New evidence on the allocation of NIH funds across diseases

Abstract

Context: The responsiveness of NIH (National Institutes of Health) funding to disease burden is a long-standing issue of policy interest. Previous analyses of this issue have been hindered by data constraints, have not specified channels through which the NIH funding process could be responsive to disease considerations, and have not examined differences across NIH institutes and centers.

Methods: We collected data from the NIH's new RCDC (Research, Condition, and Disease Categorization) database on funding for 107 diseases in 2008 and linked these to data on deaths and hospitalizations for these diseases. We used RCDC data and information from another NIH database--RePORTER--to determine institute-specific funding for these diseases and also funding by award type. We used these data to examine the overall responsiveness of NIH funding to disease burden, within-institute responsiveness, and the responsiveness of different types of NIH awards.

Findings: Overall, we found a strong and statistically significant relationship between NIH funding and deaths and hospitalizations associated with a disease. We detected some evidence that more "applied" grant mechanisms--in particular, funding for clinical trials--are more responsive than other types of funding. We also found evidence of differences across institutes in their extent of responsiveness.

Conclusions: Overall, the data suggest that NIH funding is responsive to the two measures of disease burden. More applied grant mechanisms also may serve as "safety valves" in the allocation process, allowing Congress, disease advocacy groups, and others to apply pressure to address particular health priorities in a more fine-grained way than is possible through investigator-initiated "basic" research grants alone.

FIGURE 1

Funding in 2008 versus lagged deaths and hospital admits. Notes: This figure shows scatterplots and fitted regression lines relating the natural log of funding for a disease in 2008 versus the natural log of deaths from that disease in 2005/2006 (left panel) and the natural log of hospitalizations in 2007 (right panel). The markers are the RCDC ID numbers for each disease listed in table S1. The bottom panels exclude “supercategories” (broad categories that span multiple other diseases in the RCDC). Slopes are elasticities; that is, they represent the percent increase in funding associated with a 1 percent increase in disease burden. Source: Authors’ analyses of data from the NIH's Research, Condition, and Disease Categorization database.

FIGURE 2

Estimated responsiveness of funding to deaths (left panels) and hospitalizations (right panels). Notes: This figure plots coefficient estimates and 95 percent confidence intervals from bivariate regressions relating natural log of NIH funding (in 2008) to deaths in 2005/2006 (left panels) and hospitalizations in 2007 (right panels). The bottom panels show estimates from models excluding supercategories. For the RFA and non-RFA regressions, we include only diseases with at least one RFA grant and do the same for clinical and nonclinical regressions, center versus noncenter, and so forth. In each chart, “n” indicates the number of the 107 RCDC diseases over which the models were estimated. Estimates are elasticities; that is, they represent the percent increase in funding associated with a 1 percent increase in disease burden. Source: Authors’ analyses of data from the NIH's Research, Condition, and Disease Categorization database.

FIGURE 3

Estimated responsiveness of funding to deaths (left panels) and hospitalizations (right panels) for the twelve largest funding institutes and/or centers. Notes: This figure plots coefficient estimates and 95 percent confidence intervals from bivariate regressions relating natural log of NIH funding (in 2008) to deaths in 2005/2006 (left panels) and hospitalizations in 2007 (right panels), estimated separately by institute. The models were estimated for the twelve largest institutes, ranked by total 2008 funding. The bottom panels show estimates from models excluding supercategories. In each chart, “n” indicates the number of the 107 RCDC diseases in our sample funded by the institute, that is, the sample size for that institute's regression. Slopes are elasticities; that is, they represent the percent increase in funding associated with a 1 percent increase in disease burden. Source: Authors’ analyses of data from the NIH's Research, Condition, and Disease Categorization database.