Ayesha Ahmed, Fatima Ahmed, Mohammad Zeeshan Raza, Aiman Ghani and Nadeem Rizvi
Objectives: Our aim is to identify the seasonal pattern in the frequency and outcome of hospital admissions due to acute exacerbations of asthma.
Patients and methods: A retrospective, hospital-based observational study was used to assess the seasonal patterns in hospital visits due to asthma. The study was conducted in the three tertiary care hospitals (AKUH, LNH and JPMC) of Karachi, Pakistan for a two year period from January 1, 2011 to December 31, 2012. Data was collected from hospital records department through patients discharge files of those who had a primary diagnosis for asthma. Subjects were recruited using a stratified random sampling method. Patients’ records were included on the basis of either physician diagnosis of asthma, or spirometry or clinical or radiological evidence.
Results: There were total two thousand five hundred and three patients recorded (2,503) patients recorded. The results demonstrated that the seasonal episodes of asthma increased from mid of December to February (winter season), with a peak occurring in the month of March (early spring) and significantly less cases of asthma exacerbations occurred in the month of May (summer) and November (autumn). Age and sex-specific rates showed marked predilection towards females (65%) (p=0.001) and patients above 55 years of age (64.8%) (p=0.001) with a mean age for males was 61 years, SD ± 1.92 and for females were 64 years, SD ± 1.94. There were total 64 expired cases (3.1%) recorded during the study. The most common symptoms recorded was persistence of cough after cold (66%), difficulty in breathing (57.17%), paroxysms of cough especially at night (48.7%) and wheezing (38.67%).
Conclusions: A clear seasonal pattern with higher admissions in the winter season and early spring were observed especially in the female adults and age-group of above 55 years. Strategies to combat exacerbations of asthma should taken into consideration seasonal effects on a population. In addition, temporal trends examined over many years can be used to predict frequency of severe asthma episodes in a population.