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Background: An effective referral system is essential for a high-quality health system that provides safe surgical care while optimizing patient outcomes and ensuring efficiency. The role of referral systems in countries with under-resourced health systems is poorly understood. The aim of this study was to examine the rates, preventability, reasons and patterns of outward referrals of surgical patients across three levels of the healthcare system in Northern Tanzania. Methods: Referrals from surgical and obstetric wards were assessed at 20 health facilities in five rural regions prospectively over 3 months. Trained physician data collectors used data collection forms to capture referral details daily from hospital referral letters and through discussions with clinicians and nurses. Referrals were deemed preventable if the presenting condition was one that should be managed at the referring facility level per the national surgical, obstetric and anaesthesia plan but was referred. Results: Seven hundred forty-three total outward referrals were recorded during the study period. The referral rate was highest at regional hospitals (2.9%), followed by district hospitals (1.9%) and health centers (1.5%). About 35% of all referrals were preventable, with the highest rate from regional hospitals (70%). The most common reasons for referrals were staff-related (76%), followed by equipment (55%) and drugs or supplies (21%). Patient preference accounted for 1% of referrals. Three quarters of referrals (77%) were to the zonal hospital, followed by the regional hospitals (17%) and district hospitals (12%). The most common reason for referral to zonal (84%) and regional level (66%) hospitals was need for specialist care while the most common reason for referral to district level hospitals was non-functional imaging diagnostic equipment (28%). Conclusions: Improving the referral system in Tanzania, in order to improve quality and efficiency of patient care, will require significant investments in human resources and equipment to meet the recommended standards at each level of care. Specifically, improving access to specialists at regional referral and district hospitals is likely to reduce the number of preventable referrals to higher level hospitals, thereby reducing overcrowding at higher-level hospitals and improving the efficiency of the health system.
The United Republic of Tanzania is a lower middle income country located in Eastern Africa with a population of 56 million and a gross domestic product per capita of 1,122 USD [19]. Life expectancy at birth is 65 years and the maternal mortality is 556 per 100,000 live births [20]. The country is administratively divided into seven zones, which are further sub-divided into 26 total regions. The Lake Zone surrounds the southern shore of Lake Victoria and borders Kenya, Uganda, Rwanda and Burundi. It is divided into six regions; Mwanza, Kagera, Mara, Shinyanga, Geita and Simiyu. All regions in the Lake Zone, except Mwanza Region, were included in this study. The population of these 5 regions is approximately 9 million people. Bugando Medical Center, the zonal hospital, is located in Mwanza region and serves a catchment population of over 14 million [21]. Cumulatively, the study regions have 1196 health facilities; 1021 dispensaries, 137 health centers, 18 district hospitals, 5 regional referral hospital and 15 other hospitals at the time of the study [22]. The healthcare system in Tanzania is structured such that health services begin at the community level and patients are referred up the referral chain based on the complexity of services needed. Figure 1 illustrates the referral pathway along with surgical procedures to be provided at each level of care according to the NSOAP. Referral Pathway of the Tanzania healthcare delivery system. This figure was created by the authors using Keynote version 9.0.1 (6196) In order for this system to function efficiently, patients must move up and down the referral pathway based on the complexity of care needed with few patients bypassing lower level facilities to higher level facilities. The study was part of a larger prospective longitudinal quasi-experimental Safe Surgery 2020 (SS2020) study which aims to improve the quality of surgical care in Tanzania. Details on this study have been provided elsewhere [23]. Data collection was conducted from 1st February 2018 to 15th June 2018. A sample of 20 health facilities in 5 regions in the Lake Zone was chosen. This sample included four health centers, eleven district hospitals and five regional hospitals. For the purpose of the SS2020 study, Shinyanga and Simiyu were combined during sampling. Dispensaries were omitted from sample as they do not provide surgical services. Healthcare facilities were chosen based on the following criteria: 1) minimum average monthly surgical volume of 30 major surgical procedures 2) cross sectional representation of each level of the health system and 3) geographic distribution (Fig. 2). Information on services and basic infrastructure at all health facilities in the Lake zone was obtained from the MoHCDGEC’s health facility registry (HFR) and from the President’s Office for Regional and Local Government (PO-RALG) [22]. Based on this information, health facilities providing major surgical services in the five regions were selected. Geographic coordinates of health facilities from HFR were used to map health facilities using open source software QGIS (QGIS 2.4; QGIS Development Team; online resource). Distribution of sampled healthcare facilities. This figure was created by the authors using Google My Maps As the study focused on surgical referrals, inpatient volume included all patients admitted into male, female and pediatric surgical wards as well as obstetric wards. Referral volume included all patients referred to any other health facility from these wards. Patients from non-surgical wards and those in the out-patient department were excluded from the study. Details on referrals were captured by trained physician data collectors using a pre-tested data collection form (see Additional file 1). The data collected focused on patient demographics, reasons and destinations of referrals. Patient demographics captured included sex, age, type of management received and patient condition pre-referral (elective or emergency). More than one reason for referral could be selected per patient. For all referrals, data collectors also recorded details for why the specific reason for referral was selected as free-text. Furthermore, pre-referral diagnosis based on the best judgments of the referring healthcare provider and data collectors was noted for each patient referred. Data on referrals were primarily collected from referral letters used by hospital staff for referrals. These referral letters typically contained information on pre-referral management, reason for referral and the facility to which the patient was being referred. This data was then extracted onto the standardized data collection form. To ensure the accuracy of the data captured in the health facility referral records, data collectors reviewed each referral and reason for referral with the clinical provider responsible for the referral on a daily basis. At health facilities where referrals letters were not routinely maintained, the standardized data set for the referral data collection form was obtained primarily by discussion with hospital staff. Data collectors did not provide any input on reason for referral or final decision to refer at any time. Rather, they aimed to capture the decisions made by the clinicians in the referral forms and through discussions with them. Through discussions with clinicians, the data collectors also collected unstructured informal field notes on specific referrals to inform results collected. Field notes are not presented in the results section. After completing daily data collection, all data was manually inputted into REDCap. Data quality checks were conducted by members of the study team through daily reviews on REDCap and weekly facility visits. Means and standard deviations were used to summarize numerical data while percentages and proportions were used to summarize categorical variables. Reasons for referrals were grouped into categories: staff, equipment, drugs and supplies, infrastructure and other reasons. Subgroup analysis by hospital level was performed on preventability, reasons and patterns for referrals. The following formula was used to calculate referral rates: In this study, an appropriate referral was defined as a referral to a higher level facility for a condition that should be treated at a facility higher than the referring facility as defined by the Tanzanian NSOAP. A preventable referral was defined as referral to a higher level facility for a condition that should be treated at the referring facility level defined using the same guidelines (see Fig. 1). To determine the preventability of each referral, three members of the research team with a clinical background assessed the recorded pre-referral diagnosis for each referral against the recommendations of the NSOAP for which surgical conditions should be treated at each hospital level. Conditions that were deemed non-surgical were not classified. All analysis was performed in R Studio version 1.1.456 .
