Table 2

Results of the generalised linear (Quasi-Poisson regression) analysis of social and demographic factors on COVID-19 infection rates in Barcelona from 9 March to 3 May 2020

Estimated coefficients
EstimateSEt-statisticP valueElasticity #
Change in cumulative incidenceEquivalence in cases
%95% CINumber95% CI
(Intercept)−5.98260.3791−15.78300.0000
Percentage 70+0.02280.00882.60000.0114+0.290.07 to 0.5128.07.0 to 49.0
Post-secondary education−0.26940.1011−2.66600.0096−0.27−0.47 to −0.07−26.0−45.0 to 6.8
HDI high migrants−0.09010.0693−1.30000.1979−0.09−0.22 to 0.05−8.6−22.0 to 4.4
Population density (urban)0.02950.01402.09900.0396+0.090.01 to 0.168.21.0 to 15.9
Mobility during lockdown0.27510.11822.32800.0229+0.270.04 to 0.5026.44.0 to 48.7
Nursing homes (LTCFs)0.02790.01262.21600.0301+0.040.00 to 0.084.00.0 to 7.6
Health workers0.02600.01371.89300.0626+0.18−0.01 to 0.3717.3−1.0 to 35.2
  • Seventy-six observations (neighbourhoods). Elasticity # is a form of standardised measure that estimates the relative change in the infection rate as a consequence of a relative change in a determinant. Here, we considered the effect of a 1% increase in the factor on the infection rate, keeping all other factors the same. The equivalence of cases is based on the number of cases for Barcelona as estimated by the model (9646) using the mean city-level values of each covariate as shown in table 1.

  • HDI, Human Development Index; LTCFs, long-term care facilities.