Here are Yost indexes for census tracts and block groups in the United States for various years from 1990-2019. The Yost index is a composite index of socioeconomic status that consists of a percentile score from 1 (highest SES) to 100 (lowest SES). Data for 1990 and 2000 include the 50 US states plus the District of Columbia. For years after 2000, the data additionally include Puerto Rico. To rescale the index to geographic units smaller than the US, the score column may be used, where scores range from about -1.8 for the highest SES to 1.8 for the lowest SES.More about the Yost index can be found here: Yost K, Perkins C, Cohen R, Morris C, Wright W. Socioeconomic status and breast cancer incidence in California for different race/ethnic groups. ​Cancer Causes and Control​ 2001; 12(8): 703–711.

Yu M, Tatalovich Z, Gibson JT, Cronin KA. Using a composite index of socioeconomic status to investigate health disparities while protecting the confidentiality of cancer registry data. ​Cancer Causes and Control​. 2014; 25(1): 81-92.

The composite score for Sustainable Development Goals (SDG) index of India as of 2021 was **. Of the various targets set to be achieved by 2030, many states had fared well in providing affordable and clean energy. Kerala and Chandigarh were the leading state and union territory with a score of ** and **, respectively.

This data set contains the average census tract-scale scores, from 2000-2013, for the composite HWBI, each domain within the HWBI, each indicator within domains, and each metric within indicators. Domain and composite scores at the beginning and end of the study period (2000, 2013) are also given. This dataset is associated with the following publication: Yee, S., E. Paulukonis, and K. Buck. Downscaling a human well-being index for environmental management and environmental justice applications in Puerto Rico. Applied Geography. ELSEVIER, AMSTERDAM, HOLLAND, 123: 14, (2020).

The IMF-adapted ND-GAIN index is an adaptation of the original index, adjusted by IMF staff to replace the Doing Business (DB) Index, used as source data in the original ND-GAIN, because the DB database has been discontinued by the World Bank in 2020 and it is no longer allowed in IMF work. The IMF-adapted ND-GAIN is an interim solution offered by IMF staff until the ND-GAIN compilers will review the methodology and replace the DB index.Sources: ND-GAIN; Findex - The Global Findex Database 2021; Worldwide Governance Indicators; IMF staff calculations. Category: AdaptationData series: IMF-Adapted ND-GAIN IndexIMF-Adapted Readiness scoreReadiness score, GovernanceReadiness score, IMF-Adapted EconomicReadiness score, SocialVulnerability scoreVulnerability score, CapacityVulnerability score, EcosystemsVulnerability score, ExposureVulnerability score, FoodVulnerability score, HabitatVulnerability score, HeathVulnerability score, SensitivityVulnerability score, WaterVulnerability score, InfrastructureMetadata:The IMF-adapted ND-GAIN Country Index uses 75 data sources to form 45 core indicators that reflect the vulnerability and readiness of 192 countries from 2015 to 2021. As the original indicator, a country's IMF-adapted ND-GAIN score is composed of a Readiness score and a Vulnerability score. The Readiness score is measured using three sub-components – Economic, Governance and Social. In the original ND-GAIN database, the Economic score is built on the DB index, while in the IMF-adapted ND-GAIN, the DB Index is replaced with a composite index built using the arithmetic mean of “Borrowed from a financial institution (% age 15+)” from The Global Financial Index database (FINDEX_BFI) and “Government effectiveness” from the Worldwide Governance Indicators database (WGI_GE). The Vulnerability, Social and Governance scores do not contain any DB inputs and, hence, have been sourced from the original ND-GAIN database. Methodology:The procedure for data conversion to index is the same as the original ND-GAIN and follows three steps: Step 1. Select and collect data from the sources (called “raw” data), or compute indicators from underlying data. Some data errors (i.e., tabulation errors coming from the source) are identified and corrected at this stage. If some form of transformation is needed (e.g., expressing the measure in appropriate units, log transformation to better represent the real sensitivity of the measure etc.) it happens also at this stage. Step 2. At times some years of data could be missing for one or more countries; sometimes, all years of data are missing for a country. In the first instance, linear interpolation is adopted to make up for the missing data. In the second instance, the indicator is labeled as "missing" for that country, which means the indicator will not be considered in the averaging process. Step 3. This step can be carried out after of before Step 2 above. Select baseline minimum and maximum values for the raw data. These encompass all or most of the observed range of values across countries, but in some cases the distribution of the observed raw data is highly skewed. In this case, ND-GAIN selects the 90-percentile value if the distribution is right skewed, or 10-percentile value if the distribution is left skewed, as the baseline maximum or minimum. Based on this procedure, the IMF–Adapted ND-GAIN Index is derived as follows: i. Replace the original Economic score with a composite index based on the average of WGI_GE and cubic root of FINDEX_BFI1, as follows:IMF-Adapted Economic = ½ · (WGI_GE) + ½ · (FINDEX_BFI)1/3 (1) The IMF-adapted Readiness and overall IMF-adapted ND-GAIN scores are then derived as: IMF-Adapted ND-GAIN Readiness = 1/3 · ( IMF-Adapted Economic + Governance + Social) IMF-Adapted ND-GAIN = ½·( IMF-Adapted ND-GAIN Readiness+ND-GAIN Vulnerability) ii. In case of missing data for one of the indicators in (1), IMF-Adapted ND-GAIN Economic would be based on the value of the available indicator. In case none of the two indicators is available, the IMF-Adapted Economic score would not be produced but the IMF-Adapted ND-GAIN Readiness would be computed as average of the Governance and Social scores. This approach, that replicates the approach used to derive the original ND-GAIN indexes in case of missing data, ensures that the proposed indicator has the same coverage as the original ND-GAIN database.
1 Given that the FINDEX_BFI data are positively skewed, a cubic root transformation has been implemented to induce symmetry.

AI Frailty Risk Composite Index Platform Market Outlook

According to our latest research, the global AI Frailty Risk Composite Index Platform market size reached USD 1.14 billion in 2024, demonstrating robust expansion driven by the increasing adoption of artificial intelligence in healthcare risk assessment. The market is projected to grow at a CAGR of 18.7% from 2025 to 2033, reaching an estimated USD 6.15 billion by 2033. This remarkable growth is primarily attributed to the rising prevalence of chronic diseases, the aging global population, and the urgent need for advanced predictive analytics to optimize patient care and resource allocation.

One of the core growth drivers for the AI Frailty Risk Composite Index Platform market is the escalating demand for precision medicine and early intervention strategies in geriatric care. As healthcare systems worldwide grapple with the complexities of an aging demographic, the necessity for tools that can accurately assess frailty and predict adverse outcomes has become paramount. AI-powered platforms enable healthcare providers to integrate multifactorial data, including clinical, behavioral, and social determinants, to generate comprehensive frailty risk scores. This capability significantly enhances the ability to identify at-risk individuals, tailor interventions, and ultimately reduce hospitalizations and healthcare costs. The integration of machine learning algorithms and natural language processing further refines risk predictions, making these platforms indispensable in modern care delivery models.

Another significant factor fueling the market’s expansion is the growing emphasis on value-based healthcare and outcome-driven reimbursement models. Payers and providers are increasingly incentivized to adopt technologies that improve patient outcomes while controlling expenditures. AI Frailty Risk Composite Index Platforms offer actionable insights that support proactive care planning, enabling healthcare organizations to transition from reactive to preventive care paradigms. These platforms facilitate seamless data exchange across electronic health records (EHRs), wearable devices, and remote monitoring systems, ensuring a holistic approach to patient management. Additionally, regulatory support for digital health innovation and the proliferation of cloud-based solutions have lowered barriers to adoption, accelerating market penetration across both developed and emerging economies.

The market is also benefiting from heightened research and development activities, as well as strategic collaborations between technology firms, healthcare providers, and academic institutions. These partnerships are fostering the development of more sophisticated algorithms capable of handling diverse and large-scale datasets, thereby improving the accuracy and utility of frailty risk assessments. Furthermore, the COVID-19 pandemic has underscored the importance of remote monitoring and telehealth solutions, prompting a surge in demand for AI-driven platforms that can support virtual care delivery. As a result, stakeholders across the healthcare continuum are increasingly recognizing the value proposition of AI Frailty Risk Composite Index Platforms in enhancing patient safety, optimizing clinical workflows, and supporting population health management initiatives.

From a regional perspective, North America currently dominates the AI Frailty Risk Composite Index Platform market, accounting for the largest revenue share in 2024. This leadership position is underpinned by robust healthcare infrastructure, high digital literacy, and favorable reimbursement policies. Europe follows closely, driven by progressive healthcare reforms and strong government support for digital health initiatives. The Asia Pacific region is poised for the fastest growth, with countries like China, Japan, and India investing heavily in healthcare modernization and AI adoption. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as healthcare providers in these regions increasingly recognize the benefits of AI-powered risk assessment tools.

Active Living Scores are based on access to bicycle facilities, street intersection density, transit service, walking destinations, and employment density.This data layer is used by the Active Living Index application.

This dataset provides values for LEADING COMPOSITE INDEX reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

In 2020, the composite index of public service leadership and capability in Italy stood at 0.42 in regards to the development of a diverse workforce. Compared to the OECD average, Italy's score was below the average. The index ranged on a scala from zero to one, where zero represents the worst score and one the best.

Introduction: The Mean Vertigo Score (MVS) is a composite score for defining the burden of disease of patients suffering from vestibular disorders. It has been used in clinical research for about 30 years. This study investigates discriminant validity of the MVS and describes structural relationships of the 12 single criteria used for construction of the MVS.Materials and Methods: The statistical analyses are based on the raw data of an earlier conducted randomized, doubleblind, placebo-controlled clinical trial, which compared the following four randomized treatment groups: a fixed combination of cinnarizine and dimenhydrinate (Arlevert), two groups with only one of the two study drugs, and a group with placebo. The method used for the statistical calculations is the Wei-Lachin procedure, a multivariate generalization of the Mann-Whitney test, which takes into account correlations among the 12 single symptoms of the composite score.Results: All 12 single symptoms of the composite endpoint proved to be useful for detecting differences (Mann-Whitney effect size measures: 0.58–0.73) and thus for discriminating between treatment groups. Their Pearson product-moment correlations are all positive (range 0.07–0.71) and point to the same direction, which indicates one-dimensionality and good internal consistency of the composite index MVS. Furthermore, our statistical calculations revealed that successively increasing the number of single items of the MVS to up to twelve enhances its reliability (R12 = 0.923), which leads to a substantially higher test power and reduction of the number of patients needed (sample size) in a clinical trial.Conclusion: The use of the multivariate Wei-Lachin procedure provides further evidence of the validity of the 12-item composite score MVS, based on the efficacy data of its 12 single vertigo symptoms. The present findings demonstrate that the MVS is a powerful tool, which can be used to adequately describe the patients' self-perceived vertigo complaints, both qualitatively and quantitatively. It may therefore be regarded as a clinically meaningful alternative to other questionnaires that are presently used in vestibular research.

Background: There is a current lack of any composite measure for the effective tracking and monitoring of clinical change in individuals exposed to repetitive head impacts (RHI). The aim of this study is to create a composite instrument for the purposes of detecting change over time in cognitive and behavioral function in individuals exposed to RHI.Methods: The data to derive the composite instrument came from the Professional Fighters Brain Health Study (PFBHS), a longitudinal study of active and retired professional fighters [boxers and mixed martial arts (MMA) fighters] and healthy controls. Participants in the PFBHS underwent assessment on an annual basis that included computerized cognitive testing and behavioral questionnaires. Multivariate logistic regression models were employed to compare active fighters (n = 117) with controls (n = 22), and retired fighters (n = 26) with controls to identify the predictors that could be used to differentiate the groups over time. In a second step, linear discriminant analysis was performed to derive the linear discriminant coefficients for the three groups by using the predictors from the two separate logistic regression models.Results: The composite scale is a weighted linear value of 12 standardized scores consisting of both current and yearly change scores in domains including: processing speed, choice reaction time, semantic fluency, letter fluency, and Barrett Impulsiveness Scale. Because the weighting of values differed between active and retired fighters, two versions emerged. The mean and standard deviation ratio (MSDR) showed that the new index had better sensitivity compared to the individual measures, with the ratio of MSDR of the new index to that of the existing measures of at least 1.84.Conclusion: With the increasing need for tools to follow individuals exposed to RHI and the potential of clinical trials on the horizon for CTE, the RHICI is poised to serve as an initial approach to a composite clinical measure.

Includes the windspeed per adm3-area, and a combined 1-10 score for windspeed and accumulated rainfall.

Datasets used:

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https://data.humdata.org/dataset/accumulated-gpm-imerg-data-for-haiti-hurricane-matthew-october-3-6th-2016

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https://data.humdata.org/dataset/hurricane-matthew-gust-footprint-tropical-storm-risk-university-college-londonDate Published 10/14/2016

Abstract: Which member states could leave the European Union in the years ahead? To answer this question, I develop the ‘EU Exit Index’ measuring the exit propensities of all European Union member states. The index highlights that the United Kingdom was an outlier and uniquely positioned to leave the European Union. While all other states are far behind the United Kingdom, the index still reveals substantial variation among them. Moreover, the index allows monitoring the development of exit propensities over time. It shows that the European Union is in better shape today than before the Brexit referendum and that, currently, no further exits are on the horizon. Still, this could change in the future and the EU Exit Index provides systematic and reproducible measurements to track this development. The files uploaded entail the material necessary to replicate the results from the article and Online appendix published in: Gastinger, M. (2021) ‘Introducing the EU Exit Index measuring each member state’s propensity to leave the European Union’, European Union Politics 22(3): 566–585, available at Doi: 10.1177/14651165211000138.The following files are included: data_raw.csvRaw data necessary to replicate the findings. Plese see the online Appendix for variable descriptions. data_raw.rdsConvenience upload for R users. exit_index.RThe R script. final_score.csvFinal scores of the EU Exit Index (overall score and split up in the social, economic, and political dimension). final_score.rdsConvenience upload for R users. online_appendix.pdfVariable overview, additional information on variable operationalization, correlation matrix, and robustness checks. sessionInfo.txtVersion information about R, the OS and attached or loaded packages. Please let me know if you spot any mistakes or if I may be of any further assistance!

Additional file 1. Consists of 9 worksheets. NFHS-3 scores & ranks (State). Calculation and Tabulation for computing Linear Aggregation (LA), Geometric Mean (GM) and MANUSH scores and Ranks using NFHS-3 data for States in India, 2005–06. NFHS-4 scores & ranks (State). Calculation and Tabulation for computing Linear Aggregation (LA), Geometric Mean (GM) and MANUSH scores and Ranks using NFHS-4 data for States in India, 2015–16. CNNS scores & ranks (State). Calculation and Tabulation for computing Linear Aggregation (LA), Geometric Mean (GM) and MANUSH scores and Ranks using CNNS data for States in India, 2016–18. NFHS-4 scores & ranks (Districts). Calculation and Tabulation for computing Linear Aggregation (LA), Geometric Mean (GM) and MANUSH scores and Ranks using NFHS-4 data for Districts in India, 2016–18. Monotonicity Cases. Examples explaining Monotonicity axiom. Uniformity Cases. Examples explaining Uniformity axiom. Shortfall Sensitivity Cases. Examples explaining Shortfall Sensitivity axiom. Hiatus Sensitivity Cases. Examples explaining Hiatus Sensitivity to Level. Districts under NNM and MANUSH. List and ranking of districts phased under National Nutrition Mission (NNM) and its priority categorisation based on MANUSH scores.

Human development index of Papua New Guinea increased by 0.89% from 0.56 score in 2019 to 0.57 score in 2020. A composite index measuring average achievement in three basic dimensions of human development—a long and healthy life, knowledge and a decent standard of living. 1=the most developed.

The Opportunity Mapping data includes census tract level information on the composite index score as well as the intermediate scores for the five key elements of neighborhood opportunity and positive life outcomes: education, economic health, housing and neighborhood quality, mobility and transportation, and health and environment. The level of opportunity score (very low, low, moderate, high, very high) is determined by sorting all census tracts into quintiles based on their index scores. Opportunity Mapping: Methodology and Technical Addendum (July 2019): https://www.psrc.org/media/3503

In a survey conducted in 2022 among respondents from megacities of India, Pune emerged on the top with a score of **** among all megacities of India, followed by Mumbai and Hyderabad. Megacities are defined as cities with a population of over ************, as per the survey. The Ease of Moving Index is a composite index comprising **** parameters across ** indicators. The parameters include seamless, inclusive, clean, efficient, and shared mobility and investment in the city, among others.

The Natural Resource Protection and Child Health Indicators, 2020 Release, is produced in support of the U.S. Millennium Challenge Corporation as selection criteria for funding eligibility. The Natural Resource Protection Indicator (NRPI) and Child Health Indicator (CHI) are based on proximity-to-target scores ranging from 0 to 100 (at target). The NRPI covers 250 countries and is calculated based on the weighted average percentage of biomes under protected status. The CHI is a composite index for 194 countries derived from the average of three proximity-to-target scores for access to at least basic water and sanitation, along with child mortality. The 2020 release includes a consistent time series of NRPI scores for 2010 to 2020 and CHI scores for 2010 to 2019.

The indicator is a composite index based on a combination of surveys and assessments of corruption from 13 different sources and scores and ranks countries based on how corrupt a country’s public sector is perceived to be, with a score of 0 representing a very high level of corruption and a score of 100 representing a very clean country. The sources of information used for the 2017 CPI are based on data gathered in the 24 months preceding the publication of the index. The CPI includes only sources that provide a score for a set of countries/territories and that measure perceptions of corruption in the public sector. For a country/territory to be included in the ranking, it must be included in a minimum of three of the CPI’s data sources. The CPI is published by Transparency International.

The main objective of the European Innovation Indicators Panel is to conduct a comparative analysis of the innovation performance of the EU-28 member states and to know the strengths and weaknesses of research and innovation systems. It is structured in four main blocks, with ten dimensions, for a total of 27 indicators, from which a composite index is calculated that reflects the weight of the different dimensions. Depending on the score achieved in the composite indicator, countries are grouped into four categories: leading countries in innovation, countries with high innovation, countries with moderate innovation and countries with low innovation. The performance of the Basque Country is compared in the EU-28 environment.

The Natural Resource Protection and Child Health Indicators, 2016 Release, are produced in support of the U.S. Millennium Challenge Corporation as selection criteria for funding eligibility. These indicators are successors to the Natural Resource Management Index (NRMI), which was produced from 2006 to 2011 and was based on the same underlying data. Like the NRMI, the Natural Resource Protection Indicator (NRPI) and Child Health Indicator (CHI) are based on proximity-to-target scores ranging from 0 to 100 (at target). The NRPI covers 237 countries and is calculated based on the weighted average percentage of biomes under protected status. The CHI is a composite index for 190 countries derived from the average of three proximity-to-target scores for access to improved sanitation, access to improved water, and child mortality. The 2016 release includes a consistent time series of NRPI scores for 2012-2016 and CHI scores for 2010 to 2016.