Modeling county-level spatio-temporal mortality rates using dynamic linear models

The lifestyles and backgrounds of individuals across the United States differ widely. Some of these differences are easily measurable (ethnicity, age, income, etc.) while others are not (stress levels, empathy, diet, exercise, etc.). Though every person is unique, individuals living closer together...
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Autor*in:	Gibbs, Zoe [verfasserIn] Groendyke, Chris [verfasserIn] Hartman, Brian [verfasserIn] Richardson, Robert [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Rechteinformationen:	Open Access Namensnennung 4.0 International ; CC BY 4.0

Übergeordnetes Werk:	Enthalten in: Risks - Basel : MDPI, 2013, 8(2020), 4/117 vom: Nov., Seite 1-15
Übergeordnetes Werk:	volume:8 ; year:2020 ; number:4/117 ; month:11 ; pages:1-15

Links:	Link aufrufen Link aufrufen Link aufrufen

DOI / URN:	10.3390/risks8040117

Katalog-ID:	1743779011

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allfields	10.3390/risks8040117 doi 10419/258070 hdl (DE-627)1743779011 (DE-599)KXP1743779011 DE-627 ger DE-627 rda eng Gibbs, Zoe verfasserin aut Modeling county-level spatio-temporal mortality rates using dynamic linear models Zoe Gibbs, Chris Groendyke, Brian Hartman and Robert Richardson 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 The lifestyles and backgrounds of individuals across the United States differ widely. Some of these differences are easily measurable (ethnicity, age, income, etc.) while others are not (stress levels, empathy, diet, exercise, etc.). Though every person is unique, individuals living closer together likely have more similar lifestyles than individuals living hundreds of miles apart. Because lifestyle and environmental factors contribute to mortality, spatial correlation may be an important feature in mortality modeling. However, many of the current mortality models fail to account for spatial relationships. This paper introduces spatio-temporal trends into traditional mortality modeling using Bayesian hierarchical models with conditional auto-regressive (CAR) priors. We show that these priors, commonly used for areal data, are appropriate for modeling county-level spatial trends in mortality data covering the contiguous United States. We find that mortality rates of neighboring counties are highly correlated. Additionally, we find that mortality improvement or deterioration trends between neighboring counties are also highly correlated. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Groendyke, Chris verfasserin aut Hartman, Brian verfasserin aut Richardson, Robert verfasserin aut Enthalten in Risks Basel : MDPI, 2013 8(2020), 4/117 vom: Nov., Seite 1-15 Online-Ressource (DE-627)737288485 (DE-600)2704357-5 (DE-576)379467852 2227-9091 nnns volume:8 year:2020 number:4/117 month:11 pages:1-15 https://www.mdpi.com/2227-9091/8/4/117/pdf Verlag kostenfrei https://doi.org/10.3390/risks8040117 Resolving-System kostenfrei http://hdl.handle.net/10419/258070 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 8 2020 4/117 11 1-15 26 01 0206 3829752482 x1z 04-01-21 2403 01 DE-LFER 383348554X 00 --%%-- --%%-- n --%%-- l01 09-01-21 2403 01 DE-LFER https://doi.org/10.3390/risks8040117 2403 01 DE-LFER https://www.mdpi.com/2227-9091/8/4/117/pdf 26 00 DE-206 56 mortality improvement 26 00 DE-206 56 Bayesian modeling 26 00 DE-206 56 spatial generalized linear model 26 00 DE-206 56 conditional auto-regressive priors
spelling	10.3390/risks8040117 doi 10419/258070 hdl (DE-627)1743779011 (DE-599)KXP1743779011 DE-627 ger DE-627 rda eng Gibbs, Zoe verfasserin aut Modeling county-level spatio-temporal mortality rates using dynamic linear models Zoe Gibbs, Chris Groendyke, Brian Hartman and Robert Richardson 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 The lifestyles and backgrounds of individuals across the United States differ widely. Some of these differences are easily measurable (ethnicity, age, income, etc.) while others are not (stress levels, empathy, diet, exercise, etc.). Though every person is unique, individuals living closer together likely have more similar lifestyles than individuals living hundreds of miles apart. Because lifestyle and environmental factors contribute to mortality, spatial correlation may be an important feature in mortality modeling. However, many of the current mortality models fail to account for spatial relationships. This paper introduces spatio-temporal trends into traditional mortality modeling using Bayesian hierarchical models with conditional auto-regressive (CAR) priors. We show that these priors, commonly used for areal data, are appropriate for modeling county-level spatial trends in mortality data covering the contiguous United States. We find that mortality rates of neighboring counties are highly correlated. Additionally, we find that mortality improvement or deterioration trends between neighboring counties are also highly correlated. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Groendyke, Chris verfasserin aut Hartman, Brian verfasserin aut Richardson, Robert verfasserin aut Enthalten in Risks Basel : MDPI, 2013 8(2020), 4/117 vom: Nov., Seite 1-15 Online-Ressource (DE-627)737288485 (DE-600)2704357-5 (DE-576)379467852 2227-9091 nnns volume:8 year:2020 number:4/117 month:11 pages:1-15 https://www.mdpi.com/2227-9091/8/4/117/pdf Verlag kostenfrei https://doi.org/10.3390/risks8040117 Resolving-System kostenfrei http://hdl.handle.net/10419/258070 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 8 2020 4/117 11 1-15 26 01 0206 3829752482 x1z 04-01-21 2403 01 DE-LFER 383348554X 00 --%%-- --%%-- n --%%-- l01 09-01-21 2403 01 DE-LFER https://doi.org/10.3390/risks8040117 2403 01 DE-LFER https://www.mdpi.com/2227-9091/8/4/117/pdf 26 00 DE-206 56 mortality improvement 26 00 DE-206 56 Bayesian modeling 26 00 DE-206 56 spatial generalized linear model 26 00 DE-206 56 conditional auto-regressive priors
allfields_unstemmed	10.3390/risks8040117 doi 10419/258070 hdl (DE-627)1743779011 (DE-599)KXP1743779011 DE-627 ger DE-627 rda eng Gibbs, Zoe verfasserin aut Modeling county-level spatio-temporal mortality rates using dynamic linear models Zoe Gibbs, Chris Groendyke, Brian Hartman and Robert Richardson 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 The lifestyles and backgrounds of individuals across the United States differ widely. Some of these differences are easily measurable (ethnicity, age, income, etc.) while others are not (stress levels, empathy, diet, exercise, etc.). Though every person is unique, individuals living closer together likely have more similar lifestyles than individuals living hundreds of miles apart. Because lifestyle and environmental factors contribute to mortality, spatial correlation may be an important feature in mortality modeling. However, many of the current mortality models fail to account for spatial relationships. This paper introduces spatio-temporal trends into traditional mortality modeling using Bayesian hierarchical models with conditional auto-regressive (CAR) priors. We show that these priors, commonly used for areal data, are appropriate for modeling county-level spatial trends in mortality data covering the contiguous United States. We find that mortality rates of neighboring counties are highly correlated. Additionally, we find that mortality improvement or deterioration trends between neighboring counties are also highly correlated. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Groendyke, Chris verfasserin aut Hartman, Brian verfasserin aut Richardson, Robert verfasserin aut Enthalten in Risks Basel : MDPI, 2013 8(2020), 4/117 vom: Nov., Seite 1-15 Online-Ressource (DE-627)737288485 (DE-600)2704357-5 (DE-576)379467852 2227-9091 nnns volume:8 year:2020 number:4/117 month:11 pages:1-15 https://www.mdpi.com/2227-9091/8/4/117/pdf Verlag kostenfrei https://doi.org/10.3390/risks8040117 Resolving-System kostenfrei http://hdl.handle.net/10419/258070 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 8 2020 4/117 11 1-15 26 01 0206 3829752482 x1z 04-01-21 2403 01 DE-LFER 383348554X 00 --%%-- --%%-- n --%%-- l01 09-01-21 2403 01 DE-LFER https://doi.org/10.3390/risks8040117 2403 01 DE-LFER https://www.mdpi.com/2227-9091/8/4/117/pdf 26 00 DE-206 56 mortality improvement 26 00 DE-206 56 Bayesian modeling 26 00 DE-206 56 spatial generalized linear model 26 00 DE-206 56 conditional auto-regressive priors
allfieldsGer	10.3390/risks8040117 doi 10419/258070 hdl (DE-627)1743779011 (DE-599)KXP1743779011 DE-627 ger DE-627 rda eng Gibbs, Zoe verfasserin aut Modeling county-level spatio-temporal mortality rates using dynamic linear models Zoe Gibbs, Chris Groendyke, Brian Hartman and Robert Richardson 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 The lifestyles and backgrounds of individuals across the United States differ widely. Some of these differences are easily measurable (ethnicity, age, income, etc.) while others are not (stress levels, empathy, diet, exercise, etc.). Though every person is unique, individuals living closer together likely have more similar lifestyles than individuals living hundreds of miles apart. Because lifestyle and environmental factors contribute to mortality, spatial correlation may be an important feature in mortality modeling. However, many of the current mortality models fail to account for spatial relationships. This paper introduces spatio-temporal trends into traditional mortality modeling using Bayesian hierarchical models with conditional auto-regressive (CAR) priors. We show that these priors, commonly used for areal data, are appropriate for modeling county-level spatial trends in mortality data covering the contiguous United States. We find that mortality rates of neighboring counties are highly correlated. Additionally, we find that mortality improvement or deterioration trends between neighboring counties are also highly correlated. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Groendyke, Chris verfasserin aut Hartman, Brian verfasserin aut Richardson, Robert verfasserin aut Enthalten in Risks Basel : MDPI, 2013 8(2020), 4/117 vom: Nov., Seite 1-15 Online-Ressource (DE-627)737288485 (DE-600)2704357-5 (DE-576)379467852 2227-9091 nnns volume:8 year:2020 number:4/117 month:11 pages:1-15 https://www.mdpi.com/2227-9091/8/4/117/pdf Verlag kostenfrei https://doi.org/10.3390/risks8040117 Resolving-System kostenfrei http://hdl.handle.net/10419/258070 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 8 2020 4/117 11 1-15 26 01 0206 3829752482 x1z 04-01-21 2403 01 DE-LFER 383348554X 00 --%%-- --%%-- n --%%-- l01 09-01-21 2403 01 DE-LFER https://doi.org/10.3390/risks8040117 2403 01 DE-LFER https://www.mdpi.com/2227-9091/8/4/117/pdf 26 00 DE-206 56 mortality improvement 26 00 DE-206 56 Bayesian modeling 26 00 DE-206 56 spatial generalized linear model 26 00 DE-206 56 conditional auto-regressive priors
allfieldsSound	10.3390/risks8040117 doi 10419/258070 hdl (DE-627)1743779011 (DE-599)KXP1743779011 DE-627 ger DE-627 rda eng Gibbs, Zoe verfasserin aut Modeling county-level spatio-temporal mortality rates using dynamic linear models Zoe Gibbs, Chris Groendyke, Brian Hartman and Robert Richardson 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 The lifestyles and backgrounds of individuals across the United States differ widely. Some of these differences are easily measurable (ethnicity, age, income, etc.) while others are not (stress levels, empathy, diet, exercise, etc.). Though every person is unique, individuals living closer together likely have more similar lifestyles than individuals living hundreds of miles apart. Because lifestyle and environmental factors contribute to mortality, spatial correlation may be an important feature in mortality modeling. However, many of the current mortality models fail to account for spatial relationships. This paper introduces spatio-temporal trends into traditional mortality modeling using Bayesian hierarchical models with conditional auto-regressive (CAR) priors. We show that these priors, commonly used for areal data, are appropriate for modeling county-level spatial trends in mortality data covering the contiguous United States. We find that mortality rates of neighboring counties are highly correlated. Additionally, we find that mortality improvement or deterioration trends between neighboring counties are also highly correlated. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Groendyke, Chris verfasserin aut Hartman, Brian verfasserin aut Richardson, Robert verfasserin aut Enthalten in Risks Basel : MDPI, 2013 8(2020), 4/117 vom: Nov., Seite 1-15 Online-Ressource (DE-627)737288485 (DE-600)2704357-5 (DE-576)379467852 2227-9091 nnns volume:8 year:2020 number:4/117 month:11 pages:1-15 https://www.mdpi.com/2227-9091/8/4/117/pdf Verlag kostenfrei https://doi.org/10.3390/risks8040117 Resolving-System kostenfrei http://hdl.handle.net/10419/258070 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 8 2020 4/117 11 1-15 26 01 0206 3829752482 x1z 04-01-21 2403 01 DE-LFER 383348554X 00 --%%-- --%%-- n --%%-- l01 09-01-21 2403 01 DE-LFER https://doi.org/10.3390/risks8040117 2403 01 DE-LFER https://www.mdpi.com/2227-9091/8/4/117/pdf 26 00 DE-206 56 mortality improvement 26 00 DE-206 56 Bayesian modeling 26 00 DE-206 56 spatial generalized linear model 26 00 DE-206 56 conditional auto-regressive priors
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source	Enthalten in Risks 8(2020), 4/117 vom: Nov., Seite 1-15 volume:8 year:2020 number:4/117 month:11 pages:1-15
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container_title	Risks
authorswithroles_txt_mv	Gibbs, Zoe @@aut@@ Groendyke, Chris @@aut@@ Hartman, Brian @@aut@@ Richardson, Robert @@aut@@
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author	Gibbs, Zoe
spellingShingle	Gibbs, Zoe 26 mortality improvement 26 Bayesian modeling 26 spatial generalized linear model 26 conditional auto-regressive priors Modeling county-level spatio-temporal mortality rates using dynamic linear models
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topic_title	26 00 DE-206 56 mortality improvement 26 00 DE-206 56 Bayesian modeling 26 00 DE-206 56 spatial generalized linear model 26 00 DE-206 56 conditional auto-regressive priors Modeling county-level spatio-temporal mortality rates using dynamic linear models Zoe Gibbs, Chris Groendyke, Brian Hartman and Robert Richardson
topic	26 mortality improvement 26 Bayesian modeling 26 spatial generalized linear model 26 conditional auto-regressive priors
topic_unstemmed	26 mortality improvement 26 Bayesian modeling 26 spatial generalized linear model 26 conditional auto-regressive priors
topic_browse	26 mortality improvement 26 Bayesian modeling 26 spatial generalized linear model 26 conditional auto-regressive priors
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title	Modeling county-level spatio-temporal mortality rates using dynamic linear models
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title_full	Modeling county-level spatio-temporal mortality rates using dynamic linear models Zoe Gibbs, Chris Groendyke, Brian Hartman and Robert Richardson
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title_sort	modeling county-level spatio-temporal mortality rates using dynamic linear models
title_auth	Modeling county-level spatio-temporal mortality rates using dynamic linear models
abstract	The lifestyles and backgrounds of individuals across the United States differ widely. Some of these differences are easily measurable (ethnicity, age, income, etc.) while others are not (stress levels, empathy, diet, exercise, etc.). Though every person is unique, individuals living closer together likely have more similar lifestyles than individuals living hundreds of miles apart. Because lifestyle and environmental factors contribute to mortality, spatial correlation may be an important feature in mortality modeling. However, many of the current mortality models fail to account for spatial relationships. This paper introduces spatio-temporal trends into traditional mortality modeling using Bayesian hierarchical models with conditional auto-regressive (CAR) priors. We show that these priors, commonly used for areal data, are appropriate for modeling county-level spatial trends in mortality data covering the contiguous United States. We find that mortality rates of neighboring counties are highly correlated. Additionally, we find that mortality improvement or deterioration trends between neighboring counties are also highly correlated.
abstractGer	The lifestyles and backgrounds of individuals across the United States differ widely. Some of these differences are easily measurable (ethnicity, age, income, etc.) while others are not (stress levels, empathy, diet, exercise, etc.). Though every person is unique, individuals living closer together likely have more similar lifestyles than individuals living hundreds of miles apart. Because lifestyle and environmental factors contribute to mortality, spatial correlation may be an important feature in mortality modeling. However, many of the current mortality models fail to account for spatial relationships. This paper introduces spatio-temporal trends into traditional mortality modeling using Bayesian hierarchical models with conditional auto-regressive (CAR) priors. We show that these priors, commonly used for areal data, are appropriate for modeling county-level spatial trends in mortality data covering the contiguous United States. We find that mortality rates of neighboring counties are highly correlated. Additionally, we find that mortality improvement or deterioration trends between neighboring counties are also highly correlated.
abstract_unstemmed	The lifestyles and backgrounds of individuals across the United States differ widely. Some of these differences are easily measurable (ethnicity, age, income, etc.) while others are not (stress levels, empathy, diet, exercise, etc.). Though every person is unique, individuals living closer together likely have more similar lifestyles than individuals living hundreds of miles apart. Because lifestyle and environmental factors contribute to mortality, spatial correlation may be an important feature in mortality modeling. However, many of the current mortality models fail to account for spatial relationships. This paper introduces spatio-temporal trends into traditional mortality modeling using Bayesian hierarchical models with conditional auto-regressive (CAR) priors. We show that these priors, commonly used for areal data, are appropriate for modeling county-level spatial trends in mortality data covering the contiguous United States. We find that mortality rates of neighboring counties are highly correlated. Additionally, we find that mortality improvement or deterioration trends between neighboring counties are also highly correlated.
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title_short	Modeling county-level spatio-temporal mortality rates using dynamic linear models
url	https://www.mdpi.com/2227-9091/8/4/117/pdf https://doi.org/10.3390/risks8040117 http://hdl.handle.net/10419/258070
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Modeling county-level spatio-temporal mortality rates using dynamic linear models

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