{"id":696775,"date":"2020-10-07T16:29:44","date_gmt":"2020-10-07T23:29:44","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=696775"},"modified":"2020-10-07T18:04:02","modified_gmt":"2020-10-08T01:04:02","slug":"geographic-variation-in-sudden-unexpected-infant-death-in-the-united-states","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/geographic-variation-in-sudden-unexpected-infant-death-in-the-united-states\/","title":{"rendered":"Geographic Variation in Sudden Unexpected Infant Death in the United States"},"content":{"rendered":"
\n

Objectives<\/h3>\n

To assess the geographic variation of sudden unexpected infant death (SUID) and test if variation in geographic factors, such as state, latitude, and longitude, play a role in SUID risk across the US.<\/p>\n<\/div>\n

\n

Study design<\/h3>\n

We analyzed the Centers for Disease Control and Prevention’s Cohort Linked Birth\/Infant Death dataset (2005-2010; 22\u2008882 SUID cases, 25\u2008305\u2008837 live births, rate 0.90\/1000). SUID was defined as infant deaths (ages 7-364\u00a0days) that included sudden infant death syndrome, ill-defined and unknown cause of mortality, and accidental suffocation and strangulation in bed. SUID geographic variation was analyzed using 2 statistical models, logistic regression and generalized additive model (GAM).<\/p>\n<\/div>\n

\n

Results<\/h3>\n

Both models produced similar results. Without adjustment, there was marked geographic variation in SUID rates, but the variation decreased after adjusting for covariates including known risk factors for SUID. After adjustment, nine states demonstrated significantly higher or lower SUID mortality than the national average. Geographic contribution to SUID risk in terms of latitude and longitude were also attenuated after adjustment for covariates.<\/p>\n<\/div>\n

\n

Conclusion<\/h3>\n

Understanding why some states have lower SUID rates may enhance SUID prevention strategies.<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Objectives To assess the geographic variation of sudden unexpected infant death (SUID) and test if variation in geographic factors, such as state, latitude, and longitude, play a role in SUID risk across the US. Study design We analyzed the Centers for Disease Control and Prevention’s Cohort Linked Birth\/Infant Death dataset (2005-2010; 22\u2008882 SUID cases, 25\u2008305\u2008837 […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13553],"msr-publication-type":[193715],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-696775","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-medical-health-genomics","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2020-2-12","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"The Journal of Pediatrics","msr_volume":"220","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S002234762030010X","label_id":"243109","label":0},{"type":"doi","viewUrl":"false","id":"false","title":"https:\/\/doi.org\/10.1016\/j.jpeds.2020.01.006","label_id":"243106","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"Edwin A.Mitchell","user_id":0,"rest_url":false},{"type":"text","value":"Xiaohan Yan","user_id":0,"rest_url":false},{"type":"text","value":"Shirley You Ren","user_id":0,"rest_url":false},{"type":"text","value":"Tatiana M. Anderson","user_id":0,"rest_url":false},{"type":"text","value":"Jan-Marino Ramirez","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Juan M Lavista Ferres","user_id":39552,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Juan M Lavista Ferres"},{"type":"text","value":"Richard Johnston","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[696544],"msr_project":[778522],"publication":[],"video":[],"download":[],"msr_publication_type":"article","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/696775"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-research-item"}],"version-history":[{"count":1,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/696775\/revisions"}],"predecessor-version":[{"id":696778,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/696775\/revisions\/696778"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=696775"}],"wp:term":[{"taxonomy":"msr-content-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-content-type?post=696775"},{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=696775"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=696775"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=696775"},{"taxonomy":"msr-product-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-product-type?post=696775"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=696775"},{"taxonomy":"msr-platform","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-platform?post=696775"},{"taxonomy":"msr-download-source","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-download-source?post=696775"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=696775"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=696775"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=696775"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=696775"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=696775"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=696775"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}