Today This Day Live News reported on challenges of data coordination in the health system in Nigeria. Ndubuisi Francis reported that, “The multiplicity of conflicting data on health by various agencies is a major impediment to an effective and efficient health care delivery system in the country. Director, Disease Control and Immunisation, National Primary Health Care Development Agency (NPHCDA), Dr. Emmanuel Abanida, said resolving the conflict in the national health management information system (HMIS) is a step towards getting the system right.” The problem is not unique to Nigeria.
To learn more about how this problem affects malaria data we discussed with two staff of the Jhpiego office in Abuja, Gbenga Ishola and Bright Orji, who have been involved for many years in malaria monitoring and evaluation activities at national, state and local levels. The results of this discussion follow:
1. Incorporating Community Health Worker Data into HMIS
As the country moved toward community case management to reach coverage targets, the HMIS has worked with NMCP to establish a community data collection template. However, the level of utilization of the community level register is poor. Also the integration of this into facility output remains a key challenge. Furthermore, there has not been a feedback mechanism to the community of data collected from them. So, it is not only collection of data but use of data for decision-making whether at the Local Government (LG) level, facility or community remains part of the challenge.
2. Movement of Data from Facility to District to State to National
There is an existing data flow pattern. Data from facility HMIS registers are expected to be collated on monthly basis into a monthly summary form at facility level. The summary forms are sent to the Local Government Monitoring and Evaluation Unit which then sends this to the state level. Data flow is also not as smooth as intended. Most often facilities do not collate and send to the LG, and thus state data reporting that is suppose to be quarterly is distorted. The obvious complaint is always logistics.
The National Malaria Control Program (NMCP) monitors state data reporting by aggregating total number of facilities reporting each month and determining reporting rate for the states. Each state reports total number of health facilities in the state, and how many of these facilities submitted a monthly report during review period. For example, if a state has 1,000 health facilities but only 500 submitted monthly reports, the state would have scored 50% in data reporting. This is to encourage state to improve on data collection and reporting and is part of the report and discussions at the annual malaria program’s manager meetings.
3. Parallel Reporting Systems
There are two examples of parallel systems through which malaria data move. The National Primary Health Care Development Agency, a section of the Ministry of Health responsible for assisting LGs strengthen their primary care systems collects health data from those facilities in addition to the facilities reporting through the HMIS. Recently the Director of the HMIS indicated that his unit is trying to harmonize the existing system. The completeness of each system varies depending on how LGs and states decide to report.
Since the HMIS collects only a limited selection of malaria indicators, the NMCP makes an effort to collect more detailed statistics of all services. Some of the indicators monitored by NMCP are not in HMIS. The consequence is that health workers often abandon the NMCP register because it contains more entries than HMIS.
The HMIS collects 1) Long-lasting insecticide-treated nets (LLINs) provided and 2) doses of Intermittent Preventive Treatment (IPTp) given (1st and 2nd). NMCP additionally tracks number of fever cases, Rapid Diagnostic Tests (RDTs) conducted (and whether RDT results are negative, positive or invalid), and antimalarial medicines administered (whether quinine or ACT).
While the National HMIS unit is working to harmonize the data collection formats for all diseases including malaria cases, bringing the NMCP to participate in meetings and discussions has been a major problem. This makes it very difficult for the HMIS to be able to quote data relating to specific diseases when necessary.
4. Special Data Requirements
As in other countries, the Global Fund expects countries to report of a regular, quarterly basis on achievements based on their currently operating grant. The data required for these reports is essential for maintaining the flow of funding, but this information is not necessarily within the basic HMIS set of indicators. Global Fund is interested in consumption data for forecasting for national needs, but this has been very difficult to collect due to some of the above challenges.
The number and detail of indicators reported to the Global Fund for Nigeria’s Round 8 malaria grant reflect the complexity of reporting required that would not be included in HMIS. Below are a few examples of the malaria treatment related indicators only. HMIS would not be collecting program process data like training and does not really reach the private sector.
- Number of children under five with uncomplicated malaria receiving ACT treatment according to National guidelines (all oints of care)
- Number of children under five with uncomplicated malaria receiving ACT treatment according to National guidelines through the public sector
- Number of people (over 5) with uncomplicated malaria receiving ACT treatment according to National guidelines through the public sector
- Number of person (over 5) with uncomplicated malaria receiving ACT treatment according to National guidelines through the private sector
- Percentage of participating health facilities in the public sector reporting no stock out of ACTs for 1 week or more within the last 3 months
- Number of health care providers trained in malaria case management and prevention
- Number of CSO members trained on case mangement and prevention of malaria
- Number of Health care providers trained on pharmacovigilance
In order eventually to eliminate malaria detailed monitoring and surveillance data are needed in real time. Coordinating these data needs with a routine national HMIS will always be challenging because disease elimination is anything but a routine process.