Approach for national scale earthquake risk assessment: Case study from Nepal

The paper presents the approach for earthquake hazard, exposure, vulnerability and risk assessment for Nepal. The approach amalgamates various scientific streams, which are structured and integrated on GIS platform. The exposure, vulnerability and risk assessment are carried out for primary sectors including population, housing, education, hospital, industry, power and roadways. The risk assessment is carried out in two ways targeting emergency management agencies. One aspect of risk assessment represents expected number of sector units falling in specific grade of damage and the second aspect covers expected loss and impact on GDP due to large magnitude earthquake (i.e., Bihar-Nepal Earthquake 1934). The earthquake risk for the size of the 1934 event may mean losses exceeding 15 billion USD, can lead to large fiscal and economic impacts. Based on these findings the paper recommends DRR interventions at the national scale to tackle gaps in risk reduction, risk financing and risk governance

Should nutrient profile models be ‘category specific' or ‘across-the-board'? A comparison of the two systems using diets of British adults

Background/Objectives: Nutrient profile models have the potential to help promote healthier diets. Some models treat all foods equally (across-the-board), some consider different categories of food separately (category specific). This paper assesses whether across-the-board or category-specific nutrient profile models are more appropriate tools for improving diets. Subjects/Methods: Adult respondents to a British dietary survey were split into four groups using a diet quality index. Fifteen food categories were identified. A nutrient profile model provided a measure of the healthiness of all foods consumed. The four diet quality groups were compared for differences in (a) the calories consumed from each food category and (b) the healthiness of foods consumed in each category. Evidence of a healthier diet quality groups consuming more of healthy food categories than unhealthy diet quality groups supported the adoption of across-the-board nutrient profile models. Evidence of healthier diet quality groups consuming healthier versions of foods within food categories supported adoption of category-specific nutrient profile models. Results: A significantly greater percentage of the healthiest diet quality group's diet consisted of fruit and vegetables (21 vs 16%), fish (3 vs 2%) and breakfast cereals (7 vs 2%), and significantly less meat and meat products (7 vs 14%) than the least healthy diet quality group. The foods from the meat, dairy and cereals categories consumed by the healthy diet quality groups were healthier versions than those consumed by the unhealthy diet quality groups. Conclusions: All other things being equal, nutrient profile models designed to promote an achievable healthy diet should be category specific but with a limited number of categories. However models which use large number of categories are unhelpful for promoting a healthy diet

Comparison of cardiovascular risk factors between sri lankans living in kandy and oslo

<p>Abstract</p> <p>Background</p> <p>South Asians living in western countries are known to have unfavourable cardiovascular risk profiles. Studies indicate migrants are worse off when compared to those living in country of origin. The purpose of this study was to compare selected cardiovascular risk factors between migrant Sri Lankans living in Oslo, Norway and Urban dwellers from Kandy, Sri Lanka.</p> <p>Methods</p> <p>Data on non fasting serum lipids, blood pressure, anthropometrics and socio demographics of Sri Lankan Tamils from two almost similar population based cross sectional studies in Oslo, Norway between 2000 and 2002 (1145 participants) and Kandy, Sri Lanka in 2005 (233 participants) were compared. Combined data were analyzed using linear regression analyses.</p> <p>Results</p> <p>Men and women in Oslo had higher HDL cholesterol. Men and women from Kandy had higher Total/HDL cholesterol ratios. Mean waist circumference and body mass index was higher in Oslo. Smoking among men was low (19.2% Oslo, 13.1% Kandy, P = 0.16). None of the women smoked. Mean systolic and diastolic blood pressure was significantly higher in Kandy than in Oslo.</p> <p>Conclusions</p> <p>Our comparison showed unexpected differences in risk factors between Sri Lankan migrants living in Oslo and those living in Kandy Sri Lanka. Sri Lankans in Oslo had favorable lipid profiles and blood pressure levels despite being more obese.</p

DIABRISK - SL Prevention of cardio-metabolic disease with life style modification in young urban Sri Lankan's - study protocol for a randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>Urban South-Asian's are predisposed to early onset of type 2 diabetes mellitus (T2DM) and cardiovascular disease (CVD). There is an urgent need for country specific primary prevention strategies to address the growing burden of cardio-metabolic disease in this population. The aim of this clinical trial is to evaluate whether intensive (3-monthly) lifestyle modification advice is superior to a less-intensive (12 monthly; control group) lifestyle modification advice on a primary composite cardio-metabolic end point in 'at risk' urban subjects aged between 5-40 years.</p> <p>Methods/Design</p> <p>This is an open randomised controlled parallel group clinical trial performed at a single centre in Colombo, Sri-Lanka. A cluster sampling strategy was used to select a large representative sample of subjects aged between 5-40 years at high risk of T2DM and CVD for the intervention study. We have screened 23,298 (males 47% females 53%) healthy subjects for four risk factors: obesity, elevated waist circumference, family history of diabetes and physical inactivity, using a questionnaire and anthropometry. Those with two or more risk-factors were recruited to the intervention trial. We aim to recruit 4600 subjects for the intervention trial. The primary composite cardio-metabolic end point is; new onset T2DM, impaired glucose tolerance, impaired fasting glycaemia, new onset hypertension and albuminuria, following 5 years of intervention. The effect of the intervention on pre-specified secondary endpoints will also be evaluated. The study will be conducted according to good clinical and ethical practice, data analysis and reporting guidelines.</p> <p>Discussion</p> <p>DIABRISK-SL is a large population based trial to evaluate the prevalence of diabetes, pre-diabetes and cardio-metabolic risk factors among young urban Sri-Lankans and the effect of a primary prevention strategy on cardio-metabolic disease end points. This work will enable country specific and regional cardio-metabolic risk scores to be derived. Further if the proposed intervention is successful the results of this study can be translated and implemented as a low-cost primary prevention tool in Sri-Lanka and other low/middle income developing countries.</p> <p>Trial registration</p> <p>The trial is registered with the World Health Organisation and Sri-Lanka clinical trial registry number SLCTR/2008/003</p

Mapping malaria seasonality in Madagascar using health facility data.

BACKGROUND: Many malaria-endemic areas experience seasonal fluctuations in case incidence as Anopheles mosquito and Plasmodium parasite life cycles respond to changing environmental conditions. Identifying location-specific seasonality characteristics is useful for planning interventions. While most existing maps of malaria seasonality use fixed thresholds of rainfall, temperature, and/or vegetation indices to identify suitable transmission months, we construct a statistical modelling framework for characterising the seasonal patterns derived directly from monthly health facility data. METHODS: With data from 2669 of the 3247 health facilities in Madagascar, a spatiotemporal regression model was used to estimate seasonal patterns across the island. In the absence of catchment population estimates or the ability to aggregate to the district level, this focused on the monthly proportions of total annual cases by health facility level. The model was informed by dynamic environmental covariates known to directly influence seasonal malaria trends. To identify operationally relevant characteristics such as the transmission start months and associated uncertainty measures, an algorithm was developed and applied to model realisations. A seasonality index was used to incorporate burden information from household prevalence surveys and summarise 'how seasonal' locations are relative to their surroundings. RESULTS: Positive associations were detected between monthly case proportions and temporally lagged covariates of rainfall and temperature suitability. Consistent with the existing literature, model estimates indicate that while most parts of Madagascar experience peaks in malaria transmission near March-April, the eastern coast experiences an earlier peak around February. Transmission was estimated to start in southeast districts before southwest districts, suggesting that indoor residual spraying should be completed in the same order. In regions where the data suggested conflicting seasonal signals or two transmission seasons, estimates of seasonal features had larger deviations and therefore less certainty. CONCLUSIONS: Monthly health facility data can be used to establish seasonal patterns in malaria burden and augment the information provided by household prevalence surveys. The proposed modelling framework allows for evidence-based and cohesive inferences on location-specific seasonal characteristics. As health surveillance systems continue to improve, it is hoped that more of such data will be available to improve our understanding and planning of intervention strategies

Incident type 2 diabetes attributable to suboptimal diet in 184 countries

The global burden of diet-attributable type 2 diabetes (T2D) is not well established. This risk assessment model estimated T2D incidence among adults attributable to direct and body weight-mediated effects of 11 dietary factors in 184 countries in 1990 and 2018. In 2018, suboptimal intake of these dietary factors was estimated to be attributable to 14.1 million (95% uncertainty interval (UI), 13.814.4 million) incident T2D cases, representing 70.3% (68.871.8%) of new cases globally. Largest T2D burdens were attributable to insufficient whole-grain intake (26.1% (25.027.1%)), excess refined rice and wheat intake (24.6% (22.327.2%)) and excess processed meat intake (20.3% (18.323.5%)). Across regions, highest proportional burdens were in central and eastern Europe and central Asia (85.6% (83.487.7%)) and Latin America and the Caribbean (81.8% (80.183.4%)); and lowest proportional burdens were in South Asia (55.4% (52.160.7%)). Proportions of diet-attributable T2D were generally larger in men than in women and were inversely correlated with age. Diet-attributable T2D was generally larger among urban versus rural residents and higher versus lower educated individuals, except in high-income countries, central and eastern Europe and central Asia, where burdens were larger in rural residents and in lower educated individuals. Compared with 1990, global diet-attributable T2D increased by 2.6 absolute percentage points (8.6 million more cases) in 2018, with variation in these trends by world region and dietary factor. These findings inform nutritional priorities and clinical and public health planning to improve dietary quality and reduce T2D globally. (c) 2023, The Author(s)

Global estimation of anti-malarial drug effectiveness for the treatment of uncomplicated Plasmodium falciparum malaria 1991-2019.

BACKGROUND: Anti-malarial drugs play a critical role in reducing malaria morbidity and mortality, but their role is mediated by their effectiveness. Effectiveness is defined as the probability that an anti-malarial drug will successfully treat an individual infected with malaria parasites under routine health care delivery system. Anti-malarial drug effectiveness (AmE) is influenced by drug resistance, drug quality, health system quality, and patient adherence to drug use; its influence on malaria burden varies through space and time. METHODS: This study uses data from 232 efficacy trials comprised of 86,776 infected individuals to estimate the artemisinin-based and non-artemisinin-based AmE for treating falciparum malaria between 1991 and 2019. Bayesian spatiotemporal models were fitted and used to predict effectiveness at the pixel-level (5 km × 5 km). The median and interquartile ranges (IQR) of AmE are presented for all malaria-endemic countries. RESULTS: The global effectiveness of artemisinin-based drugs was 67.4% (IQR: 33.3-75.8), 70.1% (43.6-76.0) and 71.8% (46.9-76.4) for the 1991-2000, 2006-2010, and 2016-2019 periods, respectively. Countries in central Africa, a few in South America, and in the Asian region faced the challenge of lower effectiveness of artemisinin-based anti-malarials. However, improvements were seen after 2016, leaving only a few hotspots in Southeast Asia where resistance to artemisinin and partner drugs is currently problematic and in the central Africa where socio-demographic challenges limit effectiveness. The use of artemisinin-based combination therapy (ACT) with a competent partner drug and having multiple ACT as first-line treatment choice sustained high levels of effectiveness. High levels of access to healthcare, human resource capacity, education, and proximity to cities were associated with increased effectiveness. Effectiveness of non-artemisinin-based drugs was much lower than that of artemisinin-based with no improvement over time: 52.3% (17.9-74.9) for 1991-2000 and 55.5% (27.1-73.4) for 2011-2015. Overall, AmE for artemisinin-based and non-artemisinin-based drugs were, respectively, 29.6 and 36% below clinical efficacy as measured in anti-malarial drug trials. CONCLUSIONS: This study provides evidence that health system performance, drug quality and patient adherence influence the effectiveness of anti-malarials used in treating uncomplicated falciparum malaria. These results provide guidance to countries' treatment practises and are critical inputs for malaria prevalence and incidence models used to estimate national level malaria burden