Connor Gascoigne

Connor Gascoigne

He/his

Reaseach Associate

Publications

Here you cand find my list of publications, ordered by date (most recent first) and publication stage (published or preprint).

You can also find my articles on my Google Scholar profile.

Journal Articles

Spatio-temporal trends and socio-environmental determinants of suicides in England (2002–2022): an ecological population-based study

Published in The Lancet Regional Health - Europe, 2025

Here we model suicides in England at small areas and take into account local socio-environemntal risk and protective factors.

Recommended citation: Gascoigne, Connor & Jeffery, Annie & Rotos, Ioannis & Yu, Xuewen & Geneletti, Sara & Davies, Bethan & Baio, Gianluca & Kirkbride, James B & Pitman, Alexandra & Blangiardo, Marta. (2025). "Spatio-temporal trends and socio-environmental determinants of suicides in England (2002–2022): an ecological population-based study." The Lancet Regional Health - Europe.
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Smooth predictions for age-period-cohort models: a comparison between splines and random process

Published in BMC Medical Research Methodology, 2025

Here we show theoretically and practically how penalised splines and random processes are mathematically but perform differently when predicting.

Recommended citation: Gascoigne, Connor & Reibler, Andrea & Smith, Theresa. (2025). "Smooth predictions for age-period-cohort models: a comparison between splines and random process." BMC Medical Research Methodology. 25(1).
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Estimating Subnational Under-Five Mortality Rates Using a Spatio-Temporal Age-Period-Cohort Model

Published in Spatial and Spatio-temporal Epidemiology, 2024

Here we use an age-period-cohort model extended to account for a complex survey design to model subnational U5MRs in Kenya.

Recommended citation: Gascoigne, Connor & Smith, Theresa & Paige, John & Wakefiled, Jon. (2024). "Estimating Subnational Under-Five Mortality Rates Using a Spatio-Temporal Age-Period-Cohort Model." Spatial and Spatio-temporal Epidemiology. 100708.
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A Bayesian Interrupted Time Series framework for evaluating policy change on mental well-being: An application to England’s welfare reform

Published in Spatial and Spatio-temporal Epidemiology, 2024

Here we use a develop a framework to evaluate the effect of policy on mental health.

Recommended citation: Gascoigne, Connor & Jeffery, Annie & Shao, Zejing & Geneletti, Sara & Kirkbride, James B & Baio, Gianluca & Blangiardo, Marta. (2024). "A Bayesian Interrupted Time Series framework for evaluating policy change on mental well-being: An application to England’s welfare reform." Spatial and Spatio-temporal Epidemiology. 100662.
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The effect of immigration policy reform on mental health in people from minoritised ethnic groups in England: an interrupted time series analysis of longitudinal data from the UK Household Longitudinal Study cohort

Published in The Lancet Psychiatry, 2024

Here we use a interrupted time series model fit within a Bayesian hierarchical model to evaluate the effect of the immigration policy reform on minoritised ethnic groups in England.

Recommended citation: Jeffery, Annie & Gascoigne, Connor & Dykxhoorn, Jennifer & Blangiardo, Marta & Geneletti, Sara & Baio, Gianluca & Kirkbride, James B. (2024). "The effect of immigration policy reform on mental health in people from minoritised ethnic groups in England: an interrupted time series analysis of longitudinal data from the UK Household Longitudinal Study cohort." The Lancet Psychiatry. 11(3).
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Penalized smoothing splines resolve the curvature identifiability problem in age-period-cohort models with unequal intervals

Published in Statistics in Medicine, 2023

Here we use penalized smoothing splines to resolve a identifcation problem that arises in age-period-cohort models when data comes aggregated in non-uniform widths.

Recommended citation: Gascoigne, Connor & Smith, Theresa. (2023). "Penalized smoothing splines resolve the curvature identifiability problem in age-period-cohort models with unequal intervals." Statistic in Medicine. 42(12).
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Preprints