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The evident shedding of the SARS-CoV-2 RNA particles from infected individuals into the wastewater opened up a tantalizing array of possibilities for prediction of COVID-19 prevalence prior to symptomatic case identification through community testing. Many countries have therefore explored the use of wastewater metrics as a surveillance tool, replacing traditional direct measurement of prevalence with cost-effective approaches based on SARS-CoV-2 RNA concentrations in wastewater samples. Two important aspects in building prediction models are: time over which the prediction occurs and space for which the predicted case numbers is shown. In this review, our main focus was on finding mathematical models which take into the account both the time-varying and spatial nature of wastewater-based metrics into account. We used six main characteristics as our assessment criteria: i) modelling approach; ii) temporal coverage; iii) spatial coverage; iv) sample size; v) wastewater sampling method; and vi) covariates included in the modelling. The majority of studies in the early phases of the pandemic recognized the temporal association of SARS-CoV-2 RNA concentration level in wastewater with the number of COVID-19 cases, ignoring their spatial context. We examined 15 studies up to April 2023, focusing on models considering both temporal and spatial aspects of wastewater metrics. Most early studies correlated temporal SARS-CoV-2 RNA levels with COVID-19 cases but overlooked spatial factors. Linear regression and SEIR models were commonly used (n = 10, 66.6 % of studies), along with machine learning (n = 1, 6.6 %) and Bayesian approaches (n = 1, 6.6 %) in some cases. Three studies employed spatio-temporal modelling approach (n = 3, 20.0 %). We conclude that the development, validation and calibration of further spatio-temporally explicit models should be done in parallel with the advancement of wastewater metrics before the potential of wastewater as a surveillance tool can be fu

Background: Prevalence of SARS-CoV-2 infection with Delta variant was increasing in England in late summer 2021 among children aged 5 to 17 years, and adults who had received two vaccine doses. In September 2021, a third (booster) dose was offered to vaccinated adults aged 50 years and over, vulnerable adults and healthcare/care-home workers, and a single vaccine dose already offered to 16 and 17 year-olds was extended to children aged 12 to 15 years. Methods: SARS-CoV-2 community prevalence in England was available from self-administered throat and nose swabs using reverse transcriptase polymerase chain reaction (RT-PCR) in round 13 (24 June to 12 July 2021, N = 98,233), round 14 (9 to 27 September 2021, N = 100,527) and round 15 (19 October to 5 November 2021, N = 100,112) from the REACT-1 study randomised community surveys. Linking to National Health Service (NHS) vaccination data for consenting participants, we estimated vaccine effectiveness in children aged 12 to 17 years and compared swab-positivity rates in adults who received a third dose with those who received two doses. Findings: Weighted SARS-CoV-2 prevalence was 1.57% (1.48%, 1.66%) in round 15 compared with 0.83% (0.76%, 0.89%) in round 14, and the previously observed link between infections and hospitalisations and deaths had weakened. Vaccine effectiveness against infection in children aged 12 to 17 years was estimated (round 15) at 64.0% (50.9%, 70.6%) and 67.7% (53.8%, 77.5%) for symptomatic infections. Adults who received a third vaccine dose were less likely to test positive compared to those who received two doses, with adjusted OR of 0.36 (0.25, 0.53). Interpretation: Vaccination of children aged 12 to 17 years and third (booster) doses in adults were effective at reducing infection risk. High rates of vaccination, including booster doses, are a key part of the strategy to reduce infection rates in the community.

BackgroundThe Omicron wave of COVID-19 in England peaked in January 2022 resulting from the rapid transmission of the Omicron BA.1 variant. We investigate the spread and dynamics of the SARS-CoV-2 epidemic in the population of England during February 2022, by region, age and main SARS-CoV-2 sub-lineage.MethodsIn the REal-time Assessment of Community Transmission-1 (REACT-1) study we obtained data from a random sample of 94,950 participants with valid throat and nose swab results by RT-PCR during round 18 (8 February to 1 March 2022).FindingsWe estimated a weighted mean SARS-CoV-2 prevalence of 2.88% (95% credible interval [CrI] 2.76-3.00), with a within-round effective reproduction number (R) overall of 0.94 (0·91-0.96). While within-round weighted prevalence fell among children (aged 5 to 17 years) and adults aged 18 to 54 years, we observed a level or increasing weighted prevalence among those aged 55 years and older with an R of 1.04 (1.00-1.09). Among 1,616 positive samples with sublineages determined, one (0.1% [0.0-0.3]) corresponded to XE BA.1/BA.2 recombinant and the remainder were Omicron: N=1047, 64.8% (62.4-67.2) were BA.1; N=568, 35.2% (32.8-37.6) were BA.2. We estimated an R additive advantage for BA.2 (vs BA.1) of 0.38 (0.34-0.41). The highest proportion of BA.2 among positives was found in London.InterpretationIn February 2022, infection prevalence in England remained high with level or increasing rates of infection in older people and an uptick in hospitalisations. Ongoing surveillance of both survey and hospitalisations data is required.FundingDepartment of Health and Social Care, England.

Background: Slums or informal settlements characterize most large cities in LMIC. Previous evidence suggests pharmacies may be the most frequently used source of primary care in LMICs but that pharmacy services are of variable quality. However, evidence on pharmacy use and availability is very limited for slum populations. Methods: We conducted household, individual, and healthcare provider surveys and qualitative observations on pharmacies and pharmacy use in seven slum sites in four countries (Nigeria, Kenya, Pakistan, and Bangladesh). All pharmacies and up to 1200 households in each site were sampled. Adults and children were surveyed about their use of healthcare services and pharmacies were observed and their services, equipment, and stock documented. Results: We completed 7692 household and 7451 individual adults, 2633 individual child surveys, and 157 surveys of pharmacies located within the seven sites. Visit rates to pharmacies and drug sellers varied from 0.1 (Nigeria) to 3.0 (Bangladesh) visits per person-year, almost all of which were for new conditions. We found highly variable conditions in what constituted a “pharmacy” across the sites and most pharmacies did not employ a qualified pharmacist. Analgesics and antibiotics were widely available but other categories of medications, particularly those for chronic illness were often not available anywhere. The majority of pharmacies lacked basic equipment such as a thermometer and weighing scales. Conclusions: Pharmacies are locally and widely available to residents of slums. However, the conditions of the facilities and availability of medicines were poor and prices relatively high. Pharmacies may represent a large untapped resource to improving access to primary care for the urban poor.

Accurate measurements of seroincidence are critical for infections undercounted by reported cases, such as inf luenza, arboviral diseases, and leptospirosis. However, conventional methods of interpreting paired serological samples do not account for antibody titer decay, resulting in underestimated seroincidence rates. To improve interpretation of paired sera, we modeled exponential decay of interval-censored microscopic agglutination test titers using a historical data set of leptospirosis cases traced to a point source exposure in Italy in 1984.We then applied that decay rate to a longitudinal cohort study conducted in a high-transmission setting in Salvador, Brazil (2013-2015). We estimated a decay constant of 0.926 (95% confidence interval: 0.918, 0.934) titer dilutions per month. Accounting for decay in the cohort increased the mean infection rate to 1.21 times the conventionally defined rate over 6-month intervals (range, 1.10-1.36) and 1.82 times that rate over 12-month intervals (range, 1.65-2.07). Improved estimates of infection in longitudinal data have broad epidemiologic implications, including comparing studies with different sampling intervals, improving sample size estimation, and determining risk factors for infection and the role of acquired immunity. Our method of estimating and accounting for titer decay is generalizable to other infections defined using interval-censored serological assays.

Objectives To evaluate the spatiotemporal distribution of the incidence of COVID-19 hospitalisations in Birmingham, UK during the first wave of the pandemic to support the design of public health disease control policies. Design A geospatial statistical model was estimated as part of a real-time disease surveillance system to predict local daily incidence of COVID-19. Participants All hospitalisations for COVID-19 to University Hospitals Birmingham NHS Foundation Trust between 1 February 2020 and 30 September 2020. Outcome measures Predictions of the incidence and cumulative incidence of COVID-19 hospitalisations in local areas, its weekly change and identification of predictive covariates. Results Peak hospitalisations occurred in the first and second weeks of April 2020 with significant variation in incidence and incidence rate ratios across the city. Population age, ethnicity and socioeconomic deprivation were strong predictors of local incidence. Hospitalisations demonstrated strong day of the week effects with fewer hospitalisations (10%–20% less) at the weekend. There was low temporal correlation in unexplained variance. By day 50 at the end of the first lockdown period, the top 2.5% of small areas had experienced five times as many cases per 10 000 population as the bottom 2.5%. Conclusions Local demographic factors were strong predictors of relative levels of incidence and can be used to target local areas for disease control measures. The real-time disease surveillance system provides a useful complement to other surveillance approaches by producing real-time, quantitative and probabilistic summaries of key outcomes at fine spatial resolution to inform disease control programmes.

Background Fibroblast growth factor23 (FGF23) is elevated in CKD and has been associated with outcomes such as death, cardiovascular (CV) events and progression to Renal Replacement therapy (RRT). The majority of studies have been unable to account for change in FGF23 over time and those which have demonstrate conflicting results. We performed a survival analysis looking at change in c-terminal FGF23 (cFGF23) over time to assess the relative contribution of cFGF23 to these outcomes. Methods We measured cFGF23 on plasma samples from 388 patients with CKD 3-5 who had serial measurements of cFGF23, with a mean of 4.2 samples per individual. We used linear regression analysis to assess the annual rate of change in cFGF23 and assessed the relationship between time-varying cFGF23 and the outcomes in a cox-regression analysis. Results Across our population, median baseline eGFR was 32.3mls/min/1.73m2, median baseline cFGF23 was 162 relative units/ml (RU/ml) (IQR 101-244 RU/mL). Over 70 months (IQR 53-97) median follow-up, 76 (19.6%) patients progressed to RRT, 86 (22.2%) died, and 52 (13.4%) suffered a major non-fatal CV event. On multivariate analysis, longitudinal change in cFGF23 was significantly associated with risk for death and progression to RRT but not non-fatal cardiovascular events. Conclusion In our study, increasing cFGF23 was significantly associated with risk for death and RRT.

Venomous snakebite is a neglected tropical disease that annually leads to hundreds of thousands of deaths or long-term physical and mental ailments across the developing world. Insufficient data on spatial variation in snakebite risk, incidence, human vulnerability, and accessibility of medical treatment contribute substantially to ineffective on-ground management. There is an urgent need to collect data, fill knowledge gaps and address on-ground management problems. The use of novel, and transdisciplinary approaches that take advantage of recent advances in spatio-temporal models, ‘big data’, high performance computing, and fine-scale spatial information can add value to snakebite management by strategically improving our understanding and mitigation capacity of snakebite. We review the background and recent advances on the topic of snakebite related geospatial analyses and suggest avenues for priority research that will have practical on-ground applications for snakebite management and mitigation. These include streamlined, targeted data collection on snake distributions, snakebites, envenomings, venom composition, health infrastructure, and antivenom accessibility along with fine-scale models of spatio-temporal variation in snakebite risk and incidence, intraspecific venom variation, and environmental change modifying human exposure. These measures could improve and ‘future-proof’ antivenom production methods, antivenom distribution and stockpiling systems, and human-wildlife conflict management practices, while simultaneously feeding into research on venom evolution, snake taxonomy, ecology, biogeography, and conservation. © 2021

Background: Understanding antimicrobial consumption is essential to mitigate the development of antimicrobial resistance, yet robust data in children are sparse and methodologically limited. Electronic prescribing systems provide an important opportunity to analyse and report antimicrobial consumption in detail. Objectives: We investigated the value of electronic prescribing data from a tertiary children's hospital to report temporal trends in antimicrobial consumption in hospitalized children and compare commonly used metrics of antimicrobial consumption. Methods: Daily measures of antimicrobial consumption [days of therapy (DOT) and DDDs] were derived from the electronic prescribing system between 2010 and 2018. Autoregressive moving-average models were used to infer trends and the estimates were compared with simulated point prevalence surveys (PPSs). Results: More than 1.3 million antimicrobial administrations were analysed. There was significant daily and seasonal variation in overall consumption, which reduced annually by 1.77% (95% CI 0.50% to 3.02%). Relative consumption of meropenem decreased by 6.6% annually (95% CI -3.5% to 15.8%) following the expansion of the hospital antimicrobial stewardship programme. DOT and DDDs exhibited similar trends for most antimicrobials, though inconsistencies were observed where changes to dosage guidelines altered consumption calculation by DDDs, but not DOT. PPS simulations resulted in estimates of change over time, which converged on the model estimates, but with much less precision. Conclusions: Electronic prescribing systems offer significant opportunities to better understand and report antimicrobial consumption in children. This approach to modelling administration data overcomes the limitations of using interval data and dispensary data. It provides substantially more detailed inferences on prescribing patterns and the potential impact of stewardship interventions.

The coronavirus disease 2019 (COVID-19) pandemic is heterogeneous throughout Africa and threatening millions of lives. Surveillance and short-term modeling forecasts are critical to provide timely information for decisions on control strategies. We created a strategy that helps predict the country-level case occurrences based on cases within or external to a country throughout the entire African continent, parameterized by socioeconomic and geoeconomic variations and the lagged effects of social policy and meteorological history. We observed the effect of the Human Development Index, containment policies, testing capacity, specific humidity, temperature, and landlocked status of countries on the local within-country and external between-country transmission. One-week forecasts of case numbers from the model were driven by the quality of the reported data. Seeking equitable behavioral and social interventions, balanced with coordinated country-specific strategies in infection suppression, should be a continental priority to control the COVID-19 pandemic in Africa. © 2021 National Academy of Sciences. All rights reserved.

Surveillance of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has mainly relied on case reporting, which is biased by health service performance, test availability, and test-seeking behaviors. We report a community-wide national representative surveillance program in England based on self-administered swab results from ~594,000 individuals tested for SARS-CoV-2, regardless of symptoms, between May and the beginning of September 2020. The epidemic declined between May and July 2020 but then increased gradually from mid-August, accelerating into early September 2020 at the start of the second wave. When compared with cases detected through routine surveillance, we report here a longer period of decline and a younger age distribution. Representative community sampling for SARS-CoV-2 can substantially improve situational awareness and feed into the public health response even at low prevalence.

Background: Current standard interventions are not universally sufficient for malaria elimination. The effects of community-based house improvement (HI) and larval source management (LSM) as supplementary interventions to the Malawi National Malaria Control Programme (NMCP) interventions were assessed in the context of an intensive community engagement programme. Methods: The study was a two-by-two factorial, cluster-randomized controlled trial in Malawi. Village clusters were randomly assigned to four arms: a control arm; HI; LSM; and HI + LSM. Malawi NMCP interventions and community engagement were used in all arms. Household-level, cross-sectional surveys were conducted on a rolling, 2-monthly basis to measure parasitological and entomological outcomes over 3 years, beginning with one baseline year. The primary outcome was the entomological inoculation rate (EIR). Secondary outcomes included mosquito density, Plasmodium falciparum prevalence, and haemoglobin levels. All outcomes were assessed based on intention to treat, and comparisons between trial arms were conducted at both cluster and household level. Results: Eighteen clusters derived from 53 villages with 4558 households and 20,013 people were randomly assigned to the four trial arms. The mean nightly EIR fell from 0.010 infectious bites per person (95% CI 0.006–0.015) in the baseline year to 0.001 (0.000, 0.003) in the last year of the trial. Over the full trial period, the EIR did not differ between the four trial arms (p = 0.33). Similar results were observed for the other outcomes: mosquito density and P. falciparum prevalence decreased over 3 years of sampling, while haemoglobin levels increased; and there were minimal differences between the trial arms during the trial period. Conclusions: In the context of high insecticide-treated bed net use, neither community-based HI, LSM, nor HI + LSM contributed to further reductions in malaria transmission or prevalence beyond the reductions observed over two

Leptospirosis is the leading zoonotic disease in terms of morbidity and mortality worldwide. Effective prevention is urgently needed as the drivers of disease transmission continue to intensify. The key challenge has been developing a widely applicable vaccine that protects against the >300 serovars that can cause leptospirosis. Live attenuated mutants are enticing vaccine candidates and poorly explored in the field. We evaluated a recently characterized motility-deficient mutant lacking the expression of a flagellar protein, FcpA. Although the fcpA- mutant has lost its ability to cause disease, transient bacteremia was observed. In two animal models, immunization with a single dose of the fcpA- mutant was sufficient to induce a robust anti-protein antibodies response that promoted protection against infection with different pathogenic Leptospira species. Furthermore, characterization of the immune response identified a small repertoire of biologically relevant proteins that are highly conserved among pathogenic Leptospira species and potential correlates of cross-protective immunity. © 2021, eLife Sciences Publications Ltd. All rights reserved.

We investigated a Gaussian conditional geostatistical spatio-temporal model (CGSTM) aiming to fit data observed at non-fixed locations over discrete times, based only on the observed locations. The model specifies the process state at the current time conditioning on the process state in the recent past. Particularly, the process mean uses a weighting function governing the spatio-temporal model evolution and handling the interaction between space and time. The CGSTM provides attractive features, such as it belongs to the dynamic linear model framework, models non-fixed locations over time and easily provides forecasting maps k-steps ahead. Likelihood estimation and inference are based on a Kalman filter-based algorithm. Equivalent closed form of a covariance and precision matrices of the spatio-temporal joint-distribution was obtained. We performed a simulation study considering locations of a real data example, which presents data locations varying over time. A second simulation study was ran using various scenarios for parameter values and number of observations in time and space, observing consistency and unbiasedness of model estimators. Thirdly, The model was fitted to the average monthly rainfall dataset, with 678 temporal registers at 32 stations located in western Paraná, Brazil. The rainfall station locations suffered geographical changes from 1961 to 2017. In this modelling, we used explanatory variables and provided forecasting maps.

Background: Slums or informal settlements characterize most large cities in LMIC. Previous evidence suggests pharmacies may be the most frequently used source of primary care in LMICs but that pharmacy services are of variable quality. However, evidence on pharmacy use and availability is very limited for slum populations. Methods: We conducted household, individual, and healthcare provider surveys and qualitative observations on pharmacies and pharmacy use in seven slum sites in four countries (Nigeria, Kenya, Pakistan, and Bangladesh). All pharmacies and up to 1200 households in each site were sampled. Adults and children were surveyed about their use of healthcare services and pharmacies were observed and their services, equipment, and stock documented. Results: We completed 7692 household and 7451 individual adults, 2633 individual child surveys, and 157 surveys of pharmacies located within the seven sites. Visit rates to pharmacies and drug sellers varied from 0.1 (Nigeria) to 3.0 (Bangladesh) visits per person-year, almost all of which were for new conditions. We found highly variable conditions in what constituted a “pharmacy” across the sites and most pharmacies did not employ a qualified pharmacist. Analgesics and antibiotics were widely available but other categories of medications, particularly those for chronic illness were often not available anywhere. The majority of pharmacies lacked basic equipment such as a thermometer and weighing scales. Conclusions: Pharmacies are locally and widely available to residents of slums. However, the conditions of the facilities and availability of medicines were poor and prices relatively high. Pharmacies may represent a large untapped resource to improving access to primary care for the urban poor.

Mosquito-borne Zika virus (ZIKV) transmission has almost exclusively been detected in the tropics despite the distributions of its primary vectors extending farther into temperate regions. Therefore, it is unknown whether ZIKV's range has reached a temperature-dependent limit, or if it can spread into temperate climates. Using field-collected mosquitoes for biological relevance, we found that two common temperate mosquito species, Aedes albopictus and Ochlerotatus detritus, were competent for ZIKV. We orally exposed mosquitoes to ZIKV and held them at between 17 and 31°C, estimated the time required for mosquitoes to become infectious, and applied these data to a ZIKV spatial risk model. We identified a minimum temperature threshold for the transmission of ZIKV by mosquitoes between 17 and 19°C. Using these data, we generated standardized basic reproduction number R0-based risk maps and we derived estimates for the length of the transmission season for recent and future climate conditions. Our standardized R0-based risk maps show potential risk of ZIKV transmission beyond the current observed range in southern USA, southern China and southern European countries. Transmission risk is simulated to increase over southern and Eastern Europe, northern USA and temperate regions of Asia (northern China, southern Japan) in future climate scenarios.

This paper proposes a new class of covariance functions for bivariate random fields on spheres, having the same properties as the bivariate Matérn model proposed in Euclidean spaces. The new class depends on the geodesic distance on a sphere; it allows for indexing differentiability (in the mean square sense) and fractal dimensions of the components of any bivariate Gaussian random field having such covariance structure. We find parameter conditions ensuring positive definiteness. We discuss other possible models and illustrate our findings through a simulation study, where we explore the performance of maximum likelihood estimation method for the parameters of the new covariance function. A data illustration then follows, through a bivariate data set of temperatures and precipitations, observed over a large portion of the Earth, provided by the National Oceanic and Atmospheric Administration Earth System Research Laboratory.

The incidence of hospitalized leptospirosis patients was positively associated with increased precipitation in Salvador, Brazil. However, Leptospira infection risk among a cohort of city residents was inversely associated with rainfall. These findings indicate that, although heavy rainfall may increase severe illness, Leptospira exposures can occur year-round.

Cystic fibrosis (CF) is a progressive, genetic disease characterized by frequent, prolonged drops in lung function. Accurately predicting rapid underlying lung‐function decline is essential for clinical decision support and timely intervention. Determining whether an individual is experiencing a period of rapid decline is complicated due to its heterogeneous timing and extent, and error component of the measured lung function. We construct individualized predictive probabilities for “nowcasting” rapid decline. We assume each patient's true longitudinal lung function, S(t), follows a nonlinear, nonstationary stochastic process, and accommodate between‐patient heterogeneity through random effects. Corresponding lung‐function decline at time t is defined as the rate of change, S′(t). We predict S′(t) conditional on observed covariate and measurement history by modeling a measured lung function as a noisy version of S(t). The method is applied to data on 30 879 US CF Registry patients. Results are contrasted with a currently employed decision rule using single‐center data on 212 individuals. Rapid decline is identified earlier using predictive probabilities than the center's currently employed decision rule (mean difference: 0.65 years; 95% confidence interval (CI): 0.41, 0.89). We constructed a bootstrapping algorithm to obtain CIs for predictive probabilities. We illustrate real‐time implementation with R Shiny. Predictive accuracy is investigated using empirical simulations, which suggest this approach more accurately detects peak decline, compared with a uniform threshold of rapid decline. Median area under the ROC curve estimates (Q1‐Q3) were 0.817 (0.814‐0.822) and 0.745 (0.741‐0.747), respectively, implying reasonable accuracy for both. This article demonstrates how individualized rate of change estimates can be coupled with probabilistic predictive inference and implementation for a useful medical‐monitoring approach.

Background: Despite steady gains in life expectancy, individuals with cystic fibrosis (CF) lung disease still experience rapid pulmonary decline throughout their clinical course, which can ultimately end in respiratory failure. Point-of-care tools for accurate and timely information regarding the risk of rapid decline is essential for clinical decision support. Objective: This study aims to translate a novel algorithm for earlier, more accurate prediction of rapid lung function decline in patients with CF into an interactive web-based application that can be integrated within electronic health record systems, via collaborative development with clinicians. Methods: Longitudinal clinical history, lung function measurements, and time-invariant characteristics were obtained for 30,879 patients with CF who were followed in the US Cystic Fibrosis Foundation Patient Registry (2003-2015). We iteratively developed the application using the R Shiny framework and by conducting a qualitative study with care provider focus groups (N=17). Results: A clinical conceptual model and 4 themes were identified through coded feedback from application users: (1) ambiguity in rapid decline, (2) clinical utility, (3) clinical significance, and (4) specific suggested revisions. These themes were used to revise our application to the currently released version, available online for exploration. This study has advanced the application's potential prognostic utility for monitoring individuals with CF lung disease. Further application development will incorporate additional clinical characteristics requested by the users and also a more modular layout that can be useful for care provider and family interactions. Conclusions: Our framework for creating an interactive and visual analytics platform enables generalized development of applications to synthesize, model, and translate electronic health data, thereby enhancing clinical decision support and improving care and health outcomes for chronic

This paper reports the prevalence and intensity of soil-transmitted helminth (STH) infections measured in Zimbabwe before and after a control intervention based on annual deworming of school-age children (SAC) conducted from 2012 to 2018. In 2010, epidemiological data were collected from 13 195 SAC in 255 randomly selected schools in all districts nationwide using, as diagnostic methods, the Kato–Katz and the formal ether stool concentration technique. At follow up, conducted in 2017, only Kato–Katz was performed; specimens were collected from 13 352 children in 336 schools. The data were evaluated using a geospatial approach. The national prevalence of STH infection in SAC was estimated at 5.8% at baseline, with 0.8% of infections of moderate and heavy intensity. Preventive chemotherapy (PC) targeted all 2.5 million children of school age enrolled in Zimbabwe, with coverage ranging from 49% to 85%. At follow up, national prevalence of STH in SAC was estimated at 0.8%; infections of moderate and heavy intensity almost disappeared (0.1% prevalence). As a result, Zimbabwe can suspend deworming activities in 54 districts and reduce the frequency of PC in the remaining six districts. The total amount of albendazole tablets needed will be approximately 100 000 a year. © 2020 Midzi et al.

Background: Male sex is associated with better lung function and survival in people with cystic fibrosis but it is unclear whether the survival benefit is solely due to the sex-effect on lung function. Methods: This study analyzes data between 1996 and 2015 from the longitudinal registry study of the UK Cystic Fibrosis Registry. We jointly analyze repeated measurements and time-to-event outcomes to assess how much of the sex effect on lung function also explains survival. These novel methods allow examination of association between percent of forced expiratory volume in 1 second (%FEV1) and covariates such as sex and genotype, and survival, in the same modeling framework. We estimate the probability of surviving one more year with a probit model. Results: The dataset includes 81,129 lung function measurements of %FEV1 on 9,741 patients seen between 1996 and 2015 and captures 1,543 deaths. Males compared with females experienced a more gradual decline in %FEV1 (difference 0.11 per year 95% confidence interval [CI] = 0.08, 0.14). After adjusting for confounders, both overall level of %FEV1 and %FEV1 rate of change are associated with the concurrent hazard for death. There was evidence of a male survival advantage (probit coefficient 0.15; 95% CI = 0.10, 0.19) which changed little after adjustment for %FEV1 using conventional approaches but was attenuated by 37% on adjustment for %FEV1 level and slope in the joint model (0.09; 95% CI = 0.06, 0.12). Conclusions: We estimate that about 37% of the association of sex on survival in cystic fibrosis is mediated through lung function.

The paper uses two case-studies, one in public health surveillance the other in veterinary epidemiology, to argue that the analysis strategy for spatio-temporal point process data should be guided by the scientific context in which the data were generated and, more particularly, by the objectives of the data analysis. This point of view is not specific to the point process setting and, in the author’s opinion, should influence the way that statistics is taught at postgraduate level in response to the emergence and rapid growth of data science.

Background Acute kidney injury (AKI) and chronic kidney disease (CKD) are common syndromes associated with significant morbidity, mortality and cost. The extent to which repeated AKI episodes may cumulatively affect the rate of progression of all-cause CKD has not previously been investigated. In this study, we explored the hypothesis that repeated episodes of AKI increase the rate of renal functional deterioration loss in patients recruited to a large, all-cause CKD cohort. Methods Patients from the Salford Kidney Study (SKS) were considered. Application of KDIGO criteria to all available laboratory measurements of renal function identified episodes of AKI. A competing risks model was specified for four survival events: Stage 1 AKI; stage 2 or 3 AKI; dialysis initiation or transplant before AKI event; death before AKI event. The model was adjusted for patient age, gender, smoking status, alcohol intake, diabetic status, cardiovascular co-morbidities, and primary renal disease. Analyses were performed for patients’ first, second, and third or more AKI episodes. Results A total of 48,338 creatinine measurements were available for 2287 patients (median 13 measures per patient [IQR 6–26]). There was a median age of 66.8years, median eGFR of 28.4 and 31.6% had type 1 or 2 diabetes. Six hundred and forty three (28.1%) patients suffered one or more AKI events; 1000 AKI events (58% AKI 1) in total were observed over a median follow-up of 2.6 years [IQR 1.1–3.2]. In patients who suffered an AKI, a second AKI was more likely to be a stage 2 or 3 AKI than stage 1 [HR 2.04, p 0.01]. AKI events were associated with progression to RRT, with multiple episodes of AKI progressively increasing likelihood of progression to RRT [HR 14.4 after 1 episode of AKI, HR 28.4 after 2 episodes of AKI]. However, suffering one or more AKI events was not associated with an increased risk of mortality. Conclusions AKI events are associated with more rapid CKD deterioration as hypothesised, and als

BACKGROUND: In Sri Lanka, deworming programmes for soil-transmitted helminth infections became an integral part of school health in the 1960s, whereas routine antenatal deworming with mebendazole started in the 1980s. A 2003 national soil-transmitted helminth survey done among schoolchildren found an overall prevalence of 6·9%. In our study, we aimed to reassess the national prevalence of soil-transmitted helminth infections to enable implementation of a more focused control programme that targets smaller administrative areas at risk of continued transmission. METHODS: We did a cross-sectional, school-based, national survey using multistage stratified cluster sampling, covering all nine provinces as well as populations at high risk of soil-transmitted helminth infections living in urban slums and in plantation-sector communities. Our study population was children aged 5-7 years attending state schools. Faecal samples were collected and analysed with duplicate modified Kato-Katz smears. We modelled the risk of soil-transmitted helminth infection using generalised linear mixed-effects models, and we developed prevalence maps to enable informed decision making at the smallest health administrative level in the country. FINDINGS: Between Jan 23 and May 9, 2017, we recruited 5946 children from 130 schools; 4276 (71·9%) children provided a faecal sample for examination. National prevalence of soil-transmitted helminth infection was 0·97% (95% CI 0·63-1·48) among primary schoolchildren. Prevalence in the high-risk communities surveyed was higher than national prevalence: 2·73% (0·75-6·87) in urban slum communities and 9·02% (4·29-18·0) in plantation sector communities. Our prevalence maps showed that the lowest-level health administrative regions could be categorised into low risk (prevalence <1%), high risk (prevalence >10%), or intermediate risk (1-10%) areas. INTERPRETATION: Our survey findings indicate that the national prevalence of soil-transmitted helminth infection

BACKGROUND: With over 800 million cases globally, campylobacteriosis is a major cause of food borne disease. In temperate climates incidence is highly seasonal but the underlying mechanisms are poorly understood, making human disease control difficult. We hypothesised that observed disease patterns reflect complex interactions between weather, patterns of human risk behaviour, immune status and level of food contamination. Only by understanding these can we find effective interventions. METHODS: We analysed trends in human Campylobacter cases in NE England from 2004 to 2009, investigating the associations between different risk factors and disease using time-series models. We then developed an individual-based (IB) model of risk behaviour, human immunological responses to infection and environmental contamination driven by weather and land use. We parameterised the IB model for NE England and compared outputs to observed numbers of reported cases each month in the population in 2004-2009. Finally, we used it to investigate different community level disease reduction strategies. RESULTS: Risk behaviours like countryside visits (t = 3.665, P

There is concern that climate change will lead to expansion of vector-borne diseases as, of all disease types, they are the most sensitive to climate drivers1. Such expansion may threaten human health, and food security via effects on animal and crop health. Here we quantify the potential impact of climate change on a vector-borne disease of livestock, bluetongue, which has emerged in northern Europe in response to climate change2–4, affecting tens of thousands of farms at huge financial cost and causing the deaths of millions of animals5. We derive future disease risk trends for northern Europe, and use a detailed spatial transmission model6 to simulate outbreaks in England and Wales under future climatic conditions, using an ensemble of five downscaled general circulation models7. By 2100, bluetongue risk extends further north, the transmission season lengthens by up to three months and outbreaks are larger on average. A 1 in 20-year outbreak at present-day temperatures becomes typical by the 2070s under the highest greenhouse gas emission scenario. However, animal movement restrictions are sufficient to prevent truly devastating outbreaks. Disease transmission uncertainty dominates over climate uncertainty, even at the longest prediction timescales. Our results suggest that efficient detection and control measures to limit the spread of vector-borne diseases will be increasingly vital in future, warmer climates. © 2019, The Author(s), under exclusive licence to Springer Nature Limited.

The demographics and comorbidities of patients with community acquired pneumonia (CAP) vary enormously but stratified treatment is difficult because aetiological studies have failed to comprehensively identify the pathogens. Our aim was to describe the bacterial microbiota of CAP and relate these to clinical characteristics in order to inform future trials of treatment stratified by co-morbidity. CAP patients were prospectively recruited at two UK hospitals. We used 16S rRNA gene sequencing to identify the dominant bacteria in sputum and compositional data analysis to determine associations with patient characteristics. We analysed sputum samples from 77 patients and found a Streptococcus sp. and a Haemophilus sp. were the most relatively abundant pathogens. The Haemophilus sp. was more likely to be dominant in patients with pre-existing lung disease, and its relative abundance was associated with qPCR levels of Haemophilus influenzae. The most abundant Streptococcus sp. was associated with qPCR levels of Streptococcus pneumoniae but dominance could not be predicted from clinical characteristics. These data suggest chronic lung disease influences the microbiota of sputum in patients with CAP. This finding could inform a trial of stratifying empirical CAP antibiotics to target Haemophilus spp. in addition to Streptococcus spp. in those with chronic lung disease.

Background: Undernutrition among children under 5 years of age continues to be a public health challenge in many low- and middle-income countries and can lead to growth stunting. Infectious diseases may also affect child growth, however their actual impact on the latter can be difficult to quantify. In this paper, we analyse data from 20 Demographic and Health Surveys (DHS) conducted in 13 African countries to investigate the relationship between malaria and stunting. Our objective is to make inference on the association between malaria incidence during the first year of life and height-for-age Z-scores (HAZs). Methods: We develop a geostatistical model for HAZs as a function of both measured and unmeasured child-specific and spatial risk factors. We visualize stunting risk in each of the 20 analysed surveys by mapping the predictive probability that HAZ is below - 2. Finally, we carry out a meta-analysis by modelling the estimated effects of malaria incidence on HAZ from each DHS as a linear regression on national development indicators from the World Bank. Results: A non-spatial univariate linear regression of HAZ on malaria incidence showed a negative association in 18 out of 20 surveys. However, after adjusting for spatial risk factors and controlling for confounding effects, we found a weaker association between HAZ and malaria, with a mix of positive and negative estimates, of which 3 out of 20 are significantly different from zero at the conventional 5% level. The meta-analysis showed that this variation in the estimated effect of malaria incidence on HAZ is significantly associated with the amount of arable land. Conclusion: Confounding effects on the association between malaria and stunting vary both by country and over time. Geostatistical analysis provides a useful framework that allows to account for unmeasured spatial confounders. Establishing whether the association between malaria and stunting is causal would require longitudinal follow-up data on ind

BACKGROUND: Human movement is likely an important risk factor for environmentally-transmitted pathogens. While epidemiologic studies have traditionally focused on household risk factors, individual movement data could provide critical additional information about risk of exposure to such pathogens. We conducted global positioning system (GPS) tracking of urban slum residents to quantify their fine-scale movement patterns and evaluate their exposures to environmental sources of leptospirosis transmission. METHODOLOGY/PRINCIPAL FINDINGS: We recruited participants from an ongoing cohort study in an urban slum in Brazil and tracked them for 24 hours at 30-second intervals. Among 172 subjects asked to participate in this cross-sectional study, 130 agreed to participate and 109 had good quality data and were included in analyses. The majority of recorded locations were near participant residences (87.7% within 50 meters of the house), regardless of age or gender. Similarly, exposure to environmental sources of leptospirosis transmission did not vary by age or gender. However, males, who have higher infection rates, visited a significantly larger area during the 24-hour period than did females (34,549m2 versus 22,733m2, p = 0.005). Four male participants had serologic evidence of Leptospira infection during the study period. These individuals had significantly larger activity spaces than uninfected males (61,310m2 vs 31,575m2, p = 0.006) and elevated exposure to rodent activity (p = 0.046) and trash deposits (p = 0.031). CONCLUSIONS/SIGNIFICANCE: GPS tracking was an effective tool for quantifying individual mobility in the complex urban slum environment and identifying risk exposures associated with that movement. This study suggests that in addition to source reduction, barrier interventions that reduce contact with transmission sources as slum residents move within their communities may be a useful prevention strategy for leptospirosis.

Contains fulltext : 177601.pdf (publisher's version ) (Open Access), The spatial heterogeneity of malaria suggests that interventions may be targeted for maximum impact. It is unclear to what extent different metrics lead to consistent delineation of hotspot boundaries. Using data from a large community-based malaria survey in the western Kenyan highlands, we assessed the agreement between a model-based geostatistical (MBG) approach to detect hotspots using Plasmodium falciparum parasite prevalence and serological evidence for exposure. Malaria transmission was widespread and highly heterogeneous with one third of the total population living in hotspots regardless of metric tested. Moderate agreement (Kappa = 0.424) was observed between hotspots defined based on parasite prevalence by polymerase chain reaction (PCR)- and the prevalence of antibodies to two P. falciparum antigens (MSP-1, AMA-1). While numerous biologically plausible hotspots were identified, their detection strongly relied on the proportion of the population sampled. When only 3% of the population was sampled, no PCR derived hotspots were reliably detected and at least 21% of the population was needed for reliable results. Similar results were observed for hotspots of seroprevalence. Hotspot boundaries are driven by the malaria diagnostic and sample size used to inform the model. These findings warn against the simplistic use of spatial analysis on available data to target malaria interventions in areas where hotspot boundaries are uncertain.

This paper focuses upon two issues. Firstly, the question of identifying diachronic trends, and more importantly significant outliers, in corpora which permit an investigation of a feature at many sampling points over time. Secondly, we consider how best to combine more qualitatively oriented approaches to corpus data with the type of trends that can be observed in a corpus using quantitative techniques. The work uses a recently completed ESRC-funded project as a case study, the representation of Islam in the UK press, in order to demonstrate the potential of the approach taken to establishing significant peaks in diachronic frequency development, and the fruitful interface that may be created between qualitative and quantitative techniques.