Mariza Machado Kluck

Comparison of simple and multiple imputation methods using a risk model for surgical mortality as example

INTRODUCTION It is common for studies in health to face problems with missing data. Through imputation, complete data sets are built artificially and can be analyzed by traditional statistical analysis. The objective of this paper is to compare three types of imputation based on real data. METHODS The data used came from a study on the development of risk models for surgical mortality. The sample size was 450 patients. The imputation methods applied were: two single imputations and one multiple imputation and the assumption was MAR (Missing at Random). RESULTS The variable with missing data was serum albumin with 27.1% of missing rate. The logistic models adjusted by simple imputation were similar, but differed from models obtained by multiple imputation in relation to the inclusion of variables. CONCLUSIONS The results indicate that it is important to take into account the relationship of albumin to other variables observed, because different models were obtained in single and multiple imputations. Single imputation underestimates the variability generating narrower confidence intervals. It is important to consider the use of imputation methods when there is missing data, especially multiple imputation that takes into account the variability between imputations for estimates of the model.

Mortality prediction model using data from the Hospital Information System

OBJECTIVE To develop a hospital mortality prediction model based on data from the Hospital Information System of the Brazilian National Health System. METHODS This was a cross-sectional study using data from 453,515 authorizations for hospital admission relating to 332 hospitals in Rio Grande do Sul, Southern Brazil in the year 2005. From the ratio between observed and expected deaths, the hospitals were ranked in an adjusted manner, and this was compared with the crude ranking of the mortality rate. Logistic regression was used to develop a predictive model for the likelihood of hospital mortality according to sex, age, diagnosis and use of an intensive care unit. Confidence intervals (95%) were obtained for the 206 hospitals with more than 365 hospital admissions per year. RESULTS An index for the risk of hospital mortality was obtained. Ranking the hospitals using only the crude mortality rate differed from the ranking when it was adjusted according to the predictive likelihood model. Among the 206 hospitals analyzed, 40 of them presented observed mortality that was significantly greater than what was expected, while 58 hospitals presented mortality that was significantly lower than expected. Use of an intensive care unit presented the greatest weight in making up the risk index, followed by age and diagnosis. When the hospitals attended patients with widely differing profiles, the risk adjustment did not result in a definitive indication regarding which provider was best. Among this group of hospitals, those of large size presented greater numbers of deaths than would be expected from the characteristics of the hospital admissions. CONCLUSIONS The hospital mortality risk index was shown to be an appropriate predictor for calculating the expected death rate, and it can be applied to evaluate hospital performance. It is recommended that, in comparing hospitals, the adjustment using the predictive likelihood model for the risk should be used, with stratification according to hospital size.

Perfil dos partos cesáreos em um hospital universitário

Introducao: A proporcao de partos cesarianos no Brasil esta muito alem da preconizada pela Organizacao Mundial da Saude, o mesmo ocorrendoem um Hospital Universitario de Porto Alegre, sul do Brasil. A taxa de cesareas e considerada um indicador poderoso na avaliacao da qualidade da assistencia perinatal. Nosso objetivo foi analisar o padrao dos partos cesarianos e normais em um hospital universitario no periodo de2004 a 2012 quanto a media de permanencia, faixa etaria da parturiente, taxa de infeccao relacionada ao parto, tipo de pagador e idade gestacional. Metodos: Estudo de coorte retrospectivo, observacional, com dados coletados no Sistema de Indicadores de Gestao (IG) de um Hospital Universitario, abrangendo o periodo de2004 a 2012. Resultados: A taxa de cesarea no hospital universitario nos anos analisados foi em media 33,21%. Em relacao as cesarianas realizadas nesta instituicao durante o periodo em estudo, observou-se que: ha uma maior prevalencia de cesareas em mulheres acima de 40 anos, ocorreu um crescimento de partos cesarianos pre-termo, a media de permanencia e taxa de infeccao foram superiores em relacao as mulheres submetidas ao parto vaginal, e houve predominio de cesarianas na saude suplementar quando comparada ao Sistema Unico de Saude. Conclusoes: As elevadas taxas de cesarea no hospital universitario, embora acima do recomendado pela OMS, sao justificadas por se tratar de um hospital terciario e estao em conformidade com o padrao observado no pais.