Development Index Research Articles

New regression model and machine learning for fitting proportional data with application

We construct a new regression model for proportional data under the odd log-logistic beta (OLLBe) distribution. Some of its mathematical properties are reported. Maximum likelihood method is adopted to estimate the parameters, and several simulations prove the accuracy of the estimators. Our proposal is compared to Random Forests and XGBoost machine learning techniques. The methods are illustrated to a real data set referring to the Human Development Index of the State of Rio Grande do Sul (Brazil).

Analisis Pemodelan Regresi Linear dengan Sebaran Distribusi Gamma: Hubungan antara Pendidikan, Kesehatan, dan Ekonomi Terhadap Indeks Pembangunan Manusia

The Human Development Index (HDI) is a crucial indicator reflecting the quality of life in a region through three main dimensions: education, health, and economy. This study aims to analyze the relationship between education, health, and economic variables and HDI using linear regression modeling with a gamma distribution. The data used include Mean Years of Schooling (MYS), Life Expectancy (LE), and Poverty Rate (PR) as independent variables, with HDI as the dependent variable. The analysis evaluates the influence of each independent variable, including multicollinearity testing using Tolerance and Variance Inflation Factor (VIF) values. The results show that MYS and LE have a significant positive effect on HDI, while PR does not exhibit a significant impact. The regression model with a gamma distribution demonstrates good performance in capturing the relationship between the variables. This study provides an empirical basis for formulating more effective development policies to improve the quality of life.

Impact of internet development on transportation carbon emissions: Evidence from China

The development of the internet is of great significance for carbon reduction in the transportation sector. Drawing on panel data from 31 provinces in China spanning 2011 to 2021, we present novel evidence from the transportation sector. We construct an internet development assessment system comprising 18 indicators and compute an internet development index from four dimensions. Employing OLS, mediation analysis, and dynamic threshold panel models, we validate the relationship between the internet development index and carbon emissions in the transportation sector. Results indicate a significant increase in transportation sector carbon emissions with internet development, with a 0.072% rise in emissions for every 1% increase in the internet development index. This is primarily attributed to the influence of internet development on increasing internet shopping, vehicle ownership, and travel scale. Importantly, in regions with higher transportation sector carbon emissions, such as Shanghai, internet development may also lead to emission reductions. Thus, the Chinese government should pay particular attention to strategically employing internet technology in different regions, leveraging its technical advantages to promote carbon emission reduction strategies in the transportation sector.

A Comprehensive Approach to CO2 Emissions Analysis in High-Human-Development-Index Countries Using Statistical and Time Series Approaches

Reducing carbon dioxide (CO2) emissions is vital at both global and national levels, given their significant role in exacerbating climate change. CO2 emissions, stemming from a variety of industrial and economic activities, are major contributors to the greenhouse effect and global warming, posing substantial obstacles in addressing climate issues. It is imperative to forecast CO2 emissions trends and classify countries based on their emission patterns to effectively mitigate worldwide carbon emissions. This paper presents an in-depth comparative study on the determinants of CO2 emissions in twenty countries with high Human Development Index (HDI), exploring factors related to economy, environment, energy use, and renewable resources over a span of 25 years. The study unfolds in two distinct phases: initially, statistical techniques such as Ordinary Least Squares (OLS), fixed effects, and random effects models are applied to pinpoint significant determinants of CO2 emissions. Following this, the study leverages supervised and unsupervised time series approaches to further scrutinize and understand the factors influencing CO2 emissions. Seasonal AutoRegressive Integrated Moving Average with eXogenous variables (SARIMAX), a statistical time series forecasting model, is first used to predict emission trends from historical data, offering practical insights for policy formulation. Subsequently, Dynamic Time Warping (DTW), an unsupervised time series clustering approach, is used to group countries by similar emission patterns. The dual-phase approach utilized in this study significantly improves the accuracy of CO2 emissions predictions while also providing a deeper insight into global emission trends. By adopting this thorough analytical framework, nations can develop more focused and effective carbon reduction policies, playing a vital role in the global initiative to combat climate change.

Comparison of Perceived Achievement of Complex Thinking Competency Among American, European, and Asian University Students

Despite the growing focus of educational institutions on students’ practical abilities beyond theoretical knowledge, the perception that students have of their competencies is crucial for their effective application in professional contexts. Accordingly, this paper reports a study of 435 university students attending ten universities in eight countries in the Americas (Chile, Colombia, Mexico), Asia (Pakistan and the Philippines), and Europe (Spain, Finland, and Serbia). The goal was to measure their perceptions of their achievement of complex thinking competency and its sub-competencies. The intention was to identify how cultural, educational, and socioeconomic differences among countries account for the variances in the students’ self-assessment of competencies, impacting their professional preparedness. The study focused on the competency of complex thinking, considering its critical importance in solving current environmental problems. The analysis employed the non-parametric Brown–Forsythe statistical test and Bonferroni correction, given the non-normality and heteroscedasticity of the data. It was found that (i) there is no statistically significant difference by gender; (ii) there are statistically significant differences in all types of thinking per country, geographical area (continent), and Human Development Index (HDI).

Socio-economic inequality in the nutritional deficiencies among the world countries: evidence from global burden of disease study 2019

BackgroundSocioeconomic inequality in nutritional status as one of the main social determinants of health can lead to inequality in health outcomes. In the present study, the socioeconomic inequality in the burden of nutritional deficiencies among the countries of the world using Global Burden of Disease (GBD) data was investigated.MethodsBurden data of nutritional deficiencies and its subsets including protein-energy malnutrition, iodine deficiency, vitamin A deficiency, and dietary iron deficiency form GBD study and Human Development Index (HDI), a proxy for the socio-economic status of countries, from united nations database were collected. After descriptive statistics, the concentration index (CI) curve was used to measure socioeconomic inequality. CI for nutritional deficiencies was estimated based on Disability Adjusted Life Years (DALY), Years Lived with Disability (YLD), Years of Life Lost (YLL), prevalence, incidence and death indices. Moreover, CI of DALY and prevalence was estimated and reported for four nutritional deficiencies subgroups.ResultsCIs for DALY, YLD, YLL, prevalence, incidence and death rate show negative values and their, which indicates the concentration of nutritional deficiencies burden among lower HDI countries. The highest value of CI (lowest inequality) for DALY was related to iodine deficiency (-0.3401) and the lowest (highest inequality) was related to vitamin A deficiency (-0.5884). Also, the highest value of CI for prevalence was related to protein-energy malnutrition (-0.1403) and the lowest was related to vitamin A deficiency (-0.4308). Results also show the inequality in DALY was greater than the disparity in prevalence for all subgroups of nutritional deficiencies.ConclusionsInequality in burden of nutritional deficiencies and protein-energy malnutrition, iodine deficiency, vitamin A deficiency and dietary iron deficiency are concentrated in countries with low HDI, so there is pro- poor inequality. Findings indicate that although malnutrition occurs more in low-income countries, due to the weakness of health care systems in these countries, the inequality in the final consequences of malnutrition such as DALY becomes much deeper. More attention should be paid to the development of prevention and primary treatment measures in low HDI countries, such as improving nutrition-related health education, nutritional support and early aggressive treatment, and also eliminating hunger.

A COMPARISON OF LOGISTIC REGRESSION, MIXED LOGISTIC REGRESSION, AND GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION ON PUBLIC HEALTH DEVELOPMENT IN JAVA

The Public Health Development Index (Indeks Pembangunan Kesehatan Masyarakat - IPKM) is a combined parameter that reflects progress in health development and is useful for determining areas that need assistance in improving health development. Through IPKM modeling, factors that significantly influence regional public health development can be discovered. This research aims to find an appropriate model for modeling IPKM and determine the factors that significantly influence public health development. The data used is the 2018 IPKM data collected from 119 cities/regencies in Java. We propose three models namely logistic regression (LR), mixed logistic regression (MLR), and geographically weighted logistic regression (GWLR). The research results show that the MLR is the best model for modeling IPKM in Java based on the AIC value criteria. Based on the MLR model, the factors that have a significant influence on public health development are the egg and milk consumption level and the percentage of the number of doctors per thousand population.

CONSTRUCTION OF BLUE ECONOMY DEVELOPMENT INDEX AT THE PROVINCIAL LEVEL IN INDONESIA USING EXPLORATORY FACTOR ANALYSIS

Indonesia, as an archipelagic country, holds marine resources of significant economic value in improving the welfare of its people. However, the community's use of marine resources does not pay attention to sustainability. The government then uses the Blue Economy concept to maximize the economic value obtained while maintaining the sustainability of the marine ecosystem through national policies and plans. In realizing blue economy development, enabler factors, such as technology and government governance, have an important role. This research aims to construct a Blue Economy Development Index (IPEB) at the provincial level in Indonesia in 2021, including enabler factors for blue economy development. The analytical method used is Exploratory Factor Analysis. The results show that the distribution of the minimum values for the indicators that make up the IPEB is found in the provinces of the Eastern Region of Indonesia. In contrast, the distribution of the maximum values of the indicators is found in the provinces of the Western Region of Indonesia. The province with the highest IPEB score is South Sulawesi, while the lowest is Central Sulawesi. The limitation of this study is the data derived from the Village Potential Survey (Potensi Desa) data collection, so several variables are not yet available in annual time. The results of this study are important in improving the ability to monitor implementation and assist in decision-making in increasing blue economic development, especially at the provincial level.

E-government and the Legal Culture in Eastern European Countries

The article aims to investigate the crucial elements of e-government progress contingent on the extent of legal culture in Eastern European nations, namely the Czech Republic, Poland, and Ukraine. The analysis focuses on the stages and requirements for implementing and developing e-government based on the evaluation of the E-Government Development Index (EGDI) in specific Eastern European nations. The benefits and opportunities that offer e-government for governments and citizens are given. The main barriers that the countries have to overcome to reach the higher level of digital technology integration are defined. Outlines the role of regulatory framework, transparency, accountability, citizen engagement in eGovernment development. The scientific study pays much attention to the peculiarities of national legislation of Eastern European countries in the context of global digitalization and integration of state data into the global space. Positive practices and the existence of existing problems in the implementation of the concept of e-government are shown. The conclusions propose measures to improve the development of e-government.

Food insecurity and early childhood development among children 24-59 months in Nigeria: A multilevel mixed effects modelling of the social determinants of health inequities.

Food insecurity (FI) has been identified as a determinant of child development, yet evidence quantifying this association using the newly developed Early Childhood Development Index 2030 (ECDI2030) remains limited. Herein, we provide national estimates of early childhood development (ECD) risks using the ECDI2030 and examined to what extent FI was associated with ECD among children aged 24-59 months in Nigeria. This population based cross-sectional analyses used data from the UNICEF-supported 2021 Multiple Indicator Cluster Survey in Nigeria. The analytic sample comprised children aged 24-59 months (weighted N = 12,112). We measured early childhood development for each child using the ECDI2030, measured across three domains: learning, psychosocial well-being and health. Food insecurity was assessed using the Food Insecurity Experience Scale (FIES), categorized as none/mild, moderate and severe. We fitted mixed-effects multilevel logistic regression models, with random intercepts, to estimate the odds of association between FI status and ECD. A total of 11,494 children aged 24-59 months (mean ± SD age, 43.4 ± 9.9 months), including 5,797 boys (50.2%) and 5,697 girls (49.8%), were included in the study. Approximately 46.4% of children were developmentally off track and about 76% of children lived in food-insecure households. The intercept-only model indicated significant variation in ECD prevalence across communities (τ00 = 0.94, intraclass correlation = 0.22, p < 0.0001), suggesting nonignorable variability in ECD across communities. Adjusting for confounders, we observed no significant association between FI and ECD. However, increasing child's age and disability status appeared as significant risk factors for higher odds of children being developmentally off track. These findings highlight that while FI alone may not explain ECD, a combination of individual and contextual factors plays a crucial role. Future interventions addressing ECD in Nigeria should consider these multidimensional influences to promote optimal child development.

Comparison of Machine Learning Algorithms in Classifying Districts/Cities in Indonesia According to the Human Development Index (HDI) in 2021

The human development index (HDI) is one of the measuring tools for achieving the quality of life of a region or even a country, including Indonesia. There are 3 basic components of the HDI, namely the dimensions of health, knowledge, and decent living. Development in Indonesia is uneven as indicated by the Human Development Index (HDI) of districts/cities in 2021 which varies greatly. The purpose of this study is to compare several machine learning algorithms to classify districts/cities in Indonesia according to the Human Development Index (HDI) in 2021. There are six machine learning algorithms used in this study, namely Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearset Neighbor (K-NN), Random Forest, Decision Tree, and Naive Bayes. The k-Fold Cross Validation method is applied to form the training set and testing set, with 10 folds and 1 repetition. The results of the study showed that the classification results of the SVM algorithm using the Radial Basis Function (RBF) kernel parameters with sigma = 0.4864648 and C = 1 were the best among the other five algorithms with an average accuracy of 76.08% and a maximum accuracy of 88.24%.

Noma in a Pacient whitout Sistemic Involvement

Noma, also known as Cancrum oris or gangrenous stomatitis, is an infectious disease of bacterial origin, often in association with other microorganisms, affecting orofacial tissues and manifesting through a sequence of precursors. It begins as necrotizing gingivitis and progresses to necrotizing periodontitis and necrotizing stomatitis. In advanced stages, it can lead to facial mutilations, difficulty in eating, and psychological distress due to social stigma. Untreated, it can result in a mortality rate of up to 90%, mainly due to septicemia, dehydration, and malnutrition. When addressed early, the prognosis is favorable, and by treating its precursors, it is challenging to reach severe stages. The disease is often associated with children in impoverished social conditions, weakened immune systems, and malnutrition, especially in regions with low human development indices, such as sub-Saharan Africa, although some cases have been reported elsewhere. Treatment involves several phases, including debridement of necrotic areas, antibiotics, antimicrobial mouth rinses, and supportive periodontal therapy. Despite its rarity, infection can also occur in developed countries and affect adult patients, sometimes without underlying health conditions. The aim of this article is to report a clinical case of noma in a 31-year-old man, without systemic involvement, who sought treatment at the Maria Aparecida Pedrossian University Hospital in Campo Grande, Mato Grosso do Sul, Brazil, due to swelling in the submental region. After hospitalization, biofilm control and postoperative care, there was a satisfactory improvement in the clinical condition.

How Escaping the Middle-Income Trap: Key Factors for Indonesia's Growth?

This study investigates the complex interplay between human development index, foreign direct investment, and investment efficiency in driving economic growth. Utilized time-series data during 2000 to 2022, sourced from the World Bank and the Asian Development Bank. Employing the ARDL model. The findings reveal a strong positive correlation between HDI and economic growth, highlighting the critical role of investments in education, health, and social welfare. While FDI significantly contributes to economic growth through technology transfer and productivity enhancement, the study surprisingly finds limited evidence of a direct link between investment efficiency and economic growth. This suggests that factors such as institutional quality and innovation play a more dominant role. These findings have crucial implications for Indonesia, which faces the challenge of escaping the middle-income trap. Prioritizing human development, attracting high-quality FDI, and fostering a conducive investment climate through institutional reforms are crucial for sustainable and inclusive growth. By investing in human capital, promoting technological innovation, and effectively leveraging FDI, Indonesia can overcome the middle-income trap and achieve long-term economic prosperity.

Cultural bias in the assessment of language: A closer look at the Boston naming test among multicultural Canadian older adults

Objective The Boston Naming Test (BNT) is commonly used to assess word-finding in older adults but performance may be impacted by cultural and linguistic factors. This study aimed to assess cultural bias in BNT performance among older adults, explore sources of this bias and provide clinical guidelines for its use in multicultural settings. Methods We conducted a retrospective chart review of 525 older adults referred for neuropsychological assessment at a large geriatric hospital in a multicultural Canadian city. Participants were categorized by birthplace (Canada vs. outside Canada), and relationships between BNT scores, years in Canada, sociodevelopmental context of region of birth and first-language status were examined. Results Individuals born outside of Canada had significantly lower BNT scores than Canadian-born participants. These differences were not fully explained by English as a first language status or age at immigration though a significant correlation was observed between BNT scores and years in Canada. Sociodevelopmental context, measured by the Historical Index of Human Development (HIHD), partially mediated the relationship between region of birth and BNT performance. Conclusions The BNT is influenced by cultural and linguistic factors, which may lead to inaccurate cognitive assessment in diverse populations. Clinicians should interpret BNT scores with caution in multicultural contexts and consider sociocultural factors to improve diagnostic accuracy and cultural sensitivity.

Country economic status is strongly associated with burn survival - validation of the (modified) ABSI

BackgroundPredicting burn-related mortality is vital for family counseling, triage, and resource allocation. Several of the burn-specific mortality prediction scores have been developed, including the Abbreviated Burn Severity Index (ABSI) in 1982. However, these scores are not tested for accuracy to support contemporary estimates of the global burden of burn injury. This study compares burn mortality across countries with varying economic levels, as indicated by the Human Development Index (HDI), to assess if a modified ABSI (mABSI) offers enhanced predictive accuracy.MethodsA retrospective study was performed, including over 90,000 patients from seven sources in five continents, including two burn registries and five referral burn centers. Data from 2015 to 2019 were collected, including age, gender, presence of inhalation injury, full-thickness burn, percentage of total body surface area, and outcome. The participating countries were classified based on the HDI and mortality was predicted using both the original and the modified ABSI.ResultsAfter removal of incomplete data, 74,460 sets remained for the analysis. Significant variations in population demographics, sample sizes, total body surface area (TBSA), and age distributions across the studied regions were noted. The modified ABSI demonstrated a smaller deviation from the 95% CI of the true survival probability than the original ABSI in countries with a very high or high HDI, indicating improved accuracy here. In these countries, the original ABSI overestimates mortality. Conversely, in countries with a middle or low HDI, the original ABSI remains more accurate, reflecting the significantly greater burn related mortality in countries with a low HDI.ConclusionIn burn patients, the probability of survival remains highly dependent on the level of development of a country. Our results support the use of the modified ABSI in countries with a (very) high HDI, yet in resource constraint settings, the original ABSI seems to provide more accurate predictions. These findings underscore the need for adaptive models that take into account socioeconomic variables, potentially guiding health policy adjustments and emphasizing the necessity of resource allocation and training in lower HDI countries. Such adaptations could enhance clinical outcomes and reduce disparities in burn care effectiveness globally.

Fair Taxes, Better Revenue: A Case Study on Spatial Approaches to Land Taxation in Lebak Regency, Indonesia

Land taxation, mainly the Land and Building Tax (LBT), is a critical revenue source for local governments in Indonesia, yet its contribution remains suboptimal due to inefficiencies in assessment and administration. This study focuses on the Lebak Regency to optimize the Sales Value of Taxable Objects (SVTO) assessment, a key determinant of LBT revenue. Using spatial analysis, Land Value Zones (LVZ), and socio-economic data, the research evaluates assessment ratios to identify discrepancies between SVTO and market values. Adjustments to SVTO are proposed based on spatial patterns, such as land use and infrastructure, and the Index of Developing Villages (IDV). Findings reveal significant disparities between assessed and market values, with assessment ratios ranging from 1% to 267%. Simulations indicate potential LBT revenue of IDR 224.7 billion under full compliance, compared to IDR 44.22 billion, the 2024 target. Adjustments based on spatial patterns from land use planning and village development indices enhance equity and accuracy in tax assessments, optimizing local revenues. Despite these improvements, the study’s limitations lie in the lack of community validation for the proposed methodology, which is essential to confirm its practicality and acceptance. Future research should address this gap and explore household-level dynamics, including tax affordability and spending patterns, to enhance policy inclusivity and align taxation systems with local socio-economic realities.

The increasing global burden of cancer: implications for anaesthesia and peri-operative medicine.

Most patients with cancer will require surgery at some point in their lifetime. As the global burden of cancer continues to increase, changes and challenges in cancer epidemiology and care are also borne peri-operatively. Due to its broad scope, a formal systematic electronic literature search was not performed. This narrative review examines how an increasing global burden of cancer affects anaesthesia and peri-operative medicine. We aimed to describe current cancer statistics; economic implications; the increasing survival of individuals with cancer; and the role of anaesthetists in enhancing the quality of life and survival after oncological surgery. Epidemiological reports show wide variation in cancer incidence and mortality worldwide. Countries with a lower human development index will be disproportionately affected by greater increases in cancer burden. At the same time, these countries will also suffer larger deficits in the surgical and anaesthetic workforce. Cancer has significant macro- and micro-economic implications. Thus, public health and policy should address the large economic burden of cancer surgery and care. The improvement in cancer-related survival can be attributed to research focused on a deeper understanding of tumour biology; effective awareness education campaigns and screening programmes; early diagnosis; newer cancer therapies; and patient-centred precision care. Increasing survival brings new global challenges, such as delivering an adequate survivorship care plan and addressing long-term psychosocial concerns in survivors. Anaesthetists are involved in all phases of a patient's cancer journey including facilitating diagnostic procedures; providing comprehensive peri-operative care needs for ablative procedures; provision of critical care; addressing acute and chronic pain; and provision of integrative and palliative care services. Deeper engagement of anaesthetists in cancer care programmes will be instrumental in reducing postoperative complications; improving the quality of life for patients; enhancing population health; and contributing to improved global economies.

Longitudinal monitoring of sewershed resistomes in socioeconomically diverse urban neighborhoods

BackgroundUnderstanding factors associated with antimicrobial resistance (AMR) distribution across populations is a necessary step in planning mitigation measures. While associations between AMR and socioeconomic-status (SES), including employment and education have been increasingly recognized in low- and middle-income settings, connections are less clear in high-income countries where SES remains an important influence on other health outcomes.MethodsWe explored the relationship between SES and AMR in Calgary, Canada using spatially-resolved wastewater-based surveillance of resistomes detected by metagenomics across eight socio-economically diverse urban neighborhoods. Resistomes were established by shotgun-sequencing of wastewater pellets, and qPCR of targeted-AMR genes. SES status was established using 2021 Canadian census data. Conducting this comparison during the height of COVID-related international travel restrictions (Dec. 2020–Oct. 2021) allowed the hypotheses linking SES and AMR to be assessed with limited confounding. These were compared with sewage metagenomes from 244 cities around the world, linked with Human Development Index (HDI).ResultsWastewater metagenomes from Calgary’s socioeconomically diverse neighborhoods exhibit highly similar resistomes, with no quantitative differences (p > 0.05), low Bray-Curtis dissimilarity, and no significant correlations with SES. By comparison, dissimilarity is observed between globally-sourced resistomes (p < 0.05), underscoring the homogeneity of resistomes in Calgary’s sub-populations. The analysis of globally-sourced resistomes alongside Calgary’s resistome further reveals lower AMR burden in Calgary relative to other cities around the world. This is particularly pronounced for the most clinically-relevant AMR genes (e.g., beta-lactamases, macrolide-lincosamide-streptogramin).ConclusionsThis work showcases the effectiveness of inclusive and comprehensive wastewater-based surveillance for exploring the interplay between SES and AMR.

Innovameter: Agent-based modeling of innovation determinants in American and European countries.

This article discusses the dynamics of innovation in America and Europe, focusing on variables such as access to technology, education, and life expectancy. To do this, the article proposes an agent-based model called the Innovameter. The dependent variable is the Global Innovation Index. The paper focuses on data analysis through correlation analysis and multiple hierarchical regressions to determine the contribution of specific variables related to the pillars of the Global Innovation Index and indicators of the Human Development Index. After analyzing the data, an agent-based model was built to parameterize these main variables by defining two levels of abstraction: at the global level, there is the country, where birth rates, life expectancy, ICT use, and research and development are defined. At the local level, we define the individuals who have an age, years of schooling, and income. A series of experiments were conducted by selecting data from 30 countries. From the results of the experiments, a nonparametric correlation analysis was performed, and correlation indices were obtained indicating a relationship between the predicted outcomes and the outcomes in the global index. The proposed model aims to provide suggestions on how the different variables can become the norm in most of the countries studied.

The Intersection of Human Development and Poverty: An Analysis of HDI and I-HDI in Yogyakarta based on Maqāşid Syarī’ah

Despite its high Human Development Index (HDI), Yogyakarta paradoxically suffers from one of the highest poverty rates on the island of Java. This situation underscores the limitations of conventional human development metrics, particularly in regions with a predominantly Muslim population. To address this issue, this study introduces the Islamic Human Development Index (I-HDI) as an alternative framework that more effectively captures the relationship between human development and poverty in a Muslim-majority context. Utilizing secondary data from five districts in Yogyakarta spanning the years 2012 to 2023, and employing panel data regression alongside Granger causality analysis, the findings indicate that HDI has a limited and indirect effect on poverty. In contrast, I-HDI demonstrates a stronger and more direct negative correlation with poverty levels. Furthermore, Granger causality analysis reveals that changes in I-HDI significantly influence poverty levels, and vice versa, suggesting a dynamic and bidirectional interaction between these two variables. These results imply that I-HDI offers a more accurate reflection of human development in Muslim-majority regions, capturing dimensions often overlooked by traditional indices. Ultimately, the findings highlight the intricate interaction between human development and poverty, providing valuable insights for policymakers to create more effective poverty alleviation strategies grounded in Islamic principles and local cultural values.