variation partitioning environment space time rcode rscript

Under its design, conditional Poisson (quasi-Poisson) regression allows researchers to adjust for overdispersion and autocorrelation in the count data, which is not possible for conditional logistic regression. This means the aggregated count data instead of individual data are needed. On the contrary, a conditional Poisson regression model can be performed directly on data with a time-series format (a sequence of daily cases indexed in time order), to fit space-time-stratified case-crossover design. Where a stratum consists of 1 case ( ) and its 3 or 4 controls ( ), is the conditional probability of being a case in the th stratum given the value of exposure variable and other covariates, represents the constant or intercept of stratum, stands for the exposure variable of interest in the study with its coefficient, stand for variables adjusted in the model, and denotes the coefficients of. Variables indicating the long-term trend and seasonality are not necessary to be included in the model. For every case occurring on a day, the day of the case is defined as a “case” and other days in the same stratum (on the same day of the week, month, year, and the same location) as “controls.” If there are cases in the day, there must be stratums of the day in the data set. Conditional logistic regression is similar to the analysis of matched case-control study, which requires the dataset to be expanded from time-series format to individual matched case-control format. Two statistical models, conditional logistic regression and conditional Poisson regression, can be used to perform the space-time-stratified case-crossover study. In addition, it can also adjust individual characteristics which are unlikely to change within the small-time window, such as demographic characteristics (e.g., sex, race, education, and weight) and living habits (e.g., smoking and drinking).

This design allows researchers to simultaneously control for the impacts of the day of the week, seasonality, long-term trend, and spatial variation using location-specific fixed and disjointed time strata and to avoid bias resulting from time trends by removing patterns in the placement of referents. Thus, each case has 3 or 4 control days (before and/or after the case day in the same month, Figure 1(f)). Specifically, within each ID stratum, the case day and control days are matched by day of the week in the same month, in the same year, and in the same location (e.g., city). For each case, it introduces a stratum combining two dimensions of time (e.g., month) and space (e.g., study locations).

This method is characterized by the fact that each individual serves as her or his own control. To accommodate the increasing demand for multilocation studies, we propose the space-time-stratified case-crossover design which is developed from the time-stratified case-crossover design and applied to multi-time-series data. Recently, multilocation time-series data are used to examine the health impacts of environmental exposures, to make the results generalizable, credible, and confirmable. The time-stratified case-crossover design has been widely used for location-specific time-series data. Strategies of control day selection in the case-crossover study. Time-stratified case-crossover design is the best approach to control the above biases.

However, unidirectional and bidirectional strategies introduce biases from various sources, such as time trends and seasonal patterns in exposure or health events and nonindependent selection of control days. Several strategies for choosing control days are proposed (Figures 1(a)– 1(e)).

With this design, the long-term trend and seasonality of unmeasured variables are controlled for. The control days represent the counterfactual exposure experience of each case, independently of the exposure on case day. To examine whether the events are associated with a particular exposure, it compares exposure level in the day when the health event occurs (case day) with the levels in nearby days (control days). It allows researchers to examine the acute health effects due to short-term environmental exposure.Ī case-crossover design utilizes the ID as a stratum, comparing individuals to themselves at different times. Case-crossover design with daily environmental exposure and health outcomes (e.g., deaths and hospitalisations) is one of the most common study designs. It is vital to use proper methods to quantify the impact of environmental exposure (e.g., air pollutants and extreme temperatures) on human health. We are living in a changing environment that affects human health.

Variation partitioning environment space time rcode rscript