Staff Profile - MGH Institute for Technology Assessment

Anneke L. Claypool, PhD

AAAS Science and Technology Fellow, White House Office of Science and Technology Policy

Anneke Claypool is currently an AAAS Science and Technology Fellow in the White House Office of Science and Technology Policy. Prior to this role, Anneke was an AAAS Science and Technology Fellows at the US Agency for International Development in the Malaria Division. She joined the MGH Institute for Technology Assessment (ITA) as a Postdoctoral Research Fellow in April 2021. She earned her PhD and MS in Management Science and Engineering at Stanford University and her BA in Mathematics and International Affairs at the University of Colorado at Boulder.

Dr. Claypool’s research uses mathematical models to inform health policy decisions. Her research includes cost-effectiveness analysis, modeling mosquito-transmitted diseases, and projecting county-level COVID-19 outcomes in California as part of the SC-COSMO Modeling Consortium. She is particularly interested in developing methods to improve modeling multiple diseases.

At the ITA, she is working with Dr. Mohammad Jalali on research relating to the optimization of HIV testing and treatment in settings with limited resources.

Selected Publications

2021

Reitsma, Marissa B; Claypool, Anneke L.; Vargo, Jason; Shete, Priya B; McCorvie, Ryan; Wheeler, William H; Rocha, David A; Myers, Jennifer F; Murray, Erin L; Bregman, Brooke; Dominguez, Deniz M; Nguyen, Alyssa D; Porse, Charsey; Fritz, Curtis L; Jain, Seema; Watt, James P; Salomon, Joshua A; Goldhaber-Fiebert, Jeremy D

Racial/Ethnic Disparities In COVID-19 Exposure Risk, Testing, And Cases At The Subcounty Level In California. Journal Article

In: Health affairs (Project Hope), pp. 101377hlthaff2021000, 2021, ISSN: 1544-5208, ().

Abstract | Links | BibTeX

@article{Reitsma2021,

title = {Racial/Ethnic Disparities In COVID-19 Exposure Risk, Testing, And Cases At The Subcounty Level In California.},

author = {Marissa B Reitsma and Anneke L. Claypool and Jason Vargo and Priya B Shete and Ryan McCorvie and William H Wheeler and David A Rocha and Jennifer F Myers and Erin L Murray and Brooke Bregman and Deniz M Dominguez and Alyssa D Nguyen and Charsey Porse and Curtis L Fritz and Seema Jain and James P Watt and Joshua A Salomon and Jeremy D Goldhaber-Fiebert},

doi = {10.1377/hlthaff.2021.00098},

issn = {1544-5208},

year  = {2021},

date = {2021-05-01},

journal = {Health affairs (Project Hope)},

pages = {101377hlthaff2021000},

abstract = {With a population of forty million and substantial geographic variation in sociodemographics and health services, California is an important setting in which to study disparities. Its population (37.5 percent White, 39.1 percent Latino, 5.3 percent Black, and 14.4 percent Asian) experienced 59,258 COVID-19 deaths through April 14, 2021-the most of any state. We analyzed California's racial/ethnic disparities in COVID-19 exposure risks, testing rates, test positivity, and case rates through October 2020, combining data from 15.4 million SARS-CoV-2 tests with subcounty exposure risk estimates from the American Community Survey. We defined "high-exposure-risk" households as those with one or more essential workers and fewer rooms than inhabitants. Latino people in California are 8.1 times more likely to live in high-exposure-risk households than White people (23.6 percent versus 2.9 percent), are overrepresented in cumulative cases (3,784 versus 1,112 per 100,000 people), and are underrepresented in cumulative testing (35,635 versus 48,930 per 100,000 people). These risks and outcomes were worse for Latino people than for members of other racial/ethnic minority groups. Subcounty disparity analyses can inform targeting of interventions and resources, including community-based testing and vaccine access measures. Tracking COVID-19 disparities and developing equity-focused public health programming that mitigates the effects of systemic racism can help improve health outcomes among California's populations of color.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

2019

Claypool, Anneke L.; Brandeau, Margaret L; Goldhaber-Fiebert, Jeremy D

Quantifying Positive Health Externalities of Disease Control Interventions: Modeling Chikungunya and Dengue. Journal Article

In: Medical decision making : an international journal of the Society for Medical Decision Making, vol. 39, pp. 1045–1058, 2019, ISSN: 1552-681X, ().

Abstract | Links | BibTeX

@article{Claypool2019,

title = {Quantifying Positive Health Externalities of Disease Control Interventions: Modeling Chikungunya and Dengue.},

author = {Anneke L. Claypool and Margaret L Brandeau and Jeremy D Goldhaber-Fiebert},

doi = {10.1177/0272989X19880554},

issn = {1552-681X},

year  = {2019},

date = {2019-11-01},

journal = {Medical decision making : an international journal of the Society for Medical Decision Making},

volume = {39},

pages = {1045--1058},

abstract = {Health interventions can generate positive externalities not captured in traditional, single-disease cost-effectiveness analyses (CEAs), potentially biasing results. We illustrate this with the example of mosquito-borne diseases. When a particular mosquito species can transmit multiple diseases, a single-disease CEA comparing disease-specific interventions (e.g., vaccination) with interventions targeting the mosquito population (e.g., insecticide) would underestimate the insecticide's full benefits (i.e., preventing other diseases). We developed three dynamic transmission models: chikungunya, dengue, and combined chikungunya and dengue, each calibrated to disease-specific incidence and deaths in Colombia (June 2014 to December 2017). We compared the models' predictions of the incremental benefits and cost-effectiveness of an insecticide (10% efficacy), hypothetical chikungunya and dengue vaccines (40% coverage, 95% efficacy), and combinations of these interventions. Model calibration yielded realistic parameters that produced close matches to disease-specific incidence and deaths. The chikungunya model predicted that vaccine would decrease the incidence of chikungunya and avert more total deaths than insecticide. The dengue model predicted that insecticide and the dengue vaccine would reduce dengue incidence and deaths, with no effect for the chikungunya vaccine. In the combined model, insecticide was more effective than either vaccine in reducing the incidence of and deaths from both diseases. In all models, the combined strategy was at least as effective as the most effective single strategy. In an illustrative CEA, the most frequently preferred strategy was vaccine in the chikungunya model, the status quo in the dengue model, and insecticide in the combined model. There is uncertainty in the target calibration data. Failure to capture positive externalities can bias CEA results, especially when evaluating interventions that affect multiple diseases. Multidisease modeling is a reasonable alternative for addressing such biases.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Health interventions can generate positive externalities not captured in traditional, single-disease cost-effectiveness analyses (CEAs), potentially biasing results. We illustrate this with the example of mosquito-borne diseases. When a particular mosquito species can transmit multiple diseases, a single-disease CEA comparing disease-specific interventions (e.g., vaccination) with interventions targeting the mosquito population (e.g., insecticide) would underestimate the insecticide's full benefits (i.e., preventing other diseases). We developed three dynamic transmission models: chikungunya, dengue, and combined chikungunya and dengue, each calibrated to disease-specific incidence and deaths in Colombia (June 2014 to December 2017). We compared the models' predictions of the incremental benefits and cost-effectiveness of an insecticide (10% efficacy), hypothetical chikungunya and dengue vaccines (40% coverage, 95% efficacy), and combinations of these interventions. Model calibration yielded realistic parameters that produced close matches to disease-specific incidence and deaths. The chikungunya model predicted that vaccine would decrease the incidence of chikungunya and avert more total deaths than insecticide. The dengue model predicted that insecticide and the dengue vaccine would reduce dengue incidence and deaths, with no effect for the chikungunya vaccine. In the combined model, insecticide was more effective than either vaccine in reducing the incidence of and deaths from both diseases. In all models, the combined strategy was at least as effective as the most effective single strategy. In an illustrative CEA, the most frequently preferred strategy was vaccine in the chikungunya model, the status quo in the dengue model, and insecticide in the combined model. There is uncertainty in the target calibration data. Failure to capture positive externalities can bias CEA results, especially when evaluating interventions that affect multiple diseases. Multidisease modeling is a reasonable alternative for addressing such biases.