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Space-time clustering of childhood cancers: a systematic review and pooled analysis.

ABSTRACT: The aetiology of childhood cancers remains largely unknown. Space-time clustering of cases might imply an aetiological role of infections. We aimed to review the evidence of space-time clustering of specific childhood cancers. We searched Medline and Embase for population-based studies that covered a pre-defined study area, included cases under 20 years of age and were published before July 2016. We extracted all space-time clustering tests and calculated the proportion of positive tests per diagnostic group. In a pooled analysis, we performed a Knox test of the number of pairs of cases close to each other in time and space pooled across studies. 70 studies met our eligibility criteria, 32 of which reported Knox tests. For leukaemia, the proportion of positive tests was higher than expected by chance at both time of diagnosis (26%) and birth (11%). The pooled analysis showed strong evidence of clustering at diagnosis for children aged 0-5 years for a spatial and temporal lag of 5 km and 6 months, respectively (p < 0.001). The evidence was mixed for lymphoma and tumours of the central nervous system. The current study suggests that leukaemia cases cluster in space-time due to an aetiological factor affecting children under 5 years of age. The observed pattern of clustering of young children close to time of diagnosis is compatible with Greaves' delayed-infections-hypothesis.

SUBMITTER: Kreis C

PROVIDER: S-EPMC6326085 | biostudies-other | 2019 Jan

REPOSITORIES: biostudies-other

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Space-time clustering of childhood cancers: a systematic review and pooled analysis.

Kreis Christian C Doessegger Eliane E Lupatsch Judith E JE Spycher Ben D BD

European journal of epidemiology 20181116 1

The aetiology of childhood cancers remains largely unknown. Space-time clustering of cases might imply an aetiological role of infections. We aimed to review the evidence of space-time clustering of specific childhood cancers. We searched Medline and Embase for population-based studies that covered a pre-defined study area, included cases under 20 years of age and were published before July 2016. We extracted all space-time clustering tests and calculated the proportion of positive tests per dia ...[more]

PMID: 30446850

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Project description:BackgroundA spatial and temporal study of the distribution of facility-based deliveries can identify areas of low and high facility usage and help devise more targeted interventions to improve delivery outcomes. Developing countries like Bangladesh face considerable challenges in reducing the maternal mortality ratio to the targets set by the Sustainable Development Goals. Recent studies have already identified that the progress of reducing maternal mortality has stalled. Giving birth in a health facility is one way to reduce maternal mortality.MethodsFacility delivery data from a demographic surveillance site was analyzed at both village and Bari (comprising several households with same paternal origins) level to understand spatial and temporal heterogeneity. Global spatial autocorrelation was detected using Moran's I index while local spatial clusters were detected using the local Getis G i * statistics. In addition, space-time scanning using a discrete Poisson approach facilitated the identification of space-time clusters. The likelihood of delivering at a facility when located inside a cluster was calculated using log-likelihood ratios.ResultsThe three cluster detection approaches detected significant spatial and temporal heterogeneity in the distribution of facility deliveries in the study area. The hot and cold spots indicated contiguous and relocation type diffusion and increased in number over the years. Space-time scanning revealed that when a parturient woman is located in a Bari inside the cluster, the likelihood of delivering at a health facility increases by twenty-seven times.ConclusionsSpatiotemporal studies to understand delivery patterns are quite rare. However, in resource constraint countries like Bangladesh, detecting hot and cold spot areas can aid in the detection of diffusion centers, which can be targeted to expand regions with high facility deliveries. Places and periods with reduced health facility usages can be identified using various cluster detection techniques, to assess the barriers and facilitators in promoting health facility deliveries.

Dataset Information

Space-time clustering of childhood cancers: a systematic review and pooled analysis.

Publications

Space-time clustering of childhood cancers: a systematic review and pooled analysis.

Similar Datasets

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