Age Structure and Policy

This article examines the impact of age structure and government policy in response to COVID-19 in 209 countries.  The variables included in the study were total cases and deaths (per million), shares of the median age population, those aged 65, and those aged 70+, stringency index, GDP per capita, and number of hospital beds.  Models use for analysis were the generalized linear model (GLM), generalized moments method (GMM), and quantile regression model.  The results showed that all models exhibited significant positive relationships between median age, age 65, and age 70+ to the total number of COVID-19 cases.  The 50^th quantile estimate revealed that the unit increase in the share of median age people caused the total number of COVID-19 cases to increase by 1.61 units. Moreover, 1-unit increase in the share of median age in the population, the number of total COVID-19 per million increased significantly by 7.91 units GLM) and by 6.80 units (GMM).  The GLM and GMM models indicated a negative but insignificant relationship between stringency policy measures on total cases whereas quantile regressions showed significant negative estimated impacts of stringency policy measures on total cases.  The authors also indicated that  as GDP per capita increased, the mortality rate from COVID-19 of the median age population decreased.

Spatial Modeling

We have discussed previous studies that used spatial modeling to identify clusters of COVID-19 cases and to also identify associations between cases and various demographic factors.  This study builds upon existing research related to spatial modeling.  The authors explored associations between explanatory factors and COVID-19 cases and deaths and they also examined local associations between the explanatory variables and incidence of COVID-19 using spatial regression models.   The most updated aggregated county-level datasets provided by Johns Hopkins University were used for the research.  The datasets included approximately 348 relevant variables covering multiple domains, such as demography, education, economy, health care capacity, crime statistics, public transit, climate, and housing information.  Geographically weighted regression and multiscale GWR (MGWR) were performed at the county scale to take into account the scale effects.  The counties, for which the highest R² (i.e., R²>0.90) values were derived, formed spatially clustered patterns across the country. Generalizing the results to cluster and health factors, the results showed that high values of local R² were concentrated over the Wisconsin-Indiana-Michigan region, and many parts of the states of Texas, California, Mississippi and Arkansas. The lowest R² values were found in the Northern and North-Western states (Montana, Washington, Oregon, Wyoming), Southern states (New Mexico) and North-East coast region (North Carolina and Georgia). The spatial associations between different groups (crime, demographic, education, employment, ethnicity, health and migration) and COVID-19 cases showed strong similarities in terms of their spatial patterns of local R². The highest local R² values (R² = >0.90) were found in the Southern and South-Western states, mainly Texas, Arizona, California, Utah; in the Eastern United States, or the Wisconsin-Michigan-Indiana-Illinois region; in the tri-state area of Mississippi-Arkansas-Alabama. Contrarily, health factors exhibited a different association with the COVID-19 numbers. High local associations between the health factors and cases were found in the Colorado-Utah and New Hampshire areas. For all groups, low spatial associations were found in Montana, North Dakota, Idaho, Oregon.  Lastly, high spatial associations between the explanatory variables and cases were found in Texas, New Mexico, Mississippi, Tennessee, Kentucky, Indiana, Illinois, Wisconsin and Michigan (R²> = 0.90). In April and May, high spatial associations were found in Florida and California. In June and July, Arizona, Nevada, Oregon, Idaho states had high spatial associations whereas in Washington, Oregon, Idaho, Montana, North Dakota, and South Dakota had low spatial associations.