Table 1
Contextual comparison of the DRC and Uganda.
| Country Characteristics | DRC | Uganda | |
|---|---|---|---|
| Population (2018) [16] | 84.1 million | 42.7 million | |
| Human Development Index Rank (of 189 entries) (2019) [16] | 179 | 159 | |
| HIV prevalence (age 15–49) (2018) [19] | 0.8 [95% CI 0.6–0.9] | 5.7 [95% CI 5.4–6.1] | |
| Tuberculosis incidence rate (per 100,000 people) (2018) [20] | 321 [95% CI 208–458] | 200 [95% CI 118–304] | |
| Malaria incidence rate (per 1,000 people at risk) (2018) [21] | 320 | 289 | |
| Global Fund Grant Characteristics | DRC | Uganda | |
| Portfolio Type# | High Impact | High Impact | |
| Challenging Operating Environment (COE)* | Yes | No | |
| Income category† | Low Income | Low Income | |
| Funding Requests and type of review^ | TB/HIV | Tailored | Full |
| Malaria | Program Continuation | Full | |
| Global Fund Allocation 2017–19 (US$, millions) [17] | $527.1 | $465.1 | |
| Additional catalytic matching funds (US$, millions) [18] | $16.0 | $9.4 | |
| Total number of grants signed to-date [22] | 26 | 20 | |
| Total investments signed to-date, since 2003 (US$, billions) [22] | $2.00 | $1.49 | |
[i] # In 2016, Global Fund’s Differentiation for Impact initiative resulted in three portfolio categories: Focused (<$75 million; lower disease burden); Core ($75–400 million; higher disease burden); High impact (>$400 million; mission critical disease burden) [23].
* The COE policy was approved by the Global Fund Board in April 2016 to provide guidance on Global Fund engagement in COE contexts through the principles of flexibility, partnerships, and innovation [24].
† Global Fund’s income level eligibility is based on the World Bank (Atlas Method) Income Classifications, using the latest three-year average of gross national income per capita data to determine income classification thresholds in 2016 [25].
^ A differentiated funding request model was introduced in 2017 to further streamline the application process [4].
Table 2
Characteristics of identified actors in DRC and Uganda by funding request, gender, and organizational affiliation.
| DRC | Uganda | |||||
|---|---|---|---|---|---|---|
| Funding Request | Respondent | Named in survey | Total N (% of total) | Respondent | Named in survey | Total N (% of total) |
| TB/HIV request only | 21 | 54 | 75 (49.3%) | 13 | 39 | 52 (44.1%) |
| Malaria request only | 8 | 43 | 51 (33.6%) | 6 | 24 | 30 (25.4%) |
| Both | 9 | 15 | 24 (15.8%) | 11 | 25 | 36 (30.5%) |
| Unknown | 2 | 0 | 2 (1.3%) | 0 | 0 | 0 (0.0%) |
| Gender | ||||||
| Male | 32 | 82 | 114 (75.0%) | 17 | 47 | 64 (54.2%) |
| Female | 8 | 30 | 38 (25.0%) | 13 | 41 | 54 (45.8%) |
| Organization Type | ||||||
| NGO/civil society | 7 | 29 | 36 (23.7%) | 5 | 13 | 18 (15.3%) |
| Technical partners | 5 | 25 | 30 (19.7%) | 4 | 25 | 29 (24.6%) |
| Principal Recipient: Gov# | 10 | 14 | 24 (15.8%) | 13 | 19 | 32 (27.1%) |
| Principal Recipient: NGO | 4 | 12 | 16 (10.5%) | 3 | 5 | 8 (6.8%) |
| Sub Recipient: NGO | 5 | 15 | 20 (13.1%) | – | – | – |
| Government (other)* | 6 | 11 | 17 (11.2%) | 1 | 10 | 11 (9.3%) |
| Consultant | – | – | – | 0 | 8 | 8 (6.8%) |
| CCM | 1 | 6 | 7 (4.6%) | 3 | 3 | 6 (5.1%) |
| Local Fund Agent | 1 | 0 | 1 (0.7%) | 1 | 2 | 3 (2.5%) |
| Global Fund | – | – | – | 0 | 3 | 3 (2.5%) |
| Unknown | 1 | 0 | 1 (0.7%) | – | – | – |
| Totals | 40 (26.3%) | 112 (73.7%) | 152 (100%) | 30 (25.4%) | 88 (74.6%) | 118 (100%) |
[i] # Gov = Government; In DRC, the Ministry of Health serves as the Principal Recipient for the public sector; whereas, in Uganda the Ministry of Finance is the Principal Recipient for the public sector (executing entity) and the Ministry of Health serves as the implementing entity (for the purpose of this table they are grouped).
* Includes other government agencies, departments, or ministries (e.g., Ministry of Gender, Ministry of Education, Ministry of Justice, Armed Forces, Essential Medicines/Supply Chain, Information Systems, National Health Accounts, excluding Ministry of Health which is captured under Principal Recipient: Gov).
Table 3
Comparison of network attribute definitions, values, and interpretation in the DRC and Uganda.
| Attribute | Definition | DRC | UGA | Comparison between DRC and Uganda |
|---|---|---|---|---|
| Node | An individual actor. The number of nodes denotes the network size or the total number of individuals contributing to the application process. | 152 | 118 | The network of individuals contributing to the application process in the DRC and Uganda is quite large. In both countries, there were slightly more identified nodes in the TB/HIV network (DRC: 99; Uganda: 64) than the malaria network (DRC: 75; Uganda: 49), noting that some individuals worked across both funding requests. |
| Tie | Link between two nodes, indicating collaboration between two individual actors working on the application process. | 237 | 241 | We assume all relationship ties were undirected (e.g., mutual; collaborative) during the Global Fund application process. In the DRC, more ties were identified in the TB/HIV network (169) than the malaria network (92). Similarly, in Uganda, more ties were identified in the TB/HIV network (176) than the malaria network (108). Ties where the collaborator worked on both funding requests are counted in both categories. |
| Average degree centrality | Average number of ties per node, meaning the average number of individuals each actor collaborated with. | 3 | 4 | The average node in the DRC had 3 ties, meaning the average individual actor collaborated with 3 individuals. Among individual nodes that responded to the survey, on average each reported 7 ties. Averages were slightly higher in Uganda: 4 ties per node, and 11 ties per respondent node. This suggests the overall density of ties would increase with a higher survey response rate. |
| Isolate | Unconnected node: an individual actor named in the survey with no collaborative ties to other individual actors. | 3 | 4 | In addition to listing up to 10 individuals with whom the survey respondent collaborated, respondents were asked who was “most influential” in the application process. In DRC (n = 3) and Uganda (n = 4), this resulted in isolates; however, these may not be “true” isolates given the survey response rate. |
| Density | Number of existing ties divided by the number of possible ties. | 0.02 | 0.04 | The relatively low density (meaning 2 to 4% of potential ties exist) should be interpreted with caution given the moderate survey response rate. |
| Degree centralization | Extent to which the network is dominated by one or a few focal actors. | 0.09 | 0.16 | The medium-to-low degree centralization score for the DRC (0.09) and Uganda (0.16) networks are indicative of a decentralized network with multiple collaboration hubs across funding requests, which are important for information exchange and settings requiring multiple focal actors across intersecting groups. |
| Betweenness centrality | Extent to which a node is located on the shortest paths between other actors. | See Figure 2 in Supplemental File 3 | Actors with high betweenness centrality scores serve as bridges: they are in a structural position to control the flow of information and to most efficiently transfer information to the greatest number of other actors in the network. | |
| Mean reported trust | Average trust score in the network. | 3.4 | 3.7 | Survey respondents were asked to rate levels of perceived trust (on a scale of 1 to 4) with each of the collaborators they named. The high levels of trust between individuals is indicative of strong collaborative relationships in the DRC and Uganda. |
Figure 1
DRC’s 2017 Global Fund application network with nodes represented by national vs. provincial-level stakeholders for the full network and disaggregated by malaria and TB/HIV funding requests.
Figure 2
Plots of Uganda and DRC’s 2017 Global Fund application networks with nodes represented by funding request type.
Note: A few isolates, or unconnected nodes without ties to others, were identified in each network from survey respondents listing names for the “most influential” member of the network but who were not otherwise named through the listing of individual collaborators—this might be indicative of influential leaders crucial to decision making but not involved in the collaborative work of developing the funding requests.
Figure 3
Plots of Uganda and the DRC’s 2017 Global Fund application networks with nodes represented by gender.
Figure 4
Plots of Uganda and DRC’s 2017 Global Fund application networks with nodes represented by organizational affiliation.
Table 4
Perceived benefits and drawbacks of partnership in DRC and Uganda.
| Perceived benefits of partnership | Agreed “occurred” | |
|---|---|---|
| Effectiveness | DRC | Uganda |
| Increased quality and technical soundness of the approved grants | 28 (78%) | 27 (100%) |
| Better able to execute activities | 28 (78%) | 25 (93%) |
| Better able to respond to challenges and bottlenecks that arose during process | 28 (78%) | 25 (93%) |
| Better able to identify the need for, and to acquire, additional technical support | 30 (83%) | 23 (85%) |
| Mean (effectiveness benefits) | 79% | 93% |
| Efficiency | DRC | Uganda |
| More timely execution of planned activities | 21 (58%) | 25 (93%) |
| Leveraged each organization’s comparative advantages | 16 (44%) | 23 (85%) |
| Reduced transaction costs (i.e., more streamlined grant application process) | 13 (36%) | 13 (48%) |
| Reduction in financial cost of process | 12 (33%) | 5 (19%) |
| Mean (efficiency benefits) | 43% | 61% |
| Country ownership | DRC | Uganda |
| Approved grants that are more responsive to country needs | 15 (42%) | 25 (93%) |
| Increased inclusiveness of key stakeholders in the process | 27 (75%) | 23 (85%) |
| Increased fairness of decisions made | 27 (75%) | 23 (85%) |
| Increased legitimacy of decisions made | 28 (78%) | 22 (81%) |
| Increased accountability among partners | 26 (72%) | 21 (78%) |
| Increased transparency among partners | 26 (72%) | 21 (78%) |
| Mean (country ownership benefits) | 69% | 83% |
| Perceived drawbacks of partnership | Agreed “occurred” | |
| Effectiveness | DRC | Uganda |
| Created competition and conflict among member organizations | 11 (31%) | 8 (30%) |
| Strained relations within my organization | 4 (11%) | 4 (15%) |
| Mean (effectiveness drawbacks) | 21% | 23% |
| Efficiency | DRC | Uganda |
| Forced to make decisions in a way which was not natural/typical for our organization | 7 (19%) | 7 (27%) |
| Loss of control/autonomy over decisions | 2 (6%) | 4 (15%) |
| Unnecessary management burden on my organization | 7 (19%) | 2 (8%) |
| Mean (efficiency drawbacks) | 15% | 17% |
| Country ownership | DRC | Uganda |
| Not enough credit given to my organization | 3 (8%) | 4 (15%) |
| Total (country ownership drawbacks) | 8% | 15% |