Differential and persistent risk of excess mortality from Hurricane Maria in Puerto Rico

Articles

Differential and persistent risk of excess mortality from Hurricane Maria in Puerto Rico: a time-series analysis Carlos Santos-Burgoa, John Sandberg, Erick Suárez, Ann Goldman-Hawes, Scott Zeger, Alejandra Garcia-Meza, Cynthia M Pérez, Noel Estrada-Merly, Uriyoan Colón-Ramos, Cruz María Nazario, Elizabeth Andrade, Amira Roess, Lynn Goldman

Summary

Background Hurricane Maria struck Puerto Rico on Sept 20, 2017, devastating the island. Controversy surrounded the official death toll, fuelled by estimates of excess mortality from academics and investigative journalists. We analysed all-cause excess mortality following the storm.

Methods We did a time-series analysis in Puerto Rico from September, 2017, to February, 2018. Mortality data were from the Puerto Rico Vital Statistics System. We developed two counterfactual scenarios to establish the population at risk. In the first scenario, the island’s population was assumed to track the most recent census estimates. In the second scenario, we accounted for the large-scale population displacement. Expected mortality was projected for each scenario through over-dispersed log-linear regression from July, 2010, to August, 2017, taking into account changing distributions of age, sex, and municipal socioeconomic development, as well as both long-term and seasonal trends in mortality. Excess mortality was calculated as the difference between observed and expected deaths.

Findings Between September, 2017, and February, 2018, we estimated that 1191 excess deaths (95% CI 836–1544) occurred under the census scenario. Under the preferred displacement scenario, we estimated that 2975 excess deaths (95% CI 2658–3290) occurred during the same observation period. The ratio of observed to expected mortality was highest for individuals living in municipalities with the lowest socioeconomic development (1·43, 95% CI 1·39–1·46), and for men aged 65 years or older (1·33, 95% CI 1·30–1·37). Excess risk persisted in these groups throughout the observation period.

Interpretation Analysis 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.

Funding Forensic Science Bureau, Department of Public Safety, and Milken Institute School of Public Health of The George Washington University (Washington, DC, USA).

Copyright © 2018 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.

Introduction On Sept 20, 2017, category 4 Hurricane Maria affected every region of Puerto Rico, devastating its infrastruc­ ture and disrupting its organisations. The intensity of hurricanes has continually increased globally with an even larger destructive potential per storm.1 In the after­ math of Maria, a controversy erupted over hurricanerelated mortality. By December, 2017, the Puerto Rico Government certified 64 deaths related to Hurricane Maria.

Public perception was that mortality was higher than that in the government’s report.2 In ensuing months, research by academics and investigative journalists estimated mortality to be much higher than the official count, between 800 and 100 excess deaths.2–7 Survey-based research provided the highest estimate of 4645 deaths, fuelling public debate and uncertainty.8 As a result, the Puerto Rico Government commiss­ ioned the Milken Institute School of Public Health at The

George Washington University (Washington, DC, USA) for an independent assessment of post-hurricane excess mort­ality and evaluations of death certification, as well as public risk communication processes.9 The research was done with the Graduate School of Public Health of the University of Puerto Rico (San Juan, Puerto Rico).

We aimed to investigate all-cause mortality associated with Hurricane Maria.

Published Online October 11, 2018 http://dx.doi.org/10.1016/ S2542-5196(18)30209-2 See Online/Comment http://dx.doi.org/10.1016/ S2542-5196(18)30222-5 Department of Global Health (Prof C Santos-Burgoa MD, J Sandberg PhD, A Garcia-Meza MPH, U Colón-Ramos ScD, A Roess PhD), Department of Epidemiology and Biostatistics (A Goldman-Hawes PhD), Department of Prevention and Community Health (E Andrade DrPH), and Department of Environmental and Occupational Health (L Goldman MD), Milken Institute School of Public Health, The George Washington University, Washington, DC, USA; Department of Biostatistics and Epidemiology, Graduate School of Public Health, University of Puerto Rico, San Juan, Puerto Rico (Prof Erick Suárez PhD, Prof C M Pérez PhD, N Estrada-Merly MS, Prof C M Nazario PhD); and Department of Biostatistics, Bloomberg School of Public Health, The Johns Hopkins University, Baltimore, MD, USA (Prof S Zeger PhD) Correspondence to: Prof Carlos Santos-Burgoa, Department of Global Health, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA csantosburgoa@gwu.edu

Methods

Data sources All data used in this time-series analysis are from officially published or collected federal or commonwealth sources.

We obtained vital registration mortality data, including deaths by age, sex, and residential municipality, for July 1, 2010, to Feb 28, 2018, from the Puerto Rico Vital Statistics System. We derived baseline popula­tion size estimates in each month from US Census Bureau annual estimates

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Research in context Evidence before this study Accounting for deaths is a priority to assess the magnitude of a disaster and to orient interventions. After Hurricane Maria in Puerto Rico, government-certified death counts were questioned. We did not do a systematic review, but we reviewed journals and the grey literature for methods on mortality surveillance, death certification, reporting of direct and indirect deaths, and registration quality in disasters caused by natural hazards such as hurricanes, as well as man-made disasters such as wars. No language restrictions were applied.

Several analyses attempted to estimate all-cause mortality attributable to the hurricane using vital registration data. These analyses used different strategies for estimating counterfactual mortality—or what mortality would have been if the storm had not occurred—and different methods for producing estimates.

Experts from the US Centers of Disease Control and Prevention recognised a lack of a standardised methodology for analysis of disaster-related mortality data.

Added value of this study This study considered longer timeframes and more diverse information sources than did previous studies and reports.

Unlike previous studies, this study also modelled the timeseries and the effect of a large population displacement. This study provides the methodology for a reproducible, unbiased quantification of post-disaster all-cause excess mortality, through the application of time-series analysis methods to government mortality data. The approach accounts for seasonality of mortality, age, demographic changes, population displacement (before and after the disaster), socioeconomic, and geographical differences. The study emphasises the importance of carefully establishing and defining the counterfactual estimate of what mortality would have been if a disaster such as Hurricane Maria had not occurred. We attempted identification of municipalities at greater risk and addressed issues with the choice of time-series from which this

by age, sex, and residential municipality.10 We estimated cumulative monthly popula­ tion displacement after the hurricane using US Bureau of Transportation Statistics data on monthly net dom­estic migration provided by the Puerto Rico Institute of Statistics and a Puerto Rico Planning Board survey of airline travellers.11 The George Washington University Institutional Review Board exempted the project because it posed minimal risk.

Determination of the counterfactual

See Online for appendix

2

Estimation of excess mortality from disasters associated with natural hazards requires establishing a counter­factual estimate of mortality had the disaster not occurred.12 In a time-series analysis, determination of the counterfactual crucially requires establishing the previous period for counterfactual estimation and an appropriate analytical strategy for that series.13 If the previous period chosen is too short, the counterfactual estimate might be biased because

counterfactual is drawn and, most importantly, accounted for population displacement after a disaster. The study identified differential mortality by municipal socioeconomic development, as well as by age and sex. We estimated that 2975 excess deaths (95% CI 2658–3290) occurred between September, 2017, and February, 2018, under the displacement scenario. The ratio of observed to expected mortality was highest for individuals living in municipalities with the lowest socioeconomic development (1·43, 95% CI 1·39–1·46), and for men aged 65 years or older (1·33, 95% CI 1·30–1·37).

Implications of all the available evidence The study shows that all-cause excess mortality based on registry surveillance is a practical public health approach to identify the overall impact of a disaster, and to monitor conditions after a disaster associated with natural hazards. This approach could become a standard methodology for postdisaster mortality data analysis and public health performance assessment. Since excess mortality for all affected groups in Puerto Rico did not return to baseline by February, 2018, in future practice the mortality surveillance system should incorporate the model into its routine monitoring of mortality and extend the analysis incorporating the data on all covariates as they become available. All systems should consider stratification by socioeconomic development and the ageing population for equity in the future implementation of interventions. It is crucial that governments and global institutions concerned about disasters agree on total excess mortality as a practical, timely, and sensitive indicator for impact assessment and monitoring. Quantification of the magnitude of a disaster’s mortality impact, with reduced uncertainty, greatly enhances policy makers’ capacity to develop and implement interventions to protect against the loss of life, morbidity, and disability, and to identify socioeconomically vulnerable locations in greater need of resource allocation.

of short-term systematic (such as seasonal) or random fluctuations in the outcome under consideration. If the estimate is too long, and both long-term and systematic short-term variation are not accounted for analytically, the counterfactual estimates might also be biased.

An appropriate counterfactual must also account for the size of, and changes to, the at-risk population distribution over the series. We identified three stratify­ ing dimensions for consideration: age, sex, and socio­ economic develop­ment of residential municipality. In all analyses, age was categorised as 0–39 years, 40–64 years, and 65 years or older, reflecting substantial differences in the profile of the risk of mortality under normal circumstances. Sex was defined as male and female. The Municipal Socioeconomic Development Index (SEI),14 which captures the underlying strength of municipallevel structural and institutional capacities, was grouped by tertiles (appendix).

www.thelancet.com/planetary-health Published online October 11, 2018 http://dx.doi.org/10.1016/ S2542-5196(18)30209-2

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We defined two population counterfactual scenarios for analysis. The first, census counterfactual scenario, assumes that the change rate in the resident population composition and distribution was the same before the hurricane as after the hurricane. The second, displace­ ment counterfactual scenario, estimates cumulative excess net migration from Puerto Rico from September, 2017, to February, 2018, and decrements excess migration from the census population estimates in these months.

Census counterfactual estimates of population size by strata of age, sex, and municipality of residence, aggre­ gated to SEI tertiles from July 1, 2010, to July 1, 2017, were derived from annual census estimates.10 Projections for July 1, 2018, were made through stratum-specific regres­ sions of population size from 2010 to 2017 on a quadratic specification of year. Monthly estimates over the entire series were interpolated with a cubic spline between July 1 of each year and adjusted to represent stratumspecific population size on day 15 of each month.

For the displacement counterfactual, we used net dom­ estic air travel data from August, 2009, to February, 2018, from the Bureau of Transportation Statistics and pro­ portions of travellers in each stratum estim­ ated from the Puerto Rico Planning Board survey of air travellers to develop model-based estimates of excess migration by stratum at mid-month from September, 2017, to February, 2018.

longer-term trend over the 86-month period, seasonal variation, and different specifications of dependence on the covariates age (three categories, 0–39 years, 40–64 years, and ≥65 years), sex (male or female), and SEI (tertiles of the SEI). β is the vector of coefficients associated with these covariates. Under the null assump­ tion that Hurricane Maria caused no excess deaths, we used the fitted model predictions as the counterfactual estim­ates for each month between September, 2017, and February, 2018. Our estimates of excess all-cause mort­ ality from Hurricane Maria are the differences between Observed deaths

Statistical analysis We estimated the counterfactual estimation of allcause mortality with a series of generalised linear models (GLMs)—specifically, over-dispersed log-linear re­gressions fit to monthly data for the pre-hurricane period of July, 2010, to August, 2017, of the general form:16 logE(Yit)=log(nit) + x´itβ where i indexes the strata defined by the intersection of age, sex, and municipal SEI; t indexes month and year; E(Y) is the expected number of deaths for each stratum or month combination; n is the population at risk of dying [log(n) is called an offset] corresponding to Y; and x is the vector of covariates that includes a smooth

Excess deaths (95% CI)

Ratio of observed to expected mortality

September, 2017

2906

2332

574 (515–630)

1·25

October, 2017

3015

2318

697 (635–757)

1·30

November, 2017

2657

2309

348 (285–409)

1·15

December, 2017

2797

2318

479 (416–543)

1·21

January, 2018

2799

2241

558 (497–620)

1·25

February, 2018

2434

2114

320 (260–379)

1·15

September–October, 2017

5921

4650

1271 (1154–1383)

1·27

September–December, 2017

11 375

9277

2098 (1872–2315)

1·23

September, 2017, to February, 2018

16 608

13 633

2975 (2658–3290)

1·22

Table 1: Predicted and observed excess mortality in Puerto Rico with model 3 displacement scenario

Descriptive analyses We described Puerto Rico’s crude mortality by age, sex, and month, before and after the hurricane. We compared estimated age-standardised mortality from July, 2017, to February, 2018, to mortality in the same period on a yearly basis from 2010 to 2017, using mid-2015 as the standard population,15 which allowed us to assess the degree of variation over time and departures from these trends after the hurricane. To describe differences by residential municipality, we compared crude mortality from September, 2017, to February, 2018, to mean mort­ality in the same period over 2015–16. We looked only at these years to reduce the influence of demographic changes.

Predicted deaths

Population Deaths

Estimated excess deaths (95% CI)

Ratio of observed to expected mortality (95% CI)

Observed Expected 0–39 years of age Men September, 2017

787 031

102

90

12 (5 to 19)

1·13 (1·05–1·23)

October, 2017

779 080

134

94

40 (32 to 47)

1·43 (1·33–1·56)

November, 2017

768 840

102

100

2 (–6 to 9)

1·02 (0·95–1·11)

December, 2017

761 373

124

101

23 (15 to 30)

1·23 (1·15–1·33)

January, 2018

755 020

129

94

35 (28 to 42)

1·37 (1·28–1·48)

February, 2018

750 671

91

84

7 (0 to 13)

1·08 (1·01–1·18)

September–October, 2017

783 056

236

184

52 (38 to 64)

1·28 (1·20–1·39)

September–December, 2017

774 081

462

386

76 (53 to 98)

1·20 (1·13–1·27)

September, 2017, to February, 2018

767 002

682

564

118 (90 to 146)

1·21 (1·15–1·27)

1·00 (0·91–1·07)

Women September, 2017

795 686

30

30

0 (–2 to 3)

October, 2017

790 075

36

32

4 (2 to 7)

November, 2017

783 002

26

34

1·13 (1·03–1·20)

–8 (–10 to –5)

0·76 (0·70–0·81)

December, 2017

777 516

31

34

–3 (–6 to 0)

0·91 (0·84–1·00)

January, 2018

772 769

22

31

–9 (–12 to –7)

0·71 (0·67–0·79)

February, 2018

769 282

26

28

–2 (–5 to 0)

0·93 (0·87–1·04)

September–October, 2017

792 880

66

61

5 (0 to 9)

1·08 (1·02–1·18)

September–December, 2017 786 570

123

129

–6 (–15 to 3)

0·95 (0·89–1·03)

September, 2017, to February, 2018

171

189

–18 (–29 to –7)

0·90 (0·86–0·96)

781 388

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(Table 2 continues on next page)

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Population Deaths

Estimated excess Ratio of observed deaths (95% CI) to expected mortality (95% CI)

Observed Expected (Continued from previous page) 40–64 years of age Men September, 2017

495 591

358

274

84 (72 to 95)

1·31 (1·26–1·37)

October, 2017

485 463

316

274

42 (29 to 54)

1·15 (1·10–1·21)

November, 2017

471 518

331

280

51 (38 to 63)

1·18 (1·13–1·24)

December, 2017

463 218

368

291

77 (64 to 90)

1·26 (1·21–1·32)

January, 2018

456 549

311

291

20 (7 to 33)

1·07 (1·02–1·12)

February, 2018

453 774

308

279

29 (17 to 42)

1·10 (1·05–1·15)

September–October, 2017

490 527

674

548

126 (102 to 149)

1·23 (1·18–1·29)

September–December, 2017 478 948

1373

1119

254 (213 to 294)

1·23 (1·18–1·27)

September, 2017, to February, 2018

1992

1689

303 (247 to 357)

1·18 (1·14–1·22)

471 019

Women September, 2017

572 870

168

153

15 (8 to 22)

1·10 (1·05–1·15)

October, 2017

566 330

178

154

24 (17 to 31)

1·16 (1·11–1·21)

November, 2017

557 358

176

160

16 (9 to 24)

1·10 (1·05–1·15)

December, 2017

551 856

159

167

–8 (–16 to 0)

0·95 (0·91–1·00)

January, 2018

547 446

185

168

February, 2018

545 421

129

161

September–October, 2017

17 (9 to 24)

1·10 (1·06–1·16)

–32 (–40 to –25)

0·80 (0·77–0·84)

569 600

346

307

39 (26 to 53)

1·13 (1·08–1·18)

September–December, 2017 562 104

681

633

48 (23 to 72)

1·08 (1·04–1·12)

September, 2017, to February, 2018

995

963

32 (–1 to 65)

1·03 (1·00–1·07)

556 880

≥65 years of age Men September, 2017

275 520

1131

873

258 (234 to 281)

1·30 (1·26–1·33)

October, 2017

259 943

1132

849

283 (258 to 307)

1·33 (1·30–1·37)

November, 2017

237 043

992

814

178 (155 to 201)

1·22 (1·19–1·25)

December, 2017

222 038

1076

794

282 (259 to 306)

1·36 (1·32–1·40)

January, 2018

209 674

1078

749

329 (307 to 352)

1·44 (1·40–1·48)

February, 2018

205 774

971

701

270 (248 to 291)

1·39 (1·34–1·43)

September–October, 2017

267 732

2263

1722

541 (493 to 586)

1·31 (1·28–1·35)

September–December, 2017 248 636

4331

3330

1001 (916 to 1084)

1·30 (1·27–1·33)

September, 2017, to February, 2018

6380

4780

1600 (1483 to 1715)

1·33 (1·30–1·37)

234 999

Women September, 2017

368 646

1117

912

205 (180 to 228)

1·22 (1·19–1·26)

October, 2017

358 746

1219

915

304 (277 to 329)

1·33 (1·30–1·37)

November, 2017

343 763

1030

922

108 (81 to 135)

1·12 (1·09–1·15)

December, 2017

333 745

1039

932

107 (80 to 134)

1·11 (1·08–1·15)

January, 2018

325 565

1074

908

166 (139 to 192)

1·18 (1·15–1·22)

February, 2018

323 251

909

861

September–October, 2017

363 696

2336

1827

509 (459 to 556)

1·28 (1·25–1·31)

September–December, 2017

351 225

4405

3680

725 (629 to 816)

1·20 (1·17–1·23)

September, 2017, to February, 2018

342 286

6388

5449

48 (21 to 74)

939 (801 to 1067)

1·06 (1·02–1·09)

1·17 (1·15–1·20)

Table 2: Excess mortality after Hurricane Maria by sex and age under the displacement scenario

4

each of these counter­factual estimates and the observed mortality in these months.

We used over-dispersed log-linear regression instead of Poisson regression because the variation in the number of monthly deaths is greater than its mean, violating a basic condition of the Poisson model.14 Over-dispersed log-linear models produce the same estimates but different assessments of uncertainty compared with Poisson regression.

In all GLMs, the longer-term trend was approximated by a natural spline of time with 2 degrees of freedom (df). Seasonality is modelled in two ways: with a series of monthly indicator variables (11 df), or with a more parsimonious periodic function of the form:

[

cos

2πt 12

+ sin

2πt 12

+ cos

2πt 6

+ sin

2πt 6

]

where t is the calendar month. We denominated seas­ onality as the trigonometric form of the variable in the tables and figures.

For each specification of seasonality, we estimated four models. Model 1 serves as the baseline, estimating mortality as an additive function of the covariate stra­ tum defined by interaction between age, sex, and socio­ economic development specified as indicator variables, the smooth trend, and the seasonal variation. Model 2 extends model 1, allowing the seasonal effect to interact both with age and a halfway indicator separating the observation period before and after July, 2014, to account for the apparent increase in seasonal variability observed in this period’s crude death rate. Model 3 is identical to model 2, except that the three-way interaction between age, sex, and socioeconomic development, as well as the two-way interaction between socioeconomic development and sex are constrained to be zero. Model 4 is the fully saturated version of model 3, specifying interactions between the stratum specification in that model, the annual trend, and seasonality.

In addition, we fitted a generalised additive model (GAM), assuming Poisson distribution, analogous to model 3. We used GAM to add a smoother function of year with stratum interactions, keeping month as a factors predictor, and to choose the degrees of smoothness to optimise estimates of prediction error.17 As such, GAMs are a more flexible alternative to, and specification check on, the GLMs (appendix).

Estimates of expected mortality and CIs in all models were obtained through parametric bootstrapping to estimate the joint mean and covariance matrix of the counterfactual predictors. We approximated the joint distribution of the regression coefficients by a Gaussian distribution, with mean equal to the maximum likelihood estimate and variance equal to its asymptotic covariance matrix that includes adjustment for the over-dispersion.

The CIs presented only reflect uncertainty in the estimates

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of the expected numbers of deaths for a particular para­ metric model. Another relevant measure of uncertainty is the prediction interval, which estimates the uncertainty between the observed deaths and their counterfactual value for that single year. A prediction interval includes the uncertainty in the mean value like the CI, but adds the uncertainty due to the variation from year to year in the period before the hurricane. Our methods and estimates of prediction intervals involve more technical details and will be reported elsewhere.

Role of the funding source The funder of the study facilitated access to the data but had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corres­ ponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.

Results 2119–2862 deaths occurred monthly in Puerto Rico between July, 2010, and July, 2017, the period of observation before the hurricane (data available online). 16 608 people died between September, 2017, and February, 2018 (table 1); 9054 were men and 7554 were women (table 2).

12 768 (76·9%) were adults aged 65 years or older (table 2) and 2983 (18·0%) resided in the municipa­lities with low socioeconomic development (table 3). We estimated that in mid-August, 2017, there were 3 327 917 inhabitants of Puerto Rico, and by mid-February, 2018, this number was 3 048 173 inhabitants—a reduction of approximately 8% (table 4). Age-standardised mortality by month slowly declined during the period of observation (figure 1).

Mortality showed a pronounced seasonality, peaking in winter and early spring. Of particular note, after 2014, this seasonal variation increased (figure 1).

No substantive difference in mortality was observed from the general pattern for the younger age group in any month (figure 1). For the 40–64 years age group, we saw an increase in mortality relative to previous years in September, October, and November, returning to within the general range seen in other years in December under the census scenario and January under the displacement scenario (figure 1). Among the population aged 65 years or older, mortality was higher in September and October, returning to the general seasonal pattern in November under the census scenario, but remaining higher than previous years throughout the period under the dis­ placement scenario (figure 1).

The disaster’s impact on mortality was greater for men than for women (figure 2). The declining trends by age and sex noted previously appear to have reversed in the period between September, 2017, and February, 2018, most sub­stantially under the displacement scenario.

When comparing the age-standardised monthly mort­ ality from September, 2017, to February, 2018, with speci­ ces. Under fic earlier years, we saw significant differen­

Population Deaths

Estimated excess deaths (95% CI)

Ratio of observed to expected mortality (95% CI)

Observed Expected Low-level Municipal Socioeconomic Development Index September, 2017

614 710

505

408

97 (85–107)

1·24 (1·21–1·28)

October, 2017

593 814

523

388

135 (123–146)

1·35 (1·31–1·39)

November, 2017

563 989

512

359

153 (142–163)

1·43 (1·39–1·47)

December, 2017

544 460

499

340

159 (150–169)

1·47 (1·43–1·51)

January, 2018

528 439

509

310

199 (190–208)

1·64 (1·60–1·69)

February, 2018

522 158

435

287

148 (140–157)

1·52 (1·47–1·56)

September–October, 2017

604 262

1028

796

232 (209–253)

1·29 (1·26–1·33)

September–December, 2017

579 243

2039

1495

544 (504–582)

1·36 (1·33–1·40)

September, 2017, to February, 2018

561 262

2983

2092

891 (837–944)

1·43 (1·39–1·46)

Mid-level Municipal Socioeconomic Development Index September, 2017

1 021 702

864

714

150 (131–168)

1·21 (1·18–1·24)

October, 2017

1 003 000

887

705

182 (162–201)

1·26 (1·23–1·29)

November, 2017

976 766

800

696

104 (84–123)

1·15 (1·12–1·18)

December, 2017

959 872

844

694

150 (129–170)

1·22 (1·18–1·25)

January, 2018

946 133

803

668

135 (116–154)

1·20 (1·17–1·24)

February, 2018

940 224

742

629

113 (94–131)

1·18 (1·15–1·22)

1 012 351

1751

1420

331 (294–367)

1·23 (1·20–1·27)

September–December, 2017

990 335

3395

2809

586 (514–653)

1·21 (1·18–1·24)

September, 2017, to February, 2018

974 616

4940

4106

834 (730–931)

1·20 (1·18–1·23)

1·27 (1·24–1·30)

September–October, 2017

High-level Municipal Socioeconomic Development Index September, 2017

1 658 932

1537

1210

327 (297–357

October, 2017

1 642 823

1605

1224

381 (347–412)

1·31 (1·28–1·35)

November, 2017

1 620 769

1345

1255

90 (56–124)

1·07 (1·04–1·10)

December, 2017

1 605 414

1454

1284

170 (135–205)

1·13 (1·10–1·16)

January, 2018

1 592 451

1487

1263

224 (189–260)

1·18 (1·14–1·21)

February, 2018

1 585 791

1257

1199

58 (24–93)

1·05 (1·02–1·08)

September–October, 2017

1 650 878

3142

2434

708 (645–768)

1·29 (1·26–1·33)

September–December, 2017 1 631 984

5941

4973

968 (847–1086)

1·19 (1·17–1·22)

September, 2017, to February, 2018

8685

7435

1250 (1075–1427)

1·17 (1·14–1·20)

1 617 697

Table 3: Excess mortality after Hurricane Maria in Puerto Rico, by the Municipal Socioeconomic Development Index, under the displacement scenario

Census

Displacement

Monthly net excess ··

Cumulative net excess

August, 2017

3 327 917

3 327 917

September, 2017

3 321 647

3 295 344

October, 2017

3 315 313

3 239 637

–98 741

–75 676

November, 2017

3 308 917

3 161 524

–44 693

–147 393

December, 2017

3 302 466

3 109 746

–45 964

–192 720

January, 2018

3 295 967

3 067 023

–26 481

–228 944

February, 2018

3 289 427

3 048 173

1860

–241 254

–26 303

·· –26 303

Data compiled from US Bureau of Transportation Statistics net domestic migration totals assuming 6% international net migration.

Table 4: Census and displacement counterfactual population size, net excess, and cumulative net excess displacement in Puerto Rico after Hurricane Maria

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For data see https://prstudy. publichealth.gwu.edu/datasets

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A Deaths per 10 000 population

10

Displacement Census

9 8 7 6 5

No Ju ve ly, 2 m 0 be 10 M r, 20 ar ch 10 ,2 No Ju 01 ve ly, 2 1 m 0 be 11 M r, 2 ar 01 ch 1 ,2 No Ju 01 ve ly, 2 2 m 0 be 12 M r, 2 ar 01 ch 2 ,2 No Ju 01 ve ly, 2 3 m 0 be 13 M r, 2 ar 01 ch 3 ,2 No Ju 01 ve ly, 2 4 m 0 be 14 M r, 20 ar ch 14 ,2 No Ju 01 ve ly, 2 5 m 0 be 15 M r, 2 ar 01 ch 5 ,2 No Ju 01 ve ly, 2 6 m 0 be 16 M r, 20 ar ch 16 ,2 No Ju 01 ve ly, 2 7 m 0 be 17 r, 2 01 7

0

Time (months)

Deaths per 10 000 population

B

1·5 1·4 1·3 1·2 1·1 1·0 0·9 0·8 0·7 0·6 0·5

Age 0–39 years 2010–11 2017–18 census 2011–12 2016–17 2012–13 2014–15 2013–14 2015–16 2017–18 displacement

0

Deaths per 10 000 population

6·0

Age 40–64 years

5·0

4·0

3·0 0 Age ≥65 years 50

Deaths per 10 000 population

45 40 35 30 25 20

Time (months)

ne Ju

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Ap ril

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ry Fe br ua ry

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6

the census scenario, the mortality ratio in October, 2017, increased by 27% (95% CI 20–34) compared with October, 2016 (table 5). A similar pattern was observed with the 2015 comparison, but not with the 2014 comparison (table 5).

Under the displacement scenario, a significant increment of 7% (95% CI 2–13) was shown when mortality in October, 2017, was comp­ared with that in October, 2014 (table 5).

Under the census scen­ario, mortality in February, 2018, did not show signifi­ cant differences with other years; however, under the displacement scenario, all the compari­ sons showed significant differences (table 5).

The largest post-hurricane mortality differentials were concentrated in the island’s northeast, and to a lesser extent, southwest portions (figure 3). Overall, mortality significantly increased in approximately 40% of muni­ cipalities (based on two sample tests of proportions; figure 3).

Mortality was disproportionately higher among pop­ ulat­ions in municipalities in the lowest SEI tertile than in those in other municipalities (figure 4).

Fit statistics for each model estimated—including the ratio between deviance and residual degrees of freedom and sensitivity, defined as the percentage of interval estim­ates con­taining the observed monthly mortality over the entire series and in the 1 and 2 years before September, 2017—are shown in table 2. In both the monthly and trigono­ metric specifications, models 2 and 3 had the lowest ratio of deviance to degrees of free­dom, indicating better model fit. Comparing the first three models, rela­ tive to the specifica­ tion of seasonality with monthly in­dica­tors over the 6 months under observation, the trig­ onometric specification consistently predicts slightly greater numbers of deaths in September, November, and February, and slightly lower numbers of deaths in October, December, and January (appendix). Model 1, controlling only for stratum, seasonal, and annual trends, consistently predicts fewer deaths than models 2 and 3, which also allow for the seasonal pattern to vary by age and between the two periods defined be­fore and after July, 2014. Model 3’s more parsimonious specification, allowing only main effects of the covari­ates and inter­ actions between age and sex, and age and municipal SEI, predicts virtually identical numbers of deaths in each month as model 2, which saturates interactions in the covariates (tables 2, 3, 5). Model 4’s spec­ification, com­ pared with models 1–3, ex­hibits irregular deviations in predicted deaths by month and is an un­realistic model for generalisation and predic­tion, confor­ming closely to patterns of random variation over time (appendix).

Figure 1: Age-standardised and age-specific monthly mortality by year in Puerto Rico 2017–18 estimates are presented under both the census and displacement scenarios. (A) Age-standardised mortality by month from July to February for each year from 2010–11 to 2017–18. (B) Age-specific mortality from July to February for each year from 2010–11 to 2017–18.

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12·5

Male displacement Female displacement Male census Female census

11·5

Deaths per 10 000 population

10·5 9·5 8·5 7·5 6·5 5·5 4·5

No

7

No

ve

m

be

r, 2

20 ly,

Ju

01

7

17

6

01

01 M

ar

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16

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20

5

01

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5 Ju

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3 01

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1

01

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1

0

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Time (months)

Figure 2: Age-standardised monthly mortality by sex in Puerto Rico Age-standardised monthly mortality by sex from July, 2010, to February, 2018. 2015 is used as the standard population under the census and displacement scenarios.

September

October

November

December

January

February

2018 vs 2017 SRR

NA

NA

NA

NA

0·97 (0·92–1·02)

1·05 (0·99–1·11)

2017 vs 2016 SRR

1·22 (1·16–1·29)

1·27 (1·20–1·34)

1·07 (1·01–1·13)

0·98 (0·93–1·04)

1·01 (0·96–1·06)

0·93 (0·88–0·98)

2017 vs 2015 SRR

1·27 (1·20–1·34)

1·24 (1·17–1·31)

1·16 (1·09–1·22)

1·10 (1·04–1·16)

1·00 (0·94–1·05)

0·99 (0·93–1·04)

2017 vs 2014 SRR

1·13 (1·07–1·19)

1·01 (0·96–1·07)

0·98 (0·93–1·03)

0·95 (0·90–1·00)

1·08 (1·02–1·14)

1·06 (1·00–1·12)

2017 vs 2013 SRR

1·12 (1·07–1·19)

1·21 (1·14–1·27)

1·12 (1·06–1·19

1·07 (1·01–1·13)

0·91 (0·86–0·97)

1·03 (0·97–1·08)

2017 vs 2012 SRR

1·11 (1·05–1·17)

1·16 (1·10–1·22)

1·01 (0·96–1·07)

1·02 (0·97–1·08)

0·99 (0·94–1·04)

0·91 (0·86–0·97)

2017 vs 2011 SRR

1·13 (1·07–1·19)

1·11 (1·06–1·18)

1·04 (0·98–1·10)

0·98 (0·93–1·03)

0·93 (0·88–0·98)

0·94 (0·88–0·99)

2017 vs 2010 SRR

1·14 (1·08–1·21)

1·10 (1·04–1·16)

0·95 (0·90–1·01)

0·91 (0·86–0·96)

NA

NA

Range

1·11–1·27

1·01–1·27

0·95–1·16

0·91–1·1

0·91–1·08

0·91–1·06

Median (IQR)

1·13 (1·12–1·22)

1·16 (1·10–1·24)

1·04 (0·98–1·12)

0·98 (0·95–1·07)

0·99 (0·93–1·01)

0·99 (0·93–1·05)

2018 vs 2017 SRR

NA

NA

NA

NA

1·16 (1·10–1·22)

1·27 (1·20–1·34)

2017 vs 2016 SRR

1·25 (1·18–1·32)

1·34 (1·27–1·42)

1·19 (1·13–1·26)

1·14 (1·08–1·20)

1·20 (1·14–1·27)

1·12 (1·06–1·19)

2017 vs 2015 SRR

1·29 (1·22–1·37)

1·31 (1·24–1·38)

1·29 (1·27–1·34)

1·27 (1·20–1·34)

1·19 (1·13–1·26)

1·19 (1·13–1·26)

2017 vs 2014 SRR

1·15 (1·09–1·22)

1·07 (1·02–1·13)

1·09 (1·03–1·15)

1·09 (1·04–1·15)

1·29 (1·22–1·36)

1·28 (1·21–1·35)

2017 vs 2013 SRR

1·15 (1·09–1·21)

1·28 (1·21–1·35)

1·25 (1·18–1·32)

1·23 (1·17–1·30)

1·23 (1·16–1·29)

1·17 (1·10–1·24)

2017 vs 2012 SRR

1·13 (1·07–1·19)

1·23 (1·16–1·30)

1·13 (1·07–1·19)

1·18 (1·12–1·25)

1·18 (1·12–1·25)

1·11 (1·05–1·17)

2017 vs 2011 SRR

1·15 (1·09–1·22)

1·18 (1·12–1·24)

1·16 (1·10–1·23)

1·13 (1·07–1·19)

1·12 (1·06–1·18)

1·13 (1·07–1·20)

2017 vs 2010 SRR

1·17 (1·11–1·24)

1·16 (1·10–1·22)

1·06 (1·00–1·12)

1·05 (1·00–1·11)

NA

NA

Range

1·13–1·29

1·07–1·34

1·06–1·29

1·05–1·27

1·12–1·29

1·11–1·28

Median (IQR)

1·15 (1·15–1·25)

1·23 (1·16–1·31)

1·16 (1·09–1·25)

1·14 (1·09–1·23)

1·19 (1·16–1·23)

1·17 (1·12–1·27)

Census scenario

Displacement scenario

Data are SRR (95% CI), unless otherwise stated. Puerto Rico age distribution in 2015 is the standard population. SRR=standardised rate ratio. NA=not applicable.

Table 5: Age-standardised mortality rate ratios in Puerto Rico by month

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0–10% 11–16% 17–27% ≥28% p<0·05

Figure 3: Percentage increase in crude mortality by municipality in Puerto Rico Estimated increase in crude mortality from September, 2017, to February, 2018, relative to mean mortality in the same period in 2015–16 and 2016–17 under the displacement scenario.

Although both the monthly and seasonal specifica­ tions adequately capture fluctuations due to seasonal vari­ a­tion, the trigonometric form of seasonality smooths the exaggerated variability from month to month seen in the other specification (figure 5), which is more sensitive to spikes probably associated with periodic winter increases in viral mortality, which we believe was not likely a factor in the post-hurricane period.18 As such, we believe the trigonometric specification model 3 is the preferable mortality measure over this series with which to project expected mortality in the period after the hurricance, although it should be noted that marginal predicted excess deaths under each model are almost identical (appendix).

By use of our preferred model (model 3), we esti­ mated 1271 excess deaths (95% CI 1154–1383) in September–October, 2017; 2098 excess deaths (1872–2315) from September–December, 2017; and 2975 excess deaths (2658–3290) across the entire 6-month period (table 1).

Under the census scenario, we estimated 1077 excess deaths (95% CI 952–1193) for September–October, 2017; 1237 excess deaths (993–1471) for September–December, 2017; and 1191 excess deaths (836–1544) for September, 2017, to February, 2018 (appendix).

Although municipalities at all SEI tertiles were affected by the hurricane, the greatest impact in terms of relative mortality increase was in the lowest SEI tertile (table 3).

The middle and upper SEI tertile municipalities saw immediate decreases in excess mor­ tality ratios after September, 2017, with small variation from month to month. Municipalities in the lowest category saw con­ tinual increases through December, 2017. The lowest SEI tertile had 43% higher than expected mortality (ratio of observed to expected deaths 1·43, 95% CI 1·39–1·46) from September, 2017, to February, 2018; the middle SEI tertile had 20%; and highest SEI tertile had 17% (table 3).

The largest impact of the hurricane was on the older population in all months, particularly individuals aged 65 years or older. From September, 2017, to February, 2018, the ratio of observed to expected mortality was highest for 8

men aged 65 years or older (1·33, 95% CI 1·30–1·37). A disproportionately higher number of male individuals of all ages died in all months than female individuals (table 2). Increased mortality due to the hurricane was observed for males over a longer period than for females at all ages. The impact on women, especially younger women, declined rapidly after October, 2017, whereas it persisted through February, 2018, for males (table 2).

Discussion Our study presents a methodology for an unbiased estimation of post-disaster excess mortality through a time-series analysis using vital registry and other official government data. This method can be applicable for Puerto Rico, the USA, and globally. Although measure­ ment error is an issue with all data, we believe that these sources are the most complete and accurate available for this analysis. This Article addresses the lack of standardised methodology for analysis of disaster-related mortality,19 and it provides a point estimate with a relatively narrow CI for excess mortality. This greater degree of certainty helps to move to the next stage of interventions.

The official government estimate of 64 deaths from the hurricane was low because they based their mortality count on attribution of deaths to the hurricane in death certificates. During our broader study, we found that many physicians were not familiar with the appropriate certification protocol, which translated into an inadequate indicator for monitoring mortality in the hurricane’s aftermath.

Several previous registration-based analyses have pro­ duced estimates of all-cause mortality related to Hurricane Maria between 822 and 1052 excess deaths.2–7 Key dif­ ferences between these estimates and those presented here include the definition of the counterfactual period, the adjustment for long-term trends (where applicable), seasonal trends, and the population at risk of mortality.

Such estimates are consistent with those reported here for the same time period under the census counterfactual

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12·5

Deaths per 10 000 population

11·5

Census lowest SEI Census highest SEI Displacement middle SEI

Census middle SEI Displacement lowest SEI Displacement highest SEI

10·5 9·5 8·5 7·5 6·5 5·5 4·5

No

Ju ve ly, 2 m 0 be 10 M r, 20 ar ch 10 ,2 No Ju 01 ve ly, 2 1 m 0 be 11 M r, 2 ar 01 ch 1 ,2 No Ju 01 ve ly, 2 2 m 0 be 12 M r, 2 ar 01 ch 2 ,2 No Ju 01 ve ly, 2 3 m 0 be 13 M r, 2 ar 01 ch 3 ,2 No Ju 01 ve ly, 2 4 m 0 be 14 M r, 20 ar ch 14 ,2 No Ju 01 ve ly, 2 5 m 0 be 15 M r, 2 ar 01 ch 5 ,2 No Ju 01 ve ly, 2 6 m 0 be 16 M r, 20 ar ch 16 ,2 No Ju 01 ve ly, 2 7 m 0 be 17 r, 2 01 7

0

Time (months)

Figure 4: Age-standardised mortality by municipal SEI tertiles in Puerto Rico Age-standardised mortality by SEI from July, 2010, to February, 2018, using 2015 as the standard population.

SEI=Municipal Socioeconomic Development Index.

3200 3000

Trigonometric Monthly Observed

2800 Deaths per 10 000 population

(1077, 95% CI 952–1193; appendix), which is the appropriate comparison because none attempted to account for post-hurricane population displacement.

Another study,8 based on self-reported deaths from a household survey, estimated excess mortality from Sept 20, 2017, to Dec 31, 2017, to be 4645 (95% CI 793–8498). However, this estimate was biased because it was not conditional on household size.

We produce what we conclude are appropriate estimates of all-cause excess mortality due to Hurricane Maria by month and by various aggregate periods to allow comparison of our results to previous ones. This study’s strengths include estimation of predicted mortality based on a long counterfactual period that allows for adjustment for long-term and seasonal trends in mortality; app­ ropriate adjustment for changes in population size and distribution by age, sex, and SEI over the period; and, in the displacement scenario, adjustment for the massive population displacement occurring in the hurri­ cane’s immediate aftermath. Massive population displace­ ment unequivocally has to be considered in the analysis.

A limitation of using mortality as an indicator of the overall impact of disasters on health is that it does not quantify the elevated burden of disease from morbidity and disability or large impacts on the health-care system.20–22 Our research does not attempt to address these factors, but there is a pressing need to do so.

Our operational definitions of covariates to adjust for age and municipal level effects were limited. Given the relatively small numbers of deaths in Puerto Rico in the period under observation before the storm (monthly range 2119–2862), strata along the dimensions of age, sex, and municipality were defined broadly to preserve monthly stratum-specific cell sizes that could yield accurate and efficient estimates. More finely defined strata—eg, which allow estimates of the hurricane’s effects on infants and children or on the individual municipal level—were not possible.

All information on covariates was from official govern­ ment sources. Our estimates of the number of deaths and population size are as accurate as possible, with currently available data and concordant with most other preliminary estimates.23 The distribution along these dimensions in 2018 based on the census data we used is an estimate— one that is subject to change with any future revisions published by the Census Bureau. Although official census estimates of population size and distribution after September, 2017, had the hurricane not occurred will never be produced, we believe those that we have presented here would have proved to be highly accurate if they existed. The population at risk under the displacement counterfactual, based on decrements of excess migration to the census estimates, is also poten­ tially subject to revision. Since the review of this paper for publication, for example, the Puerto Rico Institute of Statistics published a blog comment modifying the net migration data for January and February, 2018.24 Our initial review of these

2600 2400 2200 2000 1800 0 May, 2010

September, 2011

January, 2013

June, 2014

October, 2015

March, 2017

Time (months)

Figure 5: Observed and predicted deaths by month in Puerto Rico Model 3-based mortality estimates for the monthly and trigonometric forms from July, 2010, to August, 2017.

revisions suggests that they would result in only minor lished here (appendix). differences to the results pub­ Because the Puerto Rico Institute of Statistics has not yet provided enough statistical information to evaluate these new estimates, we have not included these revisions in this paper. Accounting for revisions in officially published data is a limitation of any secondary data analysis; however, it is also a strength, allowing for a continuous use of such data in surveillance to update the trends in the statistics produced by these analyses.

Differences in the functional forms and estimation methods used in our multivariate models might yield slightly different estimates from those presented here. We

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believe our models’ sensitivity with regard to monthly mortality and the general convergence of the GLM monthly and trigonometric functions of seasonality and the GAM results suggest that our estimates are relatively insensitive to changes in functional form given the covariate set we use.

Hurricane Maria affected Puerto Ricans of all ages, sex, and socioeconomic groups, and the heterogeneous mort­ ality distribution calls for action.25 6 months after the disaster, the poorest and oldest males remained at risk for excess mortality, underscoring the need for equitable disaster preparedness policies and response policies that protect the most vulnerable populations.

All-cause excess mortality is a useful indicator to monitor progress, and the method presented in this Article can be added to the standards for disaster-related mortality analysis for use throughout the USA and globally. This method can help to identify the most vulnerable groups and target them for intervention and care until indicators return to baseline conditions. Use of deaths certified as hurricane-related as the indicator for monitoring mort­ ality underestimated the exis­ting problem. Recovery is a multiyear process, and public health-relevant research is crucial to understand the causal pathways of the conditions that lead to ex­tended duration of morbidity and time to death and to establish protective measures for future disasters. The strengthening of public health surveillance, in­cluding the Puerto Rico Vital Statistics System and the death certification process, depend on this under­standing.

Next, public health actions require building resilient public health systems, identifying the context surrounding excess deaths, with disproportionately more resources targeted to those in disadvantaged groups, both immediately after the hurricane and in the months that follow.

Contributors CS-B is the principal investigator of this study, and along with LG, first conceived the study, prepared the proposal, and secured funding for it. CS-B and JS contributed equally to the study design, development of methods, data analysis, data interpretation, and writing of this manuscript. SZ and ES equally participated in the development of methods and analysis of excess mortality with contributions by NE-M. AG-H served as project coordinator and, along with AG-M, had a key role in the acquisition and organisation of data and in writing of the manuscript. CMP, UC-R, and CMN contributed to the discussion of the methodology and results. AR contributed to the discussion of methods and results, and review of the manuscript. EA reviewed the manuscript and supported the project. All authors contributed to the writing of the manuscript and approved the final version of the paper.

Declaration of interests We declare no competing interests.

Data sharing We have made all code and data sources for generating our results, tables, and figures available at https://prstudy.publichealth.gwu.edu/ datasets.

Acknowledgments This project was commissioned through the contract NCF 2018-000111 on May 15, 2018, by the Forensic Science Bureau, an agency under the Department of Public Safety of the Government of Puerto Rico.

The Milken Institute School of Public Health of The George Washington University (Washington, DC, USA) provided substantive support to the study in faculty time. The views expressed here do not represent those of

10

the Government of Puerto Rico. We thank our external review panel for their review of our methodology and results. We acknowledge the feedback and support from Debarati Guha-Sapir (Centre for Research on the Epidemiology of Disasters, University of Louvain School of Public Health, Brussels, Belgium), Matthew W Seeger (Wayne State University, Detroit, MI, USA), Bernardo Hernandez (University of Washington, Seattle, WA, USA), and Sam Clark (Ohio State University, Columbus, OH, USA). For the overall Puerto Rico study, we also received technical guidance from Ronald Waldman, Dan Hoffman, and Pietro (Peter) Marghella, long-term experts in humanitarian and disaster medicine, as well as from Sam Simmens on statistical analysis, all affiliated with The George Washington University. We thank The George Washington Office of the Vice Provost for Research, Information and Technology Support, and the administration for their assistance in making institutional arrangements. We thank Nicole Barrett (Milken Institute School of Public Health at The George Washington University) for her communication support, and also acknowledge the support of Martie Sucec, who served as the editor of the manuscript. We thank the leadership and team at the Graduate School of Public Health at the University of Puerto Rico (San Juan, Puero Rico). We thank Istoni Da Luz Sant’Ana and Israel Almodóvar (Graduate School of Public Health, University of Puerto Rico) for their advice on R programming.

We recognise the dedication, support, and orientation of the personnel at public institutions in Puerto Rico, the Demographic Registry, and the Forensic Sciences Bureau for providing support, the mortality information, and, most importantly, for helping us to understand their processes. We thank the Puerto Rico Institute of Statistics leadership and the team at the Planning Board for their support.

References 1 Geophysical Fluid Dynamics Laboratory. Global warming and hurricanes: an overview of current research results. Sept 20, 2018. https://www.gfdl.noaa.gov/global-warming-and-hurricanes/ (accessed Sept 27, 2018).

2 Robles F, Davis K, Fink S, Almukhtar S. Official toll in Puerto Rico: 64. Actual deaths may be 1,052. New York Times Dec 9, 2017. https://www.nytimes.com/interactive/2017/12/08/us/puerto-ricohurricane-maria-death-toll.html (accessed March 7, 2018).

3 Pascual OS. María dispara las muertes en Puerto Rico en un 43%.

Centro de Periodismo Investigativo Nov 8, 2017. http:// periodismoinvestigativo.com/2017/11/maria-dispara-las-muertesen-puerto-rico-en-un-43/ (accessed March 5, 2018).

4 Rivera R, Rolke W. Estimating the death toll of Hurricane Maria.

Significance 2018; 15: 8–9.

5 Santos-Lozada A, Howard J. Estimates of excess deaths in Puerto Rico following Hurricane Maria. SocArXiv 2017; published online Nov 21. DOI:10.31235/osf.io/s7dmu (preprint).

6 Santos-Lozada AR, Howard JT. Use of death counts from vital statistics to calculate excess deaths in Puerto Rico following Hurricane Maria. JAMA 2018; published online Aug 2.

DOI:10.1001/jama.2018.10929.

7 Sutter JD, Santiago L, Shah K. We surveyed 112 Puerto Rican funeral homes to check the accuracy of the hurricane death toll.

This is what we found. CNN Nov 20, 2017. https://www.cnn. com/2017/11/20/health/hurricane-maria-uncounted-deaths-invs/ index.html (accessed March 20, 2018).

8 Kishore N, Marqués D, Mahmud A, et al. Mortality in Puerto Rico after Hurricane Maria. N Engl J Med 2018; 379: 162–17.

9 Government of Puerto Rico Federal Affairs Administration.

Governor Rosselló announces The George Washington University will lead efforts to review the death count associated with Hurricane Maria in Puerto Rico. Feb 23, 2018. http://prfaa.pr.gov/ governor-rossello-announces-the-george-washington-universitywill-lead-efforts-to-review-the-death-count-associated-withhurricane-maria-in-puerto-rico/ (accessed March 5, 2018).

10 US Census Bureau. Latest population estimate data. http://census. hawaii.gov/home/population-estimate/ (accessed July 25, 2018).

11 Instituto de Estadísticas de Puerto Rico. Número de vuelos, pasajeros aéreos y carga con origen o destino en Puerto Rico.

April, 2018. https://www.indicadores.pr/dataset/vuelos-pasajerosaereos-y-carga-puerto-rico/resource/eee7916d-0e62-4604-a527312c18a813fe (accessed July 18, 2018).

12 Glass TA, Goodman SN, Hernan MA, Samet JM. Causal inference in public health. Annu Rev Public Health 2013; 34: 61–75.

www.thelancet.com/planetary-health Published online October 11, 2018 http://dx.doi.org/10.1016/ S2542-5196(18)30209-2

Articles

13 Brodersen KH, Gallusser F, Koehler J, Remy N, Scott SL. Inferring causal impact using Bayesian structural time-series models. Ann Appl Stat 2015; 9: 247–74.

14 Junta de Planificación. Índice de desarrollo socioeconómico municipal. 2017. https://estadisticas.pr/files/Inventario/ publicaciones/%C3%8DNDICE%20DE%20DESARROLLO%20 SOCIOECON%C3%93MICO%20MUNICIPAL%20Octubre%20 2016-final.pdf (accessed May 13, 2018).

15 Rosner B. Estimation. In: Fundamentals of Biostatistics, 8th edn.

Boston, MA: Cengage Learning, 2015: 154–210.

16 Olive DJ. GLMs and GAMs. In: Linear regression, 1st edn.

New York, NY: Springer International Publishing; 2017: 389–458.

17 Wood SN. Generalized additive models: an introduction with R, 2nd edn. Boca Raton, FL: CRC/Taylor & Francis, 2017.

18 Departamento de Salud, Gobierno de Puerto Rico. Vigilancia de influenza de Puerto Rico: informe seminal. March 16, 2018. http:// www.salud.gov.pr/Estadisticas-Registros-y-Publicaciones/Pages/ Influenza.aspx#InplviewHash168d71c2-08af-45fa-811757d1778553da=Paged%3DTRUE-p__Category%3D2018-p_ SortBehavior%3D0-p_ Created%3D20180720%252019%253a10%253a39-p_ID%3D335PageFirstRow%3D11-WebPartID%3D%7BC65B9706--CC79--4DCE-9FCA--C41424A81F4E%7D (accessed July 7, 2018).

19 Issa A, Ramadugu K, Mulay P, et al. Deaths related to Hurricane Irma—Florida, Georgia, and North Carolina, September 4–October 10, 2017. MMWR Morb Mortal Wkly Rep 2018; 67: 829–83.

20 Shin P, Sharac J, Gunsalus R, Leifer B, Rosenbaum S. Puerto Rico’s community health centers: struggling to recover in the wake of Hurricane Maria. November, 2017. https://publichealth.gwu.edu/ sites/default/files/downloads/GGRCHN/GG%3ARCHN%20 Policy%20Issue%20Brief %20%2350%20FV.pdf (accessed March 3, 2018).

21 Pan American Health Organization. Country Report: Puerto Rico. https://www.paho.org/salud-en-las-americas-2017/?page_id=151 (accessed July 10, 2018).

22 Noy I. A DALY measure of the impact of natural disasters.

March 13, 2015. https://voxeu.org/article/daly-measure-directimpact-natural-disasters (accessed July 31, 2018).

23 Meléndez E, Hinojosa J. Estimates of post-Hurricane Maria exodus from Puerto Rico. October, 2017. https://centropr.hunter.cuny.edu/ sites/default/files/RB2017-01-POST-MARIA%20EXODUS_V3.pdf (accessed June 27, 2018).

24 Marazzi-Santiago M. Fe de errata: movimiento neto de pasajeros aéreos en vuelos domésticos. July 18, 2018. https://estadisticas.pr/ en/blog/fe_de_errata (accessed Aug 30, 2018).

25 Lichtveld M. Disasters through the lens of disparities: elevate community resilience as an essential public health service.

Am J Public Health 2018; 108: 28–30.

www.thelancet.com/planetary-health Published online October 11, 2018 http://dx.doi.org/10.1016/ S2542-5196(18)30209-2

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