Integrated disease surveillance and response implementation in Liberia, findings from a data quality audit, 2017

Thomas Nagbe, Kwuakuan Yealue, Trokon Yeabah, Julius Monday Rude, Musoka Fallah, Laura Skrip, Chukwuemeka Agbo, Nuha Mouhamoud, Joseph Chukwudi Okeibunor, Roland Tuopileyi, Ambrose Talisuna, Ali Ahmed Yahaya, Soatiana Rajatonirina, Joseph Asamoah Frimpong, Mary Stephen, Esther Hamblion, Tolbert Nyenswah, Bernice Dahn, Alex Gasasira, Ibrahima Socé Fall

Research output: Contribution to journalArticle

Abstract

Introduction: in spite of the efforts and resources committed by the division of infectious disease and epidemiology (DIDE) of the national public health institute of Liberia (NPHIL)/Ministry of health to strengthening integrated disease surveillance and response (IDSR) across the country, quality data management system remains a challenge to the Liberia NPHIL/MoH (Ministry of health), with incomplete and inconsistent data constantly being reported at different levels of the surveillance system. As part of the monitoring and evaluation strategy for IDSR continuous improvement, data quality assessment (DQA) of the IDSR system to identify successes and gaps in the disease surveillance information system (DSIS) with the aim of ensuring data accuracy, reliability and credibility of generated data at all levels of the health system; and to inform an operational plan to address data quality needs for IDSR activities is required. Methods: multi-stage cluster sampling that included stage 1: simple random sample (SRS) of five counties, stage 2: simple random sample of two districts and stage 3: simple random sample of three health facilities was employed during the study pilot assessment done in Montserrado County with Liberia institute of bio medical research (LIBR) inclusive. A total of thirty (30) facilities was targeted, twenty nine (29) of the facilities were successfully audited: one hospital, two health centers, twenty clinics and respondents included: health facility surveillance focal persons (HFSFP), zonal surveillance officers (ZSOs), district surveillance officers (DSOs) and County surveillance officers (CSOs). Results: the assessment revealed that data use is limited to risk communication and sensitization, no examples of use of data for prioritization or decision making at the subnational level. The findings indicated the following: 23% (7/29) of health facilities having dedicated phone for reporting, 20% (6/29) reported no cell phone network, 17% (5/29) reported daily access to internet, 56.6% (17/29) reported a consistent supply of electricity, and no facility reported access to functional laptop. It was also established that 40% of health facilities have experienced a stock out of laboratory specimens packaging supplies in the past year. About half of the surveyed health facilities delivered specimens through riders and were assisted by the DSOs. There was a large variety in the reported packaging process, with many staff unable to give clear processes. The findings during the exercise also indicated that 91% of health facility staff were mentored on data quality check and data management including the importance of the timeliness and completeness of reporting through supportive supervision and mentorship; 65% of the health facility assessed received supervision on IDSR core performance indicator; and 58% of the health facility officer in charge gave feedback to the community level. Conclusion: public health is a data-intensive field which needs high-quality data and authoritative information to support public health assessment, decision-making and to assure the health of communities. Data quality assessment is important for public health. In this review completeness, accuracy, and timeliness were the three most-assessed attributes. Quantitative data quality assessment primarily used descriptive surveys and data audits, while qualitative data quality assessment methods include primarily interviews, questionnaires administration, documentation reviews and field observations. We found that data-use and data-process have not been given adequate attention, although they were equally important factors which determine the quality of data. Other limitations of the previous studies were inconsistency in the definition of the attributes of data quality, failure to address data users' concerns and a lack of triangulation of mixed methods for data quality assessment. The reliability and validity of the data quality assessment were rarely reported. These gaps suggest that in the future, data quality assessment for public health needs to consider equally the three dimensions of data quality, data use and data process. Measuring the perceptions of end users or consumers towards data quality will enrich our understanding of data quality issues. Data use is limited to risk communication and sensitization, no examples of use of data for prioritization or decision making at the sub national level.

Original languageEnglish (US)
Number of pages1
JournalThe Pan African medical journal
Volume33
DOIs
StatePublished - Jan 1 2019
Externally publishedYes

Fingerprint

Liberia
Health Facilities
Public Health
Decision Making
Data Accuracy
Health
National Institutes of Health (U.S.)
Product Packaging
Information Systems
Communication
Electricity

Keywords

  • and timeliness integrated disease surveillance and response
  • case investigation forms and eDEWS
  • completeness
  • data accuracy
  • Data quality assessment
  • disease surveillance information system
  • health management information system (HMIS)/district health informative system two (DHIS2) database
  • multi-stage cluster sampling
  • reliability and credibility
  • simple random sample

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Integrated disease surveillance and response implementation in Liberia, findings from a data quality audit, 2017. / Nagbe, Thomas; Yealue, Kwuakuan; Yeabah, Trokon; Rude, Julius Monday; Fallah, Musoka; Skrip, Laura; Agbo, Chukwuemeka; Mouhamoud, Nuha; Okeibunor, Joseph Chukwudi; Tuopileyi, Roland; Talisuna, Ambrose; Yahaya, Ali Ahmed; Rajatonirina, Soatiana; Frimpong, Joseph Asamoah; Stephen, Mary; Hamblion, Esther; Nyenswah, Tolbert; Dahn, Bernice; Gasasira, Alex; Fall, Ibrahima Socé.

In: The Pan African medical journal, Vol. 33, 01.01.2019.

Research output: Contribution to journalArticle

Nagbe, T, Yealue, K, Yeabah, T, Rude, JM, Fallah, M, Skrip, L, Agbo, C, Mouhamoud, N, Okeibunor, JC, Tuopileyi, R, Talisuna, A, Yahaya, AA, Rajatonirina, S, Frimpong, JA, Stephen, M, Hamblion, E, Nyenswah, T, Dahn, B, Gasasira, A & Fall, IS 2019, 'Integrated disease surveillance and response implementation in Liberia, findings from a data quality audit, 2017', The Pan African medical journal, vol. 33. https://doi.org/10.11604/pamj.supp.2019.33.2.17608
Nagbe, Thomas ; Yealue, Kwuakuan ; Yeabah, Trokon ; Rude, Julius Monday ; Fallah, Musoka ; Skrip, Laura ; Agbo, Chukwuemeka ; Mouhamoud, Nuha ; Okeibunor, Joseph Chukwudi ; Tuopileyi, Roland ; Talisuna, Ambrose ; Yahaya, Ali Ahmed ; Rajatonirina, Soatiana ; Frimpong, Joseph Asamoah ; Stephen, Mary ; Hamblion, Esther ; Nyenswah, Tolbert ; Dahn, Bernice ; Gasasira, Alex ; Fall, Ibrahima Socé. / Integrated disease surveillance and response implementation in Liberia, findings from a data quality audit, 2017. In: The Pan African medical journal. 2019 ; Vol. 33.
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abstract = "Introduction: in spite of the efforts and resources committed by the division of infectious disease and epidemiology (DIDE) of the national public health institute of Liberia (NPHIL)/Ministry of health to strengthening integrated disease surveillance and response (IDSR) across the country, quality data management system remains a challenge to the Liberia NPHIL/MoH (Ministry of health), with incomplete and inconsistent data constantly being reported at different levels of the surveillance system. As part of the monitoring and evaluation strategy for IDSR continuous improvement, data quality assessment (DQA) of the IDSR system to identify successes and gaps in the disease surveillance information system (DSIS) with the aim of ensuring data accuracy, reliability and credibility of generated data at all levels of the health system; and to inform an operational plan to address data quality needs for IDSR activities is required. Methods: multi-stage cluster sampling that included stage 1: simple random sample (SRS) of five counties, stage 2: simple random sample of two districts and stage 3: simple random sample of three health facilities was employed during the study pilot assessment done in Montserrado County with Liberia institute of bio medical research (LIBR) inclusive. A total of thirty (30) facilities was targeted, twenty nine (29) of the facilities were successfully audited: one hospital, two health centers, twenty clinics and respondents included: health facility surveillance focal persons (HFSFP), zonal surveillance officers (ZSOs), district surveillance officers (DSOs) and County surveillance officers (CSOs). Results: the assessment revealed that data use is limited to risk communication and sensitization, no examples of use of data for prioritization or decision making at the subnational level. The findings indicated the following: 23{\%} (7/29) of health facilities having dedicated phone for reporting, 20{\%} (6/29) reported no cell phone network, 17{\%} (5/29) reported daily access to internet, 56.6{\%} (17/29) reported a consistent supply of electricity, and no facility reported access to functional laptop. It was also established that 40{\%} of health facilities have experienced a stock out of laboratory specimens packaging supplies in the past year. About half of the surveyed health facilities delivered specimens through riders and were assisted by the DSOs. There was a large variety in the reported packaging process, with many staff unable to give clear processes. The findings during the exercise also indicated that 91{\%} of health facility staff were mentored on data quality check and data management including the importance of the timeliness and completeness of reporting through supportive supervision and mentorship; 65{\%} of the health facility assessed received supervision on IDSR core performance indicator; and 58{\%} of the health facility officer in charge gave feedback to the community level. Conclusion: public health is a data-intensive field which needs high-quality data and authoritative information to support public health assessment, decision-making and to assure the health of communities. Data quality assessment is important for public health. In this review completeness, accuracy, and timeliness were the three most-assessed attributes. Quantitative data quality assessment primarily used descriptive surveys and data audits, while qualitative data quality assessment methods include primarily interviews, questionnaires administration, documentation reviews and field observations. We found that data-use and data-process have not been given adequate attention, although they were equally important factors which determine the quality of data. Other limitations of the previous studies were inconsistency in the definition of the attributes of data quality, failure to address data users' concerns and a lack of triangulation of mixed methods for data quality assessment. The reliability and validity of the data quality assessment were rarely reported. These gaps suggest that in the future, data quality assessment for public health needs to consider equally the three dimensions of data quality, data use and data process. Measuring the perceptions of end users or consumers towards data quality will enrich our understanding of data quality issues. Data use is limited to risk communication and sensitization, no examples of use of data for prioritization or decision making at the sub national level.",
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TY - JOUR

T1 - Integrated disease surveillance and response implementation in Liberia, findings from a data quality audit, 2017

AU - Nagbe, Thomas

AU - Yealue, Kwuakuan

AU - Yeabah, Trokon

AU - Rude, Julius Monday

AU - Fallah, Musoka

AU - Skrip, Laura

AU - Agbo, Chukwuemeka

AU - Mouhamoud, Nuha

AU - Okeibunor, Joseph Chukwudi

AU - Tuopileyi, Roland

AU - Talisuna, Ambrose

AU - Yahaya, Ali Ahmed

AU - Rajatonirina, Soatiana

AU - Frimpong, Joseph Asamoah

AU - Stephen, Mary

AU - Hamblion, Esther

AU - Nyenswah, Tolbert

AU - Dahn, Bernice

AU - Gasasira, Alex

AU - Fall, Ibrahima Socé

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Introduction: in spite of the efforts and resources committed by the division of infectious disease and epidemiology (DIDE) of the national public health institute of Liberia (NPHIL)/Ministry of health to strengthening integrated disease surveillance and response (IDSR) across the country, quality data management system remains a challenge to the Liberia NPHIL/MoH (Ministry of health), with incomplete and inconsistent data constantly being reported at different levels of the surveillance system. As part of the monitoring and evaluation strategy for IDSR continuous improvement, data quality assessment (DQA) of the IDSR system to identify successes and gaps in the disease surveillance information system (DSIS) with the aim of ensuring data accuracy, reliability and credibility of generated data at all levels of the health system; and to inform an operational plan to address data quality needs for IDSR activities is required. Methods: multi-stage cluster sampling that included stage 1: simple random sample (SRS) of five counties, stage 2: simple random sample of two districts and stage 3: simple random sample of three health facilities was employed during the study pilot assessment done in Montserrado County with Liberia institute of bio medical research (LIBR) inclusive. A total of thirty (30) facilities was targeted, twenty nine (29) of the facilities were successfully audited: one hospital, two health centers, twenty clinics and respondents included: health facility surveillance focal persons (HFSFP), zonal surveillance officers (ZSOs), district surveillance officers (DSOs) and County surveillance officers (CSOs). Results: the assessment revealed that data use is limited to risk communication and sensitization, no examples of use of data for prioritization or decision making at the subnational level. The findings indicated the following: 23% (7/29) of health facilities having dedicated phone for reporting, 20% (6/29) reported no cell phone network, 17% (5/29) reported daily access to internet, 56.6% (17/29) reported a consistent supply of electricity, and no facility reported access to functional laptop. It was also established that 40% of health facilities have experienced a stock out of laboratory specimens packaging supplies in the past year. About half of the surveyed health facilities delivered specimens through riders and were assisted by the DSOs. There was a large variety in the reported packaging process, with many staff unable to give clear processes. The findings during the exercise also indicated that 91% of health facility staff were mentored on data quality check and data management including the importance of the timeliness and completeness of reporting through supportive supervision and mentorship; 65% of the health facility assessed received supervision on IDSR core performance indicator; and 58% of the health facility officer in charge gave feedback to the community level. Conclusion: public health is a data-intensive field which needs high-quality data and authoritative information to support public health assessment, decision-making and to assure the health of communities. Data quality assessment is important for public health. In this review completeness, accuracy, and timeliness were the three most-assessed attributes. Quantitative data quality assessment primarily used descriptive surveys and data audits, while qualitative data quality assessment methods include primarily interviews, questionnaires administration, documentation reviews and field observations. We found that data-use and data-process have not been given adequate attention, although they were equally important factors which determine the quality of data. Other limitations of the previous studies were inconsistency in the definition of the attributes of data quality, failure to address data users' concerns and a lack of triangulation of mixed methods for data quality assessment. The reliability and validity of the data quality assessment were rarely reported. These gaps suggest that in the future, data quality assessment for public health needs to consider equally the three dimensions of data quality, data use and data process. Measuring the perceptions of end users or consumers towards data quality will enrich our understanding of data quality issues. Data use is limited to risk communication and sensitization, no examples of use of data for prioritization or decision making at the sub national level.

AB - Introduction: in spite of the efforts and resources committed by the division of infectious disease and epidemiology (DIDE) of the national public health institute of Liberia (NPHIL)/Ministry of health to strengthening integrated disease surveillance and response (IDSR) across the country, quality data management system remains a challenge to the Liberia NPHIL/MoH (Ministry of health), with incomplete and inconsistent data constantly being reported at different levels of the surveillance system. As part of the monitoring and evaluation strategy for IDSR continuous improvement, data quality assessment (DQA) of the IDSR system to identify successes and gaps in the disease surveillance information system (DSIS) with the aim of ensuring data accuracy, reliability and credibility of generated data at all levels of the health system; and to inform an operational plan to address data quality needs for IDSR activities is required. Methods: multi-stage cluster sampling that included stage 1: simple random sample (SRS) of five counties, stage 2: simple random sample of two districts and stage 3: simple random sample of three health facilities was employed during the study pilot assessment done in Montserrado County with Liberia institute of bio medical research (LIBR) inclusive. A total of thirty (30) facilities was targeted, twenty nine (29) of the facilities were successfully audited: one hospital, two health centers, twenty clinics and respondents included: health facility surveillance focal persons (HFSFP), zonal surveillance officers (ZSOs), district surveillance officers (DSOs) and County surveillance officers (CSOs). Results: the assessment revealed that data use is limited to risk communication and sensitization, no examples of use of data for prioritization or decision making at the subnational level. The findings indicated the following: 23% (7/29) of health facilities having dedicated phone for reporting, 20% (6/29) reported no cell phone network, 17% (5/29) reported daily access to internet, 56.6% (17/29) reported a consistent supply of electricity, and no facility reported access to functional laptop. It was also established that 40% of health facilities have experienced a stock out of laboratory specimens packaging supplies in the past year. About half of the surveyed health facilities delivered specimens through riders and were assisted by the DSOs. There was a large variety in the reported packaging process, with many staff unable to give clear processes. The findings during the exercise also indicated that 91% of health facility staff were mentored on data quality check and data management including the importance of the timeliness and completeness of reporting through supportive supervision and mentorship; 65% of the health facility assessed received supervision on IDSR core performance indicator; and 58% of the health facility officer in charge gave feedback to the community level. Conclusion: public health is a data-intensive field which needs high-quality data and authoritative information to support public health assessment, decision-making and to assure the health of communities. Data quality assessment is important for public health. In this review completeness, accuracy, and timeliness were the three most-assessed attributes. Quantitative data quality assessment primarily used descriptive surveys and data audits, while qualitative data quality assessment methods include primarily interviews, questionnaires administration, documentation reviews and field observations. We found that data-use and data-process have not been given adequate attention, although they were equally important factors which determine the quality of data. Other limitations of the previous studies were inconsistency in the definition of the attributes of data quality, failure to address data users' concerns and a lack of triangulation of mixed methods for data quality assessment. The reliability and validity of the data quality assessment were rarely reported. These gaps suggest that in the future, data quality assessment for public health needs to consider equally the three dimensions of data quality, data use and data process. Measuring the perceptions of end users or consumers towards data quality will enrich our understanding of data quality issues. Data use is limited to risk communication and sensitization, no examples of use of data for prioritization or decision making at the sub national level.

KW - and timeliness integrated disease surveillance and response

KW - case investigation forms and eDEWS

KW - completeness

KW - data accuracy

KW - Data quality assessment

KW - disease surveillance information system

KW - health management information system (HMIS)/district health informative system two (DHIS2) database

KW - multi-stage cluster sampling

KW - reliability and credibility

KW - simple random sample

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