A 2020 article published in the journal Spatial and Spatio-temporal Epidemiology reports on the distributions of neighborhood characteristics across clusters of 177 New York City (U.S.) neighborhoods (define by postal codes) that differ in terms of access to COVID 19 testing and COVID 19 prevalence. As per a section from the article's abstract: "Identifying areas with low access to testing and high case burden is necessary to understand risk and allocate resources in the COVID-19 pandemic. Using zip code level data for New York City, we analyzed testing rates, positivity rates, and proportion positive. A spatial scan statistic identified clusters of high and low testing rates, high positivity rates, and high proportion positive. Boxplots and Pearson correlations determined associations between outcomes, clusters, and contextual factors. Clusters with less testing and low proportion positive tests had higher income, education, and white population, whereas clusters with high testing rates and high proportion positive tests were disproportionately black and without health insurance." (note: While we have not yet covered correlations in the course, we will get to this topic in Statistical Reasoning 2) The authors created a cluster (neighborhood) level index based on three cluster level COVID-19 measures: testing rates, positivity rates (% positive out of all residents), high proportion of positive tests (% positive out of only those tested), and the categorized this index into 4 quartiles: for naming purposed the four categories each feature a characteristic of the category: TRH (high testing rate), TRL (low testing rate), PRH (high neighborhood positivity rate), and PPH )high percentage of positive test results). However, these are mutually exclusive categories i.e. each neighborhood is in only one of the four groups. The following is a portion of a figure from this article, and show side-by-side boxplots of neighborhood characteristics across the four COVID-19 index categories. Proportion Public Transportation 0.0 0.2 0.4 0.6 0.8 1.0 Proportion Bachelors or Graduate 0.0 0.2 0.4 0.6 0.8 1.0 Median Household Income TRH TRL PRH PPH TRH TRL PRH PPH TRH TRL PRH PH 04 Proportion on Public Assistance 0.0 01 02 03 04 05 Proportion Rent 250% of income 0.0 0.1 0.2 0.3 0.4 0.5 Proportion in Poverty 0.2 0.3 0.1 0.0 TRH TRL PRH PPH TRH TRL PRH PPH TRH TRL PRH PPH Fig. 4. Covariate distribution by zip codes in clusters of high testing rates TRH), clusters of low testing rates (TRL) clusters of high positivity rates (781, and clusters of high proportion of positive test (PH) How do the distributions of household median incomes compare in shape across neighborhoods in the four COVID-19 index categories.? a. All 4 distributions have similar medians, and variability b. The income values for the TRL neighborhoods are less variable than the distributions for the other 3 COVID-19 index categories. c. All 4 distributions are left-skewed. d. The income values for the TRL neighborhoods are more variable than the distributions for the other 3 COVID-19 index categories. A 2020 article published in the journal Spatial and Spatio-temporal Epidemiology reports on the distributions of neighborhood characteristics across clusters of 177 New York City (U.S.) neighborhoods (define by postal codes) that differ in terms of access to COVID 19 testing and COVID 19 prevalence. As per a section from the article's abstract: "Identifying areas with low access to testing and high case burden is necessary to understand risk and allocate resources in the COVID-19 pandemic. Using zip code level data for New York City, we analyzed testing rates, positivity rates, and proportion positive. A spatial scan statistic identified clusters of high and low testing rates, high positivity rates, and high proportion positive. Boxplots and Pearson correlations determined associations between outcomes, clusters, and contextual factors. Clusters with less testing and low proportion positive tests had higher income, education, and white population, whereas clusters with high testing rates and high proportion positive tests were disproportionately black and without health insurance." (note: while we have not yet covered correlations in the course, we will get to this topic in Statistical Reasoning 2) The authors created a cluster (neighborhood) level index based on three cluster level COVID-19 measures: testing rates, positivity rates (% positive out of all residents), high proportion of positive tests (% positive out of only those tested), and the categorized this index into 4 quartiles: for naming purposed the four categories each feature a characteristic of the category: TRH (high testing rate), TRL (low testing rate), PRH (high neighborhood positivity rate), and PPH high percentage of positive test results). However, these are mutually exclusive categories i.e. each neighborhood is in only one of the four groups. The following is a portion of a figure from this article, and show side-by-side boxplots of neighborhood characteristics across the four CIVID-19 index categories. I Proportion Public Transportation 0.0 0.2 0.4 0.6 0.8 1.0 Proportion Bachelors or Graduate 0.0 0.2 0.4 0.6 0.8 1.0 Median Household Income TRH TRL PRH PPH TRH TRL PRH PPH TRH TRL PRH PPH 04 If we consider the COVID index a Proportion on Public Assistance 0.0 01 02 03 04 0.5 Proportion Rent 250% of income 0.0 0.1 0.2 0.3 0.4 0.5 Proportion in Poverty 0.2 0.3 0.1 an TRH TRL PRH PPH TRH TRI PRH PPH TRH TRI. PRH PPH Fig. 4. Covariate distribution by zip codes in clusters of high testing rates (TRH), clusters of low testing rates (TRL)clusters of high positivity rates (RH) and clusters of high proportion of positive test (PH neighborhood level "outcome", and each characteristics represented in the graphic an exposure", what is the study design type for this research? a. Case-control b. Observational Prospective Cohort c. Observational Cross-sectional d. Randomized Prospective Cohort