Complex systems models for causal inference in social epidemiology

Kouser, Hiba; Barnard-Mayers, Ruby; Murray, Eleanor

doi:10.1136/jech-2019-213052

Cited by 16 publications

(13 citation statements)

References 87 publications

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“…If the true data generating process for an epidemic involves the theoretically predicted voluntary behavioral avoidance response, then an empirical model that assumes away that behavioral response can fit the epidemiological data as well as a model that specifies the true data generating process (6). The problem is that the misspecified model introduces many opportunities for confounding processes in epidemiology (26), and a misspecified model cannot provide information about behavioral response, whether voluntary or because of public health mandates. Therefore, we focus on the first step in a potential causal chain.…”

Section: Significancementioning

confidence: 99%

Measuring voluntary and policy-induced social distancing behavior during the COVID-19 pandemic

Yan

Malik

Bayham

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

124

103

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Staying home and avoiding unnecessary contact is an important part of the effort to contain COVID-19 and limit deaths. Every state in the United States enacted policies to encourage distancing and some mandated staying home. Understanding how these policies interact with individuals’ voluntary responses to the COVID-19 epidemic is a critical initial step in understanding the role of these nonpharmaceutical interventions in transmission dynamics and assessing policy impacts. We use variation in policy responses along with smart device data that measures the amount of time Americans stayed home to disentangle the extent that observed shifts in staying home behavior are induced by policy. We find evidence that stay-at-home orders and voluntary response to locally reported COVID-19 cases and deaths led to behavioral change. For the median county, which implemented a stay-at-home order with about two cases, we find that the response to stay-at-home orders increased time at home as if the county had experienced 29 additional local cases. However, the relative effect of stay-at-home orders was much greater in select counties. On the one hand, the mandate can be viewed as displacing a voluntary response to this rise in cases. On the other hand, policy accelerated the response, which likely helped reduce spread in the early phase of the pandemic. It is important to be able to attribute the relative role of self-interested behavior or policy mandates to understand the limits and opportunities for relying on voluntary behavior as opposed to imposing stay-at-home orders.

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Section: Significancementioning

confidence: 99%

Measuring voluntary and policy-induced social distancing behavior during the COVID-19 pandemic

Yan

Malik

Bayham

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

124

103

View full text Add to dashboard Cite

show abstract

“…Dynamic modelling methods are used to develop mathematical representations of non-linear systems, incorporating feedback loops and multiple interdependent variables that evolve over time [ 40 ]. Dynamic models can be used to simulate the impact of an intervention at a systems level and are used increasingly to inform policy making [ 41 – 43 ]. They provide an explicit method to synthesise available evidence regarding the effectiveness and costs of alternative healthcare interventions or strategies [ 44 ].…”

Section: Dynamic Transmission Models and Incorporation Of Antimicrobi...mentioning

confidence: 99%

Modelling the Future Clinical and Economic Burden of Antimicrobial Resistance: The Feasibility and Value of Models to Inform Policy

Hillock

Merlin

Turnidge

et al. 2022

Appl Health Econ Health Policy

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Due to the increasing threat to public health and the economy, governments internationally are interested in models to estimate the future clinical and economic burden of antimicrobial resistance (AMR) and to evaluate the cost-effectiveness of interventions to prevent or control resistance and to inform resource-allocation decision making. A widely cited UK report estimated that 10 million additional deaths will occur globally per annum due to AMR by 2050; however, the utility and accuracy of this prediction has been challenged. The precision of models predicting the future economic burden of AMR is dependent upon the accuracy of predicting future resistance rates. This paper reviews the feasibility and value of modelling to inform policy and resource allocation to manage and curb AMR. Here we describe methods used to estimate future resistance in published burden-of-disease models; the sources of uncertainty are highlighted, which could potentially mislead policy decision-making. While broad assumptions can be made regarding some predictable factors contributing to future resistance rates, the unexpected emergence, establishment and spread of new resistance genes introduces substantial uncertainty into estimates of future economic burden, and in models evaluating the effectiveness of interventions or policies to address AMR. Existing reporting standards for best practice in modelling should be adapted to guide the reporting of AMR economic models, to ensure model transparency and validation for interpretation by policymakers.

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“…Firstly, Machine learning is conducive to establish the multivariate empirical relationship between causes and effects ( 23 ). Secondly, system dynamics modeling approaches or agent-based models contributes to arrange and assess the weights of interventions at multiple levels ( 24 , 25 ). Thirdly, by incorporating more information and handling missing data, machine learning can be used for resource allocation and result prediction ( 26 ).…”

Section: Introductionmentioning

confidence: 99%

Study of the Economic, Environmental, and Social Factors Affecting Chinese Residents' Health Based on Machine Learning

Pan²,

Meng³

et al. 2022

Front. Public Health

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The Healthy China Strategy puts realistic demands for residents' health levels, but the reality is that various factors can affect health. In order to clarify which factors have a great impact on residents' health, based on China's provincial panel data from 2011 to 2018, this paper selects 17 characteristic variables from the three levels of economy, environment, and society and uses the XG boost algorithm and Random forest algorithm based on recursive feature elimination to determine the influencing variables. The results show that at the economic level, the number of industrial enterprises above designated size, industrial added value, population density, and per capita GDP have a greater impact on the health of residents. At the environmental level, coal consumption, energy consumption, total wastewater discharge, and solid waste discharge have a greater impact on the health level of residents. Therefore, the Chinese government should formulate targeted measures at both economic and environmental levels, which is of great significance to realizing the Healthy China strategy.

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Complex systems models for causal inference in social epidemiology

Cited by 16 publications

References 87 publications

Measuring voluntary and policy-induced social distancing behavior during the COVID-19 pandemic

Measuring voluntary and policy-induced social distancing behavior during the COVID-19 pandemic

Modelling the Future Clinical and Economic Burden of Antimicrobial Resistance: The Feasibility and Value of Models to Inform Policy

Study of the Economic, Environmental, and Social Factors Affecting Chinese Residents' Health Based on Machine Learning

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