Natural language processing of multi-hospital electronic health records for public health surveillance of suicidality.

There is an urgent need to monitor the mental health of large populations, especially during crises such as the COVID-19 pandemic, to timely identify the most at-risk subgroups and to design targeted prevention campaigns. We therefore developed and validated surveillance indicators related to suicidality: the monthly number of hospitalisations caused by suicide attempts and the prevalence among them of five known risks factors. They were automatically computed analysing the electronic health records of fifteen university hospitals of the Paris area, France, using natural language processing algorithms based on artificial intelligence. We evaluated the relevance of these indicators conducting a retrospective cohort study. Considering 2,911,920 records contained in a common data warehouse, we tested for changes after the pandemic outbreak in the slope of the monthly number of suicide attempts by conducting an interrupted time-series analysis. We segmented the assessment time in two sub-periods: before (August 1, 2017, to February 29, 2020) and during (March 1, 2020, to June 31, 2022) the COVID-19 pandemic. We detected 14,023 hospitalisations caused by suicide attempts. Their monthly number accelerated after the COVID-19 outbreak with an estimated trend variation reaching 3.7 (95%CI 2.1-5.3), mainly driven by an increase among girls aged 8-17 (trend variation 1.8, 95%CI 1.2-2.5). After the pandemic outbreak, acts of domestic, physical and sexual violence were more often reported (prevalence ratios: 1.3, 95%CI 1.16-1.48; 1.3, 95%CI 1.10-1.64 and 1.7, 95%CI 1.48-1.98), fewer patients died (p = 0.007) and stays were shorter (p < 0.001). Our study demonstrates that textual clinical data collected in multiple hospitals can be jointly analysed to compute timely indicators describing mental health conditions of populations. Our findings also highlight the need to better take into account the violence imposed on women, especially at early ages and in the aftermath of the COVID-19 pandemic.

[Analysis of off-label prescriptions of medicines in hospital in adult patients and feasibility study of their detection using CIM-10 coding]. / Analyse des prescriptions hospitalières hors AMM des médicaments chez l'adulte et étude de la faisabilité de leur détection par le codage CIM-10 du PMSI.

Analysis of off-label prescriptions of medicines in hospital in adult patients and study of feasibility of their detection by use of international disease classification, 10th version (IDC-10 codes). CONTEXT: In order to improve the appropriate use of medicines, a method of detection of off label prescriptions, especially in hospitalised patients, should be available. STUDY OBJECTIVES: Evaluate the performance of the detection of off-label prescriptions in hospitalised patients by use of IDC-10 codes. METHODS: Data prescriptions (excluding those directly taken in charge by the national health care system), clinical history and biological results were extracted from Assistance publique des Hôpitaux de Paris (AP-HP) data-warehouse for 108 in-hospital adults patients' journeys. An adjudication committee established the classification reference for the appropriate or off label drug prescriptions status after analysis of medical information for each patient. IDC-10 codification that is performed after every hospitalisation was crossed with those IDC-10 codes that were to be expected corresponding to the marketing authorisation labelling (section 4.1 of specifications of product characteristics [SPC]). Results of IDC-10 coding were compared to the reference for off label use identification. RESULTS: Out of the 1131 analysed prescriptions, 44 (3.9%) were classified as off label by the adjudication committee. Sensitivity of detection by IDC-10 coding was 87 (95% CI [0.73-0.96]) to 92% (95% CI [0.79-0.98]) and specificity 25 (95% CI [0.22-0.27]) to 41% (95% CI [0.38-0.44]) according to the number of characters of ICD-10 that could be used. CONCLUSIONS: Incidence of in-hospital off label use of drugs (restricted to within drug related groups prescriptions) appeared relatively low (3.9%). Its semi-automatic detection by IDC-10 coding appears feasible with a good sensitivity but a low specificity. Such method could be further assessed as a first step detection focusing on one pharmacological class or on one pathologic condition.

Diabetes Increases Severe COVID-19 Outcomes Primarily in Younger Adults.

CONTEXT: Diabetes is reported as a risk factor for severe coronavirus disease 2019 (COVID-19), but whether this risk is similar in all categories of age remains unclear. OBJECTIVE: To investigate the risk of severe COVID-19 outcomes in hospitalized patients with and without diabetes according to age categories. DESIGN SETTING AND PARTICIPANTS: We conducted a retrospective observational cohort study of 6314 consecutive patients hospitalized for COVID-19 between February and 30 June 2020 in the Paris metropolitan area, France; follow-up was recorded until 30 September 2020. MAIN OUTCOME MEASURE(S): The main outcome was a composite outcome of mortality and orotracheal intubation in subjects with diabetes compared with subjects without diabetes, after adjustment for confounding variables and according to age categories. RESULTS: Diabetes was recorded in 39% of subjects. Main outcome was higher in patients with diabetes, independently of confounding variables (hazard ratio [HR] 1.13 [1.03-1.24]) and increased with age in individuals without diabetes, from 23% for those <50 to 35% for those >80 years but reached a plateau after 70 years in those with diabetes. In direct comparison between patients with and without diabetes, diabetes-associated risk was inversely proportional to age, highest in <50 years and similar after 70 years. Similarly, mortality was higher in patients with diabetes (26%) than in those without diabetes (22%, P < 0.001), but adjusted HR for diabetes was significant only in patients younger than age 50 years (HR 1.81 [1.14-2.87]). CONCLUSIONS: Diabetes should be considered as an independent risk factor for the severity of COVID-19 in young adults more so than in older adults, especially for individuals younger than 70 years.

Contribution of vaginal culture to predict early onset neonatal infection in preterm prelabor rupture of membranes.

BACKGROUND: Preterm prelabor rupture of membranes (PPROM) is a major cause of morbidity and mortality for both the mother and the newborn. The vaginal germ profile in PPROM is poorly known, particularly regarding the risk of early-onset neonatal infection (EONI). OBJECTIVE: To determine microbiological risk factors for EONI in case of PPROM before 34 weeks of gestation (WG). STUDY DESIGN: A retrospective single-center cohort of patients with PPROM before 34 W G from 2008 to 2016. Vaginal swabs were obtained at admission and at delivery as per usual care and were analyzed by Gram stain and culture for vaginal dysbiosisi.e lactobacilli depletion and/or presence of potential pathogens. RESULTS: Among 268 cases of PPROM, 39 neonates had EONI 14.55 %; (95 %CI 0.11 - 0.19) Overall, vaginal samples culture was positive in 16.67 % (95 %CI 11.95 %-22.32 %) at the time of rupture and 24.76 % (95 %CI 19.02 %-31.23 %) at delivery, with no significant differences between EONI and no-EONI groups (p = 0.797 and 0.486, respectively), including for Group B Streptococci (GBS) and Escherichia coli. EONI was significantly associated with dysbiosis at the time of rupture (23.94 % versus 10.35 % in the absence of dysbiosis, p = 0.009) and at delivery (19.70 % versus 3.90 % if no dysbiosis, p < 0.001). Clinical intra-uterine infection was present in 78.5 % (n = 31) of the EONI group versus 37.2 % (n = 85) in the non-EONI group (p < 0.001) and chorioamnionitis and/or funisitis were found in 97.3 % and 91.9 %, respectively in the EONI group, versus 56.11 % and 53.96 %, respectively, in the non-EONI group (p < 0.001). CONCLUSION: Dysbiosis following rupture and at delivery, but not the presence of pathogens in the VS culture, was associated with the risk of EONI in case of PPROM.

Targeting Malaria Hotspots to Reduce Transmission Incidence in Senegal.

In central Senegal, malaria incidence declined in response to scaling-up of control measures from 2000 to 2010 and has since remained stable, making elimination unlikely in the short term. Additional control measures are needed to reduce transmission. We simulated chemoprophylaxis interventions targeting malaria hotspots using a metapopulation mathematical model, based on a differential-equation framework and incorporating human mobility. The model was fitted to weekly malaria incidence from 45 villages. Three approaches for selecting intervention targets were compared: (a) villages with malaria cases during the low transmission season of the previous year; (b) villages with highest incidence during the high transmission season of the previous year; (c) villages with highest connectivity with adjacent populations. Our results showed that intervention strategies targeting hotspots would be effective in reducing malaria incidence in both targeted and untargeted areas. Regardless of the intervention strategy used, pre-elimination (1-5 cases per 1000 per year) would not be reached without simultaneously increasing vector control by more than 10%. A cornerstone of malaria control and elimination is the effective targeting of strategic locations. Mathematical tools help to identify those locations and estimate the impact in silico.

Application of Functional Data Analysis to Identify Patterns of Malaria Incidence, to Guide Targeted Control Strategies.

We introduce an approach based on functional data analysis to identify patterns of malaria incidence to guide effective targeting of malaria control in a seasonal transmission area. Using functional data method, a smooth function (functional data or curve) was fitted from the time series of observed malaria incidence for each of 575 villages in west-central Senegal from 2008 to 2012. These 575 smooth functions were classified using hierarchical clustering (Ward's method), and several different dissimilarity measures. Validity indices were used to determine the number of distinct temporal patterns of malaria incidence. Epidemiological indicators characterizing the resulting malaria incidence patterns were determined from the velocity and acceleration of their incidences over time. We identified three distinct patterns of malaria incidence: high-, intermediate-, and low-incidence patterns in respectively 2% (12/575), 17% (97/575), and 81% (466/575) of villages. Epidemiological indicators characterizing the fluctuations in malaria incidence showed that seasonal outbreaks started later, and ended earlier, in the low-incidence pattern. Functional data analysis can be used to identify patterns of malaria incidence, by considering their temporal dynamics. Epidemiological indicators derived from their velocities and accelerations, may guide to target control measures according to patterns.

Spatio-temporal variation of malaria hotspots in Central Senegal, 2008-2012.

BACKGROUND: In malaria endemic areas, identifying spatio-temporal hotspots is becoming an important element of innovative control strategies targeting transmission bottlenecks. The aim of this work was to describe the spatio-temporal variation of malaria hotspots in central Senegal and to identify the meteorological, environmental, and preventive factors that influence this variation. METHODS: This study analysed the weekly incidence of malaria cases recorded from 2008 to 2012 in 575 villages of central Senegal (total population approximately 500,000) as part of a trial of seasonal malaria chemoprevention (SMC). Data on weekly rainfall and annual vegetation types were obtained for each village through remote sensing. The time series of weekly malaria incidence for the entire study area was divided into periods of high and low transmission using change-point analysis. Malaria hotspots were detected during each transmission period with the SaTScan method. The effects of rainfall, vegetation type, and SMC intervention on the spatio-temporal variation of malaria hotspots were assessed using a General Additive Mixed Model. RESULTS: The malaria incidence for the entire area varied between 0 and 115.34 cases/100,000 person weeks during the study period. During high transmission periods, the cumulative malaria incidence rate varied between 7.53 and 38.1 cases/100,000 person-weeks, and the number of hotspot villages varied between 62 and 147. During low transmission periods, the cumulative malaria incidence rate varied between 0.83 and 2.73 cases/100,000 person-weeks, and the number of hotspot villages varied between 10 and 43. Villages with SMC were less likely to be hotspots (OR = 0.48, IC95%: 0.33-0.68). The association between rainfall and hotspot status was non-linear and depended on both vegetation type and amount of rainfall. The association between village location in the study area and hotspot status was also shown. CONCLUSION: In our study, malaria hotspots varied over space and time according to a combination of meteorological, environmental, and preventive factors. By taking into consideration the environmental and meteorological characteristics common to all hotspots, monitoring of these factors could lead targeted public health interventions at the local level. Moreover, spatial hotspots and foci of malaria persisting during LTPs need to be further addressed. TRIAL REGISTRATION: The data used in this work were obtained from a clinical trial registered on July 10, 2008 at www.clinicaltrials.gov under NCT00712374.

"Spatial heterogeneity of environmental risk in randomized prevention trials: consequences and modeling".

BACKGROUND: In the context of environmentally influenced communicable diseases, proximity to environmental sources results in spatial heterogeneity of risk, which is sometimes difficult to measure in the field. Most prevention trials use randomization to achieve comparability between groups, thus failing to account for heterogeneity. This study aimed to determine under what conditions spatial heterogeneity biases the results of randomized prevention trials, and to compare different approaches to modeling this heterogeneity. METHODS: Using the example of a malaria prevention trial, simulations were performed to quantify the impact of spatial heterogeneity and to compare different models. Simulated scenarios combined variation in baseline risk, a continuous protective factor (age), a non-related factor (sex), and a binary protective factor (preventive treatment). Simulated spatial heterogeneity scenarios combined variation in breeding site density and effect, location, and population density. The performances of the following five statistical models were assessed: a non-spatial Cox Proportional Hazard (Cox-PH) model and four models accounting for spatial heterogeneity-i.e., a Data-Generating Model, a Generalized Additive Model (GAM), and two Stochastic Partial Differential Equation (SPDE) models, one modeling survival time and the other the number of events. Using a Bayesian approach, we estimated the SPDE models with an Integrated Nested Laplace Approximation algorithm. For each factor (age, sex, treatment), model performances were assessed by quantifying parameter estimation biases, mean square errors, confidence interval coverage rates (CRs), and significance rates. The four models were applied to data from a malaria transmission blocking vaccine candidate. RESULTS: The level of baseline risk did not affect our estimates. However, with a high breeding site density and a strong breeding site effect, the Cox-PH and GAM models underestimated the age and treatment effects (but not the sex effect) with a low CR. When population density was low, the Cox-SPDE model slightly overestimated the effect of related factors (age, treatment). The two SPDE models corrected the impact of spatial heterogeneity, thus providing the best estimates. CONCLUSION: Our results show that when spatial heterogeneity is important but not measured, randomization alone cannot achieve comparability between groups. In such cases, prevention trials should model spatial heterogeneity with an adapted method. TRIAL REGISTRATION: The dataset used for the application example was extracted from Vaccine Trial #NCT02334462 ( ClinicalTrials.gov registry).

Socioeconomic and environmental factors associated with malaria hotspots in the Nanoro demographic surveillance area, Burkina Faso.

BACKGROUND: With limited resources and spatio-temporal heterogeneity of malaria in developing countries, it is still difficult to assess the real impact of socioeconomic and environmental factors in order to set up targeted campaigns against malaria at an accurate scale. Our goal was to detect malaria hotspots in rural area and assess the extent to which household socioeconomic status and meteorological recordings may explain the occurrence and evolution of these hotspots. METHODS: Data on malaria cases from 2010 to 2014 and on socioeconomic and meteorological factors were acquired from four health facilities within the Nanoro demographic surveillance area. Statistical cross correlation was used to quantify the temporal association between weekly malaria incidence and meteorological factors. Local spatial autocorrelation analysis was performed and restricted to each transmission period using Kulldorff's elliptic spatial scan statistic. Univariate and multivariable analysis were used to assess the principal socioeconomic and meteorological determinants of malaria hotspots using a Generalized Estimating Equation (GEE) approach. RESULTS: Rainfall and temperature were positively and significantly associated with malaria incidence, with a lag time of 9 and 14 weeks, respectively. Spatial analysis showed a spatial autocorrelation of malaria incidence and significant hotspots which was relatively stable throughout the study period. Furthermore, low socioeconomic status households were strongly associated with malaria hotspots (aOR = 1.21, 95% confidence interval: 1.03-1.40). CONCLUSION: These fine-scale findings highlight a relatively stable spatio-temporal pattern of malaria risk and indicate that social and environmental factors play an important role in malaria incidence. Integrating data on these factors into existing malaria struggle tools would help in the development of sustainable bottleneck strategies adapted to the local context for malaria control.

Spatio-temporal dynamic of malaria in Ouagadougou, Burkina Faso, 2011-2015.

BACKGROUND: Given the scarcity of resources in developing countries, malaria treatment requires new strategies that target specific populations, time periods and geographical areas. While the spatial pattern of malaria transmission is known to vary depending on local conditions, its temporal evolution has yet to be evaluated. The aim of this study was to determine the spatio-temporal dynamic of malaria in the central region of Burkina Faso, taking into account meteorological factors. METHODS: Drawing on national databases, 101 health areas were studied from 2011 to 2015, together with weekly meteorological data (temperature, number of rain events, rainfall, humidity, wind speed). Meteorological factors were investigated using a principal component analysis (PCA) to reduce dimensions and avoid collinearities. The Box-Jenkins ARIMA model was used to test the stationarity of the time series. The impact of meteorological factors on malaria incidence was measured with a general additive model. A change-point analysis was performed to detect malaria transmission periods. For each transmission period, malaria incidence was mapped and hotspots were identified using spatial cluster detection. RESULTS: Malaria incidence never went below 13.7 cases/10,000 person-weeks. The first and second PCA components (constituted by rain/humidity and temperatures, respectively) were correlated with malaria incidence with a lag of 2 weeks. The impact of temperature was significantly non-linear: malaria incidence increased with temperature but declined sharply with high temperature. A significant positive linear trend was found for the entire time period. Three transmission periods were detected: low (16.8-29.9 cases/10,000 person-weeks), high (51.7-84.8 cases/10,000 person-weeks), and intermediate (26.7-32.2 cases/10,000 person-weeks). The location of clusters identified as high risk varied little across transmission periods. CONCLUSION: This study highlighted the spatial variability and relative temporal stability of malaria incidence around the capital Ouagadougou, in the central region of Burkina Faso. Despite increasing efforts in fighting the disease, malaria incidence remained high and increased over the period of study. Hotspots, particularly those detected for low transmission periods, should be investigated further to uncover the local environmental and behavioural factors of transmission, and hence to allow for the development of better targeted control strategies.

Mathematical models for predicting human mobility in the context of infectious disease spread: introducing the impedance model.

BACKGROUND: Mathematical models of human mobility have demonstrated a great potential for infectious disease epidemiology in contexts of data scarcity. While the commonly used gravity model involves parameter tuning and is thus difficult to implement without reference data, the more recent radiation model based on population densities is parameter-free, but biased. In this study we introduce the new impedance model, by analogy with electricity. Previous research has compared models on the basis of a few specific available spatial patterns. In this study, we use a systematic simulation-based approach to assess the performances. METHODS: Five hundred spatial patterns were generated using various area sizes and location coordinates. Model performances were evaluated based on these patterns. For simulated data, comparison measures were average root mean square error (aRMSE) and bias criteria. Modeling of the 2010 Haiti cholera epidemic with a basic susceptible-infected-recovered (SIR) framework allowed an empirical evaluation through assessing the goodness-of-fit of the observed epidemic curve. RESULTS: The new, parameter-free impedance model outperformed previous models on simulated data according to average aRMSE and bias criteria. The impedance model achieved better performances with heterogeneous population densities and small destination populations. As a proof of concept, the basic compartmental SIR framework was used to confirm the results obtained with the impedance model in predicting the spread of cholera in Haiti in 2010. CONCLUSIONS: The proposed new impedance model provides accurate estimations of human mobility, especially when the population distribution is highly heterogeneous. This model can therefore help to achieve more accurate predictions of disease spread in the context of an epidemic.

Comparison of Air Impaction and Electrostatic Dust Collector Sampling Methods to Assess Airborne Fungal Contamination in Public Buildings.

Many ailments can be linked to exposure to indoor airborne fungus. However, obtaining a precise measurement of airborne fungal levels is complicated partly due to indoor air fluctuations and non-standardized techniques. Electrostatic dust collector (EDC) sampling devices have been used to measure a wide range of airborne analytes, including endotoxins, allergens, ß-glucans, and microbial DNA in various indoor environments. In contrast, viable mold contamination has only been assessed in highly contaminated environments such as farms and archive buildings. This study aimed to assess the use of EDCs, compared with repeated air-impactor measurements, to assess airborne viable fungal flora in moderately contaminated indoor environments. Indoor airborne fungal flora was cultured from EDCs and daily air-impaction samples collected in an office building and a daycare center. The quantitative fungal measurements obtained using a single EDC significantly correlated with the cumulative measurement of nine daily air impactions. Both methods enabled the assessment of fungal exposure, although a few differences were observed between the detected fungal species and the relative quantity of each species. EDCs were also used over a 32-month period to monitor indoor airborne fungal flora in a hospital office building, which enabled us to assess the impact of outdoor events (e.g. ground excavations) on the fungal flora levels on the indoor environment. In conclusion, EDC-based measurements provided a relatively accurate profile of the viable airborne flora present during a sampling period. In particular, EDCs provided a more representative assessment of fungal levels compared with single air-impactor sampling. The EDC technique is also simpler than performing repetitive air-impaction measures over the course of several consecutive days. EDC is a versatile tool for collecting airborne samples and was efficient for measuring mold levels in indoor environments.

Cholera Outbreak in Grande Comore: 1998-1999.

In 1998, a cholera epidemic in east Africa reached the Comoros Islands, an archipelago in the Mozambique Channel that had not reported a cholera case for more than 20 years. In just a little over 1 year (between January 1998 and March 1999), Grande Comore, the largest island in the Union of the Comoros, reported 7,851 cases of cholera, about 3% of the population. Using case reports and field observations during the medical response, we describe the epidemiology of the 1998-1999 cholera epidemic in Grande Comore. Outbreaks of infectious diseases on islands provide a unique opportunity to study transmission dynamics in a nearly closed population, and they may serve as stepping-stones for human pathogens to cross unpopulated expanses of ocean.

A comprehensive repertoire of prokaryotic species identified in human beings.

The compilation of the complete prokaryotic repertoire associated with human beings as commensals or pathogens is a major goal for the scientific and medical community. The use of bacterial culture techniques remains a crucial step to describe new prokaryotic species. The large number of officially acknowledged bacterial species described since 1980 and the recent increase in the number of recognised pathogenic species have highlighted the absence of an exhaustive compilation of species isolated in human beings. By means of a thorough investigation of several large culture databases and a search of the scientific literature, we built an online database containing all human-associated prokaryotic species described, whether or not they had been validated and have standing in nomenclature. We list 2172 species that have been isolated in human beings. They were classified in 12 different phyla, mostly in the Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes phyla. Our online database is useful for both clinicians and microbiologists and forms part of the Human Microbiome Project, which aims to characterise the whole human microbiota and help improve our understanding of the human predisposition and susceptibility to infectious agents.

Using mobile phone data to predict the spatial spread of cholera.

Effective response to infectious disease epidemics requires focused control measures in areas predicted to be at high risk of new outbreaks. We aimed to test whether mobile operator data could predict the early spatial evolution of the 2010 Haiti cholera epidemic. Daily case data were analysed for 78 study areas from October 16 to December 16, 2010. Movements of 2.9 million anonymous mobile phone SIM cards were used to create a national mobility network. Two gravity models of population mobility were implemented for comparison. Both were optimized based on the complete retrospective epidemic data, available only after the end of the epidemic spread. Risk of an area experiencing an outbreak within seven days showed strong dose-response relationship with the mobile phone-based infectious pressure estimates. The mobile phone-based model performed better (AUC 0.79) than the retrospectively optimized gravity models (AUC 0.66 and 0.74, respectively). Infectious pressure at outbreak onset was significantly correlated with reported cholera cases during the first ten days of the epidemic (p < 0.05). Mobile operator data is a highly promising data source for improving preparedness and response efforts during cholera outbreaks. Findings may be particularly important for containment efforts of emerging infectious diseases, including high-mortality influenza strains.