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Additional file 2. Interview Guide (used for the in-depth interviews in this study).

Practical guidelines for the early screening and field evaluation of banana (Musa spp.) for resistance to three major traits: Fusarium wilt (Fusarium oxysporum f. sp. cubense), leaf spot diseases (Pseudocercospora spp.) and drought. The guidelines have been produced by experts from the Evaluation Thematic Group of MusaNet, the global network for Musa genetic resources (www.musanet.org) coordinated by the Alliance of Bioversity and CIAT.

Additional file 1: Text A1. Details of MapSan intervention facilities. Text A2. Sampling procedures. Text A3. Further details on intervention reach.

Additional file 2. Process evaluation domains, subdomains and indicators. Process indicators used, with details of dichotomisation and role of variables in path models (exogenous / endogenous, intermediary outcome / potential mediator).

Additional file 4. Generalised structural equation model with probit link using data from small compounds (≤ 20 members). Abbreviations: HWF, handwashing facility; HWWS, handwashing with soap.

Additional file 6. Linear probability path analysis of data from small compounds (≤ 20 members) – standardised coefficients and covariances between exogenous variables. Abbreviations: HWF, handwashing facility; HWWS, handwashing with soap.

Additional file 5. Generalised structural equation model with probit link using data from large compounds (> 20 members). Abbreviations: ‘chefe’, chefe de composto (informal compound leader); HWF, handwashing facility; HWWS, handwashing with soap.

Additional file 7. Linear probability path analysis of data from large compounds (> 20 members) – standardised coefficients and covariances between exogenous variables. Abbreviations: ‘chefe’, chefe de composto (informal compound leader); HWF, handwashing facility; HWWS, handwashing with soap.

Clinical trials have shown that antiretroviral drugs are highly effective in preventing HIV acquisition. Such pre-exposure prophylaxis (PrEP), including in sub-Saharan Africa, has almost exclusively focused on certain priority groups, particularly female sex workers, men having sex with men, pregnant women, serodiscordant couples, and young women. As part of a demonstration project of PrEP for the general population at six primary healthcare facilities in Eswatini (formerly Swaziland), we conducted a randomized trial of a healthcare facility-based PrEP promotion package to increase PrEP uptake. Over the 18-month study period, 33.6% (517/1,538) of adults identified by healthcare workers as being at risk of acquiring HIV took up PrEP and 30.0% of these individuals attended all scheduled appointments during the first six months after initiation of PrEP. The PrEP promotion package was associated with a 55% (95% CI: 15% to 110%, p=0.036) relative increase in the number of individuals taking up PrEP, with an absolute increase of 2.2 individuals per month per healthcare facility. When asked how PrEP uptake could be improved in 212 accompanying in-depth qualitative interviews, interviewees perceived an expansion of PrEP promotion activities from healthcare facilities to communities to be essential. Although a healthcare facility-based promotion package improved PrEP uptake, both uptake and retention remained low. Expanding promotion activities to the community is likely needed to achieve greater PrEP coverage among adults at risk of HIV infection in Eswatini and similar settings.

Background: Several performance-based financing (PBF) evaluations have been undertaken in low-income countries, yet few have examined community perspectives of care amid PBF programme implementation. We assessed community members' perspectives of Support for Service Delivery Integration - Performance-Based Incentives ('SSDI-PBI'), a PBF intervention in Malawi, and explored some of the unintended effects that emerged amid implementation. Methods: We conducted 30 focus group discussions: 17 with community leaders and 13 with mothers within catchment areas of SSDI-PBI implementing facilities. We analysed data using the framework approach. Results: Community leaders and women had mixed impressions regarding the effect of SSDI-PBI on service delivery in facilities. They highlighted several improvements (including improved dialogue between staff and community, and cleaner, better-equipped facilities with enhanced privacy), but also persisting challenges (including inadequate and overworked staff, overcrowded facilities and long distances to facilities) related to services in SSDI-PBI-implementing facilities. Further, respondents described how four targeted service indicators related to maternal risk factor management, antenatal care (ANC) in the first trimester, skilled birth attendance and couple's HIV testing sparked unintended negative effects as experienced by women and communities. The unintended effects included women returning home for delivery, women feeling uncertain about their pregnancy status, women feeling betrayed or frustrated by the quality of care provided and partnerless women being denied ANC. Conclusion: PBF programmes such as SSDI-PBI may improve some aspects of service delivery. However, to achieve system improvement, not only should necessary tools (such as medicines, equipment and human resources) be in place, but also programme priorities must be congruent with cultural expectations. Finally, facilities must be better supported to expect and then address increases in client load and heightened expectations in relation to services.

Genetic Resources are made available to researchers, breeders, farmers and other stakeholders in the agricultural sector who need to identify and select germplasm based on a combination of passport, genotypic and phenotypic data. Therefore, documenting this germplasm is critical in order to facilitate its effective use. Integrated information systems powered by large databases are thus required to achieve this objective. The Musa Germplasm Information System (MGIS) (www.cropdiversity. org/mgis) has been developed to address those needs in a user-friendly way. Since 1997, the efforts of documenting banana (Musa spp.) gene banks worldwide have been supported in the MusaNet (www.musanet.org) by a dedicated database called Musa Germplasm Information System (MGIS) to provide standardized passport and characterization data. During recent years, tremendous progress in genotyping and phenotyping allowed scientists to generate a huge amount of data for material maintained in gene banks. Therefore, we started to map of individual accession with data from literature in order to link germplasm with those datasets. As an example, MGIS manages diversity trees with dedicated viewers, and enable retrieval of individual of underlying SNP markers using modern bioinformatic tools. User-friendly interfaces are being developed in parallel for phenotyping datasets annotated with the banana ontology. Finally, we have been testing solutions to facilitate the capture and display of in situ information. For instance, MGIS harvests worldwide observations of banana plants from anyone registered in the Banana natural biodiversity mapping project in iNaturalist (www.inaturalist.org/projects/banana-natural-biodiversity-mapping). Combining this dataset with collecting mission locations will allow us to identify possible gaps in Musa collections. The challenges remain to improve the navigation among these heterogeneous datasets in order to display meaningful information adapted to the audience and to link it with relevant environmental and socio-economic data.