Mutliscale Drivers of Global Environmental Health
- Author(s): Desai, Manish Anil
- Advisor(s): Smith, Kirk R
- Eisenberg, Joseph NS
- et al.
Environmental health’s purview, driven by an accelerating transformation of social and ecological systems, has been progressively expanding to encompass a broader array of environment health relationships. This widening perspective embraces persistent, resurgent, and nascent threats to human health that often operate at multiple scales, generating the attributable burdens of the present as well as the avoidable burdens of the future. Analyzing linkages from the planetary to the individual is a core challenge for evolving environmental health into its “global” incarnation. The complications and uncertainties involved are daunting as causality cascades through multiple scales, prompting global environmental health to expand not only its paradigm but also its toolkit.
In this dissertation, I motivate, develop, and demonstrate three such approaches for investigating multiscale drivers of global environmental health: (1) a metric for analyzing contributions and responses to climate change from global to sectoral scales, (2) a framework for unraveling the influence of environmental change on infectious diseases at regional to local scales, and (3) a model for informing the design and evaluation of clean cooking interventions at community to household scales.
The full utility of climate debt as an analytical perspective will remain untapped without tools that can be manipulated by a wide range of analysts, including global environmental health researchers. Chapter 2 explains how international natural debt (IND) apportions global radiative forcing from fossil fuel carbon dioxide and methane, the two most significant climate altering pollutants, to individual entities − primarily countries but also subnational states and economic sectors, with even finer scales possible − as a function of unique trajectories of historical emissions, taking into account the quite different radiative efficiencies and atmospheric lifetimes of each pollutant. Owing to its straightforward and transparent derivation, IND can readily operationalize climate debt to consider issues of equity and efficiency and drive scenario exercises that explore the response to climate change at multiple scales. Collectively, the analyses presented in this chapter demonstrate how IND can inform a range of key question on climate change mitigation at multiple scales, compelling environmental health towards an appraisal of the causes and not just the consequences of climate change.
The environmental change and infectious disease (EnvID) conceptual framework of Chapter 3 builds on a rich history of prior efforts in epidemiologic theory, environmental science, and mathematical modeling by: (1) articulating a flexible and logical system specification; (2) incorporating transmission groupings linked to public health intervention strategies; (3) emphasizing the intersection of proximal environmental characteristics and transmission cycles; (4) incorporating a matrix formulation to identify knowledge gaps and facilitate an integration of research; and (5) highlighting hypothesis generation amidst dynamic processes. A systems based approach leverages the reality that studies relevant to environmental change and infectious disease are embedded within a wider web of interactions. As scientific understanding advances, the EnvID framework can help integrate the various factors at play in determining environment–disease relationships and the connections between intrinsically multiscale causal networks.
In Chapter 4, the coverage effect model functions primarily as a “proof of concept” analysis to address whether the efficacy of a clean cooking technology may be determined by the extent of not only household level use but also community level coverage. Such coverage dependent efficacy, or a “coverage effect,” would transform how interventions are studied and deployed. Ensemble results are consistent with the concept that an appreciable coverage effect from clean cooking interventions can manifest within moderately dense communities. Benefits for users derive largely from direct effects; initially, at low coverage levels, almost exclusively so. Yet, as coverage expands within a user’s community, a coverage effect becomes increasingly beneficial. In contrast, non users, despite also experiencing comparable exposure reductions from community-level intervention use, cannot proportionately benefit because their exposures remain overwhelmingly dominated by household-level use of traditional solid fuel cookstoves.
The coverage effect model strengthens the rationale for public health programs and policies to encourage clean cooking technologies with an added incentive to realize high coverage within contiguous areas. The implications of the modeling exercise extend to priorities for data collection, underscoring the importance of outdoor pollution concentrations during, as well as before and/or after, community cooking windows and also routine measurement of ventilation, meteorology, time activity patterns, and cooking practices. The possibility of a coverage effect necessitates appropriate strategies to estimate not only direct effects but also coverage and total effects to avoid impaired conclusions.
The specter of accelerating social and ecological change challenges efforts to respond to climate change, re/emerging infectious diseases, and household air pollution. Environmental health possesses a well-established and well-tested repertoire of methods but contending with multiscale drivers of risk requires complementary approaches, as well. Integrating metrics, frameworks, and models − and their insights − into its analytical arsenal can help global environmental health meet the challenges of today and tomorrow.