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Modern Applied Statistics With S

https://dro.deakin.edu.au/eserv/DU:30146218/sharifulislam-globalburden-2020.pdf

Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019: Update From the GBD 2019 Study

Urban greening to cool towns and cities: a systematic review of the empirical evidence.

The Rotterdam Study is a prospective cohort study ongoing since 1990 in the city of Rotterdam in The Netherlands. The study targets cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, oncological, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. The findings of the Rotterdam Study have been presented in over a 1,000 research articles and reports (see www.erasmus-epidemiology.nl/rotterdamstudy). This article gives the rationale of the study and its design. It also presents a summary of the major findings and an update of the objectives and methods.

/pdf/the-rotterdam-study-objectives-and-design-update-2n3pqovgsq.pdf

The Rotterdam Study: Objectives and design update

Heat is an environmental and occupational hazard. The prevention of deaths in the community caused by extreme high temperatures (heat waves) is now an issue of public health concern. The risk of heat-related mortality increases with natural aging, but persons with particular social and/or physical vulnerability are also at risk. Important differences in vulnerability exist between populations, depending on climate, culture, infrastructure (housing), and other factors. Public health measures include health promotion and heat wave warning systems, but the effectiveness of acute measures in response to heat waves has not yet been formally evaluated. Climate change will increase the frequency and the intensity of heat waves, and a range of measures, including improvements to housing, management of chronic diseases, and institutional care of the elderly and the vulnerable, will need to be developed to reduce health impacts.

https://www.annualreviews.org/doi/pdf/10.1146/annurev.publhealth.29.020907.090843

Heat Stress and Public Health: A Critical Review

Impact de la COVID-19 sur la santé mentale aux urgences et chez SOS Médecins, France, 2020-21

Background The World Health Organization declared a pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), on March 11, 2020. The standardized approach of disability-adjusted life years (DALYs) allows for quantifying the combined impact of morbidity and mortality of diseases and injuries. The main objective of this study was to estimate the direct impact of COVID-19 in France in 2020, using DALYs to combine the population health impact of infection fatalities, acute symptomatic infections and their post-acute consequences, in 28 days (baseline) up to 140 days, following the initial infection. Methods National mortality, COVID-19 screening, and hospital admission data were used to calculate DALYs based on the European Burden of Disease Network consensus disease model. Scenario analyses were performed by varying the number of symptomatic cases and duration of symptoms up to a maximum of 140 days, defining COVID-19 deaths using the underlying, and associated, cause of death. Results In 2020, the estimated DALYs due to COVID-19 in France were 990 710 (1472 per 100 000), with 99% of burden due to mortality (982 531 years of life lost, YLL) and 1% due to morbidity (8179 years lived with disability, YLD), following the initial infection. The contribution of YLD reached 375%, assuming the duration of 140 days of post-acute consequences of COVID-19. Post-acute consequences contributed to 49% of the total morbidity burden. The contribution of YLD due to acute symptomatic infections among people younger than 70 years was higher (67%) than among people aged 70 years and above (33%). YLL among people aged 70 years and above, contributed to 74% of the total YLL. Conclusions COVID-19 had a substantial impact on population health in France in 2020. The majority of population health loss was due to mortality. Men had higher population health loss due to COVID-19 than women. Post-acute consequences of COVID-19 had a large contribution to the YLD component of the disease burden, even when we assume the shortest duration of 28 days, long COVID burden is large. Further research is recommended to assess the impact of health inequalities associated with these estimates.

Direct impact of COVID-19 by estimating disability-adjusted life years at national level in France in 2020

Objective This study aims to implement and evaluate two automatic classification methods of free-text medical causes of death into Mortality Syndromic Groups (MSGs) in order to be used for reactive mortality surveillance. Introduction Mortality is an indicator of the severity of the impact of an event on the population. In France mortality surveillance is part of the syndromic surveillance system SurSaUD and is carried out by Sante publique France, the French public health agency. The set-up of an Electronic Death Registration System (EDRS) in 2007 enabled to receive in real-time medical causes of death in free-text format. This data source was considered as reactive and valuable to implement a reactive mortality surveillance system using medical causes of death (1). The reactive mortality surveillance system is based on the monitoring of Mortality Syndromic Groups (MSGs). An MSG is defined as a cluster of medical causes of death (pathologies, syndromes, symptoms) that meet the objectives of early detection and impact assessment of events (2). Since causes of death are entered in free-text format, their automatic classifications into MSGs require the use of natural language processing methods. We observe a constant increase in the use of these methods to classify medical information and for health surveillance over the last two decades (3). Methods Data consisted of the medical part of electronic death certificates received in routine by Sante publique France from 2012 to 2016. We split the dataset into training and test sets. Among each set, a subset of certificates was selected by a random sampling without replacement. Two annotators manually assigned MSGs to each death certificates in all subsets. Discordances were discussed and corrected if necessary. The agreement rate between the two annotators was 0.90 on the test set. Final annotated subsets represent the ground truth against which the methods tested were evaluated. The final evaluation was performed on the test set of 1,000 death certificates while the classifiers were trained on 3500 death certificates. Two classification methods were implemented: a rule-based method and a supervised machine learning method. The rule-based method was based on four processing steps: applying standardization rules, splitting of medical expression using delimiters, spelling correction and dictionary projection. The supervised machine learning method was set up using a linear Support Vector Machine (SVM) classifier. We trained a multi-label classifier using the one-versus-all strategy. We implemented two models: one based on surface features (SVM model) and the other, a hybrid model, combining surface features and features obtained by the rule-based method. Surface features were bags-of-word unigrams and bigrams and of character trigrams. The rule-based method and the two supervised machine learning models were evaluated using the three evaluation measures: precision (Positive Predictive Value), recall (Sensitivity) and F-measure (P/R/Fm). The study focused on the classification performance of MSGs defined for the reactive detection of outbreaks and are composed of unspecific or acute pathologies, or general symptoms (related to pain, fever, cognitive disorder…). Only the 40 MSGs mentioned at least 3 times in the test set were considered in this study, they belonged to 13 topics (Respiratory conditions, Cardio and cerebrovascular conditions, Infectious diseases, Digestive conditions…). Results With the rule-based method, among the 40 MSGs, 24 obtained a P/R/Fm over 0.90. They belonged mainly to the topics Cardio and Cerebrovascular conditions (5 MSGs), Respiratory conditions (6), and General symptoms (5). Four MSGs obtained P/R/Fm below 0.85 belonging to the topics Infectious conditions (2), Blood condition (1) and Unspecified causes of death (1). The hybrid model obtained P/R/Fm over 0.90 for 25 MSGs. Among them, 21 were the same as the rule-base method. Performance of the rule-based method and the hybrid model were over 0.95 for the same 13 MSGs. The hybrid model obtained P/R/Fm below 0.85 for 4 MSGs also belonging to the same topics as those of the rule-based method. The SVM model had lower classification performance than the two other models. Conclusions For syndromic mortality surveillance both precision and recall are important for all MSGs. Indeed to meet the objective of a reactive detection of events, high precision is needed to limit false alarms. To measure the impact of an event, the surveillance system should have high recall, to avoid an underestimation of this impact. This is especially true for rarer diseases. The results showed that the rule-based method and the hybrid model are the most effective to classify causes of death into MSGs. For some MSGs with less than 5 mentions in the test set (7%), these results must be qualified. Also, to improve classification performance for MSGs with performance below 0.90, and to confirm these results further analysis must be conducted. The results suggest the relevance of these methods to set up a reactive mortality surveillance system for detection and alert based on free-text causes of death. Such a system will provide useful information to health authorities regarding the causes of death during an event, helping them to adapt counter and prevention measures. References 1. Lassalle M, Caserio-Schonemann C, Gallay A, Rey G, Fouillet A. Pertinence of electronic death certificates for real-time surveillance and alert, France, 2012–2014. Public Health. 2017;143:85-93. (1) 2. Baghdadi Y, Gallay A, Caserio-Schonemann C, Thiam M-M, Fouillet A. Towards real-time mortality surveillance by medical causes of death: A strategy of analysis for alert. Rev Epidemiol Sante Publique. 2018;66:S402. (2) 3. Wang Y, Wang L, Rastegar-Mojarad M, Moon S, Shen F, Afzal N, et al. Clinical information extraction applications: A literature review. J Biomed Inf. 2018;77:34-49. (3)

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Performance of machine learning method to classify free-text medical causes of death

Objective: Describe a case study of validation of a scarlet fever outbreak using syndromic surveillance data sources. Introduction:  Since 2004, the French syndromic surveillance system SurSaUD® [1] coordinated by the French Public Health Agency (Sante publique France) daily collects morbidity data from two data sources: the emergency departments (ED) network Oscour® and the emergency general practitioners’ associations SOS Medecins. Almost 92% of the French ED attendances are recorded by the system. SOS Medecins network is a group of 62 associations of general practitioners, dispatched all over the territory. Sante publique France received data from 61 out of 62 associations. Both data sources collect medical diagnosis, using ICD10 codes in the ED network and specific medical thesaurus in SOS Medecins associations. These data are routinely analyzed to detect and follow-up various expected or unusual public health events all over the territory [2]. The system is also used for reassurance of decision makers. In that framework, in March 2017, the French Ministry of Health requested Sante publique France to validate a potential scarlet fever outbreak in France. Methods:  ED attendances for scarlet fever were identified using the ICD10 code “A38”. SOS Medecins visits with the specific code corresponding to “scarlet fever” were considered. The weekly numbers of ED attendances and SOS Medecins visits for scarlet fever were analyzed from 02/01/2017 (week 5) to 03/31/2017 (week 13) by age group (all ages and less than 15 years old, scarlet fever affecting mainly children) and were compared to the numbers of attendances and visits registered during the same period of the two previous years. Analysis was conducted both at national and regional levels. In order to take into account the improvement of data quality during the study period, we also calculated proportion of attendances and visits for scarlet fever among the overall attendances (respectively visits) with medical coded information. Results:  The number of SOS Medecins visits for scarlet fever started to increase in week 9 of 2017. Almost 95% of visits concerned children aged less than 15 years old. SOS Medecins visits for scarlet fever represented 0.24% of the overall visits for the 2 age groups for weeks 11, 13 and 14. This proportion was never reached in 2015 and was observed twice in 2016, but later in the year (weeks 25 and 26). The regional analysis showed that all French metropolitan regions contributed to the increase, even if Paris region was the most impacted. More specifically, cases were mainly located in the east part of the Paris region (in Seine-et-Marne). In the OSCOUR® network, the analysis of the number of attendances for scarlet fever at the national level shows a limited increase from week 9 to week 12. Weekly proportion of ED attendances for scarlet fever among the total coded attendances remained comparable to those observed the two previous years on the same period. The regional analysis also showed that 35% of attendances for scarlet fever during this period were observed in Paris area. But, number of attendances for scarlet fever in this region was comparable during this period to numbers observed the two previous years. Conclusions:  The analysis of emergency syndromic data sources enables to confirm an increase of consultations for scarlet fever in SOS Medecins associations from weeks 9 to 14, mainly for children less than 15 years old. The large implementation of the SOS Medecins associations on the whole territory allowed us to provide a geographical location of the outbreak: mainly in the east part of Paris area. The temporal pattern of scarlet fever visits in this region may be in favor of a small cluster of cases. The availability of data collected routinely during a long period of time by the syndromic surveillance system enables to evaluate that the outbreak occurred earlier than the previous years, but the intensity of the outbreak was similar to those observed previously. Scarlet outbreak was not confirmed through the ED network, even if a limited increase was observed during the same period of time. The investigation of this outbreak in ED network revealed a miscoding practice in one ED structure, resulting locally in a larger number of attendances than in the other ED of Paris area. Finally, this case study led to improve data quality and highlighted the importance of the validation step of alarms by epidemiologists, even in an automatized system.

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Increase of Scarlet fever in March 2017 in France: right or wrong signal?

ObjectiveThe presentation describes the results of the daily monitoring of health indicators conducted by the French public health agency during the major floods and the cold wave that occurred in January 2018 in France, in order to early identify potential impact of those climatic events on the population.IntroductionThe Seine River rises at the north-East of France and flows through Paris before emptying into the English Channel. On January 2018 (from 22th January to 11th February, Weeks 4 to 6), major floods occurred in the Basin of Seine River, after an important rainy period. This period was also marked by the occurrence on the same area of a first cold wave on Week 6 (from 5th to 7th February), including heavy snowfall and ice conditions from 9th to 10th February. A second similar cold wave occured from 28th February and 1st March.Floods of all magnitude are known to have potential health impacts on population [1], both at short, medium and long term both on physical (injuries, diarrhoeal disease, Carbon Monoxyde poisoning, vector-borne disease) and mental health. Extreme cold weather have also the potential to further impact on human health through direct exposure to lower temperatures, and associated adverse conditions, such as snow and ice [2]. Such situations may be particularly associated to direct impact like hypothermia, frostbite and selected bone/joint injuries).MethodsSince 2004, the French Public Health Agency (Sante publique France) set up a national syndromic surveillance system SurSaUD, enabling to ensure morbidity and mortality surveillance [3]. In 2018, morbidity data were daily collected from a network involving about 700 emergency departments (ED) and 58 emergency general practitioners’ associations SOS Medecins. 92% of the national ED attendances and 95% of national SOS Medecins visits are caught by the system.Both demographic (age and gender), administrative (date and location of consultation, transport) and medical information (chief complaint, medical diagnosis using ICD10 codes in ED and specific thesauri in SOS Medecins associations, severity, hospitalization after discharge) are recorded for each patient.The daily and weekly evolution of the number of all-cause ED attendances and SOS Medecins consultations during the flooding period were compared to the evolution on the two previous years. The number of hospitalisations after ED discharge was also monitored. The immediate health impact of floods and cold waves was assessed by monitoring eight syndromic indicators: gastroenteritis, carbon monoxide poisoning, burnt, stress, faintness, drowning, injuries and hypothermia.Analyses were performed by age group (<15 years, 15-64 years, more than 65 years) and at different geographical levels (national, Paris region and districts located in the Basin of Seine River).ResultsIn 2018, syndromic surveillance did not show any major impact on all-cause ED attendances and SOS Medecins consultations from week 4 to week 6, neither in Paris area nor in other areas along the Seine River. The recorded numbers were comparable to the two precedent years in all age groups.A decrease of the all-cause ED attendances was observed during the 1st day with ice conditions in Normandy and Paris, mainly in children and adults aged 15-64 years.During week 6 in Paris area, an increase of ED attendances was observed for injuries (+4% compared to the past weeks – figure 1) and to a lesser extent for hypothermia and frostbite (16 attendances compared to less than 9 for the past weeks). Similar increase in injuries were observed in Normandy during the second cold wave (Figure 1).ConclusionsDuring the flood episode, the rising water level was slow with foreseeable evolution, compared to other sudden flood events occurring in south of France in 2010 due to violent thunderstorms. This progressive evolution allows French authority to deploy wide specific organization in order to mitigate impact on concerned populations. That may explain the absence impact observed in ED at regional and national levels during the flood disaster. The evolution of injuries during 2018 episode is attributable to the cold wave that occurred simultaneously.As the French syndromic surveillance system is implemented on the whole territory and collects emergency data routinely since several years, it constitutes a reactive tool to assess the potential public health impact of both sudden and predictable disasters. It can either contribute to adapt management action or reassure decision makers if no major impact is observed.References[1] Ahern M, Kovats S. The health impacts of floods. In: Few R, Matthies F, eds. Flood hazards and health: responding to present and future risks. London, Earthscan, 2006:28–53.[2] Hughes H, Morbey R, Hughes T. et al. Using an Emergency Department Syndromic Surveillance System to investigate the impact of extreme cold weather events Public Health. 2014 Jul;128(7):628-35.[3] Caserio-Schonemann C, Bousquet V, Fouillet A, Henry V. The French syndromic surveillance system SurSaUD (R). Bull Epidemiol Hebd 2014;3-4:38-44.

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Anne Fouillet

Papers

Impact de la COVID-19 sur la santé mentale aux urgences et chez SOS Médecins, France, 2020-21

Direct impact of COVID-19 by estimating disability-adjusted life years at national level in France in 2020

Performance of machine learning method to classify free-text medical causes of death

Increase of Scarlet fever in March 2017 in France: right or wrong signal?

Early evaluation of impacts of cold waves and floods during winter 2018 in France