By Ahmad Jamil Malik
CROSS-SECTIONAL STUDY
In an analytical cross-sectional study, the investigator measures exposure and disease simultaneously in a representative sample of the population.
Cross-sectional studies measure the association between the exposure variable and existing disease (prevalence), cohort studies which measure the rate of developing disease (incidence).
Design of a cross-sectional study:
An example of a cross-sectional study is that of “Psychological impact of COVID-19 outbreak on frontline nurses: A cross-sectional survey study”. This study aimed to portray the prevalence and associated factors of psychological distress among frontline nurses during COVID-19 outbreak. This psychological distress was predominantly described as sleep disturbance, symptoms of anxiety and depression, post-traumatic stress, inability to make decisions and even somatic symptoms. An online survey by convenience sampling. The study methods were compliant with the STROBE checklist
The results of this study were that out of the 263 frontline nurses, 66 (25.1%) were identified as psychological distress.
Positive findings of this study:
These included as working in emergency department, concern for family, being treated differently, negative coping style and COVID-19-related stress symptoms.
Negative findings of this study:
These included as receiving more social support and effective precautionary measures such as personal protective equipment (PPE).
In conclusion, the study demonstrated that COVID-19 had a significant psychological impact on frontline nurses
Advantages of cross-sectional study:
- They can be short-term, and therefore less costly than prospective studies
- They provide a wealth of data that can be of great use in health systems research.
Disadvantages of cross-sectional study:
- They provide no direct estimate of risk.
- They are prone to bias from selective survival.
- Since exposure and disease are measured at the same point in time, it is not possible to establish temporality.