Number of grants for biomedical research by funder, type of grant, duration and recipients (World RePORT)

Published May 2019

Since 2012, the World RePORT has collected research grants data from 12 major funders of health research. Presented here is 2016 grant information for 10 funders that reported to World RePORT in 2017, comprising the direct (primary) grants provided by these funders in 2016. (See more about the scope of World RePORT below). 

See also:

What you see Scope and limitations |  Data sources | Current version

What you see

The data visualization above shows grant information from World RePORT for the year 2016, as follows:

  • Number of grants (and average grant duration) by funder (chart A).
  • Number of grants by type (chart B).
  • Number of grants by WHO region and income group (chart C).
  • Number of grants by health category (chart D)
  • Number of grants by recipient country or territory (chart E).
  • Number of grants by recipient organization (chart F).

With respect to disease focus, grants for neglected tropical diseases or ‘R&D Blueprint pathogens’ can be viewed separately (select desired options using the circular buttons at the top left of the data visualization).

Points to note:

Among the grants  provided in 2016 by the 10 funders:

  • As shown in chart A, the United States of America’s National Institutes of Health (NIH) funded the highest number of grants (52,928) and gave the highest average grant duration (6 years and 10 months).
  • The total number of grants was 69,420. 70.4% of these grants were directed towards research (48,878), followed by training (13,008; 18.7%) and meetings (2,907; 4.2%) (chart B).
  • Of grant recipients by income group, low income countries received only 0.2% (165) of all grants (chart C).
  • Among the 450 grants received by African countries (click on the Africa region in chart C), South Africa received the highest number of grants (156; 34.7%), followed by Kenya (69; 15.3%) and Uganda (48; 10.7%) (chart E).
  • Almost three quarters of all grants were for noncommunicable diseases (72%; 40,035), followed by communicable, maternal, perinatal and nutritional conditions (20%; 11,123) and injuries (6%; 3,056) (chart D1).
  • Among noncommunicable diseases, 24% (9,483 grants) were for malignant neoplasms, followed by mental and substance use disorders (15%; 5,945), neurological conditions (12%; 4,981), and cardiovascular diseases (11%; 4,473). (Select the noncommunicable disease category in chart D1 and see disease names sub-category in chart D2.)
  • Among communicable, maternal, perinatal and nutritional conditions (select this category in chart D1), nearly 80% of direct grants (8,826) were for infectious and parasitic diseases, followed by respiratory infections (7%; 738), nutritional deficiencies (6%; 651) and neonatal conditions and maternal conditions (both at 4%; 496 and 412 respectively) (chart D2).
  • 83% of all grants for R&D Blueprint pathogens (select this disease category at the top right) were for Ebola virus disease (43%; 117), Zika virus disease (32%; 89) and severe acute respiratory syndrome (8%; 21) (see chart D3).
  • Of all grants for neglected tropical diseases (select this disease category at the top right), dengue (16%; 125 grants) and leishmaniasis (13%,102 grants) are the two individual diseases that received the highest number of grants (see chart D3).

Exploring the data further

  • Select a funder, a WHO region, a country or any other category (by clicking on a bar in a chart or a cell in a table) to filter data for the desired selection in the other charts.
    -- For example, by selecting noncommunicable diseases in chart D1 and malignant neoplasms in chart D2, chart D3 shows that breast cancer (aside from malignant neoplasms without specification of site) received the highest number of grants (803), followed by leukaemia (447) and brain and nervous systems cancers (432).
  • Hover the cursor on a data element (a bar or a cell) to see more information in a popup window (for example, percentages and other relevant categorizations). The popup information is updated automatically for any of the selections made by filtering the data in the previous step.
  • Hold the ‘Ctrl’ key to select more than one option, for example two regions.
  • For recipient countries or organizations, select a different number to display by clicking on the ‘>’ or ‘<’ symbol="" above="" the="" relevant="" chart.="">
  • Undo a selection by clicking ‘undo’ or ‘reset’ near the bottom of the page or by clicking the same element again.

Scope, analysis and limitations of the data

Scope
  • The World RePORT is hosted by the United States of America’s National Institutes of Health and managed through a steering committee of the agencies providing data.
  • To date, the World RePORT includes data reported by 12 major funders of health research. Not all of these funders report on a yearly basis. For example, in 2017 only 10 of the 12 funders reported data (for grants in 2016).
  • The World RePORT data include direct (primary) grants provided to recipient institutions as well as collaborations with other institutions resulting from these grants (indirect grants administered by recipient institutions). This data visualization focuses on direct grants. Indirect grants resulting from collaborations with the primary recipient institutions are analysed separately.
  • Collectively, 8 of the 12 funders that have reported since 2012 make up approximately 78% of the annual health research expenditures of 55 major public and philanthropic funders of health research according to Viergever & Hendriks 2015.
Analysis
  • Automated data mining was used to generate information on the type of grant and health category using text-based data fields for each grant.
    -- To determine the type of grant, synonyms for the type of grant categories listed in the data visualization were extracted from the title or abstract (if available) of the grant record.
    -- To assign a health category to each grant, the Observatory’s compiled list of disease synonyms was used as described below. The list was compiled using as a base the Unified Medical Language System (UMLS) and the 10th version of the International Classification of Diseases. This was complemented by synonyms drawn from the data, mostly to account for errors in data entry such as spelling errors or use of abbreviations.
    -- An automated algorithm was applied to two data fields, the grant’s title and abstract, using the list of disease synonyms to generate the disease classification field used in this analysis. The algorithm stops if a match is identified using the title field, if not a match using the abstract field is pursued. The first match closer to the beginning of the text field was selected. This was considered the primary disease focus of the grant. It is possible that a grant has more than one disease focus; this is not captured in this analysis.
    -- The algorithm was refined through various iterations and will continue to be refined in future updates but as with any automated algorithm, it is likely that some grants were not correctly matched.
  • For the calculation of the average duration of grants, we assumed a 5-year renewal term for a continuous grant (no definite end date); this adjustment was applied to less than 1% of the data.
Limitations of the data
  • For some funders, especially those with relatively new or fixed data collection platforms, the data do not accurately reflect the scale of research investments or collaborations between recipients of grants.
  • This analysis only reports the number of grants. It does not reflect the volume of investments across countries or categories of funding. While investments data exist for some funders (such as the NIH), it does not yet exist in a standardized way across all reporting funders. This area is expected to be better addressed in future years as a result of the collaborative efforts among the reporting funders of the World RePORT to improve data collection and reporting standards.
  • This analysis will be updated when new data becomes available; however, time lags with the scheduled updates by the data source are inevitable. Accuracy and completeness of the information is the responsibility of the data source, see terms and conditions of use

Data sources