Burden of disease analyses can quantify the relative impact of different exposures on population health outcomes. Gastroenteritis where the causative pathogen was not determined and respiratory illness resulting from exposure to opportunistic pathogens transmitted by water aerosols have not always been considered in waterborne burden of disease estimates. We estimated the disease burden attributable to nine enteric pathogens, unspecified pathogens leading to gastroenteritis, and three opportunistic pathogens leading primarily to respiratory illness, in Ontario, Canada (population ~14 million). Employing a burden of disease framework, we attributed a fraction of annual (year 2016) emergency department (ED) visits, hospitalisations and deaths to waterborne transmission. Attributable fractions were developed from the literature and clinical input, and unattributed disease counts were obtained using administrative data. Our Monte Carlo simulation reflected uncertainty in the inputs. The estimated mean annual attributable rates for waterborne diseases were (per 100 000 population): 69 ED visits, 12 hospitalisations and 0.52 deaths. The corresponding 5th–95th percentile estimates were (per 100 000 population): 13–158 ED visits, 5–22 hospitalisations and 0.29–0.83 deaths. The burden of disease due to unspecified pathogens dominated these rates: 99% for ED visits, 63% for hospitalisations and 40% for deaths. However, when a causative pathogen was specified, the majority of hospitalisations (83%) and deaths (97%) resulted from exposure to the opportunistic pathogens Legionella spp., non-tuberculous mycobacteria and Pseudomonas spp. The waterborne disease burden in Ontario indicates the importance of gastroenteritis not traced back to a particular pathogen and of opportunistic pathogens transmitted primarily through contact with water aerosols.

The seasonality of demographic data has been of great interest. It depends mainly on the climatic conditions, and the findings may vary from study to study. Commonly, the studies are based on monthly data. The population at risk plays a central role. For births or deaths over short periods, the population at risk is proportional to the lengths of the months. Hence, one must analyze the number of births (and deaths) per day. If one studies the seasonality of multiple maternities, the population at risk is the total monthly number of confinements and the number of multiple maternities in a given month must be compared with the monthly number of all maternities. Consequently, when one considers the monthly rates of multiple maternities, the monthly number of births is eliminated and one obtains an unaffected seasonality measure of the rates. In general, comparisons between the seasonality of different data sets presuppose standardization of the data to indices with common means, mainly 100. If one assumes seasonality as ‘non-flatness’ throughout a year, a chi-squared test would be an option, but this test calculates only the heterogeneity and the same test statistic can be obtained for data sets with extreme values occurring in consecutive months or in separate months. Hence, chi-squared tests for seasonality are weak because of this arbitrariness and cannot be considered a model test. When seasonal models are applied, one must pay special attention to how well the applied model fits the data. If the goodness of fit is poor, nonsignificant models obtained can erroneously lead to statements that the seasonality is slight, although the observed seasonal fluctuations are marked. In this study, we investigate how the application of seasonal models can be applied to different demographic variables.

This paper investigates the impact of developments in Turkish migration management policy and changes in management of the Greek-Turkish border on border deaths prior to the 2015 mass inflow of refugees. As the locus of multiple and sustained Frontex operations, as well as several autonomous major changes in relevant policies and practices over the 2000–2014 period, the Greek-Turkish border can serve as a post hoc laboratory for analyzing the implications of EU-influenced migration and border management for deaths on the border. We conclude that a chaotic mix of national politics, policy development and law enforcement practices, flexible smuggling networks, and Frontex operations contributed to the mass inflows of 2015–2016 and ensured mass casualties.

The goal of this study was to analyse the spatial pattern of tuberculosis (TB) mortality using different approaches, namely: mortality rates (MR), spatial relative risks (RR) and Bayesian rates (Global and Local) and their association with human development index (HDI), Global and its three dimensions: education, longevity and income. An ecological study was developed in Curitiba, Brazil based on data from Mortality Information System (2008–2014). Spatial scan statistics were used to compute RR and identify high-risk clusters. Bivariate Local Indicator of Spatial Associations was used to assess associations. MR ranged between 0 and 25.24/100.000 with a mean (standard deviation) of 1.07 (2.66). Corresponding values for spatial RR were 0–27.46, 1.2 (2.99) and for Bayesian rates (Global and Local) were 0.49–1.66, 0.90 (0.19) and 0–6.59, 0.98 (0.80). High-risk clusters were identified for all variables, except for HDI-income and Global Bayesian rate. Significant negative spatial relations were found between MR and income; between RR and HDI global, longevity and income; and Bayesian rates with all variables. Some areas presented different patterns: low social development/low risk and high risk/high development. These results demonstrate that social development variables should be considered, in mortality due TB.

Coffee consumption has been shown to be associated with various health outcomes, but no comprehensive review and meta-analysis of the association between coffee consumption and total mortality has been conducted. To quantitatively assess this association, we conducted a meta-analysis of prospective cohort studies. Eligible studies were identified by searching the PubMed and EMBASE databases for all articles published through June 2013 and reviewing the reference lists of the retrieved articles. Pooled relative risks (RR) with 95 % CI were calculated using a random-effects model. We identified twenty studies of coffee consumption and total mortality, including 129 538 cases of deaths among the 973 904 participants. The RR of total mortality for the high v. low category of coffee consumption was 0·86 (95 % CI 0·80, 0·92). The pooled RR for studies using ≥ 2–4 cups/d as a cut-off for the high category was similar to that for studies using ≥ 5–9 cups/d as the cut-off. By geographical region, the inverse association tended to be stronger for the eight studies conducted in Europe (RR 0·78, 95 % CI 0·70, 0·88) and three studies carried out in Japan (RR 0·82, 95 % CI 0·73, 0·92) than for the nine studies conducted in the USA (RR 0·92, 95 % CI 0·84, 1·00). The inverse association was similar for men (RR 0·81, 95 % CI 0·73, 0·90) and women (RR 0·84, 95 % CI 0·79, 0·89). A weak, but significant, inverse association was found with moderate coffee consumption (1–2 cups/d; RR 0·92, 95 % CI 0·87, 0·98). High decaffeinated coffee consumption was also found to be associated with a lower risk of death, but the data are limited. Our findings indicate that coffee consumption is associated with a reduced risk of total mortality.

This Panel Session consisted of three country reports (Democratic People's Republic of Korea; Sri Lanka; and Timor-Leste) and the common issues identified during the Panel discussions relative to industrial accidents and conflicts in the Southeast Asia Region. Important issues identified included the needs for: (1) use of medical technology; (2) stockpiling of essential supplies; (3) human resource development; (4) surveillance systems for disease detection; (5) coordination; and (6) emergency funding.

This Summary Report was prepared for Agenda item 13.3 for the meeting of the World Health Assembly convened at the World Health Organizationation (WHO) Headquarters in Geneva, Switzwitzerland on 20 May 2005. It was in part, prepared from the Reports generaged by the Conference, Health Actions in Relation to Crises and Disasters, convened by the World Health Organization in Phuket, Thailand, 04–06 May 2005.