International Journal of Health Geographics - Latest Comments

Vitamin D explains some of the findings

William B. Grant — 2011-12-19T11:27:55Z

The epidemiology of sepsis in the United States led to an ecological study finding a role for solar UVB and vitamin D. Characteristics included in that study were racial disparities, seasonality, comorbid diseases, and geographical location.

Grant WB. Solar ultraviolet-B irradiance and vitamin D may reduce the risk of septicemia Dermato-Endocrinology. 2009;1(1):37-42.
(the title should have used sepsis).

Vitamin D also reduces the risk of respiratory diseases such as influenza and pneumonia, in part through induction of cathelicidin, in part through shifting cytokine production away from pro-inflammatory ones.

The high rates of sepsis in the southeast corresponds to the region of highest deaths from lung cancer. Respiratory infections comprise the majority of sepsis-attributed deaths, suggesting that smoking and diet may contribute to risk of sepsis.

OpenNI user experience recommendations

Maged N. Kamel Boulos — 2011-07-31T11:37:35Z

A comprehensive set of guidelines from OpenNI / PrimeSense that may help guiding future developments:

http://www.webcitation.org/60aMMzpk8

Kinect Paint offers a good example of a Kinect-optimised experience, with large interface elements and big buttons:

http://www.webcitation.org/60aNlsqx8

http://paint.codeplex.com/

Multiple Testing

Martin Kulldorff — 2010-07-13T18:25:56Z

This is a really nice paper, and it is interesting to see how the different methods compare for this data set. The author provides a clear example of the important differences between global clustering tests and cluster detection tests.

For the hypothesis testing part of the kernel intensity function method, it seams that one statistical test is performed for each of the 40x40=1600 grid points (p8). If so, the method does not adjust for the multiple testing inherent in the many cluster locations evaluated. At the alpha=0.05 level, one would expect 0.05*1600=80 'statistically significant' grid points just by chance alone, which is slightly less than the 110 that were found according to figure 5. Whether the difference in 110 and 80 is statistically significant is hard to tell, since the 1600 different tests are highly correlated when the grid points are close to each other. What it means though is that, with this approach, any data set that was generated under the null hypothesis will have many 'statistically significant' clusters that are not actually statistically significant.

With the spatial scan statistic there are even more potential clusters considered, but the method adjusts for the multiple testing. That is, if the data set was generated under the null hypothesis, the probability of seeing one or more statistically significant clusters anywhere on the map is 0.05.

The lack of adjustment for multiple testing explains why the kernel based method has 'statistically significant' clusters while the other methods do not (p14). While the kernel approach is useful for descriptive purposes and the test based on 'the sum of squared log ratios of kernel intensity functions' (p8) is a nice global clustering test, the method should not be used to evaluate the statistical significance of local clusters.

Geographic variations in sepsis

Greg Martin — 2010-05-11T14:57:24Z

The finding that sepsis mortality varies significantly across the U.S. is important and likely explains part of the healthcare disparities observed with this condition. A previous study using similar methodology reported similar findings regarding geographic variation, but also included analysis of incident cases and linked geographic and seasonal variations in sepsis incidence: see Danai P, et al. Critical Care Medicine 2007; 35: 410–415.

Space-time surveillance in the R package surveillance

Michael Höhle — 2010-04-27T10:31:47Z

As the review covers software for space-time disease surveillance I would like to point out that the R package "surveillance" has a function "stcd" dedicated to space-time point referenced data not mentioned in the article.

It implements are space-time cluster detection method developed in Assuncao and Correa (2009), CSDA, 53(8):2817-2830 (see manual for details). The documentation & testing of the function reveals, that its implementation is still somewhat experimental which might be the reason for it missing in the comparison/mentioning, but I would like to make readers aware of this option.
Hopefully, the stability and documentation of stcd will improve with time.

Best regards,

Michael Höhle
-author of the R package surveillance

Solar ultraviolet-B doses/vitamin D and ethnic heritage play important roles in breast and prostate cancer incidence rate variations, respectively

William B. Grant — 2010-04-21T20:20:05Z

Sir:
The paper on spatial trends of breast and prostate cancer incidence rates in the United States [1] presents interesting data but overlooks a significant portion of the literature on ecological studies of cancer mortality rates in the United States related to solar ultraviolet-B (UVB) doses and other risk modifying factors. A set of such papers is provided here published prior to submission of this paper [2-5]. In addition, there have been more since that paper was submitted [6,7]. These and other ecological studies have been reviewed recently [8,9]. The role of vitamin D in reducing risk of cancer has also been reviewed recently [10,11]. Other factors included in the ecological studies of white Americans [3-7] were air pollution, alcohol consumption, dietary iron and zinc, Hispanic heritage, smoking, urban/rural residence, and viral infections.

As noted in [1], the patterns for breast and prostate cancer have similarities and differences. One of the similarities is related to increased risk for those eating the Western diet [12,13]. However, one of the differences is that vitamin D is not associated with reduced risk of prostate cancer incidence [14] although it is associated with reduced case-fatality rate [15]. Having studied the geographical variation of prostate cancer mortality rates in the United States for a decade, my research turned in a new direction after finding the map of country of greatest ancestry by county in the United States for the year 2000 [16]. Many of the features correlated very closely with features in the prostate cancer mortality map [17]. This finding led to the hypothesis that ethnic background played a very important role in the etiology of prostate cancer and to a paper in which it was proposed that prevalence of Apolipoprotein E 4 was an important risk factor and supported by a multi-country ecological study [18]. Prostate cancer is the only cancer for which cholesterol is a risk factor [19]. This hypothesis will be investigated by researchers who have ongoing cohort studies with tissue samples that can be used for DNA analysis.

References
1. Mandal R, St-Hilaire S, Kie JG, Derryberry D. Spatial trends of breast and prostate cancers in the United States between 2000 and 2005. Int J Health Geogr. 2009, 8:53.

2. Garland FC, Garland CF, Gorham ED, Young JF. Geographic variation in breast cancer mortality in the United States: a hypothesis involving exposure to solar radiation. Prev Med. 1990, 19(6):614-622.

3. Grant WB. An ecologic study of dietary and solar ultraviolet-B links to breast carcinoma mortality rates. Cancer. 2002, 94(1):272-281.

4. Grant WB, Garland CF. The association of solar ultraviolet B (UVB) with reducing risk of cancer: multifactorial ecologic analysis of geographic variation in age-adjusted cancer mortality rates. Anticancer Res. 2006, 26(4A):2687-2699.

5. Grant WB. Hypothesis-Ultraviolet-B irradiance and vitamin D reduce the risk of viral infections and thus their sequelae, including autoimmune diseases and some cancers. Photochem Photobiol. 2008, 84(2):356-365.

6. Grant WB. An ecological study of cancer mortality rates including indices for dietary iron and zinc. Anticancer Res. 2008, 28(3B):1955-1963.

7. Grant WB. Air pollution in relation to U.S. cancer mortality rates: An ecological study; likely role of carbonaceous aerosols and polycyclic aromatic hydrocarbons. Anticancer Res, 2009, 29(9):3537-3545.

8. Mohr SB. A brief history of vitamin D and cancer prevention. Ann Epidemiol. 2009, 19(2):79-83.

9. Grant WB, Mohr SB. Ecological studies of ultraviolet B, vitamin D and cancer since 2000. Ann Epidemiol. 2009, 19(7):446-454.

10. Grant WB. How strong is the evidence that solar ultraviolet B and vitamin D reduce the risk of cancer? An examination using Hill’s criteria for causality. Dermato-Endocrinology. 2009, 1(1):17-24.

11. Grant WB. A critical review of Vitamin D and Cancer: A report of the IARC Working Group on vitamin D. Dermato-Endocrinology. 2009, 1(1):25-33.

12. Grant WB. An ecologic study of dietary and solar ultraviolet-B links to breast carcinoma mortality rates. Cancer. 2002, 94(1):272-281.

13. Grant WB. A multicountry ecologic study of risk and risk reduction factors for prostate cancer mortality. Eur Urol. 2004, 45(3):271-279.

14. Gupta D, Lammersfeld CA, Trukova K, Lis CG. Vitamin D and prostate cancer risk: a review of the epidemiological literature. Prostate Cancer Prostatic Dis. 2009, 12(3):215-226.

15. Tretli S, Hernes E, Berg JP, Hestvik UE, Robsahm TE. Association between serum 25(OH)D and death from prostate cancer. Br J Cancer. 2009, 100(3):450-454.

16. Brittingham A, de la Cruz GP. Ancestry 2000. Census 2000 Brief CK2BR-35. U. S. Dept. of Commerce, Census Bureau. Washington, DC. 2004, p. 9. http://www.census.gov/prod/2004pubs/c2kbr-35.pdf (accessed December 16, 2009)

17. Devesa SS, Grauman DJ, Blot WJ, Pennello GA, Hoover RN and Fraumeni JFJ. Atlas of Cancer Mortality in the United States, 1950-1994. NIH Publication No. 99-4564. National Institute of Health, 1999. http://www3.cancer.gov/atlasplus/type.html (accessed April 21, 2010)

18. Grant WB. A multicountry ecological study of risk-modifying factors for prostate cancer: Apolipoprotein E 4 as a risk factor and cereals as a risk reduction factor. Anticancer Res. 2010, 30:189-199.

19. Iso H, Ikeda A, Inoue M, Sato S, Tsugane S. Serum cholesterol levels in relation to the incidence of cancer: The JPHC Study Cohorts. Int J Cancer. 2009, 125(11):2679-2686.

ColorCode 3-D

Maged Nabih Kamel Boulos — 2009-11-06T17:27:59Z

Another anaglyph-like system worth mentioning that promises good colour fidelity:
http://www.colorcode3d.com/info/pages/what_is_cc3d.pdf

Nothing's happening in Arizona

Joseph Hill — 2008-12-18T10:56:14Z

I think I have the answer to R. C. Hunsaker's query (why so few heat deaths in Arizona). The SHELDUS database only records casualties from hazard events. Here in Arizona, we don't have heat events, we just have heat. (And occasional non-heat events.) That must be why SHELDUS records only 56 heat-event-related deaths in Arizona, compared to 951 in Illinois. <br><br>If the study looked at all hazard deaths, the picture would certainly be different. I don't know total numbers for Arizona, but I do know there have been at least 4,000 heat-related deaths of undocumented migrants along the southwest border since the 1990s. (Before changes in federal border policy, there were none -- speaking of contributing factors ...) But neither these deaths nor any other "uneventful" deaths are in SHELDUS.

re: Heat deaths

K.C. Hunsaker — 2008-12-17T19:06:25Z

If heat were the primary cause of death, then what explains the relatively few deaths in Arizona, for example? It has a large, vulnerable elderly population, and very high summer temperatures. Looking further, I would hazard a guess that poverty (inability to afford central air and thus nighttime cooling) and genetic predisposition to heart disease, both leading to heat deaths, would be the more likely ultimate culprits.

A valuable advance in the application of the spatial scan statistic

Francis Boscoe — 2008-11-26T06:30:12Z

This is an important paper that engages important interpretive issues regarding the spatial scan statistic that have been generally neglected. Basically, the authors’ method involves distilling complex SaTScan output from multiple program iterations, allowing the maximum circle size to vary. A few years ago, I and three coauthors proposed a method for distilling summary information from within a single program iteration (see reference 15). The two approaches are complementary and could probably be unified.

When identifying high (or low) rate clusters, the very last geographic unit in a cluster (that is, the one farthest from the center), by definition, must always have a high (or low) rate. This leads to heterogeneous clusters with a dumbbell or ring structure, where the geographic units most contributing to the cluster are found at or near its edge. In this paper, this is seen in the numerous clusters with Los Angeles, California or El Paso, Texas at their very edge. As heterogeneous clusters are difficult for public health officials to interpret and respond to, any method that can help distinguish them from stable core clusters is helpful. The method presented here accomplishes this.

More work along these lines remains to be done in the temporal dimension. There are many papers that present something like the following scenario: Data is compared over two time periods. A cluster identified in time 1 appears to get smaller, or larger, or moves slightly, or breaks into two separate clusters, in time 2. Authors conclude that the pattern or relationship has changed in some substantial way. In fact, the change was likely trivial. The original cluster is probably still significant in the second time period, it is just no longer the most significant cluster, which is the default SaTScan output. By making use of a broader set of SaTScan output, it should be possible to develop a method or visualization tool that precludes this interpretative error.