DESIGN: This qualitative study employed an interpretive descriptive approach. Two trained researchers conducted in-depth interviews (IDIs) and focus group discussions (FGDs) using a semi-structured topic guide, which was developed based on literature review and behavioural theories. All IDIs and FGDs were audio-recorded and transcribed verbatim. Two researchers analysed the data independently using a thematic approach.
PARTICIPANTS AND SETTING: Men working in a banking institution in Kuala Lumpur were recruited to the study. They were purposively sampled according to their ethnicity, job position, age and screening status in order to achieve maximal variation.
RESULTS: Eight IDIs and five FGDs were conducted (n=31) and six themes emerged from the analysis. (1) Young men did not consider screening as part of prevention and had low risk perception. (2) The younger generation was more receptive to health screening due to their exposure to health information through the internet. (3) Health screening was not a priority in young men except for those who were married. (4) Young men had limited income and would rather invest in health insurance than screening. (5) Young men tended to follow doctors' advice when it comes to screening and preferred doctors of the same gender and ethnicity. (6) Medical overuse was also raised where young men wanted more screening tests while doctors tended to promote unnecessary screening tests to them.
CONCLUSIONS: This study identified important factors that influenced young men's screening behaviour. Health authorities should address young men's misperceptions, promote the importance of early detection and develop a reasonable health screening strategy for them. Appropriate measures must be put in place to reduce low value screening practices.
METHODS: This nested case-control study was performed by collecting data from 1 January 2015 to 30 June 2017. Univariable and multivariable logistic regressions were used to identify potential risk factors. The regression coefficients were converted into item scores by dividing each regression coefficient with the minimum coefficient in the model and rounding to the nearest integer. This value was then summed to the total score. The prediction power of the model was determined by the area under the receiver operating characteristic curve (AuROC).
RESULTS AND DISCUSSION: Six clinical risk factors, namely age ≥65 years, benzodiazepine use, history of a cerebrovascular accident, dose of hydrochlorothiazide ≥25 mg, female sex and statin use, were included in our ABCDF-S score. The model showed good power of prediction (AuROC 81.53%, 95% confidence interval [CI]: 78%-84%) and good calibration (Hosmer-Lemeshow X2 = 23.20; P = .39). The positive likelihood ratios of hyponatremia in patients with low risk (score ≤ 6) and high risk (score ≥ 8) were 0.26 (95% CI: 0.21-0.32) and 3.89 (95% CI: 3.11-4.86), respectively.
WHAT IS NEW AND CONCLUSION: The screening tool with six risk predictors provided a useful prediction index for thiazide-associated hyponatremia. However, further validation of the tool is warranted prior to its utilization in routine clinical practice.
OBJECTIVES: To identify the risk factors associated with mortality for each gender and compare differences, if any, among ST-elevation myocardial infarction (STEMI) patients.
DESIGN: Retrospective analysis.
SETTINGS: Hospitals across Malaysia.
PATIENTS AND METHODS: We analyzed data on all STEMI patients in the National Cardiovascular Database-Acute coronary syndrome (NCVD-ACS) registry for the years 2006 to 2013 (8 years). We collected demographic and risk factor data (diabetes mellitus, hypertension, smoking status, dyslipidaemia and family history of CAD). Significant variables from the univariate analysis were further analysed by a multivariate logistic analysis to identify risk factors and compare by gender.
MAIN OUTCOME MEASURES: Differential risk factors for each gender.
RESULTS: For the 19484 patients included in the analysis, the mortality rate over the 8 years was significantly higher in females (15.4%) than males (7.5%) (P < .001). The univariate analysis showed that the majority of male patients < 65 years while females were >=65 years. The most prevalent risk factors for male patients were smoking (79.3%), followed by hypertension (54.9%) and diabetes mellitus (40.4%), while the most prevalent risk factors for female patients were hypertension (76.8%), followed by diabetes mellitus (60%) and dyslipidaemia (38.1%). The final model for male STEMI patients had seven significant variables: Killip class, age group, hypertension, renal disease, percutaneous coronary intervention and family history of CVD. For female STEMI patients, the significant variables were renal disease, smoking status, Killip class and age group.
CONCLUSION: Gender differences existed in the baseline characteristics, associated risk factors, clinical presentation and outcomes among STEMI patients. For STEMI females, the rate of mortality was twice that of males. Once they reach menopausal age, when there is less protection from the estrogen hormone and there are other risk factors, menopausal females are at increased risk for STEMI.
LIMITATION: Retrospective registry data with inter-hospital variation.