Our study showed an important influence of dyspepsia on work productivity. We did not find any statistically significant difference on the influence on work between patients with organic dyspepsia and functional dyspepsia. The social impact of these findings is underscored by taking into account the prevalence (up to 40%) of this condition in Brazil.

Judicialização do acesso ao tratamento de doenças genéticas raras: a doença de Fabry no Rio Grande do Sul

Adverse drug reactions and nonadherence to treatment are important causes of morbidity and cost to the health service. Much of this resource is spent to treat preventable cases of DRM, which represents a great waste of resources.

Objectives: In advance of a new medication being introduced to market, it is important to understand contemporary patterns of care and associated clinical outcomes, in order to document current treatment gaps the new asset will address. Retrospective chart review is a powerful methodology for addressing these questions. A limitation of chart review studies is that the process of extracting comprehensive data from charts can impact sample size feasibility. In contrast to the hypothesis-testing framework, for a burden of illness study, the role of sample size is to improve precision around estimates of descriptive outcomes, and ensure sufficient representation is achieved amongst subgroups of clinical interest. Determining how many patient charts to include in such a study is not straightforward: common sample size calculations developed for a hypothesis-testing framework have limited applicability in this context. The objective in this abstract is to develop and present rigorous approaches for sample size calculation for such studies. MethOds: Proposed methods are described and an illustrative case study is presented of a retrospective chart review of advanced melanoma, and the precision obtained for a sample size of 655 patients across three countries, including ability to consider patient subgroups and compare costs by country. For cost outcomes, the ratio of standard deviation to mean ranged from 0.3-4.5 across countries, with a median of 0.7 and mean of 1.0. Results: Using the proposed methods and assuming a ratio of 1.0, to achieve precision of 20% around mean cost would require a sample size of 96; parameters can be adjusted as needed to reflect alternative requirements. cOnclusiOns: This methodological study addresses an important knowledge gap, as sample sizes are frequently determined using ad-hoc approaches and/ or based only on feasibility considerations. The approach presented here is methodologically rigorous and designed for practical application in real-world retrospective chart review studies.Objectives: To contribute to this discussion on taxation of sugar sweetened beverages and its impact, this study aims to evaluate the macroeconomic and sectorial effects of this policy in Brazil. MethOds: We used the traditional input-output price model. The analysis considers an increase of 10% in the cost of production of soft drinks and sugary drinks due to a tax increase. We used the traditional input-output price model. Results: The results of the indices show that the sugary drinks sector is weak intersectoral relations and will not appear as a key sector for the Brazilian economy. Considering these connections, simulating a tax increase of 10% on the production of sugary drinks shows a reduction of R$ 35 billion or 2.1% in spending on household consumption. This change, however, does not differ significantly between the deciles of income and reaches the highest value in the decision median (2.3% of total expenditure). The estimates point to a positive result for the total economy of 2.1%. In the case of...

and model 1 + initiating-service groups (model 3). Optimism-corrected C-statistics and root mean square error (RMSE) were used to compare model prediction accuracy. Results: The cohort consisted of 3,105 individuals; of which 38.3% had a recurrent episode, with an average cost of $3,803CAD (SD= $11,959). LCA identified two groups: high user (15.2%) and low user (84.8%), while about 51.6% of the cohort was grouped as institutionalized. Model 2 predicted time to a subsequent episode better (C-statistic = 0.94) than model 3 (C-statistic = 0.91), and model 1 (C-statistic = 0.87). Model 3 predicted cost of the subsequent episode better (RMSE= 11934.5) than model 2 (RMSE= 11999.8) and model 1 (RMSE= 12075.3). ConClusions: The findings suggest that both LCA-defined groups and initiating-service groups could be used to summarize past utilization for risk prediction modeling; the choice may depend on the outcome of interest. PRM36 NoN-DiscRetioNaRy iNPuts aRe iMPoRtaNt FactoRs iN HosPital eFFicieNcy stuDies aND Policy evaluatioNPasupathy K. , Sir M. Mayo Clinic, Rochester, MN, USA objeCtives: Hospital efficiency is the focus of several studies and increasingly, data envelopment analysis (DEA) is used to compute and compare efficiencies of hospitals for quality improvement and policy analysis. DEA is a non-parametric approach to compute efficiency considering multiple inputs and outputs and benchmark hospitals, with the presumption that the inefficient hospitals can reduce inputs and/or increase outputs to improve their efficiency. Hospitals are complex systems and have multiple non-discretionary inputs, such as type of hospital or region of location that are not under the control of administration, and hence cannot be altered. This study emphasizes the need to consider non-discretionary inputs in DEA models. Methods: One year's worth of Agency for Healthcare Research & Quality's Health Care Utilization Project data was used. Variables included full-time equivalent of registered nurses, licensed practical nurses and nurse aides as inputs and total discharges and percent of surgeries as outputs, with bed size, urban/ rural, teaching status, region and ownership as non-discretionary inputs. First a variable-returns-to-scale DEA model was run without the non-discretionary inputs. Next the model was repeated including an environmental harshness (created using regression). Results: When the efficiency scores of the 862 hospitals in the two stages were compared, 755 hospitals had increased efficiency, and mean increased more than three times from 0.11 (P< 0.000) and standard deviation doubled from 0.15. The number of efficient hospitals increased from 12 to 72. ConClusions: DEA can be a sophisticated method to measure hospital efficiency. Not accounting for non-discretionary inputs can radically alter the efficiency scores and bias study results. The first stage score has inefficiency and the effect of non-discretionary inputs. Since both mean and standard deviation increased dramatically, simple normalization cannot be used as ...