Evaluating Country Performance After Transitioning From Gavi Assistance: An Applied Synthetic Control Analysis

INTRODUCTIONOver the past decade, international development assistance for health has slowed. As donors seek to increase domestic cofinancing and ultimately transition countries from donor aid dependence, COVID-19 has severely constrained public budgets. The evaluation of sustainability and longer-t...

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Veröffentlicht in:Global health science and practice 2023-08, Vol.11 (4), p.e2200536
Hauptverfasser: Kolesar, Robert John, Spruk, Rok, Tsheten, Tsheten
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Spruk, Rok
Tsheten, Tsheten
description INTRODUCTIONOver the past decade, international development assistance for health has slowed. As donors seek to increase domestic cofinancing and ultimately transition countries from donor aid dependence, COVID-19 has severely constrained public budgets. The evaluation of sustainability and longer-term impacts of donor withdrawal is increasingly important. We assess vaccination coverage and post-neonatal mortality to estimate country performance of these outcomes among countries that no longer received assistance from Gavi, the Vaccine Alliance (Gavi) between 2000 and 2018. METHODSUsing data from all countries receiving Gavi support between 2000 and 2020, we employed a synthetic control method to generate a pre-transition counterfactual with the same characteristics as the observation of interest to predict a future that empirically never existed. The synthetic unit is constructed from the weighted average of other units with good fit to the unit of interest before transition but did not transition. RESULTSWe found substantial heterogeneity after transitioning from Gavi assistance. China, Guyana, and Turkmenistan overperformed their expected coverage rates; Albania, Bhutan, China, Guyana, and Turkmenistan maintained coverage over 90%; and Bosnia and Herzegovina and Ukraine reported precipitous drop-offs that fell well below their synthetic controls. We also observed a vaccination coverage decline in 2020 for several countries after transitioning and most synthetic controls, which we attribute to COVID-19-related service disruptions. CONCLUSIONSWe recommend that Gavi adjust its transition model to systematically assess contextual externalities and risk. In addition, countries that no longer receive Gavi assistance can leverage technical assistance and communities of practice to mutually assist each other and other countries advancing toward transition. This could also foster intracountry accountability after transition. We also recommend that Gavi systematize post-transition assessments and evaluations that leverage the expertise and experience of graduated countries to encourage cross-learning.
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As donors seek to increase domestic cofinancing and ultimately transition countries from donor aid dependence, COVID-19 has severely constrained public budgets. The evaluation of sustainability and longer-term impacts of donor withdrawal is increasingly important. We assess vaccination coverage and post-neonatal mortality to estimate country performance of these outcomes among countries that no longer received assistance from Gavi, the Vaccine Alliance (Gavi) between 2000 and 2018. METHODSUsing data from all countries receiving Gavi support between 2000 and 2020, we employed a synthetic control method to generate a pre-transition counterfactual with the same characteristics as the observation of interest to predict a future that empirically never existed. The synthetic unit is constructed from the weighted average of other units with good fit to the unit of interest before transition but did not transition. RESULTSWe found substantial heterogeneity after transitioning from Gavi assistance. China, Guyana, and Turkmenistan overperformed their expected coverage rates; Albania, Bhutan, China, Guyana, and Turkmenistan maintained coverage over 90%; and Bosnia and Herzegovina and Ukraine reported precipitous drop-offs that fell well below their synthetic controls. We also observed a vaccination coverage decline in 2020 for several countries after transitioning and most synthetic controls, which we attribute to COVID-19-related service disruptions. CONCLUSIONSWe recommend that Gavi adjust its transition model to systematically assess contextual externalities and risk. In addition, countries that no longer receive Gavi assistance can leverage technical assistance and communities of practice to mutually assist each other and other countries advancing toward transition. This could also foster intracountry accountability after transition. We also recommend that Gavi systematize post-transition assessments and evaluations that leverage the expertise and experience of graduated countries to encourage cross-learning.</description><identifier>ISSN: 2169-575X</identifier><identifier>EISSN: 2169-575X</identifier><identifier>DOI: 10.9745/GHSP-D-22-00536</identifier><identifier>PMID: 37640489</identifier><language>eng</language><publisher>Johns Hopkins Center for Communication Programs</publisher><subject>Humanities and Social Sciences ; Original</subject><ispartof>Global health science and practice, 2023-08, Vol.11 (4), p.e2200536</ispartof><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><rights>Kolesar et al. 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c405t-fb0f51cd2838146707ec74999d33250e9dde9f0c692b057ede19e77b44ba7c323</citedby><cites>FETCH-LOGICAL-c405t-fb0f51cd2838146707ec74999d33250e9dde9f0c692b057ede19e77b44ba7c323</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10461703/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10461703/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,27922,27923,53789,53791</link.rule.ids><backlink>$$Uhttps://uca.hal.science/hal-04200888$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Kolesar, Robert John</creatorcontrib><creatorcontrib>Spruk, Rok</creatorcontrib><creatorcontrib>Tsheten, Tsheten</creatorcontrib><title>Evaluating Country Performance After Transitioning From Gavi Assistance: An Applied Synthetic Control Analysis</title><title>Global health science and practice</title><description>INTRODUCTIONOver the past decade, international development assistance for health has slowed. As donors seek to increase domestic cofinancing and ultimately transition countries from donor aid dependence, COVID-19 has severely constrained public budgets. The evaluation of sustainability and longer-term impacts of donor withdrawal is increasingly important. We assess vaccination coverage and post-neonatal mortality to estimate country performance of these outcomes among countries that no longer received assistance from Gavi, the Vaccine Alliance (Gavi) between 2000 and 2018. METHODSUsing data from all countries receiving Gavi support between 2000 and 2020, we employed a synthetic control method to generate a pre-transition counterfactual with the same characteristics as the observation of interest to predict a future that empirically never existed. The synthetic unit is constructed from the weighted average of other units with good fit to the unit of interest before transition but did not transition. RESULTSWe found substantial heterogeneity after transitioning from Gavi assistance. China, Guyana, and Turkmenistan overperformed their expected coverage rates; Albania, Bhutan, China, Guyana, and Turkmenistan maintained coverage over 90%; and Bosnia and Herzegovina and Ukraine reported precipitous drop-offs that fell well below their synthetic controls. We also observed a vaccination coverage decline in 2020 for several countries after transitioning and most synthetic controls, which we attribute to COVID-19-related service disruptions. CONCLUSIONSWe recommend that Gavi adjust its transition model to systematically assess contextual externalities and risk. In addition, countries that no longer receive Gavi assistance can leverage technical assistance and communities of practice to mutually assist each other and other countries advancing toward transition. This could also foster intracountry accountability after transition. We also recommend that Gavi systematize post-transition assessments and evaluations that leverage the expertise and experience of graduated countries to encourage cross-learning.</description><subject>Humanities and Social Sciences</subject><subject>Original</subject><issn>2169-575X</issn><issn>2169-575X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNpdkU1r3DAQhk1paUKac686tgcn-rSkXorZJLuBhQaSQm9CluWsii1tJXlh_33lbihpdRmheeYZwVtVHxG8kpyy6_Xm8aG-qTGuIWSkeVOdY9TImnH24-2r-1l1mdJPWI6kCEvxvjojvKGQCnle-duDHmednX8GqzD7HI_gwcYhxEl7Y0E7ZBvBU9Q-ueyCX7i7GCaw1gcH2pRcygv4BbQetPv96GwPHo8-72x2piiLMYylqcdjYT9U7wY9Jnv5Ui-q73e3T6tNvf22vl-129pQyHI9dHBgyPRYEIFowyG3hlMpZU8IZtDKvrdygKaRuIOM294iaTnvKO00NwSTi-rrybufu8n2xpZv6FHto5t0PKqgnfq3491OPYeDQpA2iENSDJ9Pht1_c5t2q5Y3SDGEQogDKuynl20x_Jptympyydhx1N6GOSksmJBCMkwLen1CTQwpRTv8dSOollTVkqq6URirP6mS3_SSlTo</recordid><startdate>20230828</startdate><enddate>20230828</enddate><creator>Kolesar, Robert John</creator><creator>Spruk, Rok</creator><creator>Tsheten, Tsheten</creator><general>Johns Hopkins Center for Communication Programs</general><general>Global Health: Science and Practice</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>1XC</scope><scope>BXJBU</scope><scope>IHQJB</scope><scope>VOOES</scope><scope>5PM</scope></search><sort><creationdate>20230828</creationdate><title>Evaluating Country Performance After Transitioning From Gavi Assistance: An Applied Synthetic Control Analysis</title><author>Kolesar, Robert John ; Spruk, Rok ; Tsheten, Tsheten</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c405t-fb0f51cd2838146707ec74999d33250e9dde9f0c692b057ede19e77b44ba7c323</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Humanities and Social Sciences</topic><topic>Original</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kolesar, Robert John</creatorcontrib><creatorcontrib>Spruk, Rok</creatorcontrib><creatorcontrib>Tsheten, Tsheten</creatorcontrib><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>HAL-SHS: Archive ouverte en Sciences de l'Homme et de la Société</collection><collection>HAL-SHS: Archive ouverte en Sciences de l'Homme et de la Société (Open Access)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Global health science and practice</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kolesar, Robert John</au><au>Spruk, Rok</au><au>Tsheten, Tsheten</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluating Country Performance After Transitioning From Gavi Assistance: An Applied Synthetic Control Analysis</atitle><jtitle>Global health science and practice</jtitle><date>2023-08-28</date><risdate>2023</risdate><volume>11</volume><issue>4</issue><spage>e2200536</spage><pages>e2200536-</pages><issn>2169-575X</issn><eissn>2169-575X</eissn><abstract>INTRODUCTIONOver the past decade, international development assistance for health has slowed. As donors seek to increase domestic cofinancing and ultimately transition countries from donor aid dependence, COVID-19 has severely constrained public budgets. The evaluation of sustainability and longer-term impacts of donor withdrawal is increasingly important. We assess vaccination coverage and post-neonatal mortality to estimate country performance of these outcomes among countries that no longer received assistance from Gavi, the Vaccine Alliance (Gavi) between 2000 and 2018. METHODSUsing data from all countries receiving Gavi support between 2000 and 2020, we employed a synthetic control method to generate a pre-transition counterfactual with the same characteristics as the observation of interest to predict a future that empirically never existed. The synthetic unit is constructed from the weighted average of other units with good fit to the unit of interest before transition but did not transition. RESULTSWe found substantial heterogeneity after transitioning from Gavi assistance. China, Guyana, and Turkmenistan overperformed their expected coverage rates; Albania, Bhutan, China, Guyana, and Turkmenistan maintained coverage over 90%; and Bosnia and Herzegovina and Ukraine reported precipitous drop-offs that fell well below their synthetic controls. We also observed a vaccination coverage decline in 2020 for several countries after transitioning and most synthetic controls, which we attribute to COVID-19-related service disruptions. CONCLUSIONSWe recommend that Gavi adjust its transition model to systematically assess contextual externalities and risk. In addition, countries that no longer receive Gavi assistance can leverage technical assistance and communities of practice to mutually assist each other and other countries advancing toward transition. This could also foster intracountry accountability after transition. We also recommend that Gavi systematize post-transition assessments and evaluations that leverage the expertise and experience of graduated countries to encourage cross-learning.</abstract><pub>Johns Hopkins Center for Communication Programs</pub><pmid>37640489</pmid><doi>10.9745/GHSP-D-22-00536</doi><oa>free_for_read</oa></addata></record>
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title Evaluating Country Performance After Transitioning From Gavi Assistance: An Applied Synthetic Control Analysis
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