño de investigación cuasiexperimental, estimando un modelo de dosis-respuesta con puntaje de propensión generalizada. Se estimó un indicador de efectividad de la política de atención primaria (IDEAB) a través del análisis de componentes principales, con base en los objetivos recomendados por el programa. Los resultados de la estimación de la función dosis-respuesta mostraron que el número de años en el programa Pacto por la Salud tuvo un impacto positivo y estadísticamente significativo en el indicador de efectividad de la política de atención primaria para los municipios participantes. Por cada año adicional en la política, el IDEAB aumentó en un promedio de 0.011 a 0.019 unidades. Por lo tanto, los resultados sugieren que los objetivos son importantes para la gobernanza de la política de salud municipal brasileña.
Abstract This study evaluated the impact of the time a Brazilian local government stays as member of the program "Pacto pela Saúde" (Pact for Health) - by signing a Management Agreement -, and its efficiency to provide primary health care for the population. The research observed the period from 2008 to 2012. The program was an initiative of the Federal Government operated by municipalities through the Management Agreement and aimed to improve healthcare policy management adopting a results-based managerial approach. The program was in place between 2006 and 2012 and was operated by 4,587 local governments (80 percent of the Brazilian municipalities). The research question guiding the study was 'What was the effect of the time of a local government in the program (in years) on the efficiency of health care delivery to local populations? A quasi-experimental research design was adopted, estimating a dose-response model with generalized propensity score. An efficiency indicator of the primary care policy (IDEAB) was estimated via principal component analysis, based on the targets recommended by the program. The results of the dose-response model showed that the number of years in the Management Agreement had a positive and statistically significant impact on the efficiency of health care delivery in participating municipalities. For each additional year in the agreement, IDEAB increased by an average of 0.011 to 0.019 units. Therefore, the results suggest that establishing targets are important for the governance of the Brazilian health care policy.
Subject(s)
Primary Health Care , Health Policy , Local GovernmentABSTRACT
This study described methods to predict human health risk associated with exposure to environmental carciongens using animal bioassay data. Also, biological assumption for various dose-response models were reviewed. To illustrate the process of risk estimate using relevant dose-response models such as Log-normal, Mantel-Bryan, Weibull and Multistage model, we used four animal carcinogenesis bioassy data of chloroform and chloroform concentrations of tap water measured in large cities of korea from 1987 to 1995. As a result, in the case of using average concentration in exposure data and 95 % upper boud unit risk of Multistage model, excess cancer risk(RISK I) was about 1.9 x 10-6, in the case of using probability distribution of cumulative exposure data and unit risks, those risks(RISK II) which were simulated by Monte-Carlo analysis were about 2.4 x 10(-6) and 7.9 x 10(-5) at 50 and 95 percentile, respectively. Therefore risk estimated by Monte-Carlo analysis using probability distribution of input variables may be more conservative.