Is climate a primary driver of Vietnam's dengue or a shared long-term trend? A five-method analysis over 36 years
TL;DR
A provenance-preserving national annual dengue series is assembled and correlated with annual and March-May means of eight tropical sea surface temperature (SST) indices at lags of zero and one year under five trend-correction lenses, showing most of the apparent association reflects a trend shared by warming oceans and expanding surveillance.
Abstract
Background. Vietnam's reported dengue burden has risen roughly five-fold since 1990. Multi-decadal studies linking climate indices to dengue rarely separate genuine year-to-year coupling from a long-term trend the two share. Methods. We assembled a provenance-preserving national annual dengue series (1990-2025; OpenDengue plus Ministry of Health figures) and correlated it with annual and March-May means of eight tropical sea surface temperature (SST) indices at lags of zero and one year under five trend-correction lenses: raw, linear detrending, first-differencing, socio-demographic-index residualisation and AR(1) prewhitening. Findings. Cases rose 3.5 percent annually. Seven predictors were significant at zero lag, led by the annual Indian Ocean Basin-Wide index (IOBW; Spearman +0.534), but linear detrending removed all. First-differencing preserved six, led by spring IOBW (+0.468), the annual Atlantic Multidecadal Oscillation (AMO; +0.462) and annual IOBW (+0.423); three survived AR(1) prewhitening - annual AMO and annual and spring IOBW. Spring AMO and a lag-1 Tropical North Atlantic signal (-0.452) did not, and are hypothesis-generating. El Nino-Southern Oscillation indices failed throughout. Interpretation. Most of the apparent association reflects a trend shared by warming oceans and expanding surveillance; we could not demonstrate that climate is the primary driver at this scale. Trend is not the whole story: IOBW and annual AMO persist under trend- and persistence-removing transformations. Because transmission responds to climate over weeks to months, annual averaging smooths the lags through which El Nino acts; these nulls reflect temporal scale, not climate insensitivity; usable predictors will require monthly, province-level models. Funding. Center for Environmental Intelligence, VinUniversity (project VUNI.CEI.FS_0001).