High Human Development Index Research Articles

Functional host-specific adaptation of the intestinal microbiome in hominids

Fine-scale knowledge of the changes in composition and function of the human gut microbiome compared that of our closest relatives is critical for understanding the evolutionary processes underlying its developmental trajectory. To infer taxonomic and functional changes in the gut microbiome across hominids at different timescales, we perform high-resolution metagenomic-based analyzes of the fecal microbiome from over two hundred samples including diverse human populations, as well as wild-living chimpanzees, bonobos, and gorillas. We find human-associated taxa depleted within non-human apes and patterns of host-specific gut microbiota, suggesting the widespread acquisition of novel microbial clades along the evolutionary divergence of hosts. In contrast, we reveal multiple lines of evidence for a pervasive loss of diversity in human populations in correlation with a high Human Development Index, including evolutionarily conserved clades. Similarly, patterns of co-phylogeny between microbes and hosts are found to be disrupted in humans. Together with identifying individual microbial taxa and functional adaptations that correlate to host phylogeny, these findings offer insights into specific candidates playing a role in the diverging trajectories of the gut microbiome of hominids. We find that repeated horizontal gene transfer and gene loss, as well as the adaptation to transient microaerobic conditions appear to have played a role in the evolution of the human gut microbiome.

Global, regional, and national lifetime risks of developing and dying from gastrointestinal cancers in 185 countries: a population-based systematic analysis of GLOBOCAN

Gastrointestinal cancers account for a quarter of the global cancer incidence and a third of cancer-related deaths. We sought to estimate the lifetime risks of developing and dying from gastrointestinal cancers at the country, world region, and global levels in 2020. For this population-based systematic analysis, we obtained estimates of gastrointestinal cancer incidence and mortality rates from GLOBOCAN for 185 countries, alongside all-cause mortality and population data from the UN. Countries were categorised into quartiles of the Human Development Index (HDI). The lifetime risk of gastrointestinal cancers was estimated with a standard method that adjusts for multiple primaries, taking into account competing risks of death from causes other than cancer and life expectancy. The global lifetime risks of developing and dying from gastrointestinal cancers from birth to death was 8·20% (95% CI 8·18-8·21) and 6·17% (6·16-6·18) in 2020. For men, the risk of developing gastrointestinal cancers was 9·53% (95% CI 9·51-9·55) and of dying from them 7·23% (7·22-7·25); for women, the risk of developing gastrointestinal cancers was 6·84% (6·82-6·85) and of dying from them 5·09% (5·08-5·10). Colorectal cancer presented the highest risk, accounting for 38·5% of the total lifetime risk of developing, and 28·2% of dying from, gastrointestinal cancers, followed by cancers of the stomach, liver, oesophagus, pancreas, and gallbladder. Eastern Asia has the highest lifetime risks for cancers of the stomach, liver, oesophagus, and gallbladder, Australia and New Zealand for colorectal cancer, and Western Europe for pancreatic cancer. The lifetime risk of gastrointestinal cancers increased consistently with increasing level of HDI; however, high HDI countries (the third HDI quartile) had the highest death risk. The global lifetime risk of gastrointestinal cancers translates to one in 12 people developing, and one in 16 people dying from, gastrointestinal cancers. The identified high risk and observed disparities across countries warrants context-specific targeted gastrointestinal cancer control and health systems planning. Beijing Nova Program, CAMS Innovation Fund for Medical Sciences, and Talent Incentive Program of Cancer Hospital, CAMS (Hope Star).

Sustainability analysis of socio-economic aspects of fishermen’s communities reviewed from the human development index in Dumai City

Dumai City is one of the cities in Riau Province, Indonesia, located in the coastal area. As an industrial and trade city, the fisheries sector in Dumai City is under intense pressure due to this marine activity. On the other hand, Dumai City has the second highest Human Development Index (HDI) value in Riau Province after Pekanbaru City. The high HDI status of Dumai City makes it attractive to study whether it is directly proportional to the HDI value of the fishermen community and its socioeconomic sustainability status. The survey method was used in this study. This study aims to analyze the sustainability status of Dumai City’s fisheries from social and economic aspects by considering HDI indicators in health, education, and economic aspects. This study uses two analysis methods: the calculation of HDI and sustainability status using Rapfish. The HDI value of the Dumai City fishermen community is 69,35. These results show that the condition of the Dumai fishermen community is still relatively low when measured by the HDI indicator. If examined based on sustainability analysis, the social and economic aspects of the Dumai City fishermen community are 39,56, with a less sustainable status.

Corruption Vs Human Development: An Empirical Analysis

Background: Corruption is a significant issue facing the modern world. Corruption hinders social, political, economic, and environmental growth, causing inequality and a decrease in public spending. Research demonstrates that corruption adversely affects human capital improvement, ecological sustainability, and political frameworks, hindering progress at both high and low levels. Method: This paper examines the relationship between corruption and human development and how each influences the other, using 2018 data from 100 countries. This study uses a linear regression model and statistical methods like goodness of fit, hypothesis testing, and correlation coefficients to look into how the Corruption Perception Index (CPI) and the Human Development Index (HDI) affect each other. Result: The corruption perception index (CPI) and the human development index (HDI) have an important positive correlation (0.7708). Models explain 62.08% of HDI variation and 57.28% of CPI variation, depending on regression analysis. This implies that higher CPI (less corruption) notably enhances HDI (human development), and vice versa. Discussion: Findings show a strong correlation between higher human development (higher HDI) and lesser corruption (higher CPI), which has a major impact on social and economic circumstances. Though several countries have progressed with decreasing corruption, perceptions across the world remain unchanged, and numerous fields are still experiencing difficulties. Conclusion: This study's findings demonstrate a significant inverse relationship between corruption and human development. With the goal of fighting corruption and enhancing human development, it emphasizes the importance of a wide range of indicators, including technical developments, institutional reforms, and international cooperation.

The Impact of Financial Inclusion on Economic Growth and Poverty Reduction: Empirical Evidence from sub-Saharan Africa

This study examines the impact of financial inclusion (FI) on economic growth (EG) and poverty reduction in 18 sub-Saharan Africa (SSA) over the period from 2014 to 2021. The study employs fixed effects, random effects, panel-corrected standard errors (PCSE), Driscoll Kraay standard errors, the generalized method of moments (GMM), and quantile regression. A FI composite index was created using principal component analysis (PCA). According to the findings from all the models, FI has a significant positive effect on EG, and it also reduces poverty in SSA. The findings from the quantile regression indicate that countries with a higher Human Development Index (HDI) have a more significant decline in poverty. Therefore, governments and policymakers must prioritize the implementation of policies that facilitate the accessibility, availability, and usability of financial products and services. Such measures will contribute to fostering inclusive economic growth and poverty reduction in SSA.

Oral health in lower middle-income African societies - surprising outcomes and strategic consequences

Objective: To assess the oral health status of the population in selected African countries and compare the outcomes with emerging and highly developed countries worldwide using quantifiable results. Method: By means of a composite indicator - the Dental Health Index (DHI) - the dental status of a country´s entire population is measured to arrive at a ranking of oral health performance. This performance is then compared with countries belonging to other development stages. The present study is descriptive and, whenever possible, uses existing epidemiological data from national representative surveys. Results: With the exception of Rwanda, the Sub-Saharan African countries performed better than the three North African countries of Egypt, Morocco and Tunisia, although the Arabic-speaking countries have higher Human Development Indices by comparison. Within Sub-Saharan Africa, the best rankings were achieved by Uganda, Kenya and Nigeria. However, the other Sub-Saharan countries studied also exhibited similarly low DHIs. Rwanda, Egypt, Tunisia and Morocco ranked last. One striking finding is that the Sub-Saharan countries studied show more favourable DHI outcomes than emerging or high-income countries. As large parts of the African population have been widely untouched for decades by dental services, the relatively favourable outcomes in African societies are difficult to explain and contradict conventional dental theory. The reasons behind this finding are not fully understood. It is only in the area of untreated oral diseases that the African countries perform much worse than the more developed countries. Conclusion: If African countries were to concentrate their scarce resources on a few vital segments of oral health, they would have the opportunity, in light of favourable morbidity structures, to gradually improve the oral health status of large parts of the population. Should they choose this route, priority should be given to the young generation. Three key action fields are decisive: mobilising oral self-care, enlarging the dental workforce by training sufficient mid-level personnel and creating a true primary oral health care system which is community-centred and complemented by strictly preventive, minimally invasive and tooth-retaining treatments under the guidance of a strong public sector.

IMPLEMENTATION OF THE K-MEDOIDS METHOD IN CLUSTERING HUMAN DEVELOPMENT INDEXES IN INDONESIA

The Human Development Index (HDI), which takes into account three fundamental aspects of human existence, a long and healthy life, knowledge, and a reasonable level of living, is one tool used to assess the effectiveness of human progress. Clustering provinces based on the human development index is important so that development disparities can be identified and help identify provinces with high, medium or low levels of development. The purpose of this study was to use the k-medoids approach to perform a cluster analysis of HDI in Indonesia based on life expectancy, average years of schooling, expected years of schooling, and expenditure per capita adjusted for 2022. The analysis indicate that two clusters were created: cluster 1 had a high human development index, while cluster 2 had a low human development index. More provinces belonged to cluster 1 than cluster 2 suggesting that human development index in Indonesia in 2022 was largely in the high category

Pengaruh Infrastruktur dan Indeks Pembangunan Manusia terhadap Penyerapan Tenaga Kerja serta Implikasi Pertumbuhan Ekonomi

This research aims to determine the influence of infrastructure and the Human Development Index on labor absorption and its implications for economic growth in the Riau Islands. The analysis model used is Path Analysis. The data used is a time series and cross-section from 7 districts and cities from 2011 to 2021 in the Riau Islands. Research results show that the Human Development Index (HDI) positively influences labor absorption. A high HDI will produce quality and productive human resources in increasing labor absorption to increase economic output. Different findings from this research show that infrastructure has a negative effect on economic growth in the Riau Islands. It is possible that the infrastructure proxied by road transportation infrastructure in the research does not have a large economic impact on the Riau island because the transportation that is widely used in inter-island economic activities uses sea transportation which requires quite large costs.

UNVEILING THE HIDDEN LINK: HUMAN DEVELOPMENT AND VOTER TURNOUT IN BALOCHISTAN, PAKISTAN

The province of Balochistan in Pakistan provides an intriguing opportunity to analyze the complex correlation between human development and voter engagement. This study aims to determine if there is a direct relationship between the Human Development Index (HDI) and voter turnout in Balochistan. The study utilizes a quantitative research approach, analyzing data from the Human Development Index (HDI) and voter participation records for districts in Balochistan, Pakistan. The HDI data was acquired from the United Nations Development Programme (UNDP), whilst the voter turnout data was gathered from the Election Commission of Pakistan (ECP). The study's findings indicate no direct correlation between HDI and voter turnout. Some districts with low Human Development Index (HDI) values have a high voter turnout, whereas, on the other hand, certain districts with high HDI values have a low voter turnout. These findings indicate that other factors beyond HDI substantially influence voter turnout in Balochistan. Further research would be helpful to thoroughly understand the intricate relationship between HDI and voter turnout in Balochistan. These research efforts have the potential to find strategies that can increase voter turnout in the province.

Regional temperature extremes and vulnerability under net zero CO2 emissions

Signatories to the Paris Agreement have pledged to keep global warming to well below 2 °C above pre-industrial levels and preferably below 1.5 °C above pre-industrial levels. Beyond over-shooting Paris Agreement warming levels followed by net negative emissions, achieving a state of net zero carbon dioxide emissions is required to satisfy Paris Agreement warming goals. Research on climate changes under net zero CO2 emissions is very limited to date with no comprehensive analysis of changes in extremes. In this study, we use results from Earth System Models in the zero emissions commitment model intercomparison project to understand regional mean-state climate change patterns during a 100 year period following carbon dioxide emissions cessation. We also perform an initial study of the evolution of hot and cold monthly temperature extremes after net zero CO2 emissions, including an assessment of how the change in frequency of temperature extremes affects areas of different levels of socioeconomic development based on regional Human Development Index (HDI). The results show that most land regions experience a fast and continuous cooling response following emissions cessation, with large areas of significant model agreement. In contrast, the Southern Ocean continues warming over the century after emissions cessation. The frequency of land-based local monthly high temperature extremes generally stays constant or decreases during the century after emissions cessation, however, decreases in heat extreme frequencies are generally less for locations with lower modern HDI than areas with higher HDI which suggests that inequality of climate change will remain an issue even after net zero CO2 emissions. There is an evident emergence of local monthly cold extremes following emissions cessation with most significant impact over high HDI mid- and high-latitude land regions.

Too Much Development or Not Enough Democracy? Exploring an Anomaly in the Democratization of Post-Communist Countries

This study of 28 post-communist regimes distinguishes a group of countries significantly less democratic than predicted by its very high Human Development Index score: Russia, Kazakhstan, and Belarus. It also appears that, contrary to theoretical assumptions, such ‘developed dictatorships’ convert their economic growth into human development remarkably well. To measure such conversion, a new tool, the Growth Conversion Index is introduced. Considering these results, the explanatory power of several theories is briefly examined. While some theories imply possible explanations (e.g., the concept of patronal politics and the conditional approach to resource dependence), none of them discloses sufficiently the actual workings of such conversion mechanism, implying the need for more in-depth studies.

Policy Triangle Analysis of Stunting Issues in Urban Areas: A Case Study of Yogyakarta City

Stunting reduction is one of Indonesia’s major projects. While one-size-fits-all regulations were no longer promoted, understanding the characteristics of each region imposed a strong policy foundation. Many studies had been conducted in rural areas, but the analysis of urban areas was scarce. A study was conducted in Yogyakarta City to understand the characteristics of stunting intervention, despite the high human development index, education level, and access to health facilities. This made it important to examine the characteristics of stunting reduction efforts, based on the analysis of the contents, contexts, processes, and actors involved, in Yogyakarta City, to help with its ambition for zero stunting. A desk review and focus group discussion were conducted involving four local government staff, consisting of the Regional Planning and Development Agency, Health Office, and Office of Women's Empowerment, Child Protection, and Population Control and Family Planning, which were elaborated with literature following the policy triangle framework. A total of 13 open-access official documents and dashboards were obtained for analysis. Decrees from the local government showed a strong commitment to ending malnutrition. While most intervention packages were similar to those across Indonesia, targeted interventions for specific subjects were needed to support people who were unable to be covered by general services. Furthermore, Yogyakarta City has smaller open spaces than other districts in the province, a contradictory low nutrition awareness despite high school participation, and other factors were cross-linked with its culture in the community, implementer, to the authority levels. Stunting reduction programs were multisectoral, involving not only government offices but also non-government, mass media, community, private sectors, and academia. This study is expected to provide lessons learned for decision-makers, private sectors, academia, and public health practitioners to implement good collaboration in stunting reduction efforts.

Global burden of 34 cancers among women in 2020 and projections to 2040: Population-based data from 185 countries/territories.

Globally women face inequality in cancer outcomes; for example, smaller improvements in life expectancy due to decreased cancer-related deaths than men (0.5 vs 0.8 years, 1981-2010). However, comprehensive global evidence on the burden of cancer among women (including by reproductive age spectrum) as well as disparities by region, remains limited. This study aimed to address these evidence gaps by considering 34 cancer types in 2020 and their projections for 2040. The cancer burden among women in 2020 was estimated using population-based data from 185 countries/territories sourced from GLOBOCAN. Mortality to Incidence Ratios (MIR), a proxy for survival, were estimated by dividing the age-standardised mortality rates by the age-standardised incidence rates. Demographic projections were performed to 2040. In 2020, there were an estimated 9.3 million cancer cases and 4.4 million cancer deaths globally. Projections showed an increase to 13.3 million (↑44%) and 7.1 million (↑60%) in 2040, respectively, with larger proportional increases in low- and middle-income countries. MIR among women was higher (poorer survival) in rare cancers and with increasing age. Countries with low Human Development Indexes (HDIs) had higher MIRs (69%) than countries with very high HDIs (30%). There was inequality in cancer incidence and mortality worldwide among women in 2020, which will further widen by 2040. Implementing cancer prevention efforts and providing basic cancer treatments by expanding universal health coverage through a human rights approach, expanding early screening opportunities and strengthening medical infrastructure are key to improving and ensuring equity in cancer control and outcomes.

Classifying regencies and cities on human development index dimensions: Application of K-Means cluster analysis

The Human Development Index (HDI) is a measurement that analyzes a region's development in improving human development. The government's development plan aims to create a successful and peaceful life. The unbalanced development in every regency and city in Indonesia is a typical issue during the development process. It may also be shown that the HDI level changes across regencies and cities in Indonesia. This research aims to identify Indonesian regencies and cities based on HDI indices. K-Means clustering algorithm is the clustering method adopted. The results of the analysis formed 4 clusters. The first cluster consisted of 20 regencies with a low average HDI indicator. The second cluster consisted of 148 regencies and cities with an average HDI indicator is medium. The third cluster consisted of 88 regencies and cities with an average HDI indicator. The fourth cluster consists of 258 regencies and cities with high HDI indicators.

Model Regresi Nonparametrik Spline Truncated Pada Indeks Pembangunan Manusia di Indonesia

Truncated spline nonparametric regression is a nonparametric regression analysis using a segmented polynomial model. This segmented nature provides flexibility so that the regression model can adapt more effectively to the local characteristics of the data. The purpose of this study was to obtain a regression model and determine the factors that influence the Human Development Index (HDI) in all provinces in Indonesia using multivariable truncated spline nonparametric regression. The Human Development Index is an important indicator in measuring success in efforts to build the quality of human life. The Human Development Index can determine the rank or level of development of a region and a country. In development, a high Human Development Index is something that is expected to be achieved, especially for developing countries. The Human Development Index data used in this study is based on BPS data published in 2020 from all provinces in Indonesia. In this study, based on the results of the analysis, the best nonparametric truncated spline regression was obtained using 1 knot point, 2 knot point and 3 knot point. Based on the minimum Generalized Cross Validation (GCV) value, the best truncated spline regression model is 3 knots with an R2 value of 83.70%. The factors that influence the Human Development Index are the variables expected length of schooling, life expectancy at birth, and population

Global Prevalence of Anemia Among Women of Reproductive Age, 2000-2019

Introduction: A common disease with significant impacts on health and quality of life, anemia is particularly prevalent in women of reproductive age due to blood losses during menstruation and pregnancy. Data from the World Health Organization (WHO) was analyzed to compare trends in prevalence of anemia in women aged 15-49 among countries and over time with the goal of identifying regions both successful and in need of assistance in combatting anemia. Methods: Publicly available data was obtained from the WHO on the prevalence of anemia among women aged 15-49 from 194 countries from 2000-2019, data being reported as percentages. Anemia was defined as hemoglobin < 12 g/dL for non-pregnant women and < 11 g/dL for pregnant women, with severe anemia being defined as hemoglobin < 8 g/dL for non-pregnant women and < 7 g/dL for pregnant women. WHO data was from both individual-level and summary statistics sources and hemoglobin was adjusted for altitude. Human development index (HDI) data and categories (low, medium, high, and very high) were obtained from the United Nations. Data was analyzed based on year, country, geographic region, and HDI, with ANOVA and Tukey HSD tests being used to analyze the relationship between HDI and anemia. Results: Worldwide from 2000 to 2013 the prevalence of anemia decreased, and then increased from 2013 to 2019 though remained lower than at 2000 levels; severe anemia decreased throughout the world from 2000 to 2019. Throughout all years, African countries had the highest prevalence of anemia and severe anemia while American and European countries had the lowest (Figure 1). When analyzed by HDI category, there was a significant difference among HDI categories with both total and severe anemia ( P < 0.001 for both). With each decrease in HDI category (very high to high, etc.) there was a significant increase in prevalence of total anemia (Table 1, P < 0.001 for all). For severe anemia, there was a significant difference in prevalence of anemia between all HDI levels except very high and high ( P = 0.09). Discussion: This data suggests that although the prevalence of anemia among reproductive age women has decreased over time there is still much work remaining, particularly in low HDI countries, where the prevalence of anemia was over twice that of very high HDI countries in 2019, and the prevalence of severe anemia was over three times as high. More research is needed into the relationship of anemia and socioeconomic status as well as preventing, recognizing and treating anemia.

Data-Driven Modeling of Human Development Index in Eastern Indonesia's Region Using Gaussian Techniques Empowered by Machine Learning

The Human Development Index (HDI) is a statistical measure used to measure and evaluate the progress and quality of human life in a country. For the Government of Indonesia, HDI is important because it is used to create or develop effective policies and programs. In addition, HDI is also used as one of the allocators in determining the General Allocation Fund. The 2022 HDI data released by BPS shows that there has been an increase in the HDI in each district/city over the last 12 years, including in the regions of Eastern Indonesia. High and low HDI values are influenced by several factors, and there are indications that there is spatial diversity where surrounding areas tend to have HDI levels that are not far from the area. The Geographically Weighted Regression method is used in this study because it takes into account spatial aspects. However, the GWR model must be built repeatedly if there is regional expansion. Therefore, a GWR model that applies machine learning methods is needed where the model is built and tested using different datasets, namely training data and test data, so that the model can predict new data better. The results obtained are that the GWR model with test data has a better R-Square value when compared to the GWR model previously trained using training data, which is 0.9946702, based on the linear regression model shows the results that the most influential factor on HDI in Eastern Indonesia is expected years of schooling (X2).

Does the Validity of the Environmental Kuznets Curve Postulation Empirically Depend on the Level of Human Development in Africa?

Purpose: The interconnectedness between human development and environmental degradation is a complex issue that warrants investigation in Africa. Although there are a few studies on the economic growth and environmental degradation nexus for African countries, there is a huge scarcity of empirical research that explores the Environmental Kuznets Curve (EKC) postulation in the context of human development and environmental degradation in Africa. The study therefore empirically explored the relationship between Human Development and Environmental Degradation within the framework of the EKC using 50 African countries subdivided into High Human Development Index Countries (HHDICs), Medium Human Development Index Countries (MHDICs) and Low Human Development Index Countries (LHDICs) from 2000 to 2019. Data for the study were obtained from the World Bank’s development indicators and the Human Development Reports of the United Nations Development Programme (UNDP-HDR).
 Methodology: The study made use of the Panel Corrected Standard Error (PCSE) model and the Feasible Generalised Least Squares Regression model (FGLS) which are econometrically suitable to handle N > T and T > N panels respectively, and are both robust to cross sectional dependence, contemporaneous correlation, group wise heteroscedasticity and slope heterogeneity. Other econometric techniques such as Descriptive Statistics, Correlation Analysis, Variance Inflation Factor test (Multicollinearity), second generation unit root tests (CADF and CIPS), Durbin Watson, Breusch Godfrey tests (Serial Correlation) and the White Test (Heteroscedasticity) were also employed in the study.
 Findings: The empirical results produced evidence that the data is integrated of order one I(1) and exhibits cross sectional dependence, slope heterogeneity and cointegration. The data was also found to be serially correlated and heteroscedastic. However, multicollinearity was absent. The panel estimates of the PCSE and the FGLS estimators showed that Human Development is a key driver of Environmental Degradation in Africa, with the greatest proportion of the impact on HHDICs, followed by MHDICs and then finally LHDICs. The results also revealed the validity of the EKC postulation only in the HHDICs, evidenced by the presence of an inverted U-shaped relationship between Environmental Degradation and Human Development. In The LHDICs and MHDICs on the other hand, U-shaped relationships between Environmental Degradation and Human Development were found, signalling the invalidity of the EKC. There was significant evidence for the support of the feedback hypothesis between Human Development and Environmental Degradation in HHDICs, MHDICs, LHDICs and all the 50 African countries at large. Based on the findings, Policymakers were given recommendations that took into consideration the uniqueness of the economic situations in HHDICs, MHDICs, LHDICs and Africa as a whole.
 Recommendations: The study shows that the degree of environmental degradation is proportional to the level of human development in Africa and indicates that the validity of the EKC postulation in the context of human development) is exclusive to only the HHDICs in Africa. This provides an incentive for policy makers in the MHDICs and LHDICs of Africa to make human development a priority through industrialisation and other profitable economic activities. This is likely to help them afford innovations and technological developments that protect the environment through the greater use of cleaner energy than the naturally abundant fossil fuel as well as the institutionalisation of policies that protect the environment as human development appreciates.

ANALISIS KLASTER INFRASTRUKTUR SEKTOR KESEHATAN DI WILAYAH KABUPATEN WONOGIRI TAHUN 2021 MENGGUNAKAN METODE ALGORITMA K-MEANS

Equalizing the development of health infrastructure is one way to improve the Human Development Index (HDI). A high HDI indicates the successful improvement of societal well-being in a region. This research discusses the utilization of the k-means algorithm to cluster sub-districts in Wonogiri Regency based on the similarity of health infrastructure using five metrics: the number of general hospitals, inpatient community health centers, outpatient community health centers, private clinics, and integrated health posts (posyandu). Three clusters were identified based on the distribution of health infrastructure: Cluster 1 consists of the following sub-districts: Paranggupito, Giritontro, Batuwarno, Karangtengah, Wuryantoro, Manyaran, Ngadirojo, Sidoharjo, Jatiroto, Bulukerto, Puhpelem, Slogohimo, Jatisrono, and Jatipurno. Cluster 2 consists of the following sub-districts: Giriwoyo, Tirtomoyo, Nguntoronadi, Eromoko, Kismantoro, Purwantoro, and Girimarto. Cluster 3 consists of the following sub-districts: Pracimantoro, Baturetno, Selogiri, and Wonogiri. By analyzing the distribution of health infrastructure, significant differences were observed among the clusters. In enhancing the HDI of Wonogiri Regency, the findings of this research can be utilized for government program planning to promote equitable development of health infrastructure.

The impact of Economic Growth, Foreign Investment, Wages, and Human Development Index on Educated Unemployment

This study aims to measure four independent variables and one dependent variable using secondary data in the form of panel data comprising 27 districts/cities in a cross-section and a time series over 5 years. The data analysis method employed is panel data regression with the random effects method. Eviews version 12 was utilized for data processing, including testing classical assumptions, hypothesis testing, and testing the Adjusted coefficient of determination (R2). The research findings indicate significant relationships between specific variables and educated unemployment. Economic Growth negatively and significantly impacts educated unemployment, demonstrating its ability to decrease educated unemployment in the region. Minimum District Wage positively and significantly influences educated unemployment, implying that increasing UMK in an area can elevate the number of educated unemployed individuals. Additionally, the Human Development Index (HDI) exerts a negative and significant effect on educated unemployment, suggesting that higher HDI can reduce educated unemployment. However, foreign investment does not significantly affect educated unemployment. Overall, the study shows that these four independent variables explain 67.8% of educated unemployment, while the remaining 32.2% is influenced by factors beyond the study's scope.