Drug–drug interactions (DDIs) are a significant concern for patients on complex therapeutic regimens, especially involving cardiovascular medications, which are frequently implicated in these interactions.

This study used a standardised interaction database to determine the frequency, severity and risk factors associated with potential DDIs (pDDIs) among cardiovascular disease (CVD) in-patients.

The prospective cross-sectional study was conducted at a tertiary care hospital in Nepal from April 2024 to October 2024. A total of 106 eligible CVD in-patients were evaluated for pDDIs using the Lexicomp DDI checker database, and the interactions were categorised based on severity and risk rating. Binary logistic regression identified factors associated with pDDIs.

The study identified 621 pDDIs using the Lexicomp database, with median values of 8 pDDIs per patient. Patients with at least one pDDI comprised 64.2% of the sample. Most pDDIs were of moderate severity (77.3%) with risk ratings of C (65.7%). The most common cardiovascular medications involved in the detected DDI pairs were diuretics (31.2%), antiplatelets and anticoagulants (23.8%) and calcium channel blockers (12.2%). Multivariate binary logistic regression revealed that patients who stayed longer (adjusted OR (AOR) 9.08, 95% CI 1.027 to 80.216, p=0.047), those receiving more medications (AOR 18.85, 95% CI 2.975 to 119.370, p=0.002) and those who were admitted to the intensive cardiac care unit (AOR 16.31, 95% CI 2.728 to 97.461, p=0.002) were significantly more likely to experience pDDIs.

This study found a higher prevalence of pDDIs. It is advisable to incorporate medication reviews into routine cardiac care and use a drug interaction checker to identify pDDIs.

The objective of the study was to understand the smoking behaviour of adults and how societal perceptions influence the smoking behaviour of university students.

Qualitative study.

National Institute of Medical Sciences university, India.

20 face-to-face interviews were carried out among university students who were in the age group of 19–30 years using a combination of purposive sampling, followed by snowball sampling methods.

Qualitative responses revealed that stress, cravings for cigarettes and mealtimes were key triggers for smoking behaviour. Many participants felt guilty about their smoking and often became irritated by advice from non-smoking friends. All participants had experienced negative health effects, including physical and sensory issues, as well as other adverse experiences. Students expressed a dislike for judgemental attitudes from society. They respected elders and found it difficult to smoke in front of them. Rather than being blamed for their smoking, they preferred supportive assistance to help them quit.

The study highlights the importance of understanding college students’ smoking behaviour, as it greatly influences their smoking habits. Cessation efforts should target this group and emphasise the negative experiences associated with smoking. Additionally, students recommend creating a non-judgemental and supportive environment to aid in quitting, rather than a judgemental and blaming society.

A ‘cluster’ is an area with a higher occurrence of tuberculosis (TB) than would be expected in an average random distribution of that area. Tuberculosis clustering is commonly reported in Ethiopia, but most studies rely on registered data, which may miss patients who do not visit health facilities or those who attend but are not identified as having TB. This makes the detection of actual clusters challenging. This study analysed the clustering of pulmonary TB and associated risk factors using symptom-based population screening in Dale, Ethiopia.

A prospective population-based cohort study.

All households in 383 enumeration areas were visited three times over a 1-year period, at 4-month intervals.

Individuals with pulmonary TB aged ≥15 years with demographic, socioeconomic, clinical and geographical data residing in 383 enumeration areas (ie, the lowest unit/village in the kebele, each with approximately 600 residents).

Pulmonary TB (ie, bacteriologically confirmed by sputum microscopy, GeneXpert or culture plus clinically diagnosed pulmonary TB) and pulmonary TB clustering.

We identified pulmonary TB clustering in 45 out of the 383 enumeration areas. During the first round of screening, 39 enumeration areas showed pulmonary TB clustering, compared with only 3 enumeration areas in the second and third rounds. Our multilevel analysis found that enumeration areas with clusters were located farther from the health centres than other enumeration areas. No other determinants examined were associated with clustering.

The distribution of pulmonary TB was clustered in enumeration areas distant from the health centres. Routine systematic community screening may be costly, but using existing health infrastructure with health extension workers through targeted screening, they can identify and refer persons with TB symptoms more quickly for diagnosis and treatment, thereby decreasing the duration of disease transmission and contributing to the reduction of TB burden.