Service Tanzania Delivery Service Delivery Indicators Indicators Health | May 2016 Education | Health Tanzania has made some phenomenal gains in recent years. For example, the infant mortality rate has fallen by an average 3.2 percent per year, the fastest rate of decline among 20 countries in the region. Arguably, the next set of gains will be more challenging, reducing neonatal mortality and maternal mortality have proven harder and costlier, and addressing the performance gaps identified in the survey at the frontline health facilities and service providers will be a critical determinant of progress. As Tanzania’s Vision 2025 sets out the country’s de- velopment framework to achieve middle-income country status, the challenge facing the health sector is: What will be the drivers of the next set of health gains to reach the level of middle-income countries. Highlights Service Delivery Indicators in the Health Results Chain Input availability Input Availability Health Providers Health Outcomes Only half (50 percent) of the §§ Tanzanian facilities had access ■ Average Absence Rate 14% to electricity, clean water and ■ Doctors’ Absence Rate 33% improved sanitation. ■ Diagnostic Accuracy 60% ■ Adherence to guidelines 44% Although in almost all the facilities §§ ■ Infrastructure 50% ■ Caseload 7.3 patients a day ■ Maternal Mortality 454 most of the vaccines were available, ■ Mininmum Equipment 84% ■ Managing maternal and ■ Neonatal Mortality 26 2/3 of the refrigerators were not ■ Drugs Availability 60% neonatal complications 30% ■ Births in health facilities 58% compliant with the regulations for the temperature. Less than half (49 percent) of the priority drugs for §§ Delivering Health Services mothers were available; only 8 percent of facilities had all 14 tracer drugs in stock; and just one percent of rural Tanzania is facing a severe human resources for health facilities did so. (HRH) shortage. According to Tanzania’s Health Sector Strategy Paper 2009-2015 (HSSP III) HRH are a priority to Provider effort improving accessibility and quality of health services. On average 14 percent of health providers were absent §§ from the facility down from 21 percent in 2010. §§Facilities on average were staffed with 13.1 health workers. Urban facilities had more staff (24.5 providers) Absence was more prevalent in Dar es Salaam where 21 §§ percent could not be found in the facility. compared to rural facilities (6.0 providers). Public facilities had fewer staff members than their private counterparts. Doctors especially in urban areas were the most likely to §§ be absent and their absence was more likely to not have §§Over half (55 percent) of health workers were nurses. been approved. Although only 10 percent of Tanzania’s population lived in Dar es Salaam, the city was home to 45 percent of Caseload was very low with the average health worker §§ all doctors. In contrast, 70 percent of the population seeing on average 7.3 outpatients per day. and 85 percent of the poor lived in rural areas but they Provider ability were served by only 28 percent of the country’s health Health providers could correctly diagnose only 60 §§ workforce, and a mere 9 percent of its doctors. These percent of five common conditions.1 stark service delivery inequalities are likely to translate and even reinforce welfare inequality. There was a significant difference between public §§ providers in rural areas who managed to diagnose less than half (44 percent) of the conditions and those in 85 percent of the poor lived in the urban areas who correctly diagnosed 70 percent of conditions. rural areas but they were served by Only 5 percent nurses could correctly diagnose at least §§ only 28 percent of the country’s 4 of the cases. health workforce, and a mere 1 The conditions are (i) malaria with anemia, (ii) diarrhea with severe dehydration, (iii) 9 percent of its doctors. pneumonia, (iv) pulmonary tuberculosis, and (v) diabetes mellitus or type 2. SDI Results Availability of Key Inputs Tanzania performed relatively well in the availability Only 8 percent facilities had all the of basic medical equipment such as stethoscopes, tracer drugs available. Virtually no thermometers, or weighing scales. However, access to basic infrastructure and drug availability remained rural public facility (1 percent) had major challenges. Only half the facilities in Tanzania all the tracer drugs on stock had the required components for infrastructure. Drug availability, particularly, for mothers and children was also poor. facilities particularly lacked access to electricity and Drugs. On average, 60 percent of the drugs were clean water, a critical input in the health sector. The available in the Tanzanian facilities. The level of avail- infrastructure indicator steadily improved with the ability of the 14 tracer drugs was similar at 60 percent. level of the facility starting from 44 percent for dis- With just above half (53 percent) of the tracer drugs pensaries and reached 75 percent for health centers available, public facilities had a significantly lower and 87 percent for district hospitals. However, a larger score compared to the private ones (e.g. 84 percent share of dispensaries in Dar es Salaam had a better for private for-profit facilities). It is alarming that only access to infrastructure (85 percent) when compared 8 percent facilities had all the tracer drugs available. to health centers in rural areas (52 percent). Virtually no rural public facility (1 percent) had all the tracer drugs on stock and unexpired. Neither drugs Seventy percent of facilities in Tanzania had access for children nor drugs for mothers were widely avail- to clean water and 67 percent had access to electric- able with average scores of 59 percent and 49 per- ity. The public sector lagged the private sector for all cent, respectively. Given the national concern about three basic infrastructure but the gap was especially maternal mortality and efforts to improve maternal important for access to clean water. This gap was health outcomes, the availability of tracer drugs for mostly driven by the rural public sector (56 percent) women was unsettlingly low. Rural facilities did con- which itself was far behind the urban public sector sistently and significantly worse than urban facilities (81 percent). in terms of drugs availability. Infrastructure. O n l y h alf (50 percent) of the Provider effort: What providers do? health facilities had access to clean water, improved In countries which experience shortages in human toilets, and electricity. There was a large difference, resources for health, it is usually a concern that health however, between rural and urban facilities (36 per- workers are overworked i.e. their caseload is unsus- cent for public vs. 79 percent urban). Rural health tainably high potentially compromising the quality of service. In Tanzania, however, the SDI data suggest that a large share of health providers, especially those Figure 1: Rural-Urban contrast in availability in moderately sized facilities, had very low caseload of key inputs (percent of facilities) levels. 89 96 85 86 Large The average caseload in Tanzania stood at 7.3 outpa- 43 percent 79 tients per provider per day. Private for profit facilities 61 urban-rural 58 infrastructure had the highest, albeit still low, daily caseload with gap 10.8 outpatients seen by the average health provid- 36 er. The outpatient workload decreased with the size of the facility with district hospital staff consulting only 3.8 patients per day. Health staff that worked Clean water Toilet Electricity Minimum in urban dispensaries were the busiest and saw 11.5 Infrastructure outpatients a day. Despite the shortage in health (All three) Urban Rural personnel, providers’ caseload in Tanzania was low suggesting that there was room for a significant 2 Tanzania Service Delivery Indicators ■ Health improvement of health providers’ productivity with- out jeopardizing quality. Figure 2: Absence rates by cadre (percent) Compared to African as well as Asian standards, ab- 35 Doctors 14 senteeism in Tanzania’s health sector was relatively low at 14 percent. It also improved as it went down 13 from 21 percent in 2010. Absenteeism was higher Clinical Officers 16 in Dar es Salaam where 21 percent health providers were absent. Absence was particularly high in Dar es Nurses 13 Salaam’s health centers (22 percent) and hospitals (25 15 percent). Staff in private not-for-profit facilities were Para- 4 as likely to be absent as those in public or other pri- Professionals 10 vate facilities (difference in absence rates were posi- tive but not statistically significant). Urban Rural Four major themes were observed in relation to ab- sence rate: (i) Absence rates were similar in dispen- saries while significant differences were observed in Figure 3: Reasons for absence (percent of all absences) health centers; (ii) Facilities with staff in excess of six Unapproved 31 workers relative to facilities with 2 or fewer workers 2 were found to have higher absence rates; and (iii) 1-in-3 urban doctor was absent from the facility at Approved 17 29 any point in time; and (iv) While absence in private (non-profit) facilities was 40 percent lower than pub- Sick/Training/Mission 51 lic facilities, this was not statistically significant after 58 controlling for other factors. 1 Other Reason 10 Compared to African Doctors Other sta as well as Asian standards, absenteeism in Tanzania’s health characteristics, age was mildly negatively correlated sector was relatively low at 14 with absence. percent. It also improved as it The survey found that the overwhelming share (88 went down from 21 percent percent) of absence was in fact sanctioned or ap- in 2010. proved absence. It is possible that absence can be improved by more prudent sanctioning policy of absence. This suggests that management improve- Absenteeism was more acute for doctors compared ments and better organization and management of to other staff. Clinical officers and nurses were equally staff can potentially improve the availability of staff likely to be absent, but they were also more likely to for service delivery. be absent than para-professionals. Urban doctors (35 percent) were almost three more likely to be absent Provider Ability: What providers know? than their counterparts serving in rural areas (14 per- The SDI survey assessed provider ability and knowl- cent). This may be due to opportunities for moon- edge using two process quality indicators (the adher- lighting or other income generating activities. In a ence to clinical guidelines in five common conditions, more sophisticated regression analysis, very small and the management of two maternal and newborn facilities with 1 or 2 health workers still had a much complications), and an outcome quality indicator (di- lower absence rate. In terms of health providers’ agnostic accuracy in five common conditions). Tanzania Service Delivery Indicators ■ Health 3 Providers were able to correctly diagnose 60 percent of the five common conditions. Urban providers as a Only 2 out of 5 providers were whole significantly outperformed their rural counter- able to correctly diagnose at least parts (66 percent of cases versus 50 percent of cas- es). Across cadres, clinical officers performed on par 4 of the conditions and 1 out of 5 with doctors but nurses’ score was just slightly above managed to correctly diagnose all 5 half that of clinical officers. It is also noteworthy that conditions. Three out of 5 providers private-for-profit providers (54 percent) performed worse than providers in both the public (60 percent) could not identify a case of severe and not-for-profit sectors (66 percent). Within the dehydration, a fatal condition public sector, rural providers found less than half (44 for children. percent) of the cases. The best performers are doc- tors in rural facilities who accurately diagnosed 85.4 percent of cases. Nurses in faith-based organizations conditions such as acute diarrhea with severe dehy- performed the worst, correctly diagnosing only 22 dration or malaria with anemia, more than half clini- percent of cases. cians were unable to offer correct diagnosis for the Only 2 out of 5 providers were able to correctly diag- former; 3-in-5 clinicians failed the latter. nose at least 4 of the conditions and 1 out of 5 man- Due to the significance of malaria in Tanzania’s bur- aged to correctly diagnose all 5 conditions. Three out den of disease a closer look was taken at the malaria of 5 providers could not identify a case of severe de- case. The diagnosis of malaria with anemia was the hydration, a fatal condition for children. On the other second least accurate at 45 percent, although a very hand, almost 20 percent of the providers could not large majority (89 percent) of providers arrived at the correctly diagnosed more than one case. Only 3 per- diagnosis of malaria the majority among them did cent of the nurses correctly diagnosed all 5 cases, and not take the additional required step to identify the almost half of them (45 percent) diagnosed at most presence of anemia. one case. Clinical officers were the best performers as 31 percent of them diagnosed all cases, a feat man- aged by only 20 percent of doctors. The diagnostic Did providers adhere to guidelines for accuracy rate varied across case conditions, ranging maternal and neonatal complications? from 39 percent for acute diarrhea with severe de- When providers correctly diagnosed the hydration to 92 percent for pulmonary tuberculosis. condition did they provide the adequate Almost half of the providers could not diagnose dia- treatment? betes, and about 1 in 4 health providers misdiagnosed Although Tanzania has surpassed the Millenium pneumonia. Even for very common, but dangerous, Development Goals (MDGs) related to infant and under-5 mortality rates, neonatal mortality barely contributed to this achievement. Indeed, under-five Figure 4: Diagnostic accuracy (percent providers who mortality decreased from 143 per 1000 live birth to correctly diagnosed number of clinical cases) 81 per 1000 live births between 1996 and 2010, while neonatal mortality went from 31 to a mere 26 per Nurses 3 5 21 27 35 10 1000 live births. Between 2006 and 1000 one third of the Tanzanian children who did not live to celebrate their fifth birthday actually died right after birth.2 As Clinical Officers 31 18 19 22 9 2 for neonatal mortality, maternal mortality rate is not improving fast enough if at all3 and Tanzania already Doctors 20 23 32 10 14 1 missed the marked for the MDGs, and is unlikely to All Clinicians 22 17 23 20 16 3 2 “The proportion of infant deaths occurring in the first month of life is 55 per- cent in the period 0 to 4 years preceding the survey. Furthermore, […]; 72 percent of neonatal deaths were early neonatal deaths.” [2010 TDHS report] 3 Maternal mortality decreased from 578 in 2004-05 to 454 in 2010 according All 5 cases 4 cases 3 cases 2 cases 1 case No case to the 2010 DHS but that decrease was not statistically significant although it suggest a declining trend has started. 4 Tanzania Service Delivery Indicators ■ Health meet the Sustainable Development Goals (SDGs) related to maternal mortality. Maternal and neona- Figure 5: Diagnostic and Treatment Accuracy (% tal mortality are therefore two critical areas where providers offering correct diagnosis and treatment). the Tanzanian health system needs to register some 37 progress. Nurses 34 The process quality indicator is clinicians’ abil- 67 ity to manage maternal and neonatal complications. Clinical Officer 46 Overall, providers adhered to only 30 percent of the clinical guidelines for managing maternal and new- Doctors 64 born complications. Doctors were again more likely 49 to adhere more closely although they followed only 60 36 percent of clinical guidelines. All Providers 44 Less than 1 percent of providers adhered to at least Correct diagnosis Correct treatment 75 percent of the guidelines for the two maternal and neonatal complications. Using a lower threshold of 50 percent, only 20 percent of providers adhered to treatment in only 44 percent of the cases. Clinical of- at least half of the treatment actions for each of the ficers displayed the largest gap between the correct two complications. diagnosis and the correct treatment. Interestingly, nurses have a low diagnostic accuracy score but in contrast to doctors and clinical officers they provide Although providers correctly the right treatment nearly every time they correctly diagnose the condition presented to them. Still they diagnosed 60 percent of the lagged behind better trained providers in the share of conditions they provide the full correct treatments. correct treatment in only 44 Ideally any patient seeking care would like to walk out of a health facility with both a correct diagnostic and percent of the cases. a relevant and complete treatment. If 92 percent of health providers correctly diagnosed the pulmonary tuberculosis case only 1-in-10 (12 percent) could pro- For the other 5 common conditions, public providers vide the complete treatment resulting in only 11 per- adhered to only 44 percent of the clinical guidelines cent providers finding both the diagnostic and pro- for managing maternal and newborn complications, viding the treatment. TB appears as an extreme case a rate similar to private providers (46 percent). This with high diagnostic accuracy but very low treatment process quality was also found to progressively de- accuracy. Malaria with anemia is the other case where cline by cadre type, with the observed differences only 10 percent of the patients will walk out of the between rural public and urban public providers ob- facility with a satisfactory outcome. For diabetes, only served to be statistically significant: 48 percent differ- half of the providers correctly diagnosed the case but ence between rural and urban among doctors to 16 then once diagnosed nearly all of them (97 percent) percent difference between rural and urban nurses. gave the adequate treatment. The lowest scores for adherence to clinical guidelines were among rural public nurses at 39 percent com- What would providers do differently pared to 49 percent among their urban counterparts without technical capacity constraints? (percent difference of 23). The implication is that Sometimes providers do know what they should do when a child or adult patient receives treatment for but were just constrained by the technical capacity at very basic conditions like diarrhea or diabetes from a their disposal. For instance, for neonatal resuscitation rural nurse only about two fifths of the country’s clini- the provider might not have any bag or mask for that cal guidelines will be followed. purpose and then the provider would just respond Although providers correctly diagnosed 60 per- with what she could do when faced with that case in cent of the conditions they provide the full correct the facility. Providers were asked what they would do Tanzania Service Delivery Indicators ■ Health 5 differently if they were in a facility that provides all nec- essary material and technology needed to diagnose Tanzanian health providers and treat the patient. sometimes faced severe technical constraints that hampered their Figure 6: Diagnostic accuracy and treatment ability to reach the correct diagnosis accuracy in 5 common conditions 97 and provide the correct treatment. 92 77 77 71 54 51 Two important laboratory tests to diagnose pulmo- 49 45 39 nary tuberculosis are sputum examination and a 30 23 chest X-ray which is usually done in case of a smear 12 11 10 negative TB test or at the end of outpatient treat- ment. All of the providers who got the diagnosis right Pulmonary Pneumonia Diabetes Malaria with Acute Diarrhea Tuberculosis Mellitus Anemia with Severe after probing requested a sputum examination and Dehydration 73 percent requested a chest X-ray. For those who still Correct Diagnosis Correct Treatment Accurate Diagnostic & Treatment did not get the correct diagnosis, 47 percent would have requested a sputum examination and 59 per- cent a chest X-ray. Even among those who correctly diagnosed the condition from the start, 15 percent Figure 7: Tanzania’s service delivery performance said they would do a sputum examination and twice in the EAC. as many would ask for the X-ray. Tanzanian health 84 72 76 providers faced severe technical constraints that 60 60 64 hampered their ability to reach the correct diagnosis 54 58 47 47 50 47 and provide the correct treatment. 45 28 30 19 22 How does Tanzania compare EAC 14 neighbors, other African countries, Absence Drug Diagnostic Maternal & Equipment Infrastructure and itself over time? Rate Availability Accuracy Neonatal Availability Availability Complications Compared to its EAC neighbors such Uganda and Kenya, Tanzania performed fairly well on the quality Tanzania 2014 Kenya 2012 Uganda 2013 of service delivery. Kenyan and Ugandan providers were respectively twice and three times more likely to be absent than Figure 8: Trends in service delivery in Tanzania. their Tanzanian counterparts. Tanzanian facilities had also more drugs on stock and equipment compared Infrastructure 19 to facilities in neighboring countries. In terms of di- availability 50 agnostic accuracy, Kenyan providers outperformed Tanzanians and Ugandans who performed at par. For Equipment 78 maternal and neonatal complications, Kenyans again availability 84 outperformed Tanzanians who bested Ugandans. 57 Generally speaking, the three EAC countries per- Diagnostic accuracy 60 formed better than Mozambique, Nigeria, Senegal, and Togo. Absence rate 21 14 Tanzania made also noticeable progress in almost all areas of service delivery between 2010 and 2014. The Tanzania 2010 Tanzania 2014 most impressive progress were in reducing the ab- sence rate which dropped by more than 30 percent, 6 Tanzania Service Delivery Indicators ■ Health and access to infrastructure which almost tripled from needs to be paid to reducing geographic inequality in 19 percent in 2010 to 50 percent in 2014. Equipment the quality of services available to the citizens. availability and diagnostic accuracy also slightly im- A major challenge for Tanzania’s health sector is proved. It must however be noted that diagnostic ac- the shortage of skilled human resources for health curacy in rural areas deteriorated. (HRH). This survey found that provider knowledge What does this mean for and abilities were not adequate to deliver quality services. Caseload per provider and absenteeism Tanzania? are relatively low, so the issue is not over-burdened Progress has been made in Tanzania’s health sector, providers. There seems to be ample room for a sig- however, more can be done to improve service deliv- nificant increase in the caseload of Tanzanian pro- ery. Perception of quality at facilities is often a decid- viders, i.e. the level of productivity in health service ing factor in service utilization. Like many countries, delivery, without jeopardizing quality. In addition Tanzania faces an inequitable geographic distribution to increasing the volume of skilled HRH to address of service quality. Quality and provider availability is of- the shortage of providers, improvements in man- ten best in urban areas, particularly in Dar es Salaam. agement, supervision and training is important to While Dar es Salaam is home to about 10 percent of improving service delivery. Health for all in Tanzania the population, about 45 percent of the country’s doc- will mean the simultaneous availability of widely ac- tors are concentrated in Dar es Salaam.4 The availabil- cessible inputs and skilled providers. ity of medical equipment and diagnostic accuracy are also higher in urban areas than rural areas. Attention Finally to improve the quality of health care it is im- portant that other measures such as the motivation 4 World Bank. 2015. Tanzania - Strengthening Primary Health Care of health providers, or systemic issues which are not for Results Program Project. Washington, D.C.: World Bank Group. http://documents.worldbank.org/curated/en/2015/05/24481589/ covered in SDI that require attention from all stake- tanzania-strengthening-primary-health-care-results-program-project holders be addressed. Tanzania Service Delivery Indicators ■ Health 7 At-a-Glance TABLE 1: SDI Health Indicators by Geographic Area: Ability, Efforts and Inputs Across Countries Tanzania Kenya Senegal Tanzania Uganda Togo Nigeria Mozambique 2014 2012 2010 2010 2013 2013 2013 2014 What providers do (effort provider) Caseload 7.3 15.2 - - 6.0 5.2 5.2 17.4 (per provider per day) Absence from facility 14.3 27.5 20 21 46.7 37.6 31.7 23.9 (% providers) What providers know (provider ability) Diagnostic accuracy 60.2 72.2 34 57 58.1 48.5 39.6 58.3 (% clinical cases) Adherence to clinical guidelines 43.8 43.7 22 35 41.4 35.6 31.9 37.4 (% percent clinical cases) Management of maternal and neonatal 30.4 44.6 - - 19.3 26.0 19.8 29.9 complications (% clinical cases) What providers have to work with (availability of inputs) Drug availability 60.3 54.2 78 76 47.2 49.2 49.2 42.7 (% drugs) Equipment availability 83.5 76.4 53 78 21.9 92.6 21.7 79.5 (% facilities) Infrastructure Availability 50.0 46.8 39 19 63.5 39.2 23.8 34.0 (% facilities) 8 Tanzania Service Delivery Indicators ■ Health TABLE 2: SDI Health Indicators by Geographic Area: Ability, Efforts, and Inputs Across Tanzania Private Private Rural Urban Indicators TANZANIA Public Rural Urban (non-profit) (for-profit) Public Public What providers do (effort provider) Caseload 7.3 7.1 5.7 10.8 6.4 9.5 6.9 7.8 (per provider per day) Absence from facility 14.3 13.9 17.0 12.8 14.4 16.4 15.1 13.4 (% providers) What providers know (provider ability) Diagnostic accuracy 60.2 59.9 65.9 54.2 50.0 62.3 43.9 70 (% clinical cases) Adherence to clinical guidelines 43.8 43.7 45.5 42.1 37.7 46.7 34.1 49.6 (% clinical cases) Management of maternal and neonatal 30.4 31.3 30.1 26.4 25.7 32.0 24.1 35.7 complications (% clinical cases) What providers have to work with (availability of inputs) Drug availability 60.3 58.9 66.0 62.8 56.2 69.4 55.3 71.6 (% drugs) Equipment availability 83.5 81.7 92.5 84.5 80.7 87.6 79.8 88.5 (% facilities) Infrastructure availability 50.0 40.6 66.9 91.2 36.0 79.2 33.5 65.8 (% facilities) Tanzania Service Delivery Indicators ■ Health 9 Annex. Definition of the Health Service Delivery Indicators Caseload per health provider Number of outpatient visits The number of outpatient visits recorded in outpatient records in the three months prior to the survey, divided by the per clinician per day. number of days the facility was open during the three-month period and the number of health professionals who conduct patient consultations (i.e. excluding cadre-types such as public health nurses and out-reach workers). Absence rate Share of a maximum of 10 Number of health professionals that are not off duty who are absent from the facility on an unannounced visit as a share randomly selected providers of ten randomly sampled workers. Health professionals doing fieldwork (mainly community and public health professionals) absent from the facility during were counted as present. an unannounced visit. Diagnostic accuracy Average share of correct For each of the following five clinical cases: (i) acute diarrhea; (ii) pneumonia; (iii) diabetes mellitus; (iv) pulmonary diagnoses provided in the five tuberculosis; (v) malaria with anemia. For each clinical case, assign a score of one as correct diagnosis for each clinical clinical cases. case if diagnosis is mentioned. Sum the total number of correct diagnoses identified. Divide by the total number of clinical cases. Where multiple diagnoses were provided by the clinician, the diagnosis is coded as correct as long as it is mentioned, irrespective of what other alternative diagnoses were given. Adherence to clinical guidelines Unweighted average of the For each of the following five clinical cases: (i) acute diarrhea; (ii) pneumonia; (iii) diabetes mellitus; (iv) pulmonary tuberculosis; share of relevant history (v) malaria with anemia. History Taking Questions: Assign a score of one if a relevant history-taking question is asked. The taking questions, the share number of relevant history taking questions asked by the clinician during consultation is expressed as a percentage of the total of relevant examinations number of relevant history questions included in the questionnaire. Relevant Examination Questions: Assign a score of one if performed. a relevant examination question is asked. The number of relevant examination taking questions asked by the clinician during consultation is expressed as a percentage of the total number of relevant examination questions included in the questionnaire. For each clinical case: Unweighted average of the: relevant history questions asked, and the percentage of physical examination questions asked. The history and examination questions considered are based on the Nigeria National Clinical Guidelines and the guidelines for Integrated Management of Childhood Illnesses (IMCI). Management of maternal and neonatal complications Share of relevant treatment For each of the following two clinical cases: (i) post-partum hemorrhage; and (ii) neonatal asphyxia. Assign a score of one actions proposed by the if a relevant action is proposed. The number of relevant treatment actions proposed by the clinician during consultation is clinician. expressed as a percentage of the total number of relevant treatment actions included in the questionnaire. Management of maternal and neonatal complications Share of relevant treatment For each of the following two clinical cases: (i) post-partum hemorrhage; and (ii) neonatal asphyxia. Assign a score of one actions proposed by the if a relevant action is proposed. The number of relevant treatment actions proposed by the clinician during consultation is clinician. expressed as a percentage of the total number of relevant treatment actions included in the questionnaire. Drug availability Share of basic drugs which Priority medicines for mothers: Assign score of one if facility reports and enumerator confirms/observes the facility has at the time of the survey the drug available and non-expired on the day of visit. The list of priority drugs can be accessed from the Technical report. were available at the health The aggregate is adjusted by facility type to accommodate the fact that not all drugs (injectables) are expected to be at the facilities. lowest level facility, dispensaries/health posts where health workers are not expected to offer injections. Equipment availability Share of facilities with Assign score of one if enumerator confirms the facility has one or more functioning of each of the following: thermometers, thermometer, stethoscope and stethoscopes, sphygmonometers and a weighing scale (adult, child, or infant weighing scale) as defined below. Health weighing scale, refrigerator centers and first level hospitals are expected to include two additional pieces of equipment: a refrigerator and sterilization and sterilization equipment. device/equipment. Infrastructure availability Share of facilities with all Infrastructure aggregate: Assign score of one if facility reports and enumerator confirms facility has electricity and water three items: electricity and and sanitation as defined. Electricity: Assign score of one if facility reports having the electric power grid, a fuel operated water and sanitation. generator, a battery operated generator or a solar powered system as their main source of electricity. Water: Assign score of one if facility reports their main source of water is piped into the facility, piped onto facility grounds or comes from a public tap/standpipe, tubewell/borehole, a protected dug well, a protected spring, bottled water or a tanker truck. Sanitation: Assign score of one if facility reports and enumerator confirms facility has one or more functioning flush toilets or VIP latrines, or covered pit latrine (with slab). 10 Tanzania Service Delivery Indicators ■ Health Tanzania Service Delivery Indicators ■ Health 11 About the SDI surveys The SDI survey was conducted between May and September 2014. The fieldwork involved collecting information from 400 primary schools, 3,692 teachers regarding absence, 2,196 teachers for knowledge assessment, and 4,041 pupils who sat a test. The results provide a representative snapshot of the quality of service delivery and the physical environment within which education services are delivered in Tanzania’s primary schools. The survey provides information on three dimensions of service delivery: measures of (i) provider’s effort; (ii) provider’s knowledge and ability; and (iii) the availability of key inputs, such as chalk, pencils, notebooks, or a blackboard, basic equipment and infrastructure (such as availability of toilet, clean water, etc.). Tanzania was a pioneer SDI country in 2010 and the first country to implement a follow up SDI allowing trend analysis in service delivery. SDI surveys are rapidly expanding and have been implemented in eight countries: Kenya, Mozambique, Niger, Nigeria, Senegal, Tanzania, Togo, and Uganda. This allows for comparison across countries and benchmarking of country performance. The Service Delivery Indicators (SDI) Program The SDI initiative is a partnership of the World Bank, the African Economic Research Consortium (AERC), and the African Development Bank to develop and institutionalize the collection of a set of indicators that would gauge the quality of service delivery within and across countries and over time. The ultimate goal is to sharply increase accountability for service delivery across Africa, by offering important advocacy tools for citizens, governments, and donors alike; to work toward the end goal of achieving rapid improvements in the responsiveness and effectiveness of service delivery. More information on the SDI survey instruments and data, and more generally on the SDI initiative can be found at: www.SDIndicators.org and www.worldbank.org/SDI, or by contacting SDI@worldbank.org. © 2016 International Bank for Reconstruction and Development / The World Bank Group 1818 H Street NW Washington DC 20433 Telephone: +1 202-473-1000 Internet: www.worldbankgroup.org This work is a product of the Service Delivery Indicators initiative (www.SDIndicators.org, www.worldbank.org/SDI) and the staff of the International Bank for Reconstruction and Development/The World Bank. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. 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