[et_pb_section fb_built=”1″ _builder_version=”4.24.2″ _module_preset=”default” positioning=”none” z_index=”2″ custom_margin=”|20px||20px|false|true” custom_margin_tablet=”|20px||20px|false|true” custom_margin_phone=”|10px||10px|false|true” custom_margin_last_edited=”on|phone” custom_padding=”||62px|||” border_radii=”on|20px|20px|20px|20px” border_radii_tablet=”on|20px|20px|20px|20px” border_radii_phone=”on|15px|15px|15px|15px” border_radii_last_edited=”on|phone” locked=”off” global_colors_info=”{}”][et_pb_row module_id=”section1″ _builder_version=”4.14.2″ _module_preset=”default” width_tablet=”” width_phone=”90%” width_last_edited=”on|phone” custom_margin=”5px|auto||auto||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.24.2″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_3,1_3,1_3″ module_id=”section1″ _builder_version=”4.14.2″ _module_preset=”default” width=”100%” width_tablet=”” width_phone=”90%” 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header_line_height=”1.2em” body_font=”Poppins||||||||” body_text_color=”#64748B” body_font_size=”20px” body_line_height=”1.4em” text_orientation=”center” custom_margin=”15px||79px|0px|false|false” custom_padding=”|0px||0px|false|true” header_font_size_tablet=”22px” header_font_size_phone=”18px” header_font_size_last_edited=”on|phone” body_font_size_tablet=”” body_font_size_phone=”12px” body_font_size_last_edited=”on|phone” custom_css_blurb_title=”text-transform: none;” locked=”off” global_colors_info=”{}”]<\/p>\n
Anomaly Detection Methods for Overdose (ADMO)<\/span><\/i><\/b>\u00a0was developed by the Johns Hopkins Bloomberg School of Public Health’s Surveillance and Outbreak Response Team (SORT) in partnership with CSTE\u00a0and a workgroup of state, Tribal,\u00a0local, and territorial (STLT) epidemiologists.\u00a0The ADMO approach is a set of standardized and customizable statistical methods that detect anomalies in count data. By using it, users can\u00a0identify\u00a0anomalies over time and space. It is not used to detect clusters.<\/span>\u00a0<\/span><\/p>\n \u00a0<\/span><\/p>\n Below, you can find resources\u00a0for implementing ADMO in your jurisdiction,\u00a0including\u00a0on-demand training videos,\u00a0detailed instructions for preparing standardized data, customizing the R-based code, understanding the statistical methods, and interpreting results to support timely overdose surveillance and public health response.<\/span>\u00a0<\/span><\/p>\n [\/et_pb_blurb][\/et_pb_column][\/et_pb_row][et_pb_row disabled_on=”on|on|off” _builder_version=”4.14.2″ _module_preset=”default” width_tablet=”” width_phone=”90%” width_last_edited=”on|phone” custom_margin=”-51px|auto|70px|auto||” custom_padding=”||0px||false|false” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.24.2″ _module_preset=”default” global_colors_info=”{}”][et_pb_blurb image_icon_width=”40px” icon_alignment=”left” content_max_width=”100%” _builder_version=”4.14.2″ _module_preset=”default” header_text_color=”#071018″ header_font_size=”22px” header_letter_spacing=”-0.2px” header_line_height=”1.2em” body_font=”Poppins||||||||” body_text_color=”#64748B” body_font_size=”20px” body_line_height=”1.4em” text_orientation=”center” custom_margin=”15px||121px|0px|false|false” custom_padding=”|56px||56px|false|true” header_font_size_tablet=”22px” header_font_size_phone=”18px” header_font_size_last_edited=”on|phone” body_font_size_tablet=”” body_font_size_phone=”12px” body_font_size_last_edited=”on|phone” custom_css_blurb_title=”text-transform: none;” locked=”off” global_colors_info=”{}”]<\/p>\n Anomaly Detection Methods for Overdose (ADMO)<\/span><\/i><\/b>\u00a0was developed by the Johns Hopkins Bloomberg School of Public Health’s Surveillance and Outbreak Response Team (SORT) in partnership with CSTE\u00a0and a workgroup of state, Tribal,\u00a0local, and territorial (STLT) epidemiologists.\u00a0The ADMO approach is a set of standardized and customizable statistical methods that detect anomalies in count data. By using it, users can\u00a0identify\u00a0anomalies over time and space. It is not used to detect clusters.<\/span>\u00a0<\/span><\/p>\n \u00a0<\/span><\/p>\n Below, you can find resources\u00a0for implementing ADMO in your jurisdiction,\u00a0including\u00a0on-demand training videos,\u00a0detailed instructions for preparing standardized data, customizing the R-based code, understanding the statistical methods, and interpreting results to support timely overdose surveillance and public health response.<\/span>\u00a0<\/span><\/p>\n [\/et_pb_blurb][et_pb_blurb image_icon_width=”40px” icon_alignment=”left” content_max_width=”100%” _builder_version=”4.14.2″ _module_preset=”default” header_text_color=”#071018″ header_font_size=”22px” header_letter_spacing=”-0.2px” header_line_height=”1.2em” body_font=”Poppins||||||||” body_text_color=”#64748B” body_font_size=”20px” body_line_height=”1.4em” text_orientation=”center” custom_margin=”15px||121px|0px|false|false” custom_padding=”|56px||56px|false|true” hover_enabled=”0″ header_font_size_tablet=”22px” header_font_size_phone=”18px” header_font_size_last_edited=”on|phone” body_font_size_tablet=”” body_font_size_phone=”12px” body_font_size_last_edited=”on|phone” custom_css_blurb_title=”text-transform: none;” locked=”off” global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n Resources<\/span><\/b>\u00a0<\/span><\/p>\n Technical Guidance for Analytic Code<\/span><\/b><\/a><\/p>\n The purpose of the\u00a0technical guidance\u00a0is to expand upon the methods and concepts used in the analytic code. Specifically, this guidance describes:\u00a0<\/span>\u00a0<\/span><\/p>\n \u00a0<\/span><\/p>\n Analytic R Code<\/span><\/b>\u00a0<\/span><\/p>\n Mock Datasets<\/span><\/b>\u00a0<\/span><\/p>\n \u00a0<\/span><\/p>\n [\/et_pb_blurb][et_pb_blurb image_icon_width=”40px” icon_alignment=”left” content_max_width=”100%” _builder_version=”4.14.2″ _module_preset=”default” header_text_color=”#071018″ header_font_size=”22px” header_letter_spacing=”-0.2px” header_line_height=”1.2em” body_font=”Poppins||||||||” body_text_color=”#64748B” body_font_size=”20px” body_line_height=”1.4em” text_orientation=”center” custom_margin=”15px||121px|0px|false|false” custom_padding=”|56px||56px|false|true” hover_enabled=”0″ header_font_size_tablet=”22px” header_font_size_phone=”18px” header_font_size_last_edited=”on|phone” body_font_size_tablet=”” body_font_size_phone=”12px” body_font_size_last_edited=”on|phone” custom_css_blurb_title=”text-transform: none;” border_radii=”on|25px|25px|25px|25px” border_width_all=”1px” border_color_all=”#080f18″ box_shadow_style=”preset3″ locked=”off” global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n \u00a0<\/span><\/p>\n [\/et_pb_blurb][\/et_pb_column][\/et_pb_row][et_pb_row disabled_on=”off|off|on” _builder_version=”4.14.2″ _module_preset=”default” custom_margin=”-36px|auto|58px|auto||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.14.2″ _module_preset=”default” global_colors_info=”{}”][et_pb_blurb title=”Early Planning Steps” url=”https:\/\/odalerts.cste.org\/?page_id=25847531″ image=”https:\/\/odalerts.cste.org\/wp-content\/uploads\/2026\/02\/rooting.png” icon_placement=”left” image_icon_width=”62px” content_max_width=”997px” _builder_version=”4.14.2″ _module_preset=”default” header_font=”Poppins||||||||” header_font_size=”19px” body_font=”Poppins||||||||” body_font_size=”15px” link_option_url=”https:\/\/odalerts.cste.org\/?page_id=25847531″ global_colors_info=”{}”]<\/p>\n\n
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\n Sequence<\/span><\/b>\u00a0<\/span><\/td>\n Topic<\/span><\/b><\/td>\n Length (Min:Sec)<\/span><\/b>\u00a0<\/span><\/td>\n Speaker<\/span><\/b>\u00a0<\/span><\/td>\n<\/tr>\n \n 1<\/span>\u00a0<\/span><\/td>\n Welcome and ADMO Project Overview<\/span>\u00a0<\/span><\/a><\/td>\n 11:50<\/span>\u00a0<\/span><\/td>\n Melissa Marx, PhD, MPH<\/span>\u00a0<\/span><\/td>\n<\/tr>\n \n 2<\/span>\u00a0<\/span><\/td>\n Methods: Key Terms,\u00a0What\u00a0is ADMO For and Not For, and Motivating Scenario<\/span>\u00a0<\/span><\/a><\/td>\n 04:51<\/span>\u00a0<\/span><\/td>\n Alexander Perez, MPH<\/span>\u00a0<\/span><\/td>\n<\/tr>\n \n 3<\/span>\u00a0<\/span><\/td>\n Methods: Data Sources, Data Preparation, and Introduction\u00a0to Process Workflow<\/span>\u00a0<\/span><\/a><\/td>\n 01:40<\/span>\u00a0<\/span><\/td>\n Alexander Perez, MPH<\/span>\u00a0<\/span><\/td>\n<\/tr>\n \n 4<\/span>\u00a0<\/span><\/td>\n Methods: Step 1\u00a0(Nonspatial\u00a0&\u00a0Spatial)<\/span>\u00a0<\/span><\/a><\/td>\n 02:35<\/span>\u00a0<\/span><\/td>\n Alexander Perez, MPH<\/span>\u00a0<\/span><\/td>\n<\/tr>\n \n 5<\/span>\u00a0<\/span><\/td>\n Methods: Step 2.1 (Nonspatial\u00a0&\u00a0Spatial)<\/span>\u00a0<\/span><\/a><\/td>\n 03:54<\/span>\u00a0<\/span><\/td>\n Shilpi Misra, MPH<\/span>\u00a0<\/span><\/td>\n<\/tr>\n \n 6<\/span>\u00a0<\/span><\/td>\n Methods: Step 2.2 (Nonspatial\u00a0&\u00a0Spatial)<\/span>\u00a0<\/span><\/a><\/td>\n 02:38<\/span>\u00a0<\/span><\/td>\n Shilpi Misra, MPH<\/span>\u00a0<\/span><\/td>\n<\/tr>\n \n 7<\/span>\u00a0<\/span><\/td>\n Methods: Step 2.3 \u2013 Introduction (Nonspatial\u00a0&\u00a0Spatial)<\/span>\u00a0<\/span><\/a><\/td>\n 07:15<\/span>\u00a0<\/span><\/td>\n Zachary\u00a0Smith, MPH<\/span>\u00a0<\/span><\/td>\n<\/tr>\n \n 8<\/span>\u00a0<\/span><\/td>\n Methods: Steps 2.3, 2.4, 2.5 (Nonspatial)<\/span>\u00a0<\/span><\/a><\/td>\n 11:55<\/span>\u00a0<\/span><\/td>\n Zachary Smith, MPH<\/span>\u00a0<\/span><\/td>\n<\/tr>\n \n 9<\/span>\u00a0<\/span><\/td>\n Methods: Steps 2.3, 2.4, 2.5 (Spatial)<\/span>\u00a0<\/span><\/a><\/td>\n 13:10<\/span>\u00a0<\/span><\/td>\n Augustin Martin, MSc, MPP<\/span>\u00a0<\/span><\/td>\n<\/tr>\n \n 10<\/span>\u00a0<\/span><\/td>\n Methods: Step 3 (Nonspatial\u00a0&\u00a0Spatial)<\/span>\u00a0<\/span><\/a><\/td>\n 04:07<\/span>\u00a0<\/span><\/td>\n Augustin Martin, MSc, MPP<\/span>\u00a0<\/span><\/td>\n<\/tr>\n \n 11<\/span>\u00a0<\/span><\/td>\n Methods: Special Considerations<\/span>\u00a0<\/span><\/td>\n 03:12<\/span>\u00a0<\/span><\/td>\n Zachary Smith, MPH<\/span>\u00a0<\/span><\/td>\n<\/tr>\n \n 12<\/span>\u00a0<\/span><\/td>\n Interpreting Nonspatial Analyses<\/span>\u00a0<\/span><\/a><\/td>\n 10:30<\/span>\u00a0<\/span><\/td>\n Shilpi Misra, MPH<\/span>
<\/span>Zachary Smith, MPH<\/span>\u00a0<\/span><\/td>\n<\/tr>\n\n 13<\/span>\u00a0<\/span><\/td>\n Interpreting Spatial Analyses<\/span>\u00a0<\/span><\/a><\/td>\n 07:06<\/span>\u00a0<\/span><\/td>\n Augustin Martin, MSc, MPP<\/span>\u00a0<\/span><\/td>\n<\/tr>\n \n 14<\/span>\u00a0<\/span><\/td>\n Practical Applications<\/span>\u00a0<\/span><\/a><\/td>\n 04:18<\/span>\u00a0<\/span><\/td>\n Zachary Smith, MPH<\/span>\u00a0<\/span><\/td>\n<\/tr>\n \n 15<\/span>\u00a0<\/span><\/td>\n Closing<\/span><\/a>\u00a0<\/span><\/td>\n 03:10<\/span>\u00a0<\/span><\/td>\n Zachary Smith, MPH<\/span>\u00a0<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
[\/et_pb_blurb][et_pb_blurb title=”Developing Case Definitions” url=”https:\/\/odalerts.cste.org\/?page_id=25847566″ image=”https:\/\/odalerts.cste.org\/wp-content\/uploads\/2026\/02\/queue.png” icon_placement=”left” image_icon_width=”62px” content_max_width=”997px” _builder_version=”4.14.2″ _module_preset=”default” header_font=”Poppins||||||||” header_font_size=”19px” body_font=”Poppins||||||||” body_font_size=”15px” link_option_url=”https:\/\/odalerts.cste.org\/?page_id=https:\/\/odalerts.cste.org\/?page_id=25847566″ global_colors_info=”{}”]<\/p>\n
[\/et_pb_blurb][et_pb_blurb title=”Developing a Logic Model” url=”https:\/\/odalerts.cste.org\/?page_id=25847620″ image=”https:\/\/odalerts.cste.org\/wp-content\/uploads\/2026\/02\/cloud-computing.png” icon_placement=”left” image_icon_width=”62px” content_max_width=”997px” _builder_version=”4.14.2″ _module_preset=”default” header_font=”Poppins||||||||” header_font_size=”19px” body_font=”Poppins||||||||” body_font_size=”15px” link_option_url=”https:\/\/odalerts.cste.org\/?page_id=25847620″ global_colors_info=”{}”]<\/p>\n
[\/et_pb_blurb][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.14.2″ _module_preset=”default” custom_margin=”82px|auto||auto||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.14.2″ _module_preset=”default” global_colors_info=”{}”][dipi_dual_heading second_heading=”Continue The Toolkit” heading_tag=”h1″ heading_display=”column” fh_padding=”0px|20px|20px|20px|false|true” sh_style=”background” sh_background_style=”normal” sh_padding=”0px|20px|20px|20px|false|true” sh_margin=”-30px|0px|0px|20px|false|false” fh_padding_tablet=”0px|20px|20px|20px|false|true” fh_padding_phone=”0px|20px|20px|20px|false|true” fh_padding_last_edited=”on|desktop” fh_margin_last_edited=”off|desktop” sh_padding_tablet=”0px|20px|20px|20px|false|true” sh_padding_phone=”0px|20px|20px|20px|false|true” sh_padding_last_edited=”on|desktop” sh_margin_tablet=”-30px|0px|0px|20px|false|false” sh_margin_phone=”-20px|0px|0px|0px|false|false” sh_margin_last_edited=”on|phone” _builder_version=”4.16″ _module_preset=”default” first_heading_font=”Poppins||||||||” first_heading_text_color=”#919eb0″ first_heading_font_size=”80px” first_heading_letter_spacing=”-9px” first_heading_line_height=”1.1em” second_heading_font=”Poppins||||||||” second_heading_text_align=”left” second_heading_text_color=”#071018″ second_heading_font_size=”49px” second_heading_letter_spacing=”-5px” second_heading_line_height=”1em” custom_margin=”-1px||-38px||false|false” custom_padding=”25px|||||” first_heading_font_size_tablet=”100px” first_heading_font_size_phone=”60px” first_heading_font_size_last_edited=”on|tablet” first_heading_letter_spacing_tablet=”-7px” first_heading_letter_spacing_phone=”-4px” first_heading_letter_spacing_last_edited=”on|tablet” second_heading_font_size_tablet=”100px” second_heading_font_size_phone=”60px” second_heading_font_size_last_edited=”on|tablet” second_heading_letter_spacing_tablet=”-7px” second_heading_letter_spacing_phone=”-4px” second_heading_letter_spacing_last_edited=”on|tablet” locked=”off” global_colors_info=”{}” overlay_bg_color_gradient_type_tablet=”linear” overlay_bg_color_gradient_type_phone=”linear” overlay_bg_color_gradient_type__hover=”linear” image_bg_color_gradient_type__hover=”linear” first_heading_color_gradient_stops=”#2b87da 0%|#29c4a9 100%” second_heading_color_gradient_stops=”#2b87da 0%|#29c4a9 100%” background_text_color_gradient_stops=”#2b87da 0%|#29c4a9 100%” fh_reveal_effect_color_gradient_stops=”#2b87da 0%|#29c4a9 100%” sh_reveal_effect_color_gradient_stops=”#2b87da 0%|#29c4a9 100%” content_bg_color_gradient_type=”linear” content_hover_bg_color_gradient_type=”linear” item_bg_color_gradient_type=”linear” overlay_bg_color_gradient_type=”linear” image_bg_color_gradient_type=”linear” front_side_bg_color_gradient_type=”linear” back_side_bg_color_gradient_type=”linear”][\/dipi_dual_heading][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_4,1_4″ disabled_on=”on|on|off” _builder_version=”4.14.2″ _module_preset=”default” custom_margin=”-4px|auto||auto||” custom_padding=”0px|||||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.14.2″ _module_preset=”default” global_colors_info=”{}”][et_pb_blurb url=”https:\/\/odalerts.cste.org\/” _builder_version=”4.14.2″ _module_preset=”default” body_font=”Poppins||||||||” body_text_color=”#071018″ body_font_size=”24px” link_option_url=”https:\/\/odalerts.cste.org\/” global_colors_info=”{}”]<\/p>\n
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Next:<\/strong> Early Planning Steps><\/p>\n\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n \n <”h1″ class=\"dipi-dh-main\">\n \n \n \n ”Anomaly\n <\/span>\n <\/span>\n <\/span>\n \n \n \n ”Code”\n <\/span>\n <\/span>\n <\/span>\n <\/”h1″>\n <\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n","protected":false},"author":7,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"footnotes":""},"dipi_cpt_category":[],"class_list":["post-25848066","page","type-page","status-publish","hentry"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/odalerts.cste.org\/index.php?rest_route=\/wp\/v2\/pages\/25848066","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/odalerts.cste.org\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/odalerts.cste.org\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/odalerts.cste.org\/index.php?rest_route=\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/odalerts.cste.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=25848066"}],"version-history":[{"count":12,"href":"https:\/\/odalerts.cste.org\/index.php?rest_route=\/wp\/v2\/pages\/25848066\/revisions"}],"predecessor-version":[{"id":25848115,"href":"https:\/\/odalerts.cste.org\/index.php?rest_route=\/wp\/v2\/pages\/25848066\/revisions\/25848115"}],"wp:attachment":[{"href":"https:\/\/odalerts.cste.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=25848066"}],"wp:term":[{"taxonomy":"dipi_cpt_category","embeddable":true,"href":"https:\/\/odalerts.cste.org\/index.php?rest_route=%2Fwp%2Fv2%2Fdipi_cpt_category&post=25848066"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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