Multiple imputation to deal with missing clinical data in rheumatologic surveys: an application in the WHO-ILAR COPCORD study in Iran

The aim of the article is demonstrating an application of multiple imputation [MI] for handling missing clinical data in the setting of rheumatologic surveys using data derived from 10291 people participating in the first phase of the Community Oriented Program for Control of Rheumatic Disorders [COPCORD] in Iran. Five data subsets were produced from the original data set. Certain demographics were selected as complete variables. In each subset, we created a univariate pattern of missingness for knee osteoarthritis status as the outcome variable [disease] using different mechanisms and percentages. The crude disease proportion and its standard error were estimated sgscrately for each complete data set to be used as true [baseline] values for percent bias calculation. The parameters of interest were also estimated for each incomplete data subset using two approaches to deal with missing data including complete case analysis [CCA] and MI with various imputation numbers. The two approaches were compared using appropriate analysis of variance. With CCA, percent bias associated with missing data was 8.67 [95% CI: 7.81-9.53] for the proportion and 13.67 [95% CI: 12.60-14.74] for the standard error. However, they were 6.42 [95% CI: 5.56-7.29] and 10.04 [95% CI: 8.97-11.11], respectively using the MI method [M=15]. Percent bias in estimating disease proportion and its standard error was significantly lower in missing data analysis using MI compared with CCA [P< 0.05]. To estimate the prevalence of rheumatic disorders such as knee osteoarthritis, applying MI using available demographics is superior to CCA

Malaria status in economic cooperation countries; achievement and gaps toward United Nations Millennium Development Goals

Evaluating the malaria status of the Economic Cooperation Organization [ECO] member countries relation to goal 6 of 3rd Millennium Development Goals [MDGs] which includes have halted by 2015 and begun to reverse the incidence of malaria. By 2009, we reviewed the MDGs reports, extracted the data from surveillance system, published, and unpublished data. The main stakeholders, from both governmental and international organizations in the country have been visited and interviewed by the research team as part of the data validation process. The malaria incidence is very heterogeneous among ECO countries, which differ less than 200 cases in total country in Kazakhstan, Kyrgyzstan, Turkey, Turkmenistan, Uzbekistan, and Azerbaijan to 82,564 cases [2,428/100,000] in Afghanistan and 59,284 cases [881/100,000] in Pakistan and about 18/100,000 in Iran in 2008. Malaria has been a major public health problem in Pakistan and Afghanistan and will continue to pose serious threat to millions of people due to poor environmental and socioeconomic conditions conducive to the spread of disease. The main malaria endemic areas of Iran are in southeastern part of the country; consist of less developed provinces that are bordered in the east by Afghanistan and Pakistan. There are little valid information about proportion of population in malaria-risk areas using effective malaria prevention and treatment measures indicators. All ECO countries could achieve MDGs malaria indicators by 2015 except Pakistan and Afghanistan, unless preparing urgent intervention programs to fulfill the goals