Letters
To the Editor: cute disseminated encephalomyelitis is an inflammatory demyelinating disease of the central nervous system that may occur after viral illnesses or vaccinations.1 No information is available about the geographic distribution of acute disseminated encephalomyelitis, although several analyses (both clinical data2 and mouse–model response to ultraviolet radiation)3 suggest a correlation with latitude. The small size and specific regional setting of acute disseminated encephalomyelitis cohorts has hampered deep analysis of this relationship. However, the association of latitude has been examined for multiple sclerosis, the most common central nervous system demyelinating disease.4 The risk of multiple sclerosis has been reported to increase from south to north, and several factors (including ultraviolet light exposure, genetic background, diagnostic accuracy, and ascertainment probability) can explain such distribution.4 The aim of this study was to evaluate whether acute disseminated encephalomyelitis has a demonstrable geographic distribution. As there is no indication of substantial changes in the incidence of this disease over time, we conducted this analysis considering the latest available information on the number of hospital admissions in the USA (2010 and 2011 National Inpatient Sample database) and in the UK (2012–2013 England’s Hospital Episode Statistics database). We identified acute disseminated encephalomyelitis cases by searching the databases for any listed diagnosis classified with the appropriate ICD-9 and ICD-10 codes for this condition (323.61 and G040, respectively). States in the
A
Copyright © 2014 by Lippincott Williams & Wilkins ISSN: 1044-3983/14/2506-0928 DOI: 10.1097/EDE.0000000000000176
928 | www.epidem.com
USA reporting 10 or fewer cases were excluded from analysis. Disease incidence was calculated using the population of each state as the denominator. We calculated the Pearson correlation coefficients to examine the number of hospital admissions for acute disseminated encephalomyelitis in the USA and UK per year in relation to latitude. We found that disease incidence increased linearly with increased distance from the equator (r = 0.42; 95% confidence interval = 0.029–0.70) (Figure). To confirm these results, we compared the incidence rates of acute disseminated encephalomyelitis among the various Census Bureau-designated regions in the USA. We found that the estimated incidence rates were considerably higher in the North-West of USA (latitude 33°–49° North; 0.38 cases per 10,000 inhabitants) than in South (latitude 26°–39° North; 0.22 cases per 100,000). Although these results could potentially be attributable to differences in population ethnicity, we did not find any correlation between race distribution and acute disseminated encephalomyelitis incidence, based on USA census bureau data. This geographical distribution of acute disseminated encephalomyelitis is consistent with the previous analyses on the incidence of multiple sclerosis, which increases with the increasing distance from the equator in both the northern and the southern hemispheres.5 Several factors may explain the geographic pattern we reported here for acute disseminated encephalomyelitis, which cannot be attributed fully to diagnostic issues. As the most common antecedent factor leading to acute disseminated encephalomyelitis is infection,6 regional differences in the prevalence of infectious diseases and socioeconomic status may play a role in this distribution. A recent analysis highlighted the possible role of specific human leukocyte antigen alleles as risk factors for acute disseminated encephalomyelitis, which may account for a different distribution in the USA and
55
50
45
Latitude
Geoepidemiology of Acute Disseminated Encephalomyelitis
40
35
30 0.2
0.3
0.4
0.5
0.6
Incidence
FIGURE. Association of latitude (degrees North) with incidence of acute disseminated encephalomyelitis (per 100,000 inhabitants).
other countries.7 As acute disseminated encephalomyelitis and multiple sclerosis are similar in terms of clinical and pathological features, it is reasonable to suppose that factors such as ultraviolet light exposure and vitamin D levels may be common to these pathologies.3 Despite the known limitations of discharge databases,8 we provide here the first evidence of a common geographical distribution between acute disseminated encephalomyelitis and multiple sclerosis. Further analyses are required to evaluate the incidence of acute disseminated encephalomyelitis at other latitudes and to evaluate the impact of other factors, including genetic predisposition. Paolo Pellegrino Sonia Radice Emilio Clementi Unit of Clinical Pharmacology Department of Biomedical and Clinical Sciences University Hospital Luigi Sacco Università di Milano, Milan, Italy [email protected]
References 1. Pellegrino P, Carnovale C, Perrone V, et al. Acute disseminated encephalomyelitis
Epidemiology • Volume 25, Number 6, November 2014
Epidemiology • Volume 25, Number 6, November 2014 Letters
onset: evaluation based on vaccine adverse events reporting systems. PLoS One. 2013;8:e77766. 2. Torisu H, Kira R, Ishizaki Y, et al. Clinical study of childhood acute disseminated encephalomyelitis, multiple sclerosis, and acute transverse myelitis in Fukuoka Prefecture, Japan. Brain Dev. 2010;32:454–462. 3. Becklund BR, Severson KS, Vang SV, DeLuca HF. UV radiation suppresses experimental autoimmune encephalomyelitis independent of vitamin D production. Proc Natl Acad Sci USA. 2010;107:6418–6423. 4. Goodin DS. The epidemiology of multiple sclerosis: insights to disease pathogenesis. Handb Clin Neurol. 2014;122:231–266. 5. Wender M. Acute disseminated encephalomyelitis (ADEM). J Neuroimmunol. 2011;231: 92–99. 6. Tenembaum SN. Acute disseminated encephalomyelitis. Handb Clin Neurol. 2013;112:1253– 1262. 7. Alves-Leon SV, Veluttini-Pimentel ML, Gouveia ME, et al. Acute disseminated encephalomyelitis: clinical features, HLA DRB1*1501, HLA DRB1*1503, HLA DQA1*0102, HLA DQB1*0602, and HLA DPA1*0301 allelic association study. Arq Neuropsiquiatr. 2009;67:643–651. 8. Pellegrino P, Carnovale C, Perrone V, et al. Epi demiological analysis on two decades of hospitalisations for meningitis in the United States. Eur J Clin Microbiol Infect Dis. 2014;33:1519–1524.
Regression Analysis of Aggregate Continuous Data To the Editor: ndividual-level statistical analyses are paramount for obtaining accurate estimates of an exposure-outcome relation in population groups. However, data privacy and confidentiality concerns have led to a conflict between ethico-legal restrictions to access microdata and scientific accuracy achievable through analyses of individual-level data.1,2 Disclosure of results is also restricted by rules protecting subjects’ confidentiality, such as small cell data suppression, rounding, and collapsing.3
I
Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www. epidem.com). This content is not peerreviewed or copy-edited; it is the sole responsibility of the author. Copyright © 2014 by Lippincott Williams & Wilkins ISSN: 1044-3983/14/XXXXX-0000 DOI: 10.1097/EDE.0000000000000172
© 2014 Lippincott Williams & Wilkins
Data aggregation is a major way to share data publicly while protecting the confidentiality of the subjects, but regression models for summary continuous data are lacking. We have developed a method to fill this gap. To perform linear regression analyses on a continuous aggregate outcome we need only 2 parameters: the mean and the standard deviation (SD), within strata of a set of categorical predictors. The frequencies (counts) of the combinations of the predictors can be used as weights. The SD of the raw data for all combinations of the predictors can be used to calculate the pooled variance that subsequently can be used to correct the standard errors (SE) of the estimated parameters using aggregate data. To illustrate the application of the method, we focused on the association between receipt of WIC (The Special Supplemental Nutrition Program for Women, Infants, and Children) food for the mother during this pregnancy (http://www.fns.usda.gov/ wic/about-wic) and gestational weight gain. We used a subset of the 2012 Natality Public Use Births File of the National Center for Health Statistics (NCHS).4 The subset is drawn from the 2003 revision of the U.S. Standard Certificate of Live Birth and includes singleton term pregnancies (37–41 weeks gestation) of underweight (body mass index is less than 18.5) women aged 20 to 25 years, who did not complete high school, and were Medicaid recipients. Records with unknown prenatal care initiation information and “other” race/ethnicity were excluded. The final sample contains 5,270 observations and 4 variables. The aggregate dataset has 12 observations and includes the number, mean, and SD for each combination of the levels of the predictors. A technical description of the method, the SAS (SAS Institute, Cary, NC) program to analyze the data and the aggregate, and individual-level datasets are in the eAppendix (http:// links.lww.com/EDE/A830; URL). The point estimates of the regression model based on the aggregate
data are identical to those based on the microdata (Table). The SEs based on aggregate regression do not differ by more than 1% from those based on the individual-level regression. Including covariates, even product terms, improved the estimation. The main limitation is that continuous covariates cannot be accommodated. However, continuous covariates can be collapsed into categories. This method has several potential applications. It can be used to perform meta-analyses and pooled analyses of multicenter, international, or comparative studies. Perhaps more important, it can be used to analyze summary information from datasets that otherwise cannot be accessed due to data confidentiality concerns, at least until open data initiatives and validated mechanisms to share microdata are in place.5,6 The inclusion of this method in our analytic toolkit challenges us to revisit the practice of categorizing continuous endpoints. Most data repository reporting systems make summary statistics publicly available in the form of counts and proportions, even when measures are originally of continuous nature, for variables such as birthweight, body mass index, and lab tests. Categorization of continuous outcomes has some shortcomings,7,8 such as assuming risk homogeneity within groups, multiple testing, and loss of power. Categorizing continuous data also creates dissent regarding the choice of the categories and appropriateness of the cutoff points, which hampers comparison of results across studies. Using means and standard deviations across groups may avoid such shortcomings, if properly analyzed. Rahim Moineddin Department of Family and Community Medicine University of Toronto Toronto, Ontario, Canada
Marcelo Luis Urquia St. Michael’s Hospital Toronto, Ontario, Canada [email protected] www.epidem.com | 929
To the Editor: cute disseminated encephalomyelitis is an inflammatory demyelinating disease of the central nervous system that may occur after viral illnesses or vaccinations.1 No information is available about the geographic distribution of acute disseminated encephalomyelitis, although several analyses (both clinical data2 and mouse–model response to ultraviolet radiation)3 suggest a correlation with latitude. The small size and specific regional setting of acute disseminated encephalomyelitis cohorts has hampered deep analysis of this relationship. However, the association of latitude has been examined for multiple sclerosis, the most common central nervous system demyelinating disease.4 The risk of multiple sclerosis has been reported to increase from south to north, and several factors (including ultraviolet light exposure, genetic background, diagnostic accuracy, and ascertainment probability) can explain such distribution.4 The aim of this study was to evaluate whether acute disseminated encephalomyelitis has a demonstrable geographic distribution. As there is no indication of substantial changes in the incidence of this disease over time, we conducted this analysis considering the latest available information on the number of hospital admissions in the USA (2010 and 2011 National Inpatient Sample database) and in the UK (2012–2013 England’s Hospital Episode Statistics database). We identified acute disseminated encephalomyelitis cases by searching the databases for any listed diagnosis classified with the appropriate ICD-9 and ICD-10 codes for this condition (323.61 and G040, respectively). States in the
A
Copyright © 2014 by Lippincott Williams & Wilkins ISSN: 1044-3983/14/2506-0928 DOI: 10.1097/EDE.0000000000000176
928 | www.epidem.com
USA reporting 10 or fewer cases were excluded from analysis. Disease incidence was calculated using the population of each state as the denominator. We calculated the Pearson correlation coefficients to examine the number of hospital admissions for acute disseminated encephalomyelitis in the USA and UK per year in relation to latitude. We found that disease incidence increased linearly with increased distance from the equator (r = 0.42; 95% confidence interval = 0.029–0.70) (Figure). To confirm these results, we compared the incidence rates of acute disseminated encephalomyelitis among the various Census Bureau-designated regions in the USA. We found that the estimated incidence rates were considerably higher in the North-West of USA (latitude 33°–49° North; 0.38 cases per 10,000 inhabitants) than in South (latitude 26°–39° North; 0.22 cases per 100,000). Although these results could potentially be attributable to differences in population ethnicity, we did not find any correlation between race distribution and acute disseminated encephalomyelitis incidence, based on USA census bureau data. This geographical distribution of acute disseminated encephalomyelitis is consistent with the previous analyses on the incidence of multiple sclerosis, which increases with the increasing distance from the equator in both the northern and the southern hemispheres.5 Several factors may explain the geographic pattern we reported here for acute disseminated encephalomyelitis, which cannot be attributed fully to diagnostic issues. As the most common antecedent factor leading to acute disseminated encephalomyelitis is infection,6 regional differences in the prevalence of infectious diseases and socioeconomic status may play a role in this distribution. A recent analysis highlighted the possible role of specific human leukocyte antigen alleles as risk factors for acute disseminated encephalomyelitis, which may account for a different distribution in the USA and
55
50
45
Latitude
Geoepidemiology of Acute Disseminated Encephalomyelitis
40
35
30 0.2
0.3
0.4
0.5
0.6
Incidence
FIGURE. Association of latitude (degrees North) with incidence of acute disseminated encephalomyelitis (per 100,000 inhabitants).
other countries.7 As acute disseminated encephalomyelitis and multiple sclerosis are similar in terms of clinical and pathological features, it is reasonable to suppose that factors such as ultraviolet light exposure and vitamin D levels may be common to these pathologies.3 Despite the known limitations of discharge databases,8 we provide here the first evidence of a common geographical distribution between acute disseminated encephalomyelitis and multiple sclerosis. Further analyses are required to evaluate the incidence of acute disseminated encephalomyelitis at other latitudes and to evaluate the impact of other factors, including genetic predisposition. Paolo Pellegrino Sonia Radice Emilio Clementi Unit of Clinical Pharmacology Department of Biomedical and Clinical Sciences University Hospital Luigi Sacco Università di Milano, Milan, Italy [email protected]
References 1. Pellegrino P, Carnovale C, Perrone V, et al. Acute disseminated encephalomyelitis
Epidemiology • Volume 25, Number 6, November 2014
Epidemiology • Volume 25, Number 6, November 2014 Letters
onset: evaluation based on vaccine adverse events reporting systems. PLoS One. 2013;8:e77766. 2. Torisu H, Kira R, Ishizaki Y, et al. Clinical study of childhood acute disseminated encephalomyelitis, multiple sclerosis, and acute transverse myelitis in Fukuoka Prefecture, Japan. Brain Dev. 2010;32:454–462. 3. Becklund BR, Severson KS, Vang SV, DeLuca HF. UV radiation suppresses experimental autoimmune encephalomyelitis independent of vitamin D production. Proc Natl Acad Sci USA. 2010;107:6418–6423. 4. Goodin DS. The epidemiology of multiple sclerosis: insights to disease pathogenesis. Handb Clin Neurol. 2014;122:231–266. 5. Wender M. Acute disseminated encephalomyelitis (ADEM). J Neuroimmunol. 2011;231: 92–99. 6. Tenembaum SN. Acute disseminated encephalomyelitis. Handb Clin Neurol. 2013;112:1253– 1262. 7. Alves-Leon SV, Veluttini-Pimentel ML, Gouveia ME, et al. Acute disseminated encephalomyelitis: clinical features, HLA DRB1*1501, HLA DRB1*1503, HLA DQA1*0102, HLA DQB1*0602, and HLA DPA1*0301 allelic association study. Arq Neuropsiquiatr. 2009;67:643–651. 8. Pellegrino P, Carnovale C, Perrone V, et al. Epi demiological analysis on two decades of hospitalisations for meningitis in the United States. Eur J Clin Microbiol Infect Dis. 2014;33:1519–1524.
Regression Analysis of Aggregate Continuous Data To the Editor: ndividual-level statistical analyses are paramount for obtaining accurate estimates of an exposure-outcome relation in population groups. However, data privacy and confidentiality concerns have led to a conflict between ethico-legal restrictions to access microdata and scientific accuracy achievable through analyses of individual-level data.1,2 Disclosure of results is also restricted by rules protecting subjects’ confidentiality, such as small cell data suppression, rounding, and collapsing.3
I
Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www. epidem.com). This content is not peerreviewed or copy-edited; it is the sole responsibility of the author. Copyright © 2014 by Lippincott Williams & Wilkins ISSN: 1044-3983/14/XXXXX-0000 DOI: 10.1097/EDE.0000000000000172
© 2014 Lippincott Williams & Wilkins
Data aggregation is a major way to share data publicly while protecting the confidentiality of the subjects, but regression models for summary continuous data are lacking. We have developed a method to fill this gap. To perform linear regression analyses on a continuous aggregate outcome we need only 2 parameters: the mean and the standard deviation (SD), within strata of a set of categorical predictors. The frequencies (counts) of the combinations of the predictors can be used as weights. The SD of the raw data for all combinations of the predictors can be used to calculate the pooled variance that subsequently can be used to correct the standard errors (SE) of the estimated parameters using aggregate data. To illustrate the application of the method, we focused on the association between receipt of WIC (The Special Supplemental Nutrition Program for Women, Infants, and Children) food for the mother during this pregnancy (http://www.fns.usda.gov/ wic/about-wic) and gestational weight gain. We used a subset of the 2012 Natality Public Use Births File of the National Center for Health Statistics (NCHS).4 The subset is drawn from the 2003 revision of the U.S. Standard Certificate of Live Birth and includes singleton term pregnancies (37–41 weeks gestation) of underweight (body mass index is less than 18.5) women aged 20 to 25 years, who did not complete high school, and were Medicaid recipients. Records with unknown prenatal care initiation information and “other” race/ethnicity were excluded. The final sample contains 5,270 observations and 4 variables. The aggregate dataset has 12 observations and includes the number, mean, and SD for each combination of the levels of the predictors. A technical description of the method, the SAS (SAS Institute, Cary, NC) program to analyze the data and the aggregate, and individual-level datasets are in the eAppendix (http:// links.lww.com/EDE/A830; URL). The point estimates of the regression model based on the aggregate
data are identical to those based on the microdata (Table). The SEs based on aggregate regression do not differ by more than 1% from those based on the individual-level regression. Including covariates, even product terms, improved the estimation. The main limitation is that continuous covariates cannot be accommodated. However, continuous covariates can be collapsed into categories. This method has several potential applications. It can be used to perform meta-analyses and pooled analyses of multicenter, international, or comparative studies. Perhaps more important, it can be used to analyze summary information from datasets that otherwise cannot be accessed due to data confidentiality concerns, at least until open data initiatives and validated mechanisms to share microdata are in place.5,6 The inclusion of this method in our analytic toolkit challenges us to revisit the practice of categorizing continuous endpoints. Most data repository reporting systems make summary statistics publicly available in the form of counts and proportions, even when measures are originally of continuous nature, for variables such as birthweight, body mass index, and lab tests. Categorization of continuous outcomes has some shortcomings,7,8 such as assuming risk homogeneity within groups, multiple testing, and loss of power. Categorizing continuous data also creates dissent regarding the choice of the categories and appropriateness of the cutoff points, which hampers comparison of results across studies. Using means and standard deviations across groups may avoid such shortcomings, if properly analyzed. Rahim Moineddin Department of Family and Community Medicine University of Toronto Toronto, Ontario, Canada
Marcelo Luis Urquia St. Michael’s Hospital Toronto, Ontario, Canada [email protected] www.epidem.com | 929
