<b>pyJacqQ</b>: <i>Python</i> Implementation of Jacquez's Q-Statistics for Space-Time Clustering of Disease Exposure in Case-Control Studies

Jirjies, Saman; Wallstrom, Garrick; Halden, Rolf U.; Scotch, Matthew

doi:10.18637/jss.v074.i06

Cited by 2 publications

(2 citation statements)

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“…To analyze clustering in time and place throughout the residential histories, we used Jacquez’s Q with an open-access python program pyjacqQ [ 11 ]. Both binomial and false detection rate -based approaches were used to correct for multiple testing.…”

Section: Methodsmentioning

confidence: 99%

Clustering of pediatric onset inflammatory bowel disease in Finland: a nationwide register-based study

2022

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Background The incidence of pediatric inflammatory bowel disease (PIBD) has increased dramatically during the past decades. This implies involvement of environmental factors in etiology but lends no clues about specific agents. We evaluated clustering in time and place of residence at PIBD onset using a case-control setting with comprehensive nationwide register data. Methods We included all PIBD cases diagnosed at ages < 18 years during 1992–2017 (3748 cases; median age of 14.6; 2316 (58%) with ulcerative colitis (UC), 1432 with Crohn’s, and 18,740 age- and sex-matched controls) and constructed complete residential histories (including coordinates) from the national database until the date of the diagnosis of the case assigned as index date for the controls. Using the coordinates of the addresses of the subjects and the diagnosis/index dates, we evaluated clustering in time and place using the Knox test. Four temporal (2, 4, 6, 12 months) and four distance (0.25, 0.5, 1, 5 km) thresholds were used, and results were calculated separately for Crohn´s disease and UC. Similar analyses were conducted using the addresses at birth and the addresses five years before the diagnosis or index date. Based on the threshold values displaying the most clustering in the Knox test, logistic regression models were built to identify whether sex, age at diagnosis or the year of diagnosis affected the probability of belonging to a cluster. To analyze clustering in time and place throughout the residential histories, we used Jacquez’s Q with an open-access python program pyjacqQ. Results The mean number of residencies until the index date was 2.91 for cases and 3.05 for controls (p = 0.0003). Knox test indicated residential clustering for UC with thresholds of 500 m between locations and time-period of four months (p = 0.004). In the regression analysis, sex, age at diagnosis or year of UC diagnosis did not show differences between the clustered and other cases. Jacquez Q analyses showed higher than expected frequency of clustered cases throughout residential histories (p < 10− 8). Conclusion Our findings suggest that the incidence of PIBD, especially of UC, exhibits clustering in locations of residencies over time. For the clustered cases, environmental triggers warrant future studies.

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Section: Methodsmentioning

confidence: 99%

Clustering of pediatric onset inflammatory bowel disease in Finland: a nationwide register-based study

2022

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“…Again, the global statistic is formally equivalent to a join count statistic. 3 Other statistics in the same case-control design, but also including the time dimension, are the so-called Q statistics introduced in Jacquez et al 2005(see also Jacquez et al 2006, Jirjies et al 2016. Similar to Cuzick-Edwards and Rogerson, these statistics count the number of cases among the k-nearest neighbors surrounding a case.…”

Section: The Global Casementioning

confidence: 99%

Operational local join count statistics for cluster detection

Anselin

2019

J Geogr Syst

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This paper operationalizes the idea of a local indicator of spatial association (LISA) for the situation where the variables of interest are binary. This yields a conditional version of a local join count statistic. The statistic is extended to a bivariate and multivariate context, with an explicit treatment of co-location. The approach provides an alternative to point pattern based statistics for situations where all potential locations of an event are available (e.g., all parcels in a city). The statistics are implemented in the open source GeoDa software and yield maps of local clusters of binary variables, as well as co-location clusters of two (or more) binary variables. Empirical illustrations investigate local clusters of house sales in Detroit in 2013 and 2014, and urban design characteristics of Chicago census blocks in 2017.

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pyJacqQ: Python Implementation of Jacquez's Q-Statistics for Space-Time Clustering of Disease Exposure in Case-Control Studies

Cited by 2 publications

References 6 publications

Clustering of pediatric onset inflammatory bowel disease in Finland: a nationwide register-based study

Clustering of pediatric onset inflammatory bowel disease in Finland: a nationwide register-based study

Operational local join count statistics for cluster detection

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