On the temporal association of outbreaks of Zika virus infection, newborn microcephaly, and Guillain-Barré syndrome

During the last couple of years Zika virus (ZIKV) infection has been considered an international public health emergency, due to possible but yet uncertain links with newborn microcephaly and Guillain-Barré syndrome (GBS). Concerns about these links originated from case reports and surveillance data suggesting an outbreak of ZIKV in Brazil was followed by outbreaks of GBS and microcephaly (1,2). A fundamental component of the public health response has been the implementation of a system for surveillance of ZIKV disease and its complications (3).

Seminal evidence of a temporal association between these outbreaks came from a study by investigators from the Centro de Pesquisas Gonçalo Moniz (CPGM), in Salvador, Bahia, the largest city in Northeastern Brazil (1). Though other studies also reported temporal associations between these outbreaks (4-6), only CPGM investigators have tested it and have made their data available to the public. They identified cases of acute exanthematous illness (AEI) through a sentinel surveillance system established by the Centers for Information and Epidemiologic Surveillance of Salvador (CIES). AEI surveillance started in April 2015, but medical records were reviewed to identify past cases up to February 15, 2015 (Figure 1). Cases were patients with rash, who did not meet diagnosis criteria for dengue, chikungunya, measles, or rubella, and were attributed to ZIKV because this was the main arbovirus circulating at that time (1,7).

Figure 1 Epidemic curves of possible Zika virus infections (by 50), newborn microcephaly, and Guillain-Barre syndrome in Salvador, Brazil (2016).

In late May 2015 CIES started surveillance of patients hospitalized with “neurologic manifestations that might be linked to Zika”, including GBS, with retrospective search of cases hospitalized during April-May. Authors provided no explicit reason why was GBS surveillance started, but this may have been triggered by an earlier unreplicated report of an increase in GBS cases during a Chikungunya outbreak in French Polynesia (8).

In October 2015 the CIES established a newborn microcephaly case-report system, following a report of an increase in cases in Pernambuco the previous month. Cases were defined as a head circumference <32 cm in full term and less than the third percentile of the Fenton curve in preterm newborns (1), and were searched retrospectively back to the start of the year. The first case was identified in mid-July, and no cases were identified before that time through the regular Brazilian Live Birth Information System (SINASC) (2). In November 19th 2015 the Brazilian Ministry of Health implemented an ad hoc public health surveillance system for microcephaly (2).

CPGM investigators used standard cross-correlations to identify the lag times (from 0 to 40 weeks) showing the highest correlations between weekly numbers of AEI cases and ensuing weekly numbers of cases of GBS and microcephaly. The strongest positive correlations occurred 5–9 weeks later for GBS and 30–33 weeks later for microcephaly. They argued these findings provided strong support for a positive association of AEI cases to ensuing cases of GBS and microcephaly.

CPGM investigators inferred an outbreak of microcephaly had occurred, based on the shape of the epidemic curve (1). Though the shape of the curve resembled that of a point outbreak, it could be also attributed to the intensity of surveillance efforts, and did not tell whether prevalence in 2015 was higher than expected. The prevalence of suspected microcephaly was 15.6 cases/1,000 newborns. Even in its peak (31.4 cases/1,000 in December 2015) the prevalence was below the expected 32.4 cases/1,000 newborns, inferred from the head size distribution in Brazilian newborns (normal, mean: 34.2 cm, standard deviation 1.2; see online Appendix for details on calculations and analyses) (9). Thus, CIES data provided no evidence of an outbreak of microcephaly in Salvador. An apparent outbreak may have resulted from comparing number of cases from the targeted-reactive CIES case-report system to those from the unspecific-regular SINASC in previous years. Indeed, SINASC traditionally had a low case yield (10), and detected no cases of microcephaly in Salvador during a period of 20 weeks in early 2015 (1), even though 462 cases were expected (see Appendix). Indeed, a low SINASC yield has been previously proposed by Brazilian experts on microcephaly as an explanation of the apparent outbreak (11).

The incidence of GBS in Salvador was 1.74/100,000 population (1). However, this rate pertained almost exclusively to the 33 weeks after the AEI peak on week 19, since only two out of 49 cases occurred in the 12 weeks period before that time (Figure 1). This was at variance with 57% of all AEI cases occurring before the AEI peak. If the rate observed after the AEI peak also applied to the whole year, the incidence would have been 2.74/100,000, well within the worldwide range of 0.6 to 4 per 100,000 (12,13). Thus, the apparent GBS outbreak in 2015 was likely due to increased diagnosis, hospitalization, and case reporting after the start of surveillance of neurologic conditions.

CPGM investigators used time-lagged cross-correlations of moving averages to estimate induction times from ZIKV infection to GBS and microcephaly (1). Such an analysis is based on the assumption that causal links actually existed. Therefore, whatever the findings, they cannot be interpreted as evidence of assumed causal links, without incurring in the logical fallacy of circular reasoning.

It has been long established that cross-correlations of non-stationary time series are highly prone to bias (14,15). Outbreak are non-stationary series because their mean and variance change with time. For instance, the mean number of AEI cases increased with time until week 19 and decreased thereafter. Time series, like the outbreaks in question, may appear correlated, even if one does not causes the other, because they may share time-varying direct or mediated causes that confound their association (14,15). In other words, even if two outbreaks are cross-correlated, the distribution of one outbreak may not probabilistically depend on the distribution of the other, i.e., one outbreak may not independently predict the other outbreak. Also, the number of events in a time series are not independent. For instance, the number of cases of AEI in a given week would be closer to the number in the following week than to the number in more distant weeks. This autocorrelation is due to autocorrelation in the time-varying factors driving the series and must be accounted for during the analysis to get correct estimates of variability (16). Moreover, a time series X could be deemed a cause of another time series Y if and only if predictions for Y differ when the history of X is taken into account and when it is not (17).

Induction times estimated by CPGM investigators were likely biased because they were based on statistical methods that assume constant mean and variance and no autocorrelation, the times corresponding to the highest correlations were automatically selected, and the correlations of moving-averages are more extreme than those of non-smoothed data.

I used a Poisson autoregressive model of first order to account for time “effects” and autocorrelation, and assessed whether the number of AEI cases independently predicted future changes in the number of cases of GBS and microcephaly (see Appendix) (18). I modeled the number of cases of each disease as a function of cubic polynomials splines of time (16,19). Then, I drew cross-correlograms of the observed number of cases, similar to those in the original study (1), and of the residual number of cases from the Poisson models.

Findings from the cross-correlations of the original data were consistent with those in the original study (Figure 2). However, after accounting for temporal fluctuations and autocorrelation there was no discernible pattern of correlation between the number of cases of AEI per week and the number of cases of GBS or microcephaly. Accordingly, the prevalence ratio (PR) for a change of 50 cases of AEI per week, for lag times (i.e., induction times) with the highest cross-correlations, were 1.01 [95% confidence interval (CI): 0.99, 1.03] for microcephaly (at 30 weeks), and 1.03 (95% CI: 0.98, 1.08) for GBS (at 7 weeks).

Figure 2 Cross-correlograms of weekly observed and residual number of cases of possible Zika virus infections (ZIKV), microcephaly, and Guillain-Barré syndrome (GBS), in Salvador, Brazil (2016).

These findings suggest the apparent temporal correlations between the AEI outbreak and putative outbreaks of microcephaly and GBS (1), were likely a consequence of known biases in cross-correlation functions that do not account for time patterns and autocorrelation (14,15). Assigning a causal role to ZIKV infections cannot be based only on the observation of microcephaly and GBS outbreaks occurring after an AEI outbreak. Indeed, outbreaks could concur by chance or may share time-varying causes (i.e., confounders), such as time-related enhancement of surveillance efforts. They may also appear correlated due systematic errors in the data, such as non-independent errors in case ascertainment, or to the autocorrelated nature of the series. This type of causal inference based solely on temporal coincidence constitutes an example of the logical fallacy post hoc ergo propter (i.e., after this, therefore because of it).

Another important report of a temporal association between ZIKV infection and microcephaly came from investigators from the Colombian National Institute of Health (INS), the US Centers for Diseases Control (CDC), and the Colombian Ministry of Health -MINSALUD (identified collectively as ICM) (6). The report was based on Colombian national surveillance data from August 9th 2015 to November 12th 2016, and included only suspected cases of ZIKV. The latter were defined as patients with fever and rash plus at least one of conjunctivitis, eye redness, itching, arthralgia, or malaise not explained by other diseases, who had been in a place below 2,200 m above sea level (20). Microcephaly was defined as head circumference below the third percentile for gestational age and sex (6).

ICM investigators reported a 4.5-fold increase in the prevalence of microcephaly in 2016 as compared to 2015 (from 2.1 to 9.6/10,000 births), as well as a temporal association between ZIKV infection and microcephaly, with a peak in the latter occurring approximately 24 weeks after the peak in ZIKV infections.

ICM’s findings should be cautiously interpreted for various reasons. According to the Colombian INS protocol for surveillance of microcephaly, there were in average 140 cases of microcephaly per year during 2010–2015 (21). This estimate came from a mandatory registry of health care episodes, managed by MINSALUD. The registry gathers data on 80% of the Colombian population with health insurance, but it is known to capture <55% of all the episodes (22,23). Accounting for coverage, under-reporting, and duration or surveillance, 245 cases should have been expected in 2016. Thus, the prevalence in 2016 would have been 2.01 (95% CI: 1.72, 2.35) times higher than that in previous years. This increase that could be easily explained by intensified surveillance of microcephaly in 2016. Moreover, defining microcephaly as head circumference below the third percentile entailed an expected prevalence of 3% (6). Thus, the observed prevalence in 2016 (9.6/10,000) was 31 times lower than expected. This suggests that, in spite of the new guidelines (21), health care personnel continued reporting mostly cases of extreme microcephaly (head circumference ≤3 standard deviations from the mean) (24), and explains why the prevalence in Colombia was close to that estimated by passive surveillance in the United States (25). In summary, the available data gives no substantial support for the occurrence of an outbreak of microcephaly in Colombia in 2016.

Regarding the temporal association, a time series analysis following the approach described above, showed that the weekly number of ZIKV infections in Colombian pregnant women did not predict the prevalence of microcephaly weeks later (see Appendix). After accounting for autocorrelation and temporal fluctuations, there was no association between the weekly number of cases of ZIKV infections and the number of cases of microcephaly 19 weeks later, the time corresponding to the strongest correlation between these conditions (PR: 0.96; 95% CI: 0.92, 1.00).

Moreover, it is surprising that ICM investigators made no attempt to compare the prevalence of microcephaly in populations living above and below 2,200 m above sea level. Indeed, INS proposed residence below 2,000 m as a surveillance criterion to define ZIKV infections (21), and recognized that ZIKV transmission was unlikely and that over half the Colombian population lives above this altitude. Such a natural experiment should be free of biases attributable to time-varying factors, such as changes in the intensity of surveillance efforts, and could have shed light on the proposed ZIKV-microcephaly link.

Fortunately, the data provided in their supplemental table (6) made possible comparing the prevalence of microcephaly in Departments with some (374,488 births and 390 cases) and those with no population (123,514 births and 86 cases) living at or above 2,000 m (see Appendix). After adjusting for infant mortality and proportion of the population with health insurance in the Department, two likely surrogates of the strength of the surveillance system, the prevalence was only 1.62 (95% CI: 1.25, 2.10) times higher in populations living below than in those living above 2,000 m.

The above estimate may be biased towards the null due to non-differential misclassification resulting from the coarse grouping of Departments. However, a finer three-level stratification yielded similar findings (see Appendix). More important, a bias analysis assuming that sensitivity and specificity of the Department classification was non-differential regarding microcephaly status and varied from 60% to 90%, showed a highest PR of 2.84 (95% CI: 1.98, 4.10) for sensitivity of 80% and specificity of 60%. These findings could be reasonably attributed to a tradition of greater concern and better reporting of arbovirus diseases in low altitude areas, a hypothesis that could have been tested with available long term INS surveillance data on those diseases. If that hypothesis were discarded, one may be compelled to believe maternal ZIKV infection is unlikely to increase in the risk of microcephaly by more than 4 times.

The reports discussed in this commentary (1,6) exemplify the use of surveillance data to formulate causal link hypotheses and to guide health policies in a context of uncertainty. However, they also call attention to the need for caution in both instances. Surveillance data are usually not collected for the purpose of identifying causal links. In consequence, the potential for information, selection, confounding, and confirmation biases is exacerbated when surveillance data is used for this purpose. Also, political pressure to respond to ongoing outbreaks may make the need for public health action so strong that analyses of surveillance data and interpretation of findings could become mere exercises to confirm foregone conclusions. Some of these limitation may be mitigated by careful analyses and interpretation of surveillance data. This would be more feasible if institutions in charge of surveillance actively supported efforts to make data readily available in formats and by means that facilitate independent analyses by all stakeholders.

In summary, CPGM and ICM reports do not support the occurrence of microcephaly and GBS outbreaks in these populations or that the apparent outbreaks were linked to ZIKV infection outbreaks. Nevertheless, CPGM investigators should be highly commended for collecting unique and valuable data, for their transparency in describing how the data were collected, how cases were defined and identified, and, more important, for recognizing that surveillance data that informs public health and patient care decisions affecting millions of individuals should be made publicly available, so that all stakeholders have the opportunity and take responsibility for conducting their own assessments. ICM investigators should be also commended for collecting, processing and publishing some of their data, though more detailed data, and more information regarding data sources, and analytical decisions would have strengthened their report. Unfortunately, similar reports based on surveillance data have been notoriously lacking on these regards (2,4).

Figure S1 Cross-correlograms of weekly observed and residual number of cases of E coli infections in Japan (2017), and microcephaly and Guillain-Barré syndrome (GBS), in Salvador, Brazil (2016).

Figure S2 Epidemic curves of Zika virus infection in pregnant women and newborn microcephaly (based on captured data from original figure). Colombia, 2015–2016.

Figure S3 Cross-correlograms of weekly observed and residual number of cases of Zika virus infections and newborn microcephaly in Colombia (2015–2016).

Figure S4 Odds ratios for altitude and microcephaly corrected for changing levels of misclassification of the exposure. Colombia (2015–2016).

Supplementary

Calculating the expected prevalence of microcephaly in Salvador, Bahia

This calculation is based on the distribution of head size among Brazilian newborn. According to the InterGrowth-21st study (9) the mean head circumference in Brazilian children is 34.2 cm, with a standard deviation of 1.2. The probability of having a head circumference <32 cm can be calculated from this distribution using a z test (see code for this calculation in item 1.0 of the accompanying Stata do file). This results in an expected prevalence of 32.8/10,000. This estimate only applies to full term newborns. However, 11.7% of Brazilian newborns are preterm (26). In the latter group the microcephaly was defined as a head circumference <3rd percentile of the Fenton curves (1), which corresponds to a prevalence of 30.0 cases/1,000 preterm newborns. Therefore, the expected prevalence in all newborns is a weighted average of the prevalences in full term and preterm newborns: [(1-0.117)×32.8]+(0.117×30)=32.4 cases/1,000 newborns.

Acknowledgments

Funding: None.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, Journal of Public Health and Emergency. The article did not undergo external peer review.

Conflicts of Interest: The author has completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/jphe.2017.06.05). The author has no conflicts of interest to declare.

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