Volume 8 Supplement 1
Space-time clustering analyses of type 1 diabetes in children from north-east England: support for an infectious aetiology?
© McNally et al; licensee BioMed Central Ltd. 2009
Published: 21 December 2009
The aetiology of type 1 diabetes in children is uncertain. A number of recent studies have suggested an infectious aetiology. It has been postulated that an infectious agent may be involved. Support for this hypothesis may be provided by a finding of space-time clustering. The aims of this study were: (i) to determine whether there was space-time clustering in cases of childhood diabetes from north-east England; and to test for differences in space-time clustering: (ii) due to age at diagnosis; (iii) between the sexes and (iv) between levels of residential population density.
We studied incidence of type 1 diabetes diagnosed in children aged 0-14 years and diagnosed during the period 1990-2007. All cases were resident in a defined geographical region of north-east England (Northumberland, Newcastle upon Tyne and North Tyneside). We applied a second-order procedure based on K-functions to test for global clustering. Locations were residential addresses at time of diagnosis. Tests were repeated using nearest neighbour thresholds to allow for variable population density, providing the primary result for each analysis. Differences between sexes and between levels of population density were assessed.
We analysed 457 cases of type 1 diabetes. Overall, there was marginally significant evidence of global space-time clustering (P = 0.089). There was statistically significant clustering amongst pairs of cases that contained at least one female (P = 0.017), but not amongst pairs of cases that contained at least one male (P = 0.190). Furthermore, there was significant clustering amongst pairs of cases that contained at least one from a more densely populated area (P = 0.044), but not amongst pairs of cases that contained at least one from a less densely populated area (P = 0.226).
Although the analyses have only found marginally significant evidence of global space-time clustering for cases of type 1 diabetes diagnosed in north-east England, there were two notable findings. First, there was evidence of clustering amongst females and secondly clustering was confined to cases from more densely populated areas. These findings are consistent with a possible aetiological involvement of an infectious agent.
The aetiology of type 1 diabetes is uncertain, although a number of studies have suggested a role for infections and immunological responses. Higher incidence has been associated with better hygiene and later exposure to infections[1–3]. Viruses that have been postulated include enterovirsues, rotavirus, mupms, cytomegalovirus and rubella. Recent studies have also suggested that Ljungan virus (a picornavirus) may be involved in the causation of type 1 diabetes in rodents [5, 6]. It is possible that it may act similarly in human populations .
Space-time clustering is observed when excess numbers of cases are observed within small geographical locations for limited periods of time, and these excesses cannot be explained in terms of general excesses in those locations or at those times . A finding of space-time clustering is consistent with the role of an environmental factor in aetiology, such as an infection.
A previous study, from Yorkshire, UK, has found evidence of space-time clustering in 10-14 and 15-19-year olds . However, it is not clear whether space-time clustering is restricted to that geographical region, or whether such patterning is present elsewhere. To address this question, we have analysed space-time clustering in children (aged 0-14 years) who were diagnosed with type 1 diabetes in a geographically-defined region of north-east England.
The aims of the present study were: (i) to determine whether there was space-time clustering in cases of childhood diabetes from north-east England; and to test for differences in space-time clustering: (ii) due to age at diagnosis; (iii) between the sexes; and (iv) between levels of residential population density.
The study analysed children (aged 0-14 years) who were diagnosed with type 1 diabetes during the period 1st January 1990 - 31st December 2007 and resident in a geographically defined region of north-east England (Northumberland, Newcastle upon Tyne and North Tyneside). The area has a stable population, with low levels of inward or outward migration [10, 11]. Furthermore, the population is ethnically homogeneous, with fewer than 2% from ethnic minorities . Cases were identified from several independent sources (paediatric and adult clinical data bases, admission diaries, in/out patient records), thus ensuring that there was a high level of ascertainment. There were no known temporal or geographical biases.
Ordnance Survey four-digit grid references (Easting and Northing) were allocated to each case with respect to the centroid of the postcode of the residential address at diagnosis, locating each address to the nearest 0.1 km. In the whole of the UK there are approximately 1.7 million postcodes. These uniquely identify addresses for postal delivery. A single postcode may identify fifteen to twenty houses, a smaller number of multiple occupancy residences, or a single commercial building . The space-time analyses were based on time and place of diagnosis.
The following hypotheses were tested: (i) there is spatio-temporal heterogeneity in the incidence of type 1 diabetes in children residing in north-east England; (ii) geographical or temporal heterogeneity of incidence of type 1 diabetes is modulated by age; (iii) geographical or temporal heterogeneity of incidence of type 1 diabetes is modulated by sex; and (iv) geographical or temporal heterogeneity of incidence of type 1 diabetes is modulated by differences in patterns of exposure related to level of population density.
We analysed global space-time clustering using a method based on K-functions . This method may be regarded as being a generalisation of the Knox test . Briefly, in the Knox test a pair of cases is said to occur in "close proximity" if both dates of diagnosis and residential addresses at time of diagnosis are close. The number of pairs of cases observed to be in close proximity (denoted O) is obtained and the number of pairs of cases expected to be in close proximity is calculated (denoted E). If O is greater than E, then there is evidence of space-time clustering.
There is an underlying problem with the Knox test. Boundaries chosen to define "close proximity" in time and space are entirely arbitrary. Repeating of the Knox test, using a number of different boundaries in time and space, leads to multiple testing. A simplified second-order procedure, based on K-functions, is used to partly deal with this problem . In this method, a set of Knox-like calculations are performed, where the critical values change over a pre-specified grid. For close times, we have chosen t = 0.1, 0.2,....., 1.5 years and for close distances we have chosen s = 0.5, 1, 1.5,....., 7.5 km.
The method of analysis based on fixed geographical distances makes no allowances for underlying heterogeneity in population density. North-east England has a highly variable population distribution, since it includes both urban and rural areas. Any specified distance between two cases may have different meanings in urban and rural areas. For example the size of school catchment areas will be different. Use of fixed geographical distance thresholds may lead to an underestimation of expected numbers in more densely populated areas, leading to a possible inflation of the clustering effect. Conversely, the use of fixed geographical thresholds may lead to an overestimation of expected numbers in less densely populated areas, leading to a possible deflation of the clustering effect.
To allow for variations in population density we repeated the analyses, replacing fixed geographical distances by variable nearest neighbour (NN) distances to the (N-7)th,....., (N+7)th NN's, using the diagnosis locations of all the cases in the data set. N was chosen such that the mean distance was 5 km and was found to be N = 21. This method provided the primary result for each analysis and is similar to a method originally proposed by Jacquez .
We analysed the following sub-age-groups: 0-4 years, 5-9 years and 10-14 years. We analysed sex by considering: (i) clustering pairs that included at least one male case (i.e. "male: any" pairs) and (ii) clustering pairs that included at least one female case (i.e. "female: any" pairs).
Cases were assigned to a "more densely populated" group if the 21st NN was nearer than the median distance (2.5 km) of all the 21st NNs and to a "less densely populated" group otherwise. Analysis by the two levels of population density was carried out for clustering pairs that included at least one case from the "more densely populated" group (i.e. "more densely populated: any" pairs) and also for clustering pairs that included at least one case from the "less densely populated" group (i.e. "less densely populated: any" pairs). It should be noted that analyses of population density may be diluted because the areas (especially "less densely populated") are not contiguous.
The K-function analyses were performed using 999 simulations. Statistical significance was indicated if P < 0.05 and marginal significance if 0.05 ≤ P < 0.10.
Results of K-function analyses of space-time clustering for cases of type 1 diabetes diagnosed in north-east England during the period 1990-2007a,b
All case pairs
P = 0.240
P = 0.089
Case pairs aged 0-4 years only
P = 0.343
P = 0.541
Case pairs aged 5-9 years only
P = 0.112
P = 0.187
Case pairs aged 10-14 years only
P = 0.318
P = 0.478
"Male: any" case pairs
P = 0.453
P = 0.190
"Female: any" case pairs
P = 0.182
P = 0.017
"More densely populated: any" case pairs
P = 0.211
P = 0.044
"Less densely populated: any" case pairs
P = 0.350
P = 0.226
There was no evidence of clustering in any sub-age-group (ages 0-4: P = 0.343 and P= 0.541 using the fixed geographical distance and variable NN threshold versions of the K-function method, respectively; ages 5-9: P= 0.112 and P = 0.187; ages 10-14: P = 0.318 and P = 0.478) [Table 1].
There was statistically significant space-time clustering amongst "female: any" case pairs (P = 0.182 and P = 0.017 using the fixed geographical distance and variable NN threshold versions of the K-function method, respectively). However, there was no evidence of clustering amongst "male: any" case pairs (P = 0.453 and P = 0.190) [Table 1].
Also, there was statistically significant space-time clustering amongst "more densely populated: any" case pairs only (P = 0.211 and P = 0.044 using the fixed geographical distance and variable NN threshold versions of the K-function method, respectively). Clustering was not significant amongst "less densely populated: any" case pairs (P = 0.350 and P = 0.226) [Table 1].
Although the analyses have only found marginally significant evidence of global space-time clustering for cases of type 1 diabetes diagnosed in north-east England, there were two notable findings. First, there was evidence of clustering amongst females and secondly clustering was confined to cases from more densely populated areas. These findings suggest the possible aetiological involvement of an infectious agent. It should be noted that heterogeneity between- individuals in the latency from initial infection until onset of type 1 diabetes is likely to have diluted the overall clustering effect.
The results are consistent with findings of space-time clustering from Yorkshire. The analyses of type 1 diabetes from that area found space-time clustering in 10-19 year olds. The clustering amongst 10-14 year olds was also more marked in case pairs that included at least one female and in case pairs that included at least one from a more densely populated area .The findings of clustering amongst females is consistent with earlier onset of puberty and differential pubertal effects for males and females on the immune system , or differences in patterns of exposure between the sexes (possibly via social contact).
Our new analyses from north-east England confirm the possibility of an infectious aetiology. The findings also indicate possible sex-related differences in response to an infectious agent. Furthermore the putative agent may be more prevalent in more urban locations.
Further research will examine geographical heterogeneity of incidence of type 1 diabetes in north-east England and will seek to link any patterning with prevalence of putative aetiological agents (such as Ljungan virus). Additional funding is currently being sought.
The peer review of this article can be found in Additional file 1.
List of abbreviations used
- O :
number of cases observed to be in close proximity
- E :
number of cases expected to be in close proximity
We thank the Joint Environment & Human Health Programme (NERC, EA, Defra, MOD, MRC, The Wellcome Trust, ESRC, BBSRC, EPSRC and HPA) for supporting this research. The sponsors had no involvement in any aspect of this study.
This article has been published as part of Environmental Health Volume 8 Supplement 1, 2009: Proceedings of the Joint Environment and Human Health Programme: Annual Science Day Conference and Workshop. The full contents of the supplement are available online at http://www.ehjournal.net/supplements/8/S1.
- Rewers M, Norris J, Dabelea D: Epidemiology of type 1 diabetes mellitus. Adv Exp Med Biol. 2004, 552: 219-246.Google Scholar
- EURODIAB Substudy 2 Study Group: Infections and vaccinations as risk factors for childhood type 1 (insulin dependent) diabetes mellitus: a multi-centre case-control investigation. Diabetologia. 2000, 43: 47-53. 10.1007/s001250050006.View ArticleGoogle Scholar
- Kolb H, Elliott RB: Increasing incidence of IDDM a consequence of improved hygiene?. Diabetologia. 1994, 37: 729-10.1007/BF00417700.View ArticleGoogle Scholar
- Filippi CM, von Herrath MG: Viral trigger for type 1 diabetes. Diabetes. 2008, 57: 2863-2871. 10.2337/db07-1023.View ArticleGoogle Scholar
- Niklasson B, Hornfeldt B, Nyholm E, Niedrig M, Donoso-Mantke O, Gelderblom HR, Lernmark A: Type 1 diabetes in Swedish bank voles (Clethrionomys glareolus): signs of disease in both colonized and wild cyclic populations at peak density. Ann N Y Acad Sci. 2003, 1005: 170-175. 10.1196/annals.1288.020.View ArticleGoogle Scholar
- Niklasson B, Nyholm E, Feinstein RE, Samsioe A, Hornfeldt B: Diabetes and myocardis in voles and lemmings at cyclic peak densities--induced by Ljungan virus?. Oecologia. 2006, 150: 1-7. 10.1007/s00442-006-0493-1.View ArticleGoogle Scholar
- Niklasson B, Hornfeldt B, Lundman B: Could myocardis, insulin-dependent diabetes mellitus, and Guillain-Barre syndrome be caused by one or more infectious agents carried by rodents?. Emerg Infect Dis. 1998, 4: 187-193. 10.3201/eid0402.980206.View ArticleGoogle Scholar
- McNally RJ, Eden TO: An infectious aetiology for childhood acute leukaemia: a review of the evidence. Br J Haematol. 2004, 127: 243-263. 10.1111/j.1365-2141.2004.05166.x.View ArticleGoogle Scholar
- McNally RJ, Feltbower RG, Parker L, Bodansky HJ, Campbell F, McKinney PA: Space-time clustering analyses of type 1 diabetes among 0- to 29-year-olds in Yorkshire, UK. Diabetologia. 2006, 49: 900-904. 10.1007/s00125-006-0208-5.View ArticleGoogle Scholar
- Pless-Mulloli T, Howel D, King A, Stone I, Merefield J, Bessell J, Darnell R: Living near opencast coal mining sites and children's respiratory health. Occup Environ Med. 2000, 57: 145-151. 10.1136/oem.57.3.145.View ArticleGoogle Scholar
- Pless-Mulloli T, Howel D, Prince H: Prevalence of asthma and other respiratory symptoms in children living near and away from opencast mining sites. Int J Epidemiol. 2001, 30: 556-563. 10.1093/ije/30.3.556.View ArticleGoogle Scholar
- Kyambi S: Beyond black and white: Mapping new immigrant communities. 2005, London: Institute for Public Policy ResearchGoogle Scholar
- Bailey TC, Gatrell AC: Interactive Spatial Data Analysis. 1995, Harlow: LongmanGoogle Scholar
- Diggle PJ, Chetwynd AG, Haggkvist R, Morris SE: Second-order analysis of space-time clustering. Stat Methods Med Res. 1995, 4: 124-136. 10.1177/096228029500400203.View ArticleGoogle Scholar
- Knox EG: The detection of space-time interactions. Appl Stats. 1964, 13: 25-29. 10.2307/2985220.View ArticleGoogle Scholar
- Jacquez GM: A k nearest neighbour test for space-time interaction. Stat Med. 1996, 15: 1935-1949. 10.1002/(SICI)1097-0258(19960930)15:18<1935::AID-SIM406>3.0.CO;2-I.View ArticleGoogle Scholar
- Purtilo DT, Sullivan JL: Immunological bases for superior survival of females. Am J Dis Child. 1979, 133: 1251-1253.Google Scholar
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