When healthy and unhealthy counties (defined as above or below U.S. average death rates or life expectancy of the population) are mapped, clear instances of spatial clustering are visible. The next question is: If we map mortality for another time-period (i.e., introducing a temporal dimension to the spatial clustering) would the spatial patterns be the same? Put another way: Are high or low county-level mortality rates persistent over time? Investigators at Mississippi State University's Social Science Research Center (SSRC) are using several exploratory data visualization techniques and spatial statistics to determine the degree of spatial/temporal clustering and their statistical significance. These visualization techniques may also be used in a number of other research situations.
As part of a larger rural health research initiative, we are investigating the spatial and temporal patterns of high and low mortality and morbidity places in the U.S. Examinations into the health of our citizens, as well as the placement of, and access to, health care, are often dichotomized into rural and urban categories (Aday, et al. 2001; Ricketts III et al. 1999; Ricketts III et al. 1998). This dual metric has been criticized for masking the wide variety among rural settlements. As Miller, Farmer and Clarke (1994) noted: "If you've seen one rural community, you've seen one rural community. ... Thus, to speak of a singular rural America is folly," (p. 3). Since we are primarily interested in health, within the rural context, we began our inquiry with health outcomes. We ranked counties and mapped mortality rates at the county level for several periods of time.i We noted an apparent spatial "persistence" over time, i.e., the same counties ranked as having high or low mortality in multiple time-periods. The first question was: If different time-periods were mapped, would the same spatial pattern be evident? We chose to hold as constant the geography (counties), controlling for time (six 5-year time-periods), and measure the relative level of mortality rates (Sinton 1978; Goodchild 1992). This enabled us to test the hypothesis of spatial persistence of relative mortality rates among certain counties. If spatial persistence is proven, it will have important implications for both research focus and the spatial/regional allocation of health care and health care facilities (Cossman 1998).
The Compressed Mortality File from the National Center for Health Statistics (NCHS) reports all deaths of residents at the county level, the lowest level of mortality geography available (NCHS 2000; 2001a). The mortality rates for all-causes-of-death were calculated for every county in the United States.ii Five-year average mortality rates were calculated for each county to provide rate stability for low population counties (typically rural counties). To control for different age structures at the county level, the rates were age adjusted via the direct standardization method, using the Year 2000 United States Standard Million (Shryock et al. 1976; Feinleib et al.1992; Anderson et al. 1998a; Anderson et al. 1998b). The standard deviation for each five-year average was calculated and used as the cut-points for each time-period.iii
The text that follows outlines how we used different mapping schemes to address our research question. A detailed discussion of the spatial clusters is presented with the last map. We began by mapping the individual six time-periods using ArcView 3.2 and making visual comparisons among them as to whether or not the spatial clusters of high and low mortality counties were persistent over time. The following maps use red to indicate counties with a five-year average mortality rate that is more than one standard deviation
above the U.S. mean mortality rate. Counties in white are within one standard deviation of the norm. Counties in blue are more than one standard deviation
below the U.S. mean.
Figure 1. U.S. Mortality: 1968-72
Figure 2. U.S. Mortality: 1973-77
Figure 3. U.S. Mortality: 1978-82
Figure 4. U.S. Mortality: 1983-87
Figure 5. U.S. Mortality: 1988-92
Figure 6. U.S. Mortality: 1993-97
There is great individual county variation between the years, but the "regions" appear to remain somewhat consistent over time. However, given the individual maps, it is difficult to discern spatial patterns that might be played out over time. Thus, we took the next logical step, which was to animate the map series. We were actually looking at two aspects of spatial clustering. First, were the same spatial clusters persistent over time? Second, was there a spatial trend relative to the clusters, i.e., were the clusters moving east or west over time, were the clusters growing in a concentric circle fashion? We recalculated mortality rates using five-year rolling averages. This generated 27 mortality calculations that were used for mapping. The animation (Figure 7) was performed in the software package Flash. This technique was expected to increase the variability of rates, and thus the patterns that would be evident. However, by enabling the maps along the temporal dimension we thought that general patterns would become more discernable. A secondary advantage of an animated map is its value as a presentation vehicle.
Figure 7. U.S. Mortality Over Time (animation)
Note: For computer security reasons executable files are not included in this paper.
While the animated map was useful in confirming our suspicions about spatial clusters it was merely a visual confirmation. We recoded all the counties for each of the six time-periods. This was done by assigning a +1 to each county that was more than one standard deviation above the national mean mortality rate in one time-period, and a -1 to those that were below the mean for mortality rates. Tallying up the scores for all six time-periods, we classified counties as having had high mortality rates in six of the six time-periods (a score of 6), five out of six (score of 5), and four out of six (score of 4). We did the same for low mortality counties. This visualization technique focuses on the overall score of high and low mortality counties. It is entirely possible that a county could have had a high rate for three time-periods and then have dropped into the normal range, or even have been both high and low, thus canceling out each score. For this analysis we chose to focus on the overall rate as opposed to possible rate trends among counties.
Using SpaceStat, a spatial statistics program which operates as an extension to ArcView 3.2, we plotted a local Moran's I scatter plot and used a first order adjacency matrix to assess the degree of spatial clustering. First order adjacency refers to those counties that share a boundary with the target county. To identify significant spatial clustering, the standardized value of the variable of interest for the target county is compared to the standardized average of all adjacent counties. For example, if a target county has a high mortality rate and is surrounded by a number of adjacent counties whose average mortality rate is high, then the target county is classified as a "high-high" county. Using this statistical technique, we are able to identify significantly unhealthy places as those counties that have not only high mortality rates, but are also surrounded by a series of adjacent counties with a correspondingly high average mortality rate.
In the following map (Figure 8), red counties refer to high mortality counties adjacent to high mortality counties (High Cnty-High Cnty). Blue counties indicate that a relatively low mortality county is adjacent to a series of counties that share a similarly low average rate of mortality (Low Cnty-Low Cnty). The pink counties indicate that a "relatively" high mortality county is adjacent to counties with a low average mortality rate (High Cnty-Low Cnty). Light blue counties indicate that a relatively low mortality county is adjacent to a number of high mortality counties (Low Cnty-High Cnty). White counties are not statically significant indicating that the mortality rate for the target county is independent of the rates of neighboring counties (No Significance).
These results have provided a preliminary answer to the first substantive research question: Over time, do some places persistently have relatively high or low mortality rates?
Clearly, there is a temporal persistence anchored to specific locations, either place or people. This conclusion has implications for both research and policy.
First, if high mortality areas are spatially clustered and stable over time, it is logistically easier to study, and provide services to, those areas. It may also be that the processes that lead to individual mortality in these locations vary. In other words, it is not just that someone lives in an area with a high proportion of blacks (demographic), or an area of poverty (socioeconomic), or a largely humid rural area (environmental), but whether they reside in one of these persistently high and low mortality locations. For every variable just cited, there are counter examples available, as well. Thus a recategorization, along the lines of high and low mortality areas, may be a more useful way to rank these areas, as opposed to the current "rural versus urban" categorization.
Second, this naturally begs the question as to the role that a particular "place" versus "population" plays in mortality rates. To put it another way, as we hold the geography constant over time, is the population that we are observing permanent in that place, or is the population base constantly changing due to in- and out-migration? This directly addresses the issue of population mixing over time in those counties. Once we have quantified the relative "permanence", we may begin to assess the relative role of "place" versus "population" characteristics in the determination of high and low county-level mortality rates.
Researchers should note that similar temporal and spatial patterns can be visualized for different causes of death and morbidity in a particular area. Finding consistent spatial patterns in mortality would lead us to believe that there are similar patterns in the causes of mortality. Therefore, this healthy and unhealthy places model has potential uses for contextual health research, not simply mortality research.
Nonetheless, there are several fundamental issues that are yet to be resolved. Even when we have spatially situated health outcomes, the actual determinants will still remain unresolved. For example, what relative or multiplicative roles do physical place, resident population, population migration, socioeconomic status, social networks and access to medical care have on medical processes and outcome? These questions make up the next series to be addressed by this ongoing research into rural health in America at the SSRC.
Our initial task was to map relative mortality at the county-level and assess whether spatial clusters were present. Although the individual maps (Figures 1-6) and the animation (Figure 7) were useful intermediate steps, the calculation and plotting of the Local Moran's I (Figure 8) was the most useful visual display and quantification of spatial clusters. This particular map displayed a breadth of pattern that the other treatments did not, although one must keep in mind that the calculations were based on neighbors and not national averages. However, the Local Moran's I presented the data as one static map of persistence, treating the 30-year time-period as one data set. Although we had quantified spatial clustering, we still sought to visualize the temporal dimension, by recoding and assigning different classes to each county, ranging from most persistent (six of six time-periods) to not persistent (less than four out of six time-periods). The map of persistence over time (Figure 9) was the most useful in terms of visualizing the temporal persistence of high and low mortality. This map enabled us to see
where high and low rates have been spatially anchored over time, which can have important implications in terms of research focus and medical intervention. The visualization of multiple time-periods proved to be as useful a tool for assessing this aspect of
mortality as the more rigorous spatial statistics test. In this particular case, the simpler mapping procedure proved to be the more informative about the temporal permanence of high and low mortality.
This research was made possible by grant number 4 D1A RH 00005-01-01 from the Office of Rural Health Policy of the Department of Health and Human Services through the Rural Health, Safety and Security Institute, Social Science Research Center (SSRC), Mississippi State University. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the Office of Rural Health Policy.
i Implicit in this data visualization of spatial clusters technique is the assumption of an isotropic plain, a flat, featureless plain which contains no physical or social barriers to movement and access to health care (e.g., rivers, mountains, roads or lack of roads, administrative or service boundaries or discrimination in the provision of care). We recognize the possible roles that physical, financial and social barriers or facilitators could play in terms of the spatial patterns of mortality that are visually evident and we do not mean to discount them.
ii The compressed mortality file contained 3,143 records. We combined independent cities with their counties and eliminated counties with zero population, reducing the file to 3,107 records. Note that the data set does not include non-residents in either the reported deaths or population. Thus some immigrants (both legal and illegal) are not reflected in these calculations. Also excluded from these maps are rates for Alaska. Mortality at the county level in Alaska was not reported until 1988, thus eliminating that state's county level data from more than half of the time-periods being studied.
iii This methodology of calculating cut-points (above or below one standard deviation from the mean) relative to each 5-year time-period spotlights the highs and lows for those periods within the context of each of the six time-periods. While this ranking identifies counties that are relatively high or low over time, this methodology does mask possible absolute trends over time (Pickle 2002; Howell 2002). A county could begin as high and slowly drop over time, yet if it ranked as high in four of the six time-periods that it would be identified as a high county. In this article we chose to focus on "relative", (within each time-period) rankings of mortality for our initial inquiry.
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Ronald Cossman, Ph.D.
Social Science Research Center, P.O. Box 5287
Mississippi State University
Mississippi State, MS., 39762-5287
Office: (662) 325-4801
Fax: (662) 325-7966