One long-term strategy for promoting health research in developing countries is to target medical students early in their careers.

Knowledge about coronary heart disease (CHD) and its risk factors is an important pre-requisite for an individual to implement behavioral changes leading towards CHD prevention. There is scant data on the status of knowledge about CHD in the general population of Pakistan. The objective of this study was to assess knowledge of CHD in a broad Pakistani population and identify the factors associated with knowledge. Cross sectional study was carried out at four tertiary care hospitals in Pakistan using convenience sampling. Standard questionnaire was used to interview 792 patient attendants (persons accompanying patients). Knowledge was computed as a continuous variable based on correct answers to fifteen questions. Multivariable linear regression was conducted to determine the factors independently associated with knowledge. The mean age was 38.1 (+/- 13) years. 27.1% had received no formal education. The median knowledge score was 3.0 out of a possible maximum of 15. Only 14% were able to correctly describe CHD as a condition involving limitation in blood flow to the heart. Majority of respondents could identify only up to two risk factors for CHD. Most commonly identified risk factors were stress (43.4%), dietary fat (39.1%), smoking (31.9%) and lack of exercise (17.4%). About 20% were not able to identify even a single risk factor for CHD. Factors significantly associated with knowledge included age (p = 0.023), income (p < 0.001), education level (p < 0.001), residence (p < 0.001), a family history of CHD (p < 0.001) and a past history of diabetes (p = 0.004). Preventive practices were significantly lacking; 35%, 65.3% and 84.6% had never undergone assessment of blood pressure, glucose or cholesterol respectively. Only a minority felt that they would modify their diet, stop smoking or start exercising if a family member was to develop CHD. This is the first study assessing the state of CHD knowledge in a relatively diverse non-patient population in Pakistan. There are striking gaps in knowledge about CHD, its risk factors and symptoms. These translate to inadequate preventive behavior patterns. Educational programs are urgently required to improve the level of understanding of CHD in the Pakistani population.

Objective: The accuracy of the diagnosis is vital when administrative databases are used for pharmacoepidemiologic and outcome studies. Data pertaining to the utility of databases for rheumatoid arthritis (RA) are sparse and variable. We assessed the utility of various diagnostic algorithms to identify RA patients within the Veterans Health Administration (VHA) databases.Methods: Using the International Classification of Diseases, Ninth Revision code for RA at 2 visits at least 6 months apart, we identified 1,779 patients between October 1, 1998 and September 30, 2009 in our local Veterans Affairs Medical Center (VAMC) administrative database. Disease‐modifying antirheumatic drug (DMARD) use was ascertained from the pharmacy database. Cases were analyzed based on DMARD therapy and RA codes at clinic visits. A total of 543 patients' medical records, selected by stratification and random selection on the basis of their visits, were reviewed to ascertain the clinicians' diagnoses and clinical criteria documentation. Positive predictive values (PPVs) were calculated for various database case identification algorithms using diagnosis of RA by medical record review as the gold standard.Results: The PPV for identification of RA with 2 RA codes 6 months apart was 30.9%. Addition of DMARD therapy increased the PPV to 60.4%. The PPV further increased to 91.4% when having an RA code at the last VAMC rheumatology clinic visit criterion was added. An algorithm using only 2 administrative RA codes 6 months apart had a low PPV for correctly identifying patients with RA in the VHA database.Conclusion: Including DMARD therapy and requiring an RA code at the last visit with a rheumatologist increased the performance of the data extraction algorithm.