The newest study exploring the association between ZIKV and microcephaly has just been published [1]; in it, the authors use diverse datasets to model the estimated risk of microcephaly based on eight reported cases combined with the attack rates from serology in French Polynesia (FP). While the authors are refreshingly explicit about their model assumptions, there are some potentially major data limitations that may constrain the utility of this analysis.
Islands and serology
The lynchpin issue for these estimates of the risk for microcephaly is: do the attack rates from captured serology serve as an accurate proxy for ZIKV exposure in pregnant women in FP?
Three serology datasets are used:
Pre-epidemic: 593 people (18–79 yr) from Tahiti (July 2011 - October, 2013).
2nd half epidemic: 196 people (7–86 yr; median 41 yr); general pop from the five most inhabited islands (February and March, 2014).
Post-epidemic: 476 children from Tahiti (6–16 yr; median 11 yr) (May and June, 2014.).
How big can the differences really be if Tahiti (the main island) has > 60% of the population?
Well, the scale bar below is an indicator: French Polynesia is massive. Like 2,000 km-wide massive.
One recent review states (paraphrasing): dengue transmission in FP is in sharp contrast to the Caribbean islands which act as an epidemiological unit with the American continent, due to short inter-island distances plus large population movements [2]. That is, there's rapid and extensive vector-human-viral mixing in the Caribbean, specifically unlike this situation. This extensive heterogeneity in FP has also been clearly documented in entomology & arbovirology, and even in the weather on Tahiti itself [3–5], which directly impacts Aedes vectors.
Moreover, in all vector-borne disease, entomological and consequent serological heterogeneity are the rule, even on local scales. In the 2007 ZIKV outbreak on Yap, attack rates ranged from 0 to 22 per 1000 over ~ 15-km, and important differences were reported in attack rates between gender and age groups [6] (see note).
In the current study, there’s no indication of how these sero-samples were collected: multistage cluster-sample, hospital samples, or convenience sampling? This is absolutely critical to ensure they aren't heavily biased. Moreover, there are no details on which islands the microcephaly cases were captured from, so the ‘sampling frames’ are undefined, and potentially very discordant.
For diagnosis, ELISA IgG to ZIKV was used for these cohorts; - however, a recent ZIKV case series report with authros from the French National Arboviral Laboratory specifcially highlighted major challenges, including indeterminate IgG results, suggestive of cross-reactivity with other flaviviruses [7].
Thus, there are three very large sources of uncertainty in the underlying serological data: almost certain major geographic heterogeneity; incompatible demography; and inherent uncertainty in the serological diagnosis itself. Together, these strongly suggest that the captured serological data from proxy populations underlying the models in [1] are very unlikely to accurately represent the diversity of viral exposures in women of reproductive age in a diverse archipelago stretching across > 2,000 km of ocean.
Total cases from sentinel sites
The second lacuna is the total suspect cases (top panel of the figure in [1]).
While the authors state in their model assumptions 'The number of Zika vius infections in a given week is proportional to the number of consultations for suspected infection in the same week.' I think it's important to examine how realistic this assumption is.
The total suspected cases are extrapolated from a network of clinic sites across FP (presumably fairly proportional to population density?). These sentinel clinics appear to report only ‘dengue-like’ illness [11] (please correct me if this is wrong). The proportion of these cases confirmed as ZIKV was 4%, but with DENV (4 serotypes), CHIKV, JEV [8], and Ross River virus (RRV) [9] all circulating in FP [10], there’s no reason to suspect the remainder might be ZIKV. This is especially true as many arboviruses have very diverse presentations, making clinical differential diagnosis difficult or impossible (eg [12]; and “the most common clinical manifestations of RRV infections are fever, arthralgia and rash.” [9]).
There is also limited detail on how this extrapolation was carried out, but the heterogeneity in epidemiology, entomology, and ‘island-ness’ combined with the 4% confirmation would make this a challenging endeavor with very large uncertainties.
In short, there is no evidence (in this article at least) that the 'spike' in cases has anything to do with ZIKV in isolation on any of the islands beyond communities in Tahiti where serology was done. The remainder could be ZIKV, or DENV, or RRV, or CHIKV, or... ?? Transmission of all these arboviruses was very likely driven by climatic conditions that favored vector reproduction and survival, allowing all the co-circulating arboviruses to 'have a go' in island-specific ways (due to vectors, travel, and population-level immunities). Moreover, the proportion of individual viruses almost certainly changed during the time period under study, adding to the complexities of estimating trimester-specific risk.
Other issues
A key issue in all disease reporting is the so-called “spatial areal unit problem.” The spatial scale (eg, city block, citywide, or national-scale) at which data are aggregated and examined can cause major changes in observed trends [13]. Rare events (like these 8 cases) are also inherently problematic for this reason (and others) specifically in birth defect studies, and artifacts are not uncommon [14].
Directly following on this point is the use of 2 per 10,000 as the baseline. While this may be due to an abundance of caution, it is the lowest end of the rates from the US (2-12 per 10,000 births) [15]. The combination of these three issues together severely undermines the thresholds for 'unusual rates' used in these models.
Conclusions
While the authors have done an admirable job of assembling data to address a critically important and pressing public health crisis, their use of 'French Polynesia' as the unit of analysis is highly problematic due to well-documented geographic, entomological and epidemiological heterogeneity which severely undermines their serology, extrapolated case counts, and 'alert' thresholds.
While the exact impact of these data issues is difficult to predict in the published models, it certainly greatly increases the uncertainty around the risk estimates, and without island-specific serology tied to where microcephaly cases have occurred, it may simply not be possible to robustly estimate risk from this limited set of eight cases.
References
1. Cauchemez S, Besnard M, Bompard P, Dub T, Guillemette-Artur P, Eyrolle-Guignot D, et al. Association between Zika virus and microcephaly in French Polynesia, 2013–15: a retrospective study. The Lancet. 2016; doi:10.1016/S0140-6736(16)00651-6
2. Tortosa P, Pascalis H, Guernier V, Cardinale E, Le Corre M, Goodman SM, et al. Deciphering arboviral emergence within insular ecosystems. Infect Genet Evol. 2012;12: 1333–1339. doi:10.1016/j.meegid.2012.03.024
3. Vazeille-Falcoz M, Mousson L, Rodhain F, Chungue E, Failloux A-B. Variation in oral susceptibility to dengue type 2 virus of populations of Aedes aegypti from the islands of Tahiti and Moorea, French Polynesia. Am J Trop Med Hyg. 1999;60: 292–299.
4. Brelsfoard CL, Dobson SL. Population genetic structure of Aedes polynesiensis in the Society Islands of French Polynesia: implications for control using a Wolbachia-based autocidal strategy. Parasit Vectors. 2012;5: 1–12.
5. Hopuare M, Pontaud M, Céron J, Ortéga P, Laurent V. Climate change, Pacific climate drivers and observed precipitation variability in Tahiti, French Polynesia. Clim Res. 2015;63: 157–170. doi:10.3354/cr01288
6. Duffy MR, Chen T-H, Hancock WT, Powers AM, Kool JL, Lanciotti RS, et al. Zika Virus Outbreak on Yap Island, Federated States of Micronesia. N Engl J Med. 2009;360: 2536–2543. doi:10.1056/NEJMoa0805715
7. Maria AT, Maquart M, Makinson A, Flusin O, Segondy M, Leparc-Goffart I, et al. Zika virus infections in three travellers returning from South America and the Caribbean respectively, to Montpellier, France, December 2015 to January 2016. Eurosurveillance. 2016;21. doi:10.2807/1560-7917.ES.2016.21.6.30131
8. Aubry M, Finke J, Teissier A, Roche C, Broult J, Paulous S, et al. Seroprevalence of arboviruses among blood donors in French Polynesia, 2011–2013. Int J Infect Dis. 2015;41: 11–12. doi:10.1016/j.ijid.2015.10.005
9. Aubry M, Finke J, Teissier A, Roche C, Broult J, Paulous S, et al. Silent circulation of Ross River virus in French Polynesia. Int J Infect Dis. 2015;37: 19–24. doi:10.1016/j.ijid.2015.06.005
10. Roth A, Mercier A, Lepers C, Hoy D, Duituturaga S, Benyon E, et al. Concurrent outbreaks of dengue, chikungunya and Zika virus infections-an unprecedented epidemic wave of mosquito-borne viruses in the Pacific 2012-2014. Euro Surveill. 2014;19: 20929.
11. Surveillance et veille sanitaire en Polynésie française. In: Pacific Public Health Surveillance Network [Internet]. [cited 22 Mar 2016]. Available: http://www.pphsn.net/Surveillance/Routine_reports-2014-archives.htm
12. Duong V, Andries A-C, Ngan C, Sok T, Richner B, Asgari-Jirhandeh N, et al. Reemergence of chikungunya virus in Cambodia. Emerg Infect Dis. 2012;18: 2066–2069. doi:10.3201/eid1812.120471
13. Jones SG, Kulldorff M. Influence of spatial resolution on space-time disease cluster detection. PLoS ONE. 2012;7: e48036. doi:10.1371/journal.pone.0048036
14. De Wals P. Investigation of clusters of adverse reproductive outcomes, an overview. Eur J Epidemiol. 1999;15: 871–875.
15. http://www.cdc.gov/ncbddd/birthdefects/microcephaly.html
Note
Detail from [6]:
"The sex-specific attack rates were 17.9 per 1000 females and 11.4 per 1000 males. Cases occurred among all age groups, but the incidence of confirmed and probable Zika virus disease detected by health care surveillance was highest among persons 55 to 59 years of age."
NB: If the map from Yap infringes on any copyright from The Lancet, I will gladly remove it.
Islands and serology
The lynchpin issue for these estimates of the risk for microcephaly is: do the attack rates from captured serology serve as an accurate proxy for ZIKV exposure in pregnant women in FP?
Three serology datasets are used:
Pre-epidemic: 593 people (18–79 yr) from Tahiti (July 2011 - October, 2013).
2nd half epidemic: 196 people (7–86 yr; median 41 yr); general pop from the five most inhabited islands (February and March, 2014).
Post-epidemic: 476 children from Tahiti (6–16 yr; median 11 yr) (May and June, 2014.).
How big can the differences really be if Tahiti (the main island) has > 60% of the population?
Well, the scale bar below is an indicator: French Polynesia is massive. Like 2,000 km-wide massive.
http://wwp.greenwichmeantime.com/time-zone/pacific/french-polynesia/map-french-polynesia/
Moreover, in all vector-borne disease, entomological and consequent serological heterogeneity are the rule, even on local scales. In the 2007 ZIKV outbreak on Yap, attack rates ranged from 0 to 22 per 1000 over ~ 15-km, and important differences were reported in attack rates between gender and age groups [6] (see note).
Suspect and confirmed cases per 1000 popualtion; 2007 ZIKV outbreak in Yap,
figure 3 in [6] (see note).
In the current study, there’s no indication of how these sero-samples were collected: multistage cluster-sample, hospital samples, or convenience sampling? This is absolutely critical to ensure they aren't heavily biased. Moreover, there are no details on which islands the microcephaly cases were captured from, so the ‘sampling frames’ are undefined, and potentially very discordant.
For diagnosis, ELISA IgG to ZIKV was used for these cohorts; - however, a recent ZIKV case series report with authros from the French National Arboviral Laboratory specifcially highlighted major challenges, including indeterminate IgG results, suggestive of cross-reactivity with other flaviviruses [7].
Thus, there are three very large sources of uncertainty in the underlying serological data: almost certain major geographic heterogeneity; incompatible demography; and inherent uncertainty in the serological diagnosis itself. Together, these strongly suggest that the captured serological data from proxy populations underlying the models in [1] are very unlikely to accurately represent the diversity of viral exposures in women of reproductive age in a diverse archipelago stretching across > 2,000 km of ocean.
Total cases from sentinel sites
The second lacuna is the total suspect cases (top panel of the figure in [1]).
While the authors state in their model assumptions 'The number of Zika vius infections in a given week is proportional to the number of consultations for suspected infection in the same week.' I think it's important to examine how realistic this assumption is.
The total suspected cases are extrapolated from a network of clinic sites across FP (presumably fairly proportional to population density?). These sentinel clinics appear to report only ‘dengue-like’ illness [11] (please correct me if this is wrong). The proportion of these cases confirmed as ZIKV was 4%, but with DENV (4 serotypes), CHIKV, JEV [8], and Ross River virus (RRV) [9] all circulating in FP [10], there’s no reason to suspect the remainder might be ZIKV. This is especially true as many arboviruses have very diverse presentations, making clinical differential diagnosis difficult or impossible (eg [12]; and “the most common clinical manifestations of RRV infections are fever, arthralgia and rash.” [9]).
There is also limited detail on how this extrapolation was carried out, but the heterogeneity in epidemiology, entomology, and ‘island-ness’ combined with the 4% confirmation would make this a challenging endeavor with very large uncertainties.
In short, there is no evidence (in this article at least) that the 'spike' in cases has anything to do with ZIKV in isolation on any of the islands beyond communities in Tahiti where serology was done. The remainder could be ZIKV, or DENV, or RRV, or CHIKV, or... ?? Transmission of all these arboviruses was very likely driven by climatic conditions that favored vector reproduction and survival, allowing all the co-circulating arboviruses to 'have a go' in island-specific ways (due to vectors, travel, and population-level immunities). Moreover, the proportion of individual viruses almost certainly changed during the time period under study, adding to the complexities of estimating trimester-specific risk.
Other issues
A key issue in all disease reporting is the so-called “spatial areal unit problem.” The spatial scale (eg, city block, citywide, or national-scale) at which data are aggregated and examined can cause major changes in observed trends [13]. Rare events (like these 8 cases) are also inherently problematic for this reason (and others) specifically in birth defect studies, and artifacts are not uncommon [14].
Directly following on this point is the use of 2 per 10,000 as the baseline. While this may be due to an abundance of caution, it is the lowest end of the rates from the US (2-12 per 10,000 births) [15]. The combination of these three issues together severely undermines the thresholds for 'unusual rates' used in these models.
Conclusions
While the authors have done an admirable job of assembling data to address a critically important and pressing public health crisis, their use of 'French Polynesia' as the unit of analysis is highly problematic due to well-documented geographic, entomological and epidemiological heterogeneity which severely undermines their serology, extrapolated case counts, and 'alert' thresholds.
While the exact impact of these data issues is difficult to predict in the published models, it certainly greatly increases the uncertainty around the risk estimates, and without island-specific serology tied to where microcephaly cases have occurred, it may simply not be possible to robustly estimate risk from this limited set of eight cases.
References
1. Cauchemez S, Besnard M, Bompard P, Dub T, Guillemette-Artur P, Eyrolle-Guignot D, et al. Association between Zika virus and microcephaly in French Polynesia, 2013–15: a retrospective study. The Lancet. 2016; doi:10.1016/S0140-6736(16)00651-6
2. Tortosa P, Pascalis H, Guernier V, Cardinale E, Le Corre M, Goodman SM, et al. Deciphering arboviral emergence within insular ecosystems. Infect Genet Evol. 2012;12: 1333–1339. doi:10.1016/j.meegid.2012.03.024
3. Vazeille-Falcoz M, Mousson L, Rodhain F, Chungue E, Failloux A-B. Variation in oral susceptibility to dengue type 2 virus of populations of Aedes aegypti from the islands of Tahiti and Moorea, French Polynesia. Am J Trop Med Hyg. 1999;60: 292–299.
4. Brelsfoard CL, Dobson SL. Population genetic structure of Aedes polynesiensis in the Society Islands of French Polynesia: implications for control using a Wolbachia-based autocidal strategy. Parasit Vectors. 2012;5: 1–12.
5. Hopuare M, Pontaud M, Céron J, Ortéga P, Laurent V. Climate change, Pacific climate drivers and observed precipitation variability in Tahiti, French Polynesia. Clim Res. 2015;63: 157–170. doi:10.3354/cr01288
6. Duffy MR, Chen T-H, Hancock WT, Powers AM, Kool JL, Lanciotti RS, et al. Zika Virus Outbreak on Yap Island, Federated States of Micronesia. N Engl J Med. 2009;360: 2536–2543. doi:10.1056/NEJMoa0805715
7. Maria AT, Maquart M, Makinson A, Flusin O, Segondy M, Leparc-Goffart I, et al. Zika virus infections in three travellers returning from South America and the Caribbean respectively, to Montpellier, France, December 2015 to January 2016. Eurosurveillance. 2016;21. doi:10.2807/1560-7917.ES.2016.21.6.30131
8. Aubry M, Finke J, Teissier A, Roche C, Broult J, Paulous S, et al. Seroprevalence of arboviruses among blood donors in French Polynesia, 2011–2013. Int J Infect Dis. 2015;41: 11–12. doi:10.1016/j.ijid.2015.10.005
9. Aubry M, Finke J, Teissier A, Roche C, Broult J, Paulous S, et al. Silent circulation of Ross River virus in French Polynesia. Int J Infect Dis. 2015;37: 19–24. doi:10.1016/j.ijid.2015.06.005
10. Roth A, Mercier A, Lepers C, Hoy D, Duituturaga S, Benyon E, et al. Concurrent outbreaks of dengue, chikungunya and Zika virus infections-an unprecedented epidemic wave of mosquito-borne viruses in the Pacific 2012-2014. Euro Surveill. 2014;19: 20929.
11. Surveillance et veille sanitaire en Polynésie française. In: Pacific Public Health Surveillance Network [Internet]. [cited 22 Mar 2016]. Available: http://www.pphsn.net/Surveillance/Routine_reports-2014-archives.htm
12. Duong V, Andries A-C, Ngan C, Sok T, Richner B, Asgari-Jirhandeh N, et al. Reemergence of chikungunya virus in Cambodia. Emerg Infect Dis. 2012;18: 2066–2069. doi:10.3201/eid1812.120471
13. Jones SG, Kulldorff M. Influence of spatial resolution on space-time disease cluster detection. PLoS ONE. 2012;7: e48036. doi:10.1371/journal.pone.0048036
14. De Wals P. Investigation of clusters of adverse reproductive outcomes, an overview. Eur J Epidemiol. 1999;15: 871–875.
15. http://www.cdc.gov/ncbddd/birthdefects/microcephaly.html
Note
Detail from [6]:
"The sex-specific attack rates were 17.9 per 1000 females and 11.4 per 1000 males. Cases occurred among all age groups, but the incidence of confirmed and probable Zika virus disease detected by health care surveillance was highest among persons 55 to 59 years of age."
NB: If the map from Yap infringes on any copyright from The Lancet, I will gladly remove it.
