Estimated Modern Use: Employing A Service Statistics-Based Indicator to Monitor Family Planning Programs

Estimated Modern Use: Employing A Service... | Gates Open Research "use strict";function _typeof(t){return(_typeof="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}!function(){var t=function(){var t,e,o=[],n=window,r=n;for(;r;){try{if(r.frames.__tcfapiLocator){t=r;break}}catch(t){}if(r===n.top)break;r=r.parent}t||(!function t(){var e=n.document,o=!!n.frames.__tcfapiLocator;if(!o)if(e.body){var r=e.createElement("iframe");r.style.cssText="display:none",r.name="__tcfapiLocator",e.body.appendChild(r)}else setTimeout(t,5);return!o}(),n.__tcfapi=function(){for(var t=arguments.length,n=new Array(t),r=0;r 3&&2===parseInt(n[1],10)&&"boolean"==typeof n[3]&&(e=n[3],"function"==typeof n[2]&&n[2]("set",!0)):"ping"===n[0]?"function"==typeof n[2]&&n[2]({gdprApplies:e,cmpLoaded:!1,cmpStatus:"stub"}):o.push(n)},n.addEventListener("message",(function(t){var e="string"==typeof t.data,o={};if(e)try{o=JSON.parse(t.data)}catch(t){}else o=t.data;var n="object"===_typeof(o)&&null!==o?o.__tcfapiCall:null;n&&window.__tcfapi(n.command,n.version,(function(o,r){var a={__tcfapiReturn:{returnValue:o,success:r,callId:n.callId}};t&&t.source&&t.source.postMessage&&t.source.postMessage(e?JSON.stringify(a):a,"*")}),n.parameter)}),!1))};"undefined"!=typeof module?module.exports=t:t()}(); dataLayer = dataLayer || []; // Standard GTM initialization - Google Consent Mode handles consent automatically (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start': new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0], j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src= 'https://www.googletagmanager.com/gtm.js?id='+i+dl+ '>m_auth=hzk0Vc3qFsQYhCrIoHz68A>m_preview=env-1>m_cookies_win=x';f.parentNode.insertBefore(j,f); })(window,document,'script','dataLayer','GTM-MWFK8L5J'); ;window.NREUM||(NREUM={});NREUM.init={distributed_tracing:{enabled:true},privacy:{cookies_enabled:true},ajax:{deny_list:["bam.nr-data.net"]}}; ;NREUM.loader_config={accountID:"438030",trustKey:"438030",agentID:"772317073",licenseKey:"97f8f67f26",applicationID:"772317073"} ;NREUM.info={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net",licenseKey:"97f8f67f26",applicationID:"772317073",sa:1} ;/*! For license information please see nr-loader-spa-1.236.0.min.js.LICENSE.txt */ (()=>{"use strict";var e,t,r={5763:(e,t,r)=>{r.d(t,{P_:()=>l,Mt:()=>g,C5:()=>s,DL:()=>v,OP:()=>T,lF:()=>D,Yu:()=>y,Dg:()=>h,CX:()=>c,GE:()=>b,sU:()=>_});var n=r(8632),i=r(9567);const o={beacon:n.ce.beacon,errorBeacon:n.ce.errorBeacon,licenseKey:void 0,applicationID:void 0,sa:void 0,queueTime:void 0,applicationTime:void 0,ttGuid:void 0,user:void 0,account:void 0,product:void 0,extra:void 0,jsAttributes:{},userAttributes:void 0,atts:void 0,transactionName:void 0,tNamePlain:void 0},a={};function s(e){if(!e)throw new Error("All info objects require an agent identifier!");if(!a[e])throw new Error("Info for ".concat(e," was never set"));return a[e]}function c(e,t){if(!e)throw new Error("All info objects require an agent identifier!");a[e]=(0,i.D)(t,o),(0,n.Qy)(e,a[e],"info")}var u=r(7056);const d=()=>{const e={blockSelector:"[data-nr-block]",maskInputOptions:{password:!0}};return{allow_bfcache:!0,privacy:{cookies_enabled:!0},ajax:{deny_list:void 0,enabled:!0,harvestTimeSeconds:10},distributed_tracing:{enabled:void 0,exclude_newrelic_header:void 0,cors_use_newrelic_header:void 0,cors_use_tracecontext_headers:void 0,allowed_origins:void 0},session:{domain:void 0,expiresMs:u.oD,inactiveMs:u.Hb},ssl:void 0,obfuscate:void 0,jserrors:{enabled:!0,harvestTimeSeconds:10},metrics:{enabled:!0},page_action:{enabled:!0,harvestTimeSeconds:30},page_view_event:{enabled:!0},page_view_timing:{enabled:!0,harvestTimeSeconds:30,long_task:!1},session_trace:{enabled:!0,harvestTimeSeconds:10},harvest:{tooManyRequestsDelay:60},session_replay:{enabled:!1,harvestTimeSeconds:60,sampleRate:.1,errorSampleRate:.1,maskTextSelector:"*",maskAllInputs:!0,get blockClass(){return"nr-block"},get ignoreClass(){return"nr-ignore"},get maskTextClass(){return"nr-mask"},get blockSelector(){return e.blockSelector},set blockSelector(t){e.blockSelector+=",".concat(t)},get maskInputOptions(){return e.maskInputOptions},set maskInputOptions(t){e.maskInputOptions={...t,password:!0}}},spa:{enabled:!0,harvestTimeSeconds:10}}},f={};function l(e){if(!e)throw new Error("All configuration objects require an agent identifier!");if(!f[e])throw new Error("Configuration for ".concat(e," was never set"));return f[e]}function h(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");f[e]=(0,i.D)(t,d()),(0,n.Qy)(e,f[e],"config")}function g(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");var r=l(e);if(r){for(var n=t.split("."),i=0;i {r.d(t,{D:()=>i});var n=r(50);function i(e,t){try{if(!e||"object"!=typeof e)return(0,n.Z)("Setting a Configurable requires an object as input");if(!t||"object"!=typeof t)return(0,n.Z)("Setting a Configurable requires a model to set its initial properties");const r=Object.create(Object.getPrototypeOf(t),Object.getOwnPropertyDescriptors(t)),o=0===Object.keys(r).length?e:r;for(let a in o)if(void 0!==e[a])try{"object"==typeof e[a]&&"object"==typeof t[a]?r[a]=i(e[a],t[a]):r[a]=e[a]}catch(e){(0,n.Z)("An error occurred while setting a property of a Configurable",e)}return r}catch(e){(0,n.Z)("An error occured while setting a Configurable",e)}}},6818:(e,t,r)=>{r.d(t,{Re:()=>i,gF:()=>o,q4:()=>n});const n="1.236.0",i="PROD",o="CDN"},385:(e,t,r)=>{r.d(t,{FN:()=>a,IF:()=>u,Nk:()=>f,Tt:()=>s,_A:()=>o,il:()=>n,pL:()=>c,v6:()=>i,w1:()=>d});const n="undefined"!=typeof window&&!!window.document,i="undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self.navigator instanceof WorkerNavigator||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis.navigator instanceof WorkerNavigator),o=n?window:"undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis),a=""+o?.location,s=/iPad|iPhone|iPod/.test(navigator.userAgent),c=s&&"undefined"==typeof SharedWorker,u=(()=>{const e=navigator.userAgent.match(/Firefox[/\s](\d+\.\d+)/);return Array.isArray(e)&&e.length>=2?+e[1]:0})(),d=Boolean(n&&window.document.documentMode),f=!!navigator.sendBeacon},1117:(e,t,r)=>{r.d(t,{w:()=>o});var n=r(50);const i={agentIdentifier:"",ee:void 0};class o{constructor(e){try{if("object"!=typeof e)return(0,n.Z)("shared context requires an object as input");this.sharedContext={},Object.assign(this.sharedContext,i),Object.entries(e).forEach((e=>{let[t,r]=e;Object.keys(i).includes(t)&&(this.sharedContext[t]=r)}))}catch(e){(0,n.Z)("An error occured while setting SharedContext",e)}}}},8e3:(e,t,r)=>{r.d(t,{L:()=>d,R:()=>c});var n=r(2177),i=r(1284),o=r(4322),a=r(3325);const s={};function c(e,t){const r={staged:!1,priority:a.p[t]||0};u(e),s[e].get(t)||s[e].set(t,r)}function u(e){e&&(s[e]||(s[e]=new Map))}function d(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"",t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:"feature";if(u(e),!e||!s[e].get(t))return a(t);s[e].get(t).staged=!0;const r=[...s[e]];function a(t){const r=e?n.ee.get(e):n.ee,a=o.X.handlers;if(r.backlog&&a){var s=r.backlog[t],c=a[t];if(c){for(var u=0;s&&u {let[t,r]=e;return r.staged}))&&(r.sort(((e,t)=>e[1].priority-t[1].priority)),r.forEach((e=>{let[t]=e;a(t)})))}function f(e,t){var r=e[1];(0,i.D)(t[r],(function(t,r){var n=e[0];if(r[0]===n){var i=r[1],o=e[3],a=e[2];i.apply(o,a)}}))}},2177:(e,t,r)=>{r.d(t,{c:()=>f,ee:()=>u});var n=r(8632),i=r(2210),o=r(1284),a=r(5763),s="nr@context";let c=(0,n.fP)();var u;function d(){}function f(e){return(0,i.X)(e,s,l)}function l(){return new d}function h(){u.aborted=!0,u.backlog={}}c.ee?u=c.ee:(u=function e(t,r){var n={},c={},f={},g=!1;try{g=16===r.length&&(0,a.OP)(r).isolatedBacklog}catch(e){}var p={on:b,addEventListener:b,removeEventListener:y,emit:v,get:x,listeners:w,context:m,buffer:A,abort:h,aborted:!1,isBuffering:E,debugId:r,backlog:g?{}:t&&"object"==typeof t.backlog?t.backlog:{}};return p;function m(e){return e&&e instanceof d?e:e?(0,i.X)(e,s,l):l()}function v(e,r,n,i,o){if(!1!==o&&(o=!0),!u.aborted||i){t&&o&&t.emit(e,r,n);for(var a=m(n),s=w(e),d=s.length,f=0;fn,p:()=>i});var n=r(2177).ee.get("handle");function i(e,t,r,i,o){o?(o.buffer([e],i),o.emit(e,t,r)):(n.buffer([e],i),n.emit(e,t,r))}},4322:(e,t,r)=>{r.d(t,{X:()=>o});var n=r(5546);o.on=a;var i=o.handlers={};function o(e,t,r,o){a(o||n.E,i,e,t,r)}function a(e,t,r,i,o){o||(o="feature"),e||(e=n.E);var a=t[o]=t[o]||{};(a[r]=a[r]||[]).push([e,i])}},3239:(e,t,r)=>{r.d(t,{bP:()=>s,iz:()=>c,m$:()=>a});var n=r(385);let i=!1,o=!1;try{const e={get passive(){return i=!0,!1},get signal(){return o=!0,!1}};n._A.addEventListener("test",null,e),n._A.removeEventListener("test",null,e)}catch(e){}function a(e,t){return i||o?{capture:!!e,passive:i,signal:t}:!!e}function s(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;window.addEventListener(e,t,a(r,n))}function c(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;document.addEventListener(e,t,a(r,n))}},4402:(e,t,r)=>{r.d(t,{Ht:()=>u,M:()=>c,Rl:()=>a,ky:()=>s});var n=r(385);const i="xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx";function o(e,t){return e?15&e[t]:16*Math.random()|0}function a(){const e=n._A?.crypto||n._A?.msCrypto;let t,r=0;return e&&e.getRandomValues&&(t=e.getRandomValues(new Uint8Array(31))),i.split("").map((e=>"x"===e?o(t,++r).toString(16):"y"===e?(3&o()|8).toString(16):e)).join("")}function s(e){const t=n._A?.crypto||n._A?.msCrypto;let r,i=0;t&&t.getRandomValues&&(r=t.getRandomValues(new Uint8Array(31)));const a=[];for(var s=0;s {r.d(t,{Bq:()=>n,Hb:()=>o,oD:()=>i});const n="NRBA",i=144e5,o=18e5},7894:(e,t,r)=>{function n(){return Math.round(performance.now())}r.d(t,{z:()=>n})},7243:(e,t,r)=>{r.d(t,{e:()=>o});var n=r(385),i={};function o(e){if(e in i)return i[e];if(0===(e||"").indexOf("data:"))return{protocol:"data"};let t;var r=n._A?.location,o={};if(n.il)t=document.createElement("a"),t.href=e;else try{t=new URL(e,r.href)}catch(e){return o}o.port=t.port;var a=t.href.split("://");!o.port&&a[1]&&(o.port=a[1].split("/")[0].split("@").pop().split(":")[1]),o.port&&"0"!==o.port||(o.port="https"===a[0]?"443":"80"),o.hostname=t.hostname||r.hostname,o.pathname=t.pathname,o.protocol=a[0],"/"!==o.pathname.charAt(0)&&(o.pathname="/"+o.pathname);var s=!t.protocol||":"===t.protocol||t.protocol===r.protocol,c=t.hostname===r.hostname&&t.port===r.port;return o.sameOrigin=s&&(!t.hostname||c),"/"===o.pathname&&(i[e]=o),o}},50:(e,t,r)=>{function n(e,t){"function"==typeof console.warn&&(console.warn("New Relic: ".concat(e)),t&&console.warn(t))}r.d(t,{Z:()=>n})},2587:(e,t,r)=>{r.d(t,{N:()=>c,T:()=>u});var n=r(2177),i=r(5546),o=r(8e3),a=r(3325);const s={stn:[a.D.sessionTrace],err:[a.D.jserrors,a.D.metrics],ins:[a.D.pageAction],spa:[a.D.spa],sr:[a.D.sessionReplay,a.D.sessionTrace]};function c(e,t){const r=n.ee.get(t);e&&"object"==typeof e&&(Object.entries(e).forEach((e=>{let[t,n]=e;void 0===u[t]&&(s[t]?s[t].forEach((e=>{n?(0,i.p)("feat-"+t,[],void 0,e,r):(0,i.p)("block-"+t,[],void 0,e,r),(0,i.p)("rumresp-"+t,[Boolean(n)],void 0,e,r)})):n&&(0,i.p)("feat-"+t,[],void 0,void 0,r),u[t]=Boolean(n))})),Object.keys(s).forEach((e=>{void 0===u[e]&&(s[e]?.forEach((t=>(0,i.p)("rumresp-"+e,[!1],void 0,t,r))),u[e]=!1)})),(0,o.L)(t,a.D.pageViewEvent))}const u={}},2210:(e,t,r)=>{r.d(t,{X:()=>i});var n=Object.prototype.hasOwnProperty;function i(e,t,r){if(n.call(e,t))return e[t];var i=r();if(Object.defineProperty&&Object.keys)try{return Object.defineProperty(e,t,{value:i,writable:!0,enumerable:!1}),i}catch(e){}return e[t]=i,i}},1284:(e,t,r)=>{r.d(t,{D:()=>n});const n=(e,t)=>Object.entries(e||{}).map((e=>{let[r,n]=e;return t(r,n)}))},4351:(e,t,r)=>{r.d(t,{P:()=>o});var n=r(2177);const i=()=>{const e=new WeakSet;return(t,r)=>{if("object"==typeof r&&null!==r){if(e.has(r))return;e.add(r)}return r}};function o(e){try{return JSON.stringify(e,i())}catch(e){try{n.ee.emit("internal-error",[e])}catch(e){}}}},3960:(e,t,r)=>{r.d(t,{K:()=>a,b:()=>o});var n=r(3239);function i(){return"undefined"==typeof document||"complete"===document.readyState}function o(e,t){if(i())return e();(0,n.bP)("load",e,t)}function a(e){if(i())return e();(0,n.iz)("DOMContentLoaded",e)}},8632:(e,t,r)=>{r.d(t,{EZ:()=>u,Qy:()=>c,ce:()=>o,fP:()=>a,gG:()=>d,mF:()=>s});var n=r(7894),i=r(385);const o={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net"};function a(){return i._A.NREUM||(i._A.NREUM={}),void 0===i._A.newrelic&&(i._A.newrelic=i._A.NREUM),i._A.NREUM}function s(){let e=a();return e.o||(e.o={ST:i._A.setTimeout,SI:i._A.setImmediate,CT:i._A.clearTimeout,XHR:i._A.XMLHttpRequest,REQ:i._A.Request,EV:i._A.Event,PR:i._A.Promise,MO:i._A.MutationObserver,FETCH:i._A.fetch}),e}function c(e,t,r){let i=a();const o=i.initializedAgents||{},s=o[e]||{};return Object.keys(s).length||(s.initializedAt={ms:(0,n.z)(),date:new Date}),i.initializedAgents={...o,[e]:{...s,[r]:t}},i}function u(e,t){a()[e]=t}function d(){return function(){let e=a();const t=e.info||{};e.info={beacon:o.beacon,errorBeacon:o.errorBeacon,...t}}(),function(){let e=a();const t=e.init||{};e.init={...t}}(),s(),function(){let e=a();const t=e.loader_config||{};e.loader_config={...t}}(),a()}},7956:(e,t,r)=>{r.d(t,{N:()=>i});var n=r(3239);function i(e){let t=arguments.length>1&&void 0!==arguments[1]&&arguments[1],r=arguments.length>2?arguments[2]:void 0,i=arguments.length>3?arguments[3]:void 0;return void(0,n.iz)("visibilitychange",(function(){if(t)return void("hidden"==document.visibilityState&&e());e(document.visibilityState)}),r,i)}},1214:(e,t,r)=>{r.d(t,{em:()=>v,u5:()=>N,QU:()=>S,_L:()=>I,Gm:()=>L,Lg:()=>M,gy:()=>U,BV:()=>Q,Kf:()=>ee});var n=r(2177);const i="nr@original";var o=Object.prototype.hasOwnProperty,a=!1;function s(e,t){return e||(e=n.ee),r.inPlace=function(e,t,n,i,o){n||(n="");var a,s,c,u="-"===n.charAt(0);for(c=0;c 2?n-2:0),o=2;o {r(A[T],e,w),r(E[T],e,w)})),r(l._A,"fetch",y),t.on(y+"end",(function(e,r){var n=this;if(r){var i=r.headers.get("content-length");null!==i&&(n.rxSize=i),t.emit(y+"done",[null,r],n)}else t.emit(y+"done",[e],n)})),t}const O={},j=["pushState","replaceState"];function S(e){const t=function(e){return(e||n.ee).get("history")}(e);return!l.il||O[t.debugId]++||(O[t.debugId]=1,s(t).inPlace(window.history,j,"-")),t}var P=r(3239);const C={},R=["appendChild","insertBefore","replaceChild"];function I(e){const t=function(e){return(e||n.ee).get("jsonp")}(e);if(!l.il||C[t.debugId])return t;C[t.debugId]=!0;var r=s(t),i=/[?&](?:callback|cb)=([^&#]+)/,o=/(.*)\.([^.]+)/,a=/^(\w+)(\.|$)(.*)$/;function c(e,t){var r=e.match(a),n=r[1],i=r[3];return i?c(i,t[n]):t[n]}return r.inPlace(Node.prototype,R,"dom-"),t.on("dom-start",(function(e){!function(e){if(!e||"string"!=typeof e.nodeName||"script"!==e.nodeName.toLowerCase())return;if("function"!=typeof e.addEventListener)return;var n=(a=e.src,s=a.match(i),s?s[1]:null);var a,s;if(!n)return;var u=function(e){var t=e.match(o);if(t&&t.length>=3)return{key:t[2],parent:c(t[1],window)};return{key:e,parent:window}}(n);if("function"!=typeof u.parent[u.key])return;var d={};function f(){t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}function l(){t.emit("jsonp-error",[],d),t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}r.inPlace(u.parent,[u.key],"cb-",d),e.addEventListener("load",f,(0,P.m$)(!1)),e.addEventListener("error",l,(0,P.m$)(!1)),t.emit("new-jsonp",[e.src],d)}(e[0])})),t}var k=r(5763);const H={};function L(e){const t=function(e){return(e||n.ee).get("mutation")}(e);if(!l.il||H[t.debugId])return t;H[t.debugId]=!0;var r=s(t),i=k.Yu.MO;return i&&(window.MutationObserver=function(e){return this instanceof i?new i(r(e,"fn-")):i.apply(this,arguments)},MutationObserver.prototype=i.prototype),t}const z={};function M(e){const t=function(e){return(e||n.ee).get("promise")}(e);if(z[t.debugId])return t;z[t.debugId]=!0;var r=n.c,o=s(t),a=k.Yu.PR;return a&&function(){function e(r){var n=t.context(),i=o(r,"executor-",n,null,!1);const s=Reflect.construct(a,[i],e);return t.context(s).getCtx=function(){return n},s}l._A.Promise=e,Object.defineProperty(e,"name",{value:"Promise"}),e.toString=function(){return a.toString()},Object.setPrototypeOf(e,a),["all","race"].forEach((function(r){const n=a[r];e[r]=function(e){let i=!1;[...e||[]].forEach((e=>{this.resolve(e).then(a("all"===r),a(!1))}));const o=n.apply(this,arguments);return o;function a(e){return function(){t.emit("propagate",[null,!i],o,!1,!1),i=i||!e}}}})),["resolve","reject"].forEach((function(r){const n=a[r];e[r]=function(e){const r=n.apply(this,arguments);return e!==r&&t.emit("propagate",[e,!0],r,!1,!1),r}})),e.prototype=a.prototype;const n=a.prototype.then;a.prototype.then=function(){var e=this,i=r(e);i.promise=e;for(var a=arguments.length,s=new Array(a),c=0;c e())),t};function m(e,t){i.inPlace(t,["onreadystatechange"],"fn-",E)}function b(){var e=this,t=r.context(e);e.readyState>3&&!t.resolved&&(t.resolved=!0,r.emit("xhr-resolved",[],e)),i.inPlace(e,f,"fn-",E)}if(function(e,t){for(var r in e)t[r]=e[r]}(o,p),p.prototype=o.prototype,i.inPlace(p.prototype,J,"-xhr-",E),r.on("send-xhr-start",(function(e,t){m(e,t),function(e){h.push(e),a&&(y?y.then(A):u?u(A):(w=-w,x.data=w))}(t)})),r.on("open-xhr-start",m),a){var y=c&&c.resolve();if(!u&&!c){var w=1,x=document.createTextNode(w);new a(A).observe(x,{characterData:!0})}}else t.on("fn-end",(function(e){e[0]&&e[0].type===d||A()}));function A(){for(var e=0;e {r.d(t,{t:()=>n});const n=r(3325).D.ajax},6660:(e,t,r)=>{r.d(t,{A:()=>i,t:()=>n});const n=r(3325).D.jserrors,i="nr@seenError"},3081:(e,t,r)=>{r.d(t,{gF:()=>o,mY:()=>i,t9:()=>n,vz:()=>s,xS:()=>a});const n=r(3325).D.metrics,i="sm",o="cm",a="storeSupportabilityMetrics",s="storeEventMetrics"},4649:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageAction},7633:(e,t,r)=>{r.d(t,{Dz:()=>i,OJ:()=>a,qw:()=>o,t9:()=>n});const n=r(3325).D.pageViewEvent,i="firstbyte",o="domcontent",a="windowload"},9251:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageViewTiming},3614:(e,t,r)=>{r.d(t,{BST_RESOURCE:()=>i,END:()=>s,FEATURE_NAME:()=>n,FN_END:()=>u,FN_START:()=>c,PUSH_STATE:()=>d,RESOURCE:()=>o,START:()=>a});const n=r(3325).D.sessionTrace,i="bstResource",o="resource",a="-start",s="-end",c="fn"+a,u="fn"+s,d="pushState"},7836:(e,t,r)=>{r.d(t,{BODY:()=>A,CB_END:()=>E,CB_START:()=>u,END:()=>x,FEATURE_NAME:()=>i,FETCH:()=>_,FETCH_BODY:()=>v,FETCH_DONE:()=>m,FETCH_START:()=>p,FN_END:()=>c,FN_START:()=>s,INTERACTION:()=>l,INTERACTION_API:()=>d,INTERACTION_EVENTS:()=>o,JSONP_END:()=>b,JSONP_NODE:()=>g,JS_TIME:()=>T,MAX_TIMER_BUDGET:()=>a,REMAINING:()=>f,SPA_NODE:()=>h,START:()=>w,originalSetTimeout:()=>y});var n=r(5763);const i=r(3325).D.spa,o=["click","submit","keypress","keydown","keyup","change"],a=999,s="fn-start",c="fn-end",u="cb-start",d="api-ixn-",f="remaining",l="interaction",h="spaNode",g="jsonpNode",p="fetch-start",m="fetch-done",v="fetch-body-",b="jsonp-end",y=n.Yu.ST,w="-start",x="-end",A="-body",E="cb"+x,T="jsTime",_="fetch"},5938:(e,t,r)=>{r.d(t,{W:()=>o});var n=r(5763),i=r(2177);class o{constructor(e,t,r){this.agentIdentifier=e,this.aggregator=t,this.ee=i.ee.get(e,(0,n.OP)(this.agentIdentifier).isolatedBacklog),this.featureName=r,this.blocked=!1}}},9144:(e,t,r)=>{r.d(t,{j:()=>m});var n=r(3325),i=r(5763),o=r(5546),a=r(2177),s=r(7894),c=r(8e3),u=r(3960),d=r(385),f=r(50),l=r(3081),h=r(8632);function g(){const e=(0,h.gG)();["setErrorHandler","finished","addToTrace","inlineHit","addRelease","addPageAction","setCurrentRouteName","setPageViewName","setCustomAttribute","interaction","noticeError","setUserId"].forEach((t=>{e[t]=function(){for(var r=arguments.length,n=new Array(r),i=0;i 1?r-1:0),i=1;i {e.exposed&&e.api[t]&&o.push(e.api[t](...n))})),o.length>1?o:o[0]}(t,...n)}}))}var p=r(2587);function m(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},m=arguments.length>2?arguments[2]:void 0,v=arguments.length>3?arguments[3]:void 0,{init:b,info:y,loader_config:w,runtime:x={loaderType:m},exposed:A=!0}=t;const E=(0,h.gG)();y||(b=E.init,y=E.info,w=E.loader_config),(0,i.Dg)(e,b||{}),(0,i.GE)(e,w||{}),(0,i.sU)(e,x),y.jsAttributes??={},d.v6&&(y.jsAttributes.isWorker=!0),(0,i.CX)(e,y),g();const T=function(e,t){t||(0,c.R)(e,"api");const h={};var g=a.ee.get(e),p=g.get("tracer"),m="api-",v=m+"ixn-";function b(t,r,n,o){const a=(0,i.C5)(e);return null===r?delete a.jsAttributes[t]:(0,i.CX)(e,{...a,jsAttributes:{...a.jsAttributes,[t]:r}}),x(m,n,!0,o||null===r?"session":void 0)(t,r)}function y(){}["setErrorHandler","finished","addToTrace","inlineHit","addRelease"].forEach((e=>h[e]=x(m,e,!0,"api"))),h.addPageAction=x(m,"addPageAction",!0,n.D.pageAction),h.setCurrentRouteName=x(m,"routeName",!0,n.D.spa),h.setPageViewName=function(t,r){if("string"==typeof t)return"/"!==t.charAt(0)&&(t="/"+t),(0,i.OP)(e).customTransaction=(r||"http://custom.transaction")+t,x(m,"setPageViewName",!0)()},h.setCustomAttribute=function(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];if("string"==typeof e){if(["string","number"].includes(typeof t)||null===t)return b(e,t,"setCustomAttribute",r);(0,f.Z)("Failed to execute setCustomAttribute.\nNon-null value must be a string or number type, but a type of was provided."))}else(0,f.Z)("Failed to execute setCustomAttribute.\nName must be a string type, but a type of was provided."))},h.setUserId=function(e){if("string"==typeof e||null===e)return b("enduser.id",e,"setUserId",!0);(0,f.Z)("Failed to execute setUserId.\nNon-null value must be a string type, but a type of was provided."))},h.interaction=function(){return(new y).get()};var w=y.prototype={createTracer:function(e,t){var r={},i=this,a="function"==typeof t;return(0,o.p)(v+"tracer",[(0,s.z)(),e,r],i,n.D.spa,g),function(){if(p.emit((a?"":"no-")+"fn-start",[(0,s.z)(),i,a],r),a)try{return t.apply(this,arguments)}catch(e){throw p.emit("fn-err",[arguments,this,"string"==typeof e?new Error(e):e],r),e}finally{p.emit("fn-end",[(0,s.z)()],r)}}}};function x(e,t,r,i){return function(){return(0,o.p)(l.xS,["API/"+t+"/called"],void 0,n.D.metrics,g),i&&(0,o.p)(e+t,[(0,s.z)(),...arguments],r?null:this,i,g),r?void 0:this}}function A(){r.e(439).then(r.bind(r,7438)).then((t=>{let{setAPI:r}=t;r(e),(0,c.L)(e,"api")})).catch((()=>(0,f.Z)("Downloading runtime APIs failed...")))}return["actionText","setName","setAttribute","save","ignore","onEnd","getContext","end","get"].forEach((e=>{w[e]=x(v,e,void 0,n.D.spa)})),h.noticeError=function(e,t){"string"==typeof e&&(e=new Error(e)),(0,o.p)(l.xS,["API/noticeError/called"],void 0,n.D.metrics,g),(0,o.p)("err",[e,(0,s.z)(),!1,t],void 0,n.D.jserrors,g)},d.il?(0,u.b)((()=>A()),!0):A(),h}(e,v);return(0,h.Qy)(e,T,"api"),(0,h.Qy)(e,A,"exposed"),(0,h.EZ)("activatedFeatures",p.T),T}},3325:(e,t,r)=>{r.d(t,{D:()=>n,p:()=>i});const n={ajax:"ajax",jserrors:"jserrors",metrics:"metrics",pageAction:"page_action",pageViewEvent:"page_view_event",pageViewTiming:"page_view_timing",sessionReplay:"session_replay",sessionTrace:"session_trace",spa:"spa"},i={[n.pageViewEvent]:1,[n.pageViewTiming]:2,[n.metrics]:3,[n.jserrors]:4,[n.ajax]:5,[n.sessionTrace]:6,[n.pageAction]:7,[n.spa]:8,[n.sessionReplay]:9}}},n={};function i(e){var t=n[e];if(void 0!==t)return t.exports;var o=n[e]={exports:{}};return r[e](o,o.exports,i),o.exports}i.m=r,i.d=(e,t)=>{for(var r in t)i.o(t,r)&&!i.o(e,r)&&Object.defineProperty(e,r,{enumerable:!0,get:t[r]})},i.f={},i.e=e=>Promise.all(Object.keys(i.f).reduce(((t,r)=>(i.f[r](e,t),t)),[])),i.u=e=>(({78:"page_action-aggregate",147:"metrics-aggregate",242:"session-manager",317:"jserrors-aggregate",348:"page_view_timing-aggregate",412:"lazy-feature-loader",439:"async-api",538:"recorder",590:"session_replay-aggregate",675:"compressor",733:"session_trace-aggregate",786:"page_view_event-aggregate",873:"spa-aggregate",898:"ajax-aggregate"}[e]||e)+"."+{78:"ac76d497",147:"3dc53903",148:"1a20d5fe",242:"2a64278a",317:"49e41428",348:"bd6de33a",412:"2f55ce66",439:"30bd804e",538:"1b18459f",590:"cf0efb30",675:"ae9f91a8",733:"83105561",786:"06482edd",860:"03a8b7a5",873:"e6b09d52",898:"998ef92b"}[e]+"-1.236.0.min.js"),i.o=(e,t)=>Object.prototype.hasOwnProperty.call(e,t),e={},t="NRBA:",i.l=(r,n,o,a)=>{if(e[r])e[r].push(n);else{var s,c;if(void 0!==o)for(var u=document.getElementsByTagName("script"),d=0;d {s.onerror=s.onload=null,clearTimeout(h);var i=e[r];if(delete e[r],s.parentNode&&s.parentNode.removeChild(s),i&&i.forEach((e=>e(n))),t)return t(n)},h=setTimeout(l.bind(null,void 0,{type:"timeout",target:s}),12e4);s.onerror=l.bind(null,s.onerror),s.onload=l.bind(null,s.onload),c&&document.head.appendChild(s)}},i.r=e=>{"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},i.j=364,i.p="https://js-agent.newrelic.com/",(()=>{var e={364:0,953:0};i.f.j=(t,r)=>{var n=i.o(e,t)?e[t]:void 0;if(0!==n)if(n)r.push(n[2]);else{var o=new Promise(((r,i)=>n=e[t]=[r,i]));r.push(n[2]=o);var a=i.p+i.u(t),s=new Error;i.l(a,(r=>{if(i.o(e,t)&&(0!==(n=e[t])&&(e[t]=void 0),n)){var o=r&&("load"===r.type?"missing":r.type),a=r&&r.target&&r.target.src;s.message="Loading chunk "+t+" failed.\n("+o+": "+a+")",s.name="ChunkLoadError",s.type=o,s.request=a,n[1](s)}}),"chunk-"+t,t)}};var t=(t,r)=>{var n,o,[a,s,c]=r,u=0;if(a.some((t=>0!==e[t]))){for(n in s)i.o(s,n)&&(i.m[n]=s[n]);if(c)c(i)}for(t&&t(r);u {i.r(o);var e=i(3325),t=i(5763);const r=Object.values(e.D);function n(e){const n={};return r.forEach((r=>{n[r]=function(e,r){return!1!==(0,t.Mt)(r,"".concat(e,".enabled"))}(r,e)})),n}var a=i(9144);var s=i(5546),c=i(385),u=i(8e3),d=i(5938),f=i(3960),l=i(50);class h extends d.W{constructor(e,t,r){let n=!(arguments.length>3&&void 0!==arguments[3])||arguments[3];super(e,t,r),this.auto=n,this.abortHandler,this.featAggregate,this.onAggregateImported,n&&(0,u.R)(e,r)}importAggregator(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};if(this.featAggregate||!this.auto)return;const r=c.il&&!0===(0,t.Mt)(this.agentIdentifier,"privacy.cookies_enabled");let n;this.onAggregateImported=new Promise((e=>{n=e}));const o=async()=>{let t;try{if(r){const{setupAgentSession:e}=await Promise.all([i.e(860),i.e(242)]).then(i.bind(i,3228));t=e(this.agentIdentifier)}}catch(e){(0,l.Z)("A problem occurred when starting up session manager. This page will not start or extend any session.",e)}try{if(!this.shouldImportAgg(this.featureName,t))return void(0,u.L)(this.agentIdentifier,this.featureName);const{lazyFeatureLoader:r}=await i.e(412).then(i.bind(i,8582)),{Aggregate:o}=await r(this.featureName,"aggregate");this.featAggregate=new o(this.agentIdentifier,this.aggregator,e),n(!0)}catch(e){(0,l.Z)("Downloading and initializing ".concat(this.featureName," failed..."),e),this.abortHandler?.(),n(!1)}};c.il?(0,f.b)((()=>o()),!0):o()}shouldImportAgg(r,n){return r!==e.D.sessionReplay||!1!==(0,t.Mt)(this.agentIdentifier,"session_trace.enabled")&&(!!n?.isNew||!!n?.state.sessionReplay)}}var g=i(7633),p=i(7894);class m extends h{static featureName=g.t9;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];if(super(r,n,g.t9,i),("undefined"==typeof PerformanceNavigationTiming||c.Tt)&&"undefined"!=typeof PerformanceTiming){const n=(0,t.OP)(r);n[g.Dz]=Math.max(Date.now()-n.offset,0),(0,f.K)((()=>n[g.qw]=Math.max((0,p.z)()-n[g.Dz],0))),(0,f.b)((()=>{const t=(0,p.z)();n[g.OJ]=Math.max(t-n[g.Dz],0),(0,s.p)("timing",["load",t],void 0,e.D.pageViewTiming,this.ee)}))}this.importAggregator()}}var v=i(1117),b=i(1284);class y extends v.w{constructor(e){super(e),this.aggregatedData={}}store(e,t,r,n,i){var o=this.getBucket(e,t,r,i);return o.metrics=function(e,t){t||(t={count:0});return t.count+=1,(0,b.D)(e,(function(e,r){t[e]=w(r,t[e])})),t}(n,o.metrics),o}merge(e,t,r,n,i){var o=this.getBucket(e,t,n,i);if(o.metrics){var a=o.metrics;a.count+=r.count,(0,b.D)(r,(function(e,t){if("count"!==e){var n=a[e],i=r[e];i&&!i.c?a[e]=w(i.t,n):a[e]=function(e,t){if(!t)return e;t.c||(t=x(t.t));return t.min=Math.min(e.min,t.min),t.max=Math.max(e.max,t.max),t.t+=e.t,t.sos+=e.sos,t.c+=e.c,t}(i,a[e])}}))}else o.metrics=r}storeMetric(e,t,r,n){var i=this.getBucket(e,t,r);return i.stats=w(n,i.stats),i}getBucket(e,t,r,n){this.aggregatedData[e]||(this.aggregatedData[e]={});var i=this.aggregatedData[e][t];return i||(i=this.aggregatedData[e][t]={params:r||{}},n&&(i.custom=n)),i}get(e,t){return t?this.aggregatedData[e]&&this.aggregatedData[e][t]:this.aggregatedData[e]}take(e){for(var t={},r="",n=!1,i=0;i t.max&&(t.max=e),e 2&&void 0!==arguments[2])||arguments[2];super(e,r,j.t,n),c.il&&((0,t.OP)(e).initHidden=Boolean("hidden"===document.visibilityState),(0,N.N)((()=>(0,s.p)("docHidden",[(0,p.z)()],void 0,j.t,this.ee)),!0),(0,O.bP)("pagehide",(()=>(0,s.p)("winPagehide",[(0,p.z)()],void 0,j.t,this.ee))),this.importAggregator())}}var P=i(3081);class C extends h{static featureName=P.t9;constructor(e,t){let r=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(e,t,P.t9,r),this.importAggregator()}}var R,I=i(2210),k=i(1214),H=i(2177),L={};try{R=localStorage.getItem("__nr_flags").split(","),console&&"function"==typeof console.log&&(L.console=!0,-1!==R.indexOf("dev")&&(L.dev=!0),-1!==R.indexOf("nr_dev")&&(L.nrDev=!0))}catch(e){}function z(e){try{L.console&&z(e)}catch(e){}}L.nrDev&&H.ee.on("internal-error",(function(e){z(e.stack)})),L.dev&&H.ee.on("fn-err",(function(e,t,r){z(r.stack)})),L.dev&&(z("NR AGENT IN DEVELOPMENT MODE"),z("flags: "+(0,b.D)(L,(function(e,t){return e})).join(", ")));var M=i(6660);class B extends h{static featureName=M.t;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(r,n,M.t,i),this.skipNext=0;try{this.removeOnAbort=new AbortController}catch(e){}const o=this;o.ee.on("fn-start",(function(e,t,r){o.abortHandler&&(o.skipNext+=1)})),o.ee.on("fn-err",(function(t,r,n){o.abortHandler&&!n[M.A]&&((0,I.X)(n,M.A,(function(){return!0})),this.thrown=!0,(0,s.p)("err",[n,(0,p.z)()],void 0,e.D.jserrors,o.ee))})),o.ee.on("fn-end",(function(){o.abortHandler&&!this.thrown&&o.skipNext>0&&(o.skipNext-=1)})),o.ee.on("internal-error",(function(t){(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,o.ee)})),this.origOnerror=c._A.onerror,c._A.onerror=this.onerrorHandler.bind(this),c._A.addEventListener("unhandledrejection",(t=>{const r=function(e){let t="Unhandled Promise Rejection: ";if(e instanceof Error)try{return e.message=t+e.message,e}catch(t){return e}if(void 0===e)return new Error(t);try{return new Error(t+(0,D.P)(e))}catch(e){return new Error(t)}}(t.reason);(0,s.p)("err",[r,(0,p.z)(),!1,{unhandledPromiseRejection:1}],void 0,e.D.jserrors,this.ee)}),(0,O.m$)(!1,this.removeOnAbort?.signal)),(0,k.gy)(this.ee),(0,k.BV)(this.ee),(0,k.em)(this.ee),(0,t.OP)(r).xhrWrappable&&(0,k.Kf)(this.ee),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}onerrorHandler(t,r,n,i,o){"function"==typeof this.origOnerror&&this.origOnerror(...arguments);try{this.skipNext?this.skipNext-=1:(0,s.p)("err",[o||new F(t,r,n),(0,p.z)()],void 0,e.D.jserrors,this.ee)}catch(t){try{(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,this.ee)}catch(e){}}return!1}}function F(e,t,r){this.message=e||"Uncaught error with no additional information",this.sourceURL=t,this.line=r}let U=1;const q="nr@id";function G(e){const t=typeof e;return!e||"object"!==t&&"function"!==t?-1:e===c._A?0:(0,I.X)(e,q,(function(){return U++}))}function V(e){if("string"==typeof e&&e.length)return e.length;if("object"==typeof e){if("undefined"!=typeof ArrayBuffer&&e instanceof ArrayBuffer&&e.byteLength)return e.byteLength;if("undefined"!=typeof Blob&&e instanceof Blob&&e.size)return e.size;if(!("undefined"!=typeof FormData&&e instanceof FormData))try{return(0,D.P)(e).length}catch(e){return}}}var X=i(7243);class W{constructor(e){this.agentIdentifier=e,this.generateTracePayload=this.generateTracePayload.bind(this),this.shouldGenerateTrace=this.shouldGenerateTrace.bind(this)}generateTracePayload(e){if(!this.shouldGenerateTrace(e))return null;var r=(0,t.DL)(this.agentIdentifier);if(!r)return null;var n=(r.accountID||"").toString()||null,i=(r.agentID||"").toString()||null,o=(r.trustKey||"").toString()||null;if(!n||!i)return null;var a=(0,_.M)(),s=(0,_.Ht)(),c=Date.now(),u={spanId:a,traceId:s,timestamp:c};return(e.sameOrigin||this.isAllowedOrigin(e)&&this.useTraceContextHeadersForCors())&&(u.traceContextParentHeader=this.generateTraceContextParentHeader(a,s),u.traceContextStateHeader=this.generateTraceContextStateHeader(a,c,n,i,o)),(e.sameOrigin&&!this.excludeNewrelicHeader()||!e.sameOrigin&&this.isAllowedOrigin(e)&&this.useNewrelicHeaderForCors())&&(u.newrelicHeader=this.generateTraceHeader(a,s,c,n,i,o)),u}generateTraceContextParentHeader(e,t){return"00-"+t+"-"+e+"-01"}generateTraceContextStateHeader(e,t,r,n,i){return i+"@nr=0-1-"+r+"-"+n+"-"+e+"----"+t}generateTraceHeader(e,t,r,n,i,o){if(!("function"==typeof c._A?.btoa))return null;var a={v:[0,1],d:{ty:"Browser",ac:n,ap:i,id:e,tr:t,ti:r}};return o&&n!==o&&(a.d.tk=o),btoa((0,D.P)(a))}shouldGenerateTrace(e){return this.isDtEnabled()&&this.isAllowedOrigin(e)}isAllowedOrigin(e){var r=!1,n={};if((0,t.Mt)(this.agentIdentifier,"distributed_tracing")&&(n=(0,t.P_)(this.agentIdentifier).distributed_tracing),e.sameOrigin)r=!0;else if(n.allowed_origins instanceof Array)for(var i=0;i 2&&void 0!==arguments[2])||arguments[2];super(r,n,Z.t,i),(0,t.OP)(r).xhrWrappable&&(this.dt=new W(r),this.handler=(e,t,r,n)=>(0,s.p)(e,t,r,n,this.ee),(0,k.u5)(this.ee),(0,k.Kf)(this.ee),function(r,n,i,o){function a(e){var t=this;t.totalCbs=0,t.called=0,t.cbTime=0,t.end=E,t.ended=!1,t.xhrGuids={},t.lastSize=null,t.loadCaptureCalled=!1,t.params=this.params||{},t.metrics=this.metrics||{},e.addEventListener("load",(function(r){_(t,e)}),(0,O.m$)(!1)),c.IF||e.addEventListener("progress",(function(e){t.lastSize=e.loaded}),(0,O.m$)(!1))}function s(e){this.params={method:e[0]},T(this,e[1]),this.metrics={}}function u(e,n){var i=(0,t.DL)(r);i.xpid&&this.sameOrigin&&n.setRequestHeader("X-NewRelic-ID",i.xpid);var a=o.generateTracePayload(this.parsedOrigin);if(a){var s=!1;a.newrelicHeader&&(n.setRequestHeader("newrelic",a.newrelicHeader),s=!0),a.traceContextParentHeader&&(n.setRequestHeader("traceparent",a.traceContextParentHeader),a.traceContextStateHeader&&n.setRequestHeader("tracestate",a.traceContextStateHeader),s=!0),s&&(this.dt=a)}}function d(e,t){var r=this.metrics,i=e[0],o=this;if(r&&i){var a=V(i);a&&(r.txSize=a)}this.startTime=(0,p.z)(),this.listener=function(e){try{"abort"!==e.type||o.loadCaptureCalled||(o.params.aborted=!0),("load"!==e.type||o.called===o.totalCbs&&(o.onloadCalled||"function"!=typeof t.onload)&&"function"==typeof o.end)&&o.end(t)}catch(e){try{n.emit("internal-error",[e])}catch(e){}}};for(var s=0;s 1?e[1]=i:e.push(i)}else e[0]&&e[0].headers&&s(e[0].headers,n)&&(this.dt=n);function s(e,t){var r=!1;return t.newrelicHeader&&(e.set("newrelic",t.newrelicHeader),r=!0),t.traceContextParentHeader&&(e.set("traceparent",t.traceContextParentHeader),t.traceContextStateHeader&&e.set("tracestate",t.traceContextStateHeader),r=!0),r}}function x(e,t){this.params={},this.metrics={},this.startTime=(0,p.z)(),this.dt=t,e.length>=1&&(this.target=e[0]),e.length>=2&&(this.opts=e[1]);var r,n=this.opts||{},i=this.target;"string"==typeof i?r=i:"object"==typeof i&&i instanceof Y?r=i.url:c._A?.URL&&"object"==typeof i&&i instanceof URL&&(r=i.href),T(this,r);var o=(""+(i&&i instanceof Y&&i.method||n.method||"GET")).toUpperCase();this.params.method=o,this.txSize=V(n.body)||0}function A(t,r){var n;this.endTime=(0,p.z)(),this.params||(this.params={}),this.params.status=r?r.status:0,"string"==typeof this.rxSize&&this.rxSize.length>0&&(n=+this.rxSize);var o={txSize:this.txSize,rxSize:n,duration:(0,p.z)()-this.startTime};i("xhr",[this.params,o,this.startTime,this.endTime,"fetch"],this,e.D.ajax)}function E(t){var r=this.params,n=this.metrics;if(!this.ended){this.ended=!0;for(var o=0;o 2&&void 0!==arguments[2])||arguments[2];super(e,t,we.t,r),this.importAggregator()}}new class{constructor(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:(0,_.ky)(16);c._A?(this.agentIdentifier=t,this.sharedAggregator=new y({agentIdentifier:this.agentIdentifier}),this.features={},this.desiredFeatures=new Set(e.features||[]),this.desiredFeatures.add(m),Object.assign(this,(0,a.j)(this.agentIdentifier,e,e.loaderType||"agent")),this.start()):(0,l.Z)("Failed to initial the agent. Could not determine the runtime environment.")}get config(){return{info:(0,t.C5)(this.agentIdentifier),init:(0,t.P_)(this.agentIdentifier),loader_config:(0,t.DL)(this.agentIdentifier),runtime:(0,t.OP)(this.agentIdentifier)}}start(){const t="features";try{const r=n(this.agentIdentifier),i=[...this.desiredFeatures];i.sort(((t,r)=>e.p[t.featureName]-e.p[r.featureName])),i.forEach((t=>{if(r[t.featureName]||t.featureName===e.D.pageViewEvent){const n=function(t){switch(t){case e.D.ajax:return[e.D.jserrors];case e.D.sessionTrace:return[e.D.ajax,e.D.pageViewEvent];case e.D.sessionReplay:return[e.D.sessionTrace];case e.D.pageViewTiming:return[e.D.pageViewEvent];default:return[]}}(t.featureName);n.every((e=>r[e]))||(0,l.Z)("".concat(t.featureName," is enabled but one or more dependent features has been disabled (").concat((0,D.P)(n),"). This may cause unintended consequences or missing data...")),this.features[t.featureName]=new t(this.agentIdentifier,this.sharedAggregator)}})),(0,T.Qy)(this.agentIdentifier,this.features,t)}catch(e){(0,l.Z)("Failed to initialize all enabled instrument classes (agent aborted) -",e);for(const e in this.features)this.features[e].abortHandler?.();const r=(0,T.fP)();return delete r.initializedAgents[this.agentIdentifier]?.api,delete r.initializedAgents[this.agentIdentifier]?.[t],delete this.sharedAggregator,r.ee?.abort(),delete r.ee?.get(this.agentIdentifier),!1}}}({features:[J,m,S,class extends h{static featureName=oe;constructor(t,r){if(super(t,r,oe,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;const n=this.ee;let i;(0,k.QU)(n),this.eventsEE=(0,k.em)(n),this.eventsEE.on(se,(function(e,t){this.bstStart=(0,p.z)()})),this.eventsEE.on(ae,(function(t,r){(0,s.p)("bst",[t[0],r,this.bstStart,(0,p.z)()],void 0,e.D.sessionTrace,n)})),n.on(ce+ne,(function(e){this.time=(0,p.z)(),this.startPath=location.pathname+location.hash})),n.on(ce+ie,(function(t){(0,s.p)("bstHist",[location.pathname+location.hash,this.startPath,this.time],void 0,e.D.sessionTrace,n)}));try{i=new PerformanceObserver((t=>{const r=t.getEntries();(0,s.p)(te,[r],void 0,e.D.sessionTrace,n)})),i.observe({type:re,buffered:!0})}catch(e){}this.importAggregator({resourceObserver:i})}},C,xe,B,class extends h{static featureName=de;constructor(e,r){if(super(e,r,de,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;if(!(0,t.OP)(e).xhrWrappable)return;try{this.removeOnAbort=new AbortController}catch(e){}let n,i=0;const o=this.ee.get("tracer"),a=(0,k._L)(this.ee),s=(0,k.Lg)(this.ee),u=(0,k.BV)(this.ee),d=(0,k.Kf)(this.ee),f=this.ee.get("events"),l=(0,k.u5)(this.ee),h=(0,k.QU)(this.ee),g=(0,k.Gm)(this.ee);function m(e,t){h.emit("newURL",[""+window.location,t])}function v(){i++,n=window.location.hash,this[ve]=(0,p.z)()}function b(){i--,window.location.hash!==n&&m(0,!0);var e=(0,p.z)();this[pe]=~~this[pe]+e-this[ve],this[ye]=e}function y(e,t){e.on(t,(function(){this[t]=(0,p.z)()}))}this.ee.on(ve,v),s.on(be,v),a.on(be,v),this.ee.on(ye,b),s.on(ge,b),a.on(ge,b),this.ee.buffer([ve,ye,"xhr-resolved"],this.featureName),f.buffer([ve],this.featureName),u.buffer(["setTimeout"+le,"clearTimeout"+fe,ve],this.featureName),d.buffer([ve,"new-xhr","send-xhr"+fe],this.featureName),l.buffer([me+fe,me+"-done",me+he+fe,me+he+le],this.featureName),h.buffer(["newURL"],this.featureName),g.buffer([ve],this.featureName),s.buffer(["propagate",be,ge,"executor-err","resolve"+fe],this.featureName),o.buffer([ve,"no-"+ve],this.featureName),a.buffer(["new-jsonp","cb-start","jsonp-error","jsonp-end"],this.featureName),y(l,me+fe),y(l,me+"-done"),y(a,"new-jsonp"),y(a,"jsonp-end"),y(a,"cb-start"),h.on("pushState-end",m),h.on("replaceState-end",m),window.addEventListener("hashchange",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("load",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("popstate",(function(){m(0,i>1)}),(0,O.m$)(!0,this.removeOnAbort?.signal)),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}}],loaderType:"spa"})})(),window.NRBA=o})(); window.jQuery || document.write(' ') CKEDITOR_BASEPATH='https://gatesopenresearch.org/js/vendor/ckeditor/' window.reactTheme = 'gates'; window.MathJax = { CommonHTML: { linebreaks: { automatic: true } }, 'HTML-CSS': { linebreaks: { automatic: true } }, SVG: { linebreaks: { automatic: true } }, AuthorInit: function() { MathJax.Hub.Register.MessageHook('End Process', function () { let timeout = false; // holder for timeout id const delay = 250; // delay after event is "complete" to run callback const reflowMath = function() { const dispFormulas = document.querySelectorAll('.disp-formula.panel'); if (!dispFormulas) { return; } for (const dispFormula of dispFormulas) { const child = dispFormula.querySelector('.MathJax_Preview').nextSibling.firstChild; const isMultiline = MathJax.Hub.getAllJax(dispFormula)[0].root.isMultiline; if (dispFormula.offsetWidth < child.offsetWidth || isMultiline) { MathJax.Hub.Queue(['Rerender', MathJax.Hub, dispFormula]); } } }; window.addEventListener('resize', function() { clearTimeout(timeout); // clear the timeout timeout = setTimeout(reflowMath, delay); // start timing for event "completion" }); }); }, }; if (window.location.hash == '#_=_'){ window.location = window.location.href.split('#')[0] } !function(f,b,e,v,n,t,s){if(f.fbq)return;n=f.fbq=function() {n.callMethod? n.callMethod.apply(n,arguments):n.queue.push(arguments)} ;if(!f._fbq)f._fbq=n; n.push=n;n.loaded=!0;n.version='2.0';n.queue=[];t=b.createElement(e);t.async=!0; t.src=v;s=b.getElementsByTagName(e)[0];s.parentNode.insertBefore(t,s)}(window, document,'script','https://connect.facebook.net/en_US/fbevents.js'); fbq('init', '1641728616063202'); fbq('track', "PixelInitialized", {}); Skip to content Gates Open Research file_upload Submit via VeriXiv search clear search menu close clear Search Browse Gateways & Collections How to Publish Submit via VeriXiv My Submissions Article Guidelines Article Guidelines (New Versions) Open Data, Software and Code Guidelines Open Data and Accessible Source Materials Guidelines (HSS) Prepublication Checks Production Process Posters and Slides Guidelines Document Guidelines Publication Charges Finding Article Reviewers About How it Works For Reviewers Our Advisors Policies Glossary FAQs For Developers Contact Blog My Account Submissions Content and Tracking Alerts My Details Sign In Submit via VeriXiv { "@context": "https://schema.org", "@type": "ScholarlyArticle", "mainEntityOfPage": { "@type": "WebPage", "@id": "https://gatesopenresearch.org/articles/9-17" }, "headline": "Estimated Modern Use: Employing A Service Statistics-Based Indicator to Monitor Family Planning Programs", "datePublished": "2025-05-07T14:12:12", "dateModified": "2025-05-07T14:12:12", "author": [ { "@type": "Person", "name": "Kristin Bietsch" }, { "@type": "Person", "name": "Margaret Reeves" }, { "@type": "Person", "name": "Jessica Williamson" }, { "@type": "Person", "name": "Priya Emmart" }, { "@type": "Person", "name": "Emily Sonneveldt" } ], "publisher": { "@type": "Organization", "name": "Gates Open Research", "logo": { "@type": "ImageObject", "url": "https://gatesopenresearch.org/img/AMP/Gates_image.png", "height": 600, "width": 47 } }, "image": { "@type": "ImageObject", "url": "https://gatesopenresearch.org/img/AMP/Gates_image.png", "height": 1200, "width": 94 }, "description": "Estimated Modern Use (EMU) is a novel, service statistics-based indicator designed to complement Couple Years of Protection (CYP) in assessing the scale of family planning use and the first widely used metric since CYPs. Developed by the Track20 project, EMU offers a population-based proportional metric that facilitates cross-country comparisons and temporal trend analysis. By leveraging existing family planning service statistics, EMU provides a more accessible and interpretable measure of contraceptive use.The associated SS-to-EMU tool used to calculate EMU incorporates rigorous data quality review mechanisms, including data visualizations and validated review processes, to enhance the reliability and utility of family planning data for decision-making. The standardization of EMU across countries and projects promotes its integration into routine data review practices, fostering a more comprehensive approach to family planning monitoring and evaluation. Since 2014, all countries that prepare annual estimates for the FP2030 global initiative utilize the SS to EMU tool, to assess data quality and produce EMU estimates.Moreover, the EMU serves as a valuable input for the Family Planning Estimation Tool (FPET), contributing to the refinement of modeled estimates of modern contraceptive prevalence. Since its introduction, EMU has gained widespread adoption at various levels, demonstrating its effectiveness in informing global, regional, and country-level monitoring efforts. Ongoing refinements to the EMU calculation further enhance its accuracy and utility as a supplementary data source for understanding contraceptive use patterns." } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://gatesopenresearch.org/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://gatesopenresearch.org/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://gatesopenresearch.org/articles/9-17", "name": "Estimated Modern Use: Employing A Service Statistics-Based Indicator..." } } ] } Home Browse Estimated Modern Use: Employing A Service Statistics-Based Indicator... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Bietsch K, Reeves M, Williamson J et al. Estimated Modern Use: Employing A Service Statistics-Based Indicator to Monitor Family Planning Programs . Gates Open Res 2025, 9 :17 ( https://doi.org/10.12688/gatesopenres.16346.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Estimated Modern Use: Employing A Service Statistics-Based Indicator to Monitor Family Planning Programs Kristin Bietsch https://orcid.org/0000-0002-6264-8094 1 , Margaret Reeves 1 , Jessica Williamson 1 , Priya Emmart 1 , Emily Sonneveldt 1 Kristin Bietsch https://orcid.org/0000-0002-6264-8094 1 , Margaret Reeves 1 , [...] Jessica Williamson 1 , Priya Emmart 1 , Emily Sonneveldt 1 PUBLISHED 07 May 2025 Author details Author details 1 Avenir Health, Glastonbury, Connecticut, USA Kristin Bietsch Roles: Conceptualization, Data Curation, Methodology, Project Administration, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Margaret Reeves Roles: Conceptualization, Methodology, Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing Jessica Williamson Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Priya Emmart Roles: Conceptualization, Formal Analysis, Investigation, Writing – Original Draft Preparation, Writing – Review & Editing Emily Sonneveldt Roles: Conceptualization, Formal Analysis, Funding Acquisition, Methodology, Project Administration, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW REVIEWER STATUS AWAITING PEER REVIEW Originally published as a preprint: https://doi.org/10.12688/verixiv.617.1 Abstract Estimated Modern Use (EMU) is a novel, service statistics-based indicator designed to complement Couple Years of Protection (CYP) in assessing the scale of family planning use and the first widely used metric since CYPs. Developed by the Track20 project, EMU offers a population-based proportional metric that facilitates cross-country comparisons and temporal trend analysis. By leveraging existing family planning service statistics, EMU provides a more accessible and interpretable measure of contraceptive use. The associated SS-to-EMU tool used to calculate EMU incorporates rigorous data quality review mechanisms, including data visualizations and validated review processes, to enhance the reliability and utility of family planning data for decision-making. The standardization of EMU across countries and projects promotes its integration into routine data review practices, fostering a more comprehensive approach to family planning monitoring and evaluation. Since 2014, all countries that prepare annual estimates for the FP2030 global initiative utilize the SS to EMU tool, to assess data quality and produce EMU estimates. Moreover, the EMU serves as a valuable input for the Family Planning Estimation Tool (FPET), contributing to the refinement of modeled estimates of modern contraceptive prevalence. Since its introduction, EMU has gained widespread adoption at various levels, demonstrating its effectiveness in informing global, regional, and country-level monitoring efforts. Ongoing refinements to the EMU calculation further enhance its accuracy and utility as a supplementary data source for understanding contraceptive use patterns. READ ALL READ LESS Keywords Estimated Modern Use (EMU), family planning, contraceptive use indicator, routine service statistics, program monitoring, HMIS Corresponding Author(s) Kristin Bietsch ( [email protected] ) Close Corresponding author: Kristin Bietsch Competing interests: No competing interests were disclosed. Grant information: This study was funded by the Gates Foundation (INV-007530) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 Bietsch K et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Bietsch K, Reeves M, Williamson J et al. Estimated Modern Use: Employing A Service Statistics-Based Indicator to Monitor Family Planning Programs . Gates Open Res 2025, 9 :17 ( https://doi.org/10.12688/gatesopenres.16346.1 ) First published: 07 May 2025, 9 :17 ( https://doi.org/10.12688/gatesopenres.16346.1 ) Latest published: 07 May 2025, 9 :17 ( https://doi.org/10.12688/gatesopenres.16346.1 ) Background The global family planning landscape has witnessed a paradigm shift since the launch of FP2020 in 2012. The Track20 project [ 1 ], which supports the measurement mandate of FP2020, now FP2030, sought to establish a standardized approach for monitoring progress across countries. Historically, family planning programs have relied on two primary data sources: survey data and routine service statistics. Survey data, such as those collected through Demographic and Health Surveys [ 2 ] (DHS) and Multiple Indicator Cluster Surveys [ 3 ] (MICS), provide snapshots of contraceptive use at specific points in time. However, lags between surveys limit their utility for real-time monitoring and decision-making. In contrast, routine service statistics, gathered through Health Management Information Systems (HMIS) and Logistic Management Information Systems (LMIS), offer a more frequent but often less comprehensive view of program activities. Countries rely on their routine data to make decisions about resource allocation, distribution and procurement of commodities, all of which have important budgetary and spending implications. Differences in the data elements and methodologies used by countries to calculate national coverage indicators make cross-country comparison difficult, and while CYPs are widely referenced, the concept is not well suited as a measure of coverage. To address the limitations of these traditional data sources, Track20 sought to create a standardized approach to transform routine service statistics into a single metric that could track progress between surveys and could contribute to more frequent estimations of contraceptive prevalence through modeling. This led to the development of the Estimated Modern Use (EMU) indicator and its integration into the Family Planning Estimation Tool (FPET). 1 – 4 EMU offers a valuable addition to the family planning monitoring toolkit. By providing a standardized measure of contraceptive coverage that can be calculated at different administrative levels using consistently available data, EMU facilitates real-time monitoring and enables more informed decision-making. It also provides a standardized way to measure the volume of use from service statistics that can help minimize confusion across countries’ differing approaches. While EMU may not perfectly mirror levels of contraceptive use, its effectiveness has been demonstrated in tracking trends over time, making it a valuable complement to survey data. 5 , 6 EMU serves as a standalone indicator that can be used to assess program performance, identify areas for improvement, and inform resource allocation decisions. The EMU’s standardized methodology and flexibility in accommodating diverse data types ensures its comparability across countries, making it a valuable tool for global family planning monitoring and evaluation. It also solves for an important prior constraint in family planning metrics, the desire for an estimate of the number of users. The number of countries that directly produce users through their HMIS is very small. Methods Calculating Estimated Modern Use The EMU calculation is the number of estimated modern users divided by the population of women of reproductive age (WRA). The numerator combines service statistics by method with CYP factors (how long a particular method will provide protection against pregnancy) and continuation rates derived from CYP factors for long acting methods. EMU = Estimated Modern Users Population ( WRA ) Where: Estimated Modern Users are Family Planning (FP) Service Statistics (by method) using CYP factors and long acting permanent method (LAPM) continuation rates. Population (WRA) is the population of women of reproductive age (15-49), representing the pool of potential contraceptive users. As metrics, EMU and CYP start from a similar calculation, which is designed to estimate an aggregated impact from a variety of contraceptive commodities on providing protection against unintended pregnancy. CYPs are calculated as a direct conversion of contraceptive commodities (ex. 10 IUDs) into an indicator of the impact of those commodities (46 Couple Years of Protection, all allocated to the year of insertion). The CYP also serves as a single indicator representing the combined impact of different contraceptive methods with different levels of effectiveness and duration. The EMU annualizes the CYP data – carrying users over for the duration of their method while accounting for discontinuation before dividing by population. By producing a proportional indicator, rather than an absolute number, the EMU is more comparable to mCP and better aligns with annual monitoring and comparisons between regions and countries. To account for system and data variations and better represent levels of FP use over time, the EMU calculation makes several adjustments to the raw data during the process of converting routine service statistics to FP Users for the EMU numerator. Details on the conversion to FP Users and other adjustments are provided in the following sections. Estimating FP Users Except for a few countries that collect true FP Users data, which tracks individual users over time, FP routine service statistics data in most countries do not represent actual FP use, but rather, a count of FP services provided, or FP commodities distributed. To account for this, some adjustments are made to the raw data provided on FP Commodities or FP Visits to convert them to estimated FP Users to produce the numerator for the EMU. The adjustments made to convert the raw service statistics into FP Users considers 3 groups of FP Users in any given year: 1. Current Users: women using a method in the same year it was provided. 2. Continuing Users: women still using a long acting or permanent method (LAPM) provided in a prior year for which data was available. 3. Historic Users: women we assume are still using a LAPM provided prior to the first year of data available. Current Users To estimate the number of current FP Users in the year a service was provided, we vary our assumptions by method type. For short-term methods, those methods that are only effective for a short duration and require refills or revisits within the same year to continue to protect against pregnancy, we use the globally accepted CYP factors to convert services or products into users. For example, four visits for DMPA-IM, or 4 injectables dispensed, is equal to one CYP and one estimated modern user. Using condom distribution numbers from HMIS is particularly challenging. It can be difficult to determine the number of condoms used specifically to prevent pregnancy because they may be distributed for multiple purposes and through non-FP programs and are often left for clients to take freely instead of distributed in a carefully documented manner. Because of this, in countries where condoms are not a common method of family planning, they may be excluded from EMU calculations. Short-Term Methods U m STM , s , t = C m STM , s , t × 1 CYP m STM , s Where: m STM is the type of short-term method, s is the type of service statistic (commodities to clients, commodities to facilities, visits), t is year, U m STM , s , t is the number of users of a short-term method, m STM , of service statistics type s, in year t, C m STM , s , t is the number of commodities or visits of the short-term method, m STM , of service statistics type s, in year t CYP m STM , s is the couple years protection for the short-term method, m STM , and service statistics type, s For long acting reversible methods, which are provided in a given year but can continue to provide protection in subsequent years, we estimate that each service or product (IUD/Implant) inserted is equivalent to one user, with some small adjustment to account for first-year discontinuation (for example, CYP continuation curves estimate there is a 92% continuation in the first year for Copper T IUDs). More discussion on how the calculation accounts for future years is included in the next section on “Continuing Users.” Long-Term Method, Current Users U m LTM , s , t Current = C m LTM , s , t × Con m LTM , 1 Where: m LTM is the type of long-term method, s is the type of service statistic (commodities to clients, commodities to facilities, visits), t is year, U m LTM , s , t Current is the number of current users (who received their method in year t ) of a long-term method, m LTM , of service statistics type s, in year t, C m LTM , s , t is the number of commodities or visits of the long-term method, m LTM , of service statistics type s, in year t Con m LTM , 1 is the continuation rate of method m LTM in year 1 For Permanent Methods, which are provided in a given year but continue to provide protection for the duration of the user’s reproductive life, we estimate that each service is equal to one user in that year. More discussion on how the calculation accounts for future years is included in the next section on “Continuing Users”. Continuing Users A critical difference between CYP and EMU is the distribution of contraceptive use over time. CYPs apply the full impact of long acting and permanent methods (LAPMs) to the year in which the method was provided. When counted in this way, an implant, for example, would show benefit in the year it was inserted, but no impact in future years, even though it continues to provide contraceptive protection for several years into the future. A direct conversion of CYPs to FP Users would result in an overestimation of users in the year of service provision and can skew trends in users when rapid scale-up or declines in service provision occur. In the EMU calculation, a “carryover” approach is used, in which that impact is distributed across the years a method would be in use, based on the standard continuation rates used to develop the CYP factors 7 ( Figure 1 ). Figure 1. Long acting reversible contraception continuation curves. Long-term Method, Continuing Users U m LTM , s , t Continuing = ∑ x = 1 t − t 0 ( C m LTM , s , t − x × Con m LTM , x + 1 ) m LTM is the type of long-term method, s is the type of service statistic (commodities to clients, commodities to facilities, visits), t is year, t 0 is the first year of data collection, U m LTM , s , t Continuing is the number of continuing users (who received their method during data collection, prior to year t , and are still expected to be users in year t ) of a long-term method, m LTM , of service statistics type s, in year t, C m LTM , s , t − x is the number of commodities or visits of the long-term method, m LTM , of service statistics type s, x years prior to year t Con m LTM , x + 1 is the continuation rate of method m LTM in x +1 years of use In the example illustrated in Figure 2 , 100 of each LAPM were provided in 2020. Based on those services in 2020, we estimate 670 users in 2020. Each year some of those users are assumed to continue using their method until it is removed, no longer effective, or until they age out of “childbearing age”. By 2030, we assume all of those users who initiated in 2020 are no longer using that method, or in the case of sterilization, no longer of childbearing age. As a specific example, if a Copper-T 10-year IUD was inserted in 2020, all 4.6 CYPs would be counted in that year. However, because EMU estimates users in a given year, if 100 Cooper-T are inserted in 2020, the SS-to-EMU tool assumes that 92 will be used for a year, 77 for two years, 65 for three years and so on, following the continuation curve which underlies the CYP calculation. This serves to smooth out trends in long acting contraceptive use, as there may be fluctuations in the acceptance of LAPMs by clients which are unlikely to result in immediate fluctuations in use as women continue to use these methods over several years. Figure 2. Estimated users for long acting and permanent methods from one year of services. One limitation of the current approach is that a single global continuation curve is used for each method, which does not take into account the country-specific variability in method continuation indicated by DHS data. Limitations to a country-specific approach include lack of discontinuation data for many countries and timeliness of data for newly popular methods when using a historical reproductive calendar for calculating discontinuation rates. The continuation rates from CYP estimates are calculated using results from multiple studies. Cohort studies were prioritized for the CYP analysis, to reduce recall bias from the retrospective calendars in DHS. However, there were not enough cohort studies to rely solely on this approach, so the estimates were created using a combination of cohort data and secondary analysis of DHS data. Track20 tested using regional discontinuation rates, but the variation within regions (among countries with discontinuation data) were the same in scale as those using the global rates, meaning that they did not improve on the calculation. Future testing could include different ways to reflect country variation. Historic Users As previously discussed, LAPM services require special consideration when estimating users because their use/impact can continue for up to 10 years [ 4 ] after a service is provided. Routine service statistics data are often limited in terms of the time trends available due to system changes and lack of electronification of past service data. As a result, it is common that countries have less than 10 years of service statistics data available in their current system, meaning that there are women using LAPMs that were provided before the first year of available data, but about whom we have no information. Disregarding these users runs the risk of substantially underestimating current levels of contraceptive use, especially in countries where a greater proportion of women use LAPMs, as it would assume that no service provision occurred before the first year of available data. To estimate “historic users” (those still using a method provided before the first year of available data), we make some assumptions about LAPM distribution before the first year of available data. The calculation of historic users is based upon the maturity of the FP program in the first year of data collection for each LAPM- was provision of this method already at scale, still scaling up, or just beginning the year data collection began? If provision of the method started in the first year of data collection, we assume there are no historic users. If the method being provided was at scale in the first year of data collection, we assume that the number of insertions in previous years was similar to the number of insertions in the first year of data collection. If the program was scaling up distribution of this method, we assume the number of historical users falls between the two other scenarios. For example, if a data system began recording methods distributed in 2017, but the country began inserting Implanon in 2013, we expect that some women received implants in 2016 or earlier and are still implant users in 2017 and beyond ( Figure 3 ). Based on the scenario identified and the first year of data available, the EMU calculation estimates the number of women who received LAPMs before the first year of data and then applies the same continuation trends used to estimate continuing users. This allows the EMU to better approximate actual contraceptive use, integrating LAPM users from prior years into the years for which data is available. Figure 3. Estimated implanon users, by year of use and year of insertion. Long-term Method, Historic Users U m LTM , s , t Historic = { No Historic = 0 Scaling Up = 1 2 × C m LTM , s , t 0 × ∑ x = t − t 0 L m LTM max Con m LTM , x + 2 At Scale = C m LTM , s , t 0 × ∑ x = t − t 0 L m LTM max Con m LTM , x + 2 m LTM is the type of long-term method, s is the type of service statistic (commodities to clients, commodities to facilities, visits), t is year, t 0 is the first year of data collection, U m LTM , s , t Historic is the number of estimated historic users (who received their method before data collection started and are still expected to be users in year t ) of a long-term method, m LTM , of service statistics type s, in year t, C m LTM , s , t 0 is the number of commodities or visits of the long-term method, m LTM , of service statistics type s, in the first year of data collection, t 0 L m LTM max is the maximum number of years of continuation for long-term method, m LTM , Con m LTM , x + 2 is the continuation rate of method m LTM in x +2 years of use The extent to which these estimates impact a countries EMU depends on the country’s method mix, number of years they have data in their HMIS, and the extent to which their family planning program has changed or prioritized different methods in the recent past. A country that primarily relies on short acting methods will see little impact in their current number of users based on these estimations. Similarly, a country that has had a robust implant program for more than five years will not see an impact on estimates of current users, since the available data covers the period of effectiveness prior to the current year. These calculations will have the largest impact in countries that are scaling up long acting or permanent methods and have been doing so for only a few years. Importantly, as countries accumulate more years of data, these calculations have no impact on current data. While historic users will always be an estimate, the benefits of this approach outweigh the risk of ignoring prior service provision, and those risks are minimized by our approach in the following ways: 1. This approach avoids the incorrect assumption that NO services were provided prior to the first year of data. 2. As countries have more data available, historic users have less impact on the total estimate. Once a country has 10 years’ worth of data available, historic users no longer factor into the EMU for the current year or future estimates. 3. Historic users’ presence in data is dependent on duration of method. a) “Historic” sterilization/IUDs last longest, but these methods generally represent a small proportion of use in most countries. b) “Historic” implants are present for a maximum of 5 years, depending on type of implant. 4. Adjustment for “new” or “scaling” versus “stable” trends helps to mitigate risk of over-estimating prior distribution. Adding all three types of long-term method users together results in the estimated number of long-term methods users in a year. Long-term Methods, Total Users U m LTM , s , t = U m LTM , s , t Current + U m LTM , s , t Continuing + U m LTM , s , t Historic Where: U m LTM , s , t is the total number of users of long-term method, m LTM , of service statistics type s, in year t, U m LTM , s , t Current is the number of current users (who received their method in year t ) of a long-term method, m LTM , of service statistics type s, in year t, U m LTM , s , t Continuing is the number of continuing users (who received their method during data collection, prior to year t , and are still expected to be users in year t ) of a long-term method, m LTM , of service statistics type s, in year t, U m LTM , s , t Historic is the number of estimated historic users (who received their method before data collection started and are still expected to be users in year t ) of a long-term method, m LTM , of service statistics type s, in year t. Combining short-term and long-term users together leads to the total number of estimated modern users. Estimated Modern Users EMU s , t = ∑ i = 1 M STM U m STM i , s , t + ∑ i = 1 M LTM U m LTM i , s , t Where: s is the type of service statistic (commodities to clients, commodities to facilities, visits), t is year, U m STM i , s , t is the number of users of a short-term method, m STM i , of service statistics type s, in year t, U m LTM i , s , t is the number of users of a long-term method, m LTM i , of service statistics type s, in year t, M STM is the total number of short-term methods, M LTM is the total number of long-term methods, EMU s , t is the number of estimated modern users captured by service statistics type s, in year t At this point, the EMU calculation represents estimated modern users whose family planning services are recorded in a country’s HMIS. This version of the indicator is used by countries to monitor their programs and track trends in contraceptive use at the national and subnational levels. In most countries, for the EMU to be used as a data source to model contraceptive prevalence, there is one additional adjustment that needs to be made - accounting for family planning services that are not included in a country’s HMIS. Private Sector Adjustment The three groups - current users, continuing users, and historic users - combine to form an “unadjusted EMU”. It is unadjusted because it does not consider women who may have received contraceptive methods outside of the facilities and/or sectors that report into the national HMIS. The final adjustment integrated into the EMU calculation is intended to make the EMU more representative of the full FP market. In most countries, parts of the private sector report data into HMIS, but it is very rare that the full private sector reports. Efforts have been made to better integrate data from private sector providers, including NGOs providing family planning services and private hospitals and clinics, but few countries have pharmacies or other non-medical sources reporting into the system. This impacts methods differently, for example in many countries, shops are key sources of pills and condoms, and they rarely report into HMIS. The EMU calculation uses a method-specific adjustment factor, applied to the service statistics data, to account for provision in sectors not represented in the available data. This adjustment factor is based on data from DHS (where available) concerning the source of methods for current users of contraception, along with information estimating how much private sector provision is captured in their data. Multiplying unadjusted EMU (by method) by the method-specific private sector adjustment factors results in the adjusted EMU. All method-specific EMUs are aggregated to form the total EMU. Dividing the adjusted EMU by women of reproductive age gives us the EMU value that can be used in FPET to inform annual family planning estimates. These transformations are all intended to help the EMU serve as a proxy for contraceptive coverage accounting for the historic provision of LAPMs prior to available data, the continued impact of LAPMs over years following the initial provision, and the provision of services in sectors not represented in HMIS. This positions the EMU to be different, yet complimentary, to CYP, with the EMU being better positioned for active monitoring. Operationalizing EMU Calculations at the Country Level Creating the EMU at the country level requires more than the calculations described above. Unlike publicly available survey datasets which are cleaned and processed, HMIS contain raw data that are entered, aggregated, and managed within country systems which are often under resourced. Additionally, routine service statistics are a living dataset- new data is entered every month and the system may evolve over time in terms of what information is collected. It needs continuous work, which is why it is imperative that a process is embedded in the creation of the EMU to understand the completeness and quality of the data being used. The Track20 Project created the Excel-based Service Statistics to Estimated Modern Use Tool (SS-to-EMU tool), to walk individuals through the process of producing the EMU. The tool ( Copyright © 2025 Track20. All Rights Reserved. ), is free to use and available through the Track20 website and GitHub. The same process can be done using R code ( https://doi.org/10.5281/zenodo.14774507 ), which is available online through Track20’s GitHub repository. The SS-to-EMU Tool performs two functions: it takes the user through a data review and visualization process to easily assess quality and identify trends, facilitating identification and follow-up on data outliers; and calculates the EMU, providing a single metric that can be regularly assessed against other data sources as a measure of progress. SS-to-EMU Data Inputs A variety of data inputs are used for producing the EMU, see Box 1 . Some data are entered by the user, and other data are pre-populated in the tool from trusted data sources. Box 1. SS-to-EMU data inputs. Data Required for Calculation of EMU ▪ Service Statistics data – one or more of the following: ▪ FP commodities distributed (to clients or facilities) ▪ FP visits ▪ FP users ▪ Population data ▪ Public sector market share Data Used for Quality Review ▪ Reporting coverage rate ▪ FPET & Survey estimates of mCP prevalence & FP method mix Service Statistics EMU is calculated using any of the four types of family planning routine data typically collected by programs: commodities distributed to clients, commodities distributed to facilities, family planning visits, and family planning users. Service statistics are entered into SS-to-EMU by the user and when possible, users are encouraged to enter more than one type of service statistic to allow for comparison during the data review phase. The SS-to-EMU Tool makes the previously discussed adjustments to the service statistics entered to convert them to FP Users for the EMU calculation. Population Data Population estimates for all women ages 15 – 49, by year, are pre-loaded into the SS-to-EMU Tool from the UN Population Division. 8 These estimates serve as the denominator for the EMU calculation. Population estimates for all women of reproductive age are preferred as the denominator (as compared to married women of reproductive age) as aggregated service statistics data generally does not contain information about the marital status of the clients who receive FP Services [ 5 ]. If the user has a preferred data source for population estimates, such as a census, the user may use their own source and replace the pre-populated data. Public Sector Market Share Data on the distribution of modern users of each method by the source from which they received that method is pulled from DHS to inform the EMU private sector adjustment discussed previously, providing insight on the public sector market share. Reporting Rates Reporting rates are entered into SS-to-EMU by the user. The reporting rate provides an indication of the share of facilities represented in the HMIS data. Reporting rates are a standard measure in many countries’ HMIS, though they vary extensively in what they represent. Generally, reporting rates refer to the percentage of facilities that submit monthly or annual reporting forms (reporting routine service statistics data) into the national HMIS. Some systems differentiate between “on time” reporting rates (the % of facilities submitting forms by the monthly reporting deadline) and “overall” reporting rates (the % of facilities that have submitted forms at all by the time data is being extracted from the system). Reporting rates recorded in HMIS may not be specific to FP data but can refer to reporting for a broader health area. This depends on whether there is a distinct reporting form for submitting FP-related data or whether FP is included in a broader maternal and reproductive health form or if all health areas are reported up from the facility in a single form. For the purpose of the SS to EMU, FP-specific reporting rates are preferred. Method Prevalence and Method Mix Data from nationally representative surveys including DHS, MICS, Performance Monitoring for Action (PMA) , and other national surveys provide method prevalence and method mix data for benchmarking. SS-to-EMU also uses mCP estimates from FPET for benchmarking. Data Quality Review A comprehensive data quality review is part of the EMU calculation process and embedded in the SS-to-EMU tool. Although anchored in the WHO Data Quality approach, 9 Track20 adapted the methodology to center around FP and streamlined it into a largely automated process. The result is a comprehensive but easy-to-follow process that generates data visualizations to help users easily identify data anomalies. The standardized process is a first for family planning data and a critical step for improved data management and use. Assessing Data Completeness Because routine service statistics are based on facilities reporting into HMIS, data completeness, an important element of data quality, is dependent on high reporting rates. A reporting rate of 100% would indicate that all services delivered by the public sector are captured in the HMIS data. The Track20 project typically recommends a reporting rate of 80% or higher for service statistics to be included in the EMU trend to ensure data are representative and largely complete. The SS-to-EMU Tool does not adjust for low reporting rates as it does for missing private sector data. Instead, low reporting rates will result in the exclusion of EMUs in that year from trend data. However, no matter the reporting rate, working through the data review and assessment processes can be an important and routine step incorporated into a country’s family planning monitoring system. Internal Benchmarking Users are encouraged to enter as many types of service statistics into the SS-to-EMU tool as are available in their system. This allows the user to examine the relationship between different types of service statistics, which can provide important internal benchmarking. The SS-to-EMU Tool displays the inputted data in ways that allow the user to easily identify outlier data points, facilitating follow-up and potential data corrections, if necessary. Ideally, trends in the different data types should be similar, as should the distribution of methods. There are some typical relationships between data sources that users can look for. For example, LMIS data on commodities sent to facilities should typically be higher than data on commodities given to clients because not all methods sent to facilities are used. Commodities to clients and visits data for single dose methods (IUDs, DMPA-IM, implants) will be similar because one commodity unit is distributed per visit. Sterilization is more commonly recorded in visits and users, but less likely to be reported in commodities data, resulting in a potential gap when only looking at one type of data. The tool helps walk through these different relationships and uses clear visualizations that can help users see how their data relate to each other and if the relationships make sense. External Benchmarking The SS-to-EMU tool is unique in that it includes routine service statistics alongside survey data and mCP from FPET global projections, so tool users can benchmark their EMU trends (derived from their service statistics) against trusted external data. For years with survey data available, users can compare rates and absolute numbers. For absolute numbers, estimated users by method can be compared to the number of users calculated from survey method prevalence multiplied by the population of women of reproductive age. Although one would not expect estimated users from surveys and service statistics to be equal (due to methodology and adjustments), how they compare can provide some indication of whether the data is generally consistent with external sources or can point to potential issues with the data inputs. For example, one would expect that unadjusted estimated users (before one accounts for the private sector) would generally be lower than survey-based estimates of users and that the private sector adjustment would bring the estimate of users closer to the survey-based estimates. There is a specific graph included in the data review that shows the impact of the private sector adjustment factor in comparison to survey-based estimates and allows the user to revisit the adjustment factors if the impact of those adjustments appears to over- or under-estimate use. If the estimates of users are substantially higher than survey-based estimates, especially for a specific method, that may point to potential issues with double counting or quality issues in HMIS. Finally, one would also expect that estimated users is likely to diverge from survey data the farther out one is from a survey. When comparing service statistics from 2023 against a survey conducted in 2018, one would expect some variation, especially as program priorities and the scale of method use may have changed. While it is expected that EMU and surveys will trend together, there are some specific FP methods that can cause particular challenges for estimating use, generally related to how data is recorded and gaps between distribution and consumption. LAM (Lactation Amenorrhea Method) is not always recorded in the HMIS, or if it is recorded, it is in reference to LAM counseling as opposed to measured use. The tool allows for the exclusion of condoms because of the various motivations for condom use and in accurate recording in HMIS. Overall, understanding how and why different methods may result in an EMU that varies from survey data can help the user identify potential data quality issues, make decisions about how to use the data, and determine which data type is providing the most accurate picture of use. In addition to doing comparisons in the year of a survey, users also look at annual rates of growth between surveys and the corresponding trends to EMU. Major differences in rates of change can highlight potential data quality issues. Decision to Use EMU for Family Planning Monitoring After completing the data review process, the SS-to-EMU tool guides the user through a final review of how each of the EMUs derived from the different types of service statistic entered performed during the quality review. For countries where routine data quality is poor, the graphics produced by the data quality review provide a useful tool for advocating for improvement in data quality. For countries where data quality is sufficient, the EMU indicator can be used directly to monitor change and as an input in FPET, thus influencing the estimation of mCP. The number of countries whose service statistics were of high enough quality to use their EMU in their FPET estimates reported in the FP2020/FP2030 Annual Progress Report has grown from 5 countries in 2015, to as high as 20 in a single year. Because HMIS data and systems are dynamic and issues arise, a country may not be able to use EMU every year. Results EMU Use in Family Planning Program Monitoring Global Level Aligned with its original intent, the EMU indicator has supported annual progress reporting for the FP2020 initiative, and its follow-on FP2030 , as well as the Ouagadougou Partnership, through its use as an input into FPET and the resulting estimates of key family planning indicators (mCP, Unmet Need, and Demand Satisfied). EMU allows for the introduction of service statistics into family planning modeling, particularly beneficial in years without surveys. It provides better representation of recent changes in family planning programs, impacting estimates and narrowing confidence intervals. Since 2014, 26 countries have used EMU to inform their annual FPET estimates in at least one year of reporting. Because EMU is a standard measure, it has been adopted by various donors and partners as part of their annual monitoring process. EMU is a required core indicator on the USAID MOMENTUM suite of grants which implements activities across 38 countries. EMU has also been adopted by the Countdown to 2030 global movement for women’s, children’s, and adolescents’ health as its approach to measure coverage for family planning across 81 focus countries. In addition, because the EMU can be calculated at sub-national levels, it can be particularly useful for tracking progress in specific sub-national geographies where different investments are made and would provide governments, projects, and donors with a common indicator to use across national and subnational investments. Country Level The SS-to-EMU tool is used to calculate EMU independently in over 30 countries as part of routine annual family planning monitoring and EMU is now auto-calculated monthly within 13 [ 6 ] country FP HMIS environments through Track20’s FP DataPro. FP DataPro is an FP and MNH environment available for download from the DHIS2 App Hub that includes EMU calculation and aspects of the SS-to-EMU data quality review, along with complex data analytics and visualizations to support active monitoring. 10 Regular utilization of the data quality review through the SS-to-EMU tool facilitates the identification of outliers and other data anomalies and supports quick follow-up with facilities to confirm entries and make corrections if necessary, improving data quality over time. Because EMU is a service-based indicator and can be calculated annually, quarterly, or monthly, it is responsive to immediate changes in service volume. This means that the EMU will reflect trend shifts due to program scale-ups, service disruptions, and other changes long before a survey would, providing more immediate feedback. EMU proved especially helpful during the COVID-19 pandemic as countries strove to understand whether COVID-19 impacted FP uptake and use. Subnational EMU calculations provide additional depth. This has allowed countries to make more responsive and targeted programmatic changes between surveys. Discussion Creating a standardized routine service statistic based indicator has provided an opportunity to look at data across countries and provided countries with a new option for national and subnational monitoring. As an indicator based on HMIS, not surveys, understanding how to interpret and use the EMU is important. Although the EMU provides an estimate of total contraceptive use, it should not be interpreted as being the same as mCP produced from a probabilistic survey. As described above, HMIS data in most countries does not contain data representing the total market of family planning services. There are channels of delivery, such as pharmacies and markets, that are rarely captured in HMIS. Although the process of producing the EMU includes an adjustment for this missing data, the adjustment factors are from the most recent survey and may be outdated. Due to this, when comparing the EMU to mCP, the expectation is that the levels may not match, but the trends should. The assumption is that changes in the percentage of women that use services from channels captured in HMIS and those that are not happen slowly over time. As the trend of incorporating more data into HMIS continues, this adjustment will have a smaller and smaller impact. To mitigate this risk, uncertainty has been added to the private sector adjustment when the EMU is added into FPET to produce mCP. Since the EMU has been used across multiple countries for over a decade, Track20 has been able to take feedback and lessons learned from country stakeholders and refine both the indicator and the process used for its production. This has led to improvements in how the indicator is calculated. For example, there have been changes made about how the historical users are calculated which are seen in the differences if a method is labeled as being scaled up or already at scale. A current area of exploration is the global continuation rates created for CYP factors that are used for long-acting methods. As explained, the current approach is to use the global factors in the creation of the EMU. Although using country specific continuation rates would be ideal, a lack of data availability prevents this approach. Using regional continuation rates has already been explored, but did not provide an improvement because the variations seen within regions is the same as what is seen globally. There are two areas currently being explored. The first one is a simple approach that allows the user to adjust the global continuation curve up or down based on available country data. This would not change the shape of the curve, just the level at where it begins. In countries with very high discontinuation this could adjust the EMU to be more aligned with actual use. The second is to make changes to the implant curve. Looking at recent DHS data shows that continuation of IUDs is a close match to the CYP global continuation curve. However, a first look at implant data suggests there are some differences. The CYP curve was created using Norplant data because at the time there was very little data about usage of the new generation of implants. Newer DHS data shows that there may be a divergence in continuation with the new implants in the final year of effectiveness. Conceptually this makes sense because implants now, which are of shorter duration than the 7 year Norplant, are more likely to be used as a spacing method. One of the larger discrepancies seen between use in service statistics and use in surveys is for implants. Use in HMIS is often higher than use in surveys. Changes to the continuation curve used to calculate the EMU would help to mitigate these differences and could better reflect actual use. Although there are limitations in terms of the representativeness HMIS and the quality of routine service statistic data, the impulse to have a proportional indicator is seen everywhere. Most countries already have a process to do this, but it is often oversimplified and ignores data quality. The EMU is the most comprehensive effort yet to standardize the calculation within existing metrics and systems. It is adaptable to data coming from multiple systems of data collection and reporting, allowing most countries to produce the indicator. Conclusions EMU is a new routine service statistics-based family planning indicator that contributes to more accurate and timely monitoring of family planning programs. EMU can help countries more closely monitor their own progress toward family planning goals and advances the FP monitoring field by facilitating cross-country comparisons and a standardized measure to track trends. Subnational EMUs provide important information to guide targeted programming and fills gaps where survey data is unavailable. As an input into FPET, EMU can increase the confidence of annual estimates of mCP, unmet need, and demand satisfied by modern methods and provide early indications of changes in trends in family planning use. The process of data review incorporated in the SS-to-EMU Tool has been shown to improve the quality, management, and use of family planning data for decision making. Since its introduction, EMU has been included in the routine list of indicators collected and analyzed by countries, donors and partners, standardizing how FP progress is tracked and discussed. Competing interests No competing interests were disclosed. Grant information This study was funded by the Bill and Melinda Gates Foundation (INV-007530) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Data and software availability Data availability This paper is theoretical, all data in this paper is for demonstration purposes only and does not reflect any individual or country. Software availability The SS-to-EMU Excel Tool s publicly available on the Track20 website, free of charge: https://www.track20.org/pages/track20_tools/SS_to_EMU_tool.php Supplementary material: The R code used to calculate EMU is available at the following GitHub repository: https://github.com/kristinbietsch/SStoEMU-National-and-Subnational MIT LicenseCopyright (c) 2025 Kristin Bietsch Archival source code is available from https://doi.org/10.5281/zenodo.14811417 Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. The software is provided “as is”, without warranty of any kind, express or Implied, including but not limited to the warranties of merchantability, Fitness for a particular purpose and noninfringement. In no event shall the Authors or copyright holders be liable for any claim, damages or other Liability, whether in an action of contract, tort or otherwise, arising from, Out of or in connection with the software or the use or other dealings in the Software. References 1. Alkema L, Kantarova V, Menzozzi C, et al. : National, regional, and global rates and trends in contraceptive prevalence and unmet need for family planning between 1990 and 2015: a systematic and comprehensive analysis. Lancet. 2013; 381 (9878):1642–1652. PubMed Abstract | Publisher Full Text 2. Cahill N, Sonneveldt E, Stover J, et al. : Modern contraceptive use, unmet need, and demand stisfied among women of reproductive age who are married or in a union in the focus countries of the Family Planning 2020 initiative: a systematic analysis using the Family Planning Estimation Tool. Lancet. 2017; 391 (10123):870–882. PubMed Abstract | Publisher Full Text | Free Full Text 3. Kantorova V, Wheldon MC, Ueffing P, et al. : Estimating progress towards meeting women’s contraceptive needs in 185 countries: A Bayesian hierarchical modelling study. PLoS Med. 2020; 17 (2):e1003026. PubMed Abstract | Publisher Full Text | Free Full Text 4. Track20: Family Planning Estimation Tool.n.d. Retrieved from Track20. Reference Source 5. Magnani RJ, Ross J, Williamson J, et al. : Can Family Planning Service Statistics Be Used to Track Population-Level Outcomes? Glob Health Sci Pract. 2018; 6 :93–102. PubMed Abstract | Publisher Full Text | Free Full Text 6. Cahill N, Sonneveldt E, Emmart P, et al. : Using family planning service statistics to inform model-based estimates of modern contraceptive prevalence. PLoS One. 2021; 16 (10):e0258304. PubMed Abstract | Publisher Full Text | Free Full Text 7. Steiner MJ, Sonneveldt E, Lebetkin E, et al. : Updated Couple Years of Protection: Literature Review, Guidance for Updating Existing Methods, and Adding New Methods.2022, January 11. Retrieved from FHI360. Reference Source 8. United Nations Department of Economic and Social Affairs Population Division: World Population Prospects.n.d. Reference Source 9. World Health Organization: Data Quality Review Toolkit.2017. Retrieved June 01, 2024, Measure Evaluation. Reference Source 10. Track20 and Dure Technologies: FP DataPro. DHIS2 App Hub.n.d. Reference Source Footnotes 1 https://www.track20.org/ 2 https://dhsprogram.com/ 3 https://mics.unicef.org/ 4 In Bangladesh, India, and Nepal the CYP factor for female sterilization is 13 years, as opposed to the global 10, to account for their earlier age at sterilization. 5 In a few cases, specifically countries where FP services are only offered to married women or it can generally be assumed that unmarried women are not accessing FP through the public sector, population estimates for married women have been used as an alternative denominator. 6 The automated EMU calculation is through FP DataPro in 12 countries and the predecessor version, the DHIS2 FP Module, in 1 country. Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 07 May 2025 ADD YOUR COMMENT Comment Author details Author details 1 Avenir Health, Glastonbury, Connecticut, USA Kristin Bietsch Roles: Conceptualization, Data Curation, Methodology, Project Administration, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Margaret Reeves Roles: Conceptualization, Methodology, Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing Jessica Williamson Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Priya Emmart Roles: Conceptualization, Formal Analysis, Investigation, Writing – Original Draft Preparation, Writing – Review & Editing Emily Sonneveldt Roles: Conceptualization, Formal Analysis, Funding Acquisition, Methodology, Project Administration, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information This study was funded by the Gates Foundation (INV-007530) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (1) version 1 Published: 07 May 2025, 9:17 https://doi.org/10.12688/gatesopenres.16346.1 Copyright © 2025 Bietsch K et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads Gates Open Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Bietsch K, Reeves M, Williamson J et al. Estimated Modern Use: Employing A Service Statistics-Based Indicator to Monitor Family Planning Programs . Gates Open Res 2025, 9 :17 ( https://doi.org/10.12688/gatesopenres.16346.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: AWAITING PEER REVIEW AWAITING PEER REVIEW ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 07 May 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Adjust parameters to alter display View on desktop for interactive features Includes Interactive Elements View on desktop for interactive features Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Stay Updated Sign up for content alerts and receive a weekly or monthly email with all newly published articles Register with Gates Open Research Already registered? Sign in Not now, thanks close PLEASE NOTE If you are an AUTHOR of this article, please check that you signed in with the account associated with this article otherwise we cannot automatically identify your role as an author and your comment will be labelled as a “User Comment”. If you are a REVIEWER of this article, please check that you have signed in with the account associated with this article and then go to your account to submit your report, please do not post your review here. If you do not have access to your original account, please contact us . All commenters must hold a formal affiliation as per our Policies . The information that you give us will be displayed next to your comment. User comments must be in English, comprehensible and relevant to the article under discussion. We reserve the right to remove any comments that we consider to be inappropriate, offensive or otherwise in breach of the User Comment Terms and Conditions . Commenters must not use a comment for personal attacks. When criticisms of the article are based on unpublished data, the data should be made available. I accept the User Comment Terms and Conditions Please confirm that you accept the User Comment Terms and Conditions. Affiliation ✕ refresh Please enter your institution. Note: To add your institution or organisation, start typing the name and then select the correct name from the list. Where applicable, the name will appear in both the original language and in English. Do not paste in the name. If the name does not appear in the drop-down list, we will display the information you have entered. ✕ refresh Country/Region * USA UK Canada China France Germany Afghanistan Aland Islands Albania Algeria American Samoa Andorra Angola Anguilla Antarctica Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Bouvet Island Brazil British Indian Ocean Territory British Virgin Islands Brunei Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Christmas Island Cocos (Keeling) Islands Colombia Comoros Congo Cook Islands Costa Rica Cote d'Ivoire Croatia Cuba Cyprus Czech Republic Democratic Republic of the Congo Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Falkland Islands Faroe Islands Federated States of Micronesia Fiji Finland France French Guiana French Polynesia French Southern Territories Gabon Georgia Germany Ghana Gibraltar Greece Greenland Grenada Guadeloupe Guam Guatemala Guernsey Guinea Guinea-Bissau Guyana Haiti Heard Island and Mcdonald Islands Holy See (Vatican City State) Honduras Hong Kong Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jersey Jordan Kazakhstan Kenya Kiribati Kosovo (Serbia and Montenegro) Kuwait Kyrgyzstan Lao People's Democratic Republic Latvia Lebanon Lesotho Liberia Libya Liechtenstein Lithuania Luxembourg Macao Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Martinique Mauritania Mauritius Mayotte Mexico Minor Outlying Islands of the United States Moldova Monaco Mongolia Montenegro Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands Antilles New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norfolk Island North Korea North Macedonia Northern Mariana Islands Norway Oman Pakistan Palau Palestinian Territory Panama Papua New Guinea Paraguay Peru Philippines Pitcairn Poland Portugal Puerto Rico Qatar Reunion Romania Russian Federation Rwanda Saint Helena Saint Kitts and Nevis Saint Lucia Saint Pierre and Miquelon Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Georgia and the South Sandwich Is South Korea South Sudan Spain Sri Lanka Sudan Suriname Svalbard and Jan Mayen Swaziland Sweden Switzerland Syria Taiwan Tajikistan Tanzania Thailand The Gambia The Netherlands Timor-Leste Togo Tokelau Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Turks and Caicos Islands Tuvalu UK USA Uganda Ukraine United Arab Emirates United States Virgin Islands Uruguay Uzbekistan Vanuatu Venezuela Vietnam Wallis and Futuna West Bank and Gaza Strip Western Sahara Yemen Zambia Zimbabwe Please select your country/region. You must enter a comment. Competing Interests Please disclose any competing interests that might be construed to influence your judgment of the article's or peer review report's validity or importance. Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Please state your competing interests The comment has been saved. An error has occurred. Please try again. Cancel Post var lTitle = "Estimated Modern Use: Employing A Service...".replace("'", ''); var linkedInUrl = "http://www.linkedin.com/shareArticle?url=https://gatesopenresearch.org/articles/9-17/v1" + "&title=" + encodeURIComponent(lTitle) + "&summary=" + encodeURIComponent('Read the article by '); var deliciousUrl = "https://del.icio.us/post?url=https://gatesopenresearch.org/articles/9-17/v1&title=" + encodeURIComponent(lTitle); var redditUrl = "http://reddit.com/submit?url=https://gatesopenresearch.org/articles/9-17/v1" + "&title=" + encodeURIComponent(lTitle); linkedInUrl += encodeURIComponent('Bietsch K et al.'); var offsetTop = /chrome/i.test( navigator.userAgent ) ? 4 : -10; var addthis_config = { ui_offset_top: offsetTop, services_compact : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_expanded : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_custom : [ { name: "LinkedIn", url: linkedInUrl, icon:"/img/icon/at_linkedin.svg" }, { name: "Mendeley", url: "http://www.mendeley.com/import/?url=https://gatesopenresearch.org/articles/9-17/v1/mendeley", icon:"/img/icon/at_mendeley.svg" }, { name: "Reddit", url: redditUrl, icon:"/img/icon/at_reddit.svg" }, ] }; var addthis_share = { url: "https://gatesopenresearch.org/articles/9-17", templates : { twitter : "Estimated Modern Use: Employing A Service Statistics-Based Indicator.... Bietsch K et al., published by " + "Gates Open Research" + ", https://gatesopenresearch.org/articles/9-17/v1" } }; if (typeof(addthis) != "undefined"){ addthis.addEventListener('addthis.ready', checkCount); addthis.addEventListener('addthis.menu.share', checkCount); } $(".f1r-shares-twitter").attr("href", "https://twitter.com/intent/tweet?text=" + addthis_share.templates.twitter); $(".f1r-shares-facebook").attr("href", "https://www.facebook.com/sharer/sharer.php?u=" + addthis_share.url); $(".f1r-shares-linkedin").attr("href", addthis_config.services_custom[0].url); $(".f1r-shares-reddit").attr("href", addthis_config.services_custom[2].url); $(".f1r-shares-mendelay").attr("href", addthis_config.services_custom[1].url); function checkCount(){ setTimeout(function(){ $(".addthis_button_expanded").each(function(){ var count = $(this).text(); if (count !== "" && count != "0") $(this).removeClass("is-hidden"); else $(this).addClass("is-hidden"); }); }, 1000); } close How to cite this report {{reportCitation}} Cancel Copy Citation Details $(function(){R.ui.buttonDropdowns('.dropdown-for-downloads');}); $(function(){R.ui.toolbarDropdowns('.toolbar-dropdown-for-downloads');}); $.get("/articles/acj/16346/17745") new F1000.Clipboard(); new F1000.ThesaurusTermsDisplay("articles", "article", "17745"); $(document).ready(function() { $( "#frame1" ).on('load', function() { var mydiv = $(this).contents().find("div"); var h = mydiv.height(); console.log(h) }); var tooltipLivingFigure = jQuery(".interactive-living-figure-label .icon-more-info"), titleLivingFigure = tooltipLivingFigure.attr("title"); tooltipLivingFigure.simpletip({ fixed: true, position: ["-115", "30"], baseClass: 'small-tooltip', content:titleLivingFigure + " " }); tooltipLivingFigure.removeAttr("title"); $("body").on("click", ".cite-living-figure", function(e) { e.preventDefault(); var ref = $(this).attr("data-ref"); $(this).closest(".living-figure-list-container").find("#" + ref).fadeIn(200); }); $("body").on("click", ".close-cite-living-figure", function(e) { e.preventDefault(); $(this).closest(".popup-window-wrapper").fadeOut(200); }); $(document).on("mouseup", function(e) { var metricsContainer = $(".article-metrics-popover-wrapper"); if (!metricsContainer.is(e.target) && metricsContainer.has(e.target).length === 0) { $(".article-metrics-close-button").click(); } }); var articleId = $('#articleId').val(); if($("#main-article-count-box").attachArticleMetrics) { $("#main-article-count-box").attachArticleMetrics(articleId, { articleMetricsView: true }); } }); var figshareWidget = $(".new_figshare_widget"); if (figshareWidget.length > 0) { window.figshare.load("f1000", function(Widget) { // Select a tag/tags defined in your page. In this tag we will place the widget. _.map(figshareWidget, function(el){ var widget = new Widget({ articleId: $(el).attr("figshare_articleId") //height:300 // this is the height of the viewer part. [Default: 550] }); widget.initialize(); // initialize the widget widget.mount(el); // mount it in a tag that's on your page // this will save the widget on the global scope for later use from // your JS scripts. This line is optional. //window.widget = widget; }); }); } close Error Close Add Reset F1000.MICROSERVICES.AFFILIATION = ''; $(document).ready(function () { $('.js-affiliations-form').each((index, form) => { new AffiliationForm({ formId: form.id, institutionErrorSelector: '.comment-enter-institution', departmentErrorSelector: '.comment-enter-department', placeSelector: '.js-add-comment-place', stateSelector: '.js-add-comment-state', zipCodeSelector: '.js-add-comment-zipcode', countrySelector: '.js-add-comment-country', countryErrorSelector: '.comment-enter-country', }); }); }); $(document).ready(function () { var reportIds = { }; $(".referee-response-container,.js-referee-report").each(function(index, el) { var reportId = $(el).attr("data-reportid"), reportCount = reportIds[reportId] || 0; $(el).find(".comments-count-container,.js-referee-report-views").html(reportCount); }); var uuidInput = $("#article_uuid"), oldUUId = uuidInput.val(), newUUId = "de85e7d0-2707-4205-907b-d85c90e623d1"; uuidInput.val(newUUId); $("a[href*='article_uuid=']").each(function(index, el) { var newHref = $(el).attr("href").replace(oldUUId, newUUId); $(el).attr("href", newHref); }); }); Are you a Gates-funded researcher? If you are a previous or current Gates grant holder, sign up for information about developments, publishing and publications from Gates Open Research. First Name * You must provide your first name Last Name * You must provide your last name Email * You must provide a valid email address Institution You must provide an institution. Submit Thank you! We'll keep you updated on any major new updates to Gates Open Research Gates Open Research Browse Gateways How to Publish About Contact Legal For Developers Cookie Notice Privacy Notice RSS Submit via VeriXiv © F1000 Research Limited and its licensors ISSN 2572-4754 var F1000platform = new F1000.Platform({ name: "gates", displayName: "Gates Open Research", hostName: "gatesopenresearch.org", id: "3", editorialEmail: "[email protected]", infoEmail: "[email protected]", usePmcStats: true }); Sign In Remember me Forgotten your password? Sign In Cancel Email or password not correct. Please try again Please wait... $(function(){ // Note: All the setup needs to run against a name attribute and *not* the id due the clonish // nature of facebox... $("a[id=googleSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("GOOGLE"); $("form[id=oAuthForm]").submit(); }); $("a[id=facebookSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("FACEBOOK"); $("form[id=oAuthForm]").submit(); }); $("a[id=orcidSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("ORCID"); $("form[id=oAuthForm]").submit(); }); }); If you've forgotten your password, please enter your email address below and we'll send you instructions on how to reset your password. The email address should be the one you originally registered with F1000. Email address not valid, please try again You registered with F1000 via Google, so we cannot reset your password. To sign in, please click here . If you still need help with your Google account password, please click here . You registered with F1000 via Facebook, so we cannot reset your password. To sign in, please click here . If you still need help with your Facebook account password, please click here . Code not correct, please try again Reset password Cancel Email us for further assistance. Server error, please try again. If your email address is registered with us, we will email you instructions to reset your password. If you think you should have received this email but it has not arrived, please check your spam filters and/or contact for further assistance. Please wait... Register $(document).ready(function () { signIn.createSignInAsRow($("#sign-in-form-gfb-popup")); $(".target-field").each(function () { var uris = $(this).val().split("/"); if (uris.pop() === "login") { $(this).val(uris.toString().replace(",","/")); } }); });