Contribution of Results-Based Financing in Quality improvement of Health Services at Primary Healthcare Facilities: Findings from Tanzania Star Rating Assessment

Contribution of Results-Based Financing in Quality improvement of Health Services at Primary Healthcare Facilities: Findings from Tanzania Star Rating Assessment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Contribution of Results-Based Financing in Quality improvement of Health Services at Primary Healthcare Facilities: Findings from Tanzania Star Rating Assessment Joseph C. Hokororo, Radenta P. Bahegwa, Erick S. Kinyenje, Talhiya A. Yahya, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2336569/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Performance-based financing (PBF) is an important mechanism for improving the quality of health services in low- and middle- income countries. In 2014, Tanzania launched a country-wide quality approach known as Star Rating Assessment (SRA) aims to assess the quality of healthcare service delivery in all Primary Health Care (PHC) Facilities in the country. Furthermore, by 2015, the country rolled out RBF initiatives into eight regions in which PHC facilities were paid incentives based on their level of achievement in SRA assessments. This study aims to compare performance in quality between PHC facilities under RBF regions and non-RBF regions using the findings from the two-phases SRA assessments; baseline (2015/16) and follow-up (2017/18). Methods Analysis of performance of SRA indicators in the SRA service areas were identified based on the star rating tool that was used. The star rating tool had 12 service areas. For the sake of this implementation study, only seven service areas were included. The purposive sampling of the areas was used to select the areas that had direct influence of RBF in health facilities improvement. We used a t-test to determine whether there were differences in assessment star rating scores between the regions that implemented RBF and those which did not at each assessment (both baseline and reassessment). All results were considered significant at p < 0.05. The 95% Confidence Interval was also reported. Results The mean value was found to be 61.26 among facilities exposed to RBF compared to 51.28 among those not exposed to RBF. The study showed the mean difference score to be 10.79, with a confidence interval at 95% to be -1.24 to 22.84, suggesting that there was (no) a significant difference in the facilities based on RBF exposure during baseline assessment. The p-value of 0.07 was not statistically significant. Overall, there was an increment in facilities scoring the recommended 3+stars and above by 17.39% between the assessments, the difference was significant (p=0.0001). When the regions were stratified based on RBF intervention; facilities under RBF improved in 3+ stars by 10.63% higher compared to those that were not under RBF; however, the difference was not statistically significant (p=0.06) Conclusion Improvement of Health services needs to adhere to all six WHO building blocks and note to a sole financing. The six WHO building blocks are (i) service delivery, (ii) health workforce, (iii) health information systems, (iv) access to essential medicines, (v) financing, and (vi) leadership/governance. Probably, RBF found not to influence star rating because other blocks were not considered in this intervention. We need to integrate all the six WHO building blocks whenever we want to improve health services provision. Result Based Financing Star Rating Assessment Quality improvement plan Primary Healthcare Facilities Quality Improvement Key Message Implications for policy makers RBF is worth to continue as a way to improve health services in developing countries RBF implementation should take on board all six WHO building blocks for improving health services RBF should be scaled up to all regions in the implementing health facilities Developing countries should look for funds that will be used to implement RBF as sustainability mechanism. Implications for public This research intended to find out the gabs related to implementation of RBF so as to come up with solutions that will improve the intended purpose of RBF in primary health facilities. If RBF will be implemented based on WHO six building blocks, then there will be a lot of benefits to the public, that include: increased availability of health supplies (including medicine) at health facilities, increased health care utilization in primary health care facilities, Quality health service provision from the primary facilities, gains in health care providers’ productivity and efficiency in service delivery, higher quality data that is used for evidence-based decision-making as well as Health management teams, MSD, facility governing committees and Quality Improvement teams will be more accountable and responsive. Background Performance based payment models (commonly referred to as performance-based financing (PBF) or results-based financing – (RBF)) are an important mechanism for helping to improve the quality of health services in low- and middle- income countries (LMICs) (Gergen, et al, 2017 ). PBF quality tools used in verification of facility performance are more focused on structural quality and availability of resources, with few processes of care. This has called for a need to improve the tools to focus more on the quality-of-care processes (Josephson, et al, 2017 ). Effect of performance-based payment interventions depend on its design, additional funding, supportive components such as technical support, and the context in which it is implemented (Diaconu et al, 2021 ). Based on lessons from implementation of PBF in several countries in LMICs, the need for ensuring that the quality components in PBF is adapted to a country context has been noted (Gergen, et al, 2018 ). Analysis of data from Burundi, Lesotho, Senegal, Zambia and Zimbabwe have shown that PBF had no effect “ on neonatal health outcomes, health care utilization or quality ”, which indicates a need to relook at PBF if they are really effective (Gage and Bauhoff, 2021 ). However, a study by Brenner and colleagues on implementation of RBF in Malawi has shown that it has potential for improving “ effective coverage ” for obstetric services (Brenner, et al , 2021). In Zimbabwe, analysis of Demographic and Health Survey data between 2005 and 2015 was done to check for the effect of RBF implementation on health outcomes (neonatal, infant and under five mortality) and their analysis based on socio-economic groups. The findings have shown some positive effects on health outcomes but influenced by socio-economic status (Fichera, et al, 2021 ). In Tanzania, PBF (in the name of pay for performance – P4P) intervention was introduced in Pwani Region in 2011 (Borghi, et al, 2013 ; Borghi, et al, 2021 ). Its implementation was shown to be influenced by the following “ contextual factors”: salary and employment benefits; resource availability including staff, medicines and functioning equipment; supervision; facility access to utilities; and community preferences (Olafsdottir, et al, 2014 ). Other studies on its implementation found improvements in seven areas as follows. First, it improved accountability mechanisms in particular internal accountability mechanisms; external accountability mechanism improved in some aspects such as attitude to patients but did not influence functionality of Health Facility Governing Committees. (Mayumana, et al, 2017 ). Second, it reduced stock out of essential drugs in particular oxytocin; increased health care workers kindness at delivery; and also enabled supportive supervision visits to be implemented within planned timeframes (Anselmi, et al, 2017 ). Third, it improved availability of essential medicines and supplies, but had no effect on availability of functioning equipment. (Binyaruka and Borghi, 2017 ) Fourth, it was found to have potential for ensuring equity in accessing health services among the poor and in rural districts. (Binyaruka, et al, 2018 ) Fifth, it was found to have potential for influencing efficiency in particular in public facilities but it requires further improvement in its design for this to be realized. (Binyaruka, et al , 2020) Sixth, it produced some sustained improvements in user(patient) experience of care such as kindness. (Borghi, et al, 2021 ) Seventh, it showed potential for reducing women bypassing a nearby health facility. (Bezu, et al ,2021). Given, its high costs in its implementation especially management costs and costs involved in performance data generation and verification, it was suggested to consider its integration in routine health systems so as to make it more cost-effective. (Borghi, et al, 2015 ) Several studies looked at the way the P4P intervention in Tanzania was designed. A study by Songstad and colleagues ( 2012 ) on assessment of health care workers performance expectations with the P4P in comparison with the Open Performance Review and Appraisal System (OPRAS) found that the studied health care workers showed positive expectations towards P4P implementation, although the link between OPRAS and P4P was unclear. (Songstad, et al, 2012 ). However, the design was noted to be influenced by politics especially the influence of external actors in setting the agenda (Chimhutu, et al, 2015 ). Another study by Chimhutu, et al . (2016) found that the modality of distribution of bonuses in the P4P scheme was unfair and that it affected staff motivation, teamwork, as well as social relations at health facilities; which could ultimately affect the quality of health care services. Binyaruka and colleagues found that the design of how incentives are provided in P4P and some health facility characteristics influenced inequalities in health facilities performance (Binyaruka, et al, 2018 ). A study by Cassidy and colleagues observed that the roles of Health Facility Governing Committees were not included in the design of the P4P despite their key roles in enabling proper management of resources in health facilities and also linking with the community served. (Cassidy, et al, 2021 ). It was further found that apart from the potential for improving some aspects of experience of care, the way the P4P was designed with focus on certain services only, limited the generalizability of their gains in a whole health facility. (Chimhutu, et al, 2019 ) Implementation of the P4P pilot in Pwani Region took place from 2011 to 2013 and thereafter, preparation for rolling on its improved version named RBF was started in which its pilot was done in two councils in Shinyanga Region in 2015 and rolled out to the whole region in 2016. The program was then rolled out to other regions as follows: Pwani, and Mwanza (2016); Geita, Kagera, and Kigoma (2017); Simiyu, and Tabora (2018). (Ministry of Health and Social Welfare, 2015[ pp.51–52 ]) The RBF implementation in Tanzania gives payment to PHC facilities on quarterly basis based on their level of achievement that has been verified; in which 75% of the payments is allocated for facility improvement and 25% is for incentivizing the staff. (Ministry of Health, Community Development, Gender, Elderly and Children, 2019) By 2019, RBF was implemented in 8 regions of Tanzania: Pwani, Mwanza, Shinyanga, Tabora, Simiyu, Kagera, Kigoma, Geita. (Ministry of Health and Social Welfare, 2015; Ministry of Health, Community Development, Gender, Elderly and Children, 2019) The RBF implementation was envisaged as a strategy that can help to reform the health sector resulting in improvements in “ service delivery, leadership and governance, human resources, health management information system, medical supplies, vaccines, equipment, and health care financing in order to improve accountability, efficiency, and equity” . (Ministry of Health and Social Welfare, 2015[ pp.54 ]) This study aims to compare performance of health facilities in RBF regions and non-RBF regions in terms of quality of health services as measured by the Star Rating Assessment (SRA) approach. The SRA approach to quality was introduced in 2014 with the aim of assessing quality of health services in all Primary Health Care (PHC) Facilities in Tanzania. A baseline was conducted in the Fiscal Year 2015/2016 and reassessment was done in the Financial Year 2017/2018. (Yahya and Mohamed, 2018 ; Gage, et al, 2020 ) Specifically, the objectives of this study were to determine: (i) the difference in mean scores between the Star rating service areas in regions that implemented RBF versus those which did not implement in the health facilities in financial year 2017/18; (ii) whether there was an overall increment in 3 + star rating scores between baseline and follow up assessments; and whether there was difference in increment of 3 + star rating scores among regions that were exposed to RBF and those that were not between the two assessments. Methods Study design This was both descriptive and analytical comparative study on contribution of RBF in quality of services in PHC facilities from findings of SRA conducted in the Financial Year 2017/2018. Study Setting The study was conducted at primary healthcare facilities of Tanzania which is located in East Africa. Tanzania Mainland has 26 regions and 184 Local Government Authorities (shortly referred to as councils). According to health system of Tanzania Mainland, there are facilities at primary level (dispensaries, health centers and hospitals at council level) and referral hospitals (hospitals at regional, zonal and national levels). As of 2020, the country had a total of 9,813 health facilities in the following categories: Hospitals ( referral and council level ) 369, Health Centres 926, Dispensaries 7,163, others (including clinics) 1,276, and Maternity Nursing Homes 79; (TNBS, 2020). Population Selection criteria All PHC facilities that were assessed during baseline (2016/17) and re-assessment (2017/18) were included in the study. Sampling procedure and Sample size Estimation Participating regions All eight (8) regions where RBF was implemented were included in this study. From the 18 remaining regions that were not implementing RBF; 8 regions were sampled randomly by a simple random method in order to match the number of regions that implemented RBF. Before sampling, the name of each of the 18 regions was written over 18 pieces of papers then folded. Then eight papers were drawn one by one eight times to get the 8 regions by providing equal chance of participation. The total PHC facilities in these regions were 2,214 during baseline and 2,143 during reassessment. Performance areas Analysis of performance of SRA indicators in the SRA service areas were identified based on the SRA tool that was used. The SRA tool had 12 service areas. For the sake of this implementation study, only seven service areas were included. The purposive sampling of the areas was used to select the areas that had direct influence of RBF in health facilities improvement. The service areas included are: Service area 4-staff performance (4.1 staff performance appraisal system); service area 5-service provider charter (5.1 service provider charter, 5.2 client flow); service area 7-client focus (7.1 client service charter, 7.2 client satisfaction); service area 8-social accountability (8.2 functional facility governance committees or boards); service area 9-facility infrastructure (9.2 buildings); service area 11-clinical services (11.1 outpatient services; 11.2 Maternal, Neonatal and Child Health (MNCH) services); service area 12-clinical support services (12.1 pharmaceutical services). Table 1 shows the indicators for each service area that are presented in the analysis. The detail of the assessed areas is found in Table 1 . Table 1 Star rating service area (SA) and the indicators included in the analysis SA No. Star Rating Service area Star Rating Indicator 4 Staff performance 4.1 Staff performance appraisal system Staff performance methods in place Staff performance targets agreed Individual job descriptions Effective review of individual performance Staff satisfaction with performance review system 5 Service provider charter 5.1 Services provider charter Facility name working hours and on-call roster Service charter for core healthcare services Services, insurance benefits and charges are displayed Schedule for special clinics is displayed 5.2. Client flow Optimal client flow Client waiting time is monitored 7 Client Focus 7.1. Client Services charter Client services charter displayed Client services charter is monitored Client feedback mechanism and complaints handling 7.2. Client satisfaction Clients satisfied with services provided 8. Social accountability 8.2 Functional facility governance committees or boards HFGC or HFB is active and well oriented HFGC/HFB voices community concerns HFGC/HFB gives feedback to the broader community 9. Facility infrastructure 9.2 Buildings Status of the buildings and repairs Functional improved toilets for male and female, and staff Functional plumbing, drainage and sewerage system Patient privacy is ensured Availability of conducive waiting areas Health Facility rooms are well ventilated and well lit as per Health Facilities guidelines Disability-friendly facilities 11 Clinical Services 11.1. Outpatient Services Outpatient register is correctly filled Outpatients are treated according to standard treatment guidelines Good patient provided interactions 11.2 Maternal, Neonatal and Child Health (MNCH) Services ANC services follow guidelines Family planning services follow guidelines Immunization services follow guidelines The facility is performing BEmONC National guidelines available BEmONC training Partographs for mothers in labor are correctly filled The facility is performing death audit within 24 hours of maternal death Facility is offering child growth monitoring services according to the guidelines 12 Clinical supportive services 12.1 Pharmaceutical services Qualified pharmaceutical cadre Good dispensing practice Availability of essential medicines and health products ANC = Antenatal Care; BEmONC = Basic Emergency Obstetric and Newborn Care; HFGC = Health Facility Governing Committee; HFB = Hospital Advisory Board. Data Extraction, Management and Analysis Data extraction and Data Management After the identification of the performance areas was done, the next step was to extract data from the sampled regions. Secondary data of the SRA dataset of 2017/2018 were extracted and used to compare the performance of the RBF and non-RBF regions in terms of star level attainment and the scores in the assessed areas. The extracted data were checked for completeness, cleared, edited, coded and double entered in SPSS version 12 for analysis. Backup of the data was done by entering data in an excel sheet. Data Analysis Descriptive statistics were used to summarize the data in percentages. The total percentage score per performance area in a facility was computed from the sum of scores from those that scored “yes” divided by the sum for all indicators times 100%. The average percentage score of each performance area in a region was computed from percentage scores from each of the health facility in the region under the study. The final average scores for the regions that implemented RBF were developed by adding all scores of the regions divided by seven and the same was done for those that did not implement RBF. We used a t-test for unrelated samples to determine whether there were differences in rating scores between the regions that implemented RBF and those which did not at each assessment (both baseline and reassessment). The T-test for paired samples was used to determine whether there was an overall increment in proportions of facilities scoring 3 + stars between assessments. It was also used to compare if this difference was significant among the facilities that were exposed to RBF and those that were not. All results were considered significant at p < 0.05. The 95% Confidence Interval was also reported. Results Description of the participating PHC facilities A total of 4,357 facilities were involved in this study; 4,168 at baseline and 4,357 at reassessment which is (189)4.34% more facilities than those which were involved in baseline. The increment of the facilities during reassessment was due to new facilities registered across the country. During both assessments around 85% were dispensaries and 85% were owned by the government. Further description of the characteristics of the involved PHC facilities is presented in Table 2 . Table 2 Characteristics of the facilities involved in the study Variable Baseline (N) Reassessment Facility level N % N % Hospitals 129 3.1 129 2.96 Health Centres 485 11.64 488 11.2 Dispensaries 3554 85.26 3740 85.84 Ownership Private Facilities 823 19.75 835 19.16 Public Facilities 3345 80.25 3522 80.84 Total 4168 100 4357 100 This table shows the average score of the performance of the health facilities in the regions that had RBF support. The area for clinical services scored the highest (71.87%) while Clinical Support services scored the lowest (43.25%). The overall average score for all areas was 61.21%. The details of the scores are shown in Table 3 bellow. Table 3 Average Score by RBF regions during assessment in the Financial Year 2017/2018 Service Area Number Star Rating Service area Mwanza Pwani Geita Kagera Kigoma Simiyu Shinyanga Tabora Total Average 4 Staff performance 75 52 64 63 27 61 47 41 430 53.75 5 Service provider charter 76 65 69 71 51 65 68 62 527 65.87 7 Client focus 74 72 72 72 57 62 64 70 543 67.87 8 Social accountability 77 63 68 70 61 66 70 64 539 67.37 9 Infrastructure 64 63 70 58 51 51 60 51 468 58.50 11 Clinical services 89 71 76 75 48 71 73 72 575 71.87 12 Clinical supportive services 49 45 49 49 33 37 44 40 346 43.25 61.21 Average Score by non-RBF regions during assessment in the Financial Year 2017/2018 Table 4 shows the average score of the performance of the health facilities in the regions that had no RBF support. During assessment the largest score was on clinical services which was 66.62%. The lowest score was 36.37% on Clinical Support services. The average score was 50.42%. the details of scores are displayed in Table 4 Table 4 Average Score by non-RBF regions during assessment in the Financial Year 2017/2018 Service Area Number Star Rating Service area Manyara Iringa Morogoro Tanga Mara Mbeya Rukwa Songwe Total Average 4 Staff performance 44 43 35 39 37 58 41 28 325 40.62 5 Service provider charter 53 52 51 49 22 69 53 42 391 48.87 7 Client focus 60 55 61 58 39 71 60 56 460 57.50 8 Social accountability 62 60 59 51 24 68 72 63 459 57.37 9 Infrastructure 47 44 51 52 35 57 40 39 365 45.62 11 Clinical services 68 72 67 63 52 75 75 61 533 66.62 12 Clinical supportive services 39 38 42 38 19 48 38 29 291 36.37 50.42 Average difference scores during Assessment Table 5 shows the average difference scores of the performance of the health facilities in the regions that had RBF support versus those which had no support. The biggest difference appeared on Service provider charter by 17.00% while the lowest was on Clinical Services by 05.25%. The mean value was found to be 61.26% among facilities exposed to RBF compared to 50.42% among those not exposed to RBF. The study showed the mean difference score to be 10.79%, with confidence interval at 95% to be -1.24 to 22.84, suggested that there was no a significant difference in the facilities based on RBF exposure during baseline assessment. The p-value of 0.07 was not statistically significant. Table 5 Average difference scores during Assessment Service Area Number Star Rating Service area Average Score by RBF regions during reassessment in the Financial Year 2017/2018 Average Score by non-RBF regions during reassessment in the Financial Year 2017/2018 Difference CI and P value 4 Staff performance 54.71 40.71 13.12 5 Service provider charter 65.57 50.57 17.00 7 Client Focus 68.43 58.43 10.37 8. Social accountability 67.00 58.14 10.00 9 Infrastructure 58.29 47.71 12.87 11 Clinical Services 71.71 66.57 05.25 12 Clinical supportive services 43.14 36.86 06.87 Average score 61.26 51.29 10.79 P = 0.07 (CI 95% =-1.24–22.84) Table 6.0 : The overall improvement in 3 + star rating scores between baseline and follow up assessments among 16 regions During baseline assessment Tanga region had more health facilities with three stars or above 9(2.43%) while six regions did not attain 3 stars and above. When reassessment was conducted Geita had the highest percentage 38% and Songwe had the lowest 3% with health facilities attained three stars or above. Of 4168 facilities that were assessed in 2015/16 only 37 (0.89%) reached 3 stars and above whereas in 2017/18 out of 5357 health facilities assessed 794 (18%) had achieved 3 stars and above. Facilities scoring 3 + stars increased by average of 17.39 (95%CI 11.37–23.41) with P<0.0001 between the baseline assessments and reassessment. The detail is displayed in Table 6 bellow. Table 6.0 The overall improvement in 3 + star rating scores between baseline and follow up assessments among 16 regions SN Region Name Num of Facilities Star Rated (2015/16) Num of Facilities Rated 3-Stars and above (2015/16) Percentage of Facilities Rated 3-Star or Above (2015/16) Number of Facilities Star Rated (2017/18) Number of Facilities Rated 3-Star or above (2017/18) Percentage of Facilities Rated 3-Star or above (2017/18) RBF 1 Geita 153 0 0 154 58 38 Yes 2 Iringa 241 3 1.24 249 33 13 No 3 Kagera 297 7 2.35 298 95 32 Yes 4 Kigoma 253 1 0.39 269 10 4 Yes 5 Manyara 188 0 0 199 25 13 No 6 Mara 271 6 2.21 286 26 9 No 7 Mbeya 297 3 1 303 111 37 No 8 Morogoro 368 3 0.86 401 47 12 No 9 Mwanza 350 0 0 355 119 34 Yes 10 Pwani 298 2 0.67 333 61 18 Yes 11 Rukwa 207 0 0 213 17 8 No 12 Shinyanga 205 2 0.98 211 53 25 Yes 13 Simiyu 208 0 0 207 32 15 Yes 14 Songwe 163 1 0.61 178 5 3 No 15 Tabora 299 0 0 316 71 22 Yes 16 Tanga 370 9 2.43 385 30 8 No TOTAL 4168 37 0.89% 4,357 793 18% Difference increment in 3 + star rating scores between RBF and non-RBF regions in follow up assessment. The regions which had RBF implementation, Geita region had the greatest number of facilities with 3stars and above 58(38%) while Kigoma Region had the lowest 10(04%). The regions which had no RBF implementation, Mbeya region had the highest achievement for the regions which did not implement RBF. It had 111(37%) of facilities with 3 stars or above while Songwe had the lowest number of facilities with three stars or above. It had 5(03%) health facilities with three stars and above. Facilities scoring 3 + stars increased by average of 10.63 between the facilities with RBF supported regions versus those which had no RBF support and difference was not statistically significant (p = 0.06). The detail is displayed in Table 7 below Table 7 Difference increment in 3 + star rating scores between RBF and non-RBF regions in follow up assessment Pairs RBF exposure Mean N Sd Mean difference 95%CI t-value D.F p-value Yes 23.5 8 11.2 10.63 -0.92-22.17 1.97 14 0.06 no 12.9 8 10.3 Discussion In this evaluation, it was found that, the facilities that had RBF had no statistically significant difference from those which did not implement RBF. The regions which implemented RBF had 61.21% while the non-RBF facilities scored 50.42%. This means there was a difference of 10.79%. In Tanzania, the regions which were selected for the support of RBF were those which had poor performance in almost all heath indicators. Despite statistical significance in difference was not met, realistically the RBF regions had improved with the support of RBF. Hence, this study might be in keeping with the study done by Brenner and colleagues on implementation of RBF in Malawi that, it has potential for improving “effective coverage” for obstetric services. Another study that was conducted in Zimbabwe by Fichera and colleagues in 2021 found RBF had no influence in improvement of some indicators like child anthropometric measures (Fichera, et al, 2021 ); while a study by Gage and Bauhoff found that PBF had no effect “ on neonatal health outcomes, health care utilization or quality ”, in four countries (Burundi, Lesotho, Senegal, Zambia and Zimbabwe), (Gage and Bauhoff, 2021 ). The target of Star Rating Assessment was for PHC facilities to be able to perform at a star rating of three stars and above. The percentage of PHC facilities that scored 3–5 stars in the RBF regions was about a quarter (24%), while for the non-RBF regions was 13%; resulting in a non-statistically significant difference between the two. The findings are in line with a recent World Bank policy report which has looked at health systems of about 40 low-income countries around the world including Tanzania. The report assessed whether financial incentives are working to ensure effective coverage (i.e., “a measure that adjusts simple coverage of care with the quality of care provided”) using evidence accumulated in the past 15 years. The results have shown that PBF “ resulted in gains in coverage but far fewer, if any, improvements in the quality of health services delivered .” Hence, the report notes that financial reforms in health systems may need to move away from sole dependence on RBF, and focus more on its other aspects especially “ transparency, accountability, and decentralized frontline financing”. (de Walque, et al, 2022 ) In Tanzania, Star rating is considered as one of measures of the quality of services in health facilities. In this study, it was found that more health facilities with RBF support had 3stars than those with no RBF. This might reflect the intended effect of RBF to improve health facilities to attain 3stars and above were met. The country has set the cutoff point of 3 stars. Hence, the facilities with 3 stars or more are doing good in the majority of health indicators. In this study, it was found the difference of 3 stars was 10.63, but the difference was not statistically significant. The observed increase in the number of 3 stars and above at RBF regions could also have resulted from the improvement of health indicators by the support of RBF. This finding is approximately related to the study by Gage et al in 2020 on “ assessment of health facility quality improvements ” in Tanzania as well found that there was star rating improvement in the PHC facilities. (Gage, et al, 2020 ). In this evaluation it was found that, there was overall increment of the facilities that scored 3 stars and above. In the baseline assessment the facilities that scored 3 stars and above were 37 (0.89%) versus with 793(18%) and the difference was statistically significant (p>0.0001). In all health facilities the quality improvement plan was developed based on the gaps that were identified. All the facilities were required to address the gaps. The facilities with support of RBF were supposed to utilize the support to address the gaps. Other facilities with no support of RBF were supposed to continue addressing the gaps using their own funds. The achievement of quality improvement in health facilities depended on procedures within facilities and the situation in which they work. However, the improvement was enough, based on high demand for high quality health services, The government had set standard that at least 80% of all health facilities get 3 stars or above. Our evaluation in the Tanzania Mainland found that both facilities with RBF support and those without RBF support, their improvement was influenced by a facility’s capability to improve quality of healthcare services, as assessed using the star rating system. The finding of this evaluation is consistent with the study by Kinyenje and coleagues who evaluated the Status of Infection Prevention and Control in Tanzanian PHC Facilities using SRA dataset and found that there was some improvement of IPC adherence between baseline and reassessment (Kinyenje, et al, 2020). As Tanzania continues with the efforts to improve the quality of PHC services, it is important to strengthen the management of PHC facilities. Literature also has shown that for RBF to have a bigger effect in performance of PHC facilities, it is important to strengthen the PHC Facility Management. For instance, a study in Nigeria has shown that PHC facilities that had high scores in management practices had higher monthly improvement rates for institutional delivery and outpatient visits compared to the PHC facilities with low scores in management practices. (Mabuchi, et al, 2022 ) Conclusion The RBF implementation in PHC facilities in Tanzania had no significant contribution to improvement in star rating levels (3 stars and above). Probably contributed to the way the RBF was designed. Improvement of Health services needs to adhere to all six WHO health system building blocks (service delivery; health workforce; health information systems; access to essential medicines; financing; and leadership/governance) and not to focus solely on financing. Probably, RBF was found not to influence star rating because other blocks were not considered, including leaving aside the HFGCs. We recommend integrate all the six WHO health system building blocks whenever we want to improve health services provision using PBF/RBF interventions. Declarations Ethical issues: This study did not involve human subjects, hence for this type of study formal consent is not required, however, prior permission was sought from the Ministry of Health, Community Development, Gender, Elderly and Children (Recently renamed as Ministry of Health) before use of the dataset. Ministry of Health is a custodian of Health Management Information Systems data including SRA database. Ethical clearance is not necessary for this type of a study because data were collected in the course of implementing an initiative by the government and hence this analysis aims at giving feedback on prevailing circumstances after its successful implementation. Conflict of interests: The authors declared no conflict of interest, however, during 2017/2018’s Star Rating Assessment of PHCs that yielded these data, Dr Eliudi S. Eliakimu, Joseph C. Hokororo, Chrisogone J. German, Radenta P. Bahegwa, Talhiya A. Yahya, Omary A. Nassoro, Ruth R. Ngowi, Yohannes S. Msigwa, Mbwana M. Degeh, and Laura E. Marandu were working with the Health Quality Assurance Division (now called Health Quality Assurance Unit) and were responsible for the implementation of SRA and QIPs follow-up. Authors’ contributions: Conception and design: Joseph Hokororo, Eliudi S. Eliakimu, Erick S. Kinyenje, Talhiya A. Yahya, Mohamed A. Mohamed, Mbwana M. Degeh, Chrisogone J. German, Erick S. Kinyenje. Acquisition of data: Joseph Hokororo, Erick S. Kinyenje, Chrisogone J. German, Syabo Mwaisengela, Omary Nassoro, Ruth R. Ngowi, Laura E. Marandu, Radenta P. Bahegwa, Yohannes S. Msigwa. Analysis and interpretation of data: Joseph Hokororo, Erick S. Kinyenje, Chrisogone J. German, Hassan Muhomi, Jimmy Mbelwa, Eliudi S. Eliakimu. Drafting of the manuscript: Eliudi S. Eliakimu, Joseph C. Hokororo, Bush Lugoba, Edwin C. Mkwama. Critical revision of the manuscript for important intellectual content: Eliudi S. Eliakimu, Joseph C. Hokororo, Erick S. Kinyenje, Talhiya Yahya, Mbwana M. Degeh, Mohamed A. Mohamed. Statistical analysis: Joseph C. Hokororo, Erick S. Kinyenje, Chrisogone J. German, Hassan Muhomi, Jimmy Mbelwa. Obtaining funding: Not applicable Administrative, technical, or material support: Eliudi S. Eliakimu, Mbwana M. Degeh, Joseph C. Hokororo, Erick S. Kinyenje, Chrisogone J. German. Supervision: Eliudi S. Eliakimu, Joseph C. Hokororo, Erick S. Kinyenje. Other/s (specify): None. Source(s) of support/funding: No funding was received by the authors for the study. Data manipulation and analysis was conducted as a part of usual responsibilities. Most of authors are from Health Quality Assurance Unit which is responsible in implementing SRA and hence obliged to disseminate evaluation results through publications. Availability of data The data used in this study is secondary. They were collected during star rating of primary health facilities in Tanzania and it is available if it is needed. Acknowledgements: The authors would like to acknowledge the Ministry of Health for granting permission to use the Star Rating 2017/2018’s Assessment data. Furtherly, Our sincere gratitude goes to key partners in the implementation of the SRA that include directorates and units of Ministry of Health embracing Health Quality Assurance Unit, Curative Services Division, Preventive Services Division and the Health Directorate of the President’s Office – Regional Administrative and Local Government (PO-RALG), development partners including the World Bank, Centre for Disease Control and Prevention, Danish International Development Agency, The World Health Organization, Association of Private Health facilities, Christian Social Services Commission, nevertheless Regional Secretariats, Local Government Authorities and Healthcare Workers from visited Primary Health Facilities are highly appreciated. Disclaimers: The authors declare that the views expressed in this manuscript are their own and do not necessarily represent views of the institutions they are affiliated to. References Anselmi L, Binyaruka P, Borghi J. Understanding causal pathways within health systems policy evaluation through mediation analysis: an application to payment for performance (P4P) in Tanzania. Implement Sci . 2017 Feb 2;12(1):10. DOI: 10.1186/s13012-016-0540-1. PMID: 28148305; PMCID: PMC5288944. Bezu S, Binyaruka P, Mæstad O, Somville V. Pay-for-performance reduces bypassing of health facilities: Evidence from Tanzania. Soc Sci Med. 2021 Jan;268:113551. DOI: 10.1016/j.socscimed.2020.113551. Epub 2020 Nov 25. PMID: 33309150. Binyaruka P, Anselmi L. Understanding efficiency and the effect of pay-for-performance across health facilities in Tanzania. BMJ Glob Health. 2020 May;5(5):e002326. DOI: 10.1136/bmjgh-2020-002326. PMID: 32474421; PMCID: PMC7264634. Binyaruka P, Borghi J. Improving quality of care through payment for performance: examining effects on the availability and stock-out of essential medical commodities in Tanzania. Trop Med Int Health . 2017 Jan;22(1):92-102. DOI: 10.1111/tmi.12809. Epub 2016 Dec 7. PMID: 27928874. Binyaruka P, Robberstad B, Torsvik G, Borghi J. Does payment for performance increase performance inequalities across health providers? A case study of Tanzania. Health Policy Plan . 2018 Nov 1;33(9):1026-1036. DOI: 10.1093/heapol/czy084. PMID: 30380062; PMCID: PMC6263023. Binyaruka P, Robberstad B, Torsvik G, Borghi J. Who benefits from increased service utilisation? Examining the distributional effects of payment for performance in Tanzania. Int J Equity Health . 2018 Jan 29;17(1):14. DOI: 10.1186/s12939-018-0728-x. PMID: 29378658; PMCID: PMC5789643. Borghi J, Binyaruka P, Mayumana I, Lange S, Somville V, Maestad O. Long-term effects of payment for performance on maternal and child health outcomes: evidence from Tanzania. BMJ Glob Health . 2021 Dec;6(12):e006409. DOI: 10.1136/bmjgh-2021-006409. PMID: 34916272; PMCID: PMC8679076. Borghi J, Little R, Binyaruka P, Patouillard E, Kuwawenaruwa A. In Tanzania, the many costs of pay-for-performance leave open to debate whether the strategy is cost-effective. Health Aff (Millwood). 2015 Mar;34(3):406-14. DOI: 10.1377/hlthaff.2014.0608. Erratum in: Health Aff (Millwood). 2015 Sep;34(9):1611. PMID: 25732490. Borghi J, Mayumana I, Mashasi I, Binyaruka P, Patouillard E, Njau I, Maestad O, Abdulla S, Mamdani M. Protocol for the evaluation of a pay for performance programme in Pwani region in Tanzania: a controlled before and after study. Implement Sci . 2013 Jul 19;8:80. DOI: 10.1186/1748-5908-8-80. PMID: 23870717; PMCID: PMC3724689. Brenner, S., Mazalale, J., Wilhelm, D. et al. Impact of results-based financing on effective obstetric care coverage: evidence from a quasi-experimental study in Malawi. BMC Health Serv Res 18, 791 (2018). https://doi.org/10.1186/s12913-018-3589-5 Cassidy R, Tomoaia-Cotisel A, Semwanga AR, Binyaruka P, Chalabi Z, Blanchet K, Singh NS, Maiba J, Borghi J. Understanding the maternal and child health system response to payment for performance in Tanzania using a causal loop diagram approach. Soc Sci Med . 2021 Sep;285:114277. DOI: 10.1016/j.socscimed.2021.114277. Epub 2021 Jul 28. PMID: 34343830; PMCID: PMCID: PMC8434440. Chimhutu V, Songstad NG, Tjomsland M, Mrisho M, Moland KM. The inescapable question of fairness in Pay-for-performance bonus distribution: a qualitative study of health workers' experiences in Tanzania. Global Health . 2016 Nov 25;12(1):77. DOI: 10.1186/s12992-016-0213-5. PMID: 27884185; PMCID: PMC5123229. Chimhutu V, Tjomsland M, Mrisho M. Experiences of care in the context of payment for performance (P4P) in Tanzania. Global Health . 2019 Oct 16;15(1):59. DOI: 10.1186/s12992-019-0503-9. PMID: 31619291; PMCID: PMCID: PMC6796428. Chimhutu V, Tjomsland M, Songstad NG, Mrisho M, Moland KM. Introducing payment for performance in the health sector of Tanzania- the policy process. Global Health . 2015 Sep 2;11:38. DOI: 10.1186/s12992-015-0125-9. PMID: 26330198; PMCID: PMC4557903. Diaconu K, Falconer J, Verbel A, Fretheim A, Witter S. Paying for performance to improve the delivery of health interventions in low- and middle-income countries. Cochrane Database Syst Rev . 2021 May 5;5(5):CD007899. DOI: 10.1002/14651858.CD007899.pub3. PMID: 33951190; PMCID: PMC8099148. Eleonora Fichera et al (2021): Can Results-Based Financing improve health outcomes in resource poor settings? Evidence from Zimbabwe. Soc Sci Med. 2021 Jun; 279: 113959. doi: 10.1016/j.socscimed.2021.113959: PMCID: PMC8210646: PMID: 33991792 Erick Kinyenje, et al (2020): Status of Infection Prevention and Control in Tanzanian Primary Health Care Facilities: Learning from Star Rating Assessment. Volume 2, Issue 3, September 2020, 100071 Fichera E, Anselmi L, Gwati G, Brown G, Kovacs R, Borghi J. Can Results-Based Financing improve health outcomes in resource poor settings? Evidence from Zimbabwe. Soc Sci Med. 2021 Jun;279:113959. DOI: 10.1016/j.socscimed.2021.113959. Epub 2021 May 7. PMID: 33991792; PMCID: PMC8210646 Gage A. and Bauhoff S. The effects of performance-based financing on neonatal health outcomes in Burundi, Lesotho, Senegal, Zambia and Zimbabwe. Health Policy Plan . 2021 Apr 21;36(3):332-340. doi: 10.1093/heapol/czaa191. PMID: 33491082; PMCID: PMC8058947. https://doi.org/10.1093/heapol/czaa191 Gage AD, Yahya T, Kruk ME, Eliakimu E, Mohamed M, Shamba D, Roder-DeWan S. Assessment of health facility quality improvements, United Republic of Tanzania. Bull World Health Organ . 2020 Dec 1;98(12):849-858A. DOI: 10.2471/BLT.20.258145. Epub 2020 Oct 5. PMID: 33293745; PMCID: PMC7716095 Gergen J, Josephson E, Coe M, Ski S, Madhavan S, Bauhoff S . Quality of care in performance-based financing: how it is incorporated in 32 programs across 28 countries . Glob Health Sci Pract . 2017 ; 5 ( 1 ): 90 – 107 . DOI: 10.9745/GHSP-D-16-00239. PMCID: PMC5493453 Gergen J, Josephson E, Vernon C, et al. Measuring and paying for quality of care in performance-based financing: Experience from seven low and middle-income countries (Democratic Republic of Congo, Kyrgyzstan, Malawi, Mozambique, Nigeria, Senegal and Zambia). J Glob Health . 2018;8(2):021003. doi:10.7189/jogh.08.021003 https://doi.org/10.7189/jogh.08.021003 Josephson E, Gergen J, Coe M, Ski S, Madhavan S, Bauhoff S. How do performance-based financing programmes measure quality of care? A descriptive analysis of 68 quality checklists from 28 low- and middle-income countries. Health Policy Plan . 2017;32(8):1120-1126. DOI: 10.1093/heapol/czx053. PMID: 28549142 PMCID: PMC5886109 Mabuchi,S., Alonge, O., Tsugawa, Y. and Bennett, S. An Investigation of the Relationship Between the Performance and Management Practices of Health Facilities Under a Performance-Based Financing Scheme in Nigeria, Health Policy and Planning , 2022;, czac040, https://doi.org/10.1093/heapol/czac040 Published: 17 May 2022. Mayumana I, Borghi J, Anselmi L, Mamdani M, Lange S. Effects of Payment for Performance on accountability mechanisms: Evidence from Pwani, Tanzania. Soc Sci Med . 2017 Apr;179:61-73. DOI: 10.1016/j.socscimed.2017.02.022. Epub 2017 Feb 20. PMID: 28257886. Ministry of Health, Community Development, Gender, Elderly and Children. Strengthening Primary Health Care for Results - Results Based Financing - Presented to DPG H June 12, 2019. Available at: http://www.tzdpg.or.tz/fileadmin/documents/dpg_internal/dpg_working_groups_clusters/cluster_2/health/DPG_H_Meeting_Documents_2019/RBF_at_DPG_H_June_12_2019.pdf Accessed on 20 th September, 2021. Ministry of Health and Social Welfare. Result Based Financing (RBF) Operational Manual, 2015. Dar es Salaam, The United Republic of Tanzania. Available at: https://bluesquarehub.files.wordpress.com/2017/03/tanzania-operations-manual-20160422_rbf_approved-version.pdf Accessed on 09 th April, 2022. Olafsdottir AE, Mayumana I, Mashasi I, Njau I, Mamdani M, Patouillard E, Binyaruka P, Abdulla S, Borghi J. Pay for performance: an analysis of the context of implementation in a pilot project in Tanzania. BMC Health Serv Res . 2014 Sep 16;14:392. DOI: 10.1186/1472-6963-14-392. PMID: 25227620; PMCID: PMCID: PMC4261877. Songstad NG, Lindkvist I, Moland KM, Chimhutu V, Blystad A. Assessing performance enhancing tools: experiences with the open performance review and appraisal system (OPRAS) and expectations towards payment for performance (P4P) in the public health sector in Tanzania. Global Health . 2012 Sep 10;8:33. DOI: 10.1186/1744-8603-8-33. PMID: 22963317; PMCID: PMC3477072. Tanzania National Bureau of Statistics. Statistical Abstract 2020. Chapter Thirteen 13.0 Health. Available at: https://www.nbs.go.tz/index.php/en/tanzania-statistical-abstract/720-statistical-abstract-2020 Accessed on 23 rd April, 2022. Yahya T, Mohamed M. Raising a mirror to quality of care in Tanzania: the five-star assessment. Lancet Glob Health . 2018 Nov;6(11):e1155-e1157. DOI: 10.1016/S2214-109X(18)30348-6. Epub 2018 Sep 5. PMID: 30196094. de Walque, D., Kandpal, E., Wagstaff, A., Friedman, J., Neelsen, S., Piatti-Fünfkirchen, M., Sautmann, A., Shapira, G. and Van de Poel, E. 2022. Improving Effective Coverage in Health: Do Financial Incentives Work? Policy Research Report. Washington, DC: World Bank. doi:10.1596/978-1-4648-1825-7. License: Creative Commons Attribution CC BY 3.0 IGO. Available at: https://www.worldbank.org/en/research/publication/improving-effective-coverage-in-health Accessed on 15 th May, 2022. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2336569","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":158636564,"identity":"dcef6c4e-1602-451a-8dba-3967faac9164","order_by":0,"name":"Joseph C. 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Mkwama","email":"","orcid":"","institution":"Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Edwin","middleName":"C.","lastName":"Mkwama","suffix":""},{"id":158636579,"identity":"bf8ba064-7f6e-4fc9-9ada-15ca72eaf2eb","order_by":14,"name":"Jimmy Mbelya","email":"","orcid":"","institution":"University of Dar es Salaam","correspondingAuthor":false,"prefix":"","firstName":"Jimmy","middleName":"","lastName":"Mbelya","suffix":""},{"id":158636580,"identity":"7759ea9a-6b7a-4a55-8537-6fd7f402202b","order_by":15,"name":"Michael Habtu","email":"","orcid":"","institution":"World Health Organization – Tanzania Country Office","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Habtu","suffix":""},{"id":158636581,"identity":"c4431784-8adb-4d2e-9bee-0cd6364b713d","order_by":16,"name":"Eliudi S. Eliakimu","email":"","orcid":"","institution":"Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Eliudi","middleName":"S.","lastName":"Eliakimu","suffix":""},{"id":158636582,"identity":"9e0e02fb-e074-4481-8c0d-79b6b1cfa3d9","order_by":17,"name":"15.\tHassan O. Muhomi","email":"","orcid":"","institution":"University of Dar es Salaam","correspondingAuthor":false,"prefix":"","firstName":"15.\tHassan","middleName":"O.","lastName":"Muhomi","suffix":""}],"badges":[],"createdAt":"2022-12-02 08:44:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2336569/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2336569/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":47596212,"identity":"addcfb7c-f967-4a9e-b3e5-c6fce56bb128","added_by":"auto","created_at":"2023-12-05 01:22:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":768682,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2336569/v1/10624069-9037-4064-966f-62fef7b755ed.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Contribution of Results-Based Financing in Quality improvement of Health Services at Primary Healthcare Facilities: Findings from Tanzania Star Rating Assessment","fulltext":[{"header":"Key Message","content":"\u003cp\u003e\u003cstrong\u003eImplications for policy makers\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eRBF is worth to continue as a way to improve health services in developing countries\u003c/li\u003e\n \u003cli\u003eRBF implementation should take on board all six WHO building blocks for improving health services\u003c/li\u003e\n \u003cli\u003eRBF should be scaled up to all regions in the implementing health facilities\u003c/li\u003e\n \u003cli\u003eDeveloping countries should look for funds that will be used to implement RBF as sustainability mechanism.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eImplications for public\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research intended to find out the gabs related to implementation of RBF so as to come up with solutions that will improve the intended purpose of RBF in primary health facilities. If RBF will be implemented based on WHO six building blocks, then there will be a lot of benefits to the public, that include: increased availability of health supplies (including medicine) at health facilities, \u0026nbsp;increased health care utilization in primary health care facilities, Quality health service provision from the primary facilities, gains in health care providers\u0026rsquo; productivity and efficiency in service delivery, higher quality data that is used for evidence-based decision-making as well as Health management teams, MSD, facility governing committees and Quality Improvement teams will be more accountable and responsive.\u003c/p\u003e"},{"header":"Background","content":"\u003cp\u003ePerformance based payment models (commonly referred to as performance-based financing (PBF) or results-based financing \u0026ndash; (RBF)) are an important mechanism for helping to improve the quality of health services in low- and middle- income countries (LMICs) (Gergen, et al, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). PBF quality tools used in verification of facility performance are more focused on structural quality and availability of resources, with few processes of care. This has called for a need to improve the tools to focus more on the quality-of-care processes (Josephson, et al, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Effect of performance-based payment interventions depend on its design, additional funding, supportive components such as technical support, and the context in which it is implemented (Diaconu et al, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Based on lessons from implementation of PBF in several countries in LMICs, the need for ensuring that the quality components in PBF is adapted to a country context has been noted (Gergen, et al, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnalysis of data from Burundi, Lesotho, Senegal, Zambia and Zimbabwe have shown that PBF had no effect \u0026ldquo;\u003cem\u003eon neonatal health outcomes, health care utilization or quality\u003c/em\u003e\u0026rdquo;, which indicates a need to relook at PBF if they are really effective (Gage and Bauhoff, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, a study by Brenner and colleagues on implementation of RBF in Malawi has shown that it has potential for improving \u0026ldquo;\u003cem\u003eeffective coverage\u003c/em\u003e\u0026rdquo; for obstetric services (Brenner, \u003cem\u003eet al\u003c/em\u003e, 2021). In Zimbabwe, analysis of Demographic and Health Survey data between 2005 and 2015 was done to check for the effect of RBF implementation on health outcomes (neonatal, infant and under five mortality) and their analysis based on socio-economic groups. The findings have shown some positive effects on health outcomes but influenced by socio-economic status (Fichera, et al, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Tanzania, PBF (in the name of pay for performance \u0026ndash; P4P) intervention was introduced in Pwani Region in 2011 (Borghi, et al, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Borghi, et al, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Its implementation was shown to be influenced by the following \u0026ldquo;\u003cem\u003econtextual factors\u0026rdquo;: salary and employment benefits; resource availability including staff, medicines and functioning equipment; supervision; facility access to utilities; and community preferences\u003c/em\u003e (Olafsdottir, et al, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Other studies on its implementation found improvements in seven areas as follows. First, it improved accountability mechanisms in particular internal accountability mechanisms; external accountability mechanism improved in some aspects such as attitude to patients but did not influence functionality of Health Facility Governing Committees. (Mayumana, et al, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Second, it reduced stock out of essential drugs in particular oxytocin; increased health care workers kindness at delivery; and also enabled supportive supervision visits to be implemented within planned timeframes (Anselmi, et al, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Third, it improved availability of essential medicines and supplies, but had no effect on availability of functioning equipment. (Binyaruka and Borghi, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) Fourth, it was found to have potential for ensuring equity in accessing health services among the poor and in rural districts. (Binyaruka, et al, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) Fifth, it was found to have potential for influencing efficiency in particular in public facilities but it requires further improvement in its design for this to be realized. (Binyaruka, \u003cem\u003eet al\u003c/em\u003e, 2020) Sixth, it produced some sustained improvements in user(patient) experience of care such as kindness. (Borghi, et al, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) Seventh, it showed potential for reducing women bypassing a nearby health facility. (Bezu, \u003cem\u003eet al\u003c/em\u003e,2021). Given, its high costs in its implementation especially management costs and costs involved in performance data generation and verification, it was suggested to consider its integration in routine health systems so as to make it more cost-effective. (Borghi, et al, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eSeveral studies looked at the way the P4P intervention in Tanzania was designed. A study by Songstad and colleagues (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) on assessment of health care workers performance expectations with the P4P in comparison with the Open Performance Review and Appraisal System (OPRAS) found that the studied health care workers showed positive expectations towards P4P implementation, although the link between OPRAS and P4P was unclear. (Songstad, et al, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, the design was noted to be influenced by politics especially the influence of external actors in setting the agenda (Chimhutu, et al, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Another study by Chimhutu, \u003cem\u003eet al\u003c/em\u003e. (2016) found that the modality of distribution of bonuses in the P4P scheme was unfair and that it affected staff motivation, teamwork, as well as social relations at health facilities; which could ultimately affect the quality of health care services. Binyaruka and colleagues found that the design of how incentives are provided in P4P and some health facility characteristics influenced inequalities in health facilities performance (Binyaruka, et al, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). A study by Cassidy and colleagues observed that the roles of Health Facility Governing Committees were not included in the design of the P4P despite their key roles in enabling proper management of resources in health facilities and also linking with the community served. (Cassidy, et al, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It was further found that apart from the potential for improving some aspects of experience of care, the way the P4P was designed with focus on certain services only, limited the generalizability of their gains in a whole health facility. (Chimhutu, et al, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eImplementation of the P4P pilot in Pwani Region took place from 2011 to 2013 and thereafter, preparation for rolling on its improved version named RBF was started in which its pilot was done in two councils in Shinyanga Region in 2015 and rolled out to the whole region in 2016. The program was then rolled out to other regions as follows: Pwani, and Mwanza (2016); Geita, Kagera, and Kigoma (2017); Simiyu, and Tabora (2018). (Ministry of Health and Social Welfare, 2015[\u003cem\u003epp.51\u0026ndash;52\u003c/em\u003e]) The RBF implementation in Tanzania gives payment to PHC facilities on quarterly basis based on their level of achievement that has been verified; in which 75% of the payments is allocated for facility improvement and 25% is for incentivizing the staff. (Ministry of Health, Community Development, Gender, Elderly and Children, 2019) By 2019, RBF was implemented in 8 regions of Tanzania: Pwani, Mwanza, Shinyanga, Tabora, Simiyu, Kagera, Kigoma, Geita. (Ministry of Health and Social Welfare, 2015; Ministry of Health, Community Development, Gender, Elderly and Children, 2019) The RBF implementation was envisaged as a strategy that can help to reform the health sector resulting in improvements in \u0026ldquo;\u003cem\u003eservice delivery, leadership and governance, human resources, health management information system, medical supplies, vaccines, equipment, and health care financing in order to improve accountability, efficiency, and equity\u0026rdquo;\u003c/em\u003e. (Ministry of Health and Social Welfare, 2015[\u003cem\u003epp.54\u003c/em\u003e])\u003c/p\u003e \u003cp\u003eThis study aims to compare performance of health facilities in RBF regions and non-RBF regions in terms of quality of health services as measured by the Star Rating Assessment (SRA) approach. The SRA approach to quality was introduced in 2014 with the aim of assessing quality of health services in all Primary Health Care (PHC) Facilities in Tanzania. A baseline was conducted in the Fiscal Year 2015/2016 and reassessment was done in the Financial Year 2017/2018. (Yahya and Mohamed, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gage, et al, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) Specifically, the objectives of this study were to determine: \u003cb\u003e(i)\u003c/b\u003e the difference in mean scores between the Star rating service areas in regions that implemented RBF versus those which did not implement in the health facilities in financial year 2017/18; (ii) whether there was an overall increment in 3\u0026thinsp;+\u0026thinsp;star rating scores between baseline and follow up assessments; and whether there was difference in increment of 3\u0026thinsp;+\u0026thinsp;star rating scores among regions that were exposed to RBF and those that were not between the two assessments.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis was both descriptive and analytical comparative study on contribution of RBF in quality of services in PHC facilities from findings of SRA conducted in the Financial Year 2017/2018.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy Setting\u003c/h2\u003e \u003cp\u003eThe study was conducted at primary healthcare facilities of Tanzania which is located in East Africa. Tanzania Mainland has 26 regions and 184 Local Government Authorities (shortly referred to as councils). According to health system of Tanzania Mainland, there are facilities at primary level (dispensaries, health centers and hospitals at council level) and referral hospitals (hospitals at regional, zonal and national levels). As of 2020, the country had a total of 9,813 health facilities in the following categories: Hospitals (\u003cem\u003ereferral and council level\u003c/em\u003e) 369, Health Centres 926, Dispensaries 7,163, others (including clinics) 1,276, and Maternity Nursing Homes 79; (TNBS, 2020).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePopulation\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eSelection criteria\u003c/h2\u003e \u003cp\u003eAll PHC facilities that were assessed during baseline (2016/17) and re-assessment (2017/18) were included in the study.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSampling procedure and Sample size Estimation\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eParticipating regions\u003c/h2\u003e \u003cp\u003eAll eight (8) regions where RBF was implemented were included in this study. From the 18 remaining regions that were not implementing RBF; 8 regions were sampled randomly by a simple random method in order to match the number of regions that implemented RBF. Before sampling, the name of each of the 18 regions was written over 18 pieces of papers then folded. Then eight papers were drawn one by one eight times to get the 8 regions by providing equal chance of participation. The total PHC facilities in these regions were 2,214 during baseline and 2,143 during reassessment.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePerformance areas\u003c/h2\u003e \u003cp\u003eAnalysis of performance of SRA indicators in the SRA service areas were identified based on the SRA tool that was used. The SRA tool had 12 service areas. For the sake of this implementation study, only seven service areas were included. The purposive sampling of the areas was used to select the areas that had direct influence of RBF in health facilities improvement.\u003c/p\u003e \u003cp\u003eThe service areas included are: Service area 4-staff performance (4.1 staff performance appraisal system); service area 5-service provider charter (5.1 service provider charter, 5.2 client flow); service area 7-client focus (7.1 client service charter, 7.2 client satisfaction); service area 8-social accountability (8.2 functional facility governance committees or boards); service area 9-facility infrastructure (9.2 buildings); service area 11-clinical services (11.1 outpatient services; 11.2 Maternal, Neonatal and Child Health (MNCH) services); service area 12-clinical support services (12.1 pharmaceutical services). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the indicators for each service area that are presented in the analysis. The detail of the assessed areas is found in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStar rating service area (SA) and the indicators included in the analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSA No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStar Rating Service area\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStar Rating Indicator\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStaff performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e4.1 Staff performance appraisal system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStaff performance methods in place\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStaff performance targets agreed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndividual job descriptions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEffective review of individual performance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStaff satisfaction with performance review system\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eService provider charter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e5.1 Services provider charter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFacility name working hours and on-call roster\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eService charter for core healthcare services\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eServices, insurance benefits and charges are displayed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSchedule for special clinics is displayed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5.2. Client flow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOptimal client flow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClient waiting time is monitored\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClient Focus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e7.1. Client Services charter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClient services charter displayed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClient services charter is monitored\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClient feedback mechanism and complaints handling\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.2. Client satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClients satisfied with services provided\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e8.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial accountability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e8.2 Functional facility governance committees or boards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHFGC or HFB is active and well oriented\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHFGC/HFB voices community concerns\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHFGC/HFB gives feedback to the broader community\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e9.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFacility infrastructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e9.2 Buildings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStatus of the buildings and repairs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFunctional improved toilets for male and female, and staff\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFunctional plumbing, drainage and sewerage system\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatient privacy is ensured\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAvailability of conducive waiting areas\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealth Facility rooms are well ventilated and well lit as per Health Facilities guidelines\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDisability-friendly facilities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"12\" rowspan=\"13\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinical Services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e11.1. Outpatient Services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOutpatient register is correctly filled\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOutpatients are treated according to standard treatment guidelines\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood patient provided interactions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003e11.2 Maternal, Neonatal and Child Health (MNCH) Services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eANC services follow guidelines\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFamily planning services follow guidelines\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImmunization services follow guidelines\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe facility is performing BEmONC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNational guidelines available\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBEmONC training\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePartographs for mothers in labor are correctly filled\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe facility is performing death audit within 24 hours of maternal death\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFacility is offering child growth monitoring services according to the guidelines\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinical supportive services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e12.1 Pharmaceutical services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQualified pharmaceutical cadre\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood dispensing practice\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAvailability of essential medicines and health products\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eANC\u0026thinsp;=\u0026thinsp;Antenatal Care; BEmONC\u0026thinsp;=\u0026thinsp;Basic Emergency Obstetric and Newborn Care; HFGC\u0026thinsp;=\u0026thinsp;Health Facility Governing Committee; HFB\u0026thinsp;=\u0026thinsp;Hospital Advisory Board.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eData Extraction, Management and Analysis\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003eData extraction and Data Management\u003c/h2\u003e \u003cp\u003eAfter the identification of the performance areas was done, the next step was to extract data from the sampled regions. Secondary data of the SRA dataset of 2017/2018 were extracted and used to compare the performance of the RBF and non-RBF regions in terms of star level attainment and the scores in the assessed areas. The extracted data were checked for completeness, cleared, edited, coded and double entered in SPSS version 12 for analysis. Backup of the data was done by entering data in an excel sheet.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were used to summarize the data in percentages. The total percentage score per performance area in a facility was computed from the sum of scores from those that scored \u0026ldquo;yes\u0026rdquo; divided by the sum for all indicators times 100%. The average percentage score of each performance area in a region was computed from percentage scores from each of the health facility in the region under the study. The final average scores for the regions that implemented RBF were developed by adding all scores of the regions divided by seven and the same was done for those that did not implement RBF. We used a t-test for unrelated samples to determine whether there were differences in rating scores between the regions that implemented RBF and those which did not at each assessment (both baseline and reassessment).\u003c/p\u003e \u003cp\u003eThe T-test for paired samples was used to determine whether there was an overall increment in proportions of facilities scoring 3\u0026thinsp;+\u0026thinsp;stars between assessments. It was also used to compare if this difference was significant among the facilities that were exposed to RBF and those that were not. All results were considered significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The 95% Confidence Interval was also reported.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv class=\"Section2\" id=\"Sec14\"\u003e\n \u003ch2\u003eDescription of the participating PHC facilities\u003c/h2\u003e\n \u003cp\u003eA total of 4,357 facilities were involved in this study; 4,168 at baseline and 4,357 at reassessment which is (189)4.34% more facilities than those which were involved in baseline. The increment of the facilities during reassessment was due to new facilities registered across the country. During both assessments around 85% were dispensaries and 85% were owned by the government. Further description of the characteristics of the involved PHC facilities is presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of the facilities involved in the study\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBaseline (N)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eReassessment\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFacility level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHospitals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealth Centres\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDispensaries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOwnership\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate Facilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePublic Facilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4168\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e100\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4357\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e100\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eThis table shows the average score of the performance of the health facilities in the regions that had RBF support. The area for clinical services scored the highest (71.87%) while Clinical Support services scored the lowest (43.25%). The overall average score for all areas was 61.21%. The details of the scores are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e bellow.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003eAverage Score by RBF regions during assessment in the Financial Year 2017/2018\u003c/span\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eService Area Number\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStar Rating Service area\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMwanza\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePwani\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGeita\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKagera\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKigoma\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSimiyu\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eShinyanga\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTabora\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStaff performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eService provider charter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClient focus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial accountability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInfrastructure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClinical services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClinical supportive services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"12\"\u003e\n \u003cp\u003e61.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec15\"\u003e\n \u003ch2\u003eAverage Score by non-RBF regions during assessment in the Financial Year 2017/2018\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows the average score of the performance of the health facilities in the regions that had no RBF support. During assessment the largest score was on clinical services which was 66.62%. The lowest score was 36.37% on Clinical Support services. The average score was 50.42%. the details of scores are displayed in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage Score by non-RBF regions during assessment in the Financial Year 2017/2018\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eService Area Number\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStar Rating Service area\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eManyara\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIringa\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMorogoro\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTanga\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMara\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMbeya\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRukwa\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSongwe\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStaff performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eService provider charter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClient focus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial accountability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInfrastructure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClinical services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClinical supportive services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec16\"\u003e\n \u003ch2\u003eAverage difference scores during Assessment\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows the average difference scores of the performance of the health facilities in the regions that had RBF support versus those which had no support. The biggest difference appeared on Service provider charter by 17.00% while the lowest was on Clinical Services by 05.25%. The mean value was found to be 61.26% among facilities exposed to RBF compared to 50.42% among those not exposed to RBF. The study showed the mean difference score to be 10.79%, with confidence interval at 95% to be -1.24 to 22.84, suggested that there was no a significant difference in the facilities based on RBF exposure during baseline assessment. The p-value of 0.07 was not statistically significant.\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab5\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAverage difference scores during Assessment\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eService Area Number\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStar Rating Service area\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage Score by RBF regions during reassessment in the Financial Year 2017/2018\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage Score by non-RBF regions during reassessment in the Financial Year 2017/2018\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDifference\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCI and P value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStaff performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eService provider charter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClient Focus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial accountability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInfrastructure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClinical Services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e05.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClinical supportive services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e06.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.07 (CI 95% =-1.24\u0026ndash;22.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e6.0\u003c/span\u003e: \u003cstrong\u003eThe overall improvement in 3\u0026thinsp;+\u0026thinsp;star rating scores between baseline and follow up assessments among 16 regions\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eDuring baseline assessment Tanga region had more health facilities with three stars or above 9(2.43%) while six regions did not attain 3 stars and above. When reassessment was conducted Geita had the highest percentage 38% and Songwe had the lowest 3% with health facilities attained three stars or above. Of 4168 facilities that were assessed in 2015/16 only 37 (0.89%) reached 3 stars and above whereas in 2017/18 out of 5357 health facilities assessed 794 (18%) had achieved 3 stars and above. Facilities scoring 3\u0026thinsp;+\u0026thinsp;stars increased by average of 17.39 (95%CI 11.37\u0026ndash;23.41) with P\u0026lt;0.0001 between the baseline assessments and reassessment. The detail is displayed in Table 6 bellow.\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab6\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6.0\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe overall improvement in 3\u0026thinsp;+\u0026thinsp;star rating scores between baseline and follow up assessments among 16 regions\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRegion Name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNum of Facilities Star Rated (2015/16)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNum of Facilities Rated 3-Stars and above (2015/16)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage of Facilities Rated 3-Star or Above (2015/16)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of Facilities Star Rated (2017/18)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of Facilities Rated 3-Star or above (2017/18)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage of Facilities Rated 3-Star or above (2017/18)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRBF\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIringa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKagera\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKigoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eManyara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMbeya\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMorogoro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMwanza\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePwani\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRukwa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShinyanga\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimiyu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSongwe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTabora\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTanga\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTOTAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4,357\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e793\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e18%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003eDifference increment in 3\u0026thinsp;+\u0026thinsp;star rating scores between RBF and non-RBF regions in follow up assessment.\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eThe regions which had RBF implementation, Geita region had the greatest number of facilities with 3stars and above 58(38%) while Kigoma Region had the lowest 10(04%). The regions which had no RBF implementation, Mbeya region had the highest achievement for the regions which did not implement RBF. It had 111(37%) of facilities with 3 stars or above while Songwe had the lowest number of facilities with three stars or above. It had 5(03%) health facilities with three stars and above. Facilities scoring 3\u0026thinsp;+\u0026thinsp;stars increased by average of 10.63 between the facilities with RBF supported regions versus those which had no RBF support and difference was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.06). The detail is displayed in Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e below\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab7\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDifference increment in 3\u0026thinsp;+\u0026thinsp;star rating scores between RBF and non-RBF regions in follow up assessment\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePairs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRBF exposure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSd\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean difference\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eD.F\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e10.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e-0.92-22.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this evaluation, it was found that, the facilities that had RBF had no statistically significant difference from those which did not implement RBF. The regions which implemented RBF had 61.21% while the non-RBF facilities scored 50.42%. This means there was a difference of 10.79%. In Tanzania, the regions which were selected for the support of RBF were those which had poor performance in almost all heath indicators. Despite statistical significance in difference was not met, realistically the RBF regions had improved with the support of RBF. Hence, this study might be in keeping with the study done by Brenner and colleagues on implementation of RBF in Malawi that, it has potential for improving \u0026ldquo;effective coverage\u0026rdquo; for obstetric services. Another study that was conducted in Zimbabwe by Fichera and colleagues in 2021 found RBF had no influence in improvement of some indicators like child anthropometric measures (Fichera, et al, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); while a study by Gage and Bauhoff found that PBF had no effect \u0026ldquo;\u003cem\u003eon neonatal health outcomes, health care utilization or quality\u003c/em\u003e\u0026rdquo;, in four countries (Burundi, Lesotho, Senegal, Zambia and Zimbabwe), (Gage and Bauhoff, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe target of Star Rating Assessment was for PHC facilities to be able to perform at a star rating of three stars and above. The percentage of PHC facilities that scored 3\u0026ndash;5 stars in the RBF regions was about a quarter (24%), while for the non-RBF regions was 13%; resulting in a non-statistically significant difference between the two. The findings are in line with a recent World Bank policy report which has looked at health systems of about 40 low-income countries around the world including Tanzania. The report assessed whether financial incentives are working to ensure effective coverage (i.e., \u0026ldquo;a measure that adjusts simple coverage of care with the quality of care provided\u0026rdquo;) using evidence accumulated in the past 15 years. The results have shown that PBF \u0026ldquo;\u003cem\u003eresulted in gains in coverage but far fewer, if any, improvements in the quality of health services delivered\u003c/em\u003e.\u0026rdquo; Hence, the report notes that financial reforms in health systems may need to move away from sole dependence on RBF, and focus more on its other aspects especially \u0026ldquo;\u003cem\u003etransparency, accountability, and decentralized frontline financing\u0026rdquo;.\u003c/em\u003e (de Walque, et al, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eIn Tanzania, Star rating is considered as one of measures of the quality of services in health facilities. In this study, it was found that more health facilities with RBF support had 3stars than those with no RBF. This might reflect the intended effect of RBF to improve health facilities to attain 3stars and above were met. The country has set the cutoff point of 3 stars. Hence, the facilities with 3 stars or more are doing good in the majority of health indicators. In this study, it was found the difference of 3 stars was 10.63, but the difference was not statistically significant. The observed increase in the number of 3 stars and above at RBF regions could also have resulted from the improvement of health indicators by the support of RBF. This finding is approximately related to the study by Gage et al in 2020 on \u0026ldquo;\u003cem\u003eassessment of health facility quality improvements\u003c/em\u003e\u0026rdquo; in Tanzania as well found that there was star rating improvement in the PHC facilities. (Gage, et al, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this evaluation it was found that, there was overall increment of the facilities that scored 3 stars and above. In the baseline assessment the facilities that scored 3 stars and above were 37 (0.89%) versus with 793(18%) and the difference was statistically significant (p\u0026gt;0.0001). In all health facilities the quality improvement plan was developed based on the gaps that were identified. All the facilities were required to address the gaps. The facilities with support of RBF were supposed to utilize the support to address the gaps. Other facilities with no support of RBF were supposed to continue addressing the gaps using their own funds. The achievement of quality improvement in health facilities depended on procedures within facilities and the situation in which they work. However, the improvement was enough, based on high demand for high quality health services, The government had set standard that at least 80% of all health facilities get 3 stars or above. Our evaluation in the Tanzania Mainland found that both facilities with RBF support and those without RBF support, their improvement was influenced by a facility\u0026rsquo;s capability to improve quality of healthcare services, as assessed using the star rating system. The finding of this evaluation is consistent with the study by Kinyenje and coleagues who evaluated the Status of Infection Prevention and Control in Tanzanian PHC Facilities using SRA dataset and found that there was some improvement of IPC adherence between baseline and reassessment (Kinyenje, et al, 2020).\u003c/p\u003e \u003cp\u003eAs Tanzania continues with the efforts to improve the quality of PHC services, it is important to strengthen the management of PHC facilities. Literature also has shown that for RBF to have a bigger effect in performance of PHC facilities, it is important to strengthen the PHC Facility Management. For instance, a study in Nigeria has shown that PHC facilities that had high scores in management practices had higher monthly improvement rates for institutional delivery and outpatient visits compared to the PHC facilities with low scores in management practices. (Mabuchi, et al, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe RBF implementation in PHC facilities in Tanzania had no significant contribution to improvement in star rating levels (3 stars and above). Probably contributed to the way the RBF was designed. Improvement of Health services needs to adhere to all six WHO health system building blocks (service delivery; health workforce; health information systems; access to essential medicines; financing; and leadership/governance) and not to focus solely on financing. Probably, RBF was found not to influence star rating because other blocks were not considered, including leaving aside the HFGCs. We recommend integrate all the six WHO health system building blocks whenever we want to improve health services provision using PBF/RBF interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical issues:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not involve human subjects, hence for this type of study formal consent is not required, however, prior permission was sought from the Ministry of Health, Community Development, Gender, Elderly and Children (Recently renamed as Ministry of Health) before use of the dataset. Ministry of Health is a custodian of Health Management Information Systems data including SRA database. Ethical clearance is not necessary for this type of a study because data were collected in the course of implementing an initiative by the government and hence this analysis aims at giving feedback on prevailing circumstances after its successful implementation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared no conflict of interest, however, during 2017/2018\u0026rsquo;s Star Rating Assessment of PHCs that yielded these data, Dr Eliudi S. Eliakimu, Joseph C. Hokororo, Chrisogone J. German, Radenta P. Bahegwa, Talhiya A. Yahya, Omary A. Nassoro, Ruth R. \u0026nbsp;Ngowi, Yohannes S. Msigwa, Mbwana M. Degeh, and Laura E. Marandu were working with the Health Quality Assurance Division (now called Health Quality Assurance Unit) and were responsible for the implementation of SRA and QIPs follow-up.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eConception and design:\u003c/strong\u003e Joseph Hokororo, Eliudi S. Eliakimu, Erick S. Kinyenje, Talhiya A. Yahya, Mohamed A. Mohamed, Mbwana M. Degeh, Chrisogone J. German, Erick S. Kinyenje.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAcquisition of data:\u003c/strong\u003e Joseph Hokororo, Erick S. Kinyenje, Chrisogone J. German, Syabo Mwaisengela, Omary Nassoro, Ruth R. Ngowi, Laura E. Marandu, Radenta P. Bahegwa, Yohannes S. Msigwa.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAnalysis and interpretation of data:\u003c/strong\u003e Joseph Hokororo, Erick S. Kinyenje, Chrisogone J. German, Hassan Muhomi, Jimmy Mbelwa, Eliudi S. Eliakimu.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDrafting of the manuscript:\u003c/strong\u003e Eliudi S. Eliakimu, Joseph C. Hokororo, Bush Lugoba, Edwin C. Mkwama.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCritical revision of the manuscript for important intellectual content:\u003c/strong\u003e Eliudi S. Eliakimu, Joseph C. Hokororo, Erick S. Kinyenje, Talhiya Yahya, Mbwana M. Degeh, Mohamed A. Mohamed.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eStatistical analysis:\u003c/strong\u003e Joseph C. Hokororo, Erick S. Kinyenje, Chrisogone J. German, Hassan Muhomi, Jimmy Mbelwa.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eObtaining funding:\u003c/strong\u003e Not applicable\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAdministrative, technical, or material support:\u003c/strong\u003e Eliudi S. Eliakimu, Mbwana M. Degeh, Joseph C. Hokororo, Erick S. Kinyenje, Chrisogone J. German.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSupervision:\u003c/strong\u003e Eliudi S. Eliakimu, Joseph C. Hokororo, Erick S. Kinyenje.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eOther/s (specify):\u003c/strong\u003e None.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eSource(s) of support/funding:\u0026nbsp;\u003c/strong\u003eNo funding was received by the authors for the study. Data manipulation and analysis was conducted as a part of usual responsibilities. Most of authors are from Health Quality Assurance Unit which is responsible in implementing SRA and hence obliged to disseminate evaluation results through publications.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study is secondary. They were collected during star rating of primary health facilities in Tanzania and it is available if it is needed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThe authors would like to acknowledge the Ministry of Health for granting permission to use the Star Rating 2017/2018\u0026rsquo;s Assessment data. Furtherly, Our sincere gratitude goes to key partners in the implementation of the SRA that include directorates and units of Ministry of Health embracing Health Quality Assurance Unit, Curative Services Division, Preventive Services Division and the Health Directorate of the President\u0026rsquo;s Office \u0026ndash; Regional Administrative and Local Government (PO-RALG), development partners including the World Bank, Centre for Disease Control and Prevention, Danish International Development Agency, The World Health Organization, Association of Private Health facilities, Christian Social Services Commission, nevertheless Regional Secretariats, Local Government Authorities and Healthcare Workers from visited Primary Health Facilities are highly appreciated.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclaimers:\u0026nbsp;\u003c/strong\u003eThe authors declare that the views expressed in this manuscript are their own and do not necessarily represent views of the institutions they are affiliated to.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAnselmi L, Binyaruka P, Borghi J. 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Introducing payment for performance in the health sector of Tanzania- the policy process. \u003cem\u003eGlobal Health\u003c/em\u003e. 2015 Sep 2;11:38. DOI: 10.1186/s12992-015-0125-9. PMID: 26330198; PMCID: PMC4557903.\u003c/li\u003e\n \u003cli\u003eDiaconu K, Falconer J, Verbel A, Fretheim A, Witter S. Paying for performance to improve the delivery of health interventions in low- and middle-income countries. \u003cem\u003eCochrane Database Syst Rev\u003c/em\u003e. 2021 May 5;5(5):CD007899. DOI: 10.1002/14651858.CD007899.pub3. PMID: 33951190; PMCID: PMC8099148.\u003c/li\u003e\n \u003cli\u003eEleonora Fichera et al (2021): Can Results-Based Financing improve health outcomes in resource poor settings? Evidence from Zimbabwe. Soc Sci Med. 2021 Jun; 279: 113959. doi: 10.1016/j.socscimed.2021.113959: PMCID: PMC8210646: PMID: 33991792\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eErick Kinyenje, et al (2020):\u0026nbsp;\u003c/strong\u003eStatus of Infection Prevention and Control in Tanzanian Primary Health Care Facilities: Learning from Star Rating Assessment. Volume 2, Issue 3, September 2020, 100071\u003c/li\u003e\n \u003cli\u003eFichera E, Anselmi L, Gwati G, Brown G, Kovacs R, Borghi J. Can Results-Based Financing improve health outcomes in resource poor settings? Evidence from Zimbabwe. Soc Sci Med. 2021 Jun;279:113959. DOI: 10.1016/j.socscimed.2021.113959. Epub 2021 May 7. PMID: 33991792; PMCID: PMC8210646\u003c/li\u003e\n \u003cli\u003eGage A. and Bauhoff S. The effects of performance-based financing on neonatal health outcomes in Burundi, Lesotho, Senegal, Zambia and Zimbabwe. \u003cem\u003eHealth Policy Plan\u003c/em\u003e. 2021 Apr 21;36(3):332-340. doi: 10.1093/heapol/czaa191. 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PMID: 33293745; PMCID: PMC7716095\u003c/li\u003e\n \u003cli\u003eGergen J, Josephson E, Coe M, Ski S, Madhavan S, Bauhoff S\u003ccite\u003e.\u003c/cite\u003e Quality of care in performance-based financing: how it is incorporated in 32 programs across 28 countries\u003ccite\u003e.\u003c/cite\u003e \u003ccite\u003eGlob Health Sci Pract\u003c/cite\u003e\u003ccite\u003e.\u003c/cite\u003e 2017\u003ccite\u003e;\u003c/cite\u003e \u003cstrong\u003e5\u003c/strong\u003e\u003ccite\u003e(\u003c/cite\u003e1\u003ccite\u003e):\u003c/cite\u003e90\u003ccite\u003e\u0026ndash;\u003c/cite\u003e107\u003ccite\u003e.\u003c/cite\u003e DOI: 10.9745/GHSP-D-16-00239. PMCID: PMC5493453\u003c/li\u003e\n \u003cli\u003eGergen J, Josephson E, Vernon C, et al. 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An Investigation of the Relationship Between the Performance and Management Practices of Health Facilities Under a Performance-Based Financing Scheme in Nigeria, \u003cem\u003eHealth Policy and Planning\u003c/em\u003e, 2022;, czac040, https://doi.org/10.1093/heapol/czac040 \u003cstrong\u003ePublished:\u0026nbsp;\u003c/strong\u003e17 May 2022.\u003c/li\u003e\n \u003cli\u003eMayumana I, Borghi J, Anselmi L, Mamdani M, Lange S. Effects of Payment for Performance on accountability mechanisms: Evidence from Pwani, Tanzania. \u003cem\u003eSoc Sci Med\u003c/em\u003e. 2017 Apr;179:61-73. DOI: 10.1016/j.socscimed.2017.02.022. Epub 2017 Feb 20. PMID: 28257886.\u003c/li\u003e\n \u003cli\u003eMinistry of Health, Community Development, Gender, Elderly and Children. Strengthening Primary Health Care for Results - Results Based Financing - Presented to DPG H June 12, 2019. Available at: http://www.tzdpg.or.tz/fileadmin/documents/dpg_internal/dpg_working_groups_clusters/cluster_2/health/DPG_H_Meeting_Documents_2019/RBF_at_DPG_H_June_12_2019.pdf Accessed on 20\u003csup\u003eth\u003c/sup\u003e September, 2021.\u003c/li\u003e\n \u003cli\u003eMinistry of Health and Social Welfare. Result Based Financing (RBF) Operational Manual, 2015. Dar es Salaam, The United Republic of Tanzania. Available at: https://bluesquarehub.files.wordpress.com/2017/03/tanzania-operations-manual-20160422_rbf_approved-version.pdf Accessed on 09\u003csup\u003eth\u003c/sup\u003e April, 2022.\u003c/li\u003e\n \u003cli\u003eOlafsdottir AE, Mayumana I, Mashasi I, Njau I, Mamdani M, Patouillard E, Binyaruka P, Abdulla S, Borghi J. Pay for performance: an analysis of the context of implementation in a pilot project in Tanzania. \u003cem\u003eBMC Health Serv Res\u003c/em\u003e. 2014 Sep 16;14:392. DOI: 10.1186/1472-6963-14-392. PMID: 25227620; PMCID: PMCID: PMC4261877.\u003c/li\u003e\n \u003cli\u003eSongstad NG, Lindkvist I, Moland KM, Chimhutu V, Blystad A. Assessing performance enhancing tools: experiences with the open performance review and appraisal system (OPRAS) and expectations towards payment for performance (P4P) in the public health sector in Tanzania. \u003cem\u003eGlobal Health\u003c/em\u003e. 2012 Sep 10;8:33. DOI: 10.1186/1744-8603-8-33. PMID: 22963317; PMCID: PMC3477072.\u003c/li\u003e\n \u003cli\u003eTanzania National Bureau of Statistics. Statistical Abstract 2020. Chapter Thirteen 13.0 Health. Available at: https://www.nbs.go.tz/index.php/en/tanzania-statistical-abstract/720-statistical-abstract-2020 Accessed on 23\u003csup\u003erd\u003c/sup\u003e April, 2022.\u003c/li\u003e\n \u003cli\u003eYahya T, Mohamed M. Raising a mirror to quality of care in Tanzania: the five-star assessment. \u003cem\u003eLancet Glob Health\u003c/em\u003e. 2018 Nov;6(11):e1155-e1157. DOI: 10.1016/S2214-109X(18)30348-6. Epub 2018 Sep 5. PMID: 30196094.\u003c/li\u003e\n \u003cli\u003ede Walque, D., Kandpal, E., Wagstaff, A., Friedman, J., Neelsen, S., Piatti-Fünfkirchen, M., Sautmann, A., Shapira, G. and Van de Poel, E. 2022. \u003cem\u003eImproving Effective Coverage in Health: Do Financial Incentives Work?\u0026nbsp;\u003c/em\u003ePolicy Research Report. Washington, DC: World Bank. doi:10.1596/978-1-4648-1825-7. License: Creative Commons Attribution CC BY 3.0 IGO. Available at: https://www.worldbank.org/en/research/publication/improving-effective-coverage-in-health Accessed on 15\u003csup\u003eth\u003c/sup\u003e May, 2022.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Result Based Financing, Star Rating Assessment, Quality improvement plan, Primary Healthcare Facilities, Quality Improvement","lastPublishedDoi":"10.21203/rs.3.rs-2336569/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2336569/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePerformance-based financing (PBF) is an important mechanism for improving the quality of health services in low- and middle- income countries. In 2014, Tanzania launched a country-wide quality approach known as Star Rating Assessment (SRA) aims to assess the quality of healthcare service delivery in all Primary Health Care (PHC) Facilities in the country. Furthermore, by 2015, the country rolled out RBF initiatives into eight regions in which PHC facilities were paid incentives based on their level of achievement in SRA assessments. This study aims to compare performance in quality between PHC facilities under RBF regions and non-RBF regions using the findings from the two-phases SRA assessments; baseline (2015/16) and follow-up (2017/18).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis of performance of SRA indicators in the SRA service areas were identified based on the star rating tool that was used. The star rating tool had 12 service areas. For the sake of this implementation study, only seven service areas were included. The purposive sampling of the areas was used to select the areas that had direct influence of RBF in health facilities improvement. We used a t-test to determine whether there were differences in assessment star rating scores between the regions that implemented RBF and those which did not at each assessment (both baseline and reassessment). All results were considered significant at p \u0026lt; 0.05. The 95% Confidence Interval was also reported.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean value was found to be 61.26 among facilities exposed to RBF compared to 51.28 among those not exposed to RBF. The study showed the mean difference score to be 10.79, with a confidence interval at 95% to be -1.24 to 22.84, suggesting that there was (no) a significant difference in the facilities based on RBF exposure during baseline assessment. The p-value of 0.07 was not statistically significant. Overall, there was an increment in facilities scoring the recommended 3+stars and above by 17.39% between the assessments, the difference was significant (p=0.0001). When the regions were stratified based on RBF intervention; facilities under RBF improved in 3+ stars by 10.63% higher compared to those that were not under RBF; however, the difference was not statistically significant (p=0.06)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImprovement of Health services needs to adhere to all six WHO building blocks and note to a sole financing. The six WHO building blocks are (i) service delivery, (ii) health workforce, (iii) health information systems, (iv) access to essential medicines, (v) financing, and (vi) leadership/governance. Probably, RBF found not to influence star rating because other blocks were not considered in this intervention. We need to integrate all the six WHO building blocks whenever we want to improve health services provision.\u003c/p\u003e","manuscriptTitle":"Contribution of Results-Based Financing in Quality improvement of Health Services at Primary Healthcare Facilities: Findings from Tanzania Star Rating Assessment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-12-12 12:49:22","doi":"10.21203/rs.3.rs-2336569/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"50af2c0f-38a4-4599-be14-1733be7d0a08","owner":[],"postedDate":"December 12th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-12-05T01:14:20+00:00","versionOfRecord":[],"versionCreatedAt":"2022-12-12 12:49:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2336569","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2336569","identity":"rs-2336569","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}