Unpredictability dictates quality of maternal and newborn care provision in rural Tanzania-A qualitative study of health workers’ perspectives

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Study Justification:
– The study aimed to examine health worker perspectives on the conditions for maternal and newborn care provision and their perceptions of what constitutes good quality of care in rural Tanzanian health facilities.
– This study is important because health workers play a crucial role in improving the quality of care for mothers and newborns in weak health systems, particularly in Sub-Saharan Africa.
– Understanding health worker perspectives can help identify the effectiveness of existing improvement programs and inform future initiatives to strengthen maternal and newborn care.
Study Highlights:
– The study found that unpredictability was a fundamental condition for maternal and newborn care provision in rural Tanzania.
– Unpredictability affected various aspects of care, including mothers’ access to and utilization of healthcare, availability of resources, and the need for health workers to balance the situation.
– Quality of care was perceived to vary due to these unpredictable conditions.
– Health workers emphasized the importance of predictability and things going as intended as indicators of good quality care.
– The findings suggest that increasing predictability within health services and focusing on the experience of health workers should be prioritized to achieve better quality of care for mothers and newborns.
Study Recommendations:
– Prioritize increasing predictability within health services to improve the quality of maternal and newborn care.
– Focus on the experience of health workers and address the challenges they face in providing care.
– Strengthen existing improvement programs and initiatives to better address the unpredictable conditions in rural Tanzanian health facilities.
– Consider the findings of this study when planning and implementing interventions to improve maternal and newborn care in similar settings.
Key Role Players:
– Health workers: Nurses, midwives, medical attendants, and other cadres involved in maternal and newborn care.
– District Medical Officer: Responsible for overseeing healthcare services in the district.
– Policy makers: Government officials and decision-makers involved in healthcare policy and planning.
– Quality improvement project team: Individuals involved in the EQUIP project, which aimed to improve the quality of care in the district.
Cost Items for Planning Recommendations:
– Training and capacity building for health workers to address the challenges identified in the study.
– Infrastructure improvements in health facilities to enhance predictability and quality of care.
– Procurement of necessary equipment and supplies to ensure reliable availability of resources.
– Recruitment and retention strategies to address the shortage of health workers in the district.
– Monitoring and evaluation activities to assess the impact of interventions and ensure continuous improvement.
Note: The actual cost of implementing these recommendations would depend on various factors and would need to be determined through a detailed budgeting process.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is based on a qualitative study using a grounded theory approach, which provides valuable insights into health worker perspectives on maternal and newborn care provision in rural Tanzania. The study conducted 17 in-depth interviews with health workers in 14 rural health facilities. The findings highlight the unpredictability as a fundamental condition for care provision and its impact on the quality of care. However, the abstract does not provide information on the representativeness of the sample or the generalizability of the findings. To improve the evidence, future studies could consider increasing the sample size and including a more diverse range of health facilities and regions in Tanzania.

Background: Health workers are the key to realising the potential of improved quality of care for mothers and newborns in the weak health systems of Sub Saharan Africa. Their perspectives are fundamental to understand the effectiveness of existing improvement programs and to identify ways to strengthen future initiatives. The objective of this study was therefore to examine health worker perspectives of the conditions for maternal and newborn care provision and their perceptions of what constitutes good quality of care in rural Tanzanian health facilities. Methods: In February 2014, we conducted 17 in-depth interviews with different cadres of health workers providing maternal and newborn care in 14 rural health facilities in Tandahimba district, south-eastern Tanzania. These facilities included one district hospital, three health centres and ten dispensaries. Interviews were conducted in Swahili, transcribed verbatim and translated into English. A grounded theory approach was used to guide the analysis, the output of which was one core category, four main categories and several sub-categories. Results: ‘It is like rain’ was identified as the core category, delineating unpredictability as the common denominator for all aspects of maternal and newborn care provision. It implies that conditions such as mothers’ access to and utilisation of health care are unreliable; that availability of resources is uncertain and that health workers have to help and try to balance the situation. Quality of care was perceived to vary as a consequence of these conditions. Health workers stressed the importance of predictability, of ‘things going as intended’, as a sign of good quality care. Conclusions: Unpredictability emerged as a fundamental condition for maternal and newborn care provision, an important determinant and characteristic of quality in this study. We believe that this finding is also relevant for other areas of care in the same setting and may be an important defining factor of a weak health system. Increasing predictability within health services, and focusing on the experience of health workers within these, should be prioritised in order to achieve better quality of care for mothers and newborns.

This was a qualitative study using a grounded theory approach to guide the analysis [27], based on 17 in-depth interviews with health workers in Tandahimba district, Mtwara region, south-eastern Tanzania. It was nested within a district-wide quality improvement project, called EQUIP (Expanded Quality Management Using Information Power) [28]. Tandahimba district is located on the Makonde plateau in rural southern Tanzania, close to the border of Mozambique. The population of about 200,000 people is predominantly subsistence farmers but also engage in cashew nut farming. The road network within the district becomes muddy during the rainy season, making emergency transport difficult. The district has 32 health facilities providing maternal and child care, all government apart from one faith-based. The district hospital provides comprehensive Emergency Obstetric and Neonatal Care (EmONC) [25] as well as regular childbirth care. The three health centres are supposed to manage basic EmONC and efforts are underway to upgrade their services to include caesarean sections [29]. In reality however, the functions required to provide even basic EmONC are rarely fulfilled, especially in terms of assisted delivery and provision of Magnesium sulphate for eclampsia. The 28 dispensaries provide childbirth care according to their abilities including the administration of Oxytocin when available, both to prevent and manage postpartum haemorrhage. Capacity to provide neonatal resuscitation has recently increased in all facilities through provision of equipment and targeted training [28]. There is a severe shortage of health workers with 52% of clinical posts vacant in the district [29]. Lower cadre health workers, such as medical attendants, often have to take on the responsibilities of higher cadre health workers such as nurses and midwives; an example being to assist women during childbirth (Additional file 1) [29]. Utilisation of health care during pregnancy is high with all mothers attending Antenatal care (ANC) at least once during pregnancy, although only 47% attend at least four times [1, 2]. The proportion of mothers giving birth in health facilities has increased substantially in recent years and was estimated as high as 87% (CI 95%: 77–93) in 2014 [28]. The maternal mortality ratio however remains high at 579 deaths/100,000 live births in the Mtwara region, which is higher than the national MMR average of 432 [30]. Neonatal mortality in was estimated at around 31 per 1000 in 2013 [2]. Interviews with an open sample of 17 health workers providing maternal and newborn care were conducted in 14 health facilities between the 4th and 14th of February, 2014 (Table 1). Respondents were selected purposively, to represent different cadres of health workers and levels of health facilities, with the aim to achieve variation in the data (Table 1). Characteristics of health workers interviewed Permission to conduct the interviews was obtained from the District Medical Officer in Tandahimba district. Respondents were contacted in advance, by telephone where possible, else by going to see them in the health facility. They were asked if they would be willing to participate and when a suitable time for the interview would be. At the time of the interview, informed written consent was then sought from all respondents. An information sheet about the study was provided and read out by one of the interviewers. Respondents were informed that they could refuse to participate, withdraw or stop the interview without having to state any reason and without any consequences. All respondents chose to participate. Interviews were held in a private area of the health facility. Only the respondent and interviewers were present. Interruptions to allow respondents to attend to their patients were made and for this reason, some interviews took place over the course of a few hours. The median effective interview time was 1 h 12 min (range 1 h 3 min to max 2 h 7 min). Interviews were co-conducted in Swahili by the first (UB) and second (FH) authors. UB is a Swedish medical doctor with 2 years’ experience of working as a clinician and program manager in rural Tanzania. FH is a Tanzanian social scientist with experience from several qualitative studies in southern Tanzania. Both are fluent Swahili-speakers and minimal translation into English by FH to UB was done during the interviews. Neither UB nor FH were known to the respondents beforehand. The interview guide was semi-structured and developed in collaboration between all authors of the study. It was adapted after the first interviews as some questions did not yield sufficient response and to reflect new ideas emerging during the data collection (see Additional file 2). While the number of interviews was pre-determined, it was felt that saturation in the interview material was reached before the last few interviews were conducted as no or little new information emerged. No repeat interviews with respondents were done. Interviews were audio recorded and transcribed verbatim. Subsequent translation into English was conducted with careful attention to quality to ensure preservation of the original meaning. Extensive field-notes were taken during and at the end of each interview. Transcripts were not shared with respondents. Data analysis was conducted using Microsoft Word 2010. It was led by the first author, UB, with frequent and substantial input by the senior author, IH. A grounded theory approach was used for the analysis, the end result of which was a core category [27]. Transcripts and field-notes were initially read and re-read to capture the whole. Transcripts were initially coded inductively, using so called open coding close to the text to capture ‘what was going on’ [31]. Similar codes were categorised into higher order categories. After analysing half of the transcripts, a code list was prepared to harmonise emergent codes and categories, agreed upon through discussions between UB and IH. Subsequent transcripts were coded applying these new codes and categories where possible, using focused coding. Theoretical coding was applied early on using the conditional matrix by Corbin and Strauss [31], focusing on the conditions in which health workers provide care and the consequences these have for care provision. As the analysis progressed, theoretical coding continued using the concepts of mothers’ utilisation of care, the availability of resources in health facilities and the actions taken by health workers, i.e. clinical practice, as it became apparent that the data fitted well into these categories. Main categories contained health worker perceptions of the conditions for care provision and their perspectives of what constitutes good quality care. All main categories were linked to the core category. As the core category emerged, interviews were also reread to find more examples of the core category and to saturate the description of the links between the core category and the main categories. Results were not checked by the respondents.

Based on the study, the recommendation to improve access to maternal health is to increase predictability within health services. This can be achieved by addressing factors such as unreliable access to healthcare, uncertain availability of resources, and the need for health workers to balance the situation. By prioritizing the experience of health workers and addressing the challenges they face in providing care, access to maternal health can be improved.
AI Innovations Description
The recommendation that can be developed into an innovation to improve access to maternal health based on the study is to increase predictability within health services. The study found that unpredictability was a fundamental condition for maternal and newborn care provision in rural Tanzania, leading to variations in the quality of care. Therefore, focusing on increasing predictability, ensuring that “things go as intended,” can help improve the quality of care for mothers and newborns. This can be achieved by addressing factors such as unreliable access to healthcare, uncertain availability of resources, and the need for health workers to balance the situation. By prioritizing the experience of health workers and addressing the challenges they face in providing care, access to maternal health can be improved.
AI Innovations Methodology
To simulate the impact of the main recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Define the variables: Identify the key variables that are relevant to the recommendations, such as predictability within health services, access to healthcare, availability of resources, and the experience of health workers.

2. Collect baseline data: Gather data on the current state of maternal health access in the target area. This could include information on the number of healthcare facilities, the availability of resources, the utilization of healthcare services by pregnant women, and the experiences of health workers.

3. Develop a simulation model: Create a simulation model that incorporates the identified variables and their relationships. This model should be able to simulate different scenarios and measure the impact of changes in predictability, access to healthcare, and other factors on maternal health outcomes.

4. Input data and run simulations: Input the baseline data into the simulation model and run simulations to assess the impact of the main recommendations. This could involve changing variables related to predictability, access to healthcare, and other factors to see how they affect maternal health outcomes.

5. Analyze results: Analyze the results of the simulations to determine the potential impact of the main recommendations on improving access to maternal health. This could include measuring changes in maternal mortality rates, neonatal mortality rates, and other relevant indicators.

6. Validate the model: Validate the simulation model by comparing the simulated results with real-world data. This could involve comparing the simulated outcomes with data from other studies or conducting additional research to validate the findings.

7. Refine and iterate: Based on the results and validation, refine the simulation model and repeat the simulations to further assess the impact of the main recommendations. This iterative process can help refine the recommendations and identify additional areas for improvement.

By following this methodology, researchers and policymakers can gain insights into the potential impact of the main recommendations on improving access to maternal health. This can inform decision-making and help prioritize interventions to address the challenges identified in the study.

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