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.

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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.

The Generalized Additive Models for Location, Scale, and Shape (GAMLSS) are a recent class of models that further flexibilitythe distribution of the response variable. The regression analysis has been used to model biological phenomena, and its variousmodalities have met the need for its use with precision. However, there are situations in which the adjustment of models with moreflexible assumptions in the specification of the distribution of the response variable becomes indispensable, thus justifying the useof GAMLSS. The study of plant growth curves has full application in agricultural research; thus, it is crucial to know the habits ofgrowth and development of forest species is crucial for reforestation programs and in the most diverse researches. The study aimedto model the growth of Eucalyptus through the adjusting of Generalized Additive Models for Location, Scale, and Shape, in orderto promote improvements on crop productivity. Considering all parameters of the independent variable (time) under GAMLSSclass modeling, the distribution model ST3 presented better results.

Atualmente a análise de sobrevivência é uma das áreas que mais crescem no campo da análise estatística, com uma sólida teoria para ajustar modelos de regressão para estudar certos fenômenos, os quais têm, em sua estrutura, a característica de ter observações incompletas na amostra denominada censura. Embora esses modelos possam representar eficientemente o fenômeno em estudo em muitas situações, alguns deles não levam em consideração a existência de uma variável não observável presente na maioria dos estudos, denominada fragilidade. Essa fragilidade denota a suscetibilidade do evento a ocorrer por um indivíduo ou objeto determinado sob investigação. O objetivo deste trabalho foi mostrar que, em situações em que a fragilidade está presente, o uso de modelos que capturam a variabilidade dessa variável é mais viável para a análise desses dados quando comparado aos modelos convencionais em estudos de sobrevivência. Para tanto, foi realizada uma análise comparativa entre esses modelos, ajustada para um conjunto de dados de pacientes diagnosticados com retinopatia diabética, e também foi realizado um estudo de simulação para o modelo de fragilidade gama com diferentes porcentagens de censura e heterogeneidade. Após o ajuste dos modelos, observa-se que os modelos de fragilidade tiveram melhor desempenho quando comparados ao modelo de Cox, com ênfase no modelo de fragilidade gama, que gerou o menor valor para AIC e BIC. O estudo de simulação mostrou que altas taxas de censura prejudicam o grau de previsibilidade do modelo de fragilidade e que altas taxas de heterogeneidade contribuem para estimativas de parâmetros.