Patterns and predictors of mortality in a semi-urban population-based cohort in Sri Lanka: findings from the Ragama Health Study. Issue 9 (29th September 2020)

• Record Type: Journal Article
• Authors: Kasturiratne, Anuradhani; Ediriweera, Dileepa Senajith; De Silva, Shamila Thivanshi; Niriella, Madunil Anuk; Thulani, Uthuru Beddage; Pathmeswaran, Arunasalam; Dassanayake, Anuradha Supun; De Silva, Arjuna Priyadarsin; Chackrewarthy, Sureka; Ranawaka, Udaya; Kato, Norihiro; Wickremasinghe, Ananda Rajitha; de Silva, Hithanadura Janaka
• Is Part Of: BMJ open. Volume 10:Issue 9(2020)
• Publication Date: 2020-09-29
• Subjects: epidemiology -- public health -- coronary heart disease; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2020-038772
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Objective: To describe patterns and predictors of mortality in a semi-urban population in Sri Lanka. Design: A prospective population-based cohort study. Setting: Ragama Medical Officer of Health area in the Gampaha district, Sri Lanka. Participants: Adults between 35 and 64 years of age were recruited using an age stratified random sampling technique in 2007. Measures: At baseline, we recorded socio-demographic, lifestyle, anthropometric, biochemical and clinical data of the participants. Over 10 years, we obtained the cause and date of death from the death registration documents of deceased participants. We determined the survival probability of the cohort over 10 years and estimated Hazard ratios (HRs) for all-cause mortality (ACM), cardiovascular mortality (CVM) and cancer-related mortality (CRM) using Cox's proportional hazards model. We also estimated the survival probabilities for men and women in each 10-year age group and standardised mortality ratio relative to the source population. Results: There were 169 deaths over 10 years with standardised mortality rates of 5.3 and 2.4 per 1000 years of follow-up for men and women, respectively. Independent predictors of: ACM were older age, lower income, smoking and diabetes mellitus while gender, education, occupation, harmful alcohol use, waist circumference and hypertension were not; CVM were older age, lower income, smoking, diabetes and hypertension while gender and harmful alcohol use were not; CRM was older age while gender, smoking and diabetes were not. Those engaged in clerical and technical occupations or unemployed had a lower risk of CRM as compared with those engaged in elementary occupations. Conclusions: Older age, lower income, smoking, diabetes and hypertension strongly predict mortality in this cohort. Addressing the identified modifiable predictors through behavioural modification will improve longevity in similar populations.