This study conducted in Dena County is a population-based cohort study as part of the Prospective Epidemiological Research Studies in Iran (PERSIAN). The specific objectives of this study were to estimate the prevalence of region-specific modifiable risk factors and their associations with the incidence of major non-communicable diseases (NCDs).

This PERSIAN Dena Cohort Study (PDCS) was conducted on 1561 men and 2069 women aged 35–70 years from October 2016 in Dena County, Kohgiluyeh and Boyer-Ahmad Province, Southwest Iran. The overall participation rate was 82.7%.

Out of 3630 participants, the mean age was 50.16 years, 2069 (56.9%) were women and 2092 (57.6%) were rural residents. Females exhibited higher prevalence rates of diabetes, hypertension, fatty liver, psychiatric disorders, thyroiditis, kidney stones, gallstones, rheumatic disease, chronic lung disease, depression and osteoporosis compared with males (p126 mg/dL, low-density lipoprotein >100 mg/dL and haematuria, respectively; most of them were female and urban people (p

PDCS will be planned to re-evaluate NCD-related incidence, all-cause and cause-specific mortality every 5 years, along with annual follow-up for 15 years. Some examples of additional planned studies are evaluation of genetic, environmental risk, spirometry and ECG tests.

Coexisting multiple health conditions is common among older people, a population that is increasing globally. The potential for polypharmacy, adverse events, drug interactions and development of additional health conditions complicates prescribing decisions for these patients. Artificial intelligence (AI)-generated decision-making tools may help guide clinical decisions in the context of multiple health conditions, by determining which of the multiple medication options is best. This study aims to explore the perceptions of healthcare professionals (HCPs) and patients on the use of AI in the management of multiple health conditions.

A qualitative study will be conducted using semistructured interviews. Adults (≥18 years) with multiple health conditions living in the West Midlands of England and HCPs with experience in caring for patients with multiple health conditions will be eligible and purposively sampled. Patients will be identified from Clinical Practice Research Datalink (CPRD) Aurum; CPRD will contact general practitioners who will in turn, send a letter to patients inviting them to take part. Eligible HCPs will be recruited through British HCP bodies and known contacts. Up to 30 patients and 30 HCPs will be recruited, until data saturation is achieved. Interviews will be in-person or virtual, audio recorded and transcribed verbatim. The topic guide is designed to explore participants’ attitudes towards AI-informed clinical decision-making to augment clinician-directed decision-making, the perceived advantages and disadvantages of both methods and attitudes towards risk management. Case vignettes comprising a common decision pathway for patients with multiple health conditions will be presented during each interview to invite participants’ opinions on how their experiences compare. Data will be analysed thematically using the Framework Method.

This study has been approved by the National Health Service Research Ethics Committee (Reference: 22/SC/0210). Written informed consent or verbal consent will be obtained prior to each interview. The findings from this study will be disseminated through peer-reviewed publications, conferences and lay summaries.