• Users Online: 318
  • Home
  • Print this page
  • Email this page
Home About us Editorial board Search Ahead of print Current issue Archives Submit article Instructions Subscribe Contacts Login 


 
 Table of Contents  
ORIGINAL ARTICLE
Year : 2014  |  Volume : 17  |  Issue : 1  |  Page : 1-6

Challenges of data collection and disease notification in Anambra State, Nigeria


1 Departments of HIV Care and Community Medicine, Nnamdi Azikiwe University Teaching Hospital, Nnewi, Nigeria
2 Department of Community Medicine, University of Nigeria Enugu Campus, University of Nigeria, University of Nigeria Teaching Hospital, Enugu, Nigeria
3 Department of Community Medicine, Nnamdi Azikiwe University, Nnamdi Azikiwe University Teaching Hospital, Nnewi, Anambra, Nigeria

Date of Web Publication7-Apr-2014

Correspondence Address:
Chinomnso C Nnebue
Department of Community Medicine, Nnamdi Azikiwe University Teaching Hospital, PMB 5025 Nnewi, Anambra
Nigeria
Login to access the Email id


DOI: 10.4103/1119-0388.130173

Rights and Permissions
  Abstract 

Background/Objective: Disease surveillance and notification (DSN) in Nigeria have been characterized by weaknesses such as insufficiencies in health infrastructure, scientific methods, and concepts of operation; essential human, technical, and financial resources; and international or local policies as well as lack of intra- and intersectoral collaboration. These weaknesses in DSN system thus compromise efficiency and quality of data. This study examined the challenges of data collection and disease notification in Anambra state, Nigeria. Materials and Methods: This was a cross-sectional descriptive study of 270 healthcare workers selected by multistage sampling technique. Data collection was done using a mix method comprising interviewer administered questionnaires, health facility observational checklist, key informant interviews (KIIs), and desk review. Results: Commonest problems associated with DSN system as mentioned by the health workers were as follows: Most facility workers were not trained on DSN system (23.7%), lacked transportation (15.8%), poorly motivated/poor staff attitude (15.4%), inadequate supply of forms (11.8%), and poor funding (11.4%). An observational checklist on preparedness for DSN showed that 100% of primary and tertiary health facilities had facility records, while 81% at the secondary level had records. Only 51.9% facilities had community health officers (CHOs), while junior community health extension workers (JCHEWs) were more in primary health facilities compared to other levels of care (χ2 = 4.25, P = 0.040). Conclusion: Regular training program on DSN should be encouraged, while regular monthly supervision and quarterly meetings of health facilities should be organized for health facility workers. Regular and adequate information feedback should be emphasized.

Keywords: Anambra state, challenges, data collection, disease notification


How to cite this article:
Nnebue CC, Onwasigwe CN, Adogu PO, Adinma ED. Challenges of data collection and disease notification in Anambra State, Nigeria. Trop J Med Res 2014;17:1-6

How to cite this URL:
Nnebue CC, Onwasigwe CN, Adogu PO, Adinma ED. Challenges of data collection and disease notification in Anambra State, Nigeria. Trop J Med Res [serial online] 2014 [cited 2019 Aug 26];17:1-6. Available from: http://www.tjmrjournal.org/text.asp?2014/17/1/1/130173


  Introduction Top


In data collection and disease notification, insufficiencies are identified in four critical areas: Health infrastructure; scientific methods and concepts of operation; essential human, technical, and financial resources; and international or local policies. These insufficiencies challenge global surveillance of and response to infectious disease outbreak of international importance especially. [1] The current status of disease surveillance system in Nigeria is deplorable, characterized by lack of intra- and intersectoral collaboration. This leads to verticalization of programs and multiplicity of disease reporting formats, and as a result compromises efficiency and quality of data. [2] Inadequate financial and material support for disease surveillance; lack of basic infrastructure (e.g., communication equipment, transport, and data management tools); and inadequacy of skilled manpower for case detection, data collection analysis, and interpretation also militate against effective disease surveillance and notification (DSN). [2]

Nonexistence of functional public health laboratories in most states of the federation and deficient capacity at all levels to forecast and respond in a timely and appropriate manner to epidemics and disaster have been blamed for inaccurate ascertainment of cases and poor response to epidemics and disasters. [2] Bawa et al., [3] reported that diagnostic support was lacking in most of the health facilities they studied. The logistical difficulties of travel and communication, which are common in developing countries, constrain the conventional surveillance system that relies on epidemiologists visiting sites to discover and investigate cases, particularly in rural areas. [4] For effective surveillance system, the presence of simple standard case definition has been emphasized. [5],[6],[7],[8] Bawa et al., [3] reported that only 65.9 and 8.0% of facilities had up-to-date registers and the World Health Organization (WHO), State Ministry of Health (SMoH) and United Nations Children's Fund (UNICEF) Integrated Disease Surveillance and Response (IDSR) forms, respectively. Data was not analyzed at local government area (LGA) level, and forms and logistics for supervising DSN activities and feedback were inadequate. [3],[9]

Studies have also shown that too many complex forms were among major factors that hindered effective reporting, collection, and interpretation of data. [10],[11] The issue of confidentiality has been perceived as a deterrent to reporting sexually transmitted diseases and also a feeling among physicians, that reporting certain diseases was useless. [10],[12],[13],[14] It is however noteworthy that under notification leads to an underestimation of disease burden and hinders the implementation of appropriate prevention and control strategies.

Furthermore, cultural beliefs that favor the use of alternative sources of healthcare before consulting a nurse or physician, and those forbidding remembering bad events are constraints for disease surveillance. [4],[15] Therefore, the DSN reports do not capture the true epidemiological pattern of diseases. Similarly, lack of community-based approach to disease reporting as well as lack of training act as constraints to effective disease surveillance. [4] Training has been documented to positively impact the disease notification habits of health personnel as well as the information system. [3],[9] Training prepares beneficiaries for a certain defined line of action and emphasizes understanding and skills acquisition with a view to improving standard of performance.

It was reported that the time from disease onset to diagnosis generally contributed most to the delay in reporting. [16],[17] A study by Jajosky and Groseclose showed that the reporting delay, based on date of onset, ranged from 12 days for meningococcal disease to 40 days for pertussis. [18] Diseases with longer incubation periods tended to have a higher percentage of cases reported within its incubation period. It has been documented that delay in diagnosis may allow widespread transmission and lead to large epidemics. [16] If symptoms begin abruptly and progresses severely, patients tend to visit doctors more quickly, and diagnostic confirmation is faster. Difficulty in diagnosis tends to delay the reporting of diseases by doctors because they need to balance the cost of false positives and delayed reporting. A consideration of the necessity for time-consuming laboratory tests for diagnosis may also delay reporting. [16]

Epidemiological characteristics such as incidence may affect timeliness in reporting, which can be facilitated by familiarity with the disease in a given population. [16] For example measles is more common and thus is more quickly reported than malaria in England. [19] Occurrence of an epidemic is likely to have similar effects. [16] This shows the role of community participation in disease surveillance and in improving the timeliness of reporting diseases. [4]

Disease surveillance has been recognized as an effective strategy in the control and prevention of diseases most especially communicable diseases. An effective surveillance system allows early intervention for the prevention and reduction of the mortality and morbidity that may result from epidemics of communicable diseases. However, not much has been done towards the improvement of the DSN system in Nigeria and most countries in the West African sub-region.

The weaknesses in DSN systems in most countries have thus resulted in failures in detecting epidemics. This leads to the spread of diseases, human suffering, and loss of lives. Therefore, the DSN has to be repositioned and strengthened to track especially; the health related Millennium Development Goals (MDGs). The findings of the study are expected to provide information for the improvement of the DSN system.

DSN is also important in this era of health sector reforms so as to serve as a basis for recommendations of appropriate interventions and approaches that may be adopted in the strengthening of the health system. This study will no doubt identify the militating factors, proffer solutions, and help in making informed suggestions towards the formulation of policies and improvement of the DSN system in the state.

Studies on DSN have been carried out in Benin, Yobe and none has been conducted in Anambra state. The study will also contribute to research in DSN in Nigeria and in the West African sub-region. It is therefore in the light of the above that a study on the DSN in Anambra State is necessary.

Anambra State is one of the 36 states of Nigeria located in the southeast geopolitical zone of the country. There are 21 LGAs and 177 communities in the state. The major language spoken is Igbo, while the literacy level range from 48.6 to 84.1%. [20]

The health program of the state conforms to the National Health Policy and has a vast number of health facilities to support it, including: A state-owned tertiary health institution, the Anambra State University Teaching Hospital, Awka, which is however at its rudimentary stage, a federal teaching hospital, the Nnamdi Azikiwe University Teaching Hospital (NAUTH), Nnewi; 32 state government owned general hospitals; 14 mission hospitals; 189 maternity homes; and about 600 private hospitals and clinics. Each of the 21 LGAs has an equitable distribution of primary healthcare centers (210) and 166 health posts. There are five schools of nursing and midwifery and a school of health technology. [21]

This study was a cross-sectional descriptive study to determine the functional status of DSN system, and thus examined the challenges of data collection and disease notification at health facility, local government health department, and state levels in Anambra state, Nigeria. The scope of the study includes all healthcare workers involved in DSN in the health facilities in Anambra state.


  Materials and Methods Top


The sample size for this study was determined using the formula for the calculation of sample size in populations greater than 10,000, n = z 2 pq/d 2 . [22] where n = calculated sample size, z = standard normal deviate at 95% confidence interval = 1.96, P = proportion of respondents that ever reported occurrence of epidemic, q = the complementary probability of P (1 − p), that is, the proportion of respondents that never reported occurrence of epidemic, d = precision level 5%.

Using the result of a study in Yobe State Nigeria, where 79% of respondents were found to have ever reported occurrence of epidemic, [3] P = 0.79, while q = 1 − 0.79 = 0.21. Therefore,



An adjustment of the sample size estimate to cover for non-response rate was made by dividing the sample size calculated with a factor f, that is, n/f, where f is the estimated response rate. [23] Anticipating a response rate of 95%, the minimum sample size required for the study was 254 healthcare workers, the study sample size = 254/0.95 = 270. Thus, a sample size of 270 healthcare workers was used for this study.

A multistage sampling technique was used to select six LGAs from the state (three urban and three rural LGAs). Then nine health facilities were selected from each of these six LGAs and five healthcare providers were selected from each of the health facilities, thus a total of 270 healthcare providers.

Data collection was done using both quantitative and qualitative methods. These include: Interviewer administered semi-structured healthcare provider questionnaires, health facility observational checklist, and key informant interviews (KIIs).

Quantitative data collected were analyzed with the aid of the Microsoft Excel and Statistical Package for Social Sciences (SPSS) version 16, while qualitative data obtained from the KII recordings were transcribed verbatim, translated (where necessary), and field notes made.

Ethical approval was obtained from the Nnamdi Azikiwe University Teaching Hospital Ethical Committee (NAUTHEC), while the permission to conduct the study was obtained from the State Ministry of Health, Ministry of Local Government Affairs, and the Local Government PHC Department.

In addition, written informed consent was obtained from all the respondents.


  Results Top


[Table 1] highlights the socio-demographic characteristics of the respondents. A total of 270 questionnaires were distributed and 256 retrieved giving a response rate of 94.8%; however, two questionnaires were rejected because they were poorly completed and consequently 254 questionnaires were analyzed. The mean age of the respondents was 37.2 ΁ 9.2 years. A total of 227 of them were females (89.4%). One hundred and ninety-nine (78.4%) had tertiary education and 52 (20.5%) had secondary education. The respondents included 17 doctors (6.7%), 94 nurses (37%), 81 community health extension workers (CHEWs; 31.9%), 19 auxiliary nurses (7.5%), 23 health attendants (9.1%), seven record officers (2.8%), five environmental health officers (2.0%), and others-health education officers, nutritionists, pharmacy, and laboratory technicians (8; 3.1%).
Table 1: Socio-demographic characteristics of the respondents


Click here to view


[Table 2] highlights the common problems associated with DSN system. Only 82 (32.3%) of all the healthcare workers interviewed have been trained on DSN. Commonest problems associated with DSN system as mentioned by the health workers were as follows; most facility workers are not trained on DSN system by 54 health workers (23.7%), lack of transportation (36, 15.8%), poorly motivated/poor staff attitude (35, 15.4%), inadequate supply of forms (27, 11.8%), and poor funding (26, 11.4%).
Table 2: Healthcare providers' report of common problems associated with disease surveillance and notification system in the state


Click here to view


[Table 3] shows the distribution of cadre of healthcare workers by health facility type. Fifty-one facilities (94.4%) had nurses/midwives. All the secondary and tertiary health facilities had doctors, while only six (22.2%) of the primary healthcare facilities had doctors. Only 28 (51.9%) facilities had community health officers (CHOs), and they were fewest in secondary health facilities with only seven (33.3%). Junior CHEWs (JCHEWs) were more in primary health facilities compared to other levels of care (χ2 = 4.25, P = 0.040).
Table 3: Distribution of cadre of healthcare workers by HF type on observation


Click here to view


[Table 4] shows the proportion of healthcare workers ever trained on DSN by health facility type. Most of the healthcare providers surveyed have attended any form of training on DSN. Among those that have received any form of training, a greater proportion of those in the tertiary and secondary health facilities have been trained compared to healthcare providers in primary health centers. However, this difference was not statistically significant (χ2 = 5.45, P = 0.244).
Table 4: Proportion of healthcare workers ever trained on DSN by HF type


Click here to view


An observational checklist of 54 health facilities in the state to assess their level of preparedness for DSN shows that 50 (92.6%) of the health facilities had facility records. All the primary and tertiary health facilities had facility records, while 81% of the secondary health facilities had records.

The KII findings reported that the DSN officers (DSNOs) and the state epidemiologist corroborated that there are problems encountered in the sending of reports and receiving of returns. They enumerated the following problems: There is poor funding, as the monthly stipend of 5,000 naira given to them is too small. There is lack of steady means of transportation. In their own words, a 37-year-old female DSNO said, "We make several visits during the sending of reports and receiving of returns, and no means of transportation is given to us. Even the 5,000 naira given to us monthly finishes before you know it". There is lack of managerial tools such as computers for analysis of reports, internet access, and photocopiers. They were almost in unison on this. One of respondents said, "Despite that we are in the computer age, we still do things even worse than the people before us. Would it not have been better to analyze these data and send them to the appropriate authorities through the internet rather than this time- and cost-consuming archaic process?" There is shortage of supply of IDSR and other forms and materials. The DSNOs said they have been sponsored to several workshops so far this year. However, most of them said there was no provision for in-service training to further their education. It was also reported that only the DSNOs attend these workshops, thus making it difficult for the other health workers to appreciate the principle of DSN and the role they are expected to perform.

The result of the desk review showed that there was no existing state-specific data or policies on the functional status of DSN system in Anambra State. The DSN system is not computerized at the state, LGA, and health facility levels. It also lacks manpower and basic equipment like computers, photocopiers, printers, and vehicles for transportation.


  Discussion Top


Only about a third (32.3%) of all health personnel studied has been trained on the rudiments of DSN. This would translate to low reporting as training is expected to improve the knowledge, attitude, and practice of reporting by the health personnel. The knowledge gap regarding notifiable diseases can only be effectively filled by the conduct of a training program. Lack of training affects every level of health delivery system regardless of its sophistication. This corroborates the finding by Adindu which showed that lack of training affects the information system on DSN. [9] This is in keeping with the findings of the study by Osibogun et al., [24] where after a training program, health workers appreciated the value of reliable data as a means of information relevant to provision of health services. It also agrees with the result of an interventional study conducted in northern Nigeria where the percentage completeness of reporting of notifiable diseases increased from 2.3 to 52.0% and percentage of timely reports increased from 0 to 42.9% post training. [25]

From this study, the findings of the KII and desk review showed that the DSN system in the state is not computerized. The system if computerized would have improved the timeliness and also helped save the cost of transportation. The advent of information technology has led to comparison between electronic and conventional (paper-based) methods. [17],[26] It has been reported by some authors that the electronic system has improved timeliness and complete reporting of notifiable conditions. [27],[28]

Common problems associated with DSN system as mentioned by respondents of this study were; lack of training, lack of transportation, poor motivation, inadequate supply of forms and other logistics, poor funding, ignorance on the part of the public, weak/inadequate supervision, and lack of prompt feedback amongst others. The logistical difficulties of travel and communication, which are common in developing countries, constrain the conventional surveillance system that relies on epidemiologists visiting sites to discover, and investigate cases; particularly in rural areas. [2],[4] Therefore, concerted effort is required to revive the DSN system in the state.

The distribution of cadre of workers in the health facilities in the state is not ideal. Having CHOs, CHEWs, and JCHEWs in tertiary health facilities amount to putting square pegs in round holes since their training curriculum does not prepare them in any way for challenges at that level. Even at the primary healthcare level where they are expected to operate; their duty schedule stipulates that 60% of their time should be spent at the community level, while 40% is spent at the primary health facilities. These cadres of workers are trained, among other things, on data collection at the community and primary health facility levels where they are expected to contribute effectively to the overall DSN system in the state.

Supervisory visit, an important management tool was paid to the health facilities on monthly basis by the state team made up of the WHO and SMoH as well as teams from UNICEF, Malaria Control Booster Project, and the state epidemiologist who visit routinely. This is an avenue for giving 'on-the-job' training and instituting corrective measures. It is paramount for any system, and lack of it will affect the completeness and timeliness of data reporting. However, these supervisory visits to the health facilities have been described as weak and inadequate in this study. Adindu, [9] showed that about 70% of health workers did not appreciate the importance of the data they collected. Bawa et al. [3] and Adindu, [9] also reported that data was not analyzed at LGA level, and forms and logistics for supervising disease surveillance activities and feedback were inadequate. Therefore lack of adequate and supportive supervision may have been the reason the generators of data were not able to analyze and make use of these data at their various levels.


  Conclusions Top


This study has revealed that the health personnel did not operate the DSN in the state to optimal functionality. The shortcomings reported above could be attributed to maldistribution of cadres of healthcare workers in the health facilities, lack of training, lack of transportation, poor motivation, inadequate supply of forms and other logistics, poor funding, weak/inadequate supervision, and lack of prompt feedback. These, when fully addressed and implemented, would improve the DSN system in Anambra state.

 
  References Top

1.Hitchcock P, Chamberlain A, Van Wager M, Inglesby TV, O'Toole T. Challenges to global surveillance and response to infectious disease outbreaks of international importance. Biosecur Bioterror 2007;5:206-27.  Back to cited text no. 1
    
2.World Health Organization/AFRO. Country Press Releases. WHO/Nigeria. World Health Organization supports State epidemiologists; 2002. [Last accessed on 2009 May 19].  Back to cited text no. 2
    
3.Bawa SB, Olumide EA, Umar US. The knowledge attitude and practices of reporting of notifiable diseases among health workers in Yobe State, Nigeria. Afr J Med Med Sci 2003;32:49-53.  Back to cited text no. 3
    
4.Ndiaye SM, Quick L, Sanda O, Niandou S. The value of community participation in disease surveillance: A case study from Niger. Health Promot Int 2003;18:89-98.  Back to cited text no. 4
    
5.Thacker SB, Parrish RG, Trowbridge FL. A method of evaluating systems of epidemiological surveillance. World Health Stat Q 1988;41:11-8.  Back to cited text no. 5
    
6.Douglas NK, James WB, Stephen BT, Gibson PR, Fredrick LT, Ruth LB, et al. The Surveillance coordination group. Guidelines for evaluating surveillance systems. MMWR Morb Mortal Wkly Rep 1988;37:1-17.  Back to cited text no. 6
    
7.Federal Ministry of Health (Epidemiology Division, Lagos). National Conference on Disease Surveillance; September, 1988.  Back to cited text no. 7
    
8.John TJ, Samuel R, Balraj V, John R. Disease surveillance at district level: A model for developing countries. Lancet 1999;352:58-61.  Back to cited text no. 8
    
9.Adindu AU. The effect of incongruity on quality of health information system: Bama Nigeria. Primary Health Care case study (dissertation). USA: University of Hull: 1995.  Back to cited text no. 9
    
10.Abdul Karim SS, Dilraj A. Reasons for under-reporting of notifiable conditions. S Afr Med J 1996;86:834-6.  Back to cited text no. 10
    
11.Freund PJ, Kalumba K. Information for health development. World Health Forum 1986;7:185-90.  Back to cited text no. 11
    
12.Chorba TL, Berkelman RL, Safford SK, Gibbs NP, Hull HF. Mandatory reporting of infectious disease by clinicians. JAMA 1989;262:3018-26.  Back to cited text no. 12
    
13.Marier R. The reporting of communicable diseases. Am J Epidemiol 1994;10:587-90.  Back to cited text no. 13
    
14.South Australian Health Commission Sexually Transmitted Diseases Unit. Surveillance and notification of sexually transmitted diseases. Available from: http://www.google.com.disease surveillance [Last accessed on 2009 May 19].  Back to cited text no. 14
    
15.Bamisaiye A. Reports and Reporting. Transforming data into information for decision making. Abuja: Proceedings of the National Conference on Health Management Information System; 1992. p. 53-6.  Back to cited text no. 15
    
16.Yoo HS, Park O, Park HK, Lee EG, Jeong EK, Lee JK, et al. Timeliness of national notifiable diseases surveillance system in Korea: A cross-sectional study. BMC Public Health 2009;9:93.  Back to cited text no. 16
    
17.Jansson A, Arneborn M, Skarlund K, Ekdahl K. Timeliness of case reporting in the Swedish statutory surveillance of communicable diseases 1998-2002. Scand J Infect Dis 2004;36:865-72.  Back to cited text no. 17
    
18.Jajosky RA, Groseclose SL. Evaluation of reporting timeliness of public health surveillance system for infectious diseases. BMC Public Health 2004;4:29.  Back to cited text no. 18
    
19.Clarkson JA, Fine PE. Delays in notification of infectious disease. Health Trends 1987;19:9-11.  Back to cited text no. 19
[PUBMED]    
20.National Population Commission. 2006 Provisional Census Results, Vol. 94. Abuja: Federal Republic of Nigeria Official Gazette, Abuja; 2007. p. 1-64.  Back to cited text no. 20
    
21.Anambra State of Nigeria. State Economic Empowerment and Development Strategy (SEEDS), 2 nd ed. Awka; 2007.  Back to cited text no. 21
    
22.Araoye MO. Research methodology with statistics for health and social sciences, 2 nd ed. Saw-Mill: Nathadex Publications; 2008. p. 115-22.  Back to cited text no. 22
    
23.Nigerian Population Commission. Nigeria: State and Local Government Demographic Profile, 1991-2010. Abuja; 1991.  Back to cited text no. 23
    
24.Osibogun A, Jaksic J, Idowu JA, Alausa OK, Oluwole FA. For better data, better utilized. World Health Forum 1996;17:274-6.  Back to cited text no. 24
    
25.Bawa SB, Olumide EA. The effect of training on the reporting of notifiable diseases among health workers in Yobe State, Nigeria. Niger Postgrad Med J 2005;12:1-5.  Back to cited text no. 25
    
26.Panackal AA, M'ikanatha NM, Tsui FC, McMahon J, Wagner MM, Dixon BW, et al. Automatic electronic laboratory-based reporting of notifiable diseases at a large health system. Emerg Infect Dis 2002;8:685-91.  Back to cited text no. 26
    
27.Grant AD, Eke B. Application of information technology to the laboratory reporting of communicable disease in England and Wales. Commun Dis Rep CDR Rev 1993;3:R75-8.  Back to cited text no. 27
    
28.Payne JN. The introduction of computerized system for notification and improved analysis of infectious diseases in Sheffield. J Public Health Med 1992;14:62-7.  Back to cited text no. 28
[PUBMED]    



 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

 
  In this article
Abstract
Introduction
Materials and Me...
Results
Discussion
Conclusions
References
Article Tables

 Article Access Statistics
    Viewed6689    
    Printed199    
    Emailed0    
    PDF Downloaded377    
    Comments [Add]    

Recommend this journal


[TAG2]
[TAG3]
[TAG4]