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Inferring influenza infection attack rate from seroprevalence data.

Wu JT, Leung K, Perera RA, Chu DK, Lee CK, Hung IF, Lin CK, Lo SV, Lau YL, Leung GM, Cowling BJ, Peiris JS - PLoS Pathog. (2014)

Bottom Line: We hypothesize that such conventions are not necessarily robust because different thresholds may result in different IAR estimates and serologic responses of clinical cases may not be representative.IAR estimates in influenza seroprevalence studies are sensitive to seropositivity thresholds and ISP adjustments which in current practice are mostly chosen based on conventions instead of systematic criteria.The same principles are broadly applicable for seroprevalence studies of other infectious disease outbreaks.

View Article: PubMed Central - PubMed

Affiliation: Department of Community Medicine and School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, People's Republic of China.

ABSTRACT
Seroprevalence survey is the most practical method for accurately estimating infection attack rate (IAR) in an epidemic such as influenza. These studies typically entail selecting an arbitrary titer threshold for seropositivity (e.g. microneutralization [MN] 1∶40) and assuming the probability of seropositivity given infection (infection-seropositivity probability, ISP) is 100% or similar to that among clinical cases. We hypothesize that such conventions are not necessarily robust because different thresholds may result in different IAR estimates and serologic responses of clinical cases may not be representative. To illustrate our hypothesis, we used an age-structured transmission model to fully characterize the transmission dynamics and seroprevalence rises of 2009 influenza pandemic A/H1N1 (pdmH1N1) during its first wave in Hong Kong. We estimated that while 99% of pdmH1N1 infections became MN1∶20 seropositive, only 72%, 62%, 58% and 34% of infections among age 3-12, 13-19, 20-29, 30-59 became MN1∶40 seropositive, which was much lower than the 90%-100% observed among clinical cases. The fitted model was consistent with prevailing consensus on pdmH1N1 transmission characteristics (e.g. initial reproductive number of 1.28 and mean generation time of 2.4 days which were within the consensus range), hence our ISP estimates were consistent with the transmission dynamics and temporal buildup of population-level immunity. IAR estimates in influenza seroprevalence studies are sensitive to seropositivity thresholds and ISP adjustments which in current practice are mostly chosen based on conventions instead of systematic criteria. Our results thus highlighted the need for reexamining conventional practice to develop standards for analyzing influenza serologic data (e.g. real-time assessment of bias in ISP adjustments by evaluating the consistency of IAR across multiple thresholds and with mixture models), especially in the context of pandemics when robustness and comparability of IAR estimates are most needed for informing situational awareness and risk assessment. The same principles are broadly applicable for seroprevalence studies of other infectious disease outbreaks.

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Age-specific ΔS40/ΔS20 during the first wave of pdmH1N1 in Hong Kong.ΔS40 and ΔS20 at each cross-section were estimated using the method described in our previous work [11]. If ISP20 and ISP40 (among all pdmH1N1 infections) were the same as the proportions of clinical cases that became MN1:20 and MN1∶40 seropositive (i.e. around 100% and 90%, respectively [23], [24]), ΔS40/ΔS20 should have remained close to 0.9–1 (the horizontal dashed line) throughout the first wave, which was not the case in reality as shown here.
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ppat-1004054-g002: Age-specific ΔS40/ΔS20 during the first wave of pdmH1N1 in Hong Kong.ΔS40 and ΔS20 at each cross-section were estimated using the method described in our previous work [11]. If ISP20 and ISP40 (among all pdmH1N1 infections) were the same as the proportions of clinical cases that became MN1:20 and MN1∶40 seropositive (i.e. around 100% and 90%, respectively [23], [24]), ΔS40/ΔS20 should have remained close to 0.9–1 (the horizontal dashed line) throughout the first wave, which was not the case in reality as shown here.

Mentions: In our previous IAR estimates, we (i) adopted the conventional MN1∶40 seropositivity threshold because the proportion of pdmH1N1 clinical cases who became MN1∶20 and MN1∶40 seropositive during convalescence were ∼100% and 90%, respectively [23], [24]; and (ii) assumed that ISP of all pdmH1N1 cases (i.e. including mild and asymptomatic infections) were similar to the proportion of clinical cases that became seropositive, i.e., ISP20≈1 and ISP40≈0.9–1. Because IAR≈ΔSX/ISPX, it follows that ΔS40/ΔS20≈ISP40/ISP20. The assumption ISP20≈1 and ISP40≈0.9–1 thus implied ΔS40/ΔS20>0.9. However, this contradicted our serial cross-sectional seroprevalence data which suggested that ΔS40/ΔS20 was consistently much smaller than 0.9 in all cross-sections throughout the first wave for all age groups, especially among older adults (Figure 2). The contribution of seasonal influenza to ΔS20 was small because (i) <34% of influenza A isolates during the first wave were seasonal influenza (http://www.chp.gov.hk/en/epidemiology/304/518/519.html); and (ii) in a Hong Kong study of within-household influenza transmission [25], only a small percentage of subjects infected with seasonal influenza became MN1∶20 seropositive against pdmH1N1 (unpublished data, BJ Cowling). Thus, given that pdmH1N1 vaccination was absent during the study period, ΔS20 could only be attributed to pdmH1N1 infections. This preliminary analysis strongly suggested that a substantial proportion of pdmH1N1 infections (e.g. mild and asymptomatic infections) did not become MN1∶40 seropositive. To substantiate this hypothesis, we developed a mathematical model to fully characterize the transmission dynamics and seroprevalence rises of pdmH1N1 during its first wave in Hong Kong.


Inferring influenza infection attack rate from seroprevalence data.

Wu JT, Leung K, Perera RA, Chu DK, Lee CK, Hung IF, Lin CK, Lo SV, Lau YL, Leung GM, Cowling BJ, Peiris JS - PLoS Pathog. (2014)

Age-specific ΔS40/ΔS20 during the first wave of pdmH1N1 in Hong Kong.ΔS40 and ΔS20 at each cross-section were estimated using the method described in our previous work [11]. If ISP20 and ISP40 (among all pdmH1N1 infections) were the same as the proportions of clinical cases that became MN1:20 and MN1∶40 seropositive (i.e. around 100% and 90%, respectively [23], [24]), ΔS40/ΔS20 should have remained close to 0.9–1 (the horizontal dashed line) throughout the first wave, which was not the case in reality as shown here.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3974861&req=5

ppat-1004054-g002: Age-specific ΔS40/ΔS20 during the first wave of pdmH1N1 in Hong Kong.ΔS40 and ΔS20 at each cross-section were estimated using the method described in our previous work [11]. If ISP20 and ISP40 (among all pdmH1N1 infections) were the same as the proportions of clinical cases that became MN1:20 and MN1∶40 seropositive (i.e. around 100% and 90%, respectively [23], [24]), ΔS40/ΔS20 should have remained close to 0.9–1 (the horizontal dashed line) throughout the first wave, which was not the case in reality as shown here.
Mentions: In our previous IAR estimates, we (i) adopted the conventional MN1∶40 seropositivity threshold because the proportion of pdmH1N1 clinical cases who became MN1∶20 and MN1∶40 seropositive during convalescence were ∼100% and 90%, respectively [23], [24]; and (ii) assumed that ISP of all pdmH1N1 cases (i.e. including mild and asymptomatic infections) were similar to the proportion of clinical cases that became seropositive, i.e., ISP20≈1 and ISP40≈0.9–1. Because IAR≈ΔSX/ISPX, it follows that ΔS40/ΔS20≈ISP40/ISP20. The assumption ISP20≈1 and ISP40≈0.9–1 thus implied ΔS40/ΔS20>0.9. However, this contradicted our serial cross-sectional seroprevalence data which suggested that ΔS40/ΔS20 was consistently much smaller than 0.9 in all cross-sections throughout the first wave for all age groups, especially among older adults (Figure 2). The contribution of seasonal influenza to ΔS20 was small because (i) <34% of influenza A isolates during the first wave were seasonal influenza (http://www.chp.gov.hk/en/epidemiology/304/518/519.html); and (ii) in a Hong Kong study of within-household influenza transmission [25], only a small percentage of subjects infected with seasonal influenza became MN1∶20 seropositive against pdmH1N1 (unpublished data, BJ Cowling). Thus, given that pdmH1N1 vaccination was absent during the study period, ΔS20 could only be attributed to pdmH1N1 infections. This preliminary analysis strongly suggested that a substantial proportion of pdmH1N1 infections (e.g. mild and asymptomatic infections) did not become MN1∶40 seropositive. To substantiate this hypothesis, we developed a mathematical model to fully characterize the transmission dynamics and seroprevalence rises of pdmH1N1 during its first wave in Hong Kong.

Bottom Line: We hypothesize that such conventions are not necessarily robust because different thresholds may result in different IAR estimates and serologic responses of clinical cases may not be representative.IAR estimates in influenza seroprevalence studies are sensitive to seropositivity thresholds and ISP adjustments which in current practice are mostly chosen based on conventions instead of systematic criteria.The same principles are broadly applicable for seroprevalence studies of other infectious disease outbreaks.

View Article: PubMed Central - PubMed

Affiliation: Department of Community Medicine and School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, People's Republic of China.

ABSTRACT
Seroprevalence survey is the most practical method for accurately estimating infection attack rate (IAR) in an epidemic such as influenza. These studies typically entail selecting an arbitrary titer threshold for seropositivity (e.g. microneutralization [MN] 1∶40) and assuming the probability of seropositivity given infection (infection-seropositivity probability, ISP) is 100% or similar to that among clinical cases. We hypothesize that such conventions are not necessarily robust because different thresholds may result in different IAR estimates and serologic responses of clinical cases may not be representative. To illustrate our hypothesis, we used an age-structured transmission model to fully characterize the transmission dynamics and seroprevalence rises of 2009 influenza pandemic A/H1N1 (pdmH1N1) during its first wave in Hong Kong. We estimated that while 99% of pdmH1N1 infections became MN1∶20 seropositive, only 72%, 62%, 58% and 34% of infections among age 3-12, 13-19, 20-29, 30-59 became MN1∶40 seropositive, which was much lower than the 90%-100% observed among clinical cases. The fitted model was consistent with prevailing consensus on pdmH1N1 transmission characteristics (e.g. initial reproductive number of 1.28 and mean generation time of 2.4 days which were within the consensus range), hence our ISP estimates were consistent with the transmission dynamics and temporal buildup of population-level immunity. IAR estimates in influenza seroprevalence studies are sensitive to seropositivity thresholds and ISP adjustments which in current practice are mostly chosen based on conventions instead of systematic criteria. Our results thus highlighted the need for reexamining conventional practice to develop standards for analyzing influenza serologic data (e.g. real-time assessment of bias in ISP adjustments by evaluating the consistency of IAR across multiple thresholds and with mixture models), especially in the context of pandemics when robustness and comparability of IAR estimates are most needed for informing situational awareness and risk assessment. The same principles are broadly applicable for seroprevalence studies of other infectious disease outbreaks.

Show MeSH
Related in: MedlinePlus