Operational manager’s knowledge and attitudes toward data and universal health coverage indicators in primary health clinics in Ugu, South Africa

ABSTRACT

Universal health coverage (UHC) aims to ensure people have access to the health services they need. Sixteen tracer indicators were developed for implementation by countries to measure UHC in the health system. South Africa uses 15 of the proposed 16 indicators. Operational managers in the public health care sector collect data and report on these indicators at a primary health clinic level. This qualitative study explored the knowledge and attitudes of managers toward data and UHC service indicators in a sub-district in Ugu, KwaZulu-Natal, South Africa. Operational managers saw data collection as information gathering, measuring performance and driving action. They understood UHC indicators as ‘health for all’ linking them to National Department of Health Strategic plans and saw the value of indicators for health promotion. They found the lack of training, inadequate numeracy skills, requests for data from multiple spheres of government and the indicator targets that they had to reach as challenging and untenable. While operational managers made the link between data, measuring performance and action, the limited training, skills gaps and pressures from higher levels of government may impede their ability to use data for local level planning and decision making.

KEYWORDS:

• Data
• universal health coverage
• operational managers

Introduction

The World Health Organisation proposed the concept of Universal Health Coverage (UHC) in 2010 which aims for all people to have access to the health services they need, as and when they need them with no financial hardship (World Health Report, 2010). The Sustainable Development Goal 3 embraces the concept of countries achieving ‘universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all’ (Horton & Das, 2015). Two indicators have been developed to measure this target, that of ‘coverage of essential health services‘ and ‘the proportion of population with large household expenditures on health as a share of total household expenditure or income’ (World Health Organization, 2019).

The UHC service coverage index combines 16 tracer indicators of health service coverage into a single summary measure. The index is reported on a unitless scale of 0–100, which is computed as the geometric mean of 16 tracer indicators of health service coverage. The tracer indicators are organised by four components of service coverage: (1) reproductive, maternal, newborn and child health; (2) infectious diseases; (3) non-communicable diseases and (4) service capacity and access (World Health Organization, 2019).

Since 2010, countries have been working toward achieving UHC with some countries like Japan achieving a UHC service coverage index of 95 (Collaborators, 2020). Generally, low- and middle-income countries (LMICs) are still grappling with implementing UHC owing to the vast health disparities in their settings and the socio-economic challenges they face. Countries such as Somalia and the Central African Republic achieved a UHC service coverage index of 25, in 2019 (Collaborators, 2020).

South Africa has shown the political will to ensure UHC through the emergence of the National Health Insurance Bill in 2019 (South African Department of Health, 2019). South Africa had a UHC service coverage index of 46.1 from 2007 to 2008 and 56.9 from 2016 to 2017 with an overall poor performance on the tracer indicators in the poorly performing health districts (Day et al., 2021). The South African UHC service coverage index incorporates 15 of the 16 proposed tracer indicators. Day et al. in their calculation of the index have used multiple sources of data including the routine District Health Information System (Day et al., 2019).

In the past 2 years, the COVID-19 syndemic globally and in South Africa, while to a large extent impeded progress on UHC, has also provided opportunities for aiding the implementation and growth of UHC post the COVID-19 crisis (Cloete et al., 2021; Karamagi et al., 2021; Verguet et al., 2021). In some of the LMICs where progress was made in reducing mortality and improving life expectancy, COVID-19 affected the older population with comorbid disease (Karamagi et al., 2021). Further finances allocated for UHC were channelled to address COVID-19 (Hanson et al., 2022). However, in the Western Cape Province of South Africa, one saw how the Provincial Health Data Centre during the COVID-19 crisis was able to integrate data sources to provide a real-time overview of the evolving pandemic in the province. The data from the Centre provided vital information which policy makers were able to use to intervene and address a variety of elements in the crisis including disease transmission, service provision and health promotion initiatives (Cloete et al., 2021). This gave the National Department of Health and provincial Departments of Health insight into how data can be used to ensure UHC going forward in the country.

Importantly the data for the UHC indicators are being collected at the coalface by healthcare workers in the public healthcare sector, as part of routine monitoring and surveillance reporting in South Africa. To have good quality data it is required that those responsible for data collection have a good understanding of the indicators and is motivated. Thus, understanding healthcare workers’ knowledge of data and the UHC service coverage indicators and their attitudes toward data generation in the healthcare facility is important if we want to ensure good quality data is collected in facilities.

In this paper, we report on primary health clinic (PHC) operational manager’s (OM) knowledge of data and UHC service indicators and their attitudes toward data in a sub-district of the Ugu health district in KwaZulu-Natal, South Africa. Operational managers in PHCs are professional nurses who provide overall management and supervision of staff and implementation of health programmes and service delivery in the PHC. They are responsible for reporting to the District Health Management Team on the functioning of the PHC (Serapelwane & Manyedi, 2022; South African Department of Public Service and Administration, 2007).

Material and methods

Design and setting

This study employed a qualitative design using focus group interviews. We conducted this qualitative study between September 2021 and October 2021. The consolidated criteria for reporting qualitative research (COREQ) guidance have been applied in this research. The data for this study was collected in the Umdoni sub-district which is located within the Ugu district in KwaZulu Natal. Umdoni sub-district is a mix of urban towns and rural settlements. The population size of Umdoni is approximately 154,427 constituting 22 percent of the total population of Ugu district (Ugu Municipality). The population of Umdoni is growing steadily due to in-migration motivated by perceived employment opportunities. Healthcare services are provided by one district hospital, eight fixed PHC clinics and four mobile clinics. In 2019, the following UHC indices were reported; the couple-year protection rate was 60%, the immunisation under 1-year coverage was 82%, the diabetes treatment coverage was 31% and the tuberculosis effective treatment coverage was 57% (Massyn et al., 2020).

OMs and recruitment

To ensure we had information-rich cases linked to the research phenomenon we purposefully selected the OMs of the PHC clinics in the Umdoni sub-district to gather details about their experiences, understanding and knowledge of routine health data, UHC service coverage indicators and the health information system The recruitment of the OMs was done by a Manager in the Ugu Health District Office.

Data collection

Before data collection, a focus group interview guide was developed by the authors in collaboration with experts in health information systems. The focus group interview guide included broad, open-ended questions on the understanding of data, knowledge of universal health indicators, attitudes about universal health indicators, practices of data collection, analysis of data, challenges and strengths of current data collection. Two Focus group discussions (FGDs) were held and limited to a minimum of four participants (Hennink et al., 2019). Focus groups were chosen over individual interviews because it requires less time, and it is more convenient for healthcare providers in busy clinical settings.

The FGDs were held on location, at a place that was convenient and suitable for the OMs. We chose the option of piggyback focus groups. Piggybacking allows the researcher to gain access to the participants whereby the focus groups are added to another meeting. The OMs attend a weekly Nerve Centre meeting at the health facility (district hospital). The purpose of the Nerve Centre Meeting is to closely monitor the performance of HIV, TB and non-communicable diseases. Data submitted from the clinics are validated for accuracy at these meetings. The OMs report on errors and advise on the correction of data presented by the facility information officer. The FGDs were held after the Nerve Centre meeting and did not disrupt the purpose of the Nerve Centre Meeting.

The OMs were contacted by the research team to determine their attendance at the Nerve Centre Meetings and their availability to participate in the FGDs. At the closure of the Nerve Centre Meetings, information on the study was provided. Consent forms were administered and signed by OMs before the FGD. A researcher experienced in conducting focus groups, interviewing health care providers and qualitative methodology moderated the FGDs. A research assistant was present to take field notes and operate the audio recorder. The moderator initiated the discussion through introductions by each participant and then outlined the topics to be discussed. OMs were encouraged to share their perspectives. All OMs were familiar with each other, and this facilitated ‘collective remembering’, sharing of common experiences and maximising interaction between OMs. The OMs were informed that the proceedings would be audio recorded solely for research purposes and all information would be treated with confidentiality. The FGDs took approximately 60 min to complete. The OMs also completed an anonymous short questionnaire about their demographics, educational level and work experience.

The FGD was transcribed verbatim by an independent transcriber and cross-checked with the audio files by the authors to ensure accuracy and consistency. The transcribed interviews were analysed by the authors using steps outlined in the literature (Braun & Clarke, 2006) on how to conduct thematic analysis in Microsoft Word. The data was coded by the researchers using inductive reasoning. Once consensus was reached on the final codes, the authors deliberated upon the emerging themes. The final themes and subthemes were defined and reviewed by all the researchers.

The researchers took several steps to ensure the trustworthiness of the data (Shenton, 2004). This included prolonged engagement, persistent observation and researcher triangulation to enhance the credibility of the research.

Ethics approval and consent to participate

Ethical clearance was obtained from the University of KwaZulu Natal Biomedical Research Ethics Committee (BREC/00002599/2021). Approval was granted by the KwaZulu Department of Health to conduct the research in a clinical setting in Umdoni (NHRD Ref: KZ_202104_006). Written informed consent was provided by all OMs.

Results

The results of the study are presented based on the themes generated by thematic analysis. The demographic characteristics of the OMs are also presented.

Demographic characteristics of the OMs

Table 1 provides an overview of the demographic characteristics of the OMs. Two FGDs were conducted with 6 and 5 OMs in each group. The OMs’ ages ranged from 37–64 years (mean age 50.5 years). Most of the OMs were female (n = 8, 72.8%). Of the 11 OMs, 6 (54.6%) had a Diploma in Nursing while 5 (45.4%) had a primary university degree (Bachelor of Nursing). Seven OMs (63.6%) were based at fixed PHC clinics and 4 (36.4%) were mobile PHC clinic based. The OMs’ work experience in the nursing profession ranged from 16 to 38 years with a mean of 23.3 years.

Table 1. Demographic details of the OMs.

Variable	Range of variation	N	%	Mean	Standard deviation	Range
Age (in years)				50.5	9.2	37–64
	35–40	2	18.2
	41–46	2	18.2
	47–52	2	18.2
	53–58	2	18.2
	>59	3	27.2
Gender	Female	8	72.8
	Male	3	27.2
Current level or rank	Operational Manager	11	100
Facility setting	Fixed PHC	7	63.6
	Mobile PHC	4	36.4
Level of Education	Diploma	6	54.6
	Primary degree	5	45.4
Work experience (in years)				24.3	6.5	16–38
	10–20	4	36.4
	21–31	2	18.2
	>32	5	45.4
Number of years in current position				8.8	4.3	1–17
	1–5	2	18.2
	6–11	4	36.4
	11–16	4	36.4
	>17	1	9.0

Thematic analysis

Three overarching themes with subthemes emerged from the data analysis. The themes were OMs’ understanding of data, understanding and knowledge of universal health coverage indicators and attitudes towards universal health coverage indicators. The findings from the data analysis as presented here will be supported with illustrative quotations.

Theme 1: Operational manager’s understanding of data

Under the overarching theme of operational manager’s understanding of data, different interpretations and understanding of data emerged. The OMs alluded to their understanding of data through the following subthemes: data is information gathering, data measures performance, data drives action and lack of data impacts service requests.

1.1 Data is information gathering

According to the illustrative quotes of the OMs, data is information gathered in the workplace regarding clients and the health services rendered. The OMs also described how data is generated at the point of care and the tools utilised to collect the data.

Okay, data in the healthcare setting is basically gathered information. It is the information about all the services that are in the health system. We use tick registers, tally sheets, HPRS and Tier. Net. When we have collected all this data, the data needs to be verified. They are vital information, not just information. Data is information that we can use to take action. Data helps us to see if we have any impact in what's happening to the healthcare services we are delivering. [P3, FGD 1, female]

Data is the information that you get from the individual or healthcare user which is recorded and then captured on the system. The data is gathering all the information including the services accessed and utilised. The data generates our statistics. [P1, FGD 2, female]

1.2 Data measures performance

Some OMs in the focus groups acknowledged that data measures performance in healthcare service delivery. Other OMs reflected that data was linked to self-evaluation of work performance.

Data is not just ordinary numbers but performance numbers. Data monitors performance of healthcare service delivery. [P2, FGD 1, male]

I think on my side, collecting data is like assessing yourself or your performance. How are you doing in a specific service area? It gives you a sense that there is always room for improvement, because from data you can see if as a facility we are performing or not. When we are not performing well, we need to pull up our socks. Sharing data at meetings also allows us to learn from others. Because sometimes we see that others are doing great work. [P4, FGD2, female]

1.3 Data drives action

The OMs confirmed that data drives action in the healthcare setting. They described data as a reference for planning and executing interventions.

Yes, Data leads to action. For instance, the healthcare data or numbers that you collect pertains to a population and those numbers will determine your target. So now that data also guides you on decision making. One can also use the data to do correlations. This will help me and my team to determine what is expected out of us. And then we apply our remedial actions or interventions in the week, month, or the quarter. Remedial action is a cycle, and we use the data to determine if we reached our target or not. If you do not reach your target, then you must sit down to interrogate the data again. You need to do some planning on how you're going to be able to reach that target. [P1, FGD 1, male]

Data is linked to action. Data informs clinical decision making and quality improvement. Let me make an example. We had a Nerve Centre Meeting today. We look at the HAST data. We critique and analyse the indicators that have been captured. We check if we have reached our targets and if we are performing satisfactorily. If we are not reaching targets and our performance is poor, then we will have to implement remedial actions. Therefore, we use data for strategic planning. Data helps us to plan for human resources and appropriate staffing. Data also guides us on the ordering of medication and equipment. Data also helps us to learn. use as references. Data is linked to developing budgets plans and to services delivery. [P3, FGD 2, female]

1.4 Lack of data impacting service requests

The OMs also narrated their concerns about a lack of data impacting efficient service delivery. Data was needed to regulate access to certain medicines and infrastructure requests. The absence of data fuelled delays in providing clients with the appropriate healthcare and restricted resources.

Sometimes data it's prohibitive, sometimes you need something for service delivery or patient care and the data doesn't justify it. Without data, you will sometimes find it difficult to deliver the quality and safe healthcare services. Because some data, it's been easy to collect, some it's very difficult to collect. So, you will find that you need something urgently, but you need to have data to back it up. For, example, you will need a certain drug which you do not often prescribe. The drug is not available in the clinic. For example, I will have a pregnant patient with syphilis. I will need to prescribe benzathine penicillin. I will not be able to immediately issue the drug to the patient. I will have to provide the data to the pharmacy and motivate for the drug. So, in the meantime, the patient will have to wait while I submit the data first. Therefore, sometimes data is prohibitive because you want to have the medication available for the patient immediately as they require it. Here is another example of data being prohibitive. Take for instance my clinic. I need my clinic to be to be renovated and expanded because it is small and congested. But my headcount does not justify the need for renovation and expansion. So, in that sense data can be prohibitive. [P2, FGD 1, male]

Theme 2: Operational manager’s understanding and knowledge of universal health coverage indicators

The second overarching theme focused on PHC OMs understanding and knowledge of UHC indicators. This theme encompassed three subthemes, namely, (1) health for all, (2) linkage between NDOH strategic plan and UHC indicators and (3) Knowledge of Service Coverage Indicators.

2.1 Health for all

Defining or describing the UHC indicators was not a simple task for the OMS. The OMs adopted their understanding of UHC indicators. Several OMs identified UHC indicators with the concept ‘Health for All’. Moreover, they reflected on the need for health services to reach the whole population.

It is about health for all. It is about Primary Health Care re-engineering. We want to see that populations who are most affected by poverty also get health services that the government provides. That's why it's called universal health. Because it should not mean that only certain people who can afford health services should receive those services. It should be freely accessible, and it must be universally provided for all members of society, even the ones who do not have money. So that's why we have to look at those indicators and see if we can reach those targets. [P6, FGD1, female]

Now what I understand by UHC is basically we want to see that the entire population gets access to health services. Health for All. Equity, accessibility, availability. It is about the provision of services to all South Africans. It is about reproductive health, maternal, newborn and child health, infectious diseases, and non-communicable diseases. Universal Health is about providing services to everyone including the marginalized. Even poverty must not restrict people from getting all the services that they need. We want to see that the health services are distributed in the way that they should be distributed. Like whom accesses antenatal care services, delivery services, immunisation, HIV, TB, cervical cancer screening, prevention of hypertension, prevention of diabetes. So, we want to see all of that and measure these. [P3, FGD2, female]

2.2 Linkage between National Department of Health (NDOH) strategic plan and UHC indicators

For some OMs, there was a profound link between the NDOH strategic plan and UHC indicators. OMs emphasised that UHC indicators were being introduced to the strategic plans of the national, provincial and district health departments. Some expressed that they were confused about the original UHC indicators because of the continuous changes in the health strategic plans by various health departments.

I think we have an idea of the universal health indicators. This will come down to your ten-point plan or strategic plan. So now, at the end of the day, if we are saying, or maybe national states, that, by the year 2020, for example, we should have less number of people who are infected with HIV, less number of babies born with HIV. So, I think it's under those connotations. As a result, what indicator comes under that? And then what do we do? And that takes you back to what you are doing to achieve these targets? Coming to National Department of Health, they do not want you to work in silos. So, they collaborate with the World Health Organisation and adapt those recommendations and indicators for public health sector in South Africa [ P1, FGD1, male]

I think that Universal Health Indicators are those indicators mandated by the Department of Health Strategic Plan. The strategic plan is about HIV/AIDS care, maternal and child health, tuberculosis, non-communicable diseases. The Department of emphasised that Universal Health Coverage will improve life expectancy. [P4, FGD2, female]

We might have covered this Universal Health Indicators in many meetings. It is just such an add on of indicators. National Department of health will give you something like a strategic plan. And then KZN provincial Department will add more indicators to this plan. And then the District Health Office will decide to add that more indicators. Then by the time it comes to clinic level, you actually don't know which is the original universal health indicators and strategic plan that National Department of Health wants, or your District Health Office wants or your Provincial Department of Health. [P5, FGD2, female]

2.3 Knowledge of UHC indicators

Knowledge of UHC indicators was fair. Operational managers recognised service coverage indicators. Some OMs cited examples of these service coverage indicators which are linked to Universal Health Care. Reproductive, maternal, newborn and child health were listed as service area categories.

Universal Health Indicators are those health service indicators. It is about the services we cover like reproductive health, maternal and child health, infectious diseases and non-communicable diseases. Our services cover child immunisation. We even need to calculate the immunisation coverage. We have to screen for infectious diseases like TB. We also screen patients for hypertension and diabetes. We have to know how many clients are on treatment for a particular disease or health condition. At my clinic, we conduct cervical cancer screening. These indicators cover all the services we provide. [P2, FGD1, male]

So, for universal health indicators, we are collecting data on child health, antenatal care, deliveries. We collect data so we can measure immunisation coverage. We even have an indicator called couple year protection rate. These indicators talk to service coverage. In terms TB, we are focusing on screening, testing and cure rate. We also look at the 90-90-90 target for HIV/AIDS. [P4, FGD1, male]

Theme 3: Operational manager’s attitudes towards universal health coverage indicators

This overarching theme comprised seven subthemes that unpacked the OMs attitudes towards UHC indicators. These subthemes include: inadequate in-service training regarding different concepts, lack of numeracy skills for analysing complex indicators, data of UHC indicators is a by-product among competing pressures of service operations, multiple spheres of government and a proliferation of indicators, population data is not always accessible for communities and districts, health promotion and disease prevention., and untenable’ targets.

3.1. Inadequate in-service training regarding different concepts

The OMs expressed inadequate in-service training regarding universal health indicators. In this regard, the training on universal health indicators was considered as an item on the agenda rather than a training process. For some OMs, the training was synonymous with ‘touch and go’.

The training of these universal health indicators was like touch and go. Even the supervision and support visits from the Monitoring and Evaluation team was conducted twice a year. Training must be ongoing because new indicators are added to the data set. [P2, FGD1, male]

The training on universal health indicators is very sketchy. Sometimes we do not understand the different concepts. Training is not always formal. It is added on the agenda of meetings. If too much time is spent on other items of the agenda, the time for training data management, analysing and interpreting is sidelined. [P4, FGD2, female]

3.2 Lack of numeracy skills for analysing complex indicators

A prevailing trend that was noticed during the FGDs was the lack of numeracy skills for analysing complex health indicators. The OMs reported that they did not understand how couple-year protection was calculated. Often, the lack of numeracy skills also affected the interpretation of data.

We do not even know how they calculate some of the indicators like couple year protection rate or even immunisation coverage. If I see the 7% or 9% target has been reached, I always wonder how was this calculated. What is the denominator or numerator? [P2, FGD2, female]

We struggle to understand how indicators are calculated. I know that there must be a formula, but I would never be able to calculate couple year protection rate. We know that there is numerators and denominators. It becomes confusing when terms like ratios, proportions or rates are thrown at you. While we understand that these universal health indicators are important for monitoring and evaluation, complex skills are required to calculate them. This can affect the interpretation of data. Some of us do not have these skills to calculate coverage indicators. [P3, FGD2, female]

3.3 Data of UHC indicators is a by-product among competing pressures of service operations

The OMs highlighted their concerns about collecting data on UHC indicators. They felt the immense pressure of delivering quality health services and collecting data daily. Some OMs reiterated that data collection was not their core function but rather a by-product.

I feel that data collection is burden sometimes. I am an operational manager and a clinician. I have shortage of clinical staff, so I spend a lot of time executing clinical work. It is hard to allocate time between clinical work and administration or data management. We always preach that the patient comes first. With all the competing requirements of my daily work tasks, data is a byproduct. [P2, FGD1, male]

In a data driven era, healthcare workers feel the pressure of collecting data. With more health indicators, healthcare workers are burdened with this recording. Our primary operations is quality clinical services and data collection is like secondary. [P6, FGD1, female]

3.4 Multiple spheres of government and a proliferation of indicators

The OMs described how the different health actors namely the National Department of Health, Provincial Department of Health and District Department of Health contributed to the proliferation of health indicators. Some OMs commented that harmonisation was difficult with different practices among the spheres of government. The requests for data and results appeared to be increasing among the different health actors which compounded the large numbers of indicators.

It appears that the universal indicator set is evolving because National Department of Health, Provincial Department of Health and the District Health Department has its own specific data needs. There are too many indicators and more emphasis producing results. We are having to account to multiple levels of government. [P4, FGD1, male]

The different levels of government sometimes appear fragmented. And sometimes the Provincial Health Department will increase the target that the National Department of Health wants. So, they keep adding new indicators and targets. Our District Health Office and Provincial Health Office will tell us that we did not reach our targets. But whereas auditors from National Department of Health will state that we reached the targets. [P3, FDG2, female]

3.5 Population data is not always accessible for communities and districts

Several OMs perceived that the population data was incorrectly estimated, and this impacted on the planning and target estimates. The target populations for vaccination are the number of births or numbers of surviving infants. The OMs felt that immunisation coverage requires accurate target population estimates.

I have an issue with target population estimation. I don't know if the target population estimation is coming from Stats SA or the sub-district. So, you expected to immunise so many children within so many days in a certain area. I don't know if they are using number of births or numbers of surviving infants in this point in time. The published target population estimates are the very same number every year. We tend to share it with the fixed clinic and the mobile clinics that are going out for immunisations. So now when you want to work towards the vaccination, the coverage estimate is distorted because of errors in the target population estimates. I remember the many times when we had so many campaigns but didn't reach the target. [P1, FGD1, male]

The population data to determine immunisation coverage is not always reliable. The population data is subject to many changes like deaths and people moving out of the area. Even with HIV testing coverage, we need correct population data. Sometimes we do not reach our targets because of the unreliable population data. [P5, FGD1, female]

Yes, target population estimates can have consequences for immunisation coverage. I think that maybe they look at how many babies are born there, as opposed to how many are living in the area, moving from other towns and cities or using your hospital statistics. We fail to achieve coverage goals due incorrect target population size. [P4, FGD2, female]

3.6 Health promotion and disease prevention

The underlying sentiments that were conveyed by OMs during the FGDs were the positive role of the UHC indicators in domains of health promotion and disease prevention. Some OMs conveyed their appreciation for screening services which help with the early detection of disease and improving positive health outcomes.

Universal Health Indicators is very important in health promotion and disease prevention. it's very important because it's all part of like your health promotion. The screening helps with early diagnosis. Early diagnosis can help with prevent complications. [P1, FGD2, female]

I appreciate that the universal health indicators have a positive effect on health promotion and disease prevention. The universal health indicators address screening and immunisation. We can prevent polio by immunisation. We can reduce measles infection and spread in the community by immunisation. Our service coverage focuses on better health outcomes for maternal and child health. [P5, FGD2, female]

3.7 ‘Untenable’ targets

It was a common experience for the OMs to report several difficulties in reaching the targets for the UHC indicators. The barriers to reaching UHC included health system and patient-related factors. Some reported that the cultural belief and values of a given population affect contraceptive use. Contraception availability and stockouts also affected the healthcare targets such as couple year protection rate. We observed in the reports from OMs that the availability of medicines in the provision of healthcare services is fundamental to reaching universal health coverage.

Reaching the targets of the universal health indicators is a challenge. We are having the challenge with couple year protection rate. It is difficult to achieve couple year protection rate because of the attitudes and beliefs of our clients towards family planning and contraception. We cannot force those clients to take contraception but at the same time we need to meet targets. [P1, FGD2, female]

We have been experiencing a stock out of injectable contraceptives and this is impacting on us reaching the target of couple year protection rate. This has been happening for such a long time. When the clients come to the clinic and while waiting in the queue, they hear that we have no 3 months injectable contraceptives, they exit the queue and do not come back. Clients have a preference for three months injectable contraceptive. Therefore, we are failing to meet the needs of clients and failing to meet the couple year protection rate. [P4, FGD2, female]

Discussion

This exploration of OMs’ knowledge and attitudes toward data and the UHC indicators provided valuable information that can be used to inform policy initiatives related to data and health information and training in the health system but also assist higher levels of government to understand the pressures OMs experience in the field. In our assessment of OMs’ knowledge and attitudes, we believe OMs appreciate the value of data within the facility and the health system however the multiple requests from multiple sources and multiple systems of data collection are challenges for them. They understood data as being a means of information sharing, measuring performance, driving action and at times limiting. Importantly, while OMs understood data to be information collected in the health system, they also saw it as vital information which could be used to assess health service delivery, as a means of self-evaluation and to take action in the healthcare setting. Operational Managers felt that by interrogating the data and assessing how the clinic is performing in reaching key targets an OM can reflect on their work performance. Tracking improvements in data can be linked to a key performance indicator against which an OM’s performance can be appraised. The understanding of the uses of data by the OMs in this study is similar to that of a study conducted amongst programme managers and managers of health management information systems in Pakistan. In addition, the Pakistan study OMs noted that data from the health information system could be used for assessing staff performance, the quality and utilisation of services, disease burden and patterns, budgeting and financing, programme monitoring, and comparison of health facilities (Qazi & Ali, 2011).

Using data for the evaluation of health services delivery has long been the practice for most health programs globally (Eismann, 2009; Gourlay et al., 2015). However, the practice of utilising data to drive decision making has generally been weak (Chanyalew et al., 2021; Odei-Lartey et al., 2020; Tilahun et al., 2021). In a study conducted in North-West Ethiopia, researchers found that the proportion of routinely collected data used for decision-making was low, estimated at 46%. Furthermore, only 43.3% of OMs used data for target setting for annual planning (Chanyalew et al., 2021). In a study across twelve health districts involving hospitals, health centres and community health facilities in Ghana, data usage for decision-making varied between the level of facilities and the district level. The use of data for policy formation or strategy revision was low at the level of the district health management team (33.3%), health centres (43.5%) and community health facilities (23.8%) but high in district hospitals (88.9%) (Odei-Lartey et al., 2020). Evidence-based decision making is extremely important in patient care and improving health outcomes and does benefit planning. If OMs do not understand this benefit then they will not use data to plan, which will compromise health service delivery in the long run. Importantly in our study, OM’s understood the value of data and its importance in the health system. Evidence from the review of the Population Health and Implementation Training (PHIT) Partnerships in five sub-Saharan African countries (Ghana, Mozambique, Rwanda, Tanzania and Zambia) supported by the African Health Initiative suggests that data was used for decision making at several levels in the health systems of participating countries. The data-driven decisions included among others clinical management of patients, priority setting at facility and higher levels in the health system and resource allocation (Mutale et al., 2013).

Staff performance and human resource allocations are important in health service delivery. In this study, OMs felt that data collection and the health information system could be used as a means of evaluating their performances in health service delivery. In the study by Odei-Lartey across twelve health districts, hospitals, health centres and community health facilities in Ghana decisions for performance recognition and reviewing of staff responsibilities were based on data from the District Health Information Management System in 47.7% of participating facilities (Odei-Lartey et al., 2020).

Failure to use data for decision-making may be related to skills and knowledge gaps (Alemu et al., 2021; Tilahun et al., 2021). In our study, while OMs appeared to understand UHC as health for the most vulnerable people in the population, they did not have a clear understanding or knowledge of the UHC indicators. The poor understanding of UHC indicators appeared to be further compounded by the expansion in the indicators on which data was required as one moved from national to provincial levels. The requests for indicator data from multiple sources appeared to complicate their work. Multiple data collection registers can lead to a duplication of work and increases the burden on OMs (Gourlay et al., 2015). In South Africa besides the UHC indicators, data are collected on several other programmatic indicators at a facility level which may result in OMs feeling quite pressured. In fact, in this study, OMs did express their concerns about the pressure they felt having to collect data daily.

Operational managers expressed their unhappiness at the limited training they had received on the UHC indicators. A lack of adequate training on health indicators and health information in the health system is not uncommon and has been described in South Africa and in other settings (Bernardi, 2017; Mpofu et al., 2014; Nicol et al., 2017; Odei-Lartey et al., 2020; Odhiambo-Otieno, 2005). An evaluation of the district health management information system in three sites in Kenya found that 73% of staff were not trained (Flora et al., 2017). Limited training is known to severely impact health information systems and service delivery in several ways. If OMs do not understand why they are collecting data and what should be collected at the frontline, then they may not capture data correctly. This in turn leads to poor-quality data which cannot be used for making management decisions or planning (Field et al., 2018; Qazi & Ali, 2009). In this study, the lack of training appeared to be compounded by gaps in numeracy skills. A lack of training and poor numeracy skills further translates into an inability to analyse and interpret data for planning at the local level. Nicol et al. their assessment of information, and the barriers to using routine data for monitoring the prevention of mother-to-child transmission of HIV programs in two districts in South Africa reported on the impact poor numeracy skills had on data collection, interpretation and use. Owing to the poor numeracy skills, staff were unable to interpret data which limited their ability to use the data in a meaningful way (Nicol et al., 2017).

Operational managers also felt that when data was absent it could impact their daily activities especially where data was required to justify requests for patient care or infrastructure improvements. Higher levels of government require evidence to inform decisions on resource allocation at the lower levels for efficient health service delivery. In the absence of this evidence, it is difficult to justify resource allocations. This stresses the importance of accurate and complete data collection at a facility level.

The positives emanating from the discussions were that OMs were able to link the UHC indicators to the national health strategic plan and to ‘health for all’ and they saw a role for the use of the UHC indicators in health promotion. The ultimate intention of UHC is to ensure that people can access the health they need easily. Health promotion is an essential component of UHC. Using UHC indicators to inform health promotion will ensure that health systems develop responsive health policies and programmes that ensure health for all people in a country (Shilton & Barry, 2022).

The scope of the study was to explore OMs knowledge and attitudes towards UHC indicators and not UHC within the sub-district. However, the collection of routine facility data has been valuable in monitoring the progress towards UHC (Day et al., 2021). In this regard, the discussion of UHC indicators and the available data on them is essential in providing an overall picture of the universal coverage of health services in the sub-district for OMs and senior levels of management.

The dimensions that should be considered when progressing towards UHC include population coverage, package of services and financial risk (World Health Organisation, 2019). South Africa has chosen to use National Health Insurance (NHI) and the District Health System as vehicles to achieve UHC (Fusheini & Eyles, 2016). The proposed features of the South African NHI (health financing system) include progressive universalism, mandatory prepayment of healthcare, comprehensive services, financial risk protection, single fund, strategic purchaser, single payer and publicly administered (South African National Department of Health, 2017). Currently, NHI is being piloted in 11 districts within the 9 provinces in South Africa. The rationale for using districts and decentralised management is to achieve equity in the geographic sub-divisions of the country (Fusheini & Eyles, 2016).

With regards to population coverage, all South Africans will be covered by NHI regardless of their socio-economic status. Populations with the greatest need, vulnerable groups and the unemployed will be prioritised. The PHC level has been declared as the point of entry for accessing healthcare services covered by NHI. Service coverage will take into consideration the catchment population, geographic, demographic and epidemiologic profiles (South African National Department of Health, 2017). In addition, the population will access services at delivery points closest to where they reside thus improving geographic access to healthcare services.

About the study findings, the OMs understood that service delivery targets were related to catchment populations to achieve healthcare coverage within the district. PHC service benefits that will be covered under the proposed NHI include health promotion, health prevention, maternal, women and child health, family planning and reproductive healthcare, HIV, TB, chronic noncommunicable diseases, violence and injuries (South African National Department of Health, 2017). Currently, the package of services at PHC clinics in the Ugu district covers the scope of services under the proposed NHI. The discussions with the OMs revealed that PHC clinics provided the catchment populations with services in maternal and child health, family planning, reproductive health, infectious diseases and non-communicable diseases. Furthermore, the OMs communicated that health promotion and prevention were part of the PHC services rendered in the district.

The OMs were able to communicate that data was being collected on reproductive, maternal, newborn and child health, infectious diseases and non-communicable diseases as part of monitoring and evaluation of service coverage. The clinic data cited as examples by the OMs (couple-year protection rate, immunisation coverage, clients on treatment for HIV and TB) reflected on UHC indicators and service coverage. The OMs perceptions of geographic access to services did not feature in the discussions due to the scope of the study. However, it is important to note that the sub-district had mobile PHC clinics to improve geographic access to services. In terms of financial access to healthcare services, user fees are not applied at the PHC level in South Africa (South African National Department of Health, 2017).

The knowledge and attitudes towards data and UHC indicators may have an important bearing on universal coverage of healthcare services. The collection and analysis of routine facility data from districts on reproductive, maternal, newborn and child health, infectious diseases. Non-communicable diseases and service capacity has contributed to the calculation of the national UHC Service Coverage Index (coverage of essential health services) in South Africa (Day et al., 2021).

Study limitations

The study was subjected to limitations. Our participants of interest were OMs from PHC Clinics. Due to the sample size of only 12 OMs, only two focus group discussions could be conducted. Our sample included participants from one sub-district and there is a possibility of a median viewpoint. However, all 12 available OMs participated in the study. Since our focus was on OMs, we were not able to assess issues related to senior levels of staff in the management structure. This would warrant further research.

Conclusion and recommendations

South Africa is collecting data on the UHC indicators and they will be part of the indicator set required for monitoring the National Health Insurance in the country. This paper presents important findings on OM’s knowledge and attitudes toward data and UHC indicators. Importantly OMs made the link between data, measuring performance and action but limited training and skills gaps may impede their ability to use data for planning and decision making.

The findings of this study are important for policy changes and implementation within the Department of Health concerning health information systems. Operational Managers report collecting indicators through multiple means and for multiple sources. A health information system incorporating a unique patient identifier through which patients can be tracked will provide OMs with the opportunity to access data from different sources and integrate it to improve their services. The system should allow for data to be collected on a uniform set of indicators which reflect UHC. There is a need to refocus data collection to that which is relevant and feasible for health facilities, whilst at the same time achieving reporting requirements. Further OMs should be empowered and included in the discussions around indicator setting. Through this process, they will have a say in what data are collected and this will ensure that they use the data as well.

These findings should inform the training the Department of Health intends to implement for health information systems in conjunction with the implementation of National Health Insurance in the country. Operational Managers should receive continuous capacity building in data management. This would include refresher training on data collection, analysis, interpretation, synthesis and use. All new staff should also have orientation in data management at the start of employment and thereafter be included in continuous capacity building.

Acknowledgements

The authors would like to acknowledge the operational managers who participated in this study and the KwaZulu-Natal Department of Health.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability

The qualitative data may be made available on request to the authors.

Additional information

Funding

This study was funded by the Bill and Melinda Gates Foundation (Grant number: INV-025027)