Indigenous Peoples' Data During COVID-19: From External to Internal

Hear my voice: understanding how community health workers in the Peruvian Amazon expanded their roles to mitigate the impact of the COVID-19 pandemic through community-based participatory research

INTRODUCTION

The COVID-19 pandemic led to the collapse of the Peruvian health system, disrupting healthcare access for indigenous communities in the Amazon. Our study analysed how community health workers (CHWs) from indigenous communities in the Peruvian Amazon expanded their roles to mitigate the effects of the COVID-19 pandemic.

METHODS

Fourteen CHWs from Loreto, Peru, participated in a community-based participatory research project using Photovoice, a technique encouraging vulnerable groups to take photos and develop stories illustrating their lived experiences. Participants were recruited from Mamás del Río, a local university-based programme, through purposive sampling. CHWs were asked to photograph how the pandemic affected their lives and work. Participants met four times over 5 months to share photos and develop action items. Data were organised into key themes using thematic analysis. CHWs shared photo galleries with policy-makers in Loreto and Lima.

RESULTS

CHWs produced 36 photos with 33 texts highlighting their roles during COVID-19. Three core themes emerged: the (1) collapse of health infrastructure, (2) use of medicinal plants versus pharmaceuticals and (3) community adaptations and struggles. The leadership of CHWs emerged as a cross-cutting theme as CHWs supported COVID-19 efforts without government training or resources. CHWs asked policy-makers for formal integration into the health system, standardisation of training and management of community pharmacies.

CONCLUSION

CHWs demonstrated their leadership and expanded their roles during the pandemic with little to no training from the government. Global investment in robust CHW programmes can fortify healthcare delivery.

Acceptability and Feasibility of Community Gardening Interventions for the Prevention of Non-Communicable Diseases among Indigenous Populations: A Scoping Review

Compared with non-Indigenous populations, Indigenous populations experience worse health across many outcomes, including non-communicable diseases, and they are three times more likely to live in extreme poverty. The objectives were to identify (1) the content, implementation, and duration of the intervention; (2) the evaluation designs used; (3) the outcomes reported; and (4) the enablers and the challenges. Using the PRISMA-ScR guidelines, a search of research databases and grey literature was conducted. Seven studies met the inclusion criteria. Papers reported on acceptability, nutrition knowledge, fruit and vegetable intake, self-efficacy, motivation, and preference concerning fruit and vegetable, diet, and gardening. No study measured all outcomes. All papers reported on acceptability, whether implicitly or explicitly. The evaluation used mostly pre- and post-intervention assessments. The effect of gardening on nutrition and gardening knowledge and fruit and vegetable intake was inconclusive, and was related to a general lack of robust evaluations. Applying the He Pikinga Waiora Framework, however, revealed strong evidence for community engagement, cultural centeredness, integrated knowledge translation and systems thinking in increasing the acceptability and feasibility of gardening in Indigenous communities. Despite environmental challenges, the evidence signaled that gardening was an acceptable intervention for the Indigenous communities.

Use of Population-Level Administrative Data in Developmental Science

Population-level administrative data-data on individuals' interactions with administrative systems (e.g., health, criminal justice, and education)-have substantially advanced our understanding of life-course development. In this review, we focus on five areas where research using these data has made significant contributions to developmental science: (a) understanding small or difficult-to-study populations, (b) evaluating intergenerational and family influences, (c) enabling estimation of causal effects through natural experiments and regional comparisons, (d) identifying individuals at risk for negative developmental outcomes, and (e) assessing neighborhood and environmental influences. Further advances will be made by linking prospective surveys to administrative data to expand the range of developmental questions that can be tested; supporting efforts to establish new linked administrative data resources, including in developing countries; and conducting cross-national comparisons to test findings' generalizability. New administrative data initiatives should involve consultation with population subgroups including vulnerable groups, efforts to obtain social license, and strong ethical oversight and governance arrangements.

Descriptive analysis of deaths associated with COVID-19 in Fiji, 15 April to 14 November 2021

Objective

There is limited published information about deaths due to coronavirus disease 2019 (COVID-19) in Fiji, the World Health Organization's Western Pacific Region and low- and middle-income countries. This report descriptively analyses deaths directly associated with COVID-19 in Fiji by age group, sex, ethnicity, geographical location, vaccination status and place of death for the first 7 months of the 2021 community outbreak.

Methods

A retrospective analysis was conducted of deaths directly associated with COVID-19 that occurred from 15 April to 14 November 2021 in Fiji. Death rates per 100 000 population were calculated by using divisional population estimates obtained from medical zone nurses in 2021.

Results

A total of 1298 deaths relating to COVID-19 were reported, with 696 directly associated with COVID-19 and therefore included in the analysis. Of these, 71.1% (495) were reported from the Central Division, 54.6% (380) occurred among males, 75.6% (526) occurred among people of indigenous (iTaukei) ethnicity and 79.5% (553) occurred among people who were unvaccinated. Four deaths were classified as maternal deaths. The highest percentage of deaths occurred in those aged ³70 years (44.3%, 308), and the majority of deaths (56.6%, 394) occurred at home.

Discussion

At-risk populations for COVID-19 mortality in Fiji include males, iTaukei peoples, and older (³70 years) and unvaccinated individuals. A high proportion of deaths occurred either at home or during the first 2 days of hospital admission, potentially indicating both a reluctance to seek medical care and a health-care system that was stressed during the peak of the outbreak.

Transparent human – (non-) transparent technology? The Janus-faced call for transparency in AI-based health care technologies

The use of Artificial Intelligence and Big Data in health care opens up new opportunities for the measurement of the human. Their application aims not only at gathering more and better data points but also at doing it less invasive. With this change in health care towards its extension to almost all areas of life and its increasing invisibility and opacity, new questions of transparency arise. While the complex human-machine interactions involved in deploying and using AI tend to become non-transparent, the use of these technologies makes the patient seemingly transparent. Papers on the ethical implementation of AI plead for transparency but neglect the factor of the “transparent patient” as intertwined with AI. Transparency in this regard appears to be Janus-faced: The precondition for receiving help - e.g., treatment advice regarding the own health - is to become transparent for the digitized health care system. That is, for instance, to donate data and become visible to the AI and its operators. The paper reflects on this entanglement of transparent patients and (non-) transparent technology. It argues that transparency regarding both AI and humans is not an ethical principle per se but an infraethical concept. Further, it is no sufficient basis for avoiding harm and human dignity violations. Rather, transparency must be enriched by intelligibility following Judith Butler’s use of the term. Intelligibility is understood as an epistemological presupposition for recognition and the ensuing humane treatment. Finally, the paper highlights ways to testify intelligibility in dealing with AI in health care ex ante, ex post, and continuously.

Uncovering SARS-COV-2 vaccine uptake and COVID-19 impacts among First Nations, Inuit and Métis Peoples living in Toronto and London, Ontario

Background:

First Nations, Inuit and Métis Peoples across geographies are at higher risk of SARS-CoV-2 infection and COVID-19 because of high rates of chronic disease, inadequate housing and barriers to accessing health services. Most Indigenous Peoples in Canada live in cities, where SARS-CoV-2 infection is concentrated. To address gaps in SARS-CoV-2 information for these urban populations, we partnered with Indigenous agencies and sought to generate rates of SARS-CoV-2 testing and vaccination, and incidence of infection for First Nations, Inuit and Métis living in 2 Ontario cities.

Methods:

We drew on existing cohorts of First Nations, Inuit and Métis adults in Toronto (n = 723) and London (n = 364), Ontario, who were recruited using respondent-driven sampling. We linked to ICES SARS-CoV-2 databases and prospectively monitored rates of SARS-CoV-2 testing, diagnosis and vaccination for First Nations, Inuit and Métis, and comparator city and Ontario populations.

Results:

We found that SARS-CoV-2 testing rates among First Nations, Inuit and Métis were higher in Toronto (54.7%, 95% confidence interval [CI] 48.1% to 61.3%) and similar in London (44.5%, 95% CI 36.0% to 53.1%) compared with local and provincial rates. We determined that cumulative incidence of SARS-CoV-2 infection was not significantly different among First Nations, Inuit and Métis in Toronto (7364/100 000, 95% CI 2882 to 11 847) or London (7707/100 000, 95% CI 2215 to 13 200) compared with city rates. We found that rates of vaccination among First Nations, Inuit and Métis in Toronto (58.2%, 95% CI 51.4% to 64.9%) and London (61.5%, 95% CI 52.9% to 70.0%) were lower than the rates for the 2 cities and Ontario.

Interpretation:

Although Ontario government policies prioritized Indigenous populations for SARS-CoV-2 vaccination, vaccine uptake was lower than in the general population for First Nations, Inuit and Métis Peoples in Toronto and London. Ongoing access to culturally safe testing and vaccinations is urgently required to avoid disproportionate hospital admisson and mortality related to COVID-19 in these communities.

Health Literacy, Equity, and Communication in the COVID-19 Era of Misinformation: Emergence of Health Information Professionals in Infodemic Management

The health information management (HIM) field’s contribution to health care delivery is invaluable in a pandemic context where the need for accurate diagnoses will hasten responsive, evidence-based decision-making. The COVID-19 pandemic offers a unique opportunity to transform the practice of HIM and bring more awareness to the role that frontline workers play behind the scenes in safeguarding reliable, comprehensive, accurate, and timely health information. This transformation will support future research, utilization management, public health surveillance, and forecasting and enable key stakeholders to plan and ensure equitable health care resource allocation, especially for the most vulnerable populations. In this paper, we juxtapose critical health literacy, public policy, and HIM perspectives to understand the COVID-19 infodemic and new opportunities for HIM in infodemic management.

Indigenous health equity in health register ascertainment and data quality: a narrative review

Background

Health registers play an important role in monitoring distribution of disease and quality of care; however, benefit is limited if ascertainment (i.e., the process of finding and recruiting people on to a register) and data quality (i.e., the accuracy, completeness, reliability, relevance, and timeliness of data) are poor. Indigenous peoples experience significant health inequities globally, yet health data for, and about, Indigenous peoples is often of poor quality. This narrative review aimed to (i) identify perceived barriers for the ascertainment of Indigenous peoples on health registers, and (ii) collate strategies identified and used by health registers to support comprehensive ascertainment and high-quality data for Indigenous peoples.

Methods

A Kaupapa Māori theoretical framework was utilized to guide this work. Four electronic databases were systematically searched for original articles and screened for eligibility. Studies involving health registers with Indigenous population(s) identified were included if either ascertainment or data quality strategies were described. Data extraction focused on the reporting of research involving Indigenous peoples using the CONSIDER checklist domains, ascertainment, and data quality.

Results

Seventeen articles were included spanning publication between 1992 and 2020. Aspects of four of eight CONSIDER domains were identified to be included in the reporting of studies. Barriers to ascertainment were themed as relating to ‘ethnicity data collection and quality’, ‘systems and structures’, ‘health services/health professionals’, and ‘perceptions of individual and community-level barriers’. Strategies to support ascertainment were categorized as ‘collaboration’, ‘finding people’, and ‘recruitment processes’. Categorized strategies to support data quality were ‘collaboration’, ‘ethnicity data collection and quality’, ‘systems-level strategies’, and ‘health service/health professional-level strategies’.

Conclusions

Poor-quality data for Indigenous peoples in health registers prevents the achievement of health equity and exemplifies inaction in the face of need. When viewed through a critical structural determinants lens, there are visible gaps in the breadth of strategies, particularly relating to the inclusion of Indigenous peoples in health register and research governance, and actions to identify and address institutional racism. Indigenous led research, meaningful collaboration, and a sharing of knowledge and experiences between health registers is recommended to enable research and health registers that support Indigenous self-determination and health equity.

Data justice and data solidarity

Datafication shapes and gradually transforms societies. Given this impact, issues of justice around data-driven practices have received more and more attention in recent years as shown, for example, by various reports and guidelines on artificial intelligence and data ethics. In this article, we elaborate on and defend two claims. First, these discourses on justice tend to center primarily around conceptions of fairness. We argue that justice in connection with datafication relates to, but ultimately encompasses more than, solely fairness. Second, although it is an important project to clarify what justice in connection with datafication encompasses, we argue that attention toward attitudes and practices of data solidarity have so far been largely overlooked. They are, however, indispensable as a catalytic element to advance toward data justice in practice.

New technologies such as big data, machine learning, and artificial intelligence are rapidly generating new opportunities and challenges for various social practices. At the same time, they raise important ethical questions. For example, for whom will these opportunities and advantages result in benefits, and who bears the burdens? A fundamental issue is which individuals and groups with their respective claims and particularities are involved and affected by the use of ever-increasing amounts of data and the inferences drawn from them. To navigate this terrain, reference is often made to the notion of justice. Such reference can be helpful to the extent that we are clear about what justice entails and what it takes to get there. In this article, we argue that justice requires us to make visible the claims of those left out, excluded, or disadvantaged in and around the use of data. To this end, we put forward the concept of data solidarity and examine its interplay with data justice.

Racism and Structural Violence: Interconnected Threats to Health Equity

In 2020, the continuing murder of Black Americans by police officers received widespread media attention and sparked global outrage. Public health responses to these events focused on discrimination by police and structural racism in broader society. However, police violence is but one of many forms of racialized violence propagated by structural racism and anti-Black racism in particular. We aim to expand the current public health dialogue by describing how structural racism and structural violence are deeply interrelated; embedded in institutions, systems, and processes; and threaten health, safety, and well-being across the life course for racialized minority groups. Structural racism and structural violence are threats to health equity and anti-racist public health work.

Differential Impact of COVID-19 Risk Factors on Ethnicities in the United States

The coronavirus disease (COVID-19) has revealed existing health inequalities in racial and ethnic minority groups in the US. This work investigates and quantifies the non-uniform effects of geographical location and other known risk factors on various ethnic groups during the COVID-19 pandemic at a national level. To quantify the geographical impact on various ethnic groups, we grouped all the states of the US. into four different regions (Northeast, Midwest, South, and West) and considered Non-Hispanic White (NHW), Non-Hispanic Black (NHB), Hispanic, Non-Hispanic Asian (NHA) as ethnic groups of our interest. Our analysis showed that infection and mortality among NHB and Hispanics are considerably higher than NHW. In particular, the COVID-19 infection rate in the Hispanic community was significantly higher than their population share, a phenomenon we observed across all regions in the US but is most prominent in the West. To gauge the differential impact of comorbidities on different ethnicities, we performed cross-sectional regression analyses of statewide data for COVID-19 infection and mortality for each ethnic group using advanced age, poverty, obesity, hypertension, cardiovascular disease, and diabetes as risk factors. After removing the risk factors causing multicollinearity, poverty emerged as one of the independent risk factors in explaining mortality rates in NHW, NHB, and Hispanic communities. Moreover, for NHW and NHB groups, we found that obesity encapsulated the effect of several other comorbidities such as advanced age, hypertension, and cardiovascular disease. At the same time, advanced age was the most robust predictor of mortality in the Hispanic group. Our study quantifies the unique impact of various risk factors on different ethnic groups, explaining the ethnicity-specific differences observed in the COVID-19 pandemic. The findings could provide insight into focused public health strategies and interventions.

Fostering global data sharing: highlighting the recommendations of the Research Data Alliance COVID-19 working group

The systemic challenges of the COVID-19 pandemic require cross-disciplinary collaboration in a global and timely fashion. Such collaboration needs open research practices and the sharing of research outputs, such as data and code, thereby facilitating research and research reproducibility and timely collaboration beyond borders. The Research Data Alliance COVID-19 Working Group recently published a set of recommendations and guidelines on data sharing and related best practices for COVID-19 research. These guidelines include recommendations for clinicians, researchers, policy- and decision-makers, funders, publishers, public health experts, disaster preparedness and response experts, infrastructure providers from the perspective of different domains (Clinical Medicine, Omics, Epidemiology, Social Sciences, Community Participation, Indigenous Peoples, Research Software, Legal and Ethical Considerations), and other potential users. These guidelines include recommendations for researchers, policymakers, funders, publishers and infrastructure providers from the perspective of different domains (Clinical Medicine, Omics, Epidemiology, Social Sciences, Community Participation, Indigenous Peoples, Research Software, Legal and Ethical Considerations). Several overarching themes have emerged from this document such as the need to balance the creation of data adherent to FAIR principles (findable, accessible, interoperable and reusable), with the need for quick data release; the use of trustworthy research data repositories; the use of well-annotated data with meaningful metadata; and practices of documenting methods and software. The resulting document marks an unprecedented cross-disciplinary, cross-sectoral, and cross-jurisdictional effort authored by over 160 experts from around the globe. This letter summarises key points of the Recommendations and Guidelines, highlights the relevant findings, shines a spotlight on the process, and suggests how these developments can be leveraged by the wider scientific community.

Fostering global data sharing: highlighting the recommendations of the Research Data Alliance COVID-19 working group

The systemic challenges of the COVID-19 pandemic require cross-disciplinary collaboration in a global and timely fashion. Such collaboration needs open research practices and the sharing of research outputs, such as data and code, thereby facilitating research and research reproducibility and timely collaboration beyond borders. The Research Data Alliance COVID-19 Working Group recently published a set of recommendations and guidelines on data sharing and related best practices for COVID-19 research. These guidelines include recommendations for researchers, policymakers, funders, publishers and infrastructure providers from the perspective of different domains (Clinical Medicine, Omics, Epidemiology, Social Sciences, Community Participation, Indigenous Peoples, Research Software, Legal and Ethical Considerations). Several overarching themes have emerged from this document such as the need to balance the creation of data adherent to FAIR principles (findable, accessible, interoperable and reusable), with the need for quick data release; the use of trustworthy research data repositories; the use of well-annotated data with meaningful metadata; and practices of documenting methods and software. The resulting document marks an unprecedented cross-disciplinary, cross-sectoral, and cross-jurisdictional effort authored by over 160 experts from around the globe. This letter summarises key points of the Recommendations and Guidelines, highlights the relevant findings, shines a spotlight on the process, and suggests how these developments can be leveraged by the wider scientific community.