Table 1. Recent coverage trends (WUENIC estimates) in GAVI-eligible countries (excluding EUR) and health

Table 1. Recent coverage trends (WUENIC estimates) in GAVI-eligible countries (excluding EUR) and health resources 1a. High (>80/U) coverage >4 v s Drop- Govt Birth # un- # under Nurses/ THE/cap W Bank ODA DTP 3 (%) DTP1 out tiff cohort vacc vacc 10k 2006 Class CH Country 2000 2005 2009 2009 2009 2009 2009 2009 2009 pop PPP int$ 2009 S/child *1000 AFR Burundi 80 87 92 98 6 7 283 5660 22640 2 15 LI 9.6 Eritrea 90 96 99 99 0 -14 185 1850 1850 6 28 LI 5.1 Gambia 89 89 98 98 0 -4 62 1240 1240 13 56 LI 10.7 Ghana 88 84 94 96 2 0 766 30640 45960 9 100 LI 11.8 Lesotho 83 87 83 93 11 -11 59 4130 10030 6 143 LMI 5.1 Malawi 75 93 93 97 4 0 608 18240 42560 6 70 LI 14.5 Rwanda 90 95 97 98 1 rVa 413 8260 12390 4 210 LI 20.7 Sao Tome & P 82 97 98 98 0 0 5 100 100 19 141 LMI rVa Senegal 52 84 86 94 9 0 476 28560 66640 3 72 LI 11.4 U R Tanzania 79 90 85 90 6 2 1812 181200 271800 4 45 LI 8 Togo 64 82 89 93 4 0 215 15050 23650 4 70 LI 3.1 AMR BoliAa 77 85 85 87 2 0 262 34060 39300 21 204 LMI 7.9 Cuba 95 89 96 98 2 0 116 2320 4640 74 363 UMI rVa Guyana 88 93 98 98 0 0 13 260 260 23 264 LMI rVa Honduras 94 98 98 99 1 0 202 2020 4040 13 241 LMI rVa Nicaragua 83 88 98 98 0 0 140 2800 2800 11 251 LMI rVa EMR Pakistan 62 80 85 90 6 0 5,403 540300 810450 5 51 LMI 3.5 SEAR Bangladesh 81 93 94 99 5 -7 3,401 34010 204060 3 69 LI 3.3 Bhutan 92 95 96 98 2 0 15 300 600 3 107 LMI rVa D P R Korea ( 54 79 93 94 1 0 327 19620 22890 41 49 LI rVa Sri Lanka 99 99 97 98 1 0 364 7280 10920 17 213 LMI rVa WPR Cambodia 59 82 94 99 5 0 367 3670 22020 9 167 LI 4 China 85 87 97 98 1 0 18294 365880 548820 10 342 LMI 0.3 Mongolia 95 99 95 95 0 0 50 2500 2500 35 149 LMI rVa Viet Nam 96 95 96 97 1 0 1,485 44550 59400 8 264 LI rVa EFTA01121863 lb. Medium (60-80%) coverage in 2005 and/or 2009 Drop- Govt # un- # under- Nurses/ THE/ GAVI World ODA DTP 3 ("/o) DTP1 out diff vacc vacc 10k cap Grouping Bank CH Country 2000 2005 2009 2009 2009 2009 2009 2009 pop 2006 (GNI Class 5/child PPP Intl 2005) 2009 Benin 78 70 83 99 16 15 3490 59330 8 46 Poorest LI 19.6 Burkina Faso 57 82 82 89 8 17 81180 132840 5 87 Poorest LI 7.7 Cameroon 62 80 80 88 9 0 85320 142200 16 80 Least poor LI 5.1 C6te d'Ivoire 67 76 81 95 15 0 36450 138510 6 66 Fragile LMI 2.4 Guinea-Bissau 49 68 68 85 20 14 9900 21120 7 40 Poorest LI 4.2 Kenya* 82 76 75 80 6 0 306000 382500 12 105 Intermed LI 12.9 Mali 43 77 74 85 13 15 82650 143260 6 65 Poorest LI 7.6 Mauritania 53 71 64 79 19 3 22890 39240 6 45 Poorest LI 7.3 Mozambique 70 76 76 88 14 0 105240 210480 3 56 Poorest LI 10.8 Sierra Leone 44 65 75 87 14 16 29510 56750 5 41 Fragile LI 9.3 Uganda 52 64 64 90 29 19 150200 540720 7 143 Poorest LI 9.4 Zambia 85 82 81 92 r 10 17 43920 104310 20 62 Poorest LI 23.5 Zmbabwe 79 65 73 87 16 0 49270 102330 7 147 Intermed LI 6.6 EMR Yemen 61 65 66 77 14 20 198030 292740 7 82 Poorest LI 3 EUR Azerbaijan 75 72 73 79 8 21 35490 45630 84 218 Least Poor LMI 10 SEAR India 60 67 66 83 20 rVa 5E+06 9107580 13 109 Intermed LMI 2.7 Indonesia 71 72 82 89 8 0 459140 751320 8 87 Least Poor LMI 2 WPR Kiribati 90 79 86 92 7 0 n/a n/a 30 290 Least Poor LMI n/a Solomons 82 78 81 83 2 0 2720 3040 14 107 Poorest LMI n/a EFTA01121864 1c. Increasing coverage Drop- Govt # un- # under Nurses/ THE/ GAVI W Bank ODA DTP 3 %) DTP1 out diff vacc vacc 10k cap Grouping Class CH Country 2000 2005 2009 2009 2009 2009 2009 2009 pop 2006 (GNI 2005) 2009 3/child AFR PPP int$ Angola' 31 47 73 93 22 0 54880 211680 14 71 Fragile LMI 5.4 Comoros (thi 70 68 83 94 12 0 1320 3740 7 35 Poorest LI n/a Congo (the) 33 65 91 92 1 0 10080 11340 10 31 Fragile LI 1.3 DRC 40 60 77 91 15 15 263700 673900 5 18 Fragile LI 3.6 Ethiopia 56 69 79 86 8 0 438480 657720 2 22 Poorest LI 9.3 Liberia 46 60 64 75 15 28 37250 53640 3 39 Fragile LI 15.3 Madagascar 57 82 78 80 3 11 139000 152900 3 34 Poorest LI 5.7 Niger (the) 34 45 70 82 15 23 146700 244500 2 27 Poorest LI 9.1 EMR Afghanistan' 31 76 83 94 12 0 78120 221340 5 29 Fragile LI 10.3 Djibouti 46 71 89 90 1 0 2400 2640 4 100 Least Poor LMI 12.8 Sudan (they 62 78 84 92 9 7 104000 208000 9 61 Fragile LMI 11.1 SEAR Myanmar 82 73 90 93 3 0 71120 101600 10 43 Poorest LI 2.5 Nepal 80 75 82 84 2 7 116800 131400 5 78 Poorest LI 2 Timor-Leste 55 72 76 5 0 11040 12880 22 169 Fragile LMI n/a 1d. Low (<60%) coverage Drop- Govt # un- # under Nurses/ THE/ GAVI W Bank ODA DTP 3%) DTP1 out diff vacc vacc 10k cap Grouping Class CH Country 2000 2005 2009 2009 2009 2009 2009 2009 pop 2006 (GNI 2005) 2009 $/child AFR PPP int$ CAR 37 54 54 64 16 22 55440 70840 4 55 Fragile LI 5.7 Chad 26 23 23 45 49 52 279400 391160 3 40 Poorest LI 2.1 Eq Guinea 33 33 33 65 49 41 9100 17420 5 280 Poorest HI 37.8 Guinea 47 59 57 75 24 28 99250 170710 5 116 Poorest LI 4.2 Nigeria 29 36 42 52 19 29 3E+06 4E+06 17 50 Intermed LMI 6.9 AMR Haiti 49 59 59 83 29 n/a 46580 112340 1 96 Fragile LI 11.1 EMR Somalia 33 35 31 40 23 20 241200 277380 2 Fragile LI 5.8 WPR Lao PDR" 51 49 57 76 25 10 41280 73960 10 85 Poorest LI 4.7 PNG 59 61 52 70 26 12 62400 99840 5 134 Intermed LMI 12.2 ' WHO-UNICEF estimates since 2000 based entirely or almost entirely on administrative reports and WHO-UNICEF recommend a national high-quality survey be conducted " WHO-UNICEF note uncertainty in the size of the birth cohort. No recent nationally representative survey conducted Dropout = difference in DTP1 and DTP3 coverage expressed as a percentage of DTP1 coverage= ((DTP1- DTP3)*100)/DTP1 Govt DTP3 dill = absolute difference between reported DTP3 coverage and WHO-UNICEF best estimates THE: total health expenditure ODA CH : official development assistance for child heatlh services - from Greco et al Lancet 2008 - only estimated for the 68 Countdown priority countries EFTA01121865 Table 2. Main countries with internally displaced populations and/or people in refugee like situations due to conflict, 2007-8 Source: Human Development Report 2009 Country Internally displaced People in refugee•like situations Populations 2008 in other countries, 2007 Afghanistan 200000 1147800 Angola 20000 Azerbaijan 573000 Bangladesh 500000 Bosnia and Herzegovina 125000 Burundi 100000 Central African Republic 108000 Chad 186000 Colombia 0 481600 Cote d'Ivoire 621000 D.R. Congo 1400000 Eritrea 32000 Ethiopia 200000 India 500000 Iraq 2842000 30000 Kenya 400000 Myanmar 503000 Peru 150000 Philippines 314000 Serbia 248000 Somalia 1100000 Sri Lanka 500000 Sudan 6000000 Syrian Arab Republic 433000 Timor-Leste 30000 Uganda 869000 EFTA01121866 Table 3: Indicators to monitor immunization program performance (adapted from Hadler et al 2008) Program component Indicators Program outputs % Fully vaccinated children (if routine reports are used, DTP3 taken as proxy) % districts with >80% DTP3 coverage in infants* % districts with U90% measles vaccine coverage in infants* Service delivery** % of planned outreach sessions that were conducted on schedule % of planned fixed site sessions that were conducted on schedule Access to services % of children up-to-date (BCG and DTPI/OPV I) by age 2 months Tracking activities "Dropout" - difference in percentage receiving DTPI/OPV1 and either DTP3/OPV3 or measles vaccine Use of all opportunities Percentage of children receiving all vaccines for which they are eligible at each visit Safety Proportion of districts that have been supplied with adequate (equal or more) number of AD syringes for all routine immunizations during the year Logistics and cold chain Proportion of districts that had no interruption in vaccine supply' Percentage of facilities storing vaccine at recommended temperatures Vaccine effectiveness in expected range for each vaccine evaluated Transport*** Kilometers/vehicle or motorbike/month (high km = high utilization) Percent use for service delivery and service delivery support (higher=more effective) Policy of planned preventive maintenance (PPM) & % PPM activities conducted Full cost per km (low cost = more efficient use of vehicles/motorbikes) Surveillance/ monitoring % expected district disease surveillance reports received at national level * % expected district coverage reports received at national level* Management and supervision Country has 5-year immunization plan % districts having microplans that include immunization activities* % districts that did >1 supervisory visit to all Health facilities in last year* Provider knowledge*** Proportion of providers who know and follow recommended guidelines, including those on simultaneous administration, contraindications, and safe injection procedures * on the WHO-UNICEF Joint Reporting Form on Immunization (JRF) ** proposal in GPEI strategic plan that polio officers will assist in monitoring these indicators *** no indicators routinely monitored by EPI to date EFTA01121867 Table 4. Advantages and disadvantages of methods to measure vaccination coverage Method Advantages Disadvantages Register- based (electronic) Can give complete and accurate information on cumulative vaccination status of individuals and populations Can be used to set appointments, issue reminders and recalls Use of electronic systems could reduce time spent on paper registers that are widespread in low income countries and often not used Need good computer access Need complete birth registry for true denominator Need unique ID number that is kept throughout life If held locally, difficult to track vaccination of migrants If held nationally, feedback/use at local level may be slow Requires adequate funding and human resources Routine reports of vaccinations delivered Simple in conception Continuous information allows monitoring of cumulative coverage through the year and by district/health facility Can be used at local level to track coverage and dropout rates Population denominators often inaccurate Private sector often does not report Exaggeration of doses administered common (e.g. double-counting of same child if home- based record mislaid; inclusion of children outside target age group, or purposeful exaggeration) Transcription errors at each health system level when paper-based systems used Surveys If well-conducted, can provide accurate information Other indicators (e.g. missed opportunities, caretaker knowledge) can be assessed Involvement of health workers can be training opportunity Large-scale surveys for multiple programs can reduce costs Lot quality sample surveys can be used to identify poor-performing districts/health facilities Quality of data depends on training, supervision and quality control Sampling frame often based on outdated census information Home-based records may be missing or incomplete Participation rate will determine reliability of results. Often long delays until results are known. Small sample sizes give imprecise results; large sample sizes are expensive and more time- consuming EFTA01121868