Results
All six districts had a drastic shortage of doctors, either seen as absence of doctors visiting clinics or too few, if any, doctors to cover the opening times of community health centres. Some districts had no doctors for primary health care. Overall the number of doctors was only 7% of the required number.
While the total number of professional nurses was 94% of the target, two districts had overall excesses and four had overall shortages. The numbers of enrolled nurses and enrolled nurse assistants were 60% and 83%, respectively, of what they should be. Counsellors were very unevenly spread, with the overall excess concealing their absence in many facilities. Administrative staff was 30% of the target and absent in many facilities. General workers seemed to be overall in greater number than required in many facilities.
Table 1 shows examples of imbalances between some facilities within the same subdistrict. Clinics C2, C8, C11 and C12 have an excess of professional nurses (positive number in the difference column) while most of the others have shortages. Some other facilities (e.g. C9 and C10) had shortages in all categories of staff.
Besides the very poor deployment between facilities, in clinics that have an excess of professional nurses there is a shortage of other health workers, such as enrolled nurses and counsellors, cancelling out the benefits of the excess in many facilities. The table shows the importance of assessing staff requirements not just in terms of professional nurses, but in terms of the whole team. Using the ratio of patients to professional nurses as an indicator of workload can be very misleading, as it does not reflect the presence or absence of supporting staff who affect the workload of professional nurses.
Turning to the staff implications of extended hours opening in rural areas, the model showed that the minimum number of professional nurses (using professional nurses as an indicator) is 1.3 full-time employees for the core 40 hours per week and 4.2 full-time employees for the additional 128 hours, over three-times that of the core hours. This significant increase in staff requirements is not matched by a parallel increase in workload. In these rural districts, in facilities opened for 168 hours, the workload during the non-core hours was low: there were typically eight times more patients per hour during the core hours than during the non-core hours. As a consequence, the number of staff required defined by workload would have been significantly lower than that prescribed by the minimum staffing level.
Discussion
The results from the use of the tool and assumptions showed misalignments between expected and actual staff, with significant variations between facilities within the same subdistrict, between subdistricts and between districts. While there is an absolute shortage of staff (especially doctors), this is substantially exacerbated by inequitable deployment. This leads to problems in both quality (when lower categories of staff are expected to perform functions of higher categories of staff) and efficiency (when higher categories of staff are expected to perform functions of lower categories of staff). This study also found that the present proposal of increasing access to health care in rural areas by extending opening hours would entail a significant increase in staff requirement.
There are some study limitations. First, the allocation of tasks per category of staff is probably conservative. We allocate all consultations to professional nurses when, in practice, some consultations are delivered by enrolled nurses, in particular in rural areas, even if not in accordance with the current scope of practice. This approach raises the issue of whether assumptions should be closer to a target picture in line with policy or closer to what is happening on the ground, resulting in different norms for urban and rural areas, thereby entrenching inequities further. Secondly, the assumptions do not sufficiently recognize the lack of referral support in rural areas and the concomitant need for comparatively better-qualified, mainly multipurpose cadres. Finally, the establishment of the norms have been built upon several studies using different methodologies. However, the size of the problems quantified show that even a 10% margin of error would not greatly affect the results or the type of issues they highlight.
The utilization-based approach to assessing staff needs has clear advantages for management and short-term planning purposes. However, sole reliance on a workload-based model may lead to an entrenchment of inequities: well-equipped and well-staffed facilities will attract many patients, and hence will receive additional staff and resources, while poorly staffed, poorly equipped and poorly managed facilities will see fewer patients as they bypass these facilities to go where they will receive better service. Thus the utilization-based ap