Durbin-Watson Research Articles

Impact of ADA Guidelines and Medication Shortage on GLP-1 Receptor Agonists Prescribing Trends in the UK: A Time-Series Analysis with Country-Specific Insights

Background: Several GLP-1 receptor agonists (GLP-1 RAs) are used to treat type 2 diabetes (T2DM). Their cardio- and renal-protective effects and their association with substantial weight loss have been evident and progressively expanded their role in the American Diabetes Association (ADA) guidelines, which are endorsed by the European Association for the Study of Diabetes (EASD). The increased demand led to a global shortage. Methods: We utilized a repeated cross-sectional design, drawing data from national prescribing databases, to analyze six GLP-1 RAs: Dulaglutide, Exenatide, Liraglutide, Lixisenatide, Semaglutide, and Tirzepatide. AutoRegressive Integrated Moving Average (ARIMA) models with exogenous variables were applied to assess the trends over time and in different regions. Results: The prescription rates significantly differ between regions. Wales shows the highest prescribing rate for most GLP-1 RAs. The ARIMA models indicated a significant increase in their prescribing rates after the release of the 2022 ADA guidelines (e.g., Dulaglutide: Post-ADA effect of 15.22, 95% CI: [12.97, 17.47]). Following the GLP-1 RA shortages in July 2023, the prescribing rates, particularly for Semaglutide, increased (Shortage effect: 74.36, 95% CI: [71.92, 76.80]). Model diagnostics, including the Akaike Information Criterion (AIC) and Durbin–Watson statistics, confirmed the robustness of these trends. Conclusions: Informed decisions should be made by considering the prescribing trends before and after important events such as the issuing of new guidelines or safety alerts.

Mathematical modeling for the efficiency function of the Retiro small hydroelectric power plant turbine-generator set

ABSTRACT Many authors use an efficiency constant for the turbine-generator set to calculate energy generation in hydroelectric projects. This often does not reflect reality because the efficiency of this system is relative and is related to the potential of the net head height and the flow of turbine discharge. The present work aims to perform a mathematical modeling for an analysis of the efficiency of the Turbine-Generator Small Hydroelectric Power Plant (SHPP) Retiro. To perform this analysis, a model based on a second-degree polynomial function was employed. A multivariate regression technique was applied by the Least Square Method (LSM) to obtain the coefficients of the function. To verify the behavior of the measured and adjusted data, the Determination Coefficient (R2) of Root Mean Square Error (RMSE) algorithm was applied. The RMSE Mean of 0.022 and the RMSE Std of 0.029 suggest that the second-degree polynomial function is more consistent and stable across different training and validation datasets. The R2 value of 0.993 indicates that 99.3% of the variability in the data is explained by the model. The Omnibus, Durbin-Watson, and Jarque-Bera tests were applied to diagnose potential issues related to normality, autocorrelation, and adequacy, thereby validating the accuracy and significance of the model. The Hessian matrix technique was also used to verify the critical points of the function. The critical point corresponding to a water head of 11.47 meters and a turbine flow of 145.1 m3/s presented the highest operational efficiency. This study allows presenting a turbine efficiency function, its Hill Curve and a generator efficiency function with its corresponding Efficiency Curve. For efficiency analysis in several flow, height and power settings. The technique has proved efficient and can be an applicable tool in energy parameter studies for other hydroelectric projects with the same characteristics as SHPP Retiro.

K-depth tests for testing simultaneously independence and other model assumptions in time series

We consider the recently developed K-depth tests for testing simultaneously independence and other model assumptions for univariate time series with a potentially related d-dimensional process of explanatory variables. Since these tests are based only on signs of residuals, they are easy to comprehend. They can be used in a full version and in a simplified version. While former investigations already showed that the full version is appropriate for testing model assumptions, we concentrate here on either testing the independence assumption on its own or on simultaneously testing independence and model assumptions with both types of tests. In an extensive simulation study, we compare these tests with several known independence test such as the runs test, the Durbin-Watson test, and the Von-Neumann-Rank-Ratio test. Finally, we demonstrate how the K-depth tests can be used for improved modeling of crack width time series depending on temperature measurements in a bridge monitoring.

Comparison of Machine Learning-Based Predictive Models of the Nutrient Loads Delivered from the Mississippi/Atchafalaya River Basin to the Gulf of Mexico

Predicting nutrient loads is essential to understanding and managing one of the environmental issues faced by the northern Gulf of Mexico hypoxic zone, which poses a severe threat to the Gulf’s healthy ecosystem and economy. The development of hypoxia in the Gulf of Mexico is strongly associated with the eutrophication process initiated by excessive nutrient loads. Due to the complexities in the excessive nutrient loads to the Gulf of Mexico, it is challenging to understand and predict the underlying temporal variation of nutrient loads. The study was aimed at identifying an optimal predictive machine learning model to capture and predict nonlinear behavior of the nutrient loads delivered from the Mississippi/Atchafalaya River Basin (MARB) to the Gulf of Mexico. For this purpose, monthly nutrient loads (N and P) in tons were collected from US Geological Survey (USGS) monitoring station 07373420 from 1980 to 2020. Machine learning models—including autoregressive integrated moving average (ARIMA), gaussian process regression (GPR), single-layer multilayer perceptron (MLP), and a long short-term memory (LSTM) with the single hidden layer—were developed to predict the monthly nutrient loads, and model performances were evaluated by standard assessment metrics—Root Mean Square Error (RMSE) and Correlation Coefficient (R). The residuals of predictive models were examined by the Durbin–Watson statistic. The results showed that MLP and LSTM persistently achieved better accuracy in predicting monthly TN and TP loads compared to GPR and ARIMA. In addition, GPR models achieved slightly better test RMSE score than ARIMA models while their correlation coefficients are much lower than ARIMA models. Moreover, MLP performed slightly better than LSTM in predicting monthly TP loads while LSTM slightly outperformed for TN loads. Furthermore, it was found that the optimizer and number of inputs didn’t show effects on the LSTM performance while they exhibited impacts on MLP outcomes. This study explores the capability of machine learning models to accurately predict nonlinearly fluctuating nutrient loads delivered to the Gulf of Mexico. Further efforts focus on improving the accuracy of forecasting using hybrid models which combine several machine learning models with superior predictive performance for nutrient fluxes throughout the MARB.

Probing dark energy evolution post-DESI 2024

We study the evidence for dark energy (DE) evolution at low redshift, using baryonic acoustic oscillations (BAOs) from the DESI Early Data Release, Pantheon+ Type Ia supernovae (SNe-Ia), and redshift space distortions (RSDs) to constrain cosmological parameters. Furthermore, we make use of the angular acoustic scale to analyse the effect of introducing condensed CMB information on the cosmological parameters informing DE evolution. The analysis is divided into cases based on the variability of priors inferred from early-time physics. Using a quadratic parametrisation, X(z), for DE density, we find evidence for DE evolution in all cases, both with and without the angular acoustic scale. We reconstruct X(z) using best fit parameters and find that DE density starts to exhibit dynamical behaviour at z∼0.5, assuming negative values beyond z∼1.5. The data show no significant preference for X(z)CDM over a ΛCDM, with both models performing equally well according to our chosen metrics of reduced χ2 and the Durbin–Watson statistic.

Empirical likelihood ratio test for autocorrelation in least squares regression

Detecting autocorrelation in regression models is crucial for accurate estimation and testing procedures. Among the various methods available, the Durbin–Watson (D-W) test is commonly utilized. However, a notable limitation of the D-W test is its reliance on an indeterminate range due to the intractable distribution of the D-W statistic. Consequently, the test yields inconclusive results when the statistic falls within this range. This paper introduces a distribution-free Empirical Likelihood Ratio Test (ELRT) for detecting autocorrelation in regression models. A Monte Carlo simulation study is carried out to compare the performance of the proposed ELRT with the D-W test, ( a + b d U ) approximation and bootstrapped D-W test based on size and power. The results show that the proposed ELRT considerably outperforms the original D-W test and ( a + b d U ) approximation test irrespective of sample size, level of autocorrelation and number of regressors. The proposed test is competitor for bootstrapped D-W test in terms of maintaining the empirical size and power for large samples, n ≥ 100 . As a real-life application, the working of the proposed test is illustrated through air quality data of Delhi.

Estimation of macro and micronutrient concentrations in ‘valencia’ orange leaves at different phenological stages using multispectral image analysis and multiple regression

ABSTRACT This study aimed at estimating the macro- and micronutrient concentrations in leaves of Valencia oranges (Citrus sinensis [L.] Osbeck) through multiple regression analysis using multispectral images and vegetation indices (VIs) selected through the stepwise regression method. The data were collected in a commercial orchard of Valencia oranges in El Barretal, Tamaulipas, Mexico. The leaf samples were analysed in a laboratory to determine the concentrations of N, P, K, Ca, Mg, Cu, Fe, Mn, and Zn using traditional chemical methods; the relative chlorophyll values were obtained using a chlorophyll metre (SPAD502 Plus). Multi-spectral images of the tree canopy were acquired using a UAV equipped with a multispectral camera with five wavelengths (blue, green, red, red edge, and near-infrared); based on these data, 85 VIs were calculated. To select the predictor variables that were most relevant for the model, 90 candidate variables were divided into three groups with 30 VIs grouped by nutrients based on the highest and lowest absolute values found in the correlation matrix. Heteroscedasticity and autocorrelation were also assessed through the Breusch-Pagan and Durbin-Watson tests, respectively. The selected variables were used in multiple regression analysis. The resulting models were validated by mean absolute error (MAE), root-mean-square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination ( R 2 ). The prediction models showed satisfactory results, with values R 2 between 0.79 and 0.95 and relatively low (<10) MAE and RMSE for all nutrients, except Fe ( < 30 ) in all phenological stages; MAPE values were lower than 0.5% for all nutrients. This study showed that multispectral imaging combined with multiple regression analysis effectively predicts nutrient levels in the leaves of Valencia oranges in various phenological stages. This study presents a new perspective on agricultural systems, offering an effective alternative to assess the nutritional conditions of crops.

Structural Attributes and Prices of Warehouses in Uyo Urban, Akwa Ibom State, Nigeria

Warehouses have now taken on a greater significance in the development of the economy of any nation. The aim of this study was to evaluate the effects of structural attributes on prices of warehouses in Uyo Urban, Akwa Ibom State. In order to attain the stated aim, the following objectives were set; to assess the structural attributes of warehouses in the study area; to examine the average prices of sampled warehouses across the study area and to establish the relationship between the structural attributes and prices of warehouses in the study area. The research employed the survey design. The independent variables were the structural attributes measured using room size, parking space and state of repairs while the dependent variable was measured using the price of ware houses. Both descriptive and inferential statistics were employed in the analysis of the data, with the help of the SPSS and Microsoft Excel software. The research revealed the prices of 48 selected warehouses in the study area and low prices were noticed in properties with poor structural attributes and vice versa. The study revealed a 99.75% significant relationship between the dependent and the independent variables, with a Durbin-Watson statistic of 1.979 and F-ratio of 5908.123. The study concluded that there was a statistically significant relationship between structural attributes and prices of warehouses in Uyo Urban, Akwa Ibom State. However, the study recommended that investors in industrial properties including warehouses should put structural attributes into consideration as poor structural attributes were noticed to bring about poor returns in prices.

The Effect of Individual Entrepreneurial Orientation on Entrepreneurial Intention Among University Students

This study aims to evaluate the impact of individual entrepreneurial orientation on the entrepreneurial intention of Algerian university students. Specifically, it focuses on three dimensions of individual entrepreneurial orientation: risk-taking, innovativeness, and proactiveness. To achieve the main purpose of the study and test its hypotheses, the researchers employed a descriptive-analytical method, and a survey was conducted using a questionnaire on a sample of 333 second-year master’s students at the Faculty of Economics, Commercial and Management Sciences in Jijel University. The questionnaire consists of 15 statements: 3 – regarding risk-taking, 3 – innovativeness, 3 –proactiveness, and 6 – individual entrepreneurial orientation. The opinions of the sample (students) were measured using a five-point Likert scale. Cronbach’s alpha coefficient was applied to test the reliability of the study instrument. To test internal consistency, Pearson’s correlation coefficient was calculated to measure the correlation between each questionnaire item and the dimension to which it belongs. Variance Inflation Factor (VIF) and the Durbin-Watson test were also relied upon to ensure the independence of the independent variables and the absence of multiple linear correlation or autocorrelation between the residuals. Multiple regression analysis was used to evaluate the effect of the independent variables on the dependent variables to test the study hypotheses accurately. The predictive capacity of the regression model is confirmed by the results of the ANOVA regression analysis. Statistical analyses were carried out using SPSS, version 28. The findings indicate that both risk-taking and proactiveness have a direct and positive influence on entrepreneurial intention. However, innovativeness does not demonstrate any statistically significant effect on entrepreneurial intention. In light of the obtained results, a set of recommendations meant to foster the individual entrepreneurial orientation and entrepreneurial intention of university students was proposed.

Leak Detection in Urban Hydraulic Systems Using the K-BiLSTM-Monte Carlo Dropout Model

Utility companies lose approximately 35 liters of water for every 100 produced due to incorrect, illegal connections and the poor condition of pipes. This study develops an intelligent model to detect leaks using the Kalman filter, BiLSTM neural networks, and the Monte Carlo Dropout algorithm. Using data from the Empresa de Acueductos y Alcantarillados de Bogotá (EAAB), Colombia, autocorrelation analysis, PCA, cluster analysis, ADF and Durbin-Watson tests, Hurst exponent, spectral analysis, and wavelet transform were performed. Then, Kalman filtering techniques were applied, and a BiLSTM architecture controlled with Monte Carlo dropout was implemented. The results showed an accuracy of 87.48% in training and 80.48% in validation. Temporal analysis revealed a stationary behavior in the flow series, and the decrease in spectral intensity around 0.25 Hz was related to pressure perturbations caused by leaks. A detailed evaluation of pressure and flow signals identified leak patterns with high precision, demonstrating the effectiveness of the wavelet spectrogram in detecting energy disturbances. The novelty of the study lies in the integration of advanced artificial intelligence and combinatorial optimization techniques to improve water resource management, allowing early and accurate detection of leaks, significantly improving compared to traditional methods. Doi: 10.28991/CEJ-2024-010-07-01 Full Text: PDF

Moving Beyond the Reflex: Effect of a Clinical Decision Support Tool on Urine Culture Ordering Practices

Background: Interventions targeting urine culture stewardship can improve diagnostic accuracy for urinary tract infections (UTI) and decrease inappropriate antibiotic treatment of asymptomatic bacteriuria. We aimed to determine if a clinical decision support (CDS) tool which provided guidance on and required documentation of the indications would decrease inappropriately ordered urine cultures in an academic healthcare network that already uses conditional (e.g. reflex) urine testing. Methods: In October 2022, four hospitals within one academic healthcare network transitioned to a new electronic health record (EHR). We developed an embedded CDS tool that provided guidance on ordering either a urinalysis (UA) with reflex to urine culture or a non-reflex urine culture (e.g. for pregnant patients) based on the indication for testing (Figure 1). We compared median monthly UA with reflex culture and non-reflex urine culture order rates pre- (8/2017–9/2022) and post- (10/2022–9/2023) intervention using the Wilcoxon rank-sum test. We used interrupted time-series analyses allowing a one-month time window for the intervention effect to assess changes in monthly UA with reflex culture, non-reflex urine culture, and total urine culture order rates associated with the intervention. Using SAS 9.4, we generated Durbin-Watson statistics to assess for autocorrelation and adjusted for this using a stepwise autoregressive model. Result: The median monthly UA with reflex culture order rates per 1000 patient-days were similar pre- and post- intervention at 36.7 (interquartile range [IQR]: 31.0–39.7) and 35.4 (IQR: 32.8–37.0), respectively (Figure 2). Non-reflex and total urine culture rates per 1000 patient-days decreased from 8.5 (IQR: 8.1–9.1) to 4.9 (IQR: 4.7–5.1) and from 20.0 (IQR: 18.9–20.7) to 14.4 (IQR: 14.0–14.6) post-intervention, respectively. Interrupted time-series analyses revealed that the intervention was associated with a decrease in the monthly non-reflex urine culture by 4.8 cultures/1000 patient-days (p&lt; 0.001) and in the total urine culture monthly order rates by 5.0 cultures/ 1000 patient-days (p &lt; 0 .001) [Figures 3a and b]. The UA with reflex order rate did not significantly change with the intervention (not pictured). Conclusion: In an academic healthcare network that already employed conditional urine testing, the implementation of an EHR-based diagnostic stewardship tool led to additional decreases in both non-reflex and total urine cultures ordered.

Shifting Paradigms: Exploring the Tapestry of Global Assistance and Its Resonance within Nigeria's Fiscal Framework"

This study critically explores the Tapestry of Global Assistance and Its Resonance within Nigeria's Fiscal Framework from 1981 to 2021, the study objectively explored the degree to which various forms of foreign aid—such as official development assistance, foreign grants, and technical aid—impact public expenditure growth within a shifting economic paradigm. Employing statistical measures such as the Coefficient of Determination (R²), F-statistics, and the Durbin-Watson statistic. Incorporating variables such as foreign technical cooperation (FTECH), foreign grants (FGRANT), official development assistance (FODA), multilateral foreign aids (MFAID), and exchange rates (EXR), the study employs time-series data from the World Bank Development Indicators and a modified regression approach. The analysis seeks to delineate the complex dynamics between external financial inputs and internal fiscal outcomes over the period from 1981 to 2021. The findings reveal a nuanced landscape where foreign aid, though impactful, does not singularly dictate fiscal changes, suggesting a need for broader, more inclusive economic strategies that leverage both external and internal resources. Augmented Dickey-Fuller (ADF) tests confirm the stationarity of these variables, validating their reliability in modeling economic impacts. The ARDL bound test results indicate no long-term sustainable relationship between foreign aid components and public expenditure growth, pointing to a more nuanced interaction between foreign aid inflows and Nigeria’s fiscal strategies, whether expansive or restrictive.

Export Instability, Investment, Export Growth and Economic Growth in India

The “Export-led Growth Hypothesis,” as it is known in the literature, is still a popular issue in both theoretical and empirical research. According to the export-led growth hypothesis (ELGH), a key factor in determining growth is the expansion of exports. It asserts that growing capital and labour inputs into the economy as well as exports can contribute to a country’s overall growth. The association between economic development, export growth, export fluctuation, and real effective exchange rate (REER) in India from 2001 to 2021 is examined in this paper using a multiple linear regression analysis model. Additionally, the impacts of foreign exchange, export fluctuation on investment, and import of capital goods have also been studied. The autocorrelation is also tested using the Durbin-Watson (D-W) test. Investigation of the association between income growth and exports in the context of India reveals that the country’s exports haven’t increased significantly enough to prove the theory of export-led growth. For instance, ceteris paribus, one per cent change in exports over time is expected to result in a 0.007 per cent increase in GDP. Weaker global demand, inflation, dropping exports, and growing global trade conflict all contribute to a weaker-than-expected positive link between GDP and exports. However, the findings have also shown that one per cent rise in instability increases GDP by 5.04 per cent. Furthermore, research has indicated that export instability does contribute to import for capital goods and demonstrates the relationship between investment and export instability is positive but not statistically significant. The result for policy is that, in order to decrease the negative impacts of export fluctuations, India should concentrate on those goods in which it has a competitive edge. The primary factors of the excessive volatility and restricted growth must be fully investigated in order to mitigate instability, and inherent flaws must be addressed.

Effect of Macroeconomic Factors on Economic Growth: A Case Study of Bangladesh

Macroeconomic factors strongly influence the economy. It affects countries and businesses equally. The relationship between different macroeconomic factors is an area of widespread study for policymakers and economists. The ultimate purpose of this study is to assess the impact of macroeconomic factors on the economic growth. This research has taken GDP as a substitute variable for the development of the economy as well as endogenous (dependent) variable and FDI, inflation (INF), money supply (M2), public and private domestic investment (DINVEST), and foreign exchange reserve (FExR) as independent variables. Moreover, this study used time series secondary data starting from 2005-06 to 2022-23 for all econometric analysis. On the other side, we utilized the ADF (Augmented Dickey-Fuller) test to study the unit root (stationary property) of data, the Durbin Watson (DW) statistic technique to verify the autocorrelation of variables, the VIF test technique to examine the multicollinearity among independent variables and multiple linear regression to scrutinize the overall effect of macroeconomic factors on growth of the economy. Study findings show that DINVEST and FDI positively affect the growth of the economy. i.e., if macroeconomic variables- DINVEST and FDI are increased by 1 unit, they increase economic growth by 2.96 units and 0.62 units, respectively. However, inflation, money supply, and foreign exchange reserves have a positive effect on economic growth, but it is statistically insignificant. The study implies that economists, researchers, academicians, and policymakers can use the findings of the study for future research and decision-making regarding the effect of macroeconomic factors on economic growth.

Examining the Adaptation of the Fama-French Three-Factor and Five-Factor Models in the US Market

The adaptability of the Fama-French three-factor and five-factor models in distinct markets is currently a key research topic. Some researchers are investigating whether the FF3FM and FF5FM are reasonable frameworks for explaining excess returns in stock markets by testing them with data from different markets. This paper mainly explores the adaptability of FF3FM and FF5FM in the US stock market, and adds CAPM to the comparison. This article uses the daily factors’ data and the daily average value weighted returns in 6 portfolios formed on size and book-to-market in the Kenneth French database website to construct a linear regression model to study the explanatory power of FF3FM and FF5FM in the US stock market. In order to determine whether these factors are parts of an effective asset pricing model, this paper assesses the efficacy, robustness, and reliability of factors in explaining asset returns using the Durbin-Watson test, the correlation matrix, and the sub-period analysis, which also confirm whether these factors have statistical significance, economic significance, and stability. It is found that the explanatory power of FF5FM is stronger than that of FF3FM in the US stock market. While both profitability and investment factors have relatively modest explanatory power, size and value factors offer significant explanatory power for stock returns.

Lactation Curves of Mastitic Vrindavani Cattle: A Statistical Approach

Mastitis is one of the important animal disease which causes huge economic loss. The present study was aimed to find the best lactation curve model that describes the milk production pattern of Vrindavani cattle suffered from mastitis. In the study, test day milk yield data of 161 mastitic Vrindavani cattle (incidence of the disease occurring within the three months of parturition) was collected from Cattle and Buffalo Farm, LPM section, ICAR‑IVRI over 5 years (2009-2014). Eight lactation curve models [Inverse quadratic polynomial model (ND), Incomplete Gamma function (WD), Linear decline model (CL), Wilmink model (WL) Mixed log model (ML), Mitscherlich x Exponential (ME), Morant and Gnanasakthy model (MG) and Ali &amp; Schaeffer lactation curve model (AS)] were fitted. The best model was selected based on different goodness of fit criteria and the Durbin-Watson test was used to examine the presence of autocorrelation present in the data set. Kolmogorov-Smirnova test and Shapiro-Wilk were used to test the normality of the residuals. Based on the various goodness of fit test it was observed that the Mixed Log (ML) model was the best fitted model to describe the lactation pattern of mastitic Vrindavani cattle.

Weak signal detection technique based on Durbin-Watson test and one-bit sampling.

Correlation-based detection techniques are widely used in the weak periodic signal detection field. Traditionally, they are based on extracting the correlation of a weak signal from noise. Considering the impact of a weak signal on the randomness of background noise, this article takes the opposite approach and proposes a weak signal detection technique based on the Durbin-Watson (DW) test and one-bit sampling, detecting the weak signal due to the extent to which the randomness of noise is affected. The randomness of noise is analyzed through the DW test, which is a method for detecting the randomness of data sequences through first-order autocorrelation. One-bit sampling is adopted to reduce the complexity of the sampling circuit and data processing algorithm. The effectiveness of the DW test in the situation of one-bit sampling is demonstrated through simulation and analysis. Simulation results show that the proposed technique is capable of detecting weak sinusoidal and square-wave signals with a signal-to-noise ratio (SNR) above -30 dB, and the frequency or SNR of a weak signal can be further estimated based on mutual constraints. The measured results confirm the capability. In addition, the factors of coherent sampling, noise bandwidth, and comparator threshold that influence the performance of the proposed technique are simulated and discussed in detail.

The effect of the COVID-19 pandemic on TOM in the housing market

Purpose The COVID-19 pandemic, a sudden and disruptive external shock to the USA and global economy, profoundly affected various operations. Thus, it becomes imperative to investigate the repercussions of this pandemic on the US housing market. This study investigates the impact of the COVID-19 pandemic on a crucial facet of the real estate market: the Time on the Market (TOM). Therefore, this study aims to ascertain the net effect of this unprecedented event after controlling for economic influences and real estate market variations. Design/methodology/approach Monthly time series data were collected for the period of January 2010 through December 2022 for statistical analysis. Given the temporal nature of the data, we conducted the Durbin–Watson test on the OLS residuals to ascertain the presence of autocorrelation. Subsequently, we used the generalized regression model to mitigate any identified issues of autocorrelation. However, it is important to note that the response variable derived from count data (specifically, the median number of months), which may not conform to the normality assumption associated with standard regression models. To better accommodate this, we opted to use Poisson regression as an alternative approach. Additionally, recognizing the possibility of overdispersion in the count data, we also explored the application of the negative binomial model as a means to address this concern, if present. Findings This study’s findings offer an insightful perspective on the housing market’s resilience in the face of COVID-19 external shock, aligning with previous research outcomes. Although TOM showed a decrease of around 10 days with standard regression and 27% with Poisson regression during the COVID-19 pandemic, it is noteworthy that this reduction lacked statistical significance in both models. As such, the impact of COVID-19 on TOM, and consequently on the housing market, appears less dramatic than initially anticipated. Originality/value This research deepens our understanding of the complex lead–lag relationships between key factors, ultimately facilitating an early indication of housing price movements. It extends the existing literature by scrutinizing the impact of the COVID-19 pandemic on the TOM. From a pragmatic viewpoint, this research carries valuable implications for real estate professionals and policymakers. It equips them with the tools to assess the prevailing conditions of the real estate market and to prepare for potential shifts in market dynamics. Specifically, both investors and policymakers are urged to remain vigilant in monitoring changes in the inventory of houses for sale. This vigilant approach can serve as an early warning system for upcoming market changes, helping stakeholders make well-informed decisions.

Isodose mapping of terrestrial gamma radiation in Sarawak-Borneo: Insights from surface-weathered soil analysis

Presentation of baseline data on terrestrial gamma radiation (TGR) levels is crucial for assessing the annual effective dose received by the public due to natural radiation exposure. Cumulative doses from various sources can become significant, warranting a spatial understanding of TGR distribution. Few countries have comprehensively mapped TGR on a national scale, often facing challenges due to remote or inaccessible regions. This study investigated the influence of weathered soil groups on TGR dose rates in Sarawak-Borneo, Malaysia, to facilitate insights for TGR projection and isodose mapping. A total of 1044 TGR dose rate measurements were collected using NaI (Tl) scintillation detector survey meters, with a mean of 100 nGy h−1 and a range of 8–375 nGy h−1. Non-parametric statistical analyses of variance using Welch's ANOVA, Brown-Forsythe, and Kruskal-Wallis validated (P-sig.=.000) notable dissimilarities among six categories of superficial-weathered soil. Graphical analysis using Sinclair's cumulative plot revealed deviations at intervals of 50, 80, 100, 120, 175, and 205 nGy h−1. These deviations indicate distinct lithological influences. Skeletal soil (entisols) and podzolic soils had high mean dose rates (148 and 113 nGy h−1, respectively) due to limited development, thus preserving abundant uranium (U) and thorium (Th). Meanwhile, gleysols and thionic soils exhibited compatible means (90 and 82 nGy h−1, respectively), while alluvial (or transported soils) and organic soils displayed lower dose rate ranges (mean of 76 and 47 nGy h−1, respectively), reflecting rapid hydrolysis weathering processes. Simple linear regression analysis revealed a strong relationship between TGR dose rate and mean value of weathered soil groups (y = 0.851x + 0.141 nGy h−1), signifying the significance and magnitude of weathered soil groups' impact on TGR dose rates. The obtained R-value is 0.704, indicating a strong linear correlation among soil group variables, and a Durbin-Watson statistic of 1.41, suggesting positive autocorrelation among residuals, thus positive relationships. An isodose map was successfully developed using the Kriging technique, aligning with lithological features of the study area. Semivariogram analysis reveals spatial dependence within a range of 1.47°, supporting the Kriging technique's suitability for spatial inference. In conclusion, this study has successfully revealed the relationship between TGR dose rates and superficial-weathered soil in Sarawak-Borneo. While the linear relationship is applicable to the Sundaland-Borneo tectonic block, it has potential to be used as a valuable tool for spatial inference of TGR dose rates in isodose development with similar lithologial characteristics, aiding in radiation exposure assessment and environmental monitoring.

Effect of the COVID-19 pandemic on utilization of essential health services in Iran evidence from an interrupted time series analysis

BackgroundThe COVID-19 disrupted the provision of essential health services in numerous countries, potentially leading to outbreaks of deadly diseases. This study aims to investigate the effect of the COVID-19 pandemic on the utilization of essential health services in Iran.MethodsAn analytical cross-sectional study was conducted using interrupted time series (ITS) analysis. Data about five indicators, including 'childhood vaccination, infant care, hypertension screening, diabetes screening, and breast cancer screening,' were obtained from the electronic health record System in two-time intervals: 15 months before (November 2018 to January 2020) and 15 months after (January 2020 to May 2021) the onset of the COVID-19 pandemic. The data were analyzed by utilizing ITS. In addition, a Poisson model was employed due to the usage of count data. The Durbin-Watson (DW) test was used to identify the presence of lag-1 autocorrelation in the time series data. All statistical analysis was performed using R 4.3.1 software, considering a 5% significance level.ResultsThe ITS analysis showed that the COVID-19 pandemic significantly affected the utilization of all essential health services (P < 0.0001). The utilization of hypertension screening (RR = 0.51, p < 0.001), diabetes screening (RR = 0.884, p < 0.001), breast cancer screening (RR = 0.435, p < 0.001), childhood vaccination (IRR = 0.947, p < 0.001), and infant care (RR = 1.666, p < 0.001), exhibited a significant decrease in the short term following the pandemic (P < 0.0001). However, the long-term trend for all service utilization, except breast cancer screening (IRR = 0.952, p < 0.001), demonstrated a significant increase.ConclusionsThe COVID-19 pandemic affected utilization of essential health care in Iran. It is imperative to utilize this evidence to develop policies that will be translated into targeted planning and implementation to sustain provision and utilization of essential health services during public health emergencies. It is also vital to raise awareness and public knowledge regarding the consequences of interruptions in essential health services. In addition, it is important to identify the supply- and demand-side factors contributing to these disruptions.