Estimating the population-level impact of vaccines using synthetic controls

Christian A. W. Bruhn, Stephen Hetterich, Cynthia Schuck-Paim, Esra Kürüm, Robert J. Taylor, Roger Lustig, Eugene D. Shapiro, Joshua L. Warren, Lone Simonsen, Daniel M. Weinberger

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Resumé

When a new vaccine is introduced, it is critical to monitor trends in disease rates to ensure that the vaccine is effective and to quantify its impact. However, estimates from observational studies can be confounded by unrelated changes in healthcare utilization, changes in the underlying health of the population, or changes in reporting. Other diseases are often used to detect and adjust for these changes, but choosing an appropriate control disease a priori is a major challenge. The “synthetic controls” (causal impact) method, which was originally developed for website analytics and social sciences, provides an appealing solution. With this approach, potential comparison time series are combined into a composite and are used to generate a counterfactual estimate, which can be compared with the time series of interest after the intervention. We sought to estimate changes in hospitalizations for all-cause pneumonia associated with the introduction of pneumococcal conjugate vaccines (PCVs) in five countries in the Americas. Using synthetic controls, we found a substantial decline in hospitalizations for all-cause pneumonia in infants in all five countries (average of 20%), whereas estimates for young and middle-aged adults varied by country and were potentially influenced by the 2009 influenza pandemic. In contrast to previous reports, we did not detect a decline in all-cause pneumonia in older adults in any country. Synthetic controls promise to increase the accuracy of studies of vaccine impact and to increase comparability of results between populations compared with alternative approaches.
OriginalsprogEngelsk
TidsskriftProceedings of the National Academy of Sciences of the United States of America
Vol/bind114
Udgave nummer7
Sider (fra-til)1524–1529
ISSN0027-8424
DOI
StatusUdgivet - 1 feb. 2017
Udgivet eksterntJa

Citer dette

Bruhn, C. A. W., Hetterich, S., Schuck-Paim, C., Kürüm, E., Taylor, R. J., Lustig, R., ... Weinberger, D. M. (2017). Estimating the population-level impact of vaccines using synthetic controls. Proceedings of the National Academy of Sciences of the United States of America, 114(7), 1524–1529. https://doi.org/10.1073/pnas.1612833114
Bruhn, Christian A. W. ; Hetterich, Stephen ; Schuck-Paim, Cynthia ; Kürüm, Esra ; Taylor, Robert J. ; Lustig, Roger ; Shapiro, Eugene D. ; Warren, Joshua L. ; Simonsen, Lone ; Weinberger, Daniel M. / Estimating the population-level impact of vaccines using synthetic controls. I: Proceedings of the National Academy of Sciences of the United States of America. 2017 ; Bind 114, Nr. 7. s. 1524–1529.
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Bruhn, CAW, Hetterich, S, Schuck-Paim, C, Kürüm, E, Taylor, RJ, Lustig, R, Shapiro, ED, Warren, JL, Simonsen, L & Weinberger, DM 2017, 'Estimating the population-level impact of vaccines using synthetic controls', Proceedings of the National Academy of Sciences of the United States of America, bind 114, nr. 7, s. 1524–1529. https://doi.org/10.1073/pnas.1612833114

Estimating the population-level impact of vaccines using synthetic controls. / Bruhn, Christian A. W.; Hetterich, Stephen; Schuck-Paim, Cynthia; Kürüm, Esra; Taylor, Robert J.; Lustig, Roger; Shapiro, Eugene D.; Warren, Joshua L.; Simonsen, Lone; Weinberger, Daniel M.

I: Proceedings of the National Academy of Sciences of the United States of America, Bind 114, Nr. 7, 01.02.2017, s. 1524–1529.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Estimating the population-level impact of vaccines using synthetic controls

AU - Bruhn, Christian A. W.

AU - Hetterich, Stephen

AU - Schuck-Paim, Cynthia

AU - Kürüm, Esra

AU - Taylor, Robert J.

AU - Lustig, Roger

AU - Shapiro, Eugene D.

AU - Warren, Joshua L.

AU - Simonsen, Lone

AU - Weinberger, Daniel M.

PY - 2017/2/1

Y1 - 2017/2/1

N2 - When a new vaccine is introduced, it is critical to monitor trends in disease rates to ensure that the vaccine is effective and to quantify its impact. However, estimates from observational studies can be confounded by unrelated changes in healthcare utilization, changes in the underlying health of the population, or changes in reporting. Other diseases are often used to detect and adjust for these changes, but choosing an appropriate control disease a priori is a major challenge. The “synthetic controls” (causal impact) method, which was originally developed for website analytics and social sciences, provides an appealing solution. With this approach, potential comparison time series are combined into a composite and are used to generate a counterfactual estimate, which can be compared with the time series of interest after the intervention. We sought to estimate changes in hospitalizations for all-cause pneumonia associated with the introduction of pneumococcal conjugate vaccines (PCVs) in five countries in the Americas. Using synthetic controls, we found a substantial decline in hospitalizations for all-cause pneumonia in infants in all five countries (average of 20%), whereas estimates for young and middle-aged adults varied by country and were potentially influenced by the 2009 influenza pandemic. In contrast to previous reports, we did not detect a decline in all-cause pneumonia in older adults in any country. Synthetic controls promise to increase the accuracy of studies of vaccine impact and to increase comparability of results between populations compared with alternative approaches.

AB - When a new vaccine is introduced, it is critical to monitor trends in disease rates to ensure that the vaccine is effective and to quantify its impact. However, estimates from observational studies can be confounded by unrelated changes in healthcare utilization, changes in the underlying health of the population, or changes in reporting. Other diseases are often used to detect and adjust for these changes, but choosing an appropriate control disease a priori is a major challenge. The “synthetic controls” (causal impact) method, which was originally developed for website analytics and social sciences, provides an appealing solution. With this approach, potential comparison time series are combined into a composite and are used to generate a counterfactual estimate, which can be compared with the time series of interest after the intervention. We sought to estimate changes in hospitalizations for all-cause pneumonia associated with the introduction of pneumococcal conjugate vaccines (PCVs) in five countries in the Americas. Using synthetic controls, we found a substantial decline in hospitalizations for all-cause pneumonia in infants in all five countries (average of 20%), whereas estimates for young and middle-aged adults varied by country and were potentially influenced by the 2009 influenza pandemic. In contrast to previous reports, we did not detect a decline in all-cause pneumonia in older adults in any country. Synthetic controls promise to increase the accuracy of studies of vaccine impact and to increase comparability of results between populations compared with alternative approaches.

U2 - 10.1073/pnas.1612833114

DO - 10.1073/pnas.1612833114

M3 - Journal article

VL - 114

SP - 1524

EP - 1529

JO - Proceedings of the National Academy of Sciences of the United States of America

JF - Proceedings of the National Academy of Sciences of the United States of America

SN - 0027-8424

IS - 7

ER -