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Patterns and predictors of mortality in a semi-urban population-based cohort in Sri Lanka: findings from the Ragama Health Study.

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.

SUBMITTER: Kasturiratne A

PROVIDER: S-EPMC7526314 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Patterns and predictors of mortality in a semi-urban population-based cohort in Sri Lanka: findings from the Ragama Health Study.

Kasturiratne Anuradhani A Ediriweera Dileepa Senajith DS De Silva Shamila Thivanshi ST Niriella Madunil Anuk MA Thulani Uthuru Beddage UB Pathmeswaran Arunasalam A Dassanayake Anuradha Supun AS De Silva Arjuna Priyadarsin AP Chackrewarthy Sureka S Ranawaka Udaya U Kato Norihiro N Wickremasinghe Ananda Rajitha AR de Silva Hithanadura Janaka HJ

BMJ open 20200929 9

<h4>Objective</h4>To describe patterns and predictors of mortality in a semi-urban population in Sri Lanka.<h4>Design</h4>A prospective population-based cohort study.<h4>Setting</h4>Ragama Medical Officer of Health area in the Gampaha district, Sri Lanka.<h4>Participants</h4>Adults between 35 and 64 years of age were recruited using an age stratified random sampling technique in 2007.<h4>Measures</h4>At baseline, we recorded socio-demographic, lifestyle, anthropometric, biochemical and clinical ...[more]

PMID: 32994253

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Project description:BackgroundDespite the intergenerational effects of metabolic disorders, evidence is greatly lacking on early pregnancy metabolic syndrome (MetS) and its effects on pregnancy outcomes from low- and middle-income countries. Thus, this prospective cohort of South Asian pregnant women aimed to evaluate how early pregnancy MetS would affect pregnancy outcomes.MethodsA prospective cohort study was conducted among first-trimester (T1) pregnant women of Anuradhapura district, Sri Lanka recruited to the Rajarata Pregnancy Cohort in 2019. MetS was diagnosed by the Joint Interim Statement criteria before 13 weeks of gestational age (GA). Participants were followed up until their delivery, and the major outcomes measured were large for gestational age (LGA), small for gestational age (SGA), preterm birth (PTB) and miscarriage (MC). Gestational weight gain, gestational age at delivery and neonatal birth weight were used as measurements to define the outcomes. Additionally, outcome measures were re-assessed with adjusting fasting plasma glucose (FPG) thresholds of MetS to be compatible with hyperglycemia in pregnancy (Revised MetS).Results2326 T1 pregnant women with a mean age of 28.1 years (SD-5.4), and a median GA of 8.0 weeks (IQR-2) were included. Baseline MetS prevalence was 5.9% (n = 137, 95%CI-5.0-6.9). Only 2027 (87.1%) women from baseline, had a live singleton birth, while 221(9.5%) had MC and 14(0.6%) had other pregnancy losses. Additionally, 64(2.8%) were lost to follow-up. A higher cumulative incidence of LGA, PTB, and MC was noted among the T1-MetS women. T1-MetS carried significant risk (RR-2.59, 95%CI-1.65-3.93) for LGA, but reduced the risk for SGA (RR-0.41, 95%CI-0.29-0.78). Revised MetS moderately increased the risk for PTB (RR-1.54, 95%CI-1.04-2.21). T1-MetS was not associated (p = 0.48) with MC. Lowered FPG thresholds were significantly associated with risk for all major pregnancy outcomes. After adjusting for sociodemographic and anthropometric confounders, revised MetS remained the only significant risk predictor for LGA.ConclusionPregnant women with T1 MetS in this population are at an increased risk for LGA and PTB and a reduced risk for SGA. We observed that a revised MetS definition with lower threshold for FPG compatible with GDM would provide a better estimation of MetS in pregnancy in relation to predicting LGA.

Project description:BackgroundThe occurrence of climate change at an unprecedented scale has resulted in alterations of ecosystems around the world. Numerous studies have reported on the potential to slow down climate change through the sequestration of carbon in soil and trees. Freshwater wetlands hold significant potential for climate change mitigation owing to their large capacity to sequester atmospheric carbon dioxide (CO2). Wetlands among all terrestrial ecosystems have the highest carbon density and are found to store up to three to five times more carbon than terrestrial forests. The current study was undertaken to quantify carbon stocks of two carbon pools: aboveground biomass (AGB) and belowground biomass (BGB). Chosen study sites; Kolonnawa wetland and Thalawathugoda wetland park are distributed within the Colombo wetland complex. Colombo was recognized as one of the 18 global Ramsar wetland cities in 2018. A combination of field measurements and allometric tree biomass regression models was used in the study. Stratification of the project area was performed using the normalized difference vegetation index (NDVI).ResultsThe AGB carbon stock, across strata, is estimated to be in the range of 13.79?±?3.65-66.49?±?6.70 tC/ha and 8.13?±?2.42-52.63?±?10.00 tC/ha at Kolonnawa wetland and Thalawathugoda wetland park, respectively. The BGB carbon stock is estimated to be in the range of 2.47?±?0.61-10.12?±?0.89 tC/ha and 1.56?±?0.41-8.17?±?1.39 tC/ha at Kolonnawa wetland and Thalawathugoda wetland park, respectively. The total AGB carbon stock of Kolonnawa wetland was estimated at 19,803?±?1566 tCO2eq and that of Thalawathugoda wetland park was estimated at 4180?±?729 tCO2eq.ConclusionsIn conclusion, the study reveals that tropical freshwater wetlands contain considerable potential as carbon reservoirs. The study suggests the use of tropical freshwater wetlands in carbon sequestration enhancement plans in the tropics. The study also shows that Annona glabra, an invasive alien species (IAS), has the potential to enhance the net sink of AGB carbon in these non-mangrove wetlands. However, further studies are essential to confirm if enhanced carbon sequestration by Annona glabra is among the unexplored and unreported benefits of the species.

Dataset Information

Patterns and predictors of mortality in a semi-urban population-based cohort in Sri Lanka: findings from the Ragama Health Study.

Publications

Patterns and predictors of mortality in a semi-urban population-based cohort in Sri Lanka: findings from the Ragama Health Study.

Similar Datasets

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