Mortality risk within counterfactual models: Puerto Rico and Hurricane Maria – Authors' reply
Authors: Carlos Santos-Burgoa, John Sandberg, Erick Suárez, Cynthia M Pérez, Lynn Goldmann
Journal: The Lancet Planetary Health (2019)

The main argument by Jeffrey Howard and Alexis Santos-Lozada appears to be that because the predicted number of deaths from the displacement model in our analysis was lower than historical averages and previous counterfactual estimates for the period after Hurricane Maria, this estimate, and estimates of excess mortality associated with them therefore must be biased or inaccurate in some way. The opposite is in fact the case. It is estimates of excess mortality that ignore displacement that are biased. Mortality rates are defined as the number of deaths divided by the population at risk of dying over a particular period. Estimated deaths under the displacement scenario are less than historical ranges because massive out-migration reduced (by approximately 8%) the population at risk of dying on the island. For a constant mortality rate, a smaller number at risk will result in a smaller number of estimated deaths. All other estimates cited by Howard and Santos-Lozada, including their own, overestimate predicted (and underestimate excess) mortality in the current context simply because they do not take displacement into account. Their second critique is that we ignore selectivity associated with the risk of mortality between people who left the island and those who did not. The citation they offer in support of their assertion that “those who remain behind have characteristics associated with higher mortality risk” says nothing about mortality, though it does suggest selectivity in evacuation from New Orleans before Hurricane Katrina by race and education. Our estimates (and the census and displacement offsets) are conditioned on age, sex, socioeconomic status of municipality of residence, and interactions between them. These are major factors stratifying the risk of mortality, and probably, selectivity in migration after Maria. To the extent that both the risks of mortality and displacement are associated with these factors, this is accounted for in our estimates. Given the absence of any additional individual level information in the registration data, our multivariate analysis accounted for factors related to selective out-migration to the fullest extent possible. Lastly, the authors raise a seeming paradox. As they acknowledge, the only difference in the estimation of predicted deaths between our census and displacement models is the population at risk in each (by design, the counterfactual rates are the same). Why then, does there seem to be a higher mortality rate among the displaced in their simple tabular analysis? The answer is straightforward. They calculate mortality rates for each scenario from the size of the population at risk at the end of the period (February, 2018) and cumulative predicted deaths for the entire period (September, 2017, to February, 2018). This calculation ignores monthly variation both in displacement and mortality, which our analysis takes into account. Displacement cumulates over the period, with 46% of total person-months at risk lost over the entire period happening in December and January as seen in table 4 in our Article.1Santos-Burgoa C Sandberg J Suárez E et al.Differential and persistent risk of excess mortality from Hurricane Maria in Puerto Rico: a time-series analysis.Lancet Planet Health. 2018; 2: 478-488Summary Full Text Full Text PDF Scopus (101) Google Scholar This period is exactly when seasonal mortality in Puerto Rico peaks. In combination, this differential loss means that the displaced were disproportionately removed from the population at risk during the period of highest mortality. Considered over the entire period, their mortality rate would have been higher had they not left, simply because they left when mortality rates were highest, not because our model attributes them disproportionately higher mortality for some other reason, as Howard and Santos-Lozada seem to suggest. The goal of estimating excess mortality is to draw inferences about the number of deaths caused by an event. Though we estimated excess mortality both with and without displacement, we presented the displacement estimates to the Government of Puerto Rico as preferred because they capture deaths attributable to differences in mortality rates after the storm, accounting for the smaller population at risk due to displacement. We believe this yields a more accurate estimate of the number of deaths caused by the storm than does assuming (as in our census scenario, Howard and Santos-Lozada's own estimates, and the others they cite) that those displaced were still in Puerto Rico after the storm and at risk of mortality from the continuing hardships associated with its aftermath. We declare no competing interests. Mortality risk within counterfactual models: Puerto Rico and Hurricane MariaUsing data from the Puerto Rico Vital Statistics System obtained from the materials provided by Carlos Santos-Burgoa and colleagues,1 we compared the census-based and displacement models with death counts for Puerto Rico for the period after Hurricane Maria. We calculated confidence intervals for deaths from 2010 to 2016 for observed deaths (figure) and compared them with the pre-Hurricane and post-Hurricane counts (2017) and both counterfactual models. The displacement model falls outside of the pattern observed over the past 7 years before Hurricane Maria, while mortality between January and August does not (figure). Full-Text PDF Open AccessDifferential and persistent risk of excess mortality from Hurricane Maria in Puerto Rico: a time-series analysisAnalysis of all-cause mortality with vital registration data allows for unbiased estimation of the impact of disasters associated with natural hazards and is useful for public health surveillance. It does not depend on certified cause of death, the basis for the official death toll in Puerto Rico. Although all sectors of Puerto Rican society were affected, recovery varied by municipal socioeconomic development and age groups. This finding calls for equitable disaster preparedness and response to protect vulnerable populations in disasters. Full-Text PDF Open Access