Monthly Data Research Articles

Effect of gasoline prices on urban air quality: Evidence from China new vehicle registrations

The emissions from motor vehicle are increasingly becoming a major source of urban air pollution in China. This study examines the impact of fuel price on expected new vehicle prices based on a hedonic valuation model. Utilizing monthly panel data from Chinese cities and employing panel ordinary least squares regression and mediation effect models, this paper finds the effects of fuel price fluctuations on new vehicle registrations and air pollution. Empirical findings indicate that rising gasoline prices lead to an improvement in urban air quality by reducing the number of new vehicle registrations. The study categorizes vehicle engine displacement into five levels, revealing that higher gasoline prices prompt residents to reduce purchases of medium-displacement vehicles and shift to small-displacement vehicles, while the impact on the registration of larger-displacement vehicles is less pronounced. The purchase tax exemption for new small-displacement vehicles has a moderating effect. Considering China's uneven economic development, the influence of gasoline prices on new vehicle registrations gradually diminishes as regional residents' incomes rise. Gasoline prices are used by regulators to guide the restructuring of vehicle consumption, thereby helping to mitigate urban environmental problems.

The impact of COVID-19-related national lockdowns on alcohol-related traffic collisions, injuries, and fatalities in Lithuania.

The national lockdowns that occurred all over the world in response to the Coronavirus Disease 2019 (COVID-19) pandemic have been found to have impacted alcohol use. The aim was to evaluate the impact of COVID-19-related national lockdowns on alcohol-related traffic collisions, injuries, and fatalities in Lithuania. Using monthly data from the Lithuanian Road Police Service for January 2004 to December 2022, we performed interrupted time-series analyses using a generalized additive model to evaluate the impact of COVID-19-related national lockdowns on alcohol-related traffic collisions, injuries, and fatalities. In Lithuania, the COVID-19-related lockdowns occurred from March 2020 to June 2020 and from November 2020 to June 2021. Although overall rates for traffic collisions and injuries decreased during the COVID-19-related lockdowns in Lithuania, these lockdowns were associated with a 3.21% (95% CI: 1.19%, 5.23%) increase in the relative proportion of alcohol-related traffic collisions and a 2.46% (95% CI: 0.12%, 4.80%) increase in the relative proportion of alcohol-related traffic injuries. The association between the lockdowns and alcohol-related traffic fatalities was not statistically significant. The COVID-19-related national lockdowns in Lithuania were associated with a decrease in the overall rate of traffic collisions and injuries, but an increase in the relative proportion of alcohol-related traffic collisions and injuries.

The effect of police reform on overall police misconduct and misconduct that involves the use of force

PurposeThis study investigates whether the Police Reform Bill significantly reduced overall incidents of police misconduct, including those involving the use of force, in Massachusetts. MethodsThe current study employed a monthly interrupted time series (ITS) design using data on overall incidents of police misconduct and incidents involving the use of force, collected from the 438 law enforcement agencies in Massachusetts. The Police Reform Bill was introduced in December 2020. The monthly data covers the period from January 1989 through December, 2023 (N = 420), with January 1989 through December 2021 serving as the period before the implementation of the Police Reform Bill (N = 384) and January 2022 through December, 2023 as the period following the implementation of the Police Reform Bill (N = 36). ResultsThe results of the monthly interrupted time series analyses showed that, compared to before the implementation of the Police Reform Bill, the trend in the overall number of police misconduct incidents and the number of misconduct incidents involving the use of force significantly declined after the implementation of the Police Reform Bill. ConclusionsThe findings suggest that the Police Reform Bill is effective in reducing police misconduct and misconduct incidents involving the use of force.

Load Flow Analysis and Loss Reduction Activities: A Case Study on Kawasoti 132/33/11 kv Substation

This paper deals with the study of energy flow patterns in INPS connected 132KV/ 33KV/11KV Kawasoti Sub-Station. From the monthly data as of April and May 2020, the base load for 30 MVA 132/33 KV transformer is about 12 MW and peak load is around 15 MW. Load forecast of 132/33/11 KV Kawasoti Sub-Station shows that the cumulative load growth percentage is around 70 % and around 8.75% per year. The peak load in 2020 was around 23.53 MW and it would approximately reach 40 MW at the end of fiscal year 2028. Load flow analysis as of 2020 shows total distribution loss was found to be 1.7833 MW when the demand is 23.658 MW at 0.7141 lagging and reactive power loss of 8.907 MVAR is found. Estimated total distribution loss for the year 2028 would be 3.851 MW when the demand is 40.238 MW at 0.76 lagging and reactive power loss of 20.572 MVAR. After the placement of a capacitor bank of 7000 KVar and 5000 Kvar on MV and LV Bus-Bar respectively, the total distribution loss was found to be 0.8695 MW with pay-back period of 1.427 Years for year 2020. After the placement of a capacitor bank of 10000 KVar and 7000 Kvar on MV and LV Bus-Bar respectively, the total distribution loss would be 2.2563 MW with pay-back period of 1.158 Years for forecasted condition. The simulated model, result and analysis are presented in this paper.

Salt reduction attempts in workers after specific food service facility guidance provided by a public health center: A case study at a worksite in Kawagoe City, Saitama Prefecture

Objective The Kawagoe City Public Health Center implemented a project to reduce salt intake through specific food service facility guidance in workplace cafeterias. Here, we report the study results.Methods The target worksite was company A, with approximately 270 employees, located in Kawagoe City, Saitama Prefecture. There were 214 participants (approximately 80%). The project was conducted from November 2019 to November 2021. The long-term goal was to decrease the number of hypertensive cases in the city. Project outcomes included decreased salt intake, urinary sodium/potassium ratio, and average blood pressure. The project aimed to improve the cafeteria menu, focusing on salt reduction by offering Smart Meals® and reducing the salt equivalents in all menus. The project output indicators included reduced amount of salt in cafeteria menu items, increased frequency of cafeteria use, increased positive evaluations of the cafeteria, and increased daily awareness of the need for salt reduction. Spot urine samples, blood pressure measurements, and self-administered questionnaire data were collected during annual physical examinations. The amount of salt in the cafeteria menu was evaluated by receiving monthly data from the food service company to which company A outsourced its cafeteria operations.Results The analysis included 102 individuals with complete data from to 2019-2021. Estimated salt intake (g/day) from spot urine decreased from 10.3±2.1 in 2019 to 9.8±2.4 in 2020 and 9.5±2.0 in 2021 (P=0.003). Systolic blood pressure (mmHg) decreased from 114.7±12.5 in 2019 to 111.7±12.1 in 2020 and 110.6±12.0 in 2021 (P=0.010). Compared to 2019, changes in dietary salt equivalents in cafeteria menus in 2020 and 2021decreased for set meals A and B, curries, and noodles (P<0.001).Conclusion We offered Smart Meals® and reduced salt equivalents in all workplace cafeteria menus. After 1-2 years, employees' salt intake and blood pressure levels were lower. These results provide implications for the implementation of food environment improvements in public health centers and other local government agencies to promote the Health Japan 21 (third term) strategy.

Integration of Stock Markets Between Indonesia and Its Major Trading Partners

The aim of this study is to analyse the integration between the emerging stock market of Indonesia and its major trading partners The Research methodology using VECM (Vector Error Correction Model) frameworks, (i.e., US, China, Japan, and Singapore) during the period of January 2016 to December 2018 with monthly data of the Indonesian stock market. The result of is this research found that the Indonesia stock market affected by its major trading partner in short term and long term. Indonesian market (JCI) responds to US market (DJIA) in positive (+) both in short run and long run. Meanwhile, Indonesian market (JCI) responds to Japan market (N225) in negative (-) only in the long run and not significant in the short run. However, China (SCI) and Singapore market (STI) are not significant in short run and long run. This research gap is to explain how the implications of the dynamics of one country's capital market affect the development of stock indexes in other countries. The benefits of this study are to explain the interaction between the dynamics of one capital market in a country and the capital markets of other countries so that appropriate macroeconomic policies can be formulated to anticipate economic effects that have the potential to cause uncertainty in the capital market. The implications of this study provide an overview of the relationship between the development of stock indexes in one country and another.

Stock Returns And Inflation In Fiji

The relationship between stock returns and inflation has important implications for monetary policy and stock market investment and has attracted growing attention from scholars over recent years. Despite this, the empirical evidence so far has been inconclusive. The main objective of this paper is to investigate the impact of inflation on stock returns in the developing stock market of Fiji. We examine the impact of inflation on stock market returns within the GARCH and EGARCH modelling framework using monthly data from 2000:02 to 2018:06 and find that inflation negatively affects stock market returns. Our results indicate the importance of ensuring price stability and suggest that stock market investment will not help hedge against inflation in Fiji. The results suggest that Fiji’s stock market is likely to react more negatively to inflation in response to countercyclical monetary policy and emphasize the significance of portfolio diversification.

Uncertainty estimation of machine learning spatial precipitation predictions from satellite data

Abstract Merging satellite and gauge data with machine learning produces high-resolution precipitation datasets, but uncertainty estimates are often missing. We addressed the gap of how to optimally provide such estimates by benchmarking six algorithms, mostly novel even for the more general task of quantifying predictive uncertainty in spatial prediction settings. On 15 years of monthly data from over the contiguous United States (CONUS), we compared quantile regression (QR), quantile regression forests (QRF), generalized random forests (GRF), gradient boosting machines (GBM), light gradient boosting machines (LightGBM), and quantile regression neural networks (QRNN). Their ability to issue predictive precipitation quantiles at nine quantile levels (0.025, 0.050, 0.100, 0.250, 0.500, 0.750, 0.900, 0.950, 0.975), approximating the full probability distribution, was evaluated using quantile scoring functions and the quantile scoring rule. Predictors at a site were nearby values from two satellite precipitation retrievals, namely PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) and IMERG (Integrated Multi-satellitE Retrievals), and the site’s elevation. The dependent variable was the monthly mean gauge precipitation. With respect to QR, LightGBM showed improved performance in terms of the quantile scoring rule by 11.10%, also surpassing QRF (7.96%), GRF (7.44%), GBM (4.64%) and QRNN (1.73%). Notably, LightGBM outperformed all random forest variants, the current standard in spatial prediction with machine learning. To conclude, we propose a suite of machine learning algorithms for estimating uncertainty in spatial data prediction, supported with a formal evaluation framework based on scoring functions and scoring rules.

THE EFFECTS ON U.S. IMPORTATION: RISE OF CRYPTOCURRENCY AND TRADITIONAL CURRENCIES

Objective: This paper aims to investigate both short and long run interactions between the U.S. Imports, Bitcoin, Gold, and U.S. Dollar by applying monthly data from January of 2011 to December of 2023. Theoretical Framework: The United States has been experiencing trade deficit for the past several years. Hence, prices of cryptocurrency and traditional currencies are also increasing that may serve a connection with the increasing trade deficit. Method: This study utilized Unit Root, Auto-Regressive Distributed Lag (ARDL), and Granger Causality to examine the relationship between all the variables. Results and Discussion: The results of this study revealed that the U.S. Dollar has a positive relationship with the increasing U.S. Imports in the short run while the rest of the variables are insignificant. Notably, Bitcoin revealed a positive significant relationship with U.S. Imports in the long run while the rest of the variables remained insignificant. Research Implications: This study has implied that the rise cryptocurrency mainly Bitcoin and traditional currencies specifically the U.S. Dollar and Gold has an implication with the current trade deficit being faced in the United States. Originality/Value: This study unraveled the implications of cryptocurrency and traditional currencies to a macroeconomic variable, specifically imports. Furthermore, this study wishes to contribute within the field of cryptocurrency as it can change the system of financial technology and its relationship with traditional currencies implying a greater impact for the global economy, as it is showing great potential for the future of finance with its global adoption.

Downscaling TRMM Monthly Precipitation in Cloudy and Rainy Regions and Analyzing Spatiotemporal Variations: A Case Study in the Dongting Lake Basin

High-resolution and accurate precipitation data are essential for hydrological, meteorological, and ecological research at the watershed scale. However, in regions with complex terrain and significant rainfall variability, the limited number of rain gauge stations (RGS) is insufficient, and the spatial resolution of existing satellite precipitation data is too low to capture detailed precipitation patterns at the watershed scale. To address this issue, the downscaling of satellite precipitation products has become an effective method to obtain high-resolution precipitation data. This study proposes a monthly downscaling method based on a random forest model, aiming to improve the resolution of precipitation data in cloudy and rainy regions at mid-to-low latitudes. We combined the Google Earth Engine (GEE) platform with a local Python environment, introducing cloud cover characteristics into traditional downscaling variables (latitude, longitude, topography, and vegetation index). The TRMM data were downscaled from 25 km to 1 km, generating high-resolution monthly precipitation data for the Dongting Lake Basin from 2001 to 2019. Furthermore, we analyzed the spatiotemporal variation characteristics of precipitation in the study area. The results show the following: (1) In cloudy and rainy regions, our method improves resolution and detail while maintaining the accuracy of precipitation data; (2) The response of monthly precipitation to environmental variables varies, with cloud cover characteristics contributing more to the downscaling model than vegetation characteristics, helping to overcome the lag effect of vegetation characteristics; and (3) Over the past 20 years, there have been significant seasonal trends in precipitation changes in the study area, with a decreasing trend in winter and spring (January–May) and an increasing trend in summer and autumn (June–December). These results indicate that the proposed method is suitable for downscaling monthly precipitation data in cloudy and rainy regions of the Dongting Lake Basin.

Inflation and Stock Market Performance in a Developing Country: The Bangladesh Outlook

This research adds empirical evidence from a developing country to the research topic: how does inflation impact stock market performance? Stock market performance is measured by the all-stock index (DSEX) of the premiere bourse in Bangladesh – Dhaka Stock Exchange (DSE). A multivariate regression model is used to investigate the relationship between inflation and stock market performance in Bangladesh. Evidence from previous literature exists of positive and negative relationships between these two variables. By examining monthly data from June, 2012 until March, 2024. It is found that inflation is negatively related to stock market performance in Bangladesh. The research paper also uses three control variables: private sector credit growth, remittance, and exchange rate. Two of them are negatively related to the stock market index, and the other is positively related.

Effect of Macroeconomic Factors on Mutual Funds Risk and Return: An Empirical Study from Bangladesh

This study aims to identify the influence of macroeconomic factors on the risk and return of mutual funds in Bangladesh. Monthly closing price data of 27 mutual funds from Bangladesh are collected from December 2015 to December 2022. Additionally, monthly data of 6 macroeconomic variables, i.e., deposit rate, export, import payments, remittance, broad money (M2) and GDP growth rate, are gathered for this study. This study utilized standard deviation and beta as risk measures, and the Sharpe and Treynor ratios are applied as risk-adjusted return (RAR) measures. All the risk and risk-adjusted return measures are computed using 12-months rolling window method. The random effect model of panel data analysis is applied to find the influence of macroeconomic variables on the risk and return of mutual funds. Overall findings indicate that macroeconomic factors significantly influence mutual fund risk exposure. On the other hand, risk-adjusted return (RAR) is also significantly influenced by the macroeconomic variables. This empirical evidence helps practitioners and institutional investors in their decision-making in the situation of market asymmetric. Keywords: Risk-Adjusted Return, Bangladesh, Mutual Fund, Standard Deviation, Beta, Sharpe Ratio, Treynor Ratio

Do illegal Migration Affect tourism Arrivals in Greece? A Vector Autoregression Analysis

Introduction: This paper explores the impact of illegal migration on the tourism sector in Greece. Utilizing a Vector autoregression (VAR) methodology, the research examines the relationship between the influx of illegal migrants and the number of tourist arrivals. The study presents the migration as a phenomenon that takes place dynamically the last decades in Europe in general and the Greek territory particular. By providing, empirical evidence on the short-term and long-term impacts of illegal migration on tourism in Greece, this study contributes to the broader discourse on migration and economic stability. The research's findings reveal that illegal migration does not significantly influence tourism arrivals in Greece. This indicates that tourists’ decisions to visit Greece are driven by factors other than concerns related to illegal migration. Objective: The objective of this study is to investigate the relationship between the influx of illegal migrants and the number of tourist arrivals Theoretical Framework: In this topic, the main concepts and theories that underpin the research are presented: the Model of cyclicity, Lee’s migration model. Method: The research examines the relationship between the influx of illegal migrants and the number of tourist arrivals over a specific period. The methodology used for this research is Vector autoregression analysis, with monthly data for the period January 2015 to December 2023. This analytical approach allows for the examination of the dynamic interactions and potential causality between the two variables over time. Results and Discussion: The results obtained revealed reveal that illegal migration does not significantly influence tourism arrivals in Greece. Changes in the number of migrants arriving do not predict changes in tourist arrivals, and changes in tourist arrivals do not predict the number of migrants arriving in Greece. The outcome is positive for the Greek tourism industry, which is of a great importance for the country’s GDP and employment politics. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the field of tourism. These implications could encompass the socio-economic sector of the country. Originality/Value: This study contributes to the literature by a vector autoregression analysis to the broader discourse on migration and economic stability. The relevance and value of this research are evidenced by the findings, that reveal that illegal migration does not significantly impact tourism arrivals in Greece. This indicates that tourists’ decisions to visit Greece are driven by factors other than concerns related to illegal migration.

Strategies for scaling up pre-ART advanced HIV disease screening at a secondary referral hospital—Malawi: a qualitative study

BackgroundImplementation of the World Health Organization (WHO) recommended Advanced HIV Disease screening package, remains poor in most settings with limited resources. More than 50% of newly diagnosed-HIV clients are missed on screening as a result of implementation barriers. It is important to mitigate the existing barriers and leverage enablers’ inorder to maximize uptake of the advanced HIV disease screening. This study aimed to identify strategies for scaling up implementation of advanced HIV disease screening among newly HIV-diagnosed clients in pre-ART phase using a Consolidated Framework for Implementation Research-Expert Recommendation for Implementing Change (CFIR-ERIC) guiding tool.MethodsA qualitative study was conducted at Rumphi district hospital in Malawi (August – September, 2023). Two sessions of Focus group discussions (FDGs) involving key stakeholders were facilitated to identify specific strategies following the initial study on exploration of barriers and facilitators of advanced HIV disease screening package. Participants comprised healthcare providers, purposively selected from key hospital departments. A deductive approach was used to analyze FDG transcripts where emerging themes were mapped with ERIC list of strategies. CFIR-ERIC Matching tool version 1.0, was used to generate an output of the most to least expert-endorsed Level 1 and Level 2 strategies.FindingsAbout 25 key healthcare workers participated in FDGs. Overall, 6 Level 1 strategies (≥ 50% expert endorsement score) and 4 Level 2 strategies (≥ 20%, ≤ 49% expert endorsement score) were identified, targeting barriers associated with availability of resources, intervention complexity, access to knowledge and information, communication; and implementation leads. Most of the reported strategies were cross-cutting and aimed at enhancing clinical knowledge of the intervention (distributing training materials, educational meetings), developing stakeholders’ interrelations (network weaving) as well as improving clinical workflow (environmental restructuring). Use of evaluative and iterative strategies such as monthly data collection for evaluation were also recommended as part of continuous improvement while an AHD coordinator was recommended to be formally appointed inorder to spearhead coordination of AHD screening services.ConclusionThrough the involvement of key stakeholders and the use of CFIR-ERIC matching tool, this study has identified cross-cutting strategies that if well implemented, can help to mitigate contextual barriers and leverage enablers for an improved delivery of AHD screening package.

Long-Term Sea Level Periodicities over the European Seas from Altimetry and Tide Gauge Data

This study investigates the long-term temporal patterns of sea level changes by analyzing monthly tide gauge data from 1950 to 2022 (42 to 72 years) along the European coastline and monthly altimetry data from 1992 to 2024 in the surrounding European seas. The primary focus is on signals with periods longer than 5 years. The application of wavelet-based approaches and multiresolution analysis has enabled the isolation of signals with periods of approximately 8 and 16 years. However, the latter has only been observed in tide gauge data, as the altimetry time series is not sufficiently long. The same analysis was applied to the North Atlantic Oscillation (NAO) and Atlantic Multidecadal Oscillation (AMO) indices, which enabled the detection of the same signals. The reported multiyear signals of sea level are correlated with NAO and AMO indices, particularly during the period spanning from 1975 to 2010.

A fair grid connection cost-sharing model for electricity based on the random forest machine learning method

Developing countries should clarify the dedicated grid connection cost-sharing model when reforming electricity user price policy, which could be learned from developed countries’ experiences. We use the random forest method and monthly data from several developed countries or regions, including the United States and the United Kingdom in 2018–2020, to identify key features that influence decision-makers in choosing dedicated grid connection cost-sharing models and simulate the connection cost-sharing models suitable for various regions of China using provincial data. It provides experience and enlightenment for China and other developing countries to implement the connection price policy reform.

A stoichiometric insight into the seasonal imbalance of phosphorus and nitrogen in central European fishponds

BackgroundThis study examines seasonal and regional trends in chlorophyll-a concentrations and the dynamics of nitrogen (N) and phosphorus (P) in 32 fishponds (resulting in 150 pond-year cases) employed for fish production. Fishponds have a poor ecological state, requiring further insights for pond management. To gain those insights, monthly data on the pond environment were collected over the growing seasons from April to September (up to 14 years) across lowland to highland regions in Czechia. We used a ratio of dissolved inorganic nitrogen to total phosphorus (DIN:TP) to investigate seasonal patterns of N and P limitations.ResultsFishponds in the lowland region (below 199 m above sea level, a.s.l.) were predominantly N-limited (80%), while ponds in the midland region (200–449 m a.s.l.) exhibited P limitation at the beginning of the growing season (April–May) and N limitation by the end of the season (August–September; 90% of fishponds). Highland fishponds (above 450 m a.s.l.) showed frequent P limitations, especially during the beginning of the season. Chlorophyll-a concentrations varied on both regional and seasonal scales, with the overall phytoplankton biomass peak at 31 ha of pond surface area. Chlorophyll-a remained stable at the beginning of the season regardless of the DIN:TP ratio but increased with a lower DIN:TP ratio by its end. The chlorophyll-a concentrations were lowest at the beginning and highest at the end of the season and decreased with altitude.ConclusionsSeasonal and regional variations in nutrient limitations and chlorophyll-a occur in fishponds. Our study suggests that targeted seasonal nutrient input and enhanced monitoring can significantly improve fishpond management practices and ecosystem stability.

Drought effects on litter fraction and recovery in a subtropical forest

Litter input is a critical link between the circulation and flows of energy and matter in forest ecosystems. Drought events impact litter inputs, affecting forest ecosystems’ nutrient and energy cycles. To investigate the response of litter production to extreme drought, explore their recovery mechanism, and the difference between whole year, concurrent drought, non-drought period, rainy season, and dry season. We studied an extreme drought event in a subtropical evergreen broadleaved forest in the Ailao Mountain in 2009–2010. We used 10 years (2005–2014) of monthly litter production and climate data, including monthly average precipitation (MAP), soil water content (SWC), and monthly average temperature (MAT). The observation period was separated into pre-drought (2005–2008), during-drought (2009–2010), and post-drought (2011–2014) periods. We found that annual total litter production decreased by 12 % during the drought (p < 0. 05), similar to that in the drought period. Leaf litter production increased by 1 % during drought time, and other fraction decreased obviously. The contribution of water environmental factors to litter production was more evident after drought events. MAT, MAP, and SWC jointly affect litter input under extreme drought conditions, and the dominant factor is related to litter fraction and drought periods. Water environmental factors (MAP & SWC) contributed to litter input after drought events, while MAT mainly affected litter input during the non-drought and rainy seasons. Due to the growth and nutritional strategies of each fraction, the recovery time is branch < leaf < flower & fruit < moss < bark. The results further reveal litter production response to drought events and the importance of water factors in recovery.

Assessment of expanded programme on immunization routine data quality in the upper east region of Ghana

BackgroundData quality is a major challenge for most health institutions and organizations across the globe. The Ghana Health Service, supported by other non-governmental organizations, has instituted various strategies to address and improve data quality issues in regional and district health facilities in Ghana. This study sought to assess routine data quality of Expanded Programme on Immunization, specifically for Penta 1 and Penta 3 vaccines.MethodsA descriptive cross-sectional study design was used for the study. A simple random sampling method was used to select thirty-four health facilities across seven sub-municipalities. Records from the Expanded Programme on Immunization (EPI) Tally Books and Monthly Vaccination Summary Report were reviewed and compared with data entered into the District Health Information Management System 2 (DHIMS2) software for the period of January to December 2020. The World Health Organization Data quality self-assessment (DQS) tool was used to compare data recorded in the EPI tally books with monthly data from summary reports and DHIMS2. Data accuracy ratio was determined by the data quality assessment tools and STATA version 14.2 was used to run additional analysis. A data discrepancy is when two corresponding data sets don’t match.ResultsThe results showed discrepancies between recounted tallies in EPI tally books and summary reports submitted as well as DHIMS2. Verification factor of 97.4% and 99.3% and a discrepancy rate of 2.6 and 0.7 for Penta 1 and Penta 3 respectively were recorded for tallied data and summary reports. A verification factor of 100.5% and 99.9% and a discrepancy of -0.5 and 0.1 respectively for the same antigens were obtained for the summary reports and DHIMS2. Data timeliness was 90.7% and completeness was 100% for both antigens.ConclusionThe accuracy of Penta 1 and Penta 3 data on EPI in the Upper East Region of Ghana was high. The data availability, timeliness and completeness were also high.

Variations of compound warm, dry, wet, and cold climate extremes in India during 1951 to 2014

The simultaneous occurrence of climate extremes significantly impacts ecosystems, increasing the vulnerability to physical risks. Despite extensive research on hot extremes and droughts globally, there remains a significant gap in comprehending the occurrence, magnitude, spatial extent, and associated risks of compound extremes, encompassing scenarios like warm/dry, cold/dry, warm/wet, and cold/wet. This study investigates various compound extreme scenarios by examining combinations of maximum temperature (Tx) and the Standardized Precipitation Evapotranspiration Index (SPEI) using monthly data from 1951 to 2014 acquired from the India Meteorological Department (IMD) for Indian landmass. From the results, the spatial extent of warm/dry events has increased at 1.8 % per decade, while cold/wet events decreased by 1.1 % over India. The warm/wet events have shown an increased trend of about 0.3 %, and cold/dry events at modest rise of 0.7 % per decade. Furthermore, compound warm/dry and cold/wet extremes over India exhibit extreme frequency and shorter return periods, posing greater risk. Conversely, compound cold/dry and warm/wet extremes occur less often, indicating longer return periods and lower risk. Across much of the country, the frequency of warm/dry, cold/dry, warm/wet, and cold/wet extremes ranges from 30 to 45, 15–30, 20–30, and 25–45 months, respectively. Notably, warm/dry conditions exhibit increased frequency in the recent period (1983–2014) with 31 years compared to the base period (1951–1982) which had approximately 24 years for a spatial extent exceeding 5 %. The findings of this study contribute to an enhanced understanding of the changes in compound climate extremes from a multivariate perspective.