Correlation between a) total, b) industry, and c) NIH research funding in 2005 and projected disease burden in 2015 for high income countries and the world.

<p>Correlation between a) total, b) industry, and c) NIH research funding in 2005 and projected disease burden in 2015 for high income countries and the world.</p

New Molecular Entities Approved by US Food and Drug Administration by Therapeutic Area, 1995–2005.

<p>New Molecular Entities Approved by US Food and Drug Administration by Therapeutic Area, 1995–2005.</p

Research support, sponsors, and FDA new drug approvals from 1995 – 2005 for a) all therapeutic areas, b) neuroscience research, c) oncology research, d) cardiovascular research, and e) endocrine research.

<p>Research support, sponsors, and FDA new drug approvals from 1995 – 2005 for a) all therapeutic areas, b) neuroscience research, c) oncology research, d) cardiovascular research, and e) endocrine research.</p

Funding for Biomedical Research by Therapeutic Area, 1995–2005.

*<p>Adjusted for inflation by the Biomedical Research and Development Price Index.</p

Research support, sponsors, and FDA new drug approvals from 1995 – 2005 for a) infectious disease (non-HIV) research, b) HIV research, c) gastrointestinal research, d) respiratory research, and e) genitourinary research.

<p>Research support, sponsors, and FDA new drug approvals from 1995 – 2005 for a) infectious disease (non-HIV) research, b) HIV research, c) gastrointestinal research, d) respiratory research, and e) genitourinary research.</p

Total Number of Drugs Approved by US Food and Drug Administration by Therapeutic Area, 1995–2005.

<p>Total Number of Drugs Approved by US Food and Drug Administration by Therapeutic Area, 1995–2005.</p

Table quantifying variation in US outcomes.

<p>(A) Significant geographic variation exists across all outcomes measures both before and after risk-adjustment. The values in the table quantify the extent of outcomes variation between the top decile and bottom decile geographies. For example, for IQI 15 AMI inpatient mortality, we observe a 4.0-fold difference in outcomes between the top 10% and bottom 10% of hospitals. For IQIs and PSIs, "observed" refers to values that were adjusted for low-volume noise using Bayesian shrinkage method but did not risk adjust for any other factors. For PQIs, the unit of analysis was at the county level, and therefore PQIs did not need to be shrunk. Risk-adjustments are performed incrementally. "+ pop. factors adjusted" values are shrunk and adjusted for populations factors. "+ co-morb. adjusted" values are shrunk, adjusted for population factors, and adjusted for co-morbidities. Lastly, "+ system adjusted" values are shrunk, population adjusted, co-morbidities adjusted, and system factors adjusted. For IQI 15 AMI inpatient mortality, we observe a 2.3-fold difference in outcomes between the top 10% and bottom 10% of hospitals after risk-adjustment for demographic, co-morbidities, and health system factors. Dash (-) indicates numbers that were not calculated. Counties were not mapped to HSA or HRR, and therefore PQI ratios were not determined. Star (*) indicates a D1 (top decile) value of 0, such that it was not possible to calculate a ratio. (B) R-squared values with 95% confidence intervals are shown. For the confidence intervals [X, Y], X refers to the lower bound of a given R-squared value; Y refers to the upper bound of a given R-squared value. For example, for IQI 15 AMI inpatient mortality, we are able to account for 64% of the variability in outcomes after risk-adjusting for demographics, co-morbidities, and health system factors.</p

Volume-outcome relationship for inpatient mortality (IQI).

<p>We assessed the relationship between outcomes and hospital case volume by modeling the mortality (M) as M = -α*ln(V)+β, where V is hospital case volume, and α and β are constants for each of the inpatient mortality outcomes. As case volume increases in a hospital, mortality decreases across all IQI measures.</p

Fully risk-adjusted geographic distributions of select outcomes (IQI 15, PSI 07, PQI 08).

<p>Large variation in outcomes is present both between and within US states. Substantially different performances highlight the variation in outcomes across the US. This variation is observed across all outcomes plotted. (A) IQI 15 Acute Myocardial Infarction (AMI) Mortality Rate and PSI 07 Central Venous Catheter-Related Blood Stream Infection Rate are adjusted for low-volume noise using a Bayesian shrinkage methodology and are adjusted for population, co-morbidities, and health system factors. After risk-adjustment, there is 2.1-fold variation in IQI 15 between the top and bottom decile HSAs. After risk-adjustment, there is 12.6-fold variation in PSI 07 between the top and bottom decile HSAs. (B) PQI 08 Heart Failure Admission Rate data has been adjusted for population, co-morbidities, and health system factors. After risk-adjustment, there is 2.2-fold variation in PQI 08 between the top and bottom decile counties. Areas shown in white are due to HCUP not making geographically identifiable data on hospital or county performance available.</p

Correlations among outcomes after adjustment for population factors and co-morbidities and system factors.

<p>Inpatient mortality measures are weakly correlated with each other. Inpatient safety measures show little to no correlation with each other. Prevention quality measures show little to no correlation with each other. Correlation categorization after Dancey and Reidy (2004), analysis following low denominator number outlier removal and risk-adjustment based on the identified population factors, co-morbidities and system factors.</p