RR:C19 Evidence Scale rating by reviewer:
Reliable. The main study claims are generally justified by its methods and data. The results and conclusions are likely to be similar to the hypothetical ideal study. There are some minor caveats or limitations, but they would/do not change the major claims of the study. The study provides sufficient strength of evidence on its own that its main claims should be considered actionable, with some room for future revision.
Authors have developed a classification algorithm to Identify the role of household immunity in driving individual dengue virus infection risk. Their study aims to fill gaps in the understanding of risk factors of DENV infection and disease, by using data from an ongoing longitudinal study that was conducted in Kamphaeng Phet, Thailand from 2015-2019.
While the study’s main claims are generally justified by its methods and data, there are pieces that would benefit from further clarification. First, the use of HAI titres could also imply flaviviral infections and not only dengue. Likewise, additional detail as to how the cut-off titer was determined, how the XGBoost works, and how the performed analyses impact risk of infection could have been included.
In regards to their statistical methods, three logistic regression models, a univariate model, a univariate model with random effects, and a multivariate model with random effects were used — what was the interval between infections and did the different intervals impact their data? Similarly, the month and year of sampling are relevant and need to be considered as the likelihood of infection increases during periods with higher case counts or in outbreak situations. And, knowing too that the more times they are boosted has role in triggering sever dengue — how does this affect the algorithm? Finally, there could also have been further clarification in regards to the HAI cutoffs of 40 and 66, chosen for the household immunity covariate as these constituted the 33rd and 66th percentiles. Why were such percentiles chosen?
In the Discussion section, the authors write, “All of these factors present a similar picture of household factors, where households with more adults or more recent infections will have more immunity to DENV and in turn alter subsequent infection risk for the members of the household.” It is unclear what is meant by “more immunity to DENV”. They also write that their results are consistent with prior studies showing that individual antibody titers are the most important predictor of future DENV infection risks, however, there is no reference or citation for such “prior studies” — does this paper that was published in 2020 use the same cohort? If so, how different is that study from the current one and is the information obtained new?
Later in the Discussion section, authors note the open question of how long boosting post infection confers immunity and protection from clinical manifestations, however, it should be acknowledged that the number of boosters received are equally important as they could trigger development of enhancing antibodies instead of neutralizing antibodies, thus, measuring neutralizing levels would be more relevant in showing immunity. Lastly, and rightfully so, authors used multigenerational households, however, a longer period of time would have shown that immunity obtained is truly protective based on the points that pre-existing immunity is a risk factor.
Finally, only 90 data points were used to inform the classification algorithm on how yearly HAI titers can look before and after an infection despite having more than 11,000 data points. Likewise, authors should further detail the individuals who did not have an infection event during an interval, as this may have depending on timing.
Ultimately, despite these queries, the study’s findings are reliable and do support issues with regard to dengue transmission. There is novelty with regard to type of analysis used, and the the limitations noted would not change the major claims of the study.