Problems in the processes of pain identification, measurement, and treatment have been found. Results suggest that there is a need for both an investment in continuing education of professionals and the development of protocols to optimize the analgesic therapy, thus preventing increased child suffering.

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.

Background: Polypharmacy has become an increasingly public health issue as population age and novel drugs are developed. Yet, evidence on low- and middle-income countries (LMIC) is still scarce. Objective: This work aims to estimate the prevalence of polypharmacy among Brazilians aged 50 and over, and investigate associated factors. Methods: A cross-sectional study was conducted using data from the baseline assessment of the Brazilian Longitudinal Study of Aging (ELSI-Brazil), a nationally representative study of persons aged 50 years and older (n=9,412). Univariate and bivariate analyses described the sample. Robust Poisson regression was used to estimate prevalence ratios and predict probabilities of polypharmacy. Results: Prevalence of polypharmacy was estimated at 13.5% among older adults in Brazil. Important disparities were observed in regard to gender (16.1% among women and 10.5% among men), race (16.0% among whites and 10.1% among blacks) and geographic region (ranging from 5.1% in the North to 18.7% in the South). The multivariate analysis showed that polypharmacy is associated with various sociodemographic/individual factors (age, gender, race, education, region, health status, body mass index) as well as with several variables of healthcare access/utilization (number of visits, same physician, provider’s knowledge of patient’s medications, gate-keeper, and difficulty managing own medication). Overall, the more utilization of health services, the higher the probability of polypharmacy, after adjusting for all other model covariates. Conclusions: Polypharmacy prevalence is relatively low in Brazil, compared to European countries. After controlling for variables of healthcare need and demographic characteristics, there is still substantial residual variance in polypharmacy prevalence. Policies to identify inappropriate prescribing and reduce regional discrepancies are necessary.

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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 ...