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Figure 1.
Cluster analysis results for patients with pityriasis rosea (A) and psoriasis (B) in Kuwait. A, Results in a series of 154 children with pityriasis rosea. This single-cluster model has a significant value (P = .005) compared with the nonclustering model. A cluster of 35 cases is seen as a depression in the center. B, Results in a series of 197 children with psoriasis. No significant cluster was detected. The uniform distribution regression model was selected using Akaike information criteria. No depression or elevation was noted.

Cluster analysis results for patients with pityriasis rosea (A) and psoriasis (B) in Kuwait. A, Results in a series of 154 children with pityriasis rosea. This single-cluster model has a significant value (P = .005) compared with the nonclustering model. A cluster of 35 cases is seen as a depression in the center. B, Results in a series of 197 children with psoriasis. No significant cluster was detected. The uniform distribution regression model was selected using Akaike information criteria. No depression or elevation was noted.

Figure 2.
Cluster analysis results for patients with pityriasis rosea (A) and psoriasis (B) in Diyarbakir, Turkey. A, Results in a series of 286 patients with pityriasis rosea. An insignificant positive cluster of 15 cases (P = .14) is seen as a depression, and a subsequent significant negative cluster of 152 days (P<.001) is seen as an elevation. B, Results in a series of 169 children with psoriasis. The uniform distribution regression model was selected using Akaike information criteria. No depression or elevation was noted.

Cluster analysis results for patients with pityriasis rosea (A) and psoriasis (B) in Diyarbakir, Turkey. A, Results in a series of 286 patients with pityriasis rosea. An insignificant positive cluster of 15 cases (P = .14) is seen as a depression, and a subsequent significant negative cluster of 152 days (P<.001) is seen as an elevation. B, Results in a series of 169 children with psoriasis. The uniform distribution regression model was selected using Akaike information criteria. No depression or elevation was noted.

Table. 
Cluster Analysis Results in 5 Series of Patients
Cluster Analysis Results in 5 Series of Patients
1.
Drago  FRanieri  EMalaguti  FLosi  ERebora  A Human herpesvirus 7 in pityriasis rosea Lancet 1997;3491367- 1368
PubMedArticle
2.
Drago  FRanieri  EMalaguti  F  et al.  Human herpesvirus 7 in patients with pityriasis rosea: electron microscopy investigations and polymerase chain reaction in mononuclear cells, plasma and skin Dermatology 1997;195374- 378
PubMedArticle
3.
Watanabe  TSugaya  MNakamura  KTamaki  K Human herpesvirus 7 and pityriasis rosea J Invest Dermatol 1999;113288- 289
PubMedArticle
4.
Kempf  WAdams  VKleinhans  M  et al.  Pityriasis rosea is not associated with human herpesvirus 7 Arch Dermatol 1999;1351070- 1072
PubMedArticle
5.
Yoshida  M Detection of human herpesvirus 7 in patients with pityriasis rosea and healthy individuals Dermatology 1999;199197- 198
PubMedArticle
6.
Kosuge  HTanaka-Taya  KMiyoshi  H  et al.  Epidemiological study of human herpesvirus-6 and human herpesvirus-7 in pityriasis rosea Br J Dermatol 2000;143795- 879
PubMedArticle
7.
Offidani  APritelli  ESimonetti  O  et al.  Pityriasis rosea associated with herpesvirus 7 DNA J Eur Acad Dermatol Venereol 2000;14313- 314
PubMedArticle
8.
Chuh  AAChiu  SSPeiris  JS Human herpesvirus 6 and 7 DNA in peripheral blood leucocytes and plasma in patients with pityriasis rosea by polymerase chain reaction: a prospective case control study Acta Derm Venereol 2001;81289- 290
PubMedArticle
9.
Drago  FMalaguti  FRanieri  E  et al.  Human herpesvirus–like particles in pityriasis rosea lesions: an electron microscopy study J Cutan Pathol 2002;29359- 361
PubMedArticle
10.
Watanabe  TKawamura  TJacob  SE  et al.  Pityriasis rosea is associated with systemic active infection with both human herpesvirus-7 and human herpesvirus-6 J Invest Dermatol 2002;119793- 797
PubMedArticle
11.
Chuh  AAT The association of pityriasis rosea with cytomegalovirus, Epstein-Barr virus and parvovirus B19 infections: a prospective case control study by polymerase chain reaction and serology Eur J Dermatol 2003;1325- 28
PubMed
12.
Chuh  AAChan  HH Prospective case-control study of chlamydia, legionella and mycoplasma infections in patients with pityriasis rosea Eur J Dermatol 2002;12170- 173
PubMed
13.
Alexander  FEBoyle  PCarli  PM  et al.  Spatial temporal patterns in childhood leukaemia: further evidence for an infectious origin: EUROCLUS project Br J Cancer 1998;77812- 817
PubMedArticle
14.
Nakamura  YYanagawa  IKawasaki  T Temporal and geographical clustering of Kawasaki disease in Japan Prog Clin Biol Res 1987;25019- 32
PubMed
15.
Messenger  AGKnox  EGSummerly  RMuston  HLIlderton  E Case clustering in pityriasis rosea: support for role of an infective agent BMJ (Clin Res Ed) 1982;284371- 373
PubMedArticle
16.
Chuh  AATLee  AMolinari  N Case clustering in pityriasis rosea: a multicenter epidemiologic study in primary care settings in Hong Kong Arch Dermatol 2003;139489- 493
PubMedArticle
17.
Molinari  NBonaldi  CDaures  JP Multiple temporal cluster detection Biometrics 2001;57577- 583
PubMedArticle
18.
Knox  G Secular pattern of congenital oesophageal atresia Br J Prev Soc Med 1959;13222- 226
PubMed
19.
Berthier  FBoulay  FMolinari  N  et al.  Rôle de la température sur l’existence de macro agrégats hivernaux d’hémoptysies Rev Mal Respir 2000;171S125
20.
Akaike  HPetrov  BNedCsaki  Fed Information theory and an extension of the maximum likelihood principle Second International Symposium on Information Theory. Budapest, Hungary Akademiai Kiado1973;267- 281
21.
Vollum  DI Pityriasis rosea in the African Trans St Johns Hosp Dermatol Soc 1973;59269- 271
PubMed
22.
Jacyk  WK Pityriasis rosea in Nigerians Int J Dermatol 1980;19397- 399
PubMedArticle
23.
Chuang  TYIlstrup  DMPerry  HOKurland  LT Pityriasis rosea in Rochester, Minnesota, 1969 to 1978 J Am Acad Dermatol 1982;780- 89
PubMedArticle
24.
de Souza Sittart  JATayah  MSoares  Z Incidence pityriasis rosea of Gibert in the Dermatology Service of the Hospital do Servidor Publico in the state of São Paulo [in Portuguese] Med Cutan Ibero Lat Am 1984;12336- 338
PubMed
25.
Ahmed  MA Pityriasis rosea in the Sudan Int J Dermatol 1986;25184- 185
PubMedArticle
26.
Olumide  Y Pityriasis rosea in Lagos Int J Dermatol 1987;26234- 236
PubMedArticle
27.
Cheong  WKWong  KS An epidemiological study of pityriasis rosea in Middle Road Hospital Singapore Med J 1989;3060- 62
PubMed
28.
Harman  MAytekin  SAkdeniz  SInaloz  HS An epidemiological study of pityriasis rosea in the Eastern Anatolia Eur J Epidemiol 1998;14495- 497
PubMedArticle
29.
Nanda  AAl-Hasawi  FAlsaleh  QA A prospective survey of pediatric dermatology clinic patients in Kuwait: an analysis of 10,000 cases Pediatr Dermatol 1999;166- 11
PubMedArticle
30.
Tay  YKGoh  CL One-year review of pityriasis rosea at the National Skin Centre, Singapore Ann Acad Med Singapore 1999;28829- 831
PubMed
31.
Traore  AKorsaga-Some  NNiamba  P  et al.  Pityriasis rosea in secondary schools in Ouagadougou, Burkina Faso Ann Dermatol Venereol 2001;128605- 609
PubMed
32.
Ederer  FMyers  EMantel  N A statistical problem in space and time: do leukemia cases come in clusters? Biometrics 1964;20626- 638Article
33.
Naus  JI The distribution of the size of the maximum cluster of points on a line J Am Stat Assoc 1965;60532- 538Article
34.
Kulldorff  MNagarwalla  N Spatial disease clusters: detection and inference Stat Med 1995;14799- 810
PubMedArticle
35.
Larsen  RJHolmes  CLHeath  CW A statistical test for measuring unimodal clustering Biometrics 1973;29301- 309
PubMedArticle
36.
Bosc  F Is pityriasis rosea infectious [case report]? Lancet 1981;1662
PubMedArticle
37.
Davis  SWV Case clustering in pityriasis rosea: support for role of an infective agent BMJ (Clin Res Ed) 1982;2841478
38.
Halkier-Sorensen  L Recurrent pityriasis rosea: new episodes every year for five years: a case report Acta Derm Venereol 1990;70179- 180
PubMed
39.
Bjornberg  AHellgren  L Pityriasis rosea: a statistical, clinical and laboratory investigation of 826 patients and matched healthy controls Acta Derm Venereol 1962;42(suppl 50)1- 68
PubMed
40.
Cameron  DJones  IG Case clustering in pityriasis rosea: support for role of an infective agent BMJ (Clin Res Ed) 1982;2841478Article
41.
Yorke  JANathanson  NPianigiani  GMartin  J Seasonality and the requirements for perpetuation and eradication of viruses in populations Am J Epidemiol 1979;109103- 123
PubMed
Evidence-Based Dermatology: Study
June 2005

Temporal Case Clustering in Pityriasis RoseaA Regression Analysis on 1379 Patients in Minnesota, Kuwait, and Diyarbakir, Turkey

Author Affiliations
 

MICHAEL E.BIGBYMDDAMIANOABENIMD, MPHROSAMARIACORONADSc, MDURBÀ GONZÁLEZMD, PhDARBAR A.QURESHIDrPHHYWEL WILLIAMSMSc, PhD, FRCP

Arch Dermatol. 2005;141(6):767-771. doi:10.1001/archderm.141.6.767
Abstract

Objective  To detect temporal case clustering in patients with pityriasis rosea in different geographic locations.

Design  Regression analysis of dates on which 1379 patients were diagnosed as having pityriasis rosea in 3 different geographic locations. The control data consisted of dates of diagnosis of patients with psoriasis in the same settings.

Setting  Dermatology clinics in Kuwait, Minnesota, and Diyarbakir, Turkey.

Patients  Patients with pityriasis rosea and psoriasis seeking care in the clinics.

Results  Three significant positive clusters (P = .005, P =.001, and P =.01, respectively) and 1 significant negative cluster (P<.001) were detected in these series of patients. No cluster was detected in 2 corresponding series of patients with psoriasis in Kuwait and Turkey.

Conclusion  Temporal case clustering exists in pityriasis rosea.

The cause of pityriasis rosea (PR) is unknown. Studies on the association of PR with human herpesvirus 7 infection110 have yielded conflicting results. Recent studies have reported no evidence of PR associated with cytomegalovirus, Epstein-Barr virus, parvovirus B19,11Chlamydia pneumoniae, Chlamydia trachomatis, Legionella long-beachae, Legionella micdadei, Legionella pneumophila, and Mycoplasma pneumoniae12 infections.

Determining whether the epidemiological evidence supports an infectious origin is important, because it supports the need for a further laborious search for the pathogen. Cluster analysis is one such approach and has been applied in such diseases as childhood leukemia13 and Kawasaki disease.14 In 1982, Messenger et al15 reported significant spatial-temporal clustering among female patients with PR in primary care settings, but not among male patients. They also reported a temporal cluster of 16 cases within a 28-day period.

Two of us (A.A.T.C. and N.M) previously reported the findings of a multicenter retrospective study in patients with PR in Hong Kong.16 Atopic dermatitis and scabies were analyzed concomitantly as control conditions, the former as a negative control (noninfectious, temporal clustering not expected to be detectable) and the latter as a positive control (infectious, temporal clustering expected to be detectable). We detected 3 statistically significant clusters (P = .03) among our patients with PR, no significant cluster among our patients with atopic dermatitis, and 1 significant cluster (P = .025) among our patients with scabies.

If our hypothesis is true, it would be expected that temporal clusters would also be detectable among patients with PR in other time frames and geographic locations. We performed a systematic analysis of 3 previously reported series involving a total of 1379 patients with PR in 3 separate locations. Our aim was to detect temporal case clustering in these series of patients. Two corresponding series consisting of a total of 366 patients with psoriasis were also analyzed as controls. Psoriasis was selected as a control condition because it is not an infectious condition, and, to the best of our knowledge, there have been no reports of temporal case clustering of psoriasis.

METHODS

One of us (N.M.) developed a method based on a regression model to detect multiple temporal clusters.17 This method does not impose to divide the period. It detects time windows with excess events and is effective with multiple clusters. For any position of the window, the entire period of observation is scanned continuously. Positive as well as negative (periods with significantly fewer events) clusters can be detected. The existence of 1 or more clusters is determined by bootstrapped simulations that allow the robustness of the test to be increased. The validity of this method has been established by applying it to the classic Knox data set18 and to 62 spontaneous hemoptysis admissions at Nice Hospital in France.19

Once the cluster bounds were computed for each model, the model with the smallest Akaike information criteria20 value was selected to determine the number of clusters. This means that we selected the model with the smallest expected loss of information, or the best approximation to the “true” model. To avoid sample effects and to obtain a P value, the criteria were computed again on 1000 bootstrapped samples. The P value corresponded to the percentage of bootstrapped samples for which the cluster model was selected with the Akaike information criteria against the nonclustering model.

One of us (A.A.T.C.) searched MEDLINE using the keywords pityriasis rosea and retrieved reports of all epidemiological studies on PR published between January 1, 1972, and December 31, 2001 (30 years). Then, letters were sent by post to the first authors of these studies or to the dermatology departments where these studies were conducted if the addresses of the first authors were not available. All authors were asked to retrieve the dates on which the diagnoses of PR were made for the series of patients reported. Also, the authors were asked to provide the dates on which a series of patients were first diagnosed as having psoriasis in the same setting within the same period to serve as controls. Approvals from review boards were sought where local regulations apply.

One of us (N.M.) then analyzed the extent of temporal clustering in each series of patients by a method based on a regression model.17 Each data set was first transformed to produce values corresponding to the time between successive cases. These values were estimated using a constant under the nonclustering, or random, hypothesis. On the contrary, a piecewise constant model improves the fitting. This method was applied to obtain several models with different numbers of clusters for each series of patients.

RESULTS

Thirteen epidemiological studies5,15,2131 on PR were identified. One study5 was a seroepidemiological study aimed at detecting human herpesvirus 6 and 7 infections by serologic analysis and by polymerase chain reaction analysis. Another31 was a cross-sectional study based on 1-day surveys. Letters were sent to the first authors or the dermatology departments regarding the other 11 studies. Replies were received from the authors of 4 studies.23,24,28,29 Investigators in São Paulo, Brazil, were unable to send us the complete data from their study, the findings of which were reported in 1984.24 Investigators in Kuwait,29 Minnesota,23 and Turkey28 were able to contribute data.

Data on a total of 1379 patients with PR and 366 patients with psoriasis were available for analysis. One of us (A.N.) retrieved data on 154 patients (all children younger than 12 years) with PR in Kuwait from 1992 to 1998 and on 197 patients (all children younger than 12 years) whose psoriasis was diagnosed in the same setting from 1993 to 1994 for the control data. Two of us (M.H. and S.A.) retrieved data on 286 patients with PR in Diyarbakir, Turkey, from 1993 to 1995 and on 169 patients whose psoriasis was diagnosed in the same setting in 1995 for the control data. One of us (G.S.) sought approval from the internal review board and retrieved data on 939 patients with PR in Rochester, Minn, during a period of 10 years (1969-1978). Owing to the long lapse of time, a corresponding set of data on patients whose psoriasis was diagnosed in the same period was not available for analysis.

The patients and controls from Kuwait and Diyarbakir, Turkey, were seen by dermatologists. Most were referred patients. Of the 939 patients with PR from Minnesota, 57% were seen by dermatologists, while 43% were seen by internal medicine physicians or general practitioners. Most of the patients seen by dermatologists were referred, while a proportion of the patients seen by internal medicine physicians or general practitioners were not. For all series of patients, no active effort was made to recruit the patients to minimize referral bias.

The results of our analysis are summarized in the Table. In the series of patients with PR from Kuwait, 1 significant temporal cluster was detected. Figure 1A shows the cluster analysis results and the interval between successive cases. A short mean time between successive events indicates a cluster. The single-cluster statistical model has a significant value (P = .005) compared with the nonclustering statistical model. The former model detects a cluster of 35 cases between September 19, 1994, and April 30, 1995. The significant cluster is seen as a depression in the center of the figure.

For the corresponding set of data on patients with psoriasis from Kuwait, no significant cluster was detected. The uniform distribution (nonclustering) regression model (Figure 1B) was selected by Akaike information criteria, ie, best approximation to the true model.

In the series of patients with PR from Minnesota, 2 significant clusters were identified. A cluster of 98 cases was detected from October 17, 1972, to May 24, 1973 (P = .001). Another cluster of 59 cases was detected from October 28, 1974, to April 16, 1975 (P = .01). The cluster analysis result is not shown because, with a total of 939 events, the figure is congested and has low readability.

In the series of patients with PR from Turkey, a significant negative cluster (period with significantly fewer events) of 152 days was detected between May 12, 1994, and October 10, 1994 (P<.001). A positive cluster of 15 cases was also detected between November 25, 1993, and December 3, 1993. However, this cluster is statistically insignificant (P = .14). Figure 2A shows the cluster analysis results for the series of patients from Turkey. The positive cluster is seen as a depression, and the subsequent negative cluster is seen as an elevation.

For the corresponding set of data of patients with psoriasis from Turkey, no significant cluster was detected. The nonclustering regression model (Figure 2B) was selected as the best approximation to the true model.

COMMENT

There are many limitations in clustering methodology. The choice of an appropriate analysis method is difficult. The cell-occupancy approach32 needs to divide the period into disjoint subintervals arbitrarily. For the scan test,33 an arbitrary scanning window size has to be defined. To obviate the need for such a definition, a scan test with a variable window size34 can be applied. However, this test only considers clusters with a fixed minimal number of events a priori, 5 for example. The rank-order procedure35 is sensitive only to unimodal clustering and cannot distinguish between multiple clusters and randomness. Another limitation is that factors other than an infectious origin might lead to temporal clustering.

Presented as case reports, the phenomenon of temporal clustering in PR is not novel. Bosc36 described 2 sisters with the successive onset of PR 61 days apart. Davis37 described a 60-year-old farmer with PR whose 30-year-old daughter developed PR 3 months later. Halkier-Sorensen38 described a 39-year-old woman whose PR recurred annually for 5 years. Her husband had a severe episode of PR 6 years earlier. There have been other reports of 2 or more cases of PR occurring in the same family or intimate environment.39 However, these cases may be coincidental.

In 1982, Messenger et al15 reported the first study on case clustering in PR. Their findings have been criticized because the degree of clustering that they found might not be sufficient to support an infectious hypothesis.40 Control data were not analyzed concomitantly. Moreover, the rationale for their choosing a window size of 28 days was not stated. Clusters can be shorter or considerably longer than 28 days, and the cases in the aforementioned reports36,37 were separated by 2 to 3 months. The prospective nature of their study might also have led to enthusiasm of participating general practitioners in reporting the cluster.15 Another prospective study in PR also found reporting bias.31 In this regard, retrospective studies may paradoxically be superior because such reporting bias is absent.

Using a regression model and applying Akaike information criteria for selecting models, we undertook the first systematic attempt in proving that case clustering does occur in PR in different time frames and in separate geographic locations. We detected 3 statistically significant temporal clusters and 1 significant negative cluster in our 3 series of patients. In contrast to the study by Messenger et al,15 we adopted a retrospective approach to eliminate reporting bias that has been found in prospective studies.31 Our method was not based on an arbitrarily predetermined window size. Entire periods of observation were scanned continuously. We also analyzed corresponding series of patients whose psoriasis was diagnosed in the same settings and in the same time frames as controls. Our results should therefore provide adequate evidence to refute a random or nonclustering hypothesis for the occurrence of PR and establish the presence of temporal clustering in PR. We believe that such temporal clustering supports an infectious origin for PR and that further microbiological studies to search for the pathogen are indicated.

The underlying factors for the significant negative cluster found in the series of patients in Turkey in 1994 are obscure. Two of us (M.H. and S.A.) reviewed the climate and administrative conditions by means of qualitative interviews with the department staff and by qualitative review of the departmental records. We were unable to pinpoint any remarkable alteration in the climate of the region or in the organization or referral pattern in the dermatology department in 1994. We would have to assume that the negative cluster corresponded to a natural trough in the epidemiological pattern of this condition. It is well known that natural troughs do exist for many viral infections.41

Apart from the limitations mentioned in the introduction, another limitation of this study is that data from only 3 geographic locations were available for analysis. We failed to recruit more investigators into the project. Moreover, only psoriasis was used as a control condition. We also failed to retrieve data for other conditions as control data. Owing to the long lapse of time, a corresponding set of data on controls from Minnesota was not available for analysis.

A possible source of bias in our study is that most of our patients with PR were referred. We admit that a referral bias could be a confounding factor for the clustering phenomenon. However, as no active recruitment effort was made, referral bias should be minimal. Moreover, the durations of our clustering were more than 5 and 7 months for patients in Minnesota, more than 7 months for patients in Kuwait, and more than 4 months for the negative cluster of patients in Diyarbakir, Turkey. With such long durations of clusters, referral bias might have a small effect on our analysis results. Our 2 series of control patients were also referred, and no significant clustering was detected.

CONCLUSIONS

Using a regression model and applying Akaike information criteria for selecting models, we detected 3 statistically significant temporal clusters and 1 significant negative cluster in our 3 series of 1379 patients with PR from 3 separate geographic locations. No cluster was found in 2 series of 366 patients with psoriasis diagnosed in the same settings. We conclude that cases of PR do not occur at random. We believe that the presence of temporal case clustering supports an infective origin for PR and that further studies to search for the pathogen are indicated.

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Article Information

Correspondence: Antonio A. T. Chuh, MD(HK), MRCP(UK), FRCP(Ire), MRCPCH, Bonham Surgery, Shop B5, Ning Yeung Terrace, 78 Bonham Rd, Ground Floor, Hong Kong (achuh@iohk.com).

Accepted for Publication: January 27, 2005.

Acknowledgment: We thank Albert Lee, MD, and Jin Ling Tang, PhD, Chinese University of Hong Kong, for their valuable comments on the manuscript for this article. Part of this article is based on work submitted by the first author (A.A.T.C.) to the University of Hong Kong for the award of the degree of doctor of medicine.

Financial Disclosure: None.

References
1.
Drago  FRanieri  EMalaguti  FLosi  ERebora  A Human herpesvirus 7 in pityriasis rosea Lancet 1997;3491367- 1368
PubMedArticle
2.
Drago  FRanieri  EMalaguti  F  et al.  Human herpesvirus 7 in patients with pityriasis rosea: electron microscopy investigations and polymerase chain reaction in mononuclear cells, plasma and skin Dermatology 1997;195374- 378
PubMedArticle
3.
Watanabe  TSugaya  MNakamura  KTamaki  K Human herpesvirus 7 and pityriasis rosea J Invest Dermatol 1999;113288- 289
PubMedArticle
4.
Kempf  WAdams  VKleinhans  M  et al.  Pityriasis rosea is not associated with human herpesvirus 7 Arch Dermatol 1999;1351070- 1072
PubMedArticle
5.
Yoshida  M Detection of human herpesvirus 7 in patients with pityriasis rosea and healthy individuals Dermatology 1999;199197- 198
PubMedArticle
6.
Kosuge  HTanaka-Taya  KMiyoshi  H  et al.  Epidemiological study of human herpesvirus-6 and human herpesvirus-7 in pityriasis rosea Br J Dermatol 2000;143795- 879
PubMedArticle
7.
Offidani  APritelli  ESimonetti  O  et al.  Pityriasis rosea associated with herpesvirus 7 DNA J Eur Acad Dermatol Venereol 2000;14313- 314
PubMedArticle
8.
Chuh  AAChiu  SSPeiris  JS Human herpesvirus 6 and 7 DNA in peripheral blood leucocytes and plasma in patients with pityriasis rosea by polymerase chain reaction: a prospective case control study Acta Derm Venereol 2001;81289- 290
PubMedArticle
9.
Drago  FMalaguti  FRanieri  E  et al.  Human herpesvirus–like particles in pityriasis rosea lesions: an electron microscopy study J Cutan Pathol 2002;29359- 361
PubMedArticle
10.
Watanabe  TKawamura  TJacob  SE  et al.  Pityriasis rosea is associated with systemic active infection with both human herpesvirus-7 and human herpesvirus-6 J Invest Dermatol 2002;119793- 797
PubMedArticle
11.
Chuh  AAT The association of pityriasis rosea with cytomegalovirus, Epstein-Barr virus and parvovirus B19 infections: a prospective case control study by polymerase chain reaction and serology Eur J Dermatol 2003;1325- 28
PubMed
12.
Chuh  AAChan  HH Prospective case-control study of chlamydia, legionella and mycoplasma infections in patients with pityriasis rosea Eur J Dermatol 2002;12170- 173
PubMed
13.
Alexander  FEBoyle  PCarli  PM  et al.  Spatial temporal patterns in childhood leukaemia: further evidence for an infectious origin: EUROCLUS project Br J Cancer 1998;77812- 817
PubMedArticle
14.
Nakamura  YYanagawa  IKawasaki  T Temporal and geographical clustering of Kawasaki disease in Japan Prog Clin Biol Res 1987;25019- 32
PubMed
15.
Messenger  AGKnox  EGSummerly  RMuston  HLIlderton  E Case clustering in pityriasis rosea: support for role of an infective agent BMJ (Clin Res Ed) 1982;284371- 373
PubMedArticle
16.
Chuh  AATLee  AMolinari  N Case clustering in pityriasis rosea: a multicenter epidemiologic study in primary care settings in Hong Kong Arch Dermatol 2003;139489- 493
PubMedArticle
17.
Molinari  NBonaldi  CDaures  JP Multiple temporal cluster detection Biometrics 2001;57577- 583
PubMedArticle
18.
Knox  G Secular pattern of congenital oesophageal atresia Br J Prev Soc Med 1959;13222- 226
PubMed
19.
Berthier  FBoulay  FMolinari  N  et al.  Rôle de la température sur l’existence de macro agrégats hivernaux d’hémoptysies Rev Mal Respir 2000;171S125
20.
Akaike  HPetrov  BNedCsaki  Fed Information theory and an extension of the maximum likelihood principle Second International Symposium on Information Theory. Budapest, Hungary Akademiai Kiado1973;267- 281
21.
Vollum  DI Pityriasis rosea in the African Trans St Johns Hosp Dermatol Soc 1973;59269- 271
PubMed
22.
Jacyk  WK Pityriasis rosea in Nigerians Int J Dermatol 1980;19397- 399
PubMedArticle
23.
Chuang  TYIlstrup  DMPerry  HOKurland  LT Pityriasis rosea in Rochester, Minnesota, 1969 to 1978 J Am Acad Dermatol 1982;780- 89
PubMedArticle
24.
de Souza Sittart  JATayah  MSoares  Z Incidence pityriasis rosea of Gibert in the Dermatology Service of the Hospital do Servidor Publico in the state of São Paulo [in Portuguese] Med Cutan Ibero Lat Am 1984;12336- 338
PubMed
25.
Ahmed  MA Pityriasis rosea in the Sudan Int J Dermatol 1986;25184- 185
PubMedArticle
26.
Olumide  Y Pityriasis rosea in Lagos Int J Dermatol 1987;26234- 236
PubMedArticle
27.
Cheong  WKWong  KS An epidemiological study of pityriasis rosea in Middle Road Hospital Singapore Med J 1989;3060- 62
PubMed
28.
Harman  MAytekin  SAkdeniz  SInaloz  HS An epidemiological study of pityriasis rosea in the Eastern Anatolia Eur J Epidemiol 1998;14495- 497
PubMedArticle
29.
Nanda  AAl-Hasawi  FAlsaleh  QA A prospective survey of pediatric dermatology clinic patients in Kuwait: an analysis of 10,000 cases Pediatr Dermatol 1999;166- 11
PubMedArticle
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