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Analysis of space time patterns of disease risk

Outline. ContextSpace time models for disease riskUse of space time models to investigate the stability of patterns of diseaseIllustration on the analysis of congenital malformationsIllustration on the analysis of bladder cancer in UtahDiscussion. . Benefits of Space Time Analysis for chronic d

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Analysis of space time patterns of disease risk

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    3. Benefits of Space Time Analysis for chronic diseases Study the persistence of patterns over time Interpreted as associated with stable risk factors, environmental effects, distribution of health care access … Highlight unusual patterns in time profiles via the inclusion of space-time interaction terms Time localised excesses linked to e.g. emerging environmental hazards with short latency Variability in recording practices Increased epidemiological interpretability Potential tool for surveillance

    4. Case study: Congenital anomalies in England All cases of congenital anomalies (non chromosomal) recorded in England for the period 1983 – 1998 Data from national post-coded registers (Office for National Statistics) Annual post-coded data on total number of live births, still births and terminations 136,000 congenital anomalies ? 84.5 per 105 birth-years Congenital anomalies are sparse: ? Grid of 970 grid squares with variable size, to equalize the number of birth and expected cases per square Variations could be linked to socio-economic or environmental risk factors or heterogeneity in recording practises ? Interest in characterising space time patterns

    7. Case study: Bladder cancer Bladder cancer incidence in Utah (US), 1973-2004 Spatial resolution: census tracts (496) Between 0 and 11 new cases per year with mean around 0.4 Time periods: 1973-76, 1977-80,…, 2001-04 Expected counts based on sex-age incidence rates for the region and the total period 1973-2004

    8. Case study: Bladder cancer

    11. Schematic representation

    14. Prior structure for the random effects Overall spatial pattern: account for local dependence due to geographical ‘continuity’ of populations and risk factors use ‘spatial autoregressive model’ commonly employed for disease mapping Overall time trends: time dependence might be expected, e.g.for long latency chronic diseases use ‘ time structured autoregressive model’ Space time interactions: capture the non predictable part from simple space + time model what model to use ?

    15. New model for the interaction terms Investigating stability of patterns: Aim is to -- Highlight true departures from the overall predictable ‘space + time’ model the variance of nit has to be allowed to be large -- Shrink idiosynchratic (non interpretable) interactions: small variance for most nit ? Mixture model to characterise ‘stable’ and ‘unstable’ risk patterns over time

    17. Analysis strategy for investigating stability of patterns Estimate a model: space + time + interactions (mixture) Use the posterior probabilities of allocation pit into component 2 to classify areas as ‘unstable’ Rule: area i is unstable if at least for one t, pit is large, i.e. pit > pcut (threshold probability). Other rules possible For ‘stable’ areas, investigate spatial patterns, e.g. by using the rule Prob(exp(?i ) >1) > 0.8. Investigate the time profile pattern of ‘unstable’ areas

    22. Mixture estimation Using a cut off pcut = 0.5, 125 areas (13%) are classified as unstable

    23. Risk time profiles for the areas classified as unstable

    24. Bladder cancer, Utah, 1973-2004 Spatial main effect (496 Census Tracts) Posterior median of exp(?i) = spatial RR Linda va faire une nouvelle carte avec dans la 1ere classe les 96 R spatiaux elevés et dans la derniere classe les 81 R spatiaux bas. Les 3 census tracts avec des R spatiaux elevés et non persistants auront une croixLinda va faire une nouvelle carte avec dans la 1ere classe les 96 R spatiaux elevés et dans la derniere classe les 81 R spatiaux bas. Les 3 census tracts avec des R spatiaux elevés et non persistants auront une croix

    25. Bladder cancer, Utah, 1973-2004 Time main effects Posterior median of exp(?t) = Temporal RR

    26. Bladder cancer, Utah, 1973-2004 Posterior distribution of ?1 and ?2

    27. Bladder cancer, Utah, 1973-2004 Census tracts classified as ‘unstable’ using the rule pit >0.6

    28. Bladder cancer, Utah, 1973-2004 Space-time interactions for the 13 areas classified as ‘unstable’

    29. Bladder cancer, Utah, 1973-2004

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