This site needs JavaScript to work properly. Please enable it to take advantage of the complete set of features!
Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

NIH NLM Logo
Log in
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2017 Jun 5;372(1722):20160122.
doi: 10.1098/rstb.2016.0122.

Human infectious disease burdens decrease with urbanization but not with biodiversity

Affiliations

Human infectious disease burdens decrease with urbanization but not with biodiversity

Chelsea L Wood et al. Philos Trans R Soc Lond B Biol Sci. .

Abstract

Infectious disease burdens vary from country to country and year to year due to ecological and economic drivers. Recently, Murray et al. (Murray CJ et al 2012 Lancet380, 2197-2223. (doi:10.1016/S0140-6736(12)61689-4)) estimated country-level morbidity and mortality associated with a variety of factors, including infectious diseases, for the years 1990 and 2010. Unlike other databases that report disease prevalence or count outbreaks per country, Murray et al. report health impacts in per-person disability-adjusted life years (DALYs), allowing comparison across diseases with lethal and sublethal health effects. We investigated the spatial and temporal relationships between DALYs lost to infectious disease and potential demographic, economic, environmental and biotic drivers, for the 60 intermediate-sized countries where data were available and comparable. Most drivers had unique associations with each disease. For example, temperature was positively associated with some diseases and negatively associated with others, perhaps due to differences in disease agent thermal optima, transmission modes and host species identities. Biodiverse countries tended to have high disease burdens, consistent with the expectation that high diversity of potential hosts should support high disease transmission. Contrary to the dilution effect hypothesis, increases in biodiversity over time were not correlated with improvements in human health, and increases in forestation over time were actually associated with increased disease burden. Urbanization and wealth were associated with lower burdens for many diseases, a pattern that could arise from increased access to sanitation and healthcare in cities and increased investment in healthcare. The importance of urbanization and wealth helps to explain why most infectious diseases have become less burdensome over the past three decades, and points to possible levers for further progress in improving global public health.This article is part of the themed issue 'Conservation, biodiversity and infectious disease: scientific evidence and policy implications'.

Keywords: Infectious disease; dilution effect; disability-adjusted life year; global change.

PubMed Disclaimer

Conflict of interest statement

We have no competing interests.

Figures

Figure 1.
Figure 1.
Standardized regression coefficients from PLS-SEM full models (including all pathways except those deemed to be illogical from first principles; table 1) and pruned models (including only those pathways that were significant) for each of the 24 infectious diseases in the (a) spatial and (b) temporal analyses. The top box indicates the interactions among the drivers, where the rows are independent variables and the columns are dependent variables. Values are standardized regression coefficients and colours correspond to coefficient values (red, negative; blue, positive). Diseases are sorted into two groups (zoonoses + vector-borne diseases versus non-zoonoses) and by descending total global DALYs in the included countries in 2010 within those two groups. Drivers are sorted left from right by their tendency for positive (blue) or negative (red) coefficients in space. The bottom boxes show the direct effects of a driver on disease burden. In the full model panel, blank cells indicate untested associations assumed to be zero based on first principles. An underlined disease is one for which there was not a significant reduced model as determined by the adjusted R2 (shown at right). The pruned model panel shows results after performing a model selection process in which any coefficient with an associated p-value < 0.10 was removed and the model re-run. Bold-font coefficients are significant.
Figure 2.
Figure 2.
Clustering diseases by their associated drivers in the (a) spatial and (b) temporal analysis. This figure was produced by hierarchical Ward clustering based on the standardized regression coefficients in figure 1, where we assumed that illogical drivers had a coefficient = 0.
Figure 3.
Figure 3.
Trends over time. A 1 : 1 line is indicated in each plot to represent zero change; data points falling above this line represent increases over time, and those falling below the line represent decreases. Colours represent biomes in (ah) (see legend). In i, black, non-zoonosis; grey, zoonosis. Biome codes: TSMBF, tropical and subtropical moist broadleaf forests; TSGSS, tropical and subtropical grasslands, savannas and shrublands; TSDBF, tropical and subtropical dry broadleaf forest; TSCF, tropical and subtropical coniferous forests; DXS, deserts and xeric shrublands; TBMF, temperate broadleaf and mixed forests; MFWS, Mediterranean forests, woodlands and scrub; TCF, temperate conifer forests; BFT, boreal forest/taiga; T, tundra.
Figure 4.
Figure 4.
Results of spatial (x-axis) and temporal (y-axis) meta-analyses, which summarize the results of PLS-SEM across 24 diseases. Points represent mean effect sizes for the effects of wealth, per cent of population living in urban environments, population density, forest cover, temperature, precipitation and biodiversity. Error bars represent 95% confidence intervals.

References

    1. World Health Organization 2016. World Health Statistics 2016: Monitoring Health for the SDGs (Sustainable Development Goals). Geneva, Switzerland: World Health Organization.
    1. World Health Organization 2008. The global burden of disease: 2004 update. Geneva, Switzerland: World Health Organization.
    1. Guernier V, Hochberg ME, Guégan J-F. 2004. Ecology drives the worldwide distribution of human diseases. PLoS Biol. 2, e141 (10.1371/journal.pbio.0020141 - DOI - PMC - PubMed
    1. Bonds MH, Dobson AP, Keenan DC. 2012. Disease ecology, biodiversity, and the latitudinal gradient in income. PLoS Biol. 10, e1001456 (10.1371/journal.pbio.1001456) - DOI - PMC - PubMed
    1. Hay SI, et al. 2013. Global mapping of infectious disease. Phil. Trans. R. Soc. B 368, 20120250 (10.1098/rstb.2012.0250) - DOI - PMC - PubMed
Cite

AltStyle によって変換されたページ (->オリジナル) /