Table 2 Admissions by place of death: What impact do structural variables such as poverty have?

From: Impact of the COVID-19 pandemic on admissions of deceased to an institute of legal medicine in Germany

Variables

(A) Residential address

(B) Retirement/care facility

(C) Public

 

Coefficients (standard errors in parentheses)

Poverty (percentage of households receiving welfare benefits)

−0.0001 (0.0002)

−0.0006 (0.00140)

0.0068** (0.002)

Dummy variable for pandemic

0.024*** (0.003)

0.151*** (0.0184)

−0.119** (0.039)

Pandemic * Poverty (interaction term)

0.0002 (0.0003)

0.0016 (0.0015)

−0.0087** (0.0028)

Physicians per capita

−0.026 (0.250)

2.51 (1.219)

1.72 (1.95)

Percentage of residents > 64 years

−0.0001 (0.0001)

−0.0005 (0.0008)

−0.0017 (0.0016)

Time trend (months)

0.004*** (0.00006)

0.004*** (0.0003)

0.0048*** (0.0007)

Mortality in Hamburg (Monthly number of deaths)

0.0007*** (3.67e−06)

0.0006*** (0.00002)

−0.00008 (0.00005)

Constant

1.209*** (0.0406)

−0.187 (0.222)

−0.160 (0.496)

Likelihood ratio Chi2

88,556.34***

3897.29***

72.49***

Observations

13,354

2394

1026

  1. Poisson regression results for monthly number of admissions in three categories of places of death (dependent variable) with an interaction term for the poverty variable and the pandemic dummy variable while controlling for structural variables for the neighborhood in which the dead body was found as well as the time trend and the monthly number of deaths in Hamburg. A higher degree of poverty in a neighborhood has a significant and positive impact on the number of admissions of dead bodies found in public places. However, this effect was reversed during the pandemic (see interaction term). Significant results in bold; *** p ≤ 0.001, ** p ≤ 0.01.
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