UHERO Public Health Report: Health Effects and Views of COVID-19 in Hawai‘i

The COVID-19 pandemic has had significant adverse impacts in the state of Hawai‘i beyond the direct effects of COVID-19 itself, including indirect effects on mental health, food security, job security, housing, poverty, etc. In order to address these adversities systematically, the University of Hawai‘i Economic Research Organization (UHERO) is developing infrastructure to inform the design and execution of public health programs in the state for COVID-19 and other disasters while providing valuable data to our communities to make informed decisions. To this goal, we have created a cohort of over 2,000 individuals in the state of Hawai‘i to collect rapid monthly surveys about individuals’ health and social factors, attitudes, and behaviors. This cohort will be followed longitudinally. 

As Hawai‘i is experiencing yet another COVID-19 surge, we report the first results of the cohort collected in late May 2022. Highlights of the report illustrate the negative impacts that COVID-19 had in almost 2 in 3 adults in the state, from mental health, food security, job security and housing. It also estimates that almost 1 in 3 adults in Hawai‘i infected with COVID-19 experience Long-COVID. Moreover, it estimates that 96% of adults in the state have some degree of immunity to the virus due to infection or vaccination, and characterizes the unvaccinated individuals in the state.

Watch the latest UHERO Focus about the report here:


1 thought on “UHERO Public Health Report: Health Effects and Views of COVID-19 in Hawai‘i”

  1. Michael Roberts

    This is a really useful report.

    I wonder if you might consider reporting, perhaps in addition to the raw numbers, weighted values that take account of the non-representativeness of the sample. For example, the Filipino population is clearly under-represented, as are people who are less educated. Since these factors correlate with trust, vaccination status, and positivity for COVID-19, some of the statistics might look a bit different if the sample is weighted to match the population.

    I realize that this can be tricky and that there can still be bias from such adjustments, but I suspect they might be somewhat less biased. I gather that the unvaccinated and distrustful are far less likely to respond to a survey like this; it’s a tough situation.

    I found it fascinating that so many here appear to be boosted and double-boosted relative to statistics that I have seen for the national population. Based on national CDC statistics on hospitalizations and mortality of people boosted and double-boosted, I think this bodes very well for the State’s progress. See, for example, this link:


    It shows that the mortality rate for those aged 50 and over and fully vaccinated, but not boosted, is 42 times higher than those who are double boosted, and those with a single booster have 4 times the mortality rate as those who are double boosted. There may be selection issues biasing these risk differences, but I’d guess that could go either way. Boosters look very effective!

    Given the especially powerful influence of being double-boosted, and the fact that so many are double-boosted in your sample, I wonder if you might do more to break out the double-boosted from the single boosted.

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