Assessment of human disease surveillance systems in the East-Central Africa infectious disease hotspot: A case study of Uganda

Assessment of human disease surveillance systems in the East-Central Africa infectious disease hotspot: a case study of Uganda

Michael Muleme¹, Joyce Nguna¹, Richard Mafigiri¹, Joyce Nguna¹, Doreen Birungi¹, John Baligwamunsi Kaneene^2,&

¹College of Veterinary Medicine, Animal Resources and Biosecurity, Makerere University, Kampala, Uganda, ²Center for Comparative Epidemiology, Michigan State University, 736 Wilson Road, Room A-109, East Lansing, Michigan, United States of America

^&Corresponding author
John Baligwamunsi Kaneene, Center for Comparative Epidemiology, Michigan State University, 736 Wilson Road, Room A-109, East Lansing, Michigan, United States of America

Introduction    

The global community is continuously advocating for effective implementation of international health regulations (IHR) due to increasing travel and trade among countries [1]. Enforcement of IHR would result in shared responsibility in controlling and preventing public health emergencies of international concern (PHEIC) [1]. This global call can only be achieved if all nations have a functional disease surveillance system: both the capacity and capabilities to detect, assess, report and respond to PHEIC. Uganda is located in one of the world´s infectious disease hotspots, known to have continuous outbreaks of deadly infections like the viral hemorrhagic fevers (VHF) Marburg and Ebola [2]. The epidemiology of many of these transboundary, emerging, and re-emerging diseases have not been fully understood because of limited capabilities in surveillance within the region [2]. This necessitates the establishment of effective surveillance and early warning systems to reduce the impact of such disease outbreaks on human health, national income, and international trade. The integrated disease surveillance and response (IDSR) model was adopted for use in Uganda in 2000 with an aim of having a consolidated system for collection, analysis, interpretation, and dissemination of public health data [3]. The IDSR system purposed to integrate the surveillance functions of most of the categorical disease control programs. The system was also implemented to effectively link standard surveillance with laboratory results, to streamline response guidelines so as to increase timeliness and completeness of surveillance data, and to increase national-level review and use of surveillance data for response [3]. To support IDSR, a health management information system (HMIS) was developed by the World Health Organization (WHO), to include a data-sharing platform among health units [4]. All data entered at a given location should be accessible by different health units within the country [4]. A review of the IDSR system between 2001 and 2007 reported that disease surveillance indicators were performed in 80% of the health units, including the submission of reports (weekly, monthly and quarterly), timely reporting of outbreaks, trend analysis for priority diseases at district level, and statistics of priority disease outbreaks. However, this review left out crucial information about human resource capacity, laboratory surveillance, and communication channels, as well as data collection, storage, and transmission capacities of health service provision in the different regions of Uganda. This study was conducted to assess the current human disease surveillance systems in Uganda among the different levels of health service provision in the different regions of Uganda, May to October 2013. The specific objectives of this study were to describe the types of human disease surveillance systems currently used in Uganda; to document diseases and the populations targeted by surveillance systems; and to assess the availability of disease surveillance support infrastructure like laboratories, communication, and data management. The assessment is expected to extend to other countries in the region and is aimed at identifying gaps that can be used to establish a robust regional surveillance system.

Methods     

Study area

Uganda was chosen for this pilot study because of its location in the East-Central Africa infectious disease hotspot [2]. The country has a decentralized system of governance with the majority of services managed at district level. In human health service delivery, the District Health Office (DHO) supervises a hierarchy of health facilities, including Health Center (HC) I (village level), II (sub-parish level), III (parish level), IV (county level) units and the district level hospital. HC I units have no physical structures and are run by village health teams [3, 5]. HC IIs provide outpatient, immunization, antenatal and outreach services; HC III units provide environmental health and inpatient services in addition to those services at HC II; while HC IV provide additional services like surgery, supervision of lower health units and health service planning on top of those offered by HC III [6]. Conversely, the Ministry of Health (MoH) oversees all regional referral hospitals as well as national central units like the Epidemiology and Surveillance Division (ESD) and the Central Public Health Laboratory (CPHL).

Sampling and data collection

The study was conducted in twelve districts, three from each the four regions of Uganda. A multi-stage sampling technique was used at it involved purposive and random selection of districts from regions and health centers from districts. From each region (Northern, Central, Western and Eastern), districts with regional referral hospitals (Arua, Mbarara, Mbale and Kampala) were purposively selected. The other two districts from each region were randomly selected. One additional district was randomly selected from the Western region because it covers a larger area compared to other regions. The selected districts included Kampala, Wakiso and Nakasongola in Central region; Lyantonde, Mbarara, Bushenyi and Hoima in Western region; Jinja, Iganga and Mbale in Eastern region; and Arua, Gulu and Lira in Northern region. We purposively included a regional referral hospital (if present) and one district hospital in each of the district selected. At least two HC IV units, two HC III units, and two HC II units were randomly selected from each district. Representatives from the selected health care units were contacted by study researchers, who explained the goals of the study and obtained informed consent from study participants. A structured questionnaire and face-to-face interviews were used, as well as site visits to obtain information on the types of disease surveillance systems used, the diseases and population targeted by surveillance systems, and the status of support infrastructure like laboratories, communication, and data management at each of the health units visited. Ethical approval was obtained from the Research and Ethics Committee of the Makerere University College of Health Sciences prior to the data collection. Data was entered into Microsoft Excel 2010 and categorized according to level of health units and region. Descriptive statistics were generated using Excel and the statistical program R 3.1.0.

Results     

A total of 75 human health facilities were visited. These included 17 HC II units, 20 HC III units, 16 HC IV units, 11 hospitals, 8 DHOs, the CPHL, and the ESD of the MoH.

Types of surveillance systems in Uganda

Disease-specific surveillance, passive surveillance, active surveillance, and syndromic surveillance mechanisms were used at all levels of health units (Table 1). The disease-specific surveillance system was the most widely used method, and disease specific surveillance programs were running in 70 of the 75 health facilities visited. Passive surveillance, active surveillance, and syndromic surveillance were carried out in 69, 62 and 57 of the health facilities visited, respectively. Although, it appears impossible for a unit to carry out active surveillance without doing passive surveillance, it some districts, the overall functioning of passive surveillance in the district were done by mainly the district hospital leaving the DHO to implement only active surveillance. The proportion of health facilities involved in the four types of disease surveillance was higher in the Central region than in other regions.

Targets for disease surveillance

Disease specific surveillance, passive surveillance, and active surveillance programs in the majority of the health facilities visited targeted mainly infant immunizable diseases, malaria, diarrheal diseases, and AIDS. The most common targets for surveillance were malaria, AIDS, immunizable infant diseases, pneumonia, and dysentery/diarrheal diseases, being covered by 87%, 75%, 67%, 55%, and 49% of 75 facilities, respectively. Only 5% of the health units carried out surveillance programs targeting non-infectious diseases. There was active surveillance for VHF in 12% of the health units visited but only 4% of the health units visited carried out surveillance on zoonotic diseases other than VHF. Brucellosis, rabies, VHF, and anthrax were the only zoonotic diseases included in surveillance programs and were part of the disease surveillance targets in 16%, 8%, 4% and 1%, respectively, of the health units. Non-infectious diseases were only targeted by 5% of the 75 health units included in this study. Syndromic surveillance targeted mainly dysentery/diarrheal syndromes, pneumonia and other respiratory illnesses, skin rashes, and red eyes. The IDSR system was implemented by 20% of the health units, and targeted diseases listed on the 033B (Health Unit Weekly Epidemiological Surveillance Report), 105 (Health Unit Outpatient Monthly Report), and 108 (Health Unit Inpatient Monthly Report) forms [7] (Ministry of Health Uganda 2010).

Support systems available for human disease surveillance

Laboratory facilities available for surveillance, data collection and storage:

Laboratory services were available at the 64 field health units visited. No laboratory services were offered at the eight DHOs or the ESD at the MoH. The CPHL and the Infectious Disease Institute (IDI) carried out specialized laboratory testing not offered at field units. All HC II units carried out only rapid diagnostic tests for pregnancy and malaria, which were done by the in-charge nurses. Referrals to HC IV units and general hospitals were common, no health units made referrals to HC II units and only HC II units sought referral laboratory services at HC III units (Table 2). Laboratory data collection was done using handwritten records in 93% of the health facilities surveyed while computers were used to collect laboratory surveillance data in 15% of the health units. Audio and video equipment were not used by any of the health facilities for laboratory data collection or storage. Storage of laboratory surveillance data was done using hand-written records in 92% of the health units, while only 25% of the health facilities stored laboratory surveillance data on electronic devices.

Surveillance data collection and communication

Hardcopy forms, questionnaires, electronic (computer), and audio/video methods were used to collect surveillance data in 100%, 39%, 7%, and 3% of health units, respectively. Surveillance data was stored on hardcopy forms, computers, and audio/video devices in 100%, 19% and 1%, respectively. Similarly, surveillance data transmission methods was done using hardcopy forms, phones, computers, and audio/video in 100%, 99%, 33%, and 1%, respectively, of all health facilities visited. The majority (99%) of the 75 health units used telephones for communicating surveillance data. Other means of communication between primary and secondary health units included email, regular meetings, and infrequent meetings, which were respectively used by 37%, 55%, and 15% of the health units visited. Written/hardcopy reports or circulars were used for communication of surveillance information by 36% of the health units surveyed. The proportion of health units at different levels of health service provision that used email services to communicate surveillance information increased with the level of health service delivery (Table 3).

Surveillance data management and reporting

Overall, 55% of personnel involved in processing of surveillance data at all the health facilities included in this study were clinicians or health workers. Only 29% of the health units surveyed had records managers or other specialized data management personnel who were in charge of data processing. Other health units (12%) submitted their monthly surveillance data to a district biostatistician or the focal HMIS contact. Annual reports were prepared in 67% of the 75 health units. Only 56% of the health centers made disease outbreak reports, while 45% of health units had reports on special programs. Reporting of surveillance data was done weekly in 83% of the study health facilities. Of the study health units, 85%, 61%, and 63% made monthly, quarterly, and annual reports, respectively. Human surveillance data collected at the different health facilities was disaggregated according to location, gender, and age (Table 4). The forms used by the health facilities to collect data had age grouped in to categories of zero to five as well as five and above and sex grouped in to male and female. The disaggregation of health data in to gender, age, and location of the patient was least done in HC II units; with only 35%, 59% and 65% of the health units disaggregating data according to location, gender and age, respectively. Reporting of processed surveillance data was done using hardcopy forms in 97% of the 75 study health units. Only 48% of the study health units reported processed surveillance data electronically. Video processing formats were used to report surveillance data in only one of the study health units.

Discussion     

This study provides a nationally-representative assessment of the human health disease surveillance systems having been done at all levels of human health delivery. The gaps thus presented here although unique to Uganda could have a lot of similarity to those in other countries in Africa with similarly less developed health systems. This study thus adds to previous efforts of providing links to the establishment of improved disease surveillance systems in Africa. Importantly, our results showed limited surveillance of zoonotic (16%) and non-infectious diseases (5%) of the health units visited; low (20%) coverage of the internet-based IDSR; few data management personnel with health workers taking on this role in majority (55%) of the units; aggregation of data in the reporting system which could mask diseases that occur in the infants and the elderly; as well as the limited (25%) electronic storage and transmission of data from the laboratory facilities. Nonetheless, the human disease surveillance derives its strength from the broadly established disease-specific surveillance programs, the well-established and role-oriented laboratory network and a functional weekly mobile phone reporting system used for some diseases. All the four types of surveillance (disease-specific, passive, active, and syndromic) were functional at all levels of health facilities (HC II, HC III, IV, and hospitals) and in all the four regions of Uganda. This is indicative of a robust disease surveillance system that could quickly identify and monitor disease occurrence in most areas of the country [8]. The Central region had a higher proportion of health facilities involved in disease surveillance than other regions, possibly due to its proximity to the central administrative units that could have resulted in quick access to resources. The majority of the study health units had disease-specific surveillance programs targeting important diseases in Uganda. AIDS, malaria, and dysentery/diarrheal diseases, which were part of the diseases with specific surveillance programs, are also reported to have a higher prevalence in Uganda compared to other diseases, while most infant immunizable diseases are classified as PHEIC and would have their surveillance programs supported by international health organizations [9]. According to the UNAIDS, Uganda has an HIV prevalence of about 7.2% and has the sixth highest number of people living with HIV/AIDS in the world [10]. Similarly, the incidence of malaria was estimated to be 241 cases per 1000 individuals in Uganda over a ten year period [11]. These programs mainly targeted malaria, infant immunizable diseases, AIDS, and dysentery/diarrheal diseases, which could be attributed to the special surveillance programs targeting these diseases [5, 12-14]. The special control programs for malaria, AIDS, and infant immunizable diseases in Uganda include the Malaria Control Program (MCP), AIDS Control Program (ACP), and the Uganda National Expanded Program on Immunization (UNEPI) [12-14]. These special programs not only promote disease control but also monitor the occurrence of their respective target diseases. Zoonotic diseases and non-infectious diseases were part of diseases surveillance in a limited number of health facilities. Some zoonotic diseases like rabies and brucellosis are included on the list of diseases reported weekly and monthly to MoH but were not part of surveillance in the health units visited [15]. Some diseases present themselves with clinical signs similar to those of other febrile infections and with the limited laboratory definitive identification; it is possible that some conditions are over-reported while others remain under-reported and less targeted by surveillance programs [16]. The targets for syndromic surveillance mainly included dysentery and diarrheal diseases, pneumonia and respiratory diseases, red eyes, and skin rashes. Syndromes related to VHF were targeted because of their high mortality rates, the limited number of diagnostic facilities to diagnose the disease, and their increased frequency of occurrence in Uganda [17-19]. Dysentery has been associated with viral hemorrhagic fevers and cholera; pneumonia with tuberculosis and pertussis; and skin rashes or red eyes with measles [20-22]. Although syndromic surveillance was the least performed surveillance system, its inclusion in 76% of the health units is indicative of a functional disease surveillance system [8]. This could be the reason for the early identification of many unknown syndromes in Uganda. Even some of the known diseases have in the past been identified as syndromes before any definitive diagnosis could be made. The availability of rapid diagnostic tests for malaria at all health facilities including HC II units could explain why it is not targeted by syndromic surveillance [23]. The IDSR system was present in only 15% of the health units visited the system requires an internet connection, its implementation is not possible at remote health units without power. This could explain why it was not available in 75% of the health units visited. However, mobile phone-based reporting systems that utilize RapidSMS technology (https://www.rapidsms.org/), such as mTrac, appear to be a good replacement of the HMIS in Uganda [24]. All the 64 health units included in this study carried out laboratory surveillance. HC II units are mandated to carry out only rapid diagnostic tests for malaria and pregnancy; HC III units to perform tests on blood, urine, and stool; and HC IV units and hospitals to diagnose zoonotic diseases like brucellosis and tuberculosis as well as AIDS testing in their routine laboratory surveillance. However, many of the health center-based laboratories still face shortages of consumables and personnel, which may affect disease surveillance in the laboratories [25]. The DHO participated in investigating unusual or strange disease outbreaks from lower level health care units, referring cases for diagnosis to the better equipped central units. The referral system among the laboratories at different health centers was functional, with lower levels of health units referring to health units higher in the rank of health service delivery. This could be important in preventing scenarios where clinical intervention occurs without laboratory diagnosis [25].

Surveillance data collection and communication

The predominant methods of surveillance data collection were through hardcopy forms and questionnaires. Only 7% of the health centers and 15% of the laboratories used electronic data collection methods, which may be due to a lack of labor capacity at these facilities. The high patient to health worker ratio in most health facilities in Uganda would not permit electronic data collection methods unless data collection personnel are recruited at these health facilities [26]. The higher percentage of laboratories using electronic data collection devices compared to the health centers could possibly be due to the lower patient turn up for laboratory services compared to other health services in the facilities giving more time for laboratory personnel to do electronic data collection. Also, the higher percentage of laboratories using electronic data compared to health units could possibly be due to more focus on improving laboratory diagnosis than clinical care. The most commonly used data transmission system involved using mobile phones, practiced at 99% of the health facilities visited. The phone-based data transmission program mTrac (Cummins and Huddleston 2013) was used by field units to transmit weekly data about malaria treatments and occurrence of notifiable diseases, as well as requisition for drugs from the national medical stores [24]. Some of the notifiable diseases reported through mTrac include neo-natal tetanus, polio, measles, bacillary dysentery, cholera, diarrhea in children older than five years, yellow fever, plague, and meningococcal meningitis [15]. The mTrac system is also used by the MoH ESD to provide feedback to all health units [24]. The mTrac system for has been used to transmit limited amounts of data and may be suitable for transmitting large quantities of surveillance data. However, the use of mobile phones could potentially be affected by scarcity and cost of phone charging facilities, especially in rural areas where there is no electricity [5]. This underscores the need to broaden the coverage of the real-time HMIS to all health units. Electronic data is quicker and much easier to process, monitor, and retrieve compared to paper data [27]. Thus, there is need to put more effort in increasing electronic data storage from 19% coverage observed in this study to at least 80%. However, the adoption of electronic health records has been slow even in more developed countries [27]. The majority (55%) of the health centers selected for this study used health workers to process surveillance data, which could compromise their medical duties as well as the quality of data generated. The disaggregation of surveillance data was least done at HC II units possibly because of an inadequate number of staff at these units compared to other levels of health service delivery. Although disease surveillance data were disaggregated by location, age, and sex, there were limitations to the approaches used that could limit the identification of illnesses associated with physiological states, culture, socio-economic background, disability, and others. The age grouping of age data in to zero to five and above five may not reveal disease among the elderly, infants less than one year of age and youth. To effectively manage the processing of health surveillance data, there is need to recruit qualified data personnel to serve all health units. Reporting of surveillance data was done weekly, monthly, quarterly, and annually by all field health units. Overall, 67% of the health facilities visited had annual reports, and the majority (> 80%) did the weekly and monthly reporting. The MoH also provided a report of weekly health surveillance data received from districts in a nationally widespread newspaper every Monday [15]. This could be responsible for the high reporting of weekly data, as non-participating districts are easily identified and held accountable by all stakeholders. Non-reporting of health surveillance data could be due to absence of skilled data management personnel. Failure to report disease surveillance data could also be due to inadequate facilitation for the data processing process which may include data processing equipment and materials.

Conclusion     

There was limited inclusion of non-infectious diseases and zoonotic diseases in surveillance with only 16% of the study health units and 5% of the study health units being involved in surveillance of zoonotic and non-infectious diseases respectively. This underscores the need to broaden the scope of the human surveillance system. Nonetheless, disease-specific, syndromic, and active and passive surveillance programs were carried out in all regions of Uganda with malaria, AIDS, and infant immunizable diseases being included in surveillance programs at most health centers. The limited implementation of the real-time IDSR data management system, the limited number of skilled data management personnel and the limited electronic storage and transmission of laboratory data present urgent gaps that need to be addressed to improve surveillance. Nevertheless, there was widespread use of mobile phones for communication of data on notifiable diseases and malaria; there was a well-established laboratory network with a functional laboratory referral system which may aid in early identification and control of diseases targeted by the human disease surveillance system.

Competing interests     

The authors declare no competing interest.

Authors´ contributions     

All authors participated in the design and data collection during the study, as well as in writing this manuscript. All authors have read and agreed to the final version of this manuscript.

Acknowledgments     

The authors would like to acknowledge the help and cooperation of all health workers at the district, which were part of this study.

Tables     

Table 1: the number of different health units carrying out the disease-specific, syndromic, active and passive health surveillance in the different regions of Uganda, May to October 2013

Table 2: the population captured by human disease surveillance systems in Uganda, disaggregated by location, gender and age, May to October 2013

Table 3: the number of health units that used referral laboratory services at other health units, May to October 2013

Table 4: the different methods of communicating surveillance data at different levels of health centers in Uganda, May to October 2013

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