Seyed Reza Abdipour Mehrian
1 
, Zahra Ghahramani
2, Mohammad Reza Akbari
1, Elham Hashemi
1, Ehsan Shojaeefard
1, Reza Malekzadeh
3, Bita Mesgarpour
4, Abdullah Gandomkar
5, Mohammad Reza Panjehshahin
6, Jafar Hasanzadeh
7, Fatemeh Malekzadeh
8, Hossein Molavi Vardanjani
9* 1 MD-MPH Program, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
2 Hematology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
3 Liver, Pancreatic, and Biliary Diseases Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
4 Vice Chancellery for Research & Technology, Iran Ministry of Health and Medical Education, Tehran, Iran
5 Non-Communicable Disease Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
6 Faculty of Pharmacy, Shiraz University of Medical Science, Medicinal & Natural Products Chemistry Research Center, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
7 Department of Epidemiology, Shiraz University of Medical Sciences, Shiraz, Iran
8 Digestive Diseases Research Center, Digestive Diseases Research Institute, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
9 MD-MPH Program, School of Medicine, Research Center for Traditional Medicine and History of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
Abstract
Background: Drug data has been used to estimate the prevalence of chronic diseases. Disease registries and annual surveys are lacking, especially in less-developed regions. At the same time, insurance drug data and self-reports of medications are easily accessible and inexpensive. We aim to investigate the similarity of prevalence estimation between self-report data of some chronic diseases and drug data in a less developed setting in southwestern Iran.
Methods: Baseline data from the Pars Cohort Study (PCS) was re-analyzed. The use of disease-related drugs were compared against self-report of each disease (hypertension [HTN], diabetes mellitus [DM], heart disease, stroke, chronic obstructive pulmonary disease [COPD], sleep disorder, anxiety, depression, gastroesophageal reflux disease [GERD], irritable bowel syndrome [IBS], and functional constipation [FC]). We used sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the Jaccard similarity index.
Results: The top five similarities were observed in DM (54%), HTN (53%), heart disease (32%), COPD (30%), and GERD (15%). The similarity between drug use and self-report was found to be low in IBS (2%), stroke (5%), depression (9%), sleep disorders (10%), and anxiety disorders (11%).
Conclusion: Self-reports of diseases and the drug data show a different picture of most diseases’ prevalence in our setting. It seems that drug data alone cannot estimate the prevalence of diseases in settings similar to ours. We recommend using drug data in combination with self-report data for epidemiological investigation in the less-developed setting.