The Gates Foundation is part of the Grand Challenges partnership network. Visit <a href="https://www.grandchallenges.org" target="_blank">www.grandchallenges.org</a> to view the map of awarded grants across this network and grant opportunities from partners.

Grand Challenges supports exceptional scientists with the expertise to do breakthrough R&amp;D as well as the experience, networks, and skills to excel as scientific leaders. These champions co-create projects with global partners, mobilize key institutions in their countries, and improve public health in their communities and around the world.

Grand Challenges funds innovations to tackle the biggest problems in global health and development. Since 2004, through the Grand Challenges program, we have supported more than 2500 innovation projects in more than 90 countries.

For more than 20 years, Grand Challenges has built trusted relationships with global health funders and researchers. We tap into this powerful network to create unique scientific collaborations with a transformative impact on people's lives.

Innovation happens all along the journey from idea, to experiment, to product or policy, and finally to delivery at scale in communities. We fund innovation projects while supporting an environment of champions, systems, and partnerships to maximize the impact these projects make in people's lives.

The 4 Pillars of the Grand Challenges Ecosystem

Doing groundbreaking science requires the right mix of institutions, policies, and tools. Grand Challenges works with partners in low- and middle-income countries to build or strengthen the capacities they need to succeed in R&amp;D.

We challenge innovators around the world to work on urgent priorities in global health and development. We issue new challenges regularly and award the most promising proposals with grant funding.&nbsp;

Past Challenges

<h2>Background</h2>

The scale and scope of the COVID-19 pandemic have reiterated the need for access to and analysis of timely accurate health data to support the implementation of control measures and to guide health interventions and evidence-based practices. The supporting infrastructures and capacities through which researchers access and analyze these data, however, vary greatly. For several reasons, researchers in the Global South often are skeptical of broad calls for data sharing when they are left out of the analysis and results interpretation. Additionally, deploying “one-size-fits-all” analysis tools and governance processes across different regions from where they were created may produce poor analysis results and could even lead to misleading interpretations and conclusions when scientists analyze data without insight from the context in which it was collected.

As such, data science infrastructure and capacity needs to be proximally focused in the areas where the data emerge, to produce solutions that enable strong evidence-based practices to guide health interventions.

<h2>The Challenge</h2>

Collecting, sharing, analyzing and interpreting health-related data are essential to the planning, implementation, and evaluation of public health practice as well as enabling advanced knowledge for heath emergency preparedness. Many data resources are not reusable if they have not been sufficiently documented and contextualized, or appropriately collected and licensed for use, particularly in cases where secondary analyses are planned that were not originally anticipated when the data were originally generated.

The desired outcome of this request for proposals is development of tools and processes that are "fit-for-purpose" to facilitate collaboration among researchers in low- and middle-income countries. This effort is focused on filling data science gaps and challenges whose solutions will address global health problems while fostering multi-disciplinary collaborations among researchers. We are specifically interested in funding proposals led by early and mid-career researchers. We expect each primary applicant to have a senior scientific advisor who would commit to provide scientific and technical guidance and mentorship to the team and facilitate connections with international networks as needed.

Specifically, the objectives of the challenge will be to:

<ul>
	<li>Develop and improve foundational tools, standards and protocols that enable data-centered health research, interventions, and health surveillance</li>
	<li>Improve access to curated and linked datasets for research and training purposes</li>
	<li>Enable South-South data-centered collaborations and build on and strengthen existing initiatives and ecosystems</li>
	<li>Enhance Data Science infrastructure, analysis capabilities and local leadership to address public health problems</li>
	<li>Foster innovation with health data in the interest of their respective local communities</li>
	<li>Bridge the gap between data-centered research and policy through collaborations and design of appropriate data science communication tools and channels</li>
	<li>Increase health data reliability and enable reuse by developing tools and processes to curate, standardize and aggregate data</li>
	<li>Improve data collection, sharing, governance, regulatory compliance, and analysis processes to enable data-centered public health research</li>
</ul>

What We Are Looking For

We will be developing a coordinated program in order to support these objectives. This will include cross-cutting cores which will provide specific functions to the program as well as individual research projects focusing on one of more specific disease areas.

Applications are invited from researchers working in healthcare, academia, industry, or research foundations, and will be welcomed from researchers based in any of the following regions: Africa, India, Bangladesh, Pakistan, Nepal, and Brazil. Each proposal should be submitted by one primary applicant, but the awards require the participation of at least two research institutions from the regions listed above and not both in the same country.

Program structure

<img alt="Strengthening Data Science Capacity and the Ecosystem Diagram" height="329" src="https://gcgh.grandchallenges.org/sites/default/files/images/picture1.png" width="540" />

Data governance and standards core

We seek to fund one data governance and standards core. This core would:

<ul>
	<li>Support the research projects funded through this initiative including the capacity strengthening core</li>
	<li>Leverage the funded research projects as use cases for development and refinement of data sharing processes and governance</li>
	<li>Seek to strengthen global communities and multi-disciplinary collaboration from researchers in low- to middle-income countries (LMICs) by developing processes to enable South-South collaborative analysis</li>
	<li>Develop adapted standards or Standard Operating Procedures that address data sharing issues including anonymization and perturbation protocols</li>
	<li>Develop re-usable tools, protocols, and standards to collect, clean, harmonize, and commonly structure individual data and meta-data related to one or many disease areas to increase health data reliability and enable reuse. New tools and process development should leverage and build on existing work</li>
	<li>Develop data informed processes to overcome barriers to the collection of complete and accurate information; and challenges related to data and information governance and data reuse</li>
</ul>

Capacity strengthening core

We seek to fund one capacity strengthening core to:

<ul>
	<li>Support the research projects and the data infrastructure and governing core funded through this initiative</li>
	<li>Bridge the gap between data scientists, modelers, regulators and policy makers, through trainings and best practices development</li>
	<li>Develop re-usable training materials and handbooks</li>
	<li>Lead and coordinate collaborative analysis efforts through different formats (workshops, hackathons, jamborees, etc.)</li>
	<li>Lead capacity-building efforts targeting existing gaps in data science trainings in LMICs and building on existing initiatives (reusing available resources when available to avoid duplication of efforts)</li>
	<li>Develop data-informed processes to overcome barriers to the collection of complete and accurate information, and challenges related to data and information governance and data reuse</li>
</ul>

Data re-use research projects

We seek to fund two data re-use projects where research will focus on one of more specific disease areas:

<ul>
	<li>Build data-driven public health platforms, tools and dashboards that guide policy making and public health interventions</li>
	<li>Identify, use, curate and link clearly defined datasets (administrative, clinical, environmental, etc.) to address policy gaps or deliver insights and conceptual and/or technological leaps on Global Health research priorities including:
	<ul>
		<li>Maternal, Newborn and Child Health</li>
		<li>Infectious diseases</li>
		<li>COVID-19 and pandemic preparedness</li>
	</ul>
	</li>
</ul>

<ul>
	<li>Proposals could focus on a specific disease area but need to demonstrate commitment to providing access to all research products including tools and processes developed</li>
</ul>

Funding level 

Successful proposals will receive an award of up to USD $250,000 with an 18- to 24-month grant duration. While proposals require the collaborative participation of at least two investigators at institutions in different countries based in any of the regions listed above, proposals should be submitted by one primary applicant/institution. While restricted to one application per institution as the primary applicant, researchers may participate as collaborators in multiple collaborative applications.

Selected proposals will become part of a coordinated program aiming at Strengthening Data Science Capacity and the Ecosystem to Enable Data-Centered Public Health Interventions. We seek to build a network of researchers working together in close collaboration with the existing <a href="https://gcgh.grandchallenges.org/grants/JTdCJTIyc29ydCUyMiUzQSUyMmRlc2NlbmRpbmclMjIlMkMlMjJzb3J0RmllbGQlMjIlM0ElMjJkYXRlJTIyJTJDJTIybnVtYmVyUGVyUGFnZSUyMiUzQTEwMCUyQyUyMnBhZ2VOdW1iZXIlMjIlM0EwJTdE/JTdCJTIyeWVhcnMlMjIlM0ElNUIlNUQlMkMlMjJjaGFsbGVuZ2VzJTIyJTNBJTVCJTIyMTE3JTIyJTVEJTJDJTIydGVybXMlMjIlM0ElNUIlNUQlMkMlMjJpbml0aWF0aXZlcyUyMiUzQSU1QiU1RCUyQyUyMmxvY2F0aW9ucyUyMiUzQSU1QiU1RCU3RA==">Data Science Grand Challenges network</a> and its partners to build on lessons learned in different regions and further develop processes, tools and governance that maximize the impact of data-focused research studies, as well as to identify and address the global data-related challenges.

Successful proposals to be considered should: 

<ul>
	<li>Be driven by a shared commitment to open science, data sharing, and building collaboration and analysis infrastructure to enable discoveries that will benefit people everywhere</li>
	<li>Seek to strengthen global communities and multi-disciplinary collaboration from researchers in LMICs, and involve patients and the wider public</li>
	<li>Involve substantial collaboration between at least two research institutions in different countries encompassing Africa, India, Bangladesh, Pakistan, Nepal, and Brazil. The suggested collaboration should be key to advancing the project goals and yield insights that are unlikely in the absence of the co-produced approaches. In addition, however, applications could include collaborations with institutions in other geographical areas</li>
	<li>Have the potential to have impact on addressing public health issues within the proposed budget and timeframe of 18 -24 months</li>
	<li>Data re-use proposals should use well-defined datasets that directly address the core research questions and have a clear governance plan and demonstrate how the data could benefit the broader community at a later stage.</li>
	<li>Demonstrate a commitment to impact policies and the general public</li>
</ul>

We will not consider funding for: 

<ul>
	<li>Studies that aim to collect and generate new data through this funding</li>
	<li>Proposals led by institutions not based in any of these regions: Africa, India, Bangladesh, Pakistan, Nepal, and Brazil</li>
	<li>Proposals that will involve a single research institution or solely involve collaborators from multiple institutions from the same country</li>
	<li>Proposals that do not demonstrate a clear commitment to open science and making their findings, processes and/or tools developed accessible and reusable</li>
	<li>Proposals that do not demonstrate how they would leverage existing work and networks</li>
	<li>Proposals that do not demonstrate how they can benefit from and support other projects funded through this coordinated program</li>
	<li>Proposals that are not accomplishable within the grant term</li>
</ul>

Grand Challenges Global Call-to-Action

Strengthening Data Science Capacity and the Ecosystem: Enabling Data-Centered Public Health Interventions

<h2>Background</h2>
The scale and scope of the COVID-19 pandemic have reiterated the need for access to and analysis of timely accurate health data to support the implementation of control measures and to guide health interventions and evidence-based practices. The supporting infrastructures and capacities through which researchers access and analyze these data, however, vary greatly. For several reasons, researchers in the Global South often are skeptical of broad calls for data sharing when they are left out of the analysis and results interpretation. Additionally, deploying “one-size-fits-all” analysis tools and governance processes across different regions from where they were created may produce poor analysis results and could even lead to misleading interpretations and conclusions when scientists analyze data without insight from the context in which it was collected.
As such, data science infrastructure and capacity needs to be proximally focused in the areas where the data emerge, to produce solutions that enable strong evidence-based practices to guide health interventions.
<h2>The Challenge</h2>
Collecting, sharing, analyzing and interpreting health-related data are essential to the planning, implementation, and evaluation of public health practice as well as enabling advanced knowledge for heath emergency preparedness. Many data resources are not reusable if they have not been sufficiently documented and contextualized, or appropriately collected and licensed for use, particularly in cases where secondary analyses are planned that were not originally anticipated when the data were originally generated.
The desired outcome of this request for proposals is development of tools and processes that are "fit-for-purpose" to facilitate collaboration among researchers in low- and middle-income countries. This effort is focused on filling data science gaps and challenges whose solutions will address global health problems while fostering multi-disciplinary collaborations among researchers. We are specifically interested in funding proposals led by early and mid-career researchers. We expect each primary applicant to have a senior scientific advisor who would commit to provide scientific and technical guidance and mentorship to the team and facilitate connections with international networks as needed.
Specifically, the objectives of the challenge will be to:
<ul>
<li>Develop and improve foundational tools, standards and protocols that enable data-centered health research, interventions, and health surveillance</li>
<li>Improve access to curated and linked datasets for research and training purposes</li>
<li>Enable South-South data-centered collaborations and build on and strengthen existing initiatives and ecosystems</li>
<li>Enhance Data Science infrastructure, analysis capabilities and local leadership to address public health problems</li>
<li>Foster innovation with health data in the interest of their respective local communities</li>
<li>Bridge the gap between data-centered research and policy through collaborations and design of appropriate data science communication tools and channels</li>
<li>Increase health data reliability and enable reuse by developing tools and processes to curate, standardize and aggregate data</li>
<li>Improve data collection, sharing, governance, regulatory compliance, and analysis processes to enable data-centered public health research</li>
</ul>
What We Are Looking For
We will be developing a coordinated program in order to support these objectives. This will include cross-cutting cores which will provide specific functions to the program as well as individual research projects focusing on one of more specific disease areas.
Applications are invited from researchers working in healthcare, academia, industry, or research foundations, and will be welcomed from researchers based in any of the following regions: Africa, India, Bangladesh, Pakistan, Nepal, and Brazil. Each proposal should be submitted by one primary applicant, but the awards require the participation of at least two research institutions from the regions listed above and not both in the same country.
Program structure
<img alt="Strengthening Data Science Capacity and the Ecosystem Diagram" height="329" src="https://gcgh.grandchallenges.org/sites/default/files/images/picture1.png" width="540">
Data governance and standards core
We seek to fund one data governance and standards core. This core would:
<ul>
<li>Support the research projects funded through this initiative including the capacity strengthening core</li>
<li>Leverage the funded research projects as use cases for development and refinement of data sharing processes and governance</li>
<li>Seek to strengthen global communities and multi-disciplinary collaboration from researchers in low- to middle-income countries (LMICs) by developing processes to enable South-South collaborative analysis</li>
<li>Develop adapted standards or Standard Operating Procedures that address data sharing issues including anonymization and perturbation protocols</li>
<li>Develop re-usable tools, protocols, and standards to collect, clean, harmonize, and commonly structure individual data and meta-data related to one or many disease areas to increase health data reliability and enable reuse. New tools and process development should leverage and build on existing work</li>
<li>Develop data informed processes to overcome barriers to the collection of complete and accurate information; and challenges related to data and information governance and data reuse</li>
</ul>
Capacity strengthening core
We seek to fund one capacity strengthening core to:
<ul>
<li>Support the research projects and the data infrastructure and governing core funded through this initiative</li>
<li>Bridge the gap between data scientists, modelers, regulators and policy makers, through trainings and best practices development</li>
<li>Develop re-usable training materials and handbooks</li>
<li>Lead and coordinate collaborative analysis efforts through different formats (workshops, hackathons, jamborees, etc.)</li>
<li>Lead capacity-building efforts targeting existing gaps in data science trainings in LMICs and building on existing initiatives (reusing available resources when available to avoid duplication of efforts)</li>
<li>Develop data-informed processes to overcome barriers to the collection of complete and accurate information, and challenges related to data and information governance and data reuse</li>
</ul>
Data re-use research projects
We seek to fund two data re-use projects where research will focus on one of more specific disease areas:
<ul>
<li>Build data-driven public health platforms, tools and dashboards that guide policy making and public health interventions</li>
<li>Identify, use, curate and link clearly defined datasets (administrative, clinical, environmental, etc.) to address policy gaps or deliver insights and conceptual and/or technological leaps on Global Health research priorities including:
<ul>
<li>Maternal, Newborn and Child Health</li>
<li>Infectious diseases</li>
<li>COVID-19 and pandemic preparedness</li>
</ul>
</li>
</ul>
<ul>
<li>Proposals could focus on a specific disease area but need to demonstrate commitment to providing access to all research products including tools and processes developed</li>
</ul>
Funding level 
Successful proposals will receive an award of up to USD $250,000 with an 18- to 24-month grant duration. While proposals require the collaborative participation of at least two investigators at institutions in different countries based in any of the regions listed above, proposals should be submitted by one primary applicant/institution. While restricted to one application per institution as the primary applicant, researchers may participate as collaborators in multiple collaborative applications.
Selected proposals will become part of a coordinated program aiming at Strengthening Data Science Capacity and the Ecosystem to Enable Data-Centered Public Health Interventions. We seek to build a network of researchers working together in close collaboration with the existing <a href="https://gcgh.grandchallenges.org/grants/JTdCJTIyc29ydCUyMiUzQSUyMmRlc2NlbmRpbmclMjIlMkMlMjJzb3J0RmllbGQlMjIlM0ElMjJkYXRlJTIyJTJDJTIybnVtYmVyUGVyUGFnZSUyMiUzQTEwMCUyQyUyMnBhZ2VOdW1iZXIlMjIlM0EwJTdE/JTdCJTIyeWVhcnMlMjIlM0ElNUIlNUQlMkMlMjJjaGFsbGVuZ2VzJTIyJTNBJTVCJTIyMTE3JTIyJTVEJTJDJTIydGVybXMlMjIlM0ElNUIlNUQlMkMlMjJpbml0aWF0aXZlcyUyMiUzQSU1QiU1RCUyQyUyMmxvY2F0aW9ucyUyMiUzQSU1QiU1RCU3RA==">Data Science Grand Challenges network</a> and its partners to build on lessons learned in different regions and further develop processes, tools and governance that maximize the impact of data-focused research studies, as well as to identify and address the global data-related challenges.
Successful proposals to be considered should: 
<ul>
<li>Be driven by a shared commitment to open science, data sharing, and building collaboration and analysis infrastructure to enable discoveries that will benefit people everywhere</li>
<li>Seek to strengthen global communities and multi-disciplinary collaboration from researchers in LMICs, and involve patients and the wider public</li>
<li>Involve substantial collaboration between at least two research institutions in different countries encompassing Africa, India, Bangladesh, Pakistan, Nepal, and Brazil. The suggested collaboration should be key to advancing the project goals and yield insights that are unlikely in the absence of the co-produced approaches. In addition, however, applications could include collaborations with institutions in other geographical areas</li>
<li>Have the potential to have impact on addressing public health issues within the proposed budget and timeframe of 18 -24 months</li>
<li>Data re-use proposals should use well-defined datasets that directly address the core research questions and have a clear governance plan and demonstrate how the data could benefit the broader community at a later stage.</li>
<li>Demonstrate a commitment to impact policies and the general public</li>
</ul>
We will not consider funding for: 
<ul>
<li>Studies that aim to collect and generate new data through this funding</li>
<li>Proposals led by institutions not based in any of these regions: Africa, India, Bangladesh, Pakistan, Nepal, and Brazil</li>
<li>Proposals that will involve a single research institution or solely involve collaborators from multiple institutions from the same country</li>
<li>Proposals that do not demonstrate a clear commitment to open science and making their findings, processes and/or tools developed accessible and reusable</li>
<li>Proposals that do not demonstrate how they would leverage existing work and networks</li>
<li>Proposals that do not demonstrate how they can benefit from and support other projects funded through this coordinated program</li>
<li>Proposals that are not accomplishable within the grant term</li>
</ul>

The scale and scope of the COVID-19 pandemic have reiterated the need for access to and analysis of timely accurate health data to support the implementation of control measures and to guide health interventions and evidence-based practices. The supporting infrastructures and capacities through which researchers access and analyze these data, however, vary greatly. For several reasons, researchers in the Global South often are skeptical of broad calls for data sharing when they are left out of the analysis and results interpretation.

Strengthening Data Science Capacity and the Ecosystem Enabling Data-Centered Public Health Interventions

The International COVID-19 Data Alliance (ICODA) and the Bill &amp; Melinda Gates Foundation’s Grand Challenges initiative, supported by the COVID-19 Therapeutics Accelerator, are announcing an open funding call for research studies to accelerate knowledge of COVID-19 through data science: Grand Challenges ICODA COVID-19 Data Science.

July 29, 2021 update: The International COVID-19 Data Alliance (ICODA) and the Bill &amp; Melinda Gates Foundation's Grand Challenges initiative, with support from the COVID-19 Therapeutics Accelerator and the Minderoo Foundation, <a href="https://icoda-research.org/grand-challenges-grants-awarded/" target="_blank">announced</a> an international set of grants awarded by Health Data Research UK (HDRUK) to 10 research organizations in 7 countries. The grants are for research studies to accelerate knowledge of COVID-19 through data science. Read summaries of the awarded grants <a href="https://gcgh.grandchallenges.org/grants/JTdCJTIyc29ydCUyMiUzQSUyMmRlc2NlbmRpbmclMjIlMkMlMjJzb3J0RmllbGQlMjIlM0ElMjJkYXRlJTIyJTJDJTIybnVtYmVyUGVyUGFnZSUyMiUzQTEwJTJDJTIycGFnZU51bWJlciUyMiUzQTAlN0Q=/JTdCJTIyeWVhcnMlMjIlM0ElNUIlMjIyMDIxJTIyJTVEJTJDJTIyY2hhbGxlbmdlcyUyMiUzQSU1QiUyMjExNyUyMiU1RCUyQyUyMnRlcm1zJTIyJTNBJTVCJTVEJTJDJTIyaW5pdGlhdGl2ZXMlMjIlM0ElNUIlMjI3ZWExOGIzNy1jMjk5LTQzZWUtYTg2Ni05NDM3ZTAxNWVmYzklMjIlNUQlMkMlMjJsb2NhdGlvbnMlMjIlM0ElNUIlNUQlN0Q=" target="_blank">here.</a>

Grand Challenges

Grand Challenges ICODA COVID-19 Data Science

The International COVID-19 Data Alliance (ICODA) and the Bill &amp; Melinda Gates Foundation’s Grand Challenges initiative, supported by the COVID-19 Therapeutics Accelerator, are announcing an open funding call for research studies to accelerate knowledge of COVID-19 through data science: Grand Challenges ICODA COVID-19 Data Science.

The National Council for Scientific and Technological Development (CNPq), the Department of Science and Technology at the Secretariat of Science, Technology, Innovation and Strategic Inputs for Health (Decit/SCTIE/MS) and the Bill &amp; Melinda Gates Foundation (BMGF), launch this public Request for Proposals (RFP), and invite all interested parties to present their proposals on the basis of what is hereby established.

<h2>1. Objective</h2>

To support research projects that intend to contribute significantly to the scientific and technologic development and to innovation in the country, in the field of data sciences, to improve Maternal and Child Health, Women’s Health and the Health of Children in Brazil.

<ol start="1">
	<li>1 The present RFP has the objective of:
	<ol type="a">
		<li>Selecting and contracting proposals in accordance with research topics defined in subitem 1.2.</li>
		<li>Promoting actions for scientific education and dissemination to different target groups, so as to achieve broad social reach, including experts, groups and institutions that act in the area of formal and informal education, (eg. schools, extension studies, museums, science centers, zoos, botanical gardens, aquariums, conservation units' visitor centers and NGOs).</li>
	</ol>
	</li>
</ol>

<ol start="1">
	<li>2 Proposals shall comply with the research lines and study types:</li>
</ol>

<ol type="i">
	<li>Cross sectional

	<ol type="a">
		<li>To apply innovative and technical analysis that employ machine learning to identify data patterns and natural experiments (e.g. the impact of economic cycles on primary care quality);</li>
		<li>To analyze primary health care distribution and quality and its relation with health outcomes and infant and maternal nutrition;</li>
		<li>To present prospective scenarios in Infant and Maternal Health, evaluating populational and epidemiological trends, to support the planning of programmatic activities and the identification of opportunities for intervention;</li>
		<li>To develop monitoring, visualization, simulation, and health indicator projection tools that support the management of public health programs related to women health, child health, and nutrition programs.</li>
	</ol>
	</li>
	<li>Children's Health
	<ol type="a">
		<li>Development of tools to monitor indicators at the local and federal levels related to growth and development curves and tools that evaluate the impact of social and environmental determinants on child development;</li>
		<li>To develop geo-referenced prediction models with vulnerability risk stratification for infant mortality, including its components: neonatal mortality (total, early and late) and post-neonatal mortality, and / or childhood mortality (under 5 years);</li>
		<li>To assess the impact of vertical or perinatal transmission of diseases such as Zika, Syphilis, HPV and other Sexually Transmitted Infections (STIs) on the health and development of newborns and children;</li>
		<li>To assess infectious and non-infectious factors related to the occurrence of changes in growth and development during pregnancy and up until early childhood;</li>
		<li>Studies on health care for children with congenital anomalies, such as congenital Zika syndrome;</li>
		<li>To develop predictive models for the development of chronic diseases such as hypertension in children whose mothers had eclampsia, pre-eclampsia and hypertension, or even diabetes in children whose mothers were diabetic or had gestational diabetes.</li>
	</ol>
	</li>
	<li>Women's Health
	<ol type="a">
		<li>To develop and/or evaluate strategies for the reduction of maternal mortality, considering the main causes, estimation of preventable deaths and potential recommendations to improve public policies in this field;</li>
		<li>To compare the quality of care provided to women (including adolescents) during prenatal visits, labor and in services related to sexual violence, taking into account the racial variable;</li>
		<li>To evaluate access and quality of the prenatal care (according to MOH protocols) per Federation Unit and per city, taking into account urban, rural and remote areas;</li>
		<li>To assess the coverage of contraceptive methods in specific populations and in those with difficulties to access them;</li>
		<li>To identify factors associated with alcohol and drug abuse among women and their impact on health outcomes;</li>
		<li>To develop instruments to assess how the type of delivery impacts the health of woman and child;</li>
		<li>To assess the impact of teenage pregnancy on the health of the woman and child;</li>
		<li>To develop predictive models of pregnant women who may develop gestational diabetes or eclampsia;</li>
		<li>To present analyses of the causes, levels and trends of premature birth in different regions; evaluations of the effectiveness of interventions to prevent premature birth; and to develop recommendations on how to prevent premature birth.</li>
	</ol>
	</li>
	<li>Food and Nutrition
	<ol type="a">
		<li>To evaluate interventions performed in Primary Health Care to control dietary risk factors for chronic conditions and nutritional problems in women, children and during pregnancy;</li>
		<li>To analyze the supply and quality of Primary Health Care and its relationship with outcomes in maternal and child health and nutrition;</li>
		<li>To develop predictive and risk stratification models for outcomes related to the double burden of malnutrition, especially on women and children;</li>
		<li>To study breastfeeding and / or food intake and nutritional and health outcomes (nutritional status, deaths, hospitalizations) based on data from SISVAN and from other health information systems, preferably those that assess vulnerable populations at different stages of their lives;</li>
		<li>To develop geo-referencing tools that provide visualization of data on breastfeeding and / or food intake and nutritional status of the Brazilian population based on the SISVAN data, as disaggregated as possible</li>
		<li>To assess the impact of nutritional status, gestational weight gain and health status on maternal and child outcomes based on data from the SISVAN;</li>
		<li>To assess the relationship between breastfeeding and child health outcomes (nutritional status, deaths, hospitalizations) using data from the Food and Nutrition Surveillance System (SISVAN) and other information systems, preferably those including assessment in vulnerable populations.</li>
	</ol>
	</li>
</ol>

<ol start="1">
	<li>3 We encourage submitting proposals that consider the impacts of COVID-19 on the topics defined in the item 1.2, such as, for example, the impact of the pandemic on access to healthcare during the prenatal period and after delivery, on gestational health, on birth-related rates, premature birth, nutrition and other factors that can impact pregnancy and child development outcomes.</li>
</ol>

<ol start="1">
	<li>4 We encourage projects to be developed in collaboration with other research centers or individual researchers.</li>
</ol>

<ol start="1">
	<li>5 We seek proposals focused on addressing scientific matters related to the outcomes in development and in maternal and child health, women and children's health. Projects must use innovative approaches to data analysis and modeling that can be applied to databases from DATASUS/MS, in CIDACS (Cohort 100M SINASC-SIM-SISVAN), at ICICT/FIOCRUZ, or to other datasets that the candidate have access to.</li>
</ol>

<ol start="1">
	<li>6 Proposals must be based on linked data sets or on existing secondary data in Brazil, with the potential to produce practical results with the potential to be implemented in health care services and which may significantly impact public health policies. Further detail on this data can be found on <a href="https://www.synapse.org/#!Synapse:syn22088071/wiki/." target="_blank">https://www.synapse.org/#!Synapse:syn22088071/wiki/.</a></li>
</ol>

<ol start="1">
	<li>7 Studies of different designs that allow, according to the proposed methods, to achieve the expect outcomes shall be granted</li>
</ol>

<ol start="1">
	<li>7.1 Examples of what we are looking for. Proposals that:
	<ol type="a">
		<li>Support innovative collaboration among researchers, health experts and Brazilian data scientists;</li>
		<li>Answer scientific questions identified in this GCE RFP, while developing and strengthening the capacity of data sciences in Brazil;</li>
		<li>Consider the social, environmental and cultural determinants of health and incorporate in the results a broad understanding of the studied community;</li>
		<li>Include the mapping of barriers and restrictions to effective health interventions and contribute to inform the implementation of evidence-based public health programs;</li>
		<li>Contribute to a portfolio of funded projects that address regional diversities and the need to provide health equity to diverse and vulnerable populations;</li>
		<li>Explain how results are more likely to become relevant in a broad implementation in the public health system;</li>
		<li>Show relation with the implementation and evaluation of primary health care, whenever possible;</li>
		<li>Describe mechanistic models so as to establish the relationship between interventions and their results;</li>
		<li>Perform analysis of indicators, impact analysis, visualizations and predictive models that support the management of public health programs, whenever relevant.</li>
		<li>Propose and validate tools for disseminating high-volume data.</li>
	</ol>
	</li>
</ol>

<ol start="1">
	<li>8 Proposals for literature review or systematic review shall not be funded.</li>
</ol>

<ol start="1">
	<li>8.1 Examples of what we are NOT looking for:
	<ol>
	</ol>

	<ol type="a">
		<li>Proposals submitted by applicants that are not based in Brazil or that aim at studying health conditions outside of the Brazilian territory;</li>
		<li>Proposals for studies that depend on the collection of primary data;</li>
		<li>Proposals that develop tools for collecting new primary data;</li>
		<li>Proposals that do not address women's health, children’s health or maternal and child health;</li>
		<li>Projects that propose the development of a scientific data algorithm without clear relevance to the issues described in this call;</li>
		<li>Ideas without a hypothesis that is clearly defined and tested by metrics of success;</li>
		<li>Proposals that cannot be developed within the scope of GCE Phase 1 funding; (R$ 550,000,00 over 18 months);</li>
		<li>Proposals that do not describe the potential impacts of innovation on the formulation of health policies;</li>
		<li>Analyzes that would only bring minor improvements to existing approaches (e.g., in the absence of additional innovation, merely replicating an approach in a new geography).</li>
	</ol>
	</li>
</ol>

Grand Challenges Brazil

Grand Challenges Explorations – Brazil: Data Science Approaches to Improve Maternal and Child Health, Women's Health and Children's Health in Brazil

Grand Challenges Explorations – Brazil Data Science Approaches to Improve Maternal and Child Health Womens Health and Childrens Health in Brazil

Grand Challenges Africa

Data Science Approaches to Improve Maternal and Child Health in Africa

Developing and validating approaches to foster maternal and child health is difficult due to the interaction of biological, environmental, and social factors. Furthermore, policy recommendations for such approaches frequently lack sufficient supporting scientific evidence, while clinical trials are expensive, time-consuming, and increasingly difficult to implement. There is now a key opportunity to accelerate research in this area by analyzing existing data arising from multiple sources in India and formulating public health recommendations that are data-driven and cost-effective.

Grand Challenges India

Data Science Approaches to Improve Maternal and Child Health in India

Developing and validating approaches to foster maternal and child health is difficult due to the interaction of biological, environmental, and social factors. Furthermore, policy recommendations for such approaches frequently lack sufficient supporting scientific evidence, while clinical trials are expensive, time-consuming, and increasingly difficult to implement. There is now a key opportunity to accelerate research in this area by analyzing existing data arising from multiple sources in India and formulating public health recommendations that are data-driven and cost-effective.

<h2>Preamble</h2>This joint call for proposals targeted specifically to Brazilian researchers is the result of a partnership between the Ministry of Health (MoH), the National Council for Scientific and Technological Development (CNPq), the National Council for State Funding Agencies (CONFAP), engaged State Funding Agency (FAPs) and The Bill &amp; Melinda Gates Foundation (BMGF). This call is part of another initiative funded by the Gates Foundation in 2010 named Healthy Birth, Growth and Development Knowledge integration (HBGDki). The goal of this program is to use data science tools to develop a deep understanding of the risk factors contributing to poor outcomes in preterm birth, physical growth faltering and impaired neurocognitive development. Through Grand Challenges Explorations - Brazil, the above-mentioned partners share this goal and wish to build on the growing expertise in data science, epidemiology and public health in Brazil to address priority issues in maternal and child health.<h2>THE CHALLENGE</h2>There remain key knowledge gaps in understanding how nutrition, prenatal and antenatal care, maternal support, and environmental and social factors contribute to an elevated risk of poor maternal and childhood health outcomes. Such an understanding is required to determine what interventions, including health policies, should be delivered to which group of individuals at what point in their lifecycle to ensure optimal outcomes.<h2>THE OPPORTUNITY</h2>Developing and validating approaches to foster maternal and child health is challenging due to the complex interaction of biological, environmental and social factors. Furthermore, policy recommendations for such approaches frequently lack sufficient supporting scientific evidence, while clinical trials are expensive, time-consuming, and increasingly difficult to implement. There is now a key opportunity to accelerate research in this area by analyzing administrative and clinical outcomes databases in Brazil to formulate public health recommendations that are data-driven and cost effective.The purpose of this call for proposals is to promote new and novel approaches to analyzing data related to social programs and public health in Brazil to produce novel insights which can be used to improve maternal and child health in Brazil and around the world. Applicants may choose to work with large datasets available to them or to collaborate with Center for Data and Knowledge Integration for Health (CIDACS) to explore their linked anonymized dataset (100 million Brazilian Cohort), which integrates information from the Cadastro Unico (CADU) with the live births system (SINASC), the mortality system (SIM) and Bolsa Família program (BFP) - see Appendix A for more information about data access through CIDACS.Successful applicants to this call should assume that with the appropriate agreements and ethical approvals, they will not only receive funding but will be able to access anonymized subsets of these data available from Center for Data and Knowledge Integration for Health (CIDACS) researchers. The utilization of the health and social data, as a key focus of this call, is intended to engage a broad spectrum of collaborators - including research and clinical scientists working with data scientists, bioinformaticians, statisticians, epidemiologists, engineers and computer programmers - to identify how innovative data analytic approaches can be used to develop improved solutions to tackle the burden of maternal and child health problems in Brazil.We also welcome applicants who have access to other relevant data sets, including publicly available data, clinical research, cohort and survey studies and other large datasets that can help address the questions below, to submit their proposals under this call. In this case, researchers who submit applications involving other data sets are responsible for securing appropriate authorization and access to the data. Applicants are encouraged to describe the steps required to access the proposed dataset in their proposals.What we are looking for:We seek proposals designed to answer critical scientific questions related to maternal and child health and development outcomes. Proposals should use innovative data analytics and modeling approaches that can be applied to the CIDACS linked datasets or to other relevant data sets that applicants can access. Proposals should be based on these linked datasets or existing primary data in Brazil and yield actionable results with a potential to significantly impact public health policy.We will give highest priority to proposals that:<ul><li>Support innovative collaborations between Brazilian research scientists, healthcare experts, and data scientists;</li><li>Answer critical scientific questions identified in this GCE, while building and strengthening data science capacity for Brazil;</li><li>Take into account social, environmental and cultural determinants of outcomes and incorporate an understanding of the target community that includes barriers and constraints to delivery of interventions and to implementation of public health programs;</li><li>Contribute to a portfolio of funded projects that addresses regional diversity and the need to provide health equity for diverse and vulnerable populations;</li><li>Explain how answers will have the highest likelihood of being relevant for implementation broadly in the public health system;</li><li>Describe mechanistic models for establishing the relationship between interventions and its related outcomes.</li></ul>Examples of what we are looking for include analytical approaches:<ul><li>That apply innovative analyses or machine learning techniques to identify patterns in data from “natural experiments” (e.g., the impact of economic cycles on the quality of primary care and health outcomes);</li><li>That stratify risk of adverse pregnancy outcomes, including preterm birth and low birth weight;</li><li>That incorporate weight gain during pregnancy as a variable, including helping to determine the relative contributions to neonatal health outcomes of maternal diet quantity versus quality;</li><li>That determine the relative contributions to infant health outcomes of diet quantity versus quality (e.g., protein quantity versus quality);</li><li>That target underexplored subsets of data (e.g., rare but significant HBGD events that can be studied because of the large size and statistical power of the database);</li><li>That help convert correlations to causal hypotheses (e.g., health outcomes correlated to sex differences, maternal education, birth spacing, age of first pregnancy, establishing causal impact of air pollution on fetal growth);</li><li>That identify new ways to aggregate risk factors and identify vulnerable populations for adverse maternal and child health outcomes, including innovative data integration strategies and visualization tools;</li><li>That specifically incorporate the roles of women – as perceived locally – from adolescence to motherhood (including pregnancy during adolescence);</li><li>That evaluate programs for pre-pregnancy intervention for women and the effect of doing so on prenatal, maternal, fetal and neonatal mortality;</li><li>That determine the best care for low-birth-weight babies;</li><li>That help determine the window of opportunity to foster catch-up growth for preterm and low-birth-weight babies, and the most effective interventions for doing so;</li><li>That help identify critical periods for intervention during pregnancy and early childhood;</li><li>That stratify risk of stunting and wasting from birth through two years of age;</li><li>That target root causes of maternal mortality, including caesarean section, and address the most vulnerable population groups considering age and ethnicity;</li><li>That investigate the "double burden" of disease in Brazil leading to pockets of stunting and wasting in parallel with pockets with rising rates of childhood obesity.</li><li>That stratify risks for child development aiming to establish national indicators for healthy development from the neonatal period to the first two years of the children addressing preferably most vulnerable population groups considering age and ethnicity</li><li>That help to understand the relationship between social indicators, nutritional conditions and mortality from the prenatal period to the early childhood. We welcome applications addressing traditional and vulnerable populations.</li></ul>Examples of what we are NOT looking for:<ul><li>Proposals submitted by applicants from outside of Brazil.</li><li>Proposals for new studies to generate new data;</li><li>Proposals that do not focus on health outcomes in Brazil;</li><li>Proposals not related to maternal and child health challenges</li><li>Development of new primary data collection tools;</li><li>Approaches that do not meaningfully involve data from adolescents, mothers, or infants;</li><li>Applications proposing data science algorithm development without clear relevance to answering the types of questions described in this call for proposals;</li><li>Ideas without a clearly articulated and testable hypothesis together with metrics of success;</li><li>Ideas for which the described indicator of success cannot be demonstrated or significantly advanced within the scope of the GCE Phase 1 award (USD $100,000 over 18 months);</li><li>Proposals that do not describe the innovation’s potential effects on health policy making;</li><li>Analyses that are only slight improvements over existing approaches (e.g., replication of an approach in a new geography in the absence of added innovation);</li></ul><h2>APPENDIX</h2>Center for Data and Knowledge Integration for HealthThe Center for Data and Knowledge Integration for Health (CIDACS) was launched in 2016 as part of Fiocruz in Bahia to conduct innovative scientific research that broadens understanding of the effects of social and environmental determinants, including public policy, on population health in Brazil. To achieve this goal, it links information from social programs and other databases of health information systems, such as mortality, birth, communicable and non-communicable infectious diseases, among other outcomes, from different information systems maintained by the Ministry of Health. A central feature of the CIDACS platform is to produce linked datasets, whose main product is the 100 million Brazilians cohort (2006-2015). This cohort is built by linking data from individuals in the Cadastro Único (CADU) to data of the same individuals in the Bolsa Familia Program (BFP), SINASC and SIM. CADU is a national registry of individuals who could be eligible to federal government social programs. BFP is a national conditional cash transfer program which transfers cash to poor households when they comply with conditions related to health and education. (For in-depth analysis of the effects of BFP on childhood mortality, please review Rasella, et al. 2013.)<a href="#_ftn1" title="" id="_ftnref1" name="_ftnref1">[1]</a> Individuals in the BFP database must be registered in CADU. SINASC is the registry of birth certificates. SIM is the registry of death certificates.Previous analyses performed by the CIDACS team have demonstrated that the social protection program contributed to a decrease in childhood mortality overall and in particular for deaths attributable to poverty-related causes such as malnutrition and diarrhea.The CIDACS platform employs the highest levels of security to ensure the privacy, integrity, and ethical use of the data. Considerable research has been required to develop the processes for data receipt, curation, and to provide authorized access for analysis. In this Grand Challenges Explorations – Brazil program, the CIDACS platform will be available for the selected applicants to conduct innovative, interdisciplinary projects in the areas of maternal and child health.<h2>Additional Resources</h2>About <a href="http://hbgdki.org/" target="_blank">HBGDKI.org</a>About <a href="http://www.cidacs.bahia.fiocruz.br/" target="_blank">Center for Data and Knowledge Integration for Health (CIDACS)</a>About <a href="http://www.cidacs.bahia.fiocruz.br/node/93" target="_blank">the 100 million Brazilian cohort managed by CIDACS</a>__________________________________<a href="#_ftnref1" title="" id="_ftn1" name="_ftn1">[1]</a>Rasella, D. et al. (2013) Effect of a conditional cash transfer programme on childhood mortality: a nationwide analysis of Brazilian municipalities. The Lancet. 382:57.

Grand Challenges Explorations - Brazil: Data Science Approaches to Improve Maternal and Child Health in Brazil

<h2>Preamble</h2>
This joint call for proposals targeted specifically to Brazilian researchers is the result of a partnership between the Ministry of Health (MoH), the National Council for Scientific and Technological Development (CNPq), the National Council for State Funding Agencies (CONFAP), engaged State Funding Agency (FAPs) and The Bill &amp; Melinda Gates Foundation (BMGF). This call is part of another initiative funded by the Gates Foundation in 2010 named Healthy Birth, Growth and Development Knowledge integration (HBGDki). The goal of this program is to use data science tools to develop a deep understanding of the risk factors contributing to poor outcomes in preterm birth, physical growth faltering and impaired neurocognitive development. Through Grand Challenges Explorations - Brazil, the above-mentioned partners share this goal and wish to build on the growing expertise in data science, epidemiology and public health in Brazil to address priority issues in maternal and child health.
<h2>THE CHALLENGE</h2>
There remain key knowledge gaps in understanding how nutrition, prenatal and antenatal care, maternal support, and environmental and social factors contribute to an elevated risk of poor maternal and childhood health outcomes. Such an understanding is required to determine what interventions, including health policies, should be delivered to which group of individuals at what point in their lifecycle to ensure optimal outcomes.
<h2>THE OPPORTUNITY</h2>
Developing and validating approaches to foster maternal and child health is challenging due to the complex interaction of biological, environmental and social factors. Furthermore, policy recommendations for such approaches frequently lack sufficient supporting scientific evidence, while clinical trials are expensive, time-consuming, and increasingly difficult to implement. There is now a key opportunity to accelerate research in this area by analyzing administrative and clinical outcomes databases in Brazil to formulate public health recommendations that are data-driven and cost effective.
The purpose of this call for proposals is to promote new and novel approaches to analyzing data related to social programs and public health in Brazil to produce novel insights which can be used to improve maternal and child health in Brazil and around the world. Applicants may choose to work with large datasets available to them or to collaborate with Center for Data and Knowledge Integration for Health (CIDACS) to explore their linked anonymized dataset (100 million Brazilian Cohort), which integrates information from the Cadastro Unico (CADU) with the live births system (SINASC), the mortality system (SIM) and Bolsa Família program (BFP) - see Appendix A for more information about data access through CIDACS.
Successful applicants to this call should assume that with the appropriate agreements and ethical approvals, they will not only receive funding but will be able to access anonymized subsets of these data available from Center for Data and Knowledge Integration for Health (CIDACS) researchers. The utilization of the health and social data, as a key focus of this call, is intended to engage a broad spectrum of collaborators - including research and clinical scientists working with data scientists, bioinformaticians, statisticians, epidemiologists, engineers and computer programmers - to identify how innovative data analytic approaches can be used to develop improved solutions to tackle the burden of maternal and child health problems in Brazil.
We also welcome applicants who have access to other relevant data sets, including publicly available data, clinical research, cohort and survey studies and other large datasets that can help address the questions below, to submit their proposals under this call. In this case, researchers who submit applications involving other data sets are responsible for securing appropriate authorization and access to the data. Applicants are encouraged to describe the steps required to access the proposed dataset in their proposals.
What we are looking for:
We seek proposals designed to answer critical scientific questions related to maternal and child health and development outcomes. Proposals should use innovative data analytics and modeling approaches that can be applied to the CIDACS linked datasets or to other relevant data sets that applicants can access. Proposals should be based on these linked datasets or existing primary data in Brazil and yield actionable results with a potential to significantly impact public health policy.
We will give highest priority to proposals that:
<ul>
<li>Support innovative collaborations between Brazilian research scientists, healthcare experts, and data scientists;</li>
<li>Answer critical scientific questions identified in this GCE, while building and strengthening data science capacity for Brazil;</li>
<li>Take into account social, environmental and cultural determinants of outcomes and incorporate an understanding of the target community that includes barriers and constraints to delivery of interventions and to implementation of public health programs;</li>
<li>Contribute to a portfolio of funded projects that addresses regional diversity and the need to provide health equity for diverse and vulnerable populations;</li>
<li>Explain how answers will have the highest likelihood of being relevant for implementation broadly in the public health system;</li>
<li>Describe mechanistic models for establishing the relationship between interventions and its related outcomes.</li>
</ul>
Examples of what we are looking for include analytical approaches:
<ul>
<li>That apply innovative analyses or machine learning techniques to identify patterns in data from “natural experiments” (e.g., the impact of economic cycles on the quality of primary care and health outcomes);</li>
<li>That stratify risk of adverse pregnancy outcomes, including preterm birth and low birth weight;</li>
<li>That incorporate weight gain during pregnancy as a variable, including helping to determine the relative contributions to neonatal health outcomes of maternal diet quantity versus quality;</li>
<li>That determine the relative contributions to infant health outcomes of diet quantity versus quality (e.g., protein quantity versus quality);</li>
<li>That target underexplored subsets of data (e.g., rare but significant HBGD events that can be studied because of the large size and statistical power of the database);</li>
<li>That help convert correlations to causal hypotheses (e.g., health outcomes correlated to sex differences, maternal education, birth spacing, age of first pregnancy, establishing causal impact of air pollution on fetal growth);</li>
<li>That identify new ways to aggregate risk factors and identify vulnerable populations for adverse maternal and child health outcomes, including innovative data integration strategies and visualization tools;</li>
<li>That specifically incorporate the roles of women – as perceived locally – from adolescence to motherhood (including pregnancy during adolescence);</li>
<li>That evaluate programs for pre-pregnancy intervention for women and the effect of doing so on prenatal, maternal, fetal and neonatal mortality;</li>
<li>That determine the best care for low-birth-weight babies;</li>
<li>That help determine the window of opportunity to foster catch-up growth for preterm and low-birth-weight babies, and the most effective interventions for doing so;</li>
<li>That help identify critical periods for intervention during pregnancy and early childhood;</li>
<li>That stratify risk of stunting and wasting from birth through two years of age;</li>
<li>That target root causes of maternal mortality, including caesarean section, and address the most vulnerable population groups considering age and ethnicity;</li>
<li>That investigate the "double burden" of disease in Brazil leading to pockets of stunting and wasting in parallel with pockets with rising rates of childhood obesity.</li>
<li>That stratify risks for child development aiming to establish national indicators for healthy development from the neonatal period to the first two years of the children addressing preferably most vulnerable population groups considering age and ethnicity</li>
<li>That help to understand the relationship between social indicators, nutritional conditions and mortality from the prenatal period to the early childhood. We welcome applications addressing traditional and vulnerable populations.</li>
</ul>
Examples of what we are NOT looking for:
<ul>
<li>Proposals submitted by applicants from outside of Brazil.</li>
<li>Proposals for new studies to generate new data;</li>
<li>Proposals that do not focus on health outcomes in Brazil;</li>
<li>Proposals not related to maternal and child health challenges</li>
<li>Development of new primary data collection tools;</li>
<li>Approaches that do not meaningfully involve data from adolescents, mothers, or infants;</li>
<li>Applications proposing data science algorithm development without clear relevance to answering the types of questions described in this call for proposals;</li>
<li>Ideas without a clearly articulated and testable hypothesis together with metrics of success;</li>
<li>Ideas for which the described indicator of success cannot be demonstrated or significantly advanced within the scope of the GCE Phase 1 award (USD $100,000 over 18 months);</li>
<li>Proposals that do not describe the innovation’s potential effects on health policy making;</li>
<li>Analyses that are only slight improvements over existing approaches (e.g., replication of an approach in a new geography in the absence of added innovation);</li>
</ul>
<h2>APPENDIX</h2>
Center for Data and Knowledge Integration for Health
The Center for Data and Knowledge Integration for Health (CIDACS) was launched in 2016 as part of Fiocruz in Bahia to conduct innovative scientific research that broadens understanding of the effects of social and environmental determinants, including public policy, on population health in Brazil. To achieve this goal, it links information from social programs and other databases of health information systems, such as mortality, birth, communicable and non-communicable infectious diseases, among other outcomes, from different information systems maintained by the Ministry of Health. A central feature of the CIDACS platform is to produce linked datasets, whose main product is the 100 million Brazilians cohort (2006-2015). This cohort is built by linking data from individuals in the Cadastro Único (CADU) to data of the same individuals in the Bolsa Familia Program (BFP), SINASC and SIM. CADU is a national registry of individuals who could be eligible to federal government social programs. BFP is a national conditional cash transfer program which transfers cash to poor households when they comply with conditions related to health and education. (For in-depth analysis of the effects of BFP on childhood mortality, please review Rasella, et al. 2013.)1 Individuals in the BFP database must be registered in CADU. SINASC is the registry of birth certificates. SIM is the registry of death certificates.
Previous analyses performed by the CIDACS team have demonstrated that the social protection program contributed to a decrease in childhood mortality overall and in particular for deaths attributable to poverty-related causes such as malnutrition and diarrhea.
The CIDACS platform employs the highest levels of security to ensure the privacy, integrity, and ethical use of the data. Considerable research has been required to develop the processes for data receipt, curation, and to provide authorized access for analysis. In this Grand Challenges Explorations – Brazil program, the CIDACS platform will be available for the selected applicants to conduct innovative, interdisciplinary projects in the areas of maternal and child health.
<h2>Additional Resources</h2>
About <a href="http://hbgdki.org/" target="_blank" rel="noopener noreferrer">HBGDKI.org</a>
About <a href="http://www.cidacs.bahia.fiocruz.br/" target="_blank" rel="noopener noreferrer">Center for Data and Knowledge Integration for Health (CIDACS)</a>
About <a href="http://www.cidacs.bahia.fiocruz.br/node/93" target="_blank" rel="noopener noreferrer">the 100 million Brazilian cohort managed by CIDACS</a>
__________________________________
<ol>
<li>Rasella, D. et al. (2013) Effect of a conditional cash transfer programme on childhood mortality: a nationwide analysis of Brazilian municipalities. The Lancet. 382:57.</li>
</ol>

We seek to promote new approaches for analyzing data related to social programs and public health in Brazil, with the goal of insights that can be used to improve maternal and child health in Brazil and around the world. There is now a key opportunity to accelerate research in this area by analyzing administrative and clinical outcomes databases in Brazil to formulate public health recommendations that are data-driven and cost effective. Applicants must be Brazilian researchers located in Brazilian organizations.