BackgroundNon-communicable diseases (NCDs) are the leading cause of mortality in Fiji, a middle-income country in the Pacific. Some food products processed sold and marketed by the food industry are major contributors to the NCD epidemic, and the food industry is widely identified as having strong economic and political power. However, little research has been undertaken on the attempts by the food industry to influence public health-related policies and programs in its favour. The “corporate political activity” (CPA) of the food industry includes six strategies (information and messaging; financial incentives; constituency building; legal strategies; policy substitution; opposition fragmentation and destabilisation). For this study, we aimed to gain a detailed understanding of the CPA strategies and practices of major food industry actors in Fiji, interpreted through a public health lens.Methods and resultsWe implemented a systematic approach to monitor the CPA of the food industry in Fiji for three months. It consisted of document analysis of relevant publicly available information. In parallel, we conducted semi-structured interviews with 10 stakeholders involved in diet- and/or public health-related issues in Fiji. Both components of the study were thematically analysed. We found evidence that the food industry adopted a diverse range of strategies in an attempt to influence public policy in Fiji, with all six CPA strategies identified. Participants identified that there is a substantial risk that the widespread CPA of the food industry could undermine efforts to address NCDs in Fiji.ConclusionsDespite limited public disclosure of information, such as data related to food industry donations to political parties and lobbying, we were able to identify many CPA practices used by the food industry in Fiji. Greater transparency from the food industry and the government would help strengthen efforts to increase their accountability and support NCD prevention. In other low- and middle-income countries, it is likely that a systematic document analysis approach would also need to be supplemented with key informant interviews to gain insight into this important influence on NCD prevention.Electronic supplementary materialThe online version of this article (doi:10.1186/s12992-016-0158-8) contains supplementary material, which is available to authorized users.

Background: No systematic comparison has been conducted in Fiji using all suitable surveys of type 2 diabetes mellitus (T2DM) and obesity prevalence after standardizing methodology and definitions. Methods: Unit records from six surveys of Fiji adults were variously adjusted for age, ethnicity (Fiji Melanesians, i-Taukei, and Fijians of Asian Indian descent [Indians]) and urban-rural by sex to previous censuses. Trends were assessed using meta-regression (random effect models) and estimates projected to 2020. Poisson regression of strata was used to assess the effect of body mass index (BMI) increases on T2DM period trends. Results: Over 1980-2011, T2DM prevalence increased in i-Taukei men (3.2% to 11.1%; 1.32%/5 years) and women (5.3% to 13.6%; 1.40%/5 years) and Indian men (11.1% to 17.9%; 1.24%/5 years) and women (11.2% to 19.9%; 1.71%/5 years). Projected T2DM prevalence in 2020 is 13.3% and 16.7% in i-Taukei men and women, and 23.4% and 24.1% in Indian men and women, respectively. Obesity prevalence increased in i-Taukei men (12.6% to 28.9%; 2.99%/5 years) and women (30.1% to 52.9%; 3.84%/5 years) and in Indian men (2.8% to 9.4%; 1.21%/5 years) and women (13.2% to 26.6%; 2.61%/ 5 years). Projected obesity prevalence in 2020 is 34.0% and 60.0% in i-Taukei and women, and 11.4% and 31.0% in Indian men and women, respectively. After age-adjustment, an estimated 27%, 25%, 16% and 18% of the T2DM period trend is attributable to BMI in i-Taukei men and women and Indian men and women, respectively. Conclusions: Prevalence of T2DM in Fiji is projected to continue increasing, driven by rising obesity, with consequences for premature mortality and life expectancy.

BackgroundRheumatic heart disease (RHD) is considered a major public health problem in developing countries, although scarce data are available to substantiate this. Here we quantify mortality from RHD in Fiji during 2008–2012 in people aged 5–69 years.Methods and FindingsUsing 1,773,999 records derived from multiple sources of routine clinical and administrative data, we used probabilistic record-linkage to define a cohort of 2,619 persons diagnosed with RHD, observed for all-cause mortality over 11,538 person-years. Using relative survival methods, we estimated there were 378 RHD-attributable deaths, almost half of which occurred before age 40 years. Using census data as the denominator, we calculated there were 9.9 deaths (95% CI 9.8–10.0) and 331 years of life-lost (YLL, 95% CI 330.4–331.5) due to RHD per 100,000 person-years, standardised to the portion of the WHO World Standard Population aged 0–69 years. Valuing life using Fiji’s per-capita gross domestic product, we estimated these deaths cost United States Dollar $6,077,431 annually. Compared to vital registration data for 2011–2012, we calculated there were 1.6-times more RHD-attributable deaths than the number reported, and found our estimate of RHD mortality exceeded all but the five leading reported causes of premature death, based on collapsed underlying cause-of-death diagnoses.ConclusionsRheumatic heart disease is a leading cause of premature death as well as an important economic burden in this setting. Age-standardised death rates are more than twice those reported in current global estimates. Linkage of routine data provides an efficient tool to better define the epidemiology of neglected diseases.

Background There is an increasing interest in finding less costly and burdensome alternatives to measuring population-level salt intake than 24-h urine collection, such as spot urine samples. However, little is known about their usefulness in developing countries like Fiji and Samoa. The purpose of this study was to evaluate the capacity of spot urine samples to estimate mean population salt intake in Fiji and Samoa. Methods The study involved secondary analyses of urine data from cross-sectional surveys conducted in Fiji and Samoa between 2012 and 2016. Mean salt intake was estimated from spot urine samples using six equations, and compared with the measured salt intake from 24-h urine samples. Differences and agreement between the two methods were examined through paired samples t-test, intraclass correlation coefficient analysis, and Bland-Altman plots and analyses. Results A total of 414 participants from Fiji and 725 participants from Samoa were included. Unweighted mean salt intake based on 24-h urine collection was 10.58 g/day (95% CI 9.95 to 11.22) in Fiji and 7.09 g/day (95% CI 6.83 to 7.36) in Samoa. In both samples, the INTERSALT equation with potassium produced the closest salt intake estimate to the 24-h urine (difference of − 0.92 g/day, 95% CI − 1.67 to − 0.18 in the Fiji sample and + 1.53 g/day, 95% CI 1.28 to 1.77 in the Samoa sample). The presence of proportional bias was evident for all equations except for the Kawasaki equation. Conclusion These data suggest that additional studies where both 24-h urine and spot urine samples are collected are needed to further assess whether methods based on spot urine samples can be confidently used to estimate mean population salt intake in Fiji and Samoa.

Comparison of the prevalence of type 2 diabetes mellitus (T2DM) in adults aged 25–64 years in selected Pacific Island countries using whole blood and plasma glucose cut‐off points. Unit records of STEPwise approach to Surveillance (STEPS) surveys obtained from Fiji, Samoa, and Tonga Ministries of Health; T2DM prevalence recalculated using whole blood and plasma cut‐off points. Shaded bars indicate T2DM prevalence based on correct glucose cut‐off points for the glucose meter used (fasting blood glucose [FBG] ≥6.1 mmol/L for early surveys1,3,5; fasting plasma glucose [FPG] ≥7.0 mmol/L for later surveys),2,4,6 whereas open bars show T2DM prevalence based on incorrect glucose cut‐off points (FPG ≥6.1 mmol/L for later surveys).2,4,6