Variance Inflation Research Articles

Assessment of Small Scale Mine Blast Toe Volume Effect on Fragmentation Size Distribution: An Application of Edge Detection Software

This study explores the critical aspect of blast toe volume in small-scale mining operations and its impact on fragmentation size distribution. The assessment is conducted to understand the relationship between blast toe volume and the resultant blast design patterns. A comprehensive analysis is carried out using production blast result from four dolomite quarries in Akoko Edo, Nigeria, focusing on various explosive engineering parameters. The research employs advanced measurement techniques and statistical methods to quantify blast toe volume and assess its influence on fragmentation size distribution. By systematically varying blast toe volumes in controlled experiments, the study aims to establish correlations between toe volume and the resulting fragmentation size. The Variance inflation factor obtained in this study revealed that the selection of parameters during toe volume simulation must be carried out with respect to stemming length and explosive weight (MIC). The result shows that the maximum instantaneous charge has a negative correlation influence on toe volume as stemming also increases. This reveals that variation in stemming length results in low explosive energy release along the blast hole column, causing toe undulation. Finally, it was also revealed that at the blast mean size (X50) increases, the toe also increases due to the poor utilization of explosive energy at the blast column.

Faktor-Faktor yang mempengaruhi Nominal Uang Rupiah yang Hilang di Kabupaten Ende

This study aims to determine the factors that affect the nominal rupiah lost in Ende Regency through data sourced from productive workers in Ende Regency. The variabels studied are inflation and public perception, both of which are independent variables. The formulation of the problem from this research is first, (1) how much is the effect of inflation on the nominal rupiah lost in Ende Regency? (2) how is the influence of public perception on the lost rupiah currency in Ende Regency? (3) what are the three factors that are more dominant in influencing the lost rupiah currency in Ende Regency? The analysis technique used in this study is multiple linear regression analiysis, the data must meet the basic assumptions, namely classical assumption test and hypothesis testing. The results of this study indicate that: First, the inflation variable does not have a positive effect on the nominal rupiah lost in Ende Regency. This is evidenced by the results of the individual significance test (t test). Value tcount>ttable (0,478<1.660). Both public perceptions have test (t test). The value of tcount>ttable (4,023>1,660). The third factor that is more dominant in influencing the nominal value of the lost rupiah in Ende Regency is public perception.

Prediction of non-uniform shrinkage of steel-concrete composite slabs based on explainable ensemble machine learning model

The non-uniform shrinkage (NUS) of steel-concrete composite slabs has been proved to significantly affect the long-term performance of composite slabs. But the reliable analysis method has not yet been proposed due to the multi-factor coupling mechanism of material composition and boundary conditions. A database containing 782 samples was established in this paper, and the index set containing 5 feature parameters was selected by analyzing the physical mechanism of NUS of steel-concrete composite slabs. The predictive capabilities of 8 machine learning (ML) models with different working mechanisms were compared, and the reliability of ML models was verified by Taylor and box scatter from the perspective of global and single sample, respectively. Furthermore, sensitivity analysis was conducted using the SHapley Additive exPlanations (SHAP) method to evaluate the impact of each parameter on the NUS of steel-concrete composite slabs. Finally, a user-friendly graphical user interface (GUI) was developed to guide engineering design. The limitations and potential improvements of the ML models established in this paper were discussed. The results indicate that the variance inflation factor (VIF) method and correlation analysis can effectively mitigate the influence of data multicollinearity and variable collinearity, and the pearson coefficient and VIF value of the input variables are less than 0.35 and 5. Gradient boosting decision tree (GBDT) has the best prediction performance, with R2 of 0.933 and 0.927 on the training set and testing set, respectively. Relative distance (x) and age (t) are the key parameters affecting the NUS of steel-concrete composite slabs. The procedure and GUI developed in this paper can effectively guide the intelligent prediction of the composite slab NUS.

Optimization and Impact Assessment of Technology Management of Solar Energy in Rwanda

There is a rarity of research about technology management in the solar energy industry in Rwanda and very little analysis is available on the effectiveness of the technology used in solar home systems (SHSs). Using a binary logistic regression to primary data collected through Kobo Collect, this study assesses the factors that are involved in SHS business in Rwanda. The findings reveal a difference in successfully collecting money from customers who purchase SHSs on an installment payment plan concerning the type of SHS (i.e. pay-as-you-go (PAYG) and non-pay-as-you-go (non-PAYG)). In terms of the odds ratio concept, a statistical interpretation was provided. If the customer has a non-PAYG SHS, getting a reminder from the solar energy company’s agent will increase by 9.52 times. That means, it is 9.52 times higher for the non-PAYG SHS as compared to a customer with PAYG SHS. The results also show that the multicollinearity assumptions for the economic, social, technological, and management models are not violated and are optimal because each of the variance inflation factors (VIFs) was close to unity. However, only the socialization predictors were not significant enhancements in the goodness-of-fit relative to the intercept-only model. The Nagelkerke R2 indicates only an average (49.5%) relationship between the prediction and grouping of the modified economic pricing model. The Hosmer–Lemeshow measures indicate that the models in this research are optimal and good fits for the data studied. Furthermore, the predicted success overall performance for the economic, social, technological, and managerial models was 73.2% on average. The Wald criterion equally demonstrated that only the monthly installment amount (with and without outliers), type of SHS, and payment channel made significant contributions to the prediction optimization problem. Therefore, the solar energy companies in Rwanda need to rely on the findings of studies like this to successfully manage customers’ accounts optimally.

Leveraging Remotely Sensed and Climatic Data for Improved Crop Yield Prediction in the Chi Basin, Thailand

Predictions of crop production in the Chi basin are of major importance for decision support tools in countries such as Thailand, which aims to increase domestic income and global food security by implementing the appropriate policies. This research aims to establish a predictive model for predicting crop production for an internal crop growth season prior to harvest at the province scale for fourteen provinces in Thailand’s Chi basin between 2011 and 2019. We provide approaches for reducing redundant variables and multicollinearity in remotely sensed (RS) and meteorological data to avoid overfitting models using correlation analysis (CA) and the variance inflation factor (VIF). The temperature condition index (TCI), the normalized difference vegetation index (NDVI), land surface temperature (LSTnighttime), and mean temperature (Tmean) were the resulting variables in the prediction model with a p-value < 0.05 and a VIF < 5. The baseline data (2011–2017: June to November) were used to train four regression models, which revealed that eXtreme Gradient Boosting (XGBoost), random forest (RF), and XGBoost achieved R2 values of 0.95, 0.94, and 0.93, respectively. In addition, the testing dataset (2018–2019) displayed a minimum root-mean-square error (RMSE) of 0.18 ton/ha for the optimal solution by integrating variables and applying the XGBoost model. Accordingly, it is estimated that between 2020 and 2022, the total crop production in the Chi basin region will be 7.88, 7.64, and 7.72 million tons, respectively. The results demonstrated that the proposed model is proficient at greatly improving crop yield prediction accuracy when compared to a conventional regression method and that it may be deployed in different regions to assist farmers and policymakers in making more informed decisions about agricultural practices and resource allocation.

Understanding Customer Satisfaction Factors: A Logistic Regression Analysis

Background: In the dynamic landscape of modern business, understanding customer satisfaction is crucial for success and competitiveness. This research intends to analyze the various elements that affect customer contentment, concentrating on the impact of demographic characteristics, economic inclinations, and involvement in loyalty programs. Methods: Employing logistic regression analysis, this research analyzes data collected from a diverse customer base across various industries. The study explores the relationship between customer satisfaction (binary dependent variable) and key independent variables, including age, income level, and loyalty program participation. Model validation is conducted through the Hosmer-Lemeshow test, along with the assessment of model fit using Cox & Snell and Nagelkerke R² metrics. Multicollinearity is checked using the Variance Inflation Factor (VIF). Results: The logistic regression model reveals that age and income level significantly influence customer satisfaction, with younger customers and individuals with greater income levels being more probable to report satisfaction. Additionally, participation in loyalty programs emerges as a strong predictor of customer satisfaction. The model demonstrates good fit and predictive ability, as indicated by statistical tests and graphical analyses, including an ROC curve and a Predicted Probability Plot. Conclusions: The research offers significant understanding into the determinants of customer satisfaction, highlighting the importance of demographic factors, economic status, and loyalty programs. These findings offer both theoretical contributions to the field of customer satisfaction research and practical implications for business strategies focused on customer engagement and loyalty.
 
 Received: 17 November 2023 / Accepted: 17 February 2024 / Published: 5 March 2024

Determinants of Rural Development in European Union Countries and Türkiye

There are many variables that can affect rural development. The aim of the study is to find out what variables affect rural development in Türkiye and European Union countries. The data period used in the study was, annual data for the period 2002-2020. According to the results of the random effects model, Inflation, Rural Population, and Rural Population Increasing variables from 8 independent variables affect Rural Development. According to the results of the Generalized Estimation Equality Mass Average Test based on the Random Effects Model, population growth in Rural Areas, Population in Rural Areas and Inflation variables were statistically significant. In the study, the Granger causality test was performed to measure the causality links of the variables. It was found that there is a bidirectional causality relationship between the exports of Agricultural Raw Material and the employment rate of young people aged 15 -24 in rural areas. Results with unidirectional causality or no causal relationship were found among the remaining variables.

Does Banking Risk And Macroeconomics Variables Matter For Agricultural Sector? Evidence From Lampung Province

This research is conducted to investigate the factors that influence the financing of the agricultural sector on Islamic banking in Lampung Province. The data used in this research is time series data from Islamic Commercial Banks (BUS) and Islamic Business Units (UUS) in Lampung Province totaling 120 samples. The data are obtained from the statistical reports of the Financial Services Authority (OJK) on sharia banking in Indonesia, and data from the Indonesia Statistics (BPS). The method used is a quantitative analysis using multiple linear regression. This study shows that the variables of Financing to Deposit Ratio (FDR) and TPF have a positive and significant correlation. However, the SBIS variable has a negative and insignificant effect on Agricultural sector financing. On the other hand, the Non-Performing Financing (NPF) variable has a negative and significant correlation, and the variable Inflation has a positive and insignificant on the agricultural sector financing in Lampung province.

The Impact of Tax Collection and Domestic Entrepreneurial Business Ventures on Services Sector in Pakistan: An ARDL Bound Test Approach

The services sector is the leading sector of Pakistan’s economy. It creates employment, foreign exchange earnings, research, and innovation, and significantly contributes to Pakistan’s economy. A significant amount of tax revenue is collected from the services sector. Therefore, it is imperative to analyze how taxation influences the services sector in Pakistan. The objective of the present study is to analyze the impact of tax revenue, entrepreneurial ventures, trade openness, inflation, and GDP per capita on the service sector of Pakistan. For this, data from 1980 to 2020 is used. The long-run ARDL estimates show that taxation adversely impacts Pakistan’s services sector. The results show that the variables domestic entrepreneur venture, gross national expenditures, and trade openness were significant and positively related to the service sector growth of Pakistan, while the variables tax revenue, poverty, and inflation rate were negatively related to Pakistan’s service sector growth rate. By considering the study results, it is concluded that the tax system significantly impacts Pakistan’s services sector. Due to high taxes, businesses in the services industry find it challenging to expand and compete. The policy recommendations are that the government must balance raising tax income with promoting the expansion of the services sector. Further, the government should endeavor to streamline the tax system so that companies can easily comprehend and adhere to the law. However, future research can be used the panel data form for developing countries. Further, the impact of taxation on other segments of the economy, such as agriculture and large-scale manufacturing, can be analyzed.

Treatment regimens and survival among patients with head and neck squamous cell carcinoma from Mizo tribal population in northeast India – a single centre, retrospective cohort study

Patients with early-stage head and neck squamous cell carcinoma (HNSCC) are treated using a single-modality approach that involves either surgery (S) or radiotherapy (RT). Conversely, those with advanced-stage disease are treated using a multi-modality approach incorporating a combination of chemotherapy (CT), RT and S. In addition to behavioural factors, such as alcohol and tobacco use, clinical parameters, such as leukocyte and neutrophil counts and T and N classification, have been linked to the survival of patients with head and neck cancer. This retrospective study was designed to provide insights into the types of treatment (induction chemotherapy [IC], concurrent chemoradiotherapy [CCRT], S and RT) administered to patients with HNSCC in Mizoram, analyse their 2-year outcome, and identify potential factors that may affect the response to treatment. A retrospective cohort study was conducted using patients diagnosed with HNSCC between 2017 and 2020 in Mizoram, northeast India. Data on clinical and demographic factors and treatments provided were collected from medical records from the Mizoram State Cancer Institute, Mizoram. Overall survival (OS) and progression free survival (PFS) were determined for each factor using the Kaplan-Meier method and compared using the log-rank test. Cox regression analysis was used to identify the factors that affected OS and PFS. Multicollinearity test was performed between the predictors using a variance inflation factor cut-off point of2. A retrospective study was performed on 210 patients with HNSCC who were followed up for a period of 2 years. The findings revealed that hypopharynx was the most affected site, followed by the nasopharynx, oral cavity, oropharynx, and larynx. Regarding treatment regimens, 85/210 (40.5%) of the patients received IC along with CCRT or RT in a sequential manner. Moreover, 86/210 (41.0%) underwent CCRT alone, 22/210 (10.5%) received RT alone and 17/210 (8.1%) underwent surgery followed by adjuvant CCRT or RT. Two-year OS and PFS estimated using the Kaplan-Meier analysis were 78.1% (95% CI=72.4%-84.2%) and 57.4% (95% CI=50.8%-64.8%), respectively. Log-rank test showed that leucocytosis (p=0.015) and neutrophilia (p=0.014) exerted effects on OS, whereas nodal involvement (p=0.005), neutrophilia (p=0.043) and IC (p=0.010) exerted effects on PFS. Multivariate analysis indicated that leucocytosis (p=0.010 [OS], 0.025 [PFS]), neutrophilia (p=0.029, 0.033), cancer site (laryngeal) (p=0.009, 0.028) and nodal involvement (N2) (p=0.020, 0.001) were predictors of poor OS and PFS. OS was better than PFS in HNSCC patients from Mizo population. Multi-modality approach offered survival advantages over single-modality approach. Leucocytosis, neutrophilia, nodal involvement, and cancer sites were associated with poor OS and PFS. More comprehensive research with a larger sample size is needed to confirm the findings from this study. There is no funding for this study.

Effects of Macroeconomic Variables on the Performance of Mutual Funds: Evidence from Bangladesh Financial Market

Using multiple regression analysis in this research paper, this is examined that these macroeconomic variables (Money Supply-M2, Inflation Rates and Exchange Rates) have significant relationships with the performance of mutual funds (represented by monthly return based on NAV) in Bangladesh. According to this project paper, money supply M2 has negative relationship with the performance of mutual funds in the financial market of Bangladesh. Because, higher level of money supply in the market weaken the monetary value of taka which makes the market more vulnerable. And this vulnerable market leads a negative impact on the whole financial market as well as mutual fund industry. Interest rates have positive relationship with the performance of mutual funds in the financial market of Bangladesh because higher level of interest rate increases the tendency of savings in the ultimate consumers and they try to consume less and save more. This savings is going to be invested in the capital market as well as in mutual fund industry which leads the market to a better position for the fund managers. Inflation rates have negative relationship with the performance of mutual funds in the financial market of Bangladesh as the higher level of inflation make the price of commodities higher and the monetary price of the taka lower. This tendency of making the less value of money, most of the investors want to withdraw their investment from the market which leads a downturn in the financial market as well as in the mutual fund industry. At the end, exchange rates have positive relationship with the performance of mutual funds as the higher level of exchange rate makes the Bangladeshi taka more powerful in the international market.

Evaluation of mechanical properties of cement mortars containing pond ash as partial replacement of river sand and prediction of properties by regression models

Despite vast research on the utilisation of coal ashes in cement mortars, many studies lack a comprehensive evaluation of properties related to mortar for masonry and plaster work. This study investigate properties like workability, compressive strength, drying shrinkage, tensile bond strength and adhesive strength through a systematic replacement of river sand by pond ash in cement mortars. Volumetric mix proportions of 1:3, 1:4, 1:5 and 1:6 (cement: sand) were considered, where river sand was replaced in the range of 20–100% by pond ash. At 30% pond ash content, properties like the compressive strength, tensile bond strength and adhesive strength improved in the range of 21–30%, 13–20% and 14–24%, respectively, for all mix proportions. At 60% pond ash content, these strength properties were comparable with those of control mixes and found suitable for mortars for masonry works. However, the drying shrinkage increased with an increase in pond ash content. At 30% pond ash content, drying shrinkage increased in the range of 26–40% for all mix proportions. The thermogravimetric analysis (TGA) showed that mixes with pond ash demonstrated reduced portlandite content compared to the control mixes, resulting in the formation of secondary C-S-H gel. Simple regression models were developed based on experimental results to establish the relationships between different properties. Based on coefficient of determination (R 2), variance inflation factor (VIF) and Durbin–Watson statistics, relationships between the strength and shrinkage properties were very well quantified to be used by researchers and practitioners. Utilisation of pond ash as partial replacement of river sand in mortars proved to be a useful step towards achieving circular economy at the same time reducing threat to depletion of limited natural resources.

The Analysis of Factors Affecting Social Security of Labour in Indonesia

This research aims to analyze the factors affecting Social Security of labour in Indonesia in the 1991-2022 period reviewed from the variables of total active participants (JPA), Gross Domestiv Product (GDP0, Inflation, Minimum Wage (UM) and Workforce Participation Rate (TPAK). The research employs secondary data, namely annual data obtained from the Central Statistics Agency and BPJS Ketenagakerjaan (Employee Social Security System) report. This research employs regression analysis method of Autoregressive Distributed Lag (ARDL). This research result indicates that in the long term, the variables of GDP, Inflation and Minimum Wage have a positive and significant effect on the JPA, respectively, Meanwhile, TPAK has a negative and insignificant effect on JPA. Keywords: Total Active Participants, Gross Domestic Product, Inflation, Minimum Wage, Workforce Participation rate, Autoregressive Distributed Lag

Ligand based-design of potential schistosomiasis inhibitors through QSAR, homology modeling, molecular dynamics, pharmacokinetics, and DFT studies

ObjectivesSchistosomiasis, a neglected tropical disease, is a leading cause of mortality in affected geographic areas. Currently, because no vaccine for schistosomiasis is available, control measures rely on widespread administration of the drug praziquantel (PZQ). The mass administration of PZQ has prompted concerns regarding the emergence of drug resistance. Therefore, new therapeutic targets and potential compounds are necessary to combat schistosomiasis. MethodsTwenty-four potent derivatives of PZQ were optimized via density functional theory (DFT) at the B3LYP/6-31G∗ level. Quantitative structureactivity relationship (QSAR) models were generated and statistically validated, and a lead candidate was selected to develop therapeutic options with improved efficacy against schistosomiasis. The biological and binding energies of the designed compounds were evaluated. In addition, molecular dynamics; drug-likeness; absorption, distribution, metabolism, excretion, and toxicity (ADMET); and DFT studies were performed on the newly designed compounds. ResultsFive QSAR models were generated, among which model 1 had favorable validation parameters (R2train: 0.957, R2adj: 0.941, LOF: 0.101, Q2cv: 0.906, and R2test: 0.783) and was chosen to identify a lead candidate. Other statistical parameters for the chosen model included variance inflation factor values ranging from 1.242 to 1.678, and a Y-scrambling coefficient (cRp2) of 0.747. Five new compounds were designed with improved predicted activity (ranging from 5.081 to 7.022) surpassing those of both the lead compound and PZQ (predicted pEC50 of 5.545). Molecular dynamics simulation revealed high binding affinity of the proposed compounds toward the target receptor. ADMET and drug-likeness assessments indicated adherence to Lipinski's rule of five criteria, thereby suggesting pharmacological and oral safety. In addition, DFT analysis indicated resistance to electronic alteration during chemical reactions. ConclusionThe proposed compounds exhibited potential drug characteristics, thus indicating their suitability for further investigation to enhance schistosomiasis treatment options.

CREDIT RISK MANAGEMENT AND FINANCIAL PERFORMANCE: A COMPARATIVE STUDY OF BRICS SELECTED BANKS

The financial sector plays an important role in the overall growth of the nation but the banks are the main players in this field. For understanding the risks faced by these banks, this study attempted to analyse the impact of credit risk on the financial performance of BRICS-selected banks which are conducted for the period 2018-2022. Twenty-two state-owned banks have been taken for this study. Descriptive statistics, Correlation matrix, Hausman test, Variation Inflation Factor Test, and Random Effect Model have been used for analysis. The results of the study showed that NPLR and CIR have a significant impact on the ROA of selected banks and CAR and LDR do not have any significant impact on the ROA of selected banks of BRICS. KEYWORDS: Financial Performance, Credit Risk, BRICS, Banks, Random Effect Model (REM)

Housing market, oil prices, and macroeconomic volatility in the G7

AbstractIn this paper, we investigate house price shocks on the macroeconomic variables (financial market, inflation, and real sector) of the G7 economies. We use the GVAR to capture the spillover effects from the U.S. housing market and oil prices on these economies from 1991M3–2022M10. We identify the U.S. house price shock using the Structural Generalized Impulse Response Function, house supply and demand variables, and regional divergence. We find that the domestic stock markets and industrial production are the most sensitive to house price shocks. We further compare the importance of house and oil prices on domestic fluctuations. The estimates reinforce the previous findings: U.S. house prices are responsible for a quarter of the domestic volatility of the stock markets and industrial production. In the other macroeconomic segments, the effects of house prices are present, but in lower values. Our results show that house prices provoke more domestic fluctuations than oil prices. Finally, we also found that short and long‐term credit markets, as well as stock markets, transmit the house price shock to industrial production. Consequently, we provide potential channels to comprehend the spillover effect of U.S. house prices on international markets.

Dynamics of Economic Factors Influencing Human Development in ASEAN-7

Abstract
 This study analyzes the relationship between economic factors and human development in the seven ASEAN countries using the panel data regression approach and Moderated Regression Analysis (MRA). The factors investigated include international trade (TRD), per capita GDP growth (GrGDPPC), inflation (INF), and economic freedom (EFI). Panel data covers seven ASEAN countries during the 2012–2020 time period. Based on the results of the panel data regression analysis, it was found that only the inflation variable did not have a significant effect on human development. Meanwhile, the results of the MRA analysis show that economic freedom acts as a quasi-moderation in the relationship between international trade and human development. Meanwhile, economic freedom also functions as an independent variable in influencing the growth of GDP per capita and inflation in human development. These findings provide a deeper understanding of the complexity of the interactions between economic factors shaping HDI achievement in ASEAN.
 Keywords: ASEAN, economic freedom, human development
 JEL: O15, F43, E31, I32

Multi-collinearity in Research and Wayforward

In multi-linear regression, there will be two or more independent variables also known as predictor variables. The main task in multi-linear regression is to find how the predictor variable impacts the dependent variable when there is a change in predictor variables by one unit. However, without testing multi-co linearity between these predictor variables, such a model will create difficulties in terms of defining the real impact of the predictor variable (independent) on the predicted variable (dependent). This situation makes us aware in terms of variable selection while conducting multi-linear regression to find the exact impact. To explain this issue, 14 students were selected and asked to fill up the form with their marks obtained in the recent examination, the number of study hours at home, the number of hours hanging out with friends, and the number of copies added during an exam. The research aimed to observe if there is a multi-co linearity issue between predictor variables (number of study hours, number of hours hanging out with friends, and number of copies added during an exam). SPSS-version 25 is used to find a correlation matrix between predictor variables, matrix scatter plot, the value of Tolerance, Variance Inflation Factor (VIF) value, condition index, and variation proportion. Multi-co linearity issue observed in two predictor variables thus further solution explained.

Association Between Sociodemographic Factors and Self-reported Glaucoma in the National Health Interview Survey: A Population-Based Analysis

To investigate the association between social determinants of health (SDH) in the domains of social and community context, education access, environmental context, economic stability, and healthcare access, with glaucoma prevalence. Cross-sectional study. The study population consisted of adult participants who answered glaucoma-related questions on the 2017 National Health Interview Survey (NHIS), the most recent iteration that includes glaucoma-related questions. The main outcome measures included the relationships between SDH-related factors and self-reported glaucoma diagnosis as well as self-reported glaucomatous vision loss were examined using univariable and multivariable regression models. In total, 26,696 of 26,742 (99.83%) NHIS respondents were included, of whom 880 (3.30%) reported a glaucoma diagnosis and 275 (1.03%) reported glaucomatous vision loss. Participants were predominantly middle-aged (50.95 ± 18.60 years), female (54.75%), and non-Hispanic White (70.49%). In age-adjusted multivariable regression (n = 25,456), non-Hispanic Black race (odds ratio [OR] = 1.87, 99% CI = [1.37, 2.55], P < .001, compared to non-Hispanic White race) and poor health status (OR = 1.54, 99% CI = [1.00, 2.37], P = .01, compared to good health status) were significant predictors of glaucoma diagnosis. For glaucomatous vision loss, having an income below the poverty threshold (OR = 2.41, 99% CI = [1.12, 5.20], P = .003, compared to income ≥5 times the poverty threshold) was the only significant predictor in univariable analyses. No SDH-related factors were significantly associated with glaucomatous vision loss in multivariable analysis (n = 848). Multicollinearity was minimal (variation inflation factor<1.6 for all independent variables). Non-Hispanic Black race and poor health status were associated with self-reported glaucoma diagnosis. Physicians and policymakers may consider SDH when assessing clinical risk and designing public health interventions.

Analysis of Macroeconomic Variables on the Existence of Inflation in Indonesia Using the Vector Error Correction Model Approach

This research investigates the impact of macroeconomic variables, namely money supply, exchange rate, interest rate, and the joint stock index, on inflation in Indonesia. Employing the Vector Error Correction Model (VECM) dynamic model approach, the study reveals a long-term relationship among each variable (Inflation Rate, Jakarta Composite Index, Interest Rate, Exchange Rate, and Money Supply). Granger Causality Test results indicate a unidirectional relationship of interest rate and money supply variables to inflation, interest rate to Jakarta Composite Index, and money supply to the exchange rate. Conversely, there is a bidirectional relationship between exchange rate and inflation variables. In the short term, Interest Rate significantly and positively influences inflation, whereas, in the long term, it exhibits a negative and insignificant effect. Money supply, in the long run, significantly and positively affects the inflation rate. This study stands out in the macroeconomic literature due to its distinctive choice of variables and the dynamic model employed