Average Monthly Change Research Articles

Immigration Narrative and Home Prices

This study characterizes the relationship between U.S. National Home Prices and Immigration Narrative as portrayed on TV news. Integrating Narrative Economics and Natural Language Processing (NLP) sentiment analysis, I analyze a large dataset of 1.96 million TV news transcripts spanning July 2009 to December 2023 to capture the sentiment of the Immigration Narrative. Immigration Narrative Sentiment and U.S. Home Prices are associated. One standard deviation orthogonalized shock to the sentiment Granger-causes a statistically significant and economically meaningful increase in the Case–Shiller U.S. National Home Price. The cumulative increase is equivalent to about 26 percent of the average monthly change during the sample period. Moreover, the effect of a shock to the Immigration Narrative Sentiment on Home Prices is permanent, suggesting that the Immigration Narrative contains fundamental information about Home Prices not captured by standard economic variables. Conversely, there is no evidence that Home Price variation affects Immigration Narrative.

Assessment of the Impact of Climate Change on Streamflow and Sediment in the Nagavali and Vamsadhara Watersheds in India

Climate-induced changes in precipitation and temperature can have a profound impact on watershed hydrological regimes, ultimately affecting agricultural yields and the quantity and quality of surface water systems. In India, the majority of the watersheds are facing water quality and quantity issues due to changes in the precipitation and temperature, which requires assessment and adaptive measures. This study seeks to evaluate the effects of climate change on the water quality and quantity at a regional scale in the Nagavali and Vamsadhara watersheds of eastern India. The impact rainfall variations in the study watersheds were modeled using the Soil and Water Assessment Tool (SWAT) with bias-corrected, statistically downscaled models from Coupled Model Intercomparison Project-6 (CMIP-6) data for historical (1975–2014), near future (2022–2060), and far future (2061–2100) timeframes using three Shared Socioeconomic Pathways (SSP) scenarios. The range of projected changes in percentage of mean annual precipitation and mean temperature varies from 0 to 41.7% and 0.7 °C to 2.7 °C in the future climate, which indicates a warmer and wetter climate in the Nagavali and Vamsadhara watersheds. Under SSP245, the average monthly changes in precipitation range from a decrease of 4.6% to an increase of 25.5%, while the corresponding changes in streamflow and sediment yield range from −11.2% to 41.2% and −15.6% to 44.9%, respectively. Similarly, under SSP370, the average monthly change in precipitation ranges from −3.6% to 36.4%, while the corresponding changes in streamflow and sediment yield range from −21.53% to 77.71% and −28.6% to 129.8%. Under SSP585, the average monthly change in precipitation ranges from −2.5% to 60.5%, while the corresponding changes in streamflow and sediment yield range from −15.8% to 134.4% and −21% to 166.5%. In the Nagavali and Vamsadhara watersheds, historical simulations indicate that 2438 and 5120 sq. km of basin areas, respectively, were subjected to high soil erosion. In contrast, under the far future Cold-Wet SSP585 scenario, 7468 and 9426 sq. km of basin areas in the Nagavali and Vamsadhara watersheds, respectively, are projected to experience high soil erosion. These results indicate that increased rainfall in the future (compared to the present) will lead to higher streamflow and sediment yield in both watersheds. This could have negative impacts on soil properties, agricultural lands, and reservoir capacity. Therefore, it is important to implement soil and water management practices in these river basins to reduce sediment loadings and mitigate these negative impacts.

Current Status and Future Forecast of Short-lived Climate-Forced Ozone in Tehran, Iran, derived from Ground-Based and Satellite Observations.

In this study, the distribution and alterations of ozone concentrations in Tehran, Iran, in 2021 were investigated. The impacts of precursors (i.e., CO, NO2, and NO) on ozone were examined using the data collected over 12months (i.e., January 2021 to December 2021) from 21 stations of the Air Quality Control Company (AQCC). The results of monthly heat mapping of tropospheric ozone concentrations indicated the lowest value in December and the highest value in July. The lowest and highest seasonal concentrations were in winter and summer, respectively. Moreover, there was a negative correlation between ozone and its precursors. The Inverse Distance Weighting (IDW) method was then implemented to obtain air pollution zoning maps. Then, ozone concentration modeled by the IDW method was compared with the average monthly change of total column density of ozone derived from Sentinel-5 satellite data in the Google Earth Engine (GEE) cloud platform. A good agreement was discovered despite the harsh circumstances that both ground-based and satellite measurements were subjected to. The results obtained from both datasets showed that the west of the city of Tehran had the highest averaged O3 concentration. In this study, the status of the concentration of ozone precursors and tropospheric ozone in 2022 was also predicted. For this purpose, the Box-Jenkins Seasonal Autoregressive Integrated Moving Average (SARIMA) approach was implemented to predict the monthly air quality parameters. Overall, it was observed that the SARIMA approach was an efficient tool for forecasting air quality. Finally, the results showed that the trends of ozone obtained from terrestrial and satellite observations throughout 2021 were slightly different due to the contribution of the tropospheric ozone precursor concentration and meteorology conditions.

Evaluating the impact of universal varicella vaccination among preschool-aged children in Qingdao, China: An interrupted time-series analysis

ABSTRACT Varicella is a contagious disease of children. Qingdao administrated free one-dose and free two-dose universal varicella vaccination schedules in 2013 and 2016 for preschool children. The effectiveness of the vaccination was analyzed in this study. Monthly varicella incidence data of 1–6 years old children during 2007–2020 were obtained from the Qingdao Infectious Disease Reporting Information Management System. We applied Interrupted time series and segmented regression analyses to assess changes in varicella incidence at the beginning of each month and average monthly changes during the vaccination. The vaccination was associated with a reduction of 32.7% in varicella morbidity on average during the 8-year intervention, there is a statistically significant difference between the voluntary period and free vaccination period (χ2 = 290.80,P < 0.001). Immediately after the free one-dose vaccination implementation in 2013 and free two-dose vaccination implementation in 2016, varicella incidence decreased by 0.135 cases per 100 000 population (P < 0.001) and increased by 1.189 cases per 100 000 population (P = 0.039), respectively, the results were statistically significant. There were significant declining trends in varicella incidence after free vaccination: 0.135(P < 0.001) and 0.055 (P = 0.025) per month in 2013.7–2016.6 and 2016.7–2020.12, respectively. This study shows a further decaying trend of varicella incidence based on the impact of free two-dose vaccination. It is necessary to prolong free two-dose universal varicella vaccination to strengthen the immune barrier of preschool children sequentially.

A New Methodology for Reference Evapotranspiration Prediction and Uncertainty Analysis under Climate Change Conditions Based on Machine Learning, Multi Criteria Decision Making and Monte Carlo Methods

In the present study, a new methodology for reference evapotranspiration (ETo) prediction and uncertainty analysis under climate change and COVID-19 post-pandemic recovery scenarios for the period 2021–2050 at nine stations in the two basins of Lake Urmia and Sefidrood is presented. For this purpose, firstly ETo data were estimated using meteorological data and the FAO Penman–Monteith (FAO-56 PM) method. Then, ETo modeling by six machine learning techniques including multiple linear regression (MLR), multiple non-linear regression (MNLR), multivariate adaptive regression splines (MARS), model tree M5 (M5), random forest (RF) and least-squares boost (LSBoost) was carried out. The technique for order of preference by similarity to ideal solution (TOPSIS) method was used under seven scenarios to rank models with evaluation and time criteria in the next step. After proving the acceptable performance of the LSBoost model, the downscaling of temperature (T) and precipitation (P) by the delta change factor (CF) method under three models ACCESS-ESM1-5, CanESM5 and MRI-ESM2-0 (scenarios SSP245-cov-fossil (SCF), SSP245-cov-modgreen (SCM) and SSP245-cov-strgreen (SCS)) was performed. The results showed that the monthly changes in the average T increases at all stations for all scenarios. Also, the average monthly change ratio of P increases in most stations and scenarios. In the next step, ETo forecasting under climate change for periods (2021–2050) was performed using the best model. Prediction results showed that ETo increases in all scenarios and stations in a pessimistic and optimistic state. In addition, the Monte Carlo method (MCM) showed that the lowest uncertainty is related to the Mianeh station in the MRI-ESM2-0 model and SCS scenario.

Use, cost-effectiveness, and end user perspectives of a home solar lighting intervention in rural Uganda: a mixed methods, randomized controlled trial

Energy poverty is prevalent in resource-limited settings, leading households to use inefficient fuels and appliances that contribute to household air pollution. Randomized controlled trials of household energy interventions in low and middle income countries have largely focused on cooking services. Less is known about the adoption and impact of clean lighting interventions. We conducted an explanatory sequential mixed methods study as part of a randomized controlled trial of home solar lighting systems in rural Uganda in order to identify contextual factors determining the use and impact of the solar lighting intervention. We used sensors to track usage, longitudinally assessed household lighting expenditures and health-related quality of life, and performed cost-effectiveness analyses. Qualitative interviews were conducted with all 80 trial participants and coded using reflexive thematic analysis. Uptake of the intervention solar lighting system was high with daily use averaging 8.23 ± 5.30 h d−1. The intervention solar lighting system increased the EQ5D index by 0.025 (95% CI 0.002–0.048) and led to an average monthly change in household lighting costs by −1.28 (−2.52, −0.85) US dollars, with higher savings in users of fuel-based lighting. The incremental cost-effectiveness ratio for the solar lighting intervention was $2025.72 US dollars per quality adjusted life year gained making the intervention cost-effective when benchmarked against the gross domestic product per capita in Uganda. Thematic analysis of qualitative data from individual interviews showed that solar lighting was transformative and associated with numerous benefits that fit within a social determinants of health (SDOH) framework. The benefits included improved household finances, improved educational performance of children, increased household safety, improved family and community cohesion, and improved perceived household health. Our findings suggest that household solar lighting interventions may be a cost-effective approach to improve health-related quality of life by addressing SDOH.

Response of climate change impact on streamflow: the case of the Upper Awash sub-basin, Ethiopia

Abstract This paper discusses the response of climate change impact on future streamflow availability in Upper Awash River basin, Ethiopia. The change of climate was built using the CORDEX, RCM daily precipitation, maximum and minimum temperature under RCP4.5 and 8.5 scenarios. The climate model was examined in the historical period 1996–2015 for its ability to capture observed precipitation and temperature. Bias correction was performed on RCM temperature and precipitation to minimize the uncertainties that may occur from climate model projection. After the successful calibration and validation of the HBV hydrological model, streamflow was simulated for the periods of 2021–2040 and 2041–2060 and compared to streamflow of the baseline period 1996–2015 to investigate the changes. The results suggested that overall, following the precipitation increment, streamflow is expected to increase under both RCPs. The average monthly changes of streamflow are expected to increase by 40.1 and 29.4% under RCP4.5 and 16.9 and 18.5% under RCP8.5 scenarios for 2021–2040 and 2041–2060, respectively. Annual streamflow would increase by 28.5 and 23.95% under RCP4.5 and 8.5, respectively. The results of this work can help and inform the water resources planner and designer to frame an appropriate plan and management for the effective use of water in the future.

Body Fat Percentage and Availability of Oral Food Intake: Prognostic Factors and Implications for Nutrition in Amyotrophic Lateral Sclerosis.

Adequate nutritional support and high body mass index (BMI) are good prognostic factors for disease progression and survival in amyotrophic lateral sclerosis (ALS). However, whether the composition of body weight, such as body fat percentage, has an independent effect on ALS prognosis remains unclear. The clinical data of 53 ALS patients were collected by medical record review. The data included: disease onset, sex, age, time of diagnosis, survival duration, presence of percutaneous endoscopic gastrostomy (PEG), nasogastric tube, tracheostomy, and availability of oral intake throughout the course of the disease, and interval measurement values of body mass by bioelectrical impedance analysis (BIA). The interval change (∆) of the BIA parameters was calculated by subtracting the follow-up values from the baseline values. Change in body fat percentage/interval between BIA measurements (months) (hazard ratio [HR] = 0.374, p = 0.0247), and availability of oral food intake (HR = 0.167, p = 0.02), were statistically significant for survival duration in multivariate hazard proportional regression analysis. Survival analysis and Kaplan–Meier curves showed similar results. Higher average monthly change in body fat percentage and availability of oral food intake are prognostic factors in ALS survival.

Falls may trigger body weight decline in nursing home residents

ObjectivesThe effect of falls on changes in body weight is still unknown. This study investigated the extent to which falls can modify the course of body weight in nursing home residents, and aimed to identify the factors that might modulate this effect. MethodsThe sample included 132 residents aged ≥60 y who had experienced at least one fall after nursing home admission. Body weight was measured monthly in the 6 mo after the fall in the entire sample, and also in the 6 mo prefall in a subsample (n = 111). Sociodemographic and health data were obtained from medical records. Linear mixed models were used to estimate the average monthly changes in body weight after the fall in the total sample, and as a function of the sociodemographic and medical factors. ResultsFalls modified the course of body weight in the total sample (β = −0.28, 95% confidence interval, −0.44 to −0.12, for the change in slope before and after fall) in all age classes and especially in individuals with severe cognitive impairment who received less-frequent informal visits (β = −0.55, 95% confidence interval, −0.87 to −0.22). Individuals aged ≥90 y and those with severe cognitive impairment had a steeper monthly weight decline in the 6 mo postfall, of 0.23 and 0.35 kg greater, respectively, than their younger and cognitively healthier counterparts. ConclusionsFalls may trigger a body weight loss in nursing home residents, especially in the oldest old people and those with severe cognitive impairment who receive little support from informal caregivers. These findings highlight the importance of monitoring nutritional status of people who live in institutions after falls.

COVID-19 and Cacophony of Coughing: Did International Commodity Prices Catch Influenza?

The Covid-19 outbreak has led to extensive declines in international commodity prices. The outbreak as well as measures fashioned to contain it has been weighing down on global supply chains and commodity prices. The pandemic has been accompanied with unprecedented shock that has disrupted both the demand and supply of commodities. In view of the widespread global impact of Covid-19, this paper analyses the impact of the outbreak on global commodity prices with particular emphasis on the energy, agricultural and metals and materials sectors using international global prices and indices to trend the movements. In the wake of oil price war and dampened oil demand, the energy sector was the most hit with 15% average monthly decline in energy indices between December 2019 and April 2020. Oil recorded its largest one-month plunge on record in March. Movements in coal indices showed more resilience to the pandemic compared to crude oil and natural gas. Base metals were the most affected sector after energy with -3.49% average monthly changes in indices between December 2019 and April 2020. Of the major base metals and minerals, aluminum appears less affected by the outbreak compared to copper and zinc. In contrast, precious metals prices and indices remain stable and is the only commodity sector with positive monthly average change (1.99%) during the period. Gold prices maintained steady gains while silver and platinum prices followed a similar but weaker trend. The agricultural commodity indices largely maintained strong upward movement but plummeted at monthly average of 0.89% between December 2019 and April 2020. Grains and cereals proved more resilient to Covid-19 pandemic compared to timber, beverages, raw materials, and oils & meals.

Follow-Up of Bone Mineral Density Changes in de novo Kidney Transplant Recipients Treated with Two Doses of the Receptor Activator of Nuclear Factor κB Ligand Inhibitor Denosumab

Background: Studies in women with post-menopausal osteoporosis have shown that discontinuation of treatment with denosumab leads to an increased risk of vertebral fractures because of rebound bone turnover and rapid loss of bone mineral density (BMD). Methods: In a post hoc analysis of the Prolia for Osteoporosis of Transplant Operated Patient study, we analyzed the effect of denosumab withdrawal on BMD changes. Twenty-five de novo kidney transplant recipients (KTR) who were treated for 1 year with 2 six-monthly doses of denosumab on top of standard treatment (daily calcium and vitamin D) were compared to a control group of 29 KTR who received standard treatment alone. BMD changes were analyzed by repeated dual-energy X-ray absorptiometry shortly after transplantation (baseline), after 6 and 12 months (active treatment phase) and after 2–6.5 years (follow-up phase). Results: The average BMD at the lumbar spine declined markedly after discontinuation of treatment with denosumab but increased again thereafter. Thus, the average monthly change in lumbar spine BMD from month 12 onward was only 0.1 ± 2.8‰ in the denosumab group but 1.5 ± 1.9‰ in the control group (p = 0.021). The average monthly change in lumbar spine BMD from baseline to follow-up was similar in the control and denosumab group (1.1 ± 1.2‰ vs. 1.5 ± 2.4‰, p = 0.788). Similar results were seen at the total hip. Conclusions: In de novo KTR treated with 2 doses of denosumab, we detect a marked decrease in lumbar spine and hip BMD when denosumab is discontinued. Denosumab treatment should therefore not be discontinued without considering an alternative antiresorptive treatment.

Effects of unemployment news on economic perceptions – Evidence from German Federal States

This study investigates whether news coverage about unemployment affects people's perceptions of the state of the economy. I compile a German state-level data set, based on household surveys and information obtained from analyzing 35 newspapers. The data are used to separate media effects from real economic consequences, taking advantage of two sources of exogenous variation. First, I exploit the salience of “milestones” in the number of unemployed. The news value of these milestones, which is not based on economic fundamentals, causes the media to report more about unemployment than usually. Second, I show that the amount of reports decreases when competing newsworthy events occur at the time of the release of the monthly unemployment statistics. Instrumental variable estimates indicate that a one standard deviation increase in coverage accounts for about a quarter of the average monthly change in the index of economic perceptions.

Evaluation of climate modeling factors impacting the variance of streamflow

The present contribution quantifies the relative importance of climate modeling factors and chosen response variables upon controlling the variance of streamflow forecasted with global climate model (GCM) projections, which has not been attempted in previous literature to our knowledge. We designed an experiment that varied climate modeling factors, including GCM type, project phase, emission scenario, downscaling method, and bias correction. The streamflow response variable was also varied and included forecasted streamflow and difference in forecast and hindcast streamflow predictions. GCM results and the Soil Water Assessment Tool (SWAT) were used to predict streamflow for a wet, temperate watershed in central Kentucky USA. After calibrating the streamflow model, 112 climate realizations were simulated within the streamflow model and then analyzed on a monthly basis using analysis of variance. Analysis of variance results indicate that the difference in forecast and hindcast streamflow predictions is a function of GCM type, climate model project phase, and downscaling approach. The prediction of forecasted streamflow is a function of GCM type, project phase, downscaling method, emission scenario, and bias correction method. The results indicate the relative importance of the five climate modeling factors when designing streamflow prediction ensembles and quantify the reduction in uncertainty associated with coupling the climate results with the hydrologic model when subtracting the hindcast simulations. Thereafter, analysis of streamflow prediction ensembles with different numbers of realizations show that use of all available realizations is unneeded for the study system, so long as the ensemble design is well balanced. After accounting for the factors controlling streamflow variance, results show that predicted average monthly change in streamflow tends to follow precipitation changes and result in a net increase in the average annual precipitation and streamflow by 10% and 11%, respectively, for the wet, temperate watershed.

Modelling the sensitivity of agricultural systems to climate change and extreme climatic events

Little is known about the impacts of increased frequencies of extreme climatic events (ECEs) on agricultural landscapes, though such events may be much more detrimental than those of gradual climate change alone. Here we develop an approach for examining the sensitivity of agricultural systems to climatic variability and ECEs on pasture-based dairy farms.Using a combination of spreadsheet formulae, biophysical and economic tools, we compared two approaches for generating future climate scenarios: a ‘Gradual’ approach, wherein climate projections of changes in monthly average temperature and rainfall were applied without altering the pattern of ECEs, and a ‘Variable’ approach, where monthly change projections were combined with more heatwaves, longer droughts and more extreme rainfall events to generate future scenarios with increased variability.The sensitivity of each approach was compared by modelling whole-farm system impacts on pasture and milk production, feed intake and profit under ‘Low’ and ‘High’ climate change projections based on the Representative Concentration Pathways with the highest greenhouse emissions in 2080 (RCP8.5) at three sites in southern Australia. ‘Low’ change projections had average warming of 1.6–2.0°C and rainfall 10–18% higher than the historical climate, while the ‘High’ change scenario had 2.5–3.2°C of warming and 15–30% reductions in rainfall. Both future climate scenarios applied the same average monthly change in rainfall and temperature relative to historical climates, but the relative frequency of events falling in the tails of the historical climate distribution was increased in the Variable approach. When used to simulate impacts on whole farm systems, the Variable approach translated into lower annual pasture growth and utilisation, and greater variation within and across years. Exposure to more frequent ECEs led to greater reliance on purchased feeds and lower long-term profitability, particularly in the High change scenarios.We conclude that increased climate variability associated with more frequent ECEs has impacts on agricultural systems over and above those of gradual climate change, which may have two main implications. First, climate change projections following RCP8.5 will progressively depress pasture yields and profitability of pasture-based dairy systems. Second, future modelling of climate change impacts on agricultural systems must adopt methodologies that account for the variability associated with ECEs in projected climate data, since the sensitivity of losses in production and profitability becomes greater with more frequent ECEs, even if gradual long-term changes in climate are accounted for.

Rainfall–runoff modelling and water balance analysis for Al-Hindiyah barrage, Iraq using remote sensing and GIS

Investigation of rainfall–run-off modelling is an important subject to develop any available means to water supply, which maintains human life such as run-off harvesting method. This study aims to analyse and understand the rainfall–run-off relationship in a part of Babil city, Iraq. Curve number which is a function of land use, soil texture, soil moisture and land slope is used in this study. Remote sensing and GIS are used to analyse the data and to produce the run-off depth map for the study area. Then, the run-off depth is used with rainfall to investigate the relationship between them using linear correlation. This study showed a strong linear relationship between rainfall and run-off (R2 = 0.992). It indicates that in the absence of rainfall data, run-off data can be used to estimate rainfall amount. Also, the study revealed through water balance analysis that there is an average monthly change in storage.

Synchronised patterns of erosion and deposition observed at two beaches

A similarity in the observed variability through time of two or more individual beaches (here quantified by synchronous changes in shoreline position and subaerial sand volume) can have significant implications to the increased focus worldwide on the need for expanded coastal monitoring. If it can be established that one or a select few beach sites exhibit ‘regionally representative’ behaviour in response to varying and/or changing wave and other climatic forcing, then this will assist to guide the extent and scope of the ongoing monitoring effort required. In this study, two beaches located on the southeast Australian coastline of similar size and orientation but separated by 35km, were simultaneously monitored and compared for a period spanning 3years. Intensive sub-aerial surveys were conducted each month at both sites using ATV-mounted RTK-GNSS. The purpose was to establish whether detailed, embayment wide patterns of erosion and deposition quantified at either site were indicative of the behaviour observed at the other.During the data collection period, storm wave events caused substantial fluctuations in monthly sub-aerial sand volumes (in the range of 60–70% of mean volume), including month-to-month changes at the berm and episodic foredune erosion and recovery. Principal component analysis of the two beaches revealed that both were dominated by oscillation (corresponding north–south behaviour) rather than rotation (contrasting north–south behaviour) over this three-year period. Comparison of alongshore-averaged volume time-series between the two sites indicated a similar total range (68m3/m and 78m3/m) and the average difference between the two sites (4.5m3/m) was found to be of the order of 50% of the average monthly change (9.7m3/m and 10.2m3/m). Corresponding spatial patterns in the occurrence of erosion/accretion along the two sites were also compared and found to closely correspond. Five synchronised ‘snap-shot’ observations from historical aerial photogrammetry and surveys at both sites spanning the period 1974 to 2011 (one per decade) suggest coherence between the sites extends beyond the three-year intensive monitoring period reported here.

Trends in prevalence of diarrhoea, Kaposi's sarcoma, bacterial pneumonia, malaria and geohelminths among HIV positive individuals in Uganda.

BackgroundTrends in prevalence of opportunistic infections (OIs) associated with the human immunodeficiency virus (HIV) in resource poor settings have previously not been well documented. The objective of this study was to describe the trends in prevalence of Diarrhoea, Bacterial pneumonia, Kaposi’s sarcoma, Malaria and Geohelminths among HIV positive individuals over a 12 year period in Uganda.MethodsObservation data for 5972 HIV positive individuals enrolled with the AIDS support organisation (TASO) in Uganda were analysed. Study participants were drawn from three HIV clinics located in different geographical areas of Uganda and followed from January 2002 to December 2013. The prevalence trends for the above OIs were plotted using the Box Jenkins moving average technique. X2-test for trend was used to test for the significance of the trends and Pearson’s correlation coefficient used to test for the strength of linear relationship between OI prevalence and calendar time. Mixed effect linear regression was used to estimate average monthly change in prevalence with monthly variation modelled as a random effect.ResultsA total of 204,871 monthly medical reports were retrieved and analysed. 73 % (4301/5972) were female with a median age of 32 years (inter-quartile range 26–39). Overall, significant decreasing mean annual prevalence trends (p < 0.05, X2trend) were observed for Diarrhoea (<1 month) with Pearson’s correlation coefficient (r = −0.89), Malaria (r = −0.75), Bacterial Pneumonia (r = −0.52), and Geohelminth (r = −0.32). Non-significant increasing mean annual prevalence trend was observed for Kaposis sarcoma (p = 0.20, X2trend; r = +0.26). After adjusting for age, sex and clinic in a mixed effects linear regression model, average monthly prevalence declined significantly at a rate of 0.4 % for Kaposis sarcoma, 0.3 % for Geohelminths, 2 % for Malaria, 1 % for Bacterial Pneumonia and 3 % for Diarrhoea(<1 month). However, the rate of decline per month differed significantly (p < 0.05) by HIV clinic for Diarrhoea (<1 month), and age, sex and clinic for malaria.Conclusions and recommendationsOverall, decreasing trends were observed in the above OIs. However the trends differed significantly by OI, geographical location and demographic characteristics. There is urgent need to integrate interventions targeting malaria and geohelminths in HIV programmes.

Antimicrobial stewardship programs in freestanding children's hospitals.

Single-center evaluations of pediatric antimicrobial stewardship programs (ASPs) suggest that ASPs are effective in reducing and improving antibiotic prescribing, but studies are limited. Our objective was to compare antibiotic prescribing rates in a group of pediatric hospitals with formalized ASPs (ASP+) to a group of concurrent control hospitals without formalized stewardship programs (ASP-). We evaluated the impact of ASPs on antibiotic prescribing over time measured by days of therapy/1000 patient-days in a group of 31 freestanding children's hospitals (9 ASP+, 22 ASP-). We compared differences in average antibiotic use for all ASP+ and ASP- hospitals from 2004 to 2012 before and after release of 2007 Infectious Diseases Society of America guidelines for developing ASPs. Antibiotic use was compared for both all antibacterials and for a select subset (vancomycin, carbapenems, linezolid). For each ASP+ hospital, we determined differences in the average monthly changes in antibiotic use before and after the program was started by using interrupted time series via dynamic regression. In aggregate, as compared with those years preceding the guidelines, there was a larger decline in average antibiotic use in ASP+ hospitals than in ASP- hospitals from 2007 to 2012, the years after the release of Infectious Diseases Society of America guidelines (11% vs 8%, P = .04). When examined individually, relative to preimplementation trends, 8 of 9 ASP+ hospitals revealed declines in antibiotic use, with an average monthly decline in days of therapy/1000 patient-days of 5.7%. For the select subset of antibiotics, the average monthly decline was 8.2%. Formalized ASPs in children's hospitals are effective in reducing antibiotic prescribing.

Asymmetric labour force participation decisions

This article finds compelling evidence of asymmetric labour force participation decisions across demographic groups in response to changes in labour market conditions. This behaviour is consistent with predictions from the standard labour-leisure choice model and suggests that asymmetry in individual behaviour plays some role in the observed asymmetry of the aggregate unemployment rate. It is estimated that the weighted average difference in response to a one percentage point change in unemployment rates is sizeable when compared to the average monthly change in the aggregate labour force.

Analysis of output trends from Varian 2100C/D and 600C/D accelerators

Analysis of Varian linear accelerator output trends is reported. Two groups, consisting of four matched Varian 2100C/D and four matched Varian 600C/D accelerators, with different designs of monitor chamber, have been investigated and the data acquired from regular calibrated ion chamber/electrometer measurements of the output performance of the eight accelerators analysed. The trend of machine output with time, having removed the effect of adjusting the monitor chamber response, was compared on a monthly and annual basis for monitor chambers with ages ranging between 1 year and 7 years. The results indicate that the response is generally consistent within each set of accelerators with different monitor chamber designs. Those used in a Varian 600C/D machine result in a reduction in measured output over time, with an average monthly reduction of 0.35 ± 0.09% over the course of the first 4 years of use. The chambers used in a 2100C/D accelerator result in an increase in measured output over time, with an average monthly increase of 0.26 ± 0.09% over the course of the first 4 years of use. The output increase then reduces towards the end of this period of time, with the average monthly change falling to −0.03 ± 0.02% for the following 3 years. The output response trend was similar for all clinical energies used on the 2100C/D accelerators-–6, 15 MV x-ray beams, and 4, 6, 9, 12, 16 and 20 MeV electron beams. By tracking these changes it has been possible to predict the response over time to allow appropriate adjustments in monitor chamber response to maintain a measured accelerator output within tolerance and give confidence in performance. It has also provided data to indicate the need for planned preventative intervention and indicate if the monitor chamber response is behaving as expected.