Abstract
Impacts of complex emergencies or relief interventions have often been evaluated by absolute mortality compared to international standardized mortality rates. A better evaluation would be to compare with local baseline mortality of the affected populations. A projection of population-based survival data into time of emergency or intervention based on information from before the emergency may create a local baseline reference. We find a log-transformed Gaussian time series model where standard errors of the estimated rates are included in the variance to have the best forecasting capacity. However, if time-at-risk during the forecasted period is known then forecasting might be done using a Poisson time series model with overdispersion. Whatever, the standard error of the estimated rates must be included in the variance of the model either in an additive form in a Gaussian model or in a multiplicative form by overdispersion in a Poisson model. Data on which the forecasting is based must be modelled carefully concerning not only calendar-time trends but also periods with excessive frequency of events (epidemics) and seasonal variations to eliminate residual autocorrelation and to make a proper reference for comparison, reflecting changes over time during the emergency. Hence, when modelled properly it is possible to predict a reference to an emergency-affected population based on local conditions. We predicted childhood mortality during the war in Guinea-Bissau 1998–1999. We found an increased mortality in the first half-year of the war and a mortality corresponding to the expected one in the last half-year of the war.
Acknowledgement
The authors are obliged to the Bandim Health Project managed by Peter Aaby and all field assistants and supervisors who collected, followed-up on and maintained the demographic surveillance data in Bissau, Guinea-Bissau. Henrik Jensen and Per K. Andersen took part in planning the design and supervised statistical methods and analysis. Jens Nielsen designed the study, implemented data control, did all statistical analyses and wrote the first draft of the paper. All authors contributed to the final version. The authors acknowledge the Danish Council for Development Research (Ministry of Foreign Affairs, Copenhagen, Denmark) and ECHO (European Commission, Brussels, Belgium).