RESUMO

The incidence and mortality rates of colorectal cancer (CRC) in Northeast Brazil are increasing. To study the association between CRC and diet, data were obtained from 64 patients with CRC and 123 sex- and age-matched controls. The dietary details were recorded using a validated food frequency questionnaire. Nutrient intake was calculated using Dietsys software (National Cancer Institute, Maryland, USA). In a binary logistic regression model of dietary components (model 1), the chance of CRC increased by 0.2% (odds ratio [OR] = 1.002; 95% confidence interval [CI]: 1.000-1.004) for each gram of processed meat intake per week (p < 0.010). Consumption of eggs decreased the chance by 0.1% per gram (OR = 0.999; 95% CI: 0.998-1.000; p < 0.050). The use of oil (including olive oil) for served food decreased the chance by 1.8% (OR = 0.982; 95% CI: 0.970-0.992) for each time consumed (p < 0.010). In a model of nutritional factors (model 2), intake of vitamin E decreased the chance by 16.8% (OR = 0.832; 95% CI: 0.725-0.940) for each milligram intake per week (p < 0.010). In model 1 and 2 smoking increased the chance of CRC by 10.294 (95%CI: 4.240-27.670) and 2.496 (95% CI: 1.425-3.566) times (p < 0.010; p < 0.010), respectively.

Assuntos

RESUMO

BACKGROUND: The diagnosis of breast cancer requires a complicated series of diagnostic exams. The present study addressed the delay of patients who used publicly and privately financed diagnostic services. Non-governmental organizations (NGOs) donated diagnostic mammograms and biopsies. DESIGN AND METHODS: Data from 304 patients were obtained from two Brazilian referral centres. In one referral centre (FAP), diagnostic mammography, clinic-histopathological exam and immunohistochemistry were outsourced, whereas in the other centre (HNL), these services were integrated. Cox regression, Kaplan-Meier analysis and non-parametric tests were used to compare variables and time intervals. RESULTS: If diagnostic mammography was financed privately and covered by private health insurance, the likelihood of a delay of >90 days between the first medical visit and the initiation of treatment decreased 2.15-fold (95%CI: 1.06- 4.36; p=0.033) and 4.44-fold (95%CI: 1.58-12.46; p=0.004), respectively. If the clinic-histopathological exam was outsourced (FAP) and publicly or privately financed, the median time between diagnostic mammography and the diagnostic result was 53 and 65 days in the integrated (HNL) and outsourced public system, compared to 29 days in the outsourced private system (p<0.050). The median time between the first medical visit and the diagnostic results of patients who were supported by NGOs, who financed their diagnostic services privately, and who used exclusively public diagnostic services was, respectively, 28.0, 48.5 and 77.5 days (p<0.050). CONCLUSION: Patients who used privately financed health services had shorter delays. Compared to outsourcing, the integration of the publicly financed clinic- histopathological exam diminished the delay. The support of patients by NGOs accelerated patient flow.

RESUMO

BACKGROUND: System delay (SD) is a leading cause of advanced stage of disease and poor prognosis among Brazilian breast cancer patients. METHODS: Cox regression and Kaplan-Meier analysis were used to identify variables that contributed to SD among 128 breast cancer patients. Time intervals between first medical consultation and treatment initiation were compared among patients of two referral centres: Patients of a referral centre with outsourced (FAP), respectively, integrated (HNL) diagnostic services. RESULTS: Women who used a specialized private clinic at the beginning of patient flow had an 2.32 fold increased chance (95% CI: 1.17 - 4.60; p = 0.016) of hospital admission within 90 days after first medical consultation, compared to women who used a public health care provider (HCP). Of 73 and 34 patients of the FAP hospital and the HNL, respectively, 10 (13.7%) and 11 (32.5%) used one HCP prior to hospital admission (p = 0.000). The median time between first medical consultation and treatment initiation was 150 days. The median time between first medical consultation and hospital admission was 136.0 and 52.0 days for patients of the FAP hospital, respectively the HNL (p < 0.050). The median time between first medical consultation and diagnostic mammography was 36.5 and 23.0 days for patients from the FAP hospital and the HNL (p < 0.050). CONCLUSIONS: Usage of public diagnostic services was associated with increased SD, whereas the usage of private diagnostic services diminished it. The usage of a lower number of HCPs accelerated patient flow.

Assuntos

RESUMO

The present paper approaches the covariance analysis model with one factor and measurement error in the covariate. Accuracy and precision of two estimators suggested in the literature were evaluated through data simulation, for estimating parameters of a regression model with measurement error. So called Plug-in method estimates the real value based on the observed ones and then uses the common function for estimating the desired parameter. The other estimator, known as bias smoother, only performs a bias correction on the usual estimator by computing a factor. Behavior of both estimators was studied under different residual distributions, goodness of fit and sample sizes. It is worth noting that, in covariance analysis model, the high the sample size, the better for accuracy and precision. Results suggest that the Plug-in estimator presented the best performance both for accuracy and precision under normality, for the distinct evaluated situations. When the estimators had been evaluated in the model of ANCOVA with the residues distributed for Gamma, the same ones had gotten the worse performance in relation when they were evaluated by the others distributions.

Estudou-se o modelo de análise de covariância com um fator e erro de medida na covariável. Avaliou-se, neste modelo, por meio de simulação, a acurácia e precisão de dois estimadores, propostos na literatura para estimar parâmetros de um modelo de regressão com erro de medida. Sobre diferentes distribuições dos resíduos, coeficientes de determinação e tamanhos amostrais, estudou-se o comportamento de ambos os estimadores. No modelo de análise de covariância, quanto maior o tamanho amostral e o coeficiente de determinação, melhor se comportam os estimadores avaliados com relação à acurácia e à precisão. As conclusões encontradas sugerem que o estimador Plug-in obteve desempenho superior, tanto na acurácia quanto na precisão em situações de normalidade, nas diferentes configurações analisadas sobre o modelo avaliado. Quando os estimadores foram avaliados no modelo de ANCOVA com os resíduos distribuídos pela Gama, obtiveram o pior desempenho em relação a quando eram avaliados pelas demais distribuições.

RESUMO

The present paper approaches the covariance analysis model with one factor and measurement error in the covariate. Accuracy and precision of two estimators suggested in the literature were evaluated through data simulation, for estimating parameters of a regression model with measurement error. So called Plug-in method estimates the real value based on the observed ones and then uses the common function for estimating the desired parameter. The other estimator, known as bias smoother, only performs a bias correction on the usual estimator by computing a factor. Behavior of both estimators was studied under different residual distributions, goodness of fit and sample sizes. It is worth noting that, in covariance analysis model, the high the sample size, the better for accuracy and precision. Results suggest that the Plug-in estimator presented the best performance both for accuracy and precision under normality, for the distinct evaluated situations. When the estimators had been evaluated in the model of ANCOVA with the residues distributed for Gamma, the same ones had gotten the worse performance in relation when they were evaluated by the others distributions.

Estudou-se o modelo de análise de covariância com um fator e erro de medida na covariável. Avaliou-se, neste modelo, por meio de simulação, a acurácia e precisão de dois estimadores, propostos na literatura para estimar parâmetros de um modelo de regressão com erro de medida. Sobre diferentes distribuições dos resíduos, coeficientes de determinação e tamanhos amostrais, estudou-se o comportamento de ambos os estimadores. No modelo de análise de covariância, quanto maior o tamanho amostral e o coeficiente de determinação, melhor se comportam os estimadores avaliados com relação à acurácia e à precisão. As conclusões encontradas sugerem que o estimador Plug-in obteve desempenho superior, tanto na acurácia quanto na precisão em situações de normalidade, nas diferentes configurações analisadas sobre o modelo avaliado. Quando os estimadores foram avaliados no modelo de ANCOVA com os resíduos distribuídos pela Gama, obtiveram o pior desempenho em relação a quando eram avaliados pelas demais distribuições.