Honghyok Kim1, Jong-Tae Lee2,3, Kelvin C Fong4, Michelle L Bell4. 1. School of the Environment, Yale University, 195 Prospect Street, New Haven, CT, 06511, USA. honghyok.kim@yale.edu. 2. BK21PLUS Program in 'Embodiment: Health -Society Interaction', Department of Public Health Science, Graduate School, Korea University, Seoul, Republic of Korea. 3. School of Health Policy and Management, College of Health Science, Korea University, Seoul, Republic of Korea. 4. School of the Environment, Yale University, 195 Prospect Street, New Haven, CT, 06511, USA.
Abstract
BACKGROUND: Time-series analysis with case-only data is a prominent method for the effect of environmental determinants on disease events in environmental epidemiology. In this analysis, adjustment for seasonality and long-term time-trend is crucial to obtain valid findings. When applying this analysis for long-term exposure (e.g., months, years) of which effects are usually studied via survival analysis with individual-level longitudinal data, unlike its application for short-term exposure (e.g., days, weeks), a standard adjustment method for seasonality and long-term time-trend can extremely inflate standard error of coefficient estimates of the effects. Given that individual-level longitudinal data are difficult to construct and often available to limited populations, if this inflation of standard error can be solved, rich case-only data over regions and countries would be very useful to test a variety of research hypotheses considering unique local contexts. METHODS: We discuss adjustment methods for seasonality and time-trend used in time-series analysis in environmental epidemiology and explain why standard errors can be inflated. We suggest alternative methods to solve this problem. We conduct simulation analyses based on real data for Seoul, South Korea, 2002-2013, and time-series analysis using real data for seven major South Korean cities, 2006-2013 to identify whether the association between long-term exposure and health outcomes can be estimated via time-series analysis with alternative adjustment methods. RESULTS: Simulation analyses and real-data analysis confirmed that frequently used adjustment methods such as a spline function of a variable representing time extremely inflate standard errors of estimates for associations between long-term exposure and health outcomes. Instead, alternative methods such as a combination of functions of variables representing time can make sufficient adjustment with efficiency. CONCLUSIONS: Our findings suggest that time-series analysis with case-only data can be applied for estimating long-term exposure effects. Rich case-only data such as death certificates and hospitalization records combined with repeated measurements of environmental determinants across countries would have high potentials for investigating the effects of long-term exposure on health outcomes allowing for unique contexts of local populations.
BACKGROUND: Time-series analysis with case-only data is a prominent method for the effect of environmental determinants on disease events in environmental epidemiology. In this analysis, adjustment for seasonality and long-term time-trend is crucial to obtain valid findings. When applying this analysis for long-term exposure (e.g., months, years) of which effects are usually studied via survival analysis with individual-level longitudinal data, unlike its application for short-term exposure (e.g., days, weeks), a standard adjustment method for seasonality and long-term time-trend can extremely inflate standard error of coefficient estimates of the effects. Given that individual-level longitudinal data are difficult to construct and often available to limited populations, if this inflation of standard error can be solved, rich case-only data over regions and countries would be very useful to test a variety of research hypotheses considering unique local contexts. METHODS: We discuss adjustment methods for seasonality and time-trend used in time-series analysis in environmental epidemiology and explain why standard errors can be inflated. We suggest alternative methods to solve this problem. We conduct simulation analyses based on real data for Seoul, South Korea, 2002-2013, and time-series analysis using real data for seven major South Korean cities, 2006-2013 to identify whether the association between long-term exposure and health outcomes can be estimated via time-series analysis with alternative adjustment methods. RESULTS: Simulation analyses and real-data analysis confirmed that frequently used adjustment methods such as a spline function of a variable representing time extremely inflate standard errors of estimates for associations between long-term exposure and health outcomes. Instead, alternative methods such as a combination of functions of variables representing time can make sufficient adjustment with efficiency. CONCLUSIONS: Our findings suggest that time-series analysis with case-only data can be applied for estimating long-term exposure effects. Rich case-only data such as death certificates and hospitalization records combined with repeated measurements of environmental determinants across countries would have high potentials for investigating the effects of long-term exposure on health outcomes allowing for unique contexts of local populations.
Authors: Francesco Sera; Ben Armstrong; Aurelio Tobias; Ana Maria Vicedo-Cabrera; Christofer Åström; Michelle L Bell; Bing-Yu Chen; Micheline de Sousa Zanotti Stagliorio Coelho; Patricia Matus Correa; Julio Cesar Cruz; Tran Ngoc Dang; Magali Hurtado-Diaz; Dung Do Van; Bertil Forsberg; Yue Leon Guo; Yuming Guo; Masahiro Hashizume; Yasushi Honda; Carmen Iñiguez; Jouni J K Jaakkola; Haidong Kan; Ho Kim; Eric Lavigne; Paola Michelozzi; Nicolas Valdes Ortega; Samuel Osorio; Mathilde Pascal; Martina S Ragettli; Niilo R I Ryti; Paulo Hilario Nascimento Saldiva; Joel Schwartz; Matteo Scortichini; Xerxes Seposo; Shilu Tong; Antonella Zanobetti; Antonio Gasparrini Journal: Int J Epidemiol Date: 2019-08-01 Impact factor: 7.196
Authors: Richard T Burnett; Anup Dewanji; Francesca Dominici; Mark S Goldberg; Aaron Cohen; Daniel Krewski Journal: Environ Health Perspect Date: 2003-07 Impact factor: 9.031