The Open Medical Informatics Journal




ISSN: 1874-4311 ― Volume 13, 2019
RESEARCH ARTICLE

Primary Healthcare Data Management Practice and Associated Factors: The Case of Health Extension Workers in Northwest Ethiopia



Segenet Yitayew1, Mulusew A. Asemahagn2, 3, *, Atinkut A. Zeleke2, 3
1 North Gondar Health Office, Gondar, Ethiopia
2School of Public Health, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia
3 Department of Health Informatics, Institute of Public Health, University of Gondar, Gondar, Ethiopia

Abstract

Background:

Collecting quality and timely healthcare data is crucial to improve health service performance.

Objective:

This study aimed at assessing data management practice and associated factors among health extension workers in East Gojjam zone, Northwest Ethiopia.

Materials and Methods:

An institution based cross-sectional study was conducted in 2014 among 302 health extension workers. Data were collected using a self-administered questionnaire and analyzed using SPSS version 20. The study objectives were described by descriptive statistics, and factors in data management were identified by multivariable logistic regression analysis.

Results:

A total of 302 health extension workers participated in the study. About 47.4% and 53.3% of respondents had good data management knowledge and practice, respectively. Inaccessibility of transportation, communication services, reference materials, and data collection/reporting formats were the mentioned challenges. Workload, data management knowledge, supervision, urban residence, reference materials access and clarity of formats were positively associated with better data management practice (p <0.05).

Conclusion:

Based on this study, the data management practice of health extension workers was low. Factors for low data management practice were organizational and technical related. Addressing knowledge gaps through professional development and improving supportive supervision are crucial to solve the problem.

Keywords: Data, Data management practice, Health extension workers, Factors, Ethiopia, Health Extension Program.


Article Information


Identifiers and Pagination:

Year: 2019
Volume: 13
First Page: 2
Last Page: 7
Publisher Id: TOMINFOJ-13-2
DOI: 10.2174/1874431101913010002

Article History:

Received Date: 15/01/2019
Revision Received Date: 06/02/2019
Acceptance Date: 05/04/2019
Electronic publication date: 24/07/2019
Collection year: 2019

Article Metrics:

CrossRef Citations:
0

Total Statistics:

Full-Text HTML Views: 237
Abstract HTML Views: 150
PDF Downloads: 113
ePub Downloads: 93
Total Views/Downloads: 593

Unique Statistics:

Full-Text HTML Views: 153
Abstract HTML Views: 111
PDF Downloads: 87
ePub Downloads: 63
Total Views/Downloads: 414
Geographical View

© 2019 Yitayew et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


* Address correspondence to this author at the School of Public Health, College of Medicine and Health Sciences, Bahir Dar University, Ethiopia;
Email: muler.hi@gmail.com





1. BACKGROUND

With the objective of improving rural health care services, the Ethiopian government has implemented a nationwide Health Extension Program (HEP) at the community level since 2003. The program uses two to four female Health Extension Workers (HEWs) per Health Post (HP), the lowest level health facility, to serve for about 5000 people [1Banteyerga H, Kidanu A. Rapid Appraisal of Health Extension Program: Ethiopia Country Report 2008.-8Yitayal M, Berhane Y, Worku A, Kebede Y. The community-based Health Extension Program significantly improved contraceptive utilization in West Gojjam Zone, Ethiopia. J Multidiscip Healthc 2014; 7: 201-8.[http://dx.doi.org/10.2147/JMDH.S62294] [PMID: 24868165] ]. One of the important pillars of primary health care is a health information system to generate quality information for decision making [9Association for Healthcare Document Integrity (Adhi). Healthcare documentation quality assessment and management best practices Excutive summary 2011.-14Gobena T, Alemayehu T, Fantahun T. Documentation for Health Extension Workers Lecture Notes, November 2004.]. Data management is a set of procedures for collecting, record keeping, processing, using, and communicating health care data [9Association for Healthcare Document Integrity (Adhi). Healthcare documentation quality assessment and management best practices Excutive summary 2011.-11Ethiopian Federal Ministry of Health. HMIS Information use and data quality training manual 2014., 14Gobena T, Alemayehu T, Fantahun T. Documentation for Health Extension Workers Lecture Notes, November 2004.-16Shagake S S, Mengistu M Y, Zeleke A A. Data Management Knowledge, Practice and Associated Factors of Ethiopian Health Extension Workers in Gamo Gofa Zone, Southern Ethiopia: Across- Sectional Study. Health & Medical Informatics 2012; 5(1)]. Due to geographic inaccessibility, infrastructure, and resource shortage, HEWs have used non-computerized data handling system [12Ethiopian Federal ministry of Health. Health sector transformation plan from 2015/16-2020 2015., 13Ethiopian Public Health Institute. Improving Health Extension Program in Ethiopia: An Evidence-Based. Policy Brief 2014., 16Shagake S S, Mengistu M Y, Zeleke A A. Data Management Knowledge, Practice and Associated Factors of Ethiopian Health Extension Workers in Gamo Gofa Zone, Southern Ethiopia: Across- Sectional Study. Health & Medical Informatics 2012; 5(1), 17Shiferaw AM, Zegeye DT, Assefa S, Yenit MK. Routine health information system utilization and factors associated thereof among health workers at government health institutions in East Gojjam Zone, Northwest Ethiopia. BMC Med Inform Decis Mak 2017; 17(1): 116.[http://dx.doi.org/10.1186/s12911-017-0509-2] [PMID: 28784115] ], which may end up in poor data quality, improper disease classification, unfair resource allocation, and poor planning [13Ethiopian Public Health Institute. Improving Health Extension Program in Ethiopia: An Evidence-Based. Policy Brief 2014., 16Shagake S S, Mengistu M Y, Zeleke A A. Data Management Knowledge, Practice and Associated Factors of Ethiopian Health Extension Workers in Gamo Gofa Zone, Southern Ethiopia: Across- Sectional Study. Health & Medical Informatics 2012; 5(1), 18Andualem M, Kebede G, Kumie A. Information needs and seeking behaviour among health professionals working at public hospital and health centres in Bahir Dar, Ethiopia. BMC Health Serv Res 2013; 13(534): 534.[http://dx.doi.org/10.1186/1472-6963-13-534] [PMID: 24373296] -21Huong NT. Clinical data management (Process and practical Guide) 2012.]. About 85% of preventive health data are generated by HEWs, thus their data management practice requires attention [12Ethiopian Federal ministry of Health. Health sector transformation plan from 2015/16-2020 2015., 13Ethiopian Public Health Institute. Improving Health Extension Program in Ethiopia: An Evidence-Based. Policy Brief 2014., 18Andualem M, Kebede G, Kumie A. Information needs and seeking behaviour among health professionals working at public hospital and health centres in Bahir Dar, Ethiopia. BMC Health Serv Res 2013; 13(534): 534.[http://dx.doi.org/10.1186/1472-6963-13-534] [PMID: 24373296] , 20Fitsum G, Belaineh G. Health services utilization and associated factors in Jimma Zone , South West Ethiopia 2007., 22Ethiopian Public Health Institute, Federal ministry of Health.. Health Data Quality Review: Systemic review on selected indicators 2016.].

Evidence from developing countries showed the presence of poor data management practice at the facility level [10Ekwueme OC. Health Data Recording, Reporting and Utilization Practices Among Primary Health Care Workers in Enugu State, South Eastern Nigeria. J College Med 2008; 13(2): 85-90.-12Ethiopian Federal ministry of Health. Health sector transformation plan from 2015/16-2020 2015., 17Shiferaw AM, Zegeye DT, Assefa S, Yenit MK. Routine health information system utilization and factors associated thereof among health workers at government health institutions in East Gojjam Zone, Northwest Ethiopia. BMC Med Inform Decis Mak 2017; 17(1): 116.[http://dx.doi.org/10.1186/s12911-017-0509-2] [PMID: 28784115] , 18Andualem M, Kebede G, Kumie A. Information needs and seeking behaviour among health professionals working at public hospital and health centres in Bahir Dar, Ethiopia. BMC Health Serv Res 2013; 13(534): 534.[http://dx.doi.org/10.1186/1472-6963-13-534] [PMID: 24373296] , 20Fitsum G, Belaineh G. Health services utilization and associated factors in Jimma Zone , South West Ethiopia 2007., 22Ethiopian Public Health Institute, Federal ministry of Health.. Health Data Quality Review: Systemic review on selected indicators 2016.-25Simba D. Quality of a routine data collection system for health: case of Kinondoni district in the Dar es Salaam region. Tanzania South African Journal of Information Mangement 2005; 7(2)[http://dx.doi.org/10.4102/sajim.v7i2.262] ]. Factors for poor data management practice include infrastructure, poor data management knowledge, poor management support, training, resource shortage, and vague data collecting formats [13Ethiopian Public Health Institute. Improving Health Extension Program in Ethiopia: An Evidence-Based. Policy Brief 2014., 15Arrighi HM. Data management and data analysis wwwepidemiolognet, © Victor J Schoenbach rev 10/22/1999, 10/28/1999, 4/9/2000 2000., 17Shiferaw AM, Zegeye DT, Assefa S, Yenit MK. Routine health information system utilization and factors associated thereof among health workers at government health institutions in East Gojjam Zone, Northwest Ethiopia. BMC Med Inform Decis Mak 2017; 17(1): 116.[http://dx.doi.org/10.1186/s12911-017-0509-2] [PMID: 28784115] , 22Ethiopian Public Health Institute, Federal ministry of Health.. Health Data Quality Review: Systemic review on selected indicators 2016., 23Gimbel S. An assessment of routine primary care health information system data quality in Sofala Province, Mozambique. Population health metrics 2011; 9(12), 25Simba D. Quality of a routine data collection system for health: case of Kinondoni district in the Dar es Salaam region. Tanzania South African Journal of Information Mangement 2005; 7(2)[http://dx.doi.org/10.4102/sajim.v7i2.262] -28Lu Z, Su J. Clinical data management: Current status, challenges, and future directions from industry perspectives. Open Access J Clin Trials 2010; 2: 93-105.[http://dx.doi.org/10.2147/OAJCT.S8172] ].

Although HEWs are major healthcare data sources to the Ethiopian healthcare system, little is known about their data management practice. Hence, this study aimed to assess the data management practice of HEWs in East Gojjam “Zone”. The “Zone” is a second administrative level next to the Region and it is composed of a certain number of “Woredas”. A “Woreda” is a smaller administrative area having an average of 100,000 population divided into the smallest administrative structure called “Kebele” which contains a minimum of 5,000 population [29East Gojjam zone Health Department.. East Gojjam Zone Health Department annual performance report of 2016 2016.].

2. METHODS AND MATERIALS

An institution based cross-sectional study was conducted in East Gojjam zone, containing 18 Woredas and 958 HEWs working in 402 HPs [29East Gojjam zone Health Department.. East Gojjam Zone Health Department annual performance report of 2016 2016.]. Due to resource constraints, only 40% of the Woredas are included in the study. There were 20 to 25 Kebeles per Woreda, 2 to 4 HEWs for each Kebele, and including all of the HEWs (302) from the randomly selected Woredas. Sample size was determined using Epi info version7 based on data management practice (P) =50% - no earlier study, 95% confidence level (CI), the margin of error (d) = ±0.05 and 10% non-response.

Data were collected using a pretested self-administered questionnaire adapted from the Amhara Regional Health Bureau HP supportive supervision checklist, and related studies [7Jira C. ealth planning for health science students: Lecture Note Series, Jima University, Faculity of Public Health, Ethiopia 2007., 14Gobena T, Alemayehu T, Fantahun T. Documentation for Health Extension Workers Lecture Notes, November 2004., 27Center for National Health Development in Ethiopia. Assessment of Working Conditions of the First Batch of Health Extension Workers 2006., 30Center for National Health Development in Ethiopia. Ethiopia Health Extension Program Evaluation: Rural Ethiopia. PartII 2007; 10]. Seven supervisors and 21 nurse data collectors were trained and participated.

Data were edited and analysed using SPSS version 20 software. Descriptive statistics were used to describe the study objectives, and bivariable logistic regression analysis was used to identify factors of data management practice. Variables having p<0.2 on the bivariate analysis were further tested by multivariable logistic regression analysis to control confounding effect. Variables having p <0.05 were considered statistically significant to the data management practice.

The authors used the mean scores of knowledge and practice questions to measure the data management knowledge and practice of HEWs. Eight knowledge questions were asked to each HEW. The mean score of HEWs on knowledge questions was 15.38. and HEWs who scored ≥15.38 were grouped under knowledgeable. Likewise, each HEWs was asked six questions related to data management practice. The mean score of HEWs on data management practice questions was 8.27 and HEWs who scored the mean and above (≥8.27) were considered as good data managers. Based on this study, the HEWs were grouped as data users if they use routine healthcare data for at least one task beyond the reporting purpose.

The University of Gondar ethical review committee reviewed and gave ethical clearance. The Amhara Regional Health Bureau and East Gojjam District issued a support letter. Informed consent was taken from each participant after a detailed explanation of study objectives, data confidentiality, and data collection procedure.

3. RESULTS

3.1. Socio-demographic Characteristics of HEWs

A total of 302 HEWs participated in the study; 56.6% were aged between 28-35 years, and 51.3% had ≤5 years working experience. The majority (86.1%) HEWs were nine-month certificates, 87.1% were from rural, and 60% earned ≥1427 Birr ($61 USD) each month (Table 1).

Table 1
Socio-demographic description of HEWs in East Gojjam Zone, Ethiopia, 2014.


3.2. Data Management Knowledge and Practice of HEWs

Over half (52.8%) of the HEWs had poor knowledge (scored below the mean 115.38 ±3.33) of knowledge questions. On the contrary, 53.3% were good data managers (scored above the mean 8.27±3.19) of data management practice questions (Table 3).

3.3. Technical and Organization Related Factors to Data Management Practice of HEWs

About 61.6% HEWs faced difficulties in understanding reporting formats. Reporting format inconsistency (46.2%), unusual words (38.7%), and unclear abbreviations (15.1%) made reporting formats unclear. Nearly two-thirds (61.3%) HEWs were reported within a regular reporting period, and 45.4% faced challenges. The majority (85.1%, 94.7%, and 87.4%) of the HEWs reported the availability of reporting formats, supervision, and training on data management, respectively (Table 2).

Table 2
Technical variables of HEWs to data management in East Gojjam Zone, Ethiopia, 2014.


In this study, 272 (90.0%) of the HEWs used routine data: 109 (40.0%) for daily activities, 111 (40.8%) for planning, and 25 (9.2%) for monitoring and evaluation.

3.4. Factors Associated with Data Management Practice of HEWs

The regression analysis identified important factors in data management practice (Table 3). HEWs who had knowledge of data management were 2.75 times more likely to be good data managers than their counterparts (odds ratio/OR/=2.75, 95% CI= 1.62, 4.60). The odds of being a good data manager were 1.86 times more among urban HEWs than the rural ones (95% CI= 1.11, 4.05). HEWs with adequate reference materials were 1.64 times more likely to be good data managers compared to the counterparts HEWs (95% CI= 1.12, 2.78). Trained HEWs were found to be good data management practitioners (OR= 2.78, 95% CI= (1.34, 5.71) compared to the non-trained groups. The odds of being a good data management practitioner was 1.82 times more among non-overloaded HEWs compared to overloaded HEWs (95% = 1.09, 3.78) (Table 3).

Table 3
Factors to data management practice of HEWs in Northwest Ethiopia, 2014.


4. DISCUSSION

Primary healthcare units are major sources of routine healthcare data to the Ethiopian health system since they are in the rural settings where about 85% of country’s population is living [11Ethiopian Federal Ministry of Health. HMIS Information use and data quality training manual 2014., 12Ethiopian Federal ministry of Health. Health sector transformation plan from 2015/16-2020 2015., 22Ethiopian Public Health Institute, Federal ministry of Health.. Health Data Quality Review: Systemic review on selected indicators 2016., 24HMIS Reform Team. Health Management Information System (HMIS)/Monitoring and Evaluation (M&E): Strategic Plan for Ethiopian Health Sector.FMOH 2011 January,; ]. Hence, poor data management in those facilities can affect the performance of the overall Ethiopian health system. To improve data management practice, adequate knowledge about data and its management is needed [11Ethiopian Federal Ministry of Health. HMIS Information use and data quality training manual 2014., 12Ethiopian Federal ministry of Health. Health sector transformation plan from 2015/16-2020 2015., 22Ethiopian Public Health Institute, Federal ministry of Health.. Health Data Quality Review: Systemic review on selected indicators 2016.].

In this study, only 47.4% of the HEWs had good data management knowledge. This clearly indicated that over half of the HEWs practiced data management without knowhow, which leads to poor data and decision quality [11Ethiopian Federal Ministry of Health. HMIS Information use and data quality training manual 2014., 16Shagake S S, Mengistu M Y, Zeleke A A. Data Management Knowledge, Practice and Associated Factors of Ethiopian Health Extension Workers in Gamo Gofa Zone, Southern Ethiopia: Across- Sectional Study. Health & Medical Informatics 2012; 5(1), 24HMIS Reform Team. Health Management Information System (HMIS)/Monitoring and Evaluation (M&E): Strategic Plan for Ethiopian Health Sector.FMOH 2011 January,; ]. This finding is slightly lower compared to study findings from Southern Ethiopia [16Shagake S S, Mengistu M Y, Zeleke A A. Data Management Knowledge, Practice and Associated Factors of Ethiopian Health Extension Workers in Gamo Gofa Zone, Southern Ethiopia: Across- Sectional Study. Health & Medical Informatics 2012; 5(1)], where 58.2% of HEWs had good data management knowledge. The difference could be due to training access where 42% of HEWs from Southern Ethiopia took training on data management, but only 12.6% from East Gojjam had training.

Based on the current study, only 53.3% of the HEWs had good data management practice. This could highly affect the decision-making practices of the Ethiopian health system in terms of resource allocation, planning, service quality and equity. The current data management practice was different from study findings in Southern Ethiopia [16Shagake S S, Mengistu M Y, Zeleke A A. Data Management Knowledge, Practice and Associated Factors of Ethiopian Health Extension Workers in Gamo Gofa Zone, Southern Ethiopia: Across- Sectional Study. Health & Medical Informatics 2012; 5(1)], where 74.3% of HEWs were good in data management. This discrepancy could be due to differences in data management knowledge [limited training], and unclear formats. Geographic location, community type, and supervision may also play an important role in making such differences. On the contrary, it was higher compared to study findings from Ethiopia [12Ethiopian Federal ministry of Health. Health sector transformation plan from 2015/16-2020 2015.] -33.3%, and a single study from Gaza and Palestine [31State of Palestine Ministry of Health. National Health Information System Summary Assessment 2011.], where data management practices were 11.0%, and 18.0%, respectively. Study period, area coverage, and instability may cause this variation.

Majority of the HEWs reported the presence of scarcity of data management inputs (Table 2) due to no electric power, supervision, communication, and transportation in HPs. All these could have an impact on data quality and management [11Ethiopian Federal Ministry of Health. HMIS Information use and data quality training manual 2014., 16Shagake S S, Mengistu M Y, Zeleke A A. Data Management Knowledge, Practice and Associated Factors of Ethiopian Health Extension Workers in Gamo Gofa Zone, Southern Ethiopia: Across- Sectional Study. Health & Medical Informatics 2012; 5(1), 17Shiferaw AM, Zegeye DT, Assefa S, Yenit MK. Routine health information system utilization and factors associated thereof among health workers at government health institutions in East Gojjam Zone, Northwest Ethiopia. BMC Med Inform Decis Mak 2017; 17(1): 116.[http://dx.doi.org/10.1186/s12911-017-0509-2] [PMID: 28784115] , 22Ethiopian Public Health Institute, Federal ministry of Health.. Health Data Quality Review: Systemic review on selected indicators 2016.]. Evidence-based practice in the Ethiopian health system would be challenged unless health information system officials give special attention to it [4Ethiopian Federal Ministry of Health. Health Extension Program in Ethiopia 2013., 6Haile GM. Community based program in Ethiopia: Form CBD to a Massive, State run Health Etension Program 2012., 16Shagake S S, Mengistu M Y, Zeleke A A. Data Management Knowledge, Practice and Associated Factors of Ethiopian Health Extension Workers in Gamo Gofa Zone, Southern Ethiopia: Across- Sectional Study. Health & Medical Informatics 2012; 5(1), 19Asemahagn. Knowledge and experience sharing practices among health professionals in hospitals under the Addis Ababa health bureau, Ethiopia. BMC Health Serv Res 2014; 14(431), 32Umar US, Olumide EA, Bawa SB. Village health workers’ and traditional birth attendants’ record keeping practices in two rural LGAs in Oyo State, Nigeria. Afr J Med Med Sci 2003; 32(2): 183-92.[PMID: 15032467] ].

Based on this study, 90.4% of the HEWs used health data; 40% for daily activities, 40.8% for planning and 9.2% for evaluation. This greater figure may be due to the weaker definition given for health information use in this study; using data for at least one activity in addition to reporting which may lead to an artificial increase. This utilization was higher compared to study findings from Jimma [33Abajebel S ea. Utilization of health information system at district information communication technology level in Jimma zone Oromia regional state,south west Ethiopia. Ethiop J Health Sci 2011; 21: 65-76.[PMID: 22435010] ], and North Gondar [34Andargi G, Addisse M. Assessment of Utilization of HMIS at district level with particular emphasis on the HIV/AIDS program in North Gondar zone, Ethiopia 2007.], where daily data utilizations were 26.7% and 22.5%, respectively. It could be due to differences in the study period and awareness level. However, it is lower compared to study findings from Southern Ethiopia [16Shagake S S, Mengistu M Y, Zeleke A A. Data Management Knowledge, Practice and Associated Factors of Ethiopian Health Extension Workers in Gamo Gofa Zone, Southern Ethiopia: Across- Sectional Study. Health & Medical Informatics 2012; 5(1)] where 87% of the HEWs used data for decision making.

Urban HEWs showed better data management practice (OR=1.86, 95% CI= [1.11, 4.05]) compared to the rural HEWs which could be due to relatively better access to transport, materials, information, and supervision. In addition, their overall education level (urban- three years diploma certificate, and rural nine months certificate) may cause the variation [11Ethiopian Federal Ministry of Health. HMIS Information use and data quality training manual 2014., 24HMIS Reform Team. Health Management Information System (HMIS)/Monitoring and Evaluation (M&E): Strategic Plan for Ethiopian Health Sector.FMOH 2011 January,; ].

HEWs having good data management knowledge were more likely to be good data managers compared to counterpart HEWs (2.75 [1.62, 4.60]. It is clear that data management knowledge is a prerequisite to data management practice. Likewise, access to the reference materials was a key factor in data management practice of HEWs (Table 3). Various studies supported this finding [4Ethiopian Federal Ministry of Health. Health Extension Program in Ethiopia 2013., 10Ekwueme OC. Health Data Recording, Reporting and Utilization Practices Among Primary Health Care Workers in Enugu State, South Eastern Nigeria. J College Med 2008; 13(2): 85-90., 11Ethiopian Federal Ministry of Health. HMIS Information use and data quality training manual 2014., 16Shagake S S, Mengistu M Y, Zeleke A A. Data Management Knowledge, Practice and Associated Factors of Ethiopian Health Extension Workers in Gamo Gofa Zone, Southern Ethiopia: Across- Sectional Study. Health & Medical Informatics 2012; 5(1), 24HMIS Reform Team. Health Management Information System (HMIS)/Monitoring and Evaluation (M&E): Strategic Plan for Ethiopian Health Sector.FMOH 2011 January,; , 32Umar US, Olumide EA, Bawa SB. Village health workers’ and traditional birth attendants’ record keeping practices in two rural LGAs in Oyo State, Nigeria. Afr J Med Med Sci 2003; 32(2): 183-92.[PMID: 15032467] ]. Congruently, HEWs with no communication access were poor data management practitioners compared to their counterparts. Likewise, the workload was a key factor in poor data management practice of HEWs. It is because of time shortage to practice on routine data, and evidence from Ethiopia supported this finding [16Shagake S S, Mengistu M Y, Zeleke A A. Data Management Knowledge, Practice and Associated Factors of Ethiopian Health Extension Workers in Gamo Gofa Zone, Southern Ethiopia: Across- Sectional Study. Health & Medical Informatics 2012; 5(1), 32Umar US, Olumide EA, Bawa SB. Village health workers’ and traditional birth attendants’ record keeping practices in two rural LGAs in Oyo State, Nigeria. Afr J Med Med Sci 2003; 32(2): 183-92.[PMID: 15032467] -34Andargi G, Addisse M. Assessment of Utilization of HMIS at district level with particular emphasis on the HIV/AIDS program in North Gondar zone, Ethiopia 2007.].

CONCLUSION

The data management knowledge and practice of Ethiopian HEWs are poor. Improving infrastructure, communication media, professional development, management supports, training, and reference materials access are important to solve the problem. We suggest a large scale qualitative study to identify enabling and barrier factors at individual and organizational domains.

LIMITATIONS OF THE STUDY

Recall bias may lower data management practice level and the study was not adequately supported by qualitative data. Determining data management knowledge and practice using the mean score of knowledge and practice questions may also be a limitation to this study.

AUTHORS' CONTRIBUTIONS

All participated in proposal development; SY handled data collection; all did data edition/ analysis; and MA did the manuscript draft, and review process.

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

The University of Gondar ethical review committee reviewed and gave ethical clearance.

HUMAN AND ANIMAL RIGHTS

No animals/humans were used for studies that are the basis of this research.

CONSENT FOR PUBLICATION

Written informed consent have been obtained for the study.

AVAILABILITY OF DATA AND MATERIALS

Not applicable.

FUNDING

The budget funder was Amhara Regional Health Bureau with a funding Id of 121/2014.

CONFLICT OF INTEREST

The author declares no conflict of interest, financial or otherwise.

ACKNOWLEDGEMENTS

We would like to thank the University of Gondar for giving ethical clearance, and the Amhara Regional Health Bureau for allocating the budget. We also thank the East Gojjam Zone Health office, data collectors, supervisors and study participants for their candid supports.

REFERENCES

[1] Banteyerga H, Kidanu A. Rapid Appraisal of Health Extension Program: Ethiopia Country Report 2008.
[2] Bilal NK, Herbst CH, Zhao F, Soucat A, Lemiere C. Health Extension Workers in Ethiopia: Improved Access and Coverage for the Rural Poor. Chapter 24.
[3] Center for National Health Development in Ethiopia. Ethiopian Health Extension Program and Health post performance survey Columbia University: The Rural Ethiopia 2010.
[4] Ethiopian Federal Ministry of Health. Health Extension Program in Ethiopia 2013.
[5] Ethiopian Federal Minstry of Health.. Training of Health Extension Workers (HEW) On Family Folder and HMIS Procedures. Facilitators’ Guide 2011.
[6] Haile GM. Community based program in Ethiopia: Form CBD to a Massive, State run Health Etension Program 2012.
[7] Jira C. ealth planning for health science students: Lecture Note Series, Jima University, Faculity of Public Health, Ethiopia 2007.
[8] Yitayal M, Berhane Y, Worku A, Kebede Y. The community-based Health Extension Program significantly improved contraceptive utilization in West Gojjam Zone, Ethiopia. J Multidiscip Healthc 2014; 7: 201-8.[http://dx.doi.org/10.2147/JMDH.S62294] [PMID: 24868165]
[9] Association for Healthcare Document Integrity (Adhi). Healthcare documentation quality assessment and management best practices Excutive summary 2011.
[10] Ekwueme OC. Health Data Recording, Reporting and Utilization Practices Among Primary Health Care Workers in Enugu State, South Eastern Nigeria. J College Med 2008; 13(2): 85-90.
[11] Ethiopian Federal Ministry of Health. HMIS Information use and data quality training manual 2014.
[12] Ethiopian Federal ministry of Health. Health sector transformation plan from 2015/16-2020 2015.
[13] Ethiopian Public Health Institute. Improving Health Extension Program in Ethiopia: An Evidence-Based. Policy Brief 2014.
[14] Gobena T, Alemayehu T, Fantahun T. Documentation for Health Extension Workers Lecture Notes, November 2004.
[15] Arrighi HM. Data management and data analysis wwwepidemiolognet, © Victor J Schoenbach rev 10/22/1999, 10/28/1999, 4/9/2000 2000.
[16] Shagake S S, Mengistu M Y, Zeleke A A. Data Management Knowledge, Practice and Associated Factors of Ethiopian Health Extension Workers in Gamo Gofa Zone, Southern Ethiopia: Across- Sectional Study. Health & Medical Informatics 2012; 5(1)
[17] Shiferaw AM, Zegeye DT, Assefa S, Yenit MK. Routine health information system utilization and factors associated thereof among health workers at government health institutions in East Gojjam Zone, Northwest Ethiopia. BMC Med Inform Decis Mak 2017; 17(1): 116.[http://dx.doi.org/10.1186/s12911-017-0509-2] [PMID: 28784115]
[18] Andualem M, Kebede G, Kumie A. Information needs and seeking behaviour among health professionals working at public hospital and health centres in Bahir Dar, Ethiopia. BMC Health Serv Res 2013; 13(534): 534.[http://dx.doi.org/10.1186/1472-6963-13-534] [PMID: 24373296]
[19] Asemahagn. Knowledge and experience sharing practices among health professionals in hospitals under the Addis Ababa health bureau, Ethiopia. BMC Health Serv Res 2014; 14(431)
[20] Fitsum G, Belaineh G. Health services utilization and associated factors in Jimma Zone , South West Ethiopia 2007.
[21] Huong NT. Clinical data management (Process and practical Guide) 2012.
[22] Ethiopian Public Health Institute, Federal ministry of Health.. Health Data Quality Review: Systemic review on selected indicators 2016.
[23] Gimbel S. An assessment of routine primary care health information system data quality in Sofala Province, Mozambique. Population health metrics 2011; 9(12)
[24] HMIS Reform Team. Health Management Information System (HMIS)/Monitoring and Evaluation (M&E): Strategic Plan for Ethiopian Health Sector.FMOH 2011 January,;
[25] Simba D. Quality of a routine data collection system for health: case of Kinondoni district in the Dar es Salaam region. Tanzania South African Journal of Information Mangement 2005; 7(2)[http://dx.doi.org/10.4102/sajim.v7i2.262]
[26] Awale P. Determinants of health information in Nethenje health area of Liongue district 2011.
[27] Center for National Health Development in Ethiopia. Assessment of Working Conditions of the First Batch of Health Extension Workers 2006.
[28] Lu Z, Su J. Clinical data management: Current status, challenges, and future directions from industry perspectives. Open Access J Clin Trials 2010; 2: 93-105.[http://dx.doi.org/10.2147/OAJCT.S8172]
[29] East Gojjam zone Health Department.. East Gojjam Zone Health Department annual performance report of 2016 2016.
[30] Center for National Health Development in Ethiopia. Ethiopia Health Extension Program Evaluation: Rural Ethiopia. PartII 2007; 10
[31] State of Palestine Ministry of Health. National Health Information System Summary Assessment 2011.
[32] Umar US, Olumide EA, Bawa SB. Village health workers’ and traditional birth attendants’ record keeping practices in two rural LGAs in Oyo State, Nigeria. Afr J Med Med Sci 2003; 32(2): 183-92.[PMID: 15032467]
[33] Abajebel S ea. Utilization of health information system at district information communication technology level in Jimma zone Oromia regional state,south west Ethiopia. Ethiop J Health Sci 2011; 21: 65-76.[PMID: 22435010]
[34] Andargi G, Addisse M. Assessment of Utilization of HMIS at district level with particular emphasis on the HIV/AIDS program in North Gondar zone, Ethiopia 2007.

Endorsements



"Open access will revolutionize 21st century knowledge work and accelerate the diffusion of ideas and evidence that support just in time learning and the evolution of thinking in a number of disciplines."


Daniel Pesut
(Indiana University School of Nursing, USA)

"It is important that students and researchers from all over the world can have easy access to relevant, high-standard and timely scientific information. This is exactly what Open Access Journals provide and this is the reason why I support this endeavor."


Jacques Descotes
(Centre Antipoison-Centre de Pharmacovigilance, France)

"Publishing research articles is the key for future scientific progress. Open Access publishing is therefore of utmost importance for wider dissemination of information, and will help serving the best interest of the scientific community."


Patrice Talaga
(UCB S.A., Belgium)

"Open access journals are a novel concept in the medical literature. They offer accessible information to a wide variety of individuals, including physicians, medical students, clinical investigators, and the general public. They are an outstanding source of medical and scientific information."


Jeffrey M. Weinberg
(St. Luke's-Roosevelt Hospital Center, USA)

"Open access journals are extremely useful for graduate students, investigators and all other interested persons to read important scientific articles and subscribe scientific journals. Indeed, the research articles span a wide range of area and of high quality. This is specially a must for researchers belonging to institutions with limited library facility and funding to subscribe scientific journals."


Debomoy K. Lahiri
(Indiana University School of Medicine, USA)

"Open access journals represent a major break-through in publishing. They provide easy access to the latest research on a wide variety of issues. Relevant and timely articles are made available in a fraction of the time taken by more conventional publishers. Articles are of uniformly high quality and written by the world's leading authorities."


Robert Looney
(Naval Postgraduate School, USA)

"Open access journals have transformed the way scientific data is published and disseminated: particularly, whilst ensuring a high quality standard and transparency in the editorial process, they have increased the access to the scientific literature by those researchers that have limited library support or that are working on small budgets."


Richard Reithinger
(Westat, USA)

"Not only do open access journals greatly improve the access to high quality information for scientists in the developing world, it also provides extra exposure for our papers."


J. Ferwerda
(University of Oxford, UK)

"Open Access 'Chemistry' Journals allow the dissemination of knowledge at your finger tips without paying for the scientific content."


Sean L. Kitson
(Almac Sciences, Northern Ireland)

"In principle, all scientific journals should have open access, as should be science itself. Open access journals are very helpful for students, researchers and the general public including people from institutions which do not have library or cannot afford to subscribe scientific journals. The articles are high standard and cover a wide area."


Hubert Wolterbeek
(Delft University of Technology, The Netherlands)

"The widest possible diffusion of information is critical for the advancement of science. In this perspective, open access journals are instrumental in fostering researches and achievements."


Alessandro Laviano
(Sapienza - University of Rome, Italy)

"Open access journals are very useful for all scientists as they can have quick information in the different fields of science."


Philippe Hernigou
(Paris University, France)

"There are many scientists who can not afford the rather expensive subscriptions to scientific journals. Open access journals offer a good alternative for free access to good quality scientific information."


Fidel Toldrá
(Instituto de Agroquimica y Tecnologia de Alimentos, Spain)

"Open access journals have become a fundamental tool for students, researchers, patients and the general public. Many people from institutions which do not have library or cannot afford to subscribe scientific journals benefit of them on a daily basis. The articles are among the best and cover most scientific areas."


M. Bendandi
(University Clinic of Navarre, Spain)

"These journals provide researchers with a platform for rapid, open access scientific communication. The articles are of high quality and broad scope."


Peter Chiba
(University of Vienna, Austria)

"Open access journals are probably one of the most important contributions to promote and diffuse science worldwide."


Jaime Sampaio
(University of Trás-os-Montes e Alto Douro, Portugal)

"Open access journals make up a new and rather revolutionary way to scientific publication. This option opens several quite interesting possibilities to disseminate openly and freely new knowledge and even to facilitate interpersonal communication among scientists."


Eduardo A. Castro
(INIFTA, Argentina)

"Open access journals are freely available online throughout the world, for you to read, download, copy, distribute, and use. The articles published in the open access journals are high quality and cover a wide range of fields."


Kenji Hashimoto
(Chiba University, Japan)

"Open Access journals offer an innovative and efficient way of publication for academics and professionals in a wide range of disciplines. The papers published are of high quality after rigorous peer review and they are Indexed in: major international databases. I read Open Access journals to keep abreast of the recent development in my field of study."


Daniel Shek
(Chinese University of Hong Kong, Hong Kong)

"It is a modern trend for publishers to establish open access journals. Researchers, faculty members, and students will be greatly benefited by the new journals of Bentham Science Publishers Ltd. in this category."


Jih Ru Hwu
(National Central University, Taiwan)


Browse Contents



Webmaster Contact: info@benthamopen.net
Copyright © 2019 Bentham Open