Background Health-care services are necessary for sustaining and improving human wellbeing, yet they have an environmental footprint that contributes to environment-related threats to human health. Previous studies have quantified the carbon emissions resulting from health care at a global level. We aimed to provide a global assessment of the wide-ranging environmental impacts of this sector.Methods In this multiregional input-output analysis, we evaluated the contribution of health-care sectors in driving environmental damage that in turn puts human health at risk. Using a global supply-chain database containing detailed information on health-care sectors, we quantified the direct and indirect supply-chain environmental damage driven by the demand for health care. We focused on seven environmental stressors with known adverse feedback cycles: greenhouse gas emissions, particulate matter, air pollutants (nitrogen oxides and sulphur dioxide), malaria risk, reactive nitrogen in water, and scarce water use.Findings Health care causes global environmental impacts that, depending on which indicator is considered, range between 1% and 5% of total global impacts, and are more than 5% for some national impacts.Interpretation Enhancing health-care expenditure to mitigate negative health effects of environmental damage is often promoted by health-care practitioners. However, global supply chains that feed into the enhanced activity of health-care sectors in turn initiate adverse feedback cycles by increasing the environmental impact of health care, thus counteracting the mission of health care.
The precise role that deforestation for agricultural settlements and commercial forest products plays in promoting or inhibiting malaria incidence in Amazonian Brazil is controversial. Using publically available databases, we analyzed temporal malaria incidence (2009–2015) in municipalities of nine Amazonian states in relation to ecologically defined variables: (i) deforestation (rate of forest clearing over time); (ii) degraded forest (degree of human disturbance and openness of forest canopy for logging) and (iii) impacted forest (sum of deforested and degraded forest patches). We found that areas affected by one kilometer square of deforestation produced 27 new malaria cases (r² = 0.78; F1,10 = 35.81; P < 0.001). Unexpectedly, we found both a highly significant positive correlation between number of impacted forest patches less than 5 km2 and malaria cases, and that these patch sizes accounted for greater than ~95% of all patches in the study area. There was a significantly negative correlation between extraction forestry economic indices and malaria cases. Our results emphasize not only that deforestation promotes malaria incidence, but also that it directly or indirectly results in a low Human Development Index, and favors environmental conditions that promote malaria vector proliferation.
Background Brazilian malaria control programmes successfully reduced the incidence and mortality rates from 2005 to 2016. Since 2017, increased malaria has been reported across the Amazon. Few field studies focus on the primary malaria vector in high to moderate endemic areas, Nyssorhynchus darlingi , as the key entomological component of malaria risk, and on the metrics of Plasmodium vivax propagation in Amazonian rural communities. Methods Human landing catch collections were carried out in 36 houses of 26 communities in five municipalities in the Brazilian states of Acre, Amazonas and Rondônia states, with API (> 30). In addition, data on the number of locally acquired symptomatic infections were employed in mathematical modelling analyses carried out to determine Ny. darlingi vector competence and vectorial capacity to P. vivax ; and to calculate the basic reproduction number for P. vivax . Results Entomological indices and malaria metrics ranged among localities: prevalence of P. vivax infection in Ny. darlingi, from 0.243% in Mâncio Lima, Acre to 3.96% in Machadinho D’Oeste, Rondônia; daily human-biting rate per person from 23 ± 1.18 in Cruzeiro do Sul, Acre, to 66 ± 2.41 in Lábrea, Amazonas; vector competence from 0.00456 in São Gabriel da Cachoeira, Amazonas to 0.04764 in Mâncio Lima, Acre; vectorial capacity from 0.0836 in Mâncio Lima, to 1.5 in Machadinho D’Oeste. The estimated R 0 for P. vivax ( PvR 0 ) was 3.3 in Mâncio Lima, 7.0 in Lábrea, 16.8 in Cruzeiro do Sul, 55.5 in São Gabriel da Cachoeira, and 58.7 in Machadinho D’Oeste. Correlation between P. vivax prevalence in Ny. darlingi and vector competence was non-linear whereas association between prevalence of P. vivax in mosquitoes, vectorial capacity and R 0 was linear and positive. Conclusions In spite of low vector competence of Ny. darlingi to P. vivax , parasite propagation in the human population is enhanced by the high human-biting rate, and relatively high vectorial capacity. The high PvR 0 values suggest hyperendemicity in Machadinho D’Oeste and São Gabriel da Cachoeira at levels similar to those found for P. falciparum in sub-Saharan Africa regions. Mass screening for parasite reservoirs, effective anti-malarial drugs and vector control interventions will be necessary to shrinking transmission in Amazonian rural communities, Brazil. Electronic supplementary material The on...
Deforestation can increase the transmission of malaria. Here, we build upon the existing link between malaria risk and deforestation by investigating how the global demand for commodities that increase deforestation can also increase malaria risk. We use a database of trade relationships to link the consumption of deforestation-implicated commodities in developed countries to estimates of country-level malaria risk in developing countries. We estimate that about 20% of the malaria risk in deforestation hotspots is driven by the international trade of deforestation-implicated export commodities, such as timber, wood products, tobacco, cocoa, coffee and cotton. By linking malaria risk to final consumers of commodities, we contribute information to support demand-side policy measures to complement existing malaria control interventions, with co-benefits for reducing deforestation and forest disturbance.
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