New Evidence on the Allocation of NIH Funds across Diseases

The NIH (National Institutes of Health) is the largest single funder of biomedical research in the world. The agency's cross-disease allocation decisions reveal a fundamental trade-off in health policy: in a world of finite resources, who shall live? (Fuchs 1975). Not surprisingly, the fundamental question of whether NIH's cross-disease allocation patterns are appropriate—that is, whether particular diseases command too much or too little funding—has been a perennial topic of debate throughout the agency's history (Rettig 1977; Strickland 1972). A memorable recent episode in this saga occurred in the early 1990s, with congressional concerns that AIDS, breast cancer, and other high-profile diseases got more than they should, given their burden (Johnson 1998). Theoretical perspectives on the “optimal” allocation of health resources also suggest that disease burden ought to be a consideration in NIH funding choices (Lichtenberg 2001; Weisbrod 1983; Zeckhauser 1967). Previous empirical work on the NIH's allocation process (Gillum et al. 2011; Gross, Anderson, and Powe 1999; Lichtenberg 2001; Mushkin 1979) found mixed evidence on NIH's responsiveness to standard measures of disease burden. But this research faced a major data constraint, a lack of consistently collected data on funding by disease. One indicator of this, in his response to one pioneering study (Gross, Anderson, and Powe 1999), the then NIH director Harold Varmus noted that “the method of coding research dollars, while consistent from year to year for a specific disorder, may differ from one disease to another” (Varmus 1999b, 1914). Similarly, in congressional testimony on priority setting, Varmus observed: “Coding of funds by disease category across the NIH, though useful for some purposes, is also inherently imprecise” (Varmus 1997). An influential IOM report (IOM 1998, 5) also raised serious concerns about the measurement of NIH funding, as well as the NIH leadership's unwillingness to stand behind them, noting that the data that then existed were “not of the quality that they should be and [that] NIH should work to improve the data and better explain the data to the public.” In 2008, the NIH created the Research, Condition, and Disease Categorization (RCDC) system, which uses standard methodologies to classify funds by area. This was in part a response to the IOM report's call for “a regular process [of disease categorization] that is supported by a clear philosophy and that is sensitive to [a] scientific understanding of the meaning of such data and their interpretation” (IOM 1998, 40) and also to the 2006 NIH Reauthorization Act, which required “the Director of NIH, when reporting on research activities relating to a specific disease, disorder, or other adverse health condition,” to “present information in a standardized format.” Whereas each of the NIH's twenty-seven institutes and centers had previously linked its grants to diseases in an ad hoc, nonstandard, way, the RCDC instituted new, standard, category definitions to classify grants, developed with input from disease groups and the scientific community. Thus under the old system, the agency had routinely provided disease-specific funding figures tentatively and with many caveats, whereas after implementation of the RCDC, the agency's stance shifted. The NIH website now states that “RCDC provides consistent and transparent information to the public about NIH-funded research. For the first time, a complete list of all NIH-funded projects related to each category is available” (NIH 2011a). We used the RCDC data to examine the responsiveness of NIH funding to two measures of disease burden, U.S. deaths and hospitalizations. In addition to more reliable funding figures than were available before, the RCDC has other advantages. For example, while earlier work was based on a smaller sample of diseases (typically fewer than thirty), the RCDC provides funding data for 215 categories (about half of which are diseases). One of the contributions of this article is that we used the new data to reassess the relationships between funding and measures of disease burden. A second contribution of our article is that we explored channels through which the agency's funding choices could respond to disease burden. Previous analyses relating funding to disease burden said nothing about the mechanisms through which the NIH might respond to disease burden. These analyses simply assumed that such mechanisms exist, even though this is not obvious. Numerous observers have noted that the NIH peer review process, which focuses mainly on scientific considerations, may lack the ability to respond to disease burden in a more fine-tuned way (IOM 1998; Varmus 1997). The 1998 IOM report on NIH priority setting (discussed earlier) suggested: “There is a sense that NIH has evolved mechanisms for judging scientific opportunity and merit that surpass its capabilities for assessing and being influenced by public health needs” (IOM 1998, 8). Although the NIH (1997) acknowledges an obligation to consider “the incidence, severity, and cost of specific disorders” (4) and claims data on these are “closely monitored” (Varmus 1999a, 1914), it is unclear where in the allocation process these variables are used to prioritize investments (Callahan 1999; IOM 1998). The IOM report also observed that while “basic” research funded through investigator-initiated grants are the agency's mainstay, “other mechanisms, such as grants for research centers, clinical trials, and R&D contracts, tend to be more directed in nature and more tied to an institute's mission. They are usually solicited by NIH through requests for applications (RFAs) rather than initiated by extramural scientists.… They also tend to be used more in problem-oriented research efforts” (IOM 1998, 19). These more applied mechanisms have been crucial to several major initiatives at the NIH (e.g., the War on Cancer, the Artificial Kidney Program, the NIH Artificial Heart Program), reflecting the difficulties of targeting research through investigator-initiated peer-reviewed grants (Sampat 2012). They therefore may serve as “safety valves” in the allocation process, enabling the agency to respond from time to time to political pressures for more targeted funding (Sampat 2012). The RCDC database also can disaggregate funds by disease to individual grants, thereby allowing an examination of the degrees to which different types of grants—those emanating from RFAs, clinical trials grants, center grants, and contracts—are responsive to the disease burden. A third contribution of our article is to provide new evidence on allocation choices within individual NIH institutes. Some scholars have suggested that one way in which patterns of NIH funding could respond to disease burden is through congressional allocations to the NIH's twenty-seven institutes and centers (Mowery, Nelson, and Martin 2010). Some of the institutes (e.g., the National Cancer Institute) specialize in particular diseases, and a common political strategy for increasing funding for specific diseases has been through the creation of new institutes (National Research Council 2003; Strickland 1972). Various observers have also proposed that linkages between disease and institute are fuzzy (Mushkin 1979; NIH 1997), which would make cross-institute allocations a blunt priority-setting mechanism (see also Sampat 2012). The data allow, too, an estimation of the extent of each of the institutes’ disease-based priority setting. This is useful not only to compare them with the responsiveness of NIH funding overall but also to address the suggestion made by previous observers that different NIH institutes may have differing commitments to priority setting (IOM 1998). Kastor (2010) emphasized the institutes’ autonomy and the considerable power and discretion of individual institute directors. Indeed, the institutes have been characterized as “mega-silos” (Kastor 2010, 41), and the NIH as “a group of largely independent fiefdoms…institutes led by directors who consider themselves the final authority on matters affecting their programs” (Cohen 1993, 1675). In this context, the various institutes’ responsiveness to disease burden seems plausible. Next we discuss the funding and disease burden data, and then we report our analyses relating NIH funding to disease burden. Finally, we summarize our findings and limitations and discuss their implications.