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Using a real-time syndromic surveillance system to track heat-related illnesses during a heat wave Shandy Dearth, Kenneth Mulanya and Julia Butwin 40. Norovirus disease surveillance using Google search data

Countries across the world are experiencing syndemic health crises where infectious pathogens including COVID‐19 interact with epidemics of communicable and non‐communicable diseases. Combined with war, environmental instability and the effects of soaring inflation, a public health crisis has emerged requiring an integrated response. Increasingly, national public health institutes (NPHIs) are at the forefront of leading this, as demonstrated at the 2022 Annual Meeting of the International Association of National Public Health Institutes (IANPHI). These effects are particularly evident where conflict is exacerbating health crises in Ukraine and Somalia. In Ukraine, medical and public health workers have been killed and infrastructure destroyed, which require major efforts to rebuild to international standards. In Somalia, these crises are magnified by the effects of climate change, leading to greater food insecurity, heat‐related deaths and famine. National public health institutes are crucial in these contexts and many others to support integrated political responses where health challenges span local, national and international levels and involve multiple stakeholders. This can be seen in strengthening of Integrated Disease Surveillance and work towards the Sustainable Development Goals. National public health institutes also provide integration through the international system, working jointly to build national capacities to deliver essential public health functions. In this context, the 2022 IANPHI Annual meeting agreed the Stockholm Statement, highlighting the role that NPHIs play in tackling the causes and effects of interconnected global and local challenges to public health. This represents an important step in addressing complex health crises and syndemics which require whole‐of‐society responses, with NPHIs uniquely placed to work across sectors and provide system leadership in response.

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Background: Health surveillance is a reactive process, with no real hindsight for dealing with signals and alerts. It may fail to detect more radical changes with a major medium-term or long-term impact on public health. To increase proactivity, the French Institute for Public Health Surveillance has opted for a prospective monitoring approach.Methods: Several steps were necessary: 1) Identification of public health determinants. 2) Identification of key variables based on a combination of determinants. Variables were classified into three groups (health event trigger factors, dissemination factors and response factors) and were submitted to future development assumptions. 3) Identification, in each of the three groups, of micro-scenarios derived from variable trends. 4) Identification of macro-scenarios, each built from the three micro-scenarios for each of the three groups. 5) Identification of issues for the future of public health.Results: The exercise identified 22 key variables, 17 micro-scenarios and 5 macro-scenarios. The topics retained relate to issues on social and territorial health inequalities, health burden, individual and collective responsibilities in terms of health, ethical aspects, emerging phenomena, ‘Big data’, data mining, new health technologies, interlocking of analysis scales.Conclusions: The approach presented here guides the programming of activities of a health safety agency, particularly for monitoring and surveillance. By describing possible future scenarios, health surveillance can help decision-makers to influence the context towards one or more favourable futures.

Background and Objective: Hot and cold temperatures significantly increase the risk of death in many regions of the world. Different measures of temperature, including minimum, maximum and apparent temperature, have been used in previous research. Which temperature measure is the best predictor of mortality is not known. Methods: We used mortality data from 106 cities in the US NMMAPS study (years 1987-2000). We examined the association between temperature and mortality using Poisson regression and fitted a non-linear spline for temperature. We examined five measures of temperature, the effect of including relative humidity, and various degrees of freedom for the temperature spline. The best model was defined as that with the minimum absolute residual. The residuals were calculated using crossvalidation. Results: Maximum temperature was selected as the best temperature measure the most often (40 cities in the Ն65-year age group), and apparent temperature the least often (8 cities in the Ͻ65-year age group). Maximum temperature was the best measure in 10 out of 12 months in both age groups. Geographically, maximum temperature was the best measure in cold regions, and minimum temperature in warm regions. Humidity was important in almost every city in the Ն65 year age group. The seasonal variation in humidity showed a surprising peak in usefulness in winter. Conclusion: Apparent temperature is no better than standard measures of temperature in predicting mortality. Maximum temperature was generally the best measure in cold climates and minimum temperature in warm climates. Humidity is an important predictor of mortality in the elderly and its effect should be estimated separately from temperature.