BACKGROUND: Anaemia is a disease of public health importance with multi-causal pathways. Previous literature suggests the role of indoor air pollution (IAP) on haemoglobin levels, but this has been studied less due to logistic constraints. A high proportion of the population in developing countries, including India, still depends on unclean fuel, which exacerbates IAP. The objective was to study the association between anaemia and IAP among the older Indian adult population (≥ 45 years) as per gender.
METHODS: Our study analysed the nationally representative dataset of the Longitudinal Ageing Study in India (LASI 2017-18, Wave-1). We have documented the association of anaemia (outcome variable) with IAP (explanatory variable). To reduce the confounding effects of demographic and socioeconomic; health related and behavioural covariates; propensity score matching (PSM) was conducted. Nested multilevel regression modelling was conducted. States and union territories were categorised cross tabulated as low, middle and high as per anaemia and IAP exposure. P value < 0.05 was considered statistically significant. SATA version 17 was used for analysis.
RESULTS: More than half (52.52%) of the participants were exposed to IAP (male (53.55%) > female (51.63%)). The odds of having anaemia was significantly 1.19 times higher (AOR 1.19 (1.09-1.31)) among participants using unclean/ solid fuel. The adjusted odds were significantly higher among participants exposed to pollution-generating sources (AOR 1.30; 1.18-1.43), and household indoor smoking (AOR 1.17 (1.07-1.29). The odds of having anaemia were significantly higher (AOR 1.26; 1.15-1.38) among participants exposed to IAP, which was higher in males (AOR 1.36; 1.15-1.61) than females (AOR 1.21; 1.08-1.35). Empowered Action Group (EAG) states like Uttar Pradesh, Chhattisgarh, Madhya Pradesh, Bihar had both high anaemia and IAP exposure.
CONCLUSIONS: This study established the positive association of anaemia with indoor air pollution among older Indian adults through a nationally representative large dataset. The association was higher among men. Further research is recommended to understand detailed causation and to establish temporality. It is a high time to implement positive intervention nationally to decrease solid/ unclean fuel usage, vulnerable ventilation, indoor smoking, IAP and health hazards associated with these with more focused actions towards EAG states.

Floating photovoltaics (FPV) are an emerging renewable energy technology. Although they have received extensive attention in recent years, understanding of their environmental impacts is limited. To address this knowledge gap, we measured water temperature and meteorological parameters for six months under FPV arrays and in the control open water site and constructed a numerical model reflecting the water energy balance. Our results showed that FPV arrays caused diurnal variation in water temperature and microclimate. Specifically, we found that FPV had a cooling effect on their host waterbody during the daytime and a heat preservation effect at night, reducing diurnal variation. The diel oscillation of water temperature below FPV panels lagged behind that of open waters by approximately two hours. The microclimate conditions below FPV panels also changed, with wind speed decreasing by 70%, air temperature increasing during the daytime (averaging +2.01°C) and decreasing at night (averaging -1.27°C). Notably, the trend in relative humidity was the opposite (-3.72%, +14.43%). Correlation analysis showed that the degree of water temperature affected by FPV was related to local climate conditions. The numerical model could capture the energy balance characteristics with a correlation coefficient of 0.80 between the simulated and actual data. The shortwave radiation and latent heat flux below FPV panels was significantly reduced, and the longwave radiation emitted by FPV panels became one of the heat sources during the daytime. The combined variations of these factors dominated the water energy balance below FPV panels. The measured data and simulation results serve as a foundation for evaluating the impact of FPV systems on water temperature, energy budget, and aquatic environment, which would also provide a more comprehensive understanding of FPV systems.

BACKGROUND: In 2018, the Mozambique Ministry of Health launched guidelines for implementing differentiated service delivery models (DSDMs) to optimize HIV service delivery, improve retention in care, and ultimately reduce HIV-associated mortality. The models were fast-track, 3-month antiretrovirals dispensing, community antiretroviral therapy groups, adherence clubs, family approach and three one-stop shop models: adolescent-friendly health services, maternal and child health, and tuberculosis. We conducted a cost-effectiveness analysis and budget impact analysis to compare these models to conventional services.
METHODS: We constructed a decision tree model based on the percentage of enrolment in each model and the probability of the outcome (12-month retention in treatment) for each year of the study period-three for the cost-effectiveness analysis (2019-2021) and three for the budget impact analysis (2022-2024). Costs for these analyses were primarily estimated per client-year from the health system perspective. A secondary cost-effectiveness analysis was conducted from the societal perspective. Budget impact analysis costs included antiretrovirals, laboratory tests and service provision interactions. Cost-effectiveness analysis additionally included start-up, training and clients\' opportunity costs. Effectiveness was estimated using an uncontrolled interrupted time series analysis comparing the outcome before and after the implementation of the differentiated models. A one-way sensitivity analysis was conducted to identify drivers of uncertainty.
RESULTS: After implementation of the DSDMs, there was a mean increase of 14.9 percentage points (95% CI: 12.2, 17.8) in 12-month retention, from 47.6% (95% CI, 44.9-50.2) to 62.5% (95% CI, 60.9-64.1). The mean cost difference comparing DSDMs and conventional care was US$ -6 million (173,391,277 vs. 179,461,668) and -32.5 million (394,705,618 vs. 433,232,289) from the health system and the societal perspective, respectively. Therefore, DSDMs dominated conventional care. Results were most sensitive to conventional care interaction costs in the one-way sensitivity analysis. For a population of 1.5 million, the base-case 3-year financial costs associated with the DSDMs was US$550 million, compared with US$564 million for conventional care.
CONCLUSIONS: DSDMs were less expensive and more effective in retaining clients 12 months after antiretroviral therapy initiation and were estimated to save approximately US$14 million for the health system from 2022 to 2024.

UNASSIGNED: The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) allows researchers to explore cortico-cortical connections. To study effective connections, the first few tens of milliseconds of the TMS-evoked potentials are the most critical. Yet, TMS-evoked artifacts complicate the interpretation of early-latency data. Data-processing strategies like independent component analysis (ICA) and the combined signal-space projection-source-informed reconstruction approach (SSP-SIR) are designed to mitigate artifacts, but their objective assessment is challenging because the true neuronal EEG responses under large-amplitude artifacts are generally unknown. Through simulations, we quantified how the spatiotemporal properties of the artifacts affect the cleaning performances of ICA and SSP-SIR.
UNASSIGNED: We simulated TMS-induced muscle artifacts and superposed them on pre-processed TMS-EEG data, serving as the ground truth. The simulated muscle artifacts were varied both in terms of their topography and temporal profiles. The signals were then cleaned using ICA and SSP-SIR, and subsequent comparisons were made with the ground truth data.
UNASSIGNED: ICA performed better when the artifact time courses were highly variable across the trials, whereas the effectiveness of SSP-SIR depended on the congruence between the artifact and neuronal topographies, with the performance of SSP-SIR being better when difference between topographies was larger. Overall, SSP-SIR performed better than ICA across the tested conditions. Based on these simulations, SSP-SIR appears to be more effective in suppressing TMS-evoked muscle artifacts. These artifacts are shown to be highly time-locked to the TMS pulse and manifest in topographies that differ substantially from the patterns of neuronal potentials.
UNASSIGNED: Selecting between ICA and SSP-SIR should be guided by the characteristics of the artifacts. SSP-SIR might be better equipped for suppressing time-locked artifacts, provided that their topographies are sufficiently different from the neuronal potential patterns of interest, and that the SSP-SIR algorithm can successfully find those artifact topographies from the high-pass-filtered data. ICA remains a powerful tool for rejecting artifacts that are not strongly time locked to the TMS pulse.

UNASSIGNED: With efforts underway to develop a universal coronavirus vaccine, otherwise known as a pan-coronavirus vaccine, this is the time to offer potential funders, researchers, and manufacturers guidance on the potential value of such a vaccine and how this value may change with differing vaccine and vaccination characteristics.
UNASSIGNED: Using a computational model representing the United States (U.S.) population, the spread of SARS-CoV-2 and the various clinical and economic outcomes of COVID-19 such as hospitalisations, deaths, quality-adjusted life years (QALYs) lost, productivity losses, direct medical costs, and total societal costs, we explored the impact of a universal vaccine under different circumstances. We developed and populated this model using data reported by the CDC as well as observational studies conducted during the COVID-19 pandemic.
UNASSIGNED: A pan-coronavirus vaccine would be cost saving in the U.S. as a standalone intervention as long as its vaccine efficacy is ≥10% and vaccination coverage is ≥10%. Every 1% increase in efficacy between 10% and 50% could avert an additional 395,000 infections and save $1.0 billion in total societal costs ($45.3 million in productivity losses, $1.1 billion in direct medical costs). It would remain cost saving even when a strain-specific coronavirus vaccine would be subsequently available, as long as it takes at least 2-3 months to develop, test, and bring that more specific vaccine to the market.
UNASSIGNED: Our results provide support for the development and stockpiling of a pan-coronavirus vaccine and help delineate the vaccine characteristics to aim for in development of such a vaccine.
UNASSIGNED: The National Science Foundation, the Agency for Healthcare Research and Quality, the National Institute of General Medical Sciences, the National Center for Advancing Translational Sciences, and the City University of New York.

OBJECTIVE: Screening colonoscopy, recommended every 10 years, reduces mortality from colorectal cancer (CRC) by early detection of prevalent but undiagnosed CRC, as well as by removal of precursor lesions. The aim of this study was to assess the relative contribution of both components to total CRC mortality reduction over time.
METHODS: Using a validated multistate Markov model, we simulated hypothetical cohorts of 100,000 individuals aged 55 to 64 years with and without screening at baseline. Main outcomes included proportions of prevented CRC deaths arising from (asymptomatic) CRC already present at baseline and from newly developed CRC during 15 years of follow-up, and mortality rate ratios of screened versus nonscreened groups over time.
RESULTS: Early detection of prevalent cases accounted for 52%, 30%, and 18% of deaths prevented by screening colonoscopy within 5, 10, and 15 years, respectively. Relative reduction of mortality was estimated to be much larger for mortality from incident cancers than for mortality from cancers that were already present and detected early at screening endoscopy and for total CRC mortality (ie, 88% versus 67% and 79%, respectively, within 10 years from screening).
CONCLUSIONS: Reduction of CRC mortality mainly arises from early detection of prevalent cancers during the early years after screening colonoscopy, but prevention of incident cases accounts for the majority of prevented deaths in the longer run. Prevention of incident cases leads to sustained strong reduction of CRC mortality, possibly warranting an extension of screening intervals.

Background COVID-19 has been the worst pandemic of this century, resulting in economic, social, and educational disruptions. Residency training is no exception, with training restrictions delaying the progression and graduation of residents. We sought to utilize simulation modelling to predict the impact on future cohorts in the event of repeated and prolonged movement restrictions due to COVID-19 and future pandemics of a similar nature. Method A Delphi study was conducted to determine key Accreditation Council for Graduate Medical Education-International (ACGME-I) training variables affected by COVID-19. Quantitative resident datasets on these variables were collated and analysed from 2018 to 2021. Using the Vensim® software (Ventana Systems, Inc., Harvard, MA), historical resident data and pandemic progression delays were used to create a novel simulation model to predict future progression delay. Various durations of delay were also programmed into the software to simulate restrictions of varying severity that would impact resident progression. Results Using the model with scenarios simulating varying pandemic length, we found that the estimated average delay for residents in each accredited year ranged from an increase of one month for year 2 residents to more than three months for year 4 residents. Movement restrictions lasting a year would require up to six years before the program returned to a pre-pandemic equilibrium. Conclusion Systems dynamic modelling can be used to predict delays in residency training programs during a pandemic. The impact on the workforce can thus be projected, allowing residency programs to institute mitigating measures to avoid progression delay.

BACKGROUND: Prediction models have gained immense importance in various fields for decision-making purposes. In the context of tennis, relying solely on the probability of winning a single match may not be sufficient for predicting a player\'s future performance or ranking. The performance of a tennis player is influenced by the timing of their matches throughout the year, necessitating the incorporation of time as a crucial factor. This study aims to focus on prediction models for performance indicators that can assist both tennis players and sports analysts in forecasting player standings in future matches.
METHODS: To predict player performance, this study employs a dynamic technique that analyzes the structure of performance using both linear and nonlinear time series models. A novel approach has been taken, comparing the performance of the non-linear Neural Network Auto-Regressive (NNAR) model with conventional stochastic linear and nonlinear models such as Auto-Regressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS), and TBATS (Trigonometric Seasonal Decomposition Time Series).
RESULTS: The study finds that the NNAR model outperforms all other competing models based on lower values of Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). This superiority in performance metrics suggests that the NNAR model is the most appropriate approach for predicting player performance in tennis. Additionally, the prediction results obtained from the NNAR model demonstrate narrow 95% Confidence Intervals, indicating higher accuracy and reliability in the forecasts.
CONCLUSIONS: In conclusion, this study highlights the significance of incorporating time as a factor when predicting player performance in tennis. It emphasizes the potential benefits of using the NNAR model for forecasting future player standings in matches. The findings suggest that the NNAR model is a recommended approach compared to conventional models like ARIMA, ETS, and TBATS. By considering time as a crucial factor and employing the NNAR model, both tennis players and sports analysts can make more accurate predictions about player performance.

BACKGROUND: Contact precautions (CPs) have been widely implemented in hospitals, particularly in intensive care units (ICUs), as the standard approach for managing meticillin-resistant Staphylococcus aureus (MRSA). However, the effectiveness of CPs in preventing MRSA transmission remains a subject of debate.
OBJECTIVE: To assess the effectiveness of CPs in reducing MRSA transmission within ICUs.
METHODS: A comprehensive analysis was conducted using data from 16 sets of parameters collected from 13 field studies investigating the effectiveness of CPs in ICUs, and an epidemiologic model was developed to simulate the dynamics of MRSA incidence in the hospital setting.
RESULTS: The analysis demonstrated a mean reduction of 20.52% (95% confidence interval -30.52 to 71.60%) in the MRSA transmission rate associated with the implementation of CPs. Furthermore, reducing the time-delay of screening tests and increasing the percentage of patients identified with MRSA through screening at admission were found to contribute to the effectiveness of CPs.
CONCLUSIONS: Proper implementation of CPs, with a particular emphasis on early identification of MRSA-colonized/infected patients, demonstrated a strong association with reduced MRSA transmission within the hospital setting. However, further research is necessary to investigate the effectiveness and safety of decolonization and other interventions used in conjunction with CPs to mitigate the risk of infection among colonized patients.

OBJECTIVE: Many factors influence patient flow through an ED, including streaming, treatment spaces and staff resources. This pilot study explored and compared real time patient flow using a single-stream system versus varying configurations of possible two-stream systems using computer simulation.
METHODS: Simulation modelling was used to assess the delay in treatment of a rapid-antigen-tested-based, two-stream model for patient flow through ED during the peak phase of the COVID pandemic.
RESULTS: Modelling two-stream configuration for all patients (minimum time to be seen for both COVID-positive and COVID-negative patients) showed that in the case study ED, a two-stream system and linked changes in bed configuration for managing the risks of infection can impact delays in treatment.
CONCLUSIONS: Data-driven modelling within specific clinical settings can inform the (in)efficiency of patient flow processes and help clinicians and managers make evidence-based decisions about patient transition through EDs. This can assist with reconfiguration of ED patient streaming particularly during periods of unique need, such as the recent COVID-19 pandemic.