Mapping the Alignment of NIH’s Portfolio to Burden of DiseaseDavid Bochner1, Sara Dodson1, Brian Haugen2, Jessica Lobo2, Laura Cameron2, Judy Riggie2, Rick Ikeda2, and Marina Volkov1

David BochnerNIH Office of Science PolicyEmail: david.bochner@nih.gov

ContactOffice of the Director, Office of Science Policy1, Office of Extramural Research2

Affiliations

• In early 2016, OSP and OER collaborated with the National Center for

Health Statistics (NCHS) at CDC to match RCDC categories with.

NCHS provided the following 2014 data:

o Matches were made between RCDC categories and International

Classification of Disease (ICD)-9/10 codes with NCHS experts

o 103 categories of mortality from the National Vital Statistics System

o 36 categories of prevalence from the National Health Interview

Survey (Not shown)

• In addition, OSP and OER have been collaborating since 2014 to match

RCDC categories with the WHO/Gates Foundation multi-site Global

Burden of Disease (GBD) study, allowing for side by side analysis of

both US and global disease burden

o The GBD dataset covers 240 causes of death and disability

across 188 countries, with six different modeling strategies to

estimate underlying rates from reported data

o GBD data provided on 2013 Disability Adjusted Life Years

(DALYs), mortality(not shown) both US and global, as well as

economic costs within the US.

o OSP staff familiar with the GBD dataset collaborated with OER staff

familiar with RCDC category definitions to make matches

o Straightforward matches don’t always exist—subjective judgement

calls are required

o Granularity isn’t always similar between data sources: GBD records

# of wheels on vehicle involved in accidents, RCDC records “injury”,

RCDC contains rare diseases not measured by GBD.

o RCDC categories are non-mutually exclusive, and may overlap

(ex. Lung and Pneumonia), whereas GBD categories are largely

non-overlapping

METHODS

• Report language with the FY15 and 16 appropriations bills included a request to post “number of Americans affected” alongside RCDC data

• Multiple metrics approximate that request, and different metrics may be more appropriate for some diseases and conditions than others

• In general, large-scale estimates of disease burden are difficult to conduct, are resource-intensive, and can cover a variety of metrics, including prevalence/incidence, mortality, or a combined measurement like disability-adjusted life years, or DALYs

• Few datasets exist to match to a wide range of RCDC categories in a comparable and consistent manner

BACKGROUND

I. US Mortality vs RCDC Funding (2014)

II. US DALYs vs RCDC Funding (2013)

RESULTS

• NIH funding positively correlates (via power law regression) with disease

burden—varying amounts of correlation, depending on metric

• Different metrics mark different diseases as “over/underfunded”

• Ongoing efforts can inform future funding decisions

Conclusions

III. Global DALYs vs RCDC Funding (2013)

IV. US Spending vs RCDC Funding (2013)

RESULTS