Routine Monitoring Data Research Articles

Interventions to reduce ambient particulate matter air pollution and their effect on health.

Interventions to reduce ambient particulate matter air pollution and their effect on health.

Model-Based Quantification of Left Ventricular Diastolic Function in Critically Ill Patients with Atrial Fibrillation from Routine Data: A Feasibility Study.

Introduction Left ventricular diastolic dysfunction (LVDD) and atrial fibrillation (AF) are connected by pathophysiology and prevalence. LVDD remains underdiagnosed in critically ill patients despite potentially significant therapeutic implications since direct measurement cannot be performed in routine care at the bedside, and echocardiographic assessment of LVDD in AF is impaired. We propose a novel approach that allows us to infer the diastolic stiffness, β, a key quantitative parameter of diastolic function, from standard monitoring data by solving the nonlinear, ill-posed inverse problem of parameter estimation for a previously described mechanistic, physiological model of diastolic filling. The beat-to-beat variability in AF offers an advantageous setting for this. Methods By employing a global optimization algorithm, β is inferred from a simple six parameter and an expanded seven parameter model of left ventricular filling. Optimization of all parameters was limited to the interval ]0, 400[ and initialized randomly on large intervals encompassing the support of the likelihood function. Routine ECG and arterial pressure recordings of 17 AF and 3 sinus rhythm (SR) patients from the PhysioNet MGH/MF Database were used as inputs. Results Estimation was successful in 15 of 17 AF patients, while in the 3 SR patients, no reliable estimation was possible. For both models, the inferred β (0.065 ± 0.044 ml−1 vs. 0.038 ± 0.033 ml−1 (p=0.02) simple vs. expanded) was compatible with the previously described (patho) physiological range. Aortic compliance, α, inferred from the expanded model (1.46 ± 1.50 ml/mmHg) also compared well with literature values. Conclusion The proposed approach successfully inferred β within the physiological range. This is the first report of an approach quantifying LVDF from routine monitoring data in critically ill AF patients. Provided future successful external validation, this approach may offer a tool for minimally invasive online monitoring of this crucial parameter.

Routine Herd Health Data as Cow-Based Risk Factors Associated with Lameness in Pasture-Based, Spring Calving Irish Dairy Cows.

Simple SummaryDairy cow lameness is considered one of the most important animal welfare and economic concerns for the dairy industry. Cow-based risk factors for lameness are not well described, especially in comparison to herd-level risk factors such as housing environment and roadway condition. This study investigates the use of routinely gathered herd health data as cow-based risk factors for lameness in dairy cows. A total of 1715 cows in 10 pasture-based Irish dairy herds were monitored for lameness during the spring and summer of 2013 and 2014 as part of the University College Dublin herd health programme. Herd health monitoring data analysed to identify potential risk factors for lameness consisted of milk production data, genetic merit information, calving details, peri-parturient disease records and body condition scores. This analysis showed a significant effect of increasing parity, lower body condition score at calving and excessive body condition loss after calving on the risk of cows being diagnosed as lame during the lactation. In conclusion, routinely gathered herd health monitoring data can be used to identify cows at increased risk of being lame and to implement lameness control strategies.Herd-level risk factors related to the cow’s environment have been associated with lameness. Uncomfortable stall surface and inadequate depth of bedding as well as abrasive alley way surface are contributing factors to increased levels of lameness. Access to pasture has been found as having a beneficial effect on cows’ locomotion. However, dairy cattle managed under grazing conditions are exposed to a different set of risk factors for lameness, mainly associated with cow tracks. Cow-based risk factors for lameness are not as clearly defined as the herd level risk factors. The objective of the present study was to use routine herd health monitoring data to identify cow-based risk factors for lameness and utilise this information to indicate cows at risk of developing lameness in the first 150 days of lactation. Lameness data were gathered from 10 pasture-based dairy herds. A total of 1715 cows were monitored, of which 1675 cows were available for analysis. Associations between lameness status and potential cow-level risk factors were determined using multivariable logistic regression. Parity 3 and 4 + cows showed odd ratios (OR’s) for lameness of 3.92 and 8.60 respectively (95% confidence interval (CI) 2.46–6.24; 5.68–13.0). Maximum loss of Body condition score (BCS) after calving exhibits OR’s for lameness of 1.49 (95% CI 1.08–2.04) if cows lost 0.5 in BCS after calving and 2.26 (95% CI 1.30–3.95) for cows losing more than 0.5 BCS. Animals calving in BCS 3.25 and ≥ 3.5 had correlating OR’s of 0.54 (95% CI 0.34–0.87) and 0.33 (95% CI 0.16–0.65) for being lame compared to cows calving with BCS ≤ 2.75. Data gathered as part of herd health monitoring can be used in conjunction with lameness records to identify shortcomings in lameness management. Findings and recommendations on lameness management can be formulated from readily available information on cow-based risk factors for lameness.

Bias through selective inclusion and attrition: Representativeness when comparing provider performance with routine outcome monitoring data.

BackgroundObservational research based on routine outcome monitoring is prone to missing data, and outcomes can be biased due to selective inclusion at baseline or selective attrition at posttest. As patients with complete data may not be representative of all patients of a provider, missing data may bias results, especially when missingness is not random but systematic.MethodsThe present study establishes clinical and demographic patient variables relevant for representativeness of the outcome information. It applies strategies to estimate sample selection bias (weighting by inclusion propensity) and selective attrition bias (multiple imputation based on multilevel regression analysis) and estimates the extent of their impact on an index of provider performance. The association between estimated bias and response rate is also investigated.ResultsProvider‐based analyses showed that in current practice, the effect of selective inclusion was minimal, but attrition had a more substantial effect, biasing results in both directions: overstating and understating performance. For 22% of the providers, attrition bias was estimated to be in excess of 0.05 ES. Bias was associated with overall response rate (r = .50). When selective inclusion and attrition bring providers' response below 50%, it is more likely that selection bias increased beyond a critical level, and conclusions on the comparative performance of such providers may be misleading.ConclusionsEstimates of provider performance were biased by selection, especially by missing data at posttest. Results on the extent and direction of bias and minimal requirements for response rates to arrive at unbiased performance indicators are discussed.

Increase Adherence to waste Management Policy at Healthcare Facility in Egypt

Abstract Background: Proper management of medical wastes can help prevent serious consequences of catastrophic events or accidents in the hospital and its near or far environment. A performance improvement PI project has been carried out to increase adherence to waste management policy of infection control, to ensure that departments are following basic health rules of their communities. Method: In this project, the team utilized the basic Six Sigma break through problem-solving methodology; Define, Measure, Analyze, Improve and Control (DMAIC) to examine the original (root) causes of the problem, in a healthcare facility in Egypt from January to August 2016. Results : The team re-modulated the place for interim storage of hospital medical waste and managed to enhance and implement the waste disposal steps. Shortage in workers was compensated for by participation of a housekeeping company and so the improving project resulted in marked increase in adherence to waste management policy. Conclusion: Infection Preventionists using their routine monitoring data, could help the organization to fulfill accreditation requirements for quality assurance through the performance improvement project and to keep all hospital staff, visitors and the surrounding environment safe and secure.

Factors associated with low birthweight in Palestinian refugees at Al-Wehdat camp, Jordan: a cross-sectional study using routine data

BackgroundLow birthweight (LBW), defined as a birthweight of less than 2500 g, is a leading cause of infant morbidity and mortality worldwide. Previous studies have shown that the risk of LBW is higher in vulnerable populations, such as refugees. Few studies have assessed the prevalence and associated risk factors of LBW in refugee populations using routine monitoring data. This study sought to measure the prevalence of LBW and associated risk factors in refugees in Al-Wehdat Camp in Jordan using routine monitoring data. MethodsThe study examined a dataset that included 3916 births from mothers registered in the UNRWA e-Health database between June 1, 2016, and June 30, 2017. The dataset was cleaned and variables were analysed. The variables were parity, mother's age, maternal education level, marital status, mother's occupation, multiple pregnancies, presence of complications, diastolic hypertension above 90 mm Hg, place of delivery, number of antenatal visits, sex of infant, infant year of birth, infant month of birth, and type of service received (family planning or antenatal services). Continuous variables were grouped into categorical (eg, parity, number of antenatal visits, mother's age) or binary variables (eg, infant's birthweight). Bivariable and multivariable logistic regression were employed to evaluate the association between previously identified potential risk factors and LBW outcomes. The resulting odds ratios (ORs) and adjusted ORs were evaluated. FindingsThe prevalence of LBW was 6·3% (246 of 3916). In the crude analysis, significant associations were found between the presence of maternal complications (OR 2·86; 95% CI 2·02–4·04) with LBW outcomes. Significant associations were also found between women that did not receive full antenatal services (OR 1·36; 95% CI 1·03–1·78) and LBW outcomes. Multivariable analyses showed that LBW outcomes were higher with maternal complications (adjusted OR 2·85; 95% CI 2·10–4·24) and receipt of full antenatal services over receiving only family planning (1·37; 95% CI 1·04–1·81), when adjusting for the a priori confounders. InterpretationThe study identified positive associations between LBW outcomes and maternal complications and the type of health service received. No other associations were significant, but some variables confounded the relationship between maternal complications and LBW outcomes. The study supports findings from previous studies, such as an increased risk of LBW outcomes when maternal complications are coupled with no family planning. The implications of this study are that prevention of maternal complications should be improved and that there should be better care of women with such complications. In addition, family planning services should be available to all women. Further research is required to form robust policies to reduce the burden of LBW in the context of vulnerable Palestinian refugees. FundingNone.

Population pharmacokinetics and dosage optimization of tacrolimus in pediatric patients with nephrotic syndrome .

The aims of this study were to investigate the population pharmacokinetic (PPK) characteristics of tacrolimus in Chinese children with nephrotic syndrome and to apply it in clinical practice. A total of 137 concentrations from 61 patients were collected from routine therapeutic drug monitoring data between 2011 and 2018. Population modeling was performed with the nonlinear mixed-effects model (NONMEM) program, using a one-compartment model with first-order absorption and elimination. The mean population estimate values of clearance (CL/F) and volume of distribution (V/F) were determined. Common demographic and clinical variables were tested for their influence on these parameters. External validation was conducted, and Monte Carlo simulation, based on the final model, was carried out to determine optimal dosage regimen. Age and body weight were the covariates that displayed a significant influence on CL/F and V/F according to the final regression model. Goodness-of-fit plots, bootstrap outcomes, and external validation confirmed the relatively good stability and prediction capability of the model. The interindividual variability of CL/F was 31.10%, and the residual variability was 0.91 ng/mL. Mean prediction error (MPE, %) and Mean absolute prediction error (MAPE, %) were 10.3% and 16.6%, respectively. Monte Carlo simulation based on the final model was carried out to determine optimal dosage regimen. A PPK model of tacrolimus in children with nephrotic syndrome was developed. Age and bodyweight could partly explain the interindividual variability in the CL/F and V/F of tacrolimus. The final model could be used to accurately predict tacrolimus individual pharmacokinetic parameters and assist in dosage optimization. .

Increase adherence to waste management policy at healthcare facility in Egypt

BackgroundProper management of medical wastes can help prevent serious consequences of catastrophic events or accidents in the hospital and its near or far environment.MethodA quality performance improvement project has been carried out to increase adherence to waste management policy of infection control, to ensure that departments are following basic health rules of their communities. In this project, the team utilized the basic Six Sigma breakthrough problem-solving methodology: Define, Measure, Analyze, Improve and Control (DMAIC) to examine the original (root) causes of the problem, in a healthcare facility in Egypt from January to August 2016. The team re-modulated a place for interim storage of hospital-regulated hazardous medical waste and compensate for workers’ fatigue by participation of professional housekeeping company.ResultsWeekly recording of errors in performance in waste management process demonstrates process stability through the first 12 weeks, and shifting towards the desired direction of reduced errors in waste management performance was obtained from the 13th week ongoing.ConclusionInfection preventionists using their routine monitoring data could help the organization to fulfill accreditation requirements for quality assurance through the performance improvement project and to keep all hospital staff, visitors, and the surrounding environment safe and secure.

Trajectories of patients with severe mental illness in two-year contact with Flexible Assertive Community Treatment teams using Routine Outcome Monitoring data: An observational study.

ObjectiveUsing outcome data collected routinely over a continuous two-year treatment period, we wished to distinguish homogeneous subgroups of patients with a severe mental illness whose psychosocial problems followed a similar pattern over time. By identifying the effectiveness of health services for different patient groups, this approach allowed us to identify patients at risk of deterioration and those recovering from their symptoms.MethodsIn total we included 2,660 patients who were in two-year continuous contact with a Flexible Assertive Community Treatment team (FACT). We collected outcome data on psychosocial functioning, needs for care and quality of life. We performed a latent class growth analysis (LCGA).ResultsThe LCGA identified six homogenous patient subgroups using trajectories of HoNOS scores. On the basis of the patterns of patients’ psychosocial problems over time, we labelled these as follows: 1) stable at a low problem-severity level (N = 709; 27%); 2) stable at a low medium problem-severity level (N = 1,208; 45%); 3) stable at a high medium problem-severity level (N = 528; 20%); 4) stable at a high problem-severity level (N = 116; 4%); 5) amelioration of problems (N = 42; 2%); and 6) deterioration of problems (N = 57; 2%). Patients with stable and a high severity of psychosocial problems had more practical and somatic unmet needs than those in other subgroups, and also had the fewest decrease in the number of unmet needs.DiscussionAfter linking patient subgroups with clinical features such as the need for care, we found that, over two years, most patients remained relatively stable in terms of psychosocial functioning, but that their unmet needs decreased over time. However, in terms of needs for treatment during two years of contact with a FACT team, patients in the subgroup with a stable and high problem-severity level tended to derive little or no benefit.

A Compilation of Serum Concentrations of 12 Antipsychotic Drugs in a Therapeutic Drug Monitoring Setting.

Background:No comprehensive collection of routine therapeutic drug monitoring data for antipsychotic drugs has been published.Methods:In this compilation, data on 12 antipsychotics are presented. The drugs included are amisulpride (n = 506), aripiprazole (n = 1610), clozapine (n = 1189), flupentixol (n = 215), haloperidol (n = 390), olanzapine (n = 10,268), perphenazine (n = 1065), quetiapine (n = 5853), risperidone (n = 3255), sertindole (n = 111), ziprasidone (n = 1235), and zuclopenthixol (n = 691). Because only one sample per patient is included, the number of patients equals the number of samples. For each drug, median serum concentrations as well as that of the 10th and 90th percentiles are given for a range of daily doses. Comparisons are made between males and females, between patients younger than 65 years and 65 years and older, and between those treated with a low and a high dose of each drug. The concentration-to-dose (C/D) ratio is the primary variable used in these comparisons. Coefficients of variation (CVs) for the serum concentrations of each drug within and between subjects are presented.Results:In general, the C/D ratios were higher in females than in males, higher in those 65 years and older than in younger subjects, and lower in those treated with higher doses than in those treated with lower doses. CVs between individuals were larger than within subjects, and the CVs were highest for the drugs with short elimination half-lives.Conclusions:For each antipsychotic drug, the results presented can serve as a reference tool for pharmacokinetic interpretation of the individual patient's serum drug level. The compiled serum concentrations and the C/D ratios can support the physician's decision when individualizing dosing and determining treatment strategies for a specific patient.

Age Impacts Olanzapine Exposure Differently During Use of Oral Versus Long-Acting Injectable Formulations: An Observational Study Including 8,288 Patients.

Olanzapine is a commonly prescribed antipsychotic available as oral and long-acting injectable (LAI) formulations. Data are lacking on the use and safety of olanzapine-LAI in older patients. The aim of this study was to investigate the effect of increasing age on olanzapine exposure during oral versus LAI administration in a real-life setting. This observational study was based on routine therapeutic drug monitoring data collected during 2005-2017. As a measure of exposure, absolute concentrations and concentration/dose ratios of olanzapine were defined as outcome variables. Linear mixed-model analyzes were used to allow for inclusion of multiple samples per patient and adjustment for covariate effects. Olanzapine concentrations and doses from 8,288 patients (21,378 measurements) were included. The number of patients on oral treatment was 7,893 (42%, 50 years or older), while 395 were using olanzapine-LAI (27%, 50 years or older). In contrast to oral use, where the dose-adjusted concentration of olanzapine increased significantly for patients 50 years or older (P < 0.001), increasing age had no effect on olanzapine concentration following LAI administration (P = 0.550). The effects of smoking habits and gender were equal in oral and olanzapine-LAI users. While the dose-adjusted systemic exposure of olanzapine increases by age after oral administration, these novel findings from a large patient population show that systemic exposure of olanzapine-LAI is unaffected by age, probably due to the lacking influence of age-related changes in gastrointestinal absorption and/or presystemic metabolism. From a pharmacokinetic point of view, it is therefore no reason to restrict the use of olanzapine-LAI in older patients requiring long-term treatment.

Reducing health inequalities in England: does the demise of NHS Stop Smoking Services matter? Analysis of mandatory monitoring data.

Tobacco smoking is a key cause of mortality, morbidity and health inequalities. The unprecedented English health inequalities strategy (1999-2010) sought to reduce health inequalities, by, in part, instigating NHS Stop Smoking Services (SSS), initially targeted in deprived 'Spearhead' localities. Performance of SSS is assessed here in light of its role supporting the strategy, which evidence suggests achieved a reduction in health inequalities. SSS enrolment and four-week quits in Spearhead and non-Spearhead localities were compared during and after the strategy period, using regression models and routine monitoring data. Changes in SSS expenditure were estimated. After similar increases in enrolment and quits between Spearhead and other localities between 2003/4 and 2008/9, SSS in Spearhead localities experienced a 2-fold better rate of improvement in enrolment and quit performance over the 4 years to 2011/12. Since 2011/12, SSS have dramatically reduced, and expenditure had fallen by half in Spearhead localities by 2016/17. SSS, particularly in Spearhead localities, were expanded up to 2011/12, and this broadly coincides with the reduction in health inequalities. This suggests that although SSS did not achieve the scale anticipated, they have important potential, and the current demise of SSS should not be tolerated.

Evaluate the Validity of Land Use Regression Models for NO2 Based on Routine and Campaign Monitoring Data from an Urban Area of Japan

Background: Generally, land use regression (LUR) models have been built using campaign monitoring data rather than routine monitoring data. However, the routine monitoring data have advantages such as low cost and long-term coverage. The objective of this study was to compare the predictability of LUR models for NO2 based on routine and campaign monitoring data obtained from an urban area. This evaluation considered that LUR models that could represent regional differences in air pollution exposure and regional road structure were optimal.Method: We selected the city of Suita in Osaka (Japan). We built a model based on routine monitoring data obtained from all sites (routine-LUR), and a model based on campaign monitoring data (campaign-LUR) within the city. The routine LUR models were based on monitoring networks across two prefectures (i.e., Osaka and Hyogo prefectures). Next, we selected 30 evaluation sites and developed LUR models excluding these evaluations sites. We calculated the predictability of each model, and compared the predicted NO2 concentrations from each model with measured annual average NO2 concentrations from evaluation sites.Results: The predictability for NO2 concentrations of routine-LUR model was better than that of campaign-LUR model: adjusted R2 &amp;#61; 0.68 and 0.59, respectively, and root mean square error &amp;#61; 3.4 ppb and 3.9 ppb, respectively. The routine-LUR model were highly correlated with the measured NO2 concentrations at evaluation sites [&amp;#961; &amp;#61; 0.88] comparing with the campaign-LUR model [&amp;#961; &amp;#61; 0.54]. Although the predicted NO2 concentrations from each model were correlated, the LUR models based on routine networks provided better visual representations of the local road conditions in the city.Conclusion: The present study demonstrated that LUR models based on routine data could estimate local traffic-related air pollution in an urban area. The importance and usefulness of data from routine monitoring networks should be acknowledged.

Research on spatial-temporal distribution characteristics of main pollutants of the rivers in the Linyi Development Zone

Abstract The pollution issues with urban rivers is one of the key factors affecting the urban ecological environment, and water pollution control has become the top task for urban ecological construction. The first step of controlling water environment pollution is to have an objective knowledge of the water quality. This research selects the rivers in the Linyi Development Zone as the subject and analyzes the major pollution factors and the spatial-temporal distribution characteristics of the main pollutants in the river system based on routine monitoring data of the river system in the zone from August 2014 to May 2017, and also accurately judges the variation trend of the water quality with the time through the Spearman rank relational coefficient. The research result shows that NH3-N, TP, CODCr, CODMn, BOD5 are the main pollution factors. Between 2014 and 2017, the overall water quality variation trend of the river system in the development zone is very significant, and the water quality variation basically remains stable. The water quality variation and major pollution sources of the rivers in the development zone are judged, providing an important basis for the water ecology construction of the development zone.

Spatial PM2.5, NO2, O3 and BC models for Western Europe – Evaluation of spatiotemporal stability

BackgroundIn order to investigate associations between air pollution and adverse health effects consistent fine spatial air pollution surfaces are needed across large areas to provide cohorts with comparable exposures. The aim of this paper is to develop and evaluate fine spatial scale land use regression models for four major health relevant air pollutants (PM2.5, NO2, BC, O3) across Europe. MethodsWe developed West-European land use regression models (LUR) for 2010 estimating annual mean PM2.5, NO2, BC and O3 concentrations (including cold and warm season estimates for O3). The models were based on AirBase routine monitoring data (PM2.5, NO2 and O3) and ESCAPE monitoring data (BC), and incorporated satellite observations, dispersion model estimates, land use and traffic data. Kriging was performed on the residual spatial variation from the LUR models and added to the exposure estimates. One model was developed using all sites (100%). Robustness of the models was evaluated by performing a five-fold hold-out validation and for PM2.5 and NO2 additionally with independent comparison at ESCAPE measurements. To evaluate the stability of each model's spatial structure over time, separate models were developed for different years (NO2 and O3: 2000 and 2005; PM2.5: 2013). ResultsThe PM2.5, BC, NO2, O3 annual, O3 warm season and O3 cold season models explained respectively 72%, 54%, 59%, 65%, 69% and 83% of spatial variation in the measured concentrations. Kriging proved an efficient technique to explain a part of residual spatial variation for the pollutants with a strong regional component explaining respectively 10%, 24% and 16% of the R2 in the PM2.5, O3 warm and O3 cold models. Explained variance at fully independent sites vs the internal hold-out validation was slightly lower for PM2.5 (65% vs 66%) and lower for NO2 (49% vs 57%). Predictions from the 2010 model correlated highly with models developed in other years at the overall European scale. ConclusionsWe developed robust PM2.5, NO2, O3 and BC hybrid LUR models. At the West-European scale models were robust in time, becoming less robust at smaller spatial scales. Models were applied to 100 × 100 m surfaces across Western Europe to allow for exposure assignment for 35 million participants from 18 European cohorts participating in the ELAPSE study.

Using a cascade approach to assess condom uptake in female sex workers in India: a review of the Avahan data

BackgroundThe Avahan India AIDS Initiative was implemented to provide HIV prevention services to key populations including female sex workers (FSWs) who carry the burden of India’s concentrated HIV epidemic. Established in 2003 and handed over to the Indian government in 2009, the Initiative included peer-led outreach education, condom promotion and distribution and STI treatment. This study aimed to determine if HIV prevention cascades could be generated using routine monitoring and evaluation data from the Avahan program and to assess their value in identifying and responding to program gaps for FSWs.MethodsTwo data sources were used namely the Integrated Behavioural and Biological Assessment reports and the Centralized Management Information System dataset. Indicators selected for the cascades were: FSWs at risk, belief that HIV can be prevented, condom access and consistent condom use with an occasional partner. Six districts were selected and stratified by HIV prevalence at baseline and two cascades were generated per district reflecting changes over time.ResultsConsistent condom use with occasional partners in this population increased in all six districts during program implementation, with statistically significant increases in four of the six. No patterns in the cascades were detected according to HIV prevalence either at baseline (2005) or at follow-up (2009). Cascades were able to identify key programmatic bottlenecks at baseline that could assist with focusing program efforts and direct resources at district levels. In some districts the belief that HIV could not be prevented contributed to inconsistent condom use, while in others, low levels of condom access were a more important barrier to consistent condom use.ConclusionThis HIV prevention cascade analysis among FSWs in India suggests that cascades could assist in identifying program gaps, focus intervention efforts and monitor their effect. However, cascades cannot replace a detailed understanding of the multiple factors at individual, community and structural levels that lead to consistent condom use in this key population. Careful indicator selection coupled with innovative data collection methods will be required. Pilot projects are proposed to formally evaluate the value of HIV prevention cascades at district level.

Real-Time Nowcasting of Microbiological Water Quality at Recreational Beaches: A Wavelet and Artificial Neural Network-Based Hybrid Modeling Approach.

The number of beach closings caused by bacterial contamination has continued to rise in recent years, putting beachgoers at risk of exposure to contaminated water. Current approaches predict levels of indicator bacteria using regression models containing a number of explanatory variables. Data-based modeling approaches can supplement routine monitoring data and provide highly accurate short-term forecasts of beach water quality. In this paper, we apply the nonlinear autoregressive network with exogenous inputs (NARX) method with explanatory variables to predict Escherichia coli concentrations at four Lake Michigan beach sites. We also apply the nonlinear input-output network (NIO) and nonlinear autoregressive neural network (NAR) methods in addition to a hybrid wavelet-NAR (WA-NAR) model and demonstrate their application. All models were tested using 3 months of observed data. Results revealed that the NARX models provided the best performance and that the WA-NAR model, which requires no explanatory variables, outperformed the NIO and NAR models; therefore, the WA-NAR model is suitable for application to data scarce regions. The models proposed in this paper were evaluated using multiple performance metrics, including sensitivity and specificity measures, and produced results comparable or superior to those of previous mechanistic and statistical models developed for the same beach sites. The relatively high R2 values between data and the NARX models ( R2 values of ∼0.8 for the beach sites and ∼0.9 for the river site) indicate that the new class of models shows promise for beach management.

Clinical and sociodemographic associations with treatment selection in major depression.

To investigate treatment selection in a naturalistic sample of MDD outpatients and the factors influencing treatment selection in specialized psychiatric care. Multinomial Logistic Regression analysis investigated associations between treatment selection and patients' sociodemographic and clinical characteristics, using retrospective chart review data and Routine Outcome Monitoring (ROM) data of MDD outpatients. Of the patients included for analyses (N = 263), 34% received psychotherapy, 32% received an antidepressant (AD) and 35% received a combination. Men were more likely than women to receive AD with reference to psychotherapy (ORAD = 5.57, 95% CI 2.38-13.00). Patients with severe depression and patients with AD use upon referral, prescribed by their general practitioner, were more likely to receive AD (ORsevere depression = 5.34, 95% CI 1.70-16.78/ORAD GP = 9.26, 95% CI 2.53-33.90) or combined treatment (ORsevere depression = 6.32, 95% CI 1.86-21.49/ORAD GP = 22.36, 95% CI 5.89-83.59) with respect to psychotherapy. More severe patients with AD upon referral received combined treatment less often compared to psychotherapy (OR = 0.14, 95% CI 0.03-0.68). AD prescriptions in primary care, severity and gender influenced treatment selection for depressive disorders in secondary psychiatric care. Other factors such as the accessibility of treatment and patient preferences may have played a role in treatment selection in this setting and need further investigation.

Assessment of the Water Treatment Process’s Empirical Model Predictions for the Management of Aesthetic and Health Risks Associated with Cyanobacteria

Potentially toxic cyanobacteria have been increasingly detected worldwide in water supply systems in recent years. The management of cyanobacteria in source water and through drinking water treatment processes has been a focus of global research for over thirty years. However, despite the volume of research outcomes and the publication of guidance documents, gaps still exist in the knowledge base that inhibits the confident application of individual treatment strategies for the mitigation of aesthetic and health risks associated with cyanobacteria and their metabolites at the full-scale. The main objective of this project is to deliver a suite of tools and other resources to the water industry to support the implementation of a regulatory framework for the management of water quality for the assessment and management of aesthetic and toxicity risks associated with cyanobacteria. This study includes (1) the development of a guide (based on real-world examples) for treatment plant operators to perform plant audits and investigative sampling to assess the risk associated with cyanobacteria in their plants, and validate the performance of existing unit processes, and (2) the validation of a treatment model that can be applied at any plant and used to as a guide to the removals of cyanobacteria and metabolites and the expected quality of treated water under a range of challenges from cyanobacteria. Full-scale sampling was undertaken at three Australian regions in 14 water treatment plants to validate the model. The results presented in this paper represent a comprehensive database of full-scale removal efficiencies of 2-methylisoborneol (MIB) and geosmin for a range of water quality and treatment processes. The major findings and conclusions from this project include: (1) the investigative sampling procedures developed are effective and have been successfully applied by utilities; and (2) while routine monitoring data is important, investigative sampling within the water treatment plant provides more detailed and insightful information about the effectiveness of unit processes within the plant. This paper also identifies the knowledge gaps and needs for further studies.

Mental health information systems in resource-challenged countries: experiences from India.

Mental health information systems are increasingly being used to measure the effectiveness of mental health interventions. Little or no data is available for mental health service availability and service uptake in low- and middle-income countries. Through a narrative review, this paper illustrates the importance of routine monitoring data and suggests methods for developing, implementing and evaluating mental health indicators in low- and middle-income countries with a primary focus on India.