Institutionalized Data Quality Assessments: A Critical Pathway to Improving the Accuracy of Integrated Disease Surveillance Data in Sierra Leone

Abstract Background Public health agencies require valid, timely and complete health information for early detection of outbreaks. Towards the end of the Ebola Virus Disease (EVD) outbreak in 2015, the Ministry of Health and Sanitation (MoHS), Sierra Leone revitalized the Integrated Disease Surveillance and Response System (IDSR). Data quality assessments were conducted to monitor the accuracy of data generated through the IDSR system.Methods Starting 2016, regular data quality assessments (DQA)were conducted in randomly selected health facilities. A structured electronic checklist was used to interview district health management team (DHMT) members and health facility staff. We used malaria data to assess data accuracy as malaria was endemic in Sierra Leone. Verification factors (VF) calculated as the ratio of verified malaria cases in the health facility register to the number of malaria cases recorded in the national health information database, were used to assess data accuracy. Allowing a 5% margin of error, VF <95% were considered over reporting while a VF >105 was underreporting. Differences in the proportion of accurate reports in the first and fourth assessments were compared using Z-test for two proportions.Results Between 2016 -2018, four DQA were conducted in 444 health facilities where 1,729 IDSR reports were reviewed. Registers and IDSR technical guidelines were widely available in health facilities and health care workers were conversant with reporting requirements. Overall data accuracy improved from VF of 95.3% in 2016 to 100.2% in 2018. Compared to the baseline in 2016, the proportion of accurate IDSR reports in 2018 increased by 19.5% (CI 12.5% -26.5%). Over reporting was more common in private clinics and not for profit facilities while under-reporting was more common in lower level government health facilities. Leading reasons for data discrepancies included counting errors in 358 (80.6%) health facilities, and missing source documents in 47 (10.6%) health facilities.Conclusion This is the first attempt to institutionalize routine monitoring of IDSR data quality in Sierra Leone. Regular data quality assessments may have contributed to improved data accuracy over time. Data compilation errors accounted for most discrepancies and should be minimized to improve accuracy of IDSR data.

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Institutionalized Data Quality Assessments: A Critical Pathway to Improving the Accuracy of Integrated Disease Surveillance Data in Sierra Leone

10.21203/rs.2.19221/v2 ◽

2020 ◽

Author(s):

Charles Kuria Njuguna ◽

Mohamed Vandi ◽

Malimbo Mugagga ◽

Joseph Kanu ◽

Evans Liyosi ◽

...

Keyword(s):

Data Quality ◽

Health Information ◽

Sierra Leone ◽

Health Facility ◽

Disease Surveillance ◽

Virus Disease ◽

Health Facilities ◽

Data Accuracy ◽

Critical Pathway ◽

Quality Assessments

Abstract Background Public health agencies require valid, timely and complete health information for early detection of outbreaks. Towards the end of the Ebola Virus Disease (EVD) outbreak in 2015, the Ministry of Health and Sanitation (MoHS), Sierra Leone revitalized the Integrated Disease Surveillance and Response System (IDSR). Data quality assessments were conducted to monitor accuracy of IDSR data. Methods Starting 2016, data quality assessments (DQA) were conducted in randomly selected health facilities. Structured electronic checklist was used to interview district health management teams (DHMT) and health facility staff. We used malaria data, to assess data accuracy as malaria was endemic in Sierra Leone. Verification factors (VF) calculated as the ratio of verified malaria cases in health facility registers to the number of malaria cases in the national health information database, were used to assess data accuracy. Allowing a 5% margin of error, VF <95% were considered over reporting while VF >105 was underreporting. Differences in the proportion of accurate reports at baseline and subsequent assessments were compared using Z-test for two proportions. Results Between 2016 -2018, four DQA were conducted in 444 health facilities where 1,729 IDSR reports were reviewed. Registers and IDSR technical guidelines were available in health facilities and health care workers were conversant with reporting requirements. Overall data accuracy improved from over- reporting of 4.7% (VF 95.3%) in 2016 to under-reporting of 0.2% (VF 100.2%) in 2018. Compared to 2016, proportion of accurate IDSR reports increased by 14.8 % (95% CI 7.2%, 22.3%) in May 2017 and 19.5% (95% CI 12.5% -26.5%) by 2018. Over reporting was more common in private clinics and not- for profit facilities while under-reporting was more common in lower level government health facilities. Leading reasons for data discrepancies included counting errors in 358 (80.6%) health facilities and missing source documents in 47 (10.6%) health facilities. Conclusion This is the first attempt to institutionalize routine monitoring of IDSR data quality in Sierra Leone. Regular data quality assessments may have contributed to improved data accuracy over time. Data compilation errors accounted for most discrepancies and should be minimized to improve accuracy of IDSR data.

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Institutionalized Data Quality Assessments: A Critical Pathway to Improving the Accuracy of Integrated Disease Surveillance Data in Sierra Leone

10.21203/rs.2.19221/v3 ◽

2020 ◽

Author(s):

Charles Kuria Njuguna ◽

Mohamed Vandi ◽

Malimbo Mugagga ◽

Joseph Kanu ◽

Evans Liyosi ◽

...

Keyword(s):

Data Quality ◽

Health Information ◽

Sierra Leone ◽

Health Facility ◽

Disease Surveillance ◽

Virus Disease ◽

Health Facilities ◽

Data Accuracy ◽

Critical Pathway ◽

Quality Assessments

Abstract Background Public health agencies require valid, timely and complete health information for early detection of outbreaks. Towards the end of the Ebola Virus Disease (EVD) outbreak in 2015, the Ministry of Health and Sanitation (MoHS), Sierra Leone revitalized the Integrated Disease Surveillance and Response System (IDSR). Data quality assessments were conducted to monitor accuracy of IDSR data. Methods Starting 2016, data quality assessments (DQA) were conducted in randomly selected health facilities. Structured electronic checklist was used to interview district health management teams (DHMT) and health facility staff. We used malaria data, to assess data accuracy, as malaria was endemic in Sierra Leone. Verification factors (VF) calculated as the ratio of confirmed malaria cases recorded in health facility registers to the number of malaria cases in the national health information database, were used to assess data accuracy. Allowing a 5% margin of error, VF <95% were considered over reporting while VF >105 was underreporting. Differences in the proportion of accurate reports at baseline and subsequent assessments were compared using Z-test for two proportions. Results: Between 2016 -2018, four DQA were conducted in 444 health facilities where 1,729 IDSR reports were reviewed. Registers and IDSR technical guidelines were available in health facilities and health care workers were conversant with reporting requirements. Overall data accuracy improved from over- reporting of 4.7% (VF 95.3%) in 2016 to under-reporting of 0.2% (VF 100.2%) in 2018. Compared to 2016, proportion of accurate IDSR reports increased by 14.8 % (95% CI 7.2%, 22.3%) in May 2017 and 19.5% (95% CI 12.5% -26.5%) by 2018. Over reporting was more common in private clinics and not- for profit facilities while under-reporting was more common in lower level government health facilities. Leading reasons for data discrepancies included counting errors in 358 (80.6%) health facilities and missing source documents in 47 (10.6%) health facilities. Conclusion This is the first attempt to institutionalize routine monitoring of IDSR data quality in Sierra Leone. Regular data quality assessments may have contributed to improved data accuracy over time. Data compilation errors accounted for most discrepancies and should be minimized to improve accuracy of IDSR data.

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Institutionalized data quality assessments: a critical pathway to improving the accuracy of integrated disease surveillance data in Sierra Leone

BMC Health Services Research ◽

10.1186/s12913-020-05591-x ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Charles Njuguna ◽

Mohamed Vandi ◽

Malimbo Mugagga ◽

Joseph Kanu ◽

Evans Liyosi ◽

...

Keyword(s):

Data Quality ◽

Sierra Leone ◽

Disease Surveillance ◽

Surveillance Data ◽

Critical Pathway ◽

Quality Assessments

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Development of a Master Health Facility List in Nigeria

Online Journal of Public Health Informatics ◽

10.5210/ojphi.v6i2.5287 ◽

2014 ◽

Vol 6 (2) ◽

Cited By ~ 14

Author(s):

Olusesan Ayodeji Makinde ◽

Aderemi Azeez ◽

Samson Bamidele ◽

Akin Oyemakinde ◽

Kolawole A Oyediran ◽

...

Keyword(s):

Health Information ◽

Health Facility ◽

Health Information Systems ◽

Facility Management ◽

Health Facilities ◽

Unique Identifier ◽

Explicit Processes ◽

National Health Information Infrastructure ◽

Multi Level ◽

State Code

Introduction: Routine Health Information Systems (RHIS) are increasingly transitioning to electronic platforms in several developing countries. Establishment of a Master Facility List (MFL) to standardize the allocation of unique identifiers for health facilities can overcome identification issues and support health facility management. The Nigerian Federal Ministry of Health (FMOH) recently developed a MFL, and we present the process and outcome.Methods: The MFL was developed from the ground up, and includes a state code, a local government area (LGA) code, health facility ownership (public or private), the level of care, and an exclusive LGA level health facility serial number, as part of the unique identifier system in Nigeria. To develop the MFL, the LGAs sent the list of all health facilities in their jurisdiction to the state, which in turn collated for all LGAs under them before sending to the FMOH. At the FMOH, a group of RHIS experts verified the list and identifiers for each state.Results: The national MFL consists of 34,423 health facilities uniquely identified. The list has been published and is available for worldwide access; it is currently used for planning and management of health services in Nigeria.Discussion: Unique identifiers are a basic component of any information system. However, poor planning and execution of implementing this key standard can diminish the success of the RHIS.Conclusion: Development and adherence to standards is the hallmark for a national health information infrastructure. Explicit processes and multi-level stakeholder engagement is necessary to ensuring the success of the effort.

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Quality of Routine Health Information System Data and Associated Factors Among Departments in Public Health Facilities of Harari Region, Ethiopia

10.21203/rs.3.rs-576218/v1 ◽

2021 ◽

Author(s):

Adisu Tafari Shama ◽

Hirbo Shore Roba ◽

Admas Abera ◽

Negga Baraki

Keyword(s):

Public Health ◽

Information System ◽

Data Quality ◽

Health Information ◽

Health Information System ◽

Health Facilities ◽

Quality Data ◽

Public Health Facilities ◽

System Data

Abstract Background: Despite the improvements in the knowledge and understanding of the role of health information in the global health system, the quality of data generated by a routine health information system is still very poor in low and middle-income countries. There is a paucity of studies as to what determines data quality in health facilities in the study area. Therefore, this study was aimed to assess the quality of routine health information system data and associated factors in public health facilities of Harari region, Ethiopia.Methods: A cross-sectional study was conducted in all public health facilities in Harari region of Ethiopia. The department-level data were collected from respective department heads through document reviews, interviews, and observation check-lists. Descriptive statistics were used to data quality and multivariate logistic regression was run to identify factors influencing data quality. The level of significance was declared at P-value <0.05. Result: The study found a good quality data in 51.35% (95% CI, 44.6-58.1) of the departments in public health facilities in Harari Region. Departments found in the health centers were 2.5 times more likely to have good quality data as compared to departments found in the health posts. The presence of trained staffs able to fill reporting formats (AOR=2.474; 95%CI: 1.124-5.445) and provision of feedback (AOR=3.083; 95%CI: 1.549-6.135) were also significantly associated with data quality. Conclusion: The level of good data quality in the public health facilities was less than the expected national level. Training should be provided to increase the knowledge and skills of the health workers.

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Use of Peripheral Health Units in Low-Transmission Ebola Virus Disease Surveillance

Online Journal of Public Health Informatics ◽

10.5210/ojphi.v8i1.6596 ◽

2016 ◽

Vol 8 (1) ◽

Cited By ~ 4

Author(s):

Alyssa J. Young ◽

Allison Connolly ◽

Adam Hoar ◽

Brooke Mancuso ◽

John Mark Esplana ◽

...

Keyword(s):

Sierra Leone ◽

Surveillance System ◽

Disease Surveillance ◽

Ebola Virus ◽

Virus Disease ◽

Malaria Prevalence ◽

Ebola Virus Disease ◽

Alert System ◽

Community Based

Surveillance strategies for Ebola Virus Disease (EVD) in Sierra Leone use a centralized "live alert" system to refer suspect cases from the community to specialized Ebola treatment centers. As EVD case burden declined in Port Loko District, Sierra Leone so did the number of reported alerts. Because EVD presents similarly to malaria, the number of alerts should remain consistent with malaria prevalence in malaria-endemic areas, irrespective of the reduction in true EVD cases. A community-based EVD surveillance system with improved symptom recording and follow-up of malaria-confirmed patients at PHUs was implemented in order to strengthen the sensitivity of EVD reporting.

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Assessment of electronic disease early warning system for improved disease surveillance and outbreak response in Yemen

BMC Public Health ◽

10.1186/s12889-020-09460-4 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Fekri Dureab ◽

Kamran Ahmed ◽

Claudia Beiersmann ◽

Claire J. Standley ◽

Ali Alwaleedi ◽

...

Keyword(s):

Public Health ◽

Data Quality ◽

Early Warning ◽

Predictive Value ◽

Disease Surveillance ◽

Early Warning System ◽

Warning System ◽

Health Facilities ◽

Public Health Response ◽

Attributes Data

Abstract Background Diseases Surveillance is a continuous process of data collection, analysis interpretation and dissemination of information for swift public health action. Recent advances in health informatics have led to the implementation of electronic tools to facilitate such critical disease surveillance processes. This study aimed to assess the performance of the national electronic Disease Early Warning System in Yemen (eDEWS) using system attributes: data quality, timeliness, stability, simplicity, predictive value positive, sensitivity, acceptability, flexibility, and representativeness, based on the Centres for Disease Control & Prevention (US CDC) standard indicators. Methods We performed a mixed methods study that occurred in two stages: first, the quantitative data was collected from weekly epidemiological bulletins from 2013 to 2017, all alerts of 2016, and annual eDEWS reports, and then the qualitative method using in-depth interviews was carried out in a convergent strategy. The CDC guideline used to describe the following system attributes: data quality (reporting, and completeness), timeliness, stability, simplicity, predictive value positive, sensitivity, acceptability, flexibility and representativeness. Results The finding of this assessment showed that eDEWS is a resilient and reliable system, and despite the conflict in Yemen, the system is still functioning and expanding. The response timeliness remains a challenge, since only 21% of all eDEWS alerts were verified within the first 24 h of detection in 2016. However, identified gaps did not affect the system’s ability to identify outbreaks in the current fragile situation. Findings show that eDEWS data is representative, since it covers the entire country. Although, eDEWS covers only 37% of all health facilities, this represents 83% of all functional health facilities in all 23 governorates and all 333 districts. Conclusion The quality and timeliness of responses are major challenges to eDEWS’ functionality, the eDEWS remains the only system that provides regular data on communicable diseases in Yemen. In particular, public health response timeliness needs improvement.

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