The Shared Savings Program County-level Aggregate Expenditure and Risk Score Data on Assignable Beneficiaries Public Use File (PUF) for the Medicare Shared Savings Program (Shared Savings Program) provides aggregate data consisting of per capita Parts A and B FFS expenditures, average CMS-HCC prospective risk scores, average demographic risk scores and total person-years for Shared Savings Program assignable beneficiaries by Medicare enrollment type (End Stage Renal Disease (ESRD), disabled, aged/dual eligible, aged/non-dual eligible). DISCLAIMER: This information is current as of the last update. Changes to Shared Savings Program Accountable Care Organization (ACO) information occur periodically. Each Shared Savings Program ACO has the most up-to-date information about their organization. Consider contacting the Shared Savings Program ACO for the latest information. Contact information is available in the ACO PUF and the ACO Participants PUF.

Protein aggregation occurs when misfolded or unfolded proteins physically bind together and can promote the development of various amyloid diseases. This study aimed to construct surrogate models for predicting protein aggregation via data-driven methods using two types of databases. First, an aggregation propensity score database was constructed by calculating the scores for protein structures in the Protein Data Bank using Aggrescan3D 2.0. Moreover, feature- and graph-based models for predicting protein aggregation have been developed by using this database. The graph-based model outperformed the feature-based model, resulting in an R2 of 0.95, although it intrinsically required protein structures. Second, for the experimental data, a feature-based model was built using the Curated Protein Aggregation Database 2.0 to predict the aggregated intensity curves. In summary, this study suggests approaches that are more effective in predicting protein aggregation, depending on the type of descriptor and the database.

Despite extensive research into the nature and determinants of party identification, links between individual-level partisan persistence and the degree of permanence in aggregate-level partisanship have largely been ignored. The failure to link the two levels of analysis leaves a gap in our collective understanding of the dynamics of aggregate partisanship. To remedy this, a set of ideal types are identified in this collection that capture the essential arguments made about individual-level party identification. The behavioral assumptions for each ideal type are then combined with existing results on statistical aggregation to deduce the specific temporal pattern that each ideal type implies for aggregate levels of partisanship. Using new diagnostic tests and a highly general time series model, the investigators found that aggregate measures of partisanship from 1953 through 1992 are fractionally integrated. The evidence that the effects of a shock to aggregate partisanship last for years -- not months or decades -- challenges previous work by party systems theorists (e.g., Burnham, 1970) and students of "macropartisanship" (e.g., MacKuen, Erikson, and Stimson, 1989). The arguments and empirical evidence of the degree of persistence in macro-level partisanship provides a conceptually richer and empirically more precise basis for existing theories -- such as those of issue evolution (Carmines and Stimson, 1989) or endogenous preferences (Gerber and Jackson, 1993) -- in which partisanship plays a central role.

This paper compares two methods of analyzing aggregate data that is classified by period and age. Because there is a linear relationship among age, period, and cohort, it is not possible to distinguish the separate effects without employing an identifying assumption. The first method, which is applied in the economics literature, assumes that period effects are orthogonal to a linear time trend. The second method, which is applied in the statistics literature, assumes that the effect parameters change gradually. Simulation results suggest that the performances of both methods are comparable. The results of applying the second method to household saving rates suggest that period effects had a negligible influence in the United States but considerable influence in Japan.

In the fall of 2013, the Detroit Blight Removal Task Force commissioned Data Driven Detroit, the Michigan Nonprofit Association, and LOVELAND Technologies to conduct a survey of every parcel in the City of Detroit. The goal of the survey was to collect data on property condition and vacancy. The effort, called Motor City Mapping, leveraged relationships with the Rock Ventures family of companies and the Detroit Employment Solutions Corporation to assemble a dedicated team of over 200 resident surveyors, drivers, and quality control associates. Data collection occurred from December 4, 2013 until February 16, 2014, and the initiative resulted in survey information for over 370,000 parcels of land in the city of Detroit, identifying condition, occupancy, and use. The data were then extensively reviewed by the Motor City Mapping quality control team, a process that concluded on September 30, 2014. This file contains the official certified results from the Winter 2013/2014 survey, aggregated to 2010 Census Tracts for easy mapping and analysis. The topics covered in the dataset include totals and calculated percentages for parcels in the categories of illegal dumping, fire damage, structural condition, existence of a structure or accessory structure, and improvements on lots without structures.Metadata associated with this file includes field description metadata and a narrative summary documenting the process of creating the dataset.

1Yield calculated as tonnes/hectare from production data and cultivated hectares; DD = directly dependent crops; ID = indirectly dependent crops; df = 1 all effects; na = not applicable.

Public Transport Accessibility Levels (PTAL) are aggregated at the Middle Super Output Area (MSOA) level, a standard geographical unit in the UK. This dataset includes summary statistics (min, max, and mean PTAL scores) per MSOA. The aggregation is based on Access Index values of PTAL (Public Transport Accessibility Level) dataset. PTAL dataset measures accessibility to public transport services across Greater London using a 100m x 100m grid resolution. PTAL scores are derived from walking times to nearby public transport services and service frequencies. This granular dataset is aggregated to the MSOA level. The mean of PTAL is calculated based on access indices of grid centroids within each MSOA and classified into six PTAL categories again. Similarly, the minimum and maximum values are identified using grid centroids within each MSOA. The spatial boundary layer displaying MSOAs on the map sourced from Office for National Statistics licensed under the Open Government Licence v.3.0.

This dataset includes First Street's aggregated flood risk summary statistics. The data is available in CSV format and is aggregated at the congressional district, county, and zip code level. The data allows you to compare FSF data with FEMA data. You can also view aggregated flood risk statistics for various modeled return periods (5-, 100-, and 500-year) and see how risk changes due to climate change (compare FSF 2020 and 2050 data). There are various Flood Factor risk score aggregations available including the average risk score for all properties (flood factor risk scores 1-10) and the average risk score for properties with risk (i.e. flood factor risk scores of 2 or greater). This is version 1.3 of the data and it covers the 48 contiguous United States. There will be updated versions to follow.

If you are interested in acquiring First Street flood data, you can request to access the data here. More information on First Street's flood risk statistics can be found here and information on First Street's hazards can be found here.

Public Transport Accessibility Levels (PTAL) are aggregated at the Lower Layer Super Output Area (LSOA) level, a standard geographical unit in the UK. This dataset includes summary statistics (min, max, and mean PTAL scores) per LSOA. The aggregation is based on Access Index values of PTAL (Public Transport Accessibility Level) dataset. PTAL dataset measures accessibility to public transport services across Greater London using a 100m x 100m grid resolution. PTAL scores are derived from walking times to nearby public transport services and service frequencies. This granular dataset is aggregated to the LSOA level. The mean of PTAL is calculated based on access indices of grid centroids within each LSOA and classified into six PTAL categories again. Similarly, the minimum and maximum values are identified using grid centroids within each LSOA.The spatial boundary layer displaying LSOAs on the map sourced from Office for National Statistics licensed under the Open Government Licence v.3.0.

1millions; DD = directly dependent crops; ID = indirectly dependent crops; x = year; na = not applicable; df = 1 all effects.

Private Education Financing and Expenditure Statistic: Main structural results by Type of indicator and Educational level (aggregate). National.

Meta-analyses of clinical trials often treat the number of patients experiencing a medical event as binomially distributed when individual patient data for fitting standard time-to-event models are unavailable. Assuming identical drop-out time distributions across arms, random censorship and low proportions of patients with an event, a binomial approach results in a valid test of the null hypothesis of no treatment effect with minimal loss in efficiency compared to time-to-event methods. To deal with differences in follow-up - at the cost of assuming specific distributions for event and drop-out times - we propose a hierarchical multivariate meta-analysis model using the aggregate data likelihood based on the number of cases, fatal cases and discontinuations in each group, as well as the planned trial duration and groups sizes. Such a model also enables exchangeability assumptions about parameters of survival distributions, for which they are more appropriate than for the expected proportion of patients with an event across trials of substantially different length. Borrowing information from other trials within a meta-analysis or from historical data is particularly useful for rare events data. Prior information or exchangeability assumptions also avoid the parameter identifiability problems that arise when using more flexible event and drop-out time distributions than the exponential one. We discuss the derivation of robust historical priors and illustrate the discussed methods using an example. We also compare the proposed approach against other aggregate data meta-analysis methods in a simulation study.

Data and code necessary to replicate the results of the article "Advisers and Aggregation in Foreign Policy Decision-Making" and its supplementary material.

These maps shows the aggregate potential across Ireland. To produce these maps, scores were given to each area based on several factors such as rock type suitability, number of quarries, area, elevation etc. The final score was a number between 5 and 100 The maps shows the scores sorted into five different ranges; * Very High potential - red * High potential - orange * Moderate Potential - yellow * Low Potential - green * Very Low Potential - blue Aggregate maps include:Granular and Crushed Rock. Also available are: Sand and gravel deposits. Pits and Quarry Locations.

A principal components decomposition of sectoral IP data reveals that the contribution of aggregate shocks to the variance of aggregate output declined from about 70 percent in the period 1967–1983 to about 30 percent after 1983. We develop an "islands" model with two sectors and costly labor reallocation to investigate how this change in the relative importance of shocks alters business cycle moments. A version of the model with relatively more important sectoral shocks results in a sizeable decline in the cyclicality of labor productivity and is consistent with changes in several other business cycle moments observed in the data.

In 2019, Russia's aggregate score in the global competitiveness index reached 66.7 points, which was the highest figure among other countries in the CIS region. The most significant improvement was observed for Azerbaijan, with an increase by 1.6 points from 2018 to 2019.

These data make it possible to replicate all numerical results in Gary King (1997), A SOLUTION TO THE ECOLOGICAL INFERENCE PROBLEM: RECONSTRUCTING INDIVIDUAL BEHAVIOR FROM AGGREGATE DATA. Princeton, NJ: Princeton University Press.

Datasets and results associated with "Sample, estimate, aggregate: A recipe for causal discovery foundation models"

This dataset tracks the updates made on the dataset "County-level Aggregate Expenditure and Risk Score Data on Assignable Beneficiaries" as a repository for previous versions of the data and metadata.

Recently, Imbs, Mumtaz, Ravn and Rey (2005, hereinafter IMRR) have argued that much of the purchasing power parity (PPP) puzzle is due to upwardly biased estimates of persistence. According to them, the source of the bias is the existence of heterogeneous price adjustment dynamics at the sectoral level that established time series or panel data methods fail to control for. This paper re-examines this claim in two steps. Firstly, we demonstrate that IMRR's measures of sectoral persistence are systematically downwardly biased because they are based on an inaccurate definition of the average impulse response function (IRF). We then show that standard estimates of shock persistence are recovered after this bias is corrected. Secondly, building on the results in Mayoral (2008), which prove that aggregate and micro models induce the same shock persistence behavior, we show that estimates based on aggregate and sectoral exchange rates are, in fact, highly consistent. Thus, aggregation is not the solution to the PPP puzzle.