Surveillance systems in Africa are generally weak and characterized by vertical programs, delayed reporting, and low level of involvement of laboratories to confirm cases or outbreaks (WHO, 1999). In Zimbabwe, it was found that only 31% of the reports from the city primary care clinics were timely. The same findings were true for the rest of the WHO member states of Africa (WHO, 1999). A study in Mauritius in 1996 found incomplete filling of medical records generalized shortage of staff, low awareness among staff of the importance of the system, lack of feedback and lack of utilization of information at the point of collection to be factors contributing towards poor compliance with the surveillance schedule, (WHO/AFRO, 1997).

Due to the problems above the WHO regional office for Africa has proposed to strengthen the national disease surveillance systems of the member states using an integrated approach (WHO, 1999). Implementation of the Integrated Disease Surveillance and Response (IDSR) started in 1998 in Tanzania with the rest of the countries in the region starting by 2000. At the time of this study, the implementation in the Great Lakes Region of Africa stood at 52%.

Although there were significant improvements in the reporting of HMIS data nationally, with the national average scores of the weekly surveillance being 82% for timeliness and 86% for completeness, Arua district was performing far below the national target of 80% (MOH, 2002). Between March 2001 and March 2002, only 39% of the Weekly Surveillance Reports from of the health units in Arua district had reached the MOH. Although the reporting structures for surveillance existed, the District Health Management Team (DHMT) had expressed dissatisfaction with the reporting of HMIS data during their supervision meetings. Money had been spent on the training of health unit staff and district focal persons and data was being collected in health units. The performance scores above seemed to suggest some operational difficulties. There was the risk of an epidemic occurring unnoticed and taking the district health authorities too long to respond. It was thought that information obtained with this study could be useful for better management of the district disease surveillance system, especially because there were already other initiatives and programs aimed at eradication of diseases such as polio, guinea worms, onchocerciasis, etc.

The study aimed at assessing the reporting component of the disease surveillance system in the district (HMIS and weekly surveillance) in the financial 2001/2002. Assessing the timeliness, completeness, accuracy and the reasons for the poor performance in these parameters could help the district tackle their root causes and help improve their preparedness. The objectives were

1. To determine the completeness at health unit and district levels of the monthly health unit reports and Weekly Surveillance reports against the national standards for HMIS completeness
2. To determine the timeliness of weekly and monthly reporting to HSD and District levels against the national standards for HMIS timeliness
3. To determine the accuracy of the weekly surveillance and monthly reports and
4. To determine the factors influencing the reporting of data at the levels of the district health system

At health unit, Health Sub-District and District levels, we reviewed the records of two HMIS reports (monthly report and weekly surveillance report) for the year 2001/2002 and interviewed the health workers responsible for the HMIS to identify the factors influencing data reporting. Assuming 60% of the units to be reporting to the District level and accepting a margin of error of 5% at the 95% level of confidence, we determined that the acceptable minimum number of records representing the entire study population of records would be 384 records of each report by using the formula [sample size = P (100-p)/d2]. Since we had seven Health Sub-Districts, we got a minimum of 55 monthly reports and 55 weekly surveillance reports per Health Sub-District for the whole year, or a minimum of 5 records per HSD per month. We analysed 6 records of each form per HSD per month.

The study variables were timeliness, completeness, accuracy and the factors that affect data reporting as detailed below. We assessed performance in each of these variables against the standards set by the Ministry of Health. Regarding timeliness, the ministry required that 80% of the expected weekly surveillance reports should have reached the district by Tuesday of every subsequent week and that 80% of the expected monthly reports should have reached the district level by the 14th day of every subsequent month. For completeness, the ministry expected two approaches. First, there was completeness in the sense of having enough reports and, second, there was completeness in the sense of having the report forms fully filled in. The first parameter measured completeness at the district level (80% of expected reports received from health units by dates above) while the second parameter measured completeness at health unit level (80% of the forms completely filled in at the health units). We assessed accuracy by comparing the individual reports with the data sources (case registers) at the health units for concordance. For the accuracy of the monthly reports, we used records of two common and killer diseases (malaria and diarrhoea) and the records of two common notifiable diseases (dysentery and measles) to verify the accuracy of Weekly Surveillance Reports. We randomly selected two months (October 2001 and April 2002) and three HUs per HSD for this exercise.

We obtained the factors influencing data reporting by interviewing key people involved in the management of data at all levels in the district using an interview guide and checklists. We interviewed all the members of the District Health Team and the individual Health Sub-District Management Teams. We modified Table 2 of the WHO checklist for surveillance and used the modified version to guide the interview. While at the HSD, we tracked the late reports earlier identified at the district office in order to verify the points of delay in the health system. We tracked one late report per randomly sampled health unit and interviewed the person in charge of the unit in charge, the senior nursing officer and the records assistant for an explanation for the delay. We studied late reports from 21 lower level health units (3 per HSD) and all hospitals and HC IV.

Table 1: Percentage completeness of monthly reports per HSD (July 2001 to May 2002)

Table 2: Completeness of weekly surveillance reports per HSD

Over the study period, the overall mean monthly completeness of the Weekly Surveillance Forms was 27% ranging from 1% in the month of February to 81% in May 2002. There was a general tendency to improve in reporting to wards the end of the financial year as shown by the moving average in Graph 2 below. In general, the compliance of the individual HSD in submitting the Weekly Surveillance Forms was not only very poor but also fluctuant, with a mean of 27% (range: 21% - 36%) and a median of 24% (range: 18% - 40%)

Completeness at health unit level

We assessed the completeness of the monthly reports using the second aspect of completeness, the filling in of all the provided spaces at health unit level.

Table 3: Completeness of weekly surveillance reports per HSD

On average only 62% of the reports sent by health facilities were complete, varying between 38% and 76% throughout the year.Graph 2 illustrates monthly variation.

The level of completeness varied greatly among the HSDs. A wide difference existed between them, ranging from 38% to 77% as shown in Graph 3 below.

There were more serious variations in the completeness of the weekly report over time as shown in Graph 4 below. There was even a total breakdown of the weekly surveillance system in the month of February 2002.

We assessed the timeliness of reporting to the district by determining the percentage of of the monthly reports received at the District office by the required date as shown in Table 4 below.

Table 4: Timeliness monthly reports at the district office

On average, over the study period, 55% (range: 38 - 76%) of the monthly reports arrived at the district in time as expected. There was no particular monthly trend in timeliness. However, the timeliness per subdistrict varied greatly with average timeliness ranging from 24 to 88%. Arua Municipality HSD had the worst timeliness average despite being at the District headquarters.

We also assessed the timeliness of weekly surveillance reporting by comparing the date of receipt of the report at the district to the date on which the reports
were expected. This is the most important report as far as the surveillance for new disease outbreaks is concerned and it must be reported accurately and quickly. The data for the weekly report are colleted every Monday and the report must be at the district on Tuesday, ready for transmission to the Ministry every Wednesday. The table below shows the percentage of the reports which were received on time per HSD during the period under study.

Table 5: Timeliness of weekly surveillance reports per HSD at the district

The accuracy of a report tells the manager whether selected HUs (3 per HSD) by cross-checking the the report should be acted upon or not. It makes the reported records against the registered records of two manager decide whether to deploy resources to indicator diagnoses responsible for the highest contain a situation or not and whether any action morbidity rates. There were general inaccuracies in should be taken or not. The accuracy of reporting the reporting. The findings per HSD were aggregated was assessed in the OPD records of 21 randomly and are presented in Table 6 below.

Table 6: Accuracy of the monthly report for two diagnoses

As shown above, data reporting in the district was below the national target of 80% for both timeliness and completeness and in some months there was a complete break down of the system. We sought for the factors that constrained the process of data reporting by interviewing the health workers. These were mainly the shortage of personnel leading to an overall heavy workload, poor managerial skills, lack of accommodation, lack of transport, lack of an innovative spirit, lack of computer training and lack of funds.

Workload: When interviewed about the ability to fill the forms, the staff reported finding no difficulties with filling any of the two forms under study. They affirmed that the forms were easy to understand and simple enough to fill. However, the health units are under-staffed and have difficulties in coping with the workload of the health facilities. Due to the shortage of staff, a single health worker often has to register patients, take history and examine them, dispense drugs, give injections and dress wounds etc, all by himself or herself. With no Records Assistants in some of the HUs and no specific incentives to do the duty, surveillance is often looked at as an extra burden.

Besides the time spent on compiling the reports, most HSDs spend one day collecting the weekly surveillance reports from the health units and one other day to take them to the office of the DDHS; hence two person-days per HSD per week. Given that the position of 'HMIS focal person' is not established, it means that this is a duty combined with other official duties of the person. This and other issues individually or in combination take away critical staff from the clinical care of patients. This worsens the already constrained staffing situation in the health units. The clinical staff not involved in surveillance activities expressed their frustration at the amount of work they have to do alone, since selection of focal persons has been classified as 'technical work' requiring somebody with a technical background in health issues.

Managerial factors: In most of the units visited, the forms were only filled by either the person in charge of the health unit or the Records Assistant. The duty was never delegated to any other person and whenever they were out of station, the reports were never compiled. This could have a bearing to the perception of heavy work involved or to the expectation of allowances for the task. This ultimately affected the timeliness and completeness of reporting. In some cases, there was clear evidence of procrastination. We found that in 75% of the units studied, records were not analysed on daily basis as recommended. Tallying was only done at the end of a month at the time of preparation of the report. This was done hurriedly and probably contributed to the tallying errors because of the heavy workload to be completed in a short time.

Staff accommodation: Two of the health units (10%) studied had no accommodation for staff at all. The staff were staying about 10 Km away from the health facilities and a substantial amount of their time was spent on travelling to the health units and back home. This affected the output at the workplace in clinical work and in data management.

Means of transport: Of the health units studied, only 35% had means of transport in form of a car or motorcycle. However, all the HSDs have either a car or a motorcycle that could be used for surveillance. The main problem with transport was therefore the fear of excessive running costs for the HSD if the vehicles were regularly used for reporting and data collection. In a few units, the means of transport were used for multiple purposes, competing more favourably against disease surveillance. In any case, most of the vehicles were old and had frequent breakdowns and frequently led to simultaneous breakdown of the reporting system e.g. Koboko, Terego and L/ Madi HSDs. The HSDs also cited irregular and unreliable supply of fuel by the district headquarters as a common constraint.

Communication: Transmission of the surveillance reports from one level to another was by hand delivery. No health HSD used any other means even when the option for doing so existed to varying extents e.g. telephone, radio, mail. All the HSDs had networks for mobile cellular telephones in their catchment area and 5/7 (71.4% ) had radio call facilities. None of the HSDs had ever used either the telephone or the radio call to communicate surveillance data to the district. Use of these facilities would be cheaper, faster and markedly improve the performance of data reporting in the district. This option could be used with the aim of improving timeliness but not as a replacement alternative to the use of reporting forms, which could still be sent for record purposes. Apart from the communication media, some HSDs lacked a clear policy on the flow of reports covering compilation of the data for the reports and collection of the reports after they were filled. In some HSDs, some units would take their reports directly to the district office, bypassing the HSD and there was no agreed mechanism for the HSD to get its copy.

Financial resources: Given the procedures for data reporting in the district there was little need for the individual health units to budget for reporting, as their role is limited to data compilation within the facility. The stationery was provided by the district and was never out of stock in all the units for all the study period. The data were collected by the HSD. Therefore, there should have been few financial constraints for that level. In fact, the staff at the lower level health units raised no complaint about finances being a barrier to data reporting. The budget for the activity should have been at the HSD level. However, none of the HSDs had a budget specific for disease surveillance activities. However, they had budgets for disaster preparedness and response which where in practice reallocated for surveillance. For the most part, these were small amounts of money, which could not sustain the surveillance activities throughout the financial year. The highest amount was Uganda Shillings 890,000/= (about US $ 520) budgeted by Lower Madi HSD. The budgets of the other HSDs were much lower. In Lower Madi itself, the shortest distance possible to be covered by a focal person collecting reports and delivering them to the district office was 370km.

Staff training: About 80% of the people involved responsible for filling the forms in the health facilities studied had received some training in the exercise. Exceptions were in Arua Regional Referral Hospital and Oriajini Hospital. However, some of the people who had been trained were not involved in compiling the surveillance forms. The consequences of not training the Records Assistant for a big hospital such as Arua could probably explain the high level of inaccuracy (+15%) seen in the hospital data - the highest among the sampled units. Some irregularities were observed in the selection of the health units and the participants for the training. Two of the sampled units (10%) were not involved in the training at all, e.g. Ludara and Oriajini. This could not be explained by a limit in the number of participants the district was capable of training because some neighbouring health units (Koboko HC IV and Wandi HC III) had sent three participants each. In fact, staff reported lack of skills in analysis of data and drawing of graphs as their major weakness. This could have suggested a weakness in the curriculum of the training but it was not possible to get a copy of it. Although all the HSD offices in the district had computers, none was used for data management, not even at the district office. Lack of computer skills was reported to be the main reason for not using them.

Staff attitudes: Many staff saw disease surveillance as a vertical program and extra work that should be done for an allowance. This was mainly observed in Maracha and Vurra HSDs but could have been prevalent in others areas as well. In fact, all the HSDs reported cases where at some health units, staff were reluctant to fill the forms and the focal persons had to fill them out themselves when they arrived for data collection. This delayed the focal persons and prolonged the data collection process. It had a significant effect on the timeliness of reporting to the district. For some, reports of 'zero' cases were given to 'make life easy'. Such an attitude would have a profound effect on accuracy of reports and detection of epidemics. Finally, due to the geographical distribution of the units, some units reported directly to the district office, by-passing the HSD. This meant that, unlike the HMIS focal person for the entire HSD who was paid an allowance, the health unit staff did not receive any payment. Such irregularities in payments were found to be silently eroding the morale of the staff. On their part, Records Assistants complained of unclear job descriptions and lack of a clear career path.

Demarcation of the HSDs: Finally, owing to the large size of some of the HSDs and the difficulties faced in managing them from the HC IV, some HSDs were re-demarcated. The HSDs of Vurra, Madi, and Terego did not correspond to their political constituencies. Partly due to this, we found that some of the HSDs did not to have a good mastery of the HUs under their care. For instance, Siripi HC originally under Terego but now under Lower Madi had accumulated weekly surveillance reports for three months. Neither HSD had collected them. This raised questions of who supervised the unit in other matters.

Table 7: Estimated HSD Efficiency in monthly reporting (HMIS 105)

Using the same reasoning as above, we worked out the ranks for weekly reporting.

Table 8: HSD Efficiency in Weekly reporting

'Completeness' in both cases refers to completeness at the district office.

This study set out to establish the performance of the integrated disease surveillance system in Arua District, with respect to the reporting of the two commonest surveillance forms: the monthly (HMIS 105) and weekly surveillance forms. We assessed some parameters of performance such as, completeness, timeliness and accuracy as well as the factors affecting them. We observed that whereas the completeness of the monthly form at the district was acceptable, that of the weekly surveillance form was unacceptably low and with occasional breakdowns. The majority of the reports were received at the district in time but were inaccurate, with a tendency for over-reporting figures. However, there were wide differences between the HSDs of the district with respect to completeness, timeliness and accuracy. Multiple factors affected the performance of data reporting in the district, the majority being financial and administrative. However, even with the resources available then, there was still room for improvement. As a general conclusion, the integrated disease surveillance system of the district was ineffective and placed the population in danger of an undetected disease outbreak if left unaddressed.

The key issues to be addressed were human resources, planning, and management. There was a need for recruitment or designation and training of staff as Records Assistants in all the health units. The available means of communication needed to be fully exploited and utilised for timely transmission of reports. There was a need for rendering somebody responsible for data aggregation, analysis and feedback at district level. In order to improve on data storage and retrieval at all the levels of the district, there was a need for training HSD HMIS focal staff in computer literacy and skills to be able to enter, retrieve and analyse HMIS data.