Risk Factors and Treatment Types for Asthma Severity Among Adult Patients

Determinants of asthma among adults in Tigray, Northern Ethiopia: a facility-based case-control study

Background

Asthma is a public health concern affecting millions of productive age groups. Several studies were conducted on the determinants of asthma in children. However, little is known about the determinants of asthma among adults in Ethiopia. Understanding the determinants of asthma among adults can help reduce its burden. This study was aimed at identifying determinant factors for developing asthma among adults in Tigray hospitals.

Methods

A facility-based, unmatched case-control study design was conducted from January 1 to April 26, 2019. A total of 698 participants (228 cases and 470 controls) completed their guided interviews using structured and pretested questionnaires by trained data collectors. A modified standard questionnaire from the European Community Respiratory Health Survey II (ECRHS II) was used to collect the data. The case definition was patients having asthma, and the control definition was patients without asthma. Data were entered and cleaned using Epi Data Manager Version 3.1 software and imported to statistical packages for social sciences Version 25 software for analysis. To identify asthma determinants, bivariate and multivariable logistic regression models were fitted.

Results

The response rate for both cases and controls was 95.9%. The odds of developing asthma was nearly twice higher among those who resided in urban (AOR = 1.68; 95% CI [1.13-2.50]), more than twice higher among those who have income less than 1000 ETB (AOR = 2.3; 95% CI [1.17-4.56]), twice higher among those who had history of skin allergy (AOR = 2.09; 95% CI [1.14-3.86]), over four times higher among those with family history of asthma (AOR = 4.26; 95% CI [2.63-6.91]), three times higher among those having house dust or smoke exposure (AOR = 3.01; 95% CI [1.96-4.64]), over five times higher among those lifetime firewood users (AOR = 5.39; 95% CI [3.34-8.72]), door opening while cooking (AOR = 0.35; 95% CI [0.26-0.55]), nearly two times higher among those having house dampness (AOR = 1.98; 95% CI [1.069-3.68]), over seven times higher among pet owners (AOR = 7.46; 95% CI [4.04-13] and almost twice higher among those who were physically inactive (AOR = 1.75; 95% CI [1.11-2.85]).

Conclusion

Asthma has been associated with urbanization, low income, a history of allergic diseases, indoor smoke or dust, firewood use, pet ownership, and a sedentary lifestyle. The community should be informed about the known risks and implement preventive steps like opening a door while cooking to lower the risk of asthma.

Development of a risk prediction model to predict the risk of hospitalization due to exacerbated asthma among adult asthma patients in a lower middle-income country

BACKGROUND

Asthma patients experience higher rates of hospitalizations due to exacerbations leaving a considerable clinical and economic burden on the healthcare system. The use of a simple, risk prediction tool offers a low-cost mechanism to identify these high-risk asthma patients for specialized care. The study aimed to develop and validate a risk prediction model to identify high-risk asthma patients for hospitalization due to exacerbations.

METHODS

Hospital-based, case-control study was carried out among 466 asthma patients aged ≥ 20 years recruited from four tertiary care hospitals in a district of Sri Lanka to identify risk factors for asthma-related hospitalizations. Patients (n = 116) hospitalized due to an exacerbation with respiratory rate > 30/min, pulse rate > 120 bpm, O2 saturation (on air) < 90% on admission, selected consecutively from medical wards; controls (n = 350;1:3 ratio) randomly selected from asthma/medical clinics. Data was collected via a pre-tested Interviewer-Administered Questionnaire (IAQ). Logistic Regression (LR) analyses were performed to develop the model with consensus from an expert panel. A second case-control study was carried out to assess the criterion validity of the new model recruiting 158 cases and 101 controls from the same hospitals. Data was collected using an IAQ based on the newly developed risk prediction model.

RESULTS

The developed model consisted of ten predictors with an Area Under the Curve (AUC) of 0.83 (95% CI: 0.78 to 0.88, P < 0.001), sensitivity 69.0%, specificity 86.1%, positive predictive value (PPV) 88.6%, negative predictive value (NPV) 63.9%. Positive and negative likelihood ratios were 4.9 and 0.3, respectively.

CONCLUSIONS

The newly developed model was proven valid to identify adult asthma patients who are at risk of hospitalization due to exacerbations. It is recommended as a simple, low-cost tool for identifying and prioritizing high-risk asthma patients for specialized care.