Suchergebnisse

'Vertical' responses focused primarily on preventing and containing COVID-19 have been implemented in countries around the world with negative consequences for other health services, people's access to and use of them, and associated health outcomes, especially in low-income and middle-income countries (LMICs). 'Lockdowns' and restrictive measures, especially, have complicated service provision and access, and disrupted key supply chains. Such interventions, alongside more traditional public health measures, interact with baseline health, health system, and social and economic vulnerabilities in LMICs to compound negative impacts. This analysis, based on a rapid evidence assessment by the Social Science in Humanitarian Action Platform in mid-2020, highlights the drivers and evidence of these impacts, emphasises the additional vulnerabilities experienced by marginalised social groups, and provides insight for governments, agencies, organisations and communities to implement more proportionate, appropriate, comprehensive and socially just responses that address COVID-19 in the context of and alongside other disease burdens. In the short term, there is an urgent need to monitor and mitigate impacts of pandemic responses on health service provision, access and use, including through embedding COVID-19 response within integrated health systems approaches. These efforts should also feed into longer-term strategies to strengthen health systems, expand universal healthcare coverage and attend to the social determinants of health—commitments, both existing and new—which governments, donors and international agencies must make and be held accountable to. Crucially, affected communities must be empowered to play a central role in identifying health priorities, allocating resources, and designing and delivering services.

Abstract
Background
The impacts of infectious disease outbreaks, epidemics, and pandemics are not gender neutral. Instead, infectious diseases and gender-based violence (GBV) mutually reinforce each other. Women and girls in humanitarian settings are disproportionately impacted as crises exacerbate gender inequality, violence, and community transmission. A syndemic model of infectious disease and GBV draws attention to their critical linkage, enabling more effective approaches to address both infectious disease transmission and GBV prevalence.

Main body
Implementation of infectious disease control measures have been consistently absent of critical gender considerations in humanitarian settings. We drew learnings from Ebola, Zika, and COVID-19 to highlight how women and girls living in humanitarian settings have faced bi-directional syndemic vulnerabilities between GBV and infectious disease. Our findings indicate that Ebola, Zika, and COVID-19 exacerbated GBV risk and experience of GBV increased community transmission of these infectious diseases. Moreover, we identified a failure of existing policies to address this mutually deleterious linkage. Thus, we advocate for policymakers to ask three foundational questions: (i) What are the gendered bi-directional risk pathways between infectious disease and GBV?; (ii) How can we act on the gendered risk pathways?; and, (iii) Who should be involved in designing, implementing, and evaluating gender-sensitive policies?

Conclusion
Our syndemic policy framework challenges existing thinking on a neglected issue that disproportionally impacts women and girls. By offering foundational guidance to address and thwart the syndemic of infectious disease and GBV in humanitarian settings, we endeavor to proactively and holistically address the reinforcing linkage between GBV and current or emergent infectious diseases.

COVID-19 is but one of many public health crises facing the people of the Democratic Republic of the Congo (DRC). On 25 June 2020, the DRC government announced the end of the country's largest Ebola outbreak on record and the second largest Ebola outbreak worldwide, a mere few weeks after a new outbreak (11th) started on 1 June 2020, in Mbandaka, Equateur Province.1 In 2019, measles claimed the lives of over 6000 people including 4500 children under the age of 5, malaria killed 17000 individuals, and cholera outbreaks affected 20 of 26 provinces, resulting in 31000 cases.

The emerging field of outbreak analytics calls attention to the need for data from multiple sources to inform evidence-based decision making in managing infectious diseases outbreaks. To date, these approaches have not systematically integrated evidence from social and behavioural sciences. During the 2018–2020 Ebola outbreak in Eastern Democratic Republic of the Congo, an innovative solution to systematic and timely generation of integrated and actionable social science evidence emerged in the form of the Cellulle d'Analyse en Sciences Sociales (Social Sciences Analytics Cell) (CASS), a social science analytical cell. CASS worked closely with data scientists and epidemiologists operating under the Epidemiological Cell to produce integrated outbreak analytics (IOA), where quantitative epidemiological analyses were complemented by behavioural field studies and social science analyses to help better explain and understand drivers and barriers to outbreak dynamics. The primary activity of the CASS was to conduct operational social science analyses that were useful to decision makers. This included ensuring that research questions were relevant, driven by epidemiological data from the field, that research could be conducted rapidly (ie, often within days), that findings were regularly and systematically presented to partners and that recommendations were co-developed with response actors. The implementation of the recommendations based on CASS analytics was also monitored over time, to measure their impact on response operations. This practice paper presents the CASS logic model, developed through a field-based externally led consultation, and documents key factors contributing to the usefulness and adaption of CASS and IOA to guide replication for future outbreaks.

The emerging field of outbreak analytics calls attention to the need for data from multiple sources to inform evidence-based decision making in managing infectious diseases outbreaks. To date, these approaches have not systematically integrated evidence from social and behavioural sciences. During the 2018-2020 Ebola outbreak in Eastern Democratic Republic of the Congo, an innovative solution to systematic and timely generation of integrated and actionable social science evidence emerged in the form of the Cellulle d'Analyse en Sciences Sociales (Social Sciences Analytics Cell) (CASS), a social science analytical cell. CASS worked closely with data scientists and epidemiologists operating under the Epidemiological Cell to produce integrated outbreak analytics (IOA), where quantitative epidemiological analyses were complemented by behavioural field studies and social science analyses to help better explain and understand drivers and barriers to outbreak dynamics. The primary activity of the CASS was to conduct operational social science analyses that were useful to decision makers. This included ensuring that research questions were relevant, driven by epidemiological data from the field, that research could be conducted rapidly (ie, often within days), that findings were regularly and systematically presented to partners and that recommendations were co-developed with response actors. The implementation of the recommendations based on CASS analytics was also monitored over time, to measure their impact on response operations. This practice paper presents the CASS logic model, developed through a field-based externally led consultation, and documents key factors contributing to the usefulness and adaption of CASS and IOA to guide replication for future outbreaks.