The authors have declared that no competing interests exist.
As part of a partnership between the Institute for Healthcare Improvement and the Ethiopian Federal Ministry of Health, woreda-based quality improvement collaboratives took place between November 2016 and December 2017 aiming to accelerate reduction of maternal and neonatal mortality in Lemu Bilbilu, Tanqua Abergele and Duguna Fango woredas. Before starting the collaboratives, assessments found inaccuracies in core measures obtained from Health Management Information System reports.
Building on the quality improvement collaborative design, data quality improvement activities were added and we used the World Health Organization review methodology to drive a verification factor for the core measures of number of pregnant women that received their first antenatal care visit, number of pregnant women that received antenatal care on at least four visits, number of pregnant women tested for syphilis and number of births attended by skilled health personnel. Impact of the data quality improvement was assessed using interrupted time series analysis. We found accurate data across all time periods for Tanqua Abergele. In Lemu Bilbilu and Duguna Fango, data quality improved for all core metrics over time. In Duguna Fango, the verification factor for number of pregnant women that received their first antenatal care visit improved from 0.794 (95%CI 0.753, 0.836; p<0.001) pre-intervention by 0.173 (95%CI 0.128, 0.219; p<0.001) during the collaborative; and the verification factor for number of pregnant women tested for syphilis improved from 0.472 (95%CI 0.390, 0.554; p<0.001) pre-intervention by 0.460 (95%CI 0.369, 0.552; p<0.001) during the collaborative. In Lemu Bilbilu, the verification factor for number of pregnant women receiving a fourth antenatal visit rose from 0.589 (95%CI 0.513, 0.664; p<0.001) at baseline by 0.358 (95%CI 0.258, 0.458; p<0.001) post-intervention; and skilled birth attendance rose from 0.917 (95%CI 0.869, 0.965) at baseline by 0.083 (95%CI 0.030, 0.136; p<0.001) during the collaborative.
A Data quality improvement initiative embedded within woreda clinical improvement collaborative improved accuracy of data used to monitor maternal and newborn health services in Ethiopia.
Since October 2013, the Institute for Healthcare Improvement (IHI) has worked in partnership with the Ethiopian Federal Ministry of Health (FMoH), with the support of the Bill and Melinda Gates Foundation and Margaret A. Cargill Philanthropies to explore how quality improvement (QI) methodologies can accelerate progress of the FMoH to improve maternal and neonatal health in Ethiopia. As part of this work, woreda (woreda) based focused improvement collaboratives, based on the IHI Breakthrough Series collaborative [
Recent studies in Nigeria [
Prior to the start of the improvement collaboratives, HMIS data accuracy was assessed in the period May to October 2016, revealing inaccurate data in the core process and outcome metrics. We sought to avoid data falsification/intentional manipulation of data by focusing on improving data quality through training participants on the importance of high-quality data, monthly data review and feedback and trust building with health care leaders and workers. Consequently, the improvement collaboratives embedded a data quality improvement initiative aimed at improving the accuracy of maternal and neonatal health data from the HMIS system in participating sites. This paper describes the extent to which the accuracy of the HMIS data was improved during the improvement collaborative, as measured using the WHO review methodology.
The Federal Ministry of Health (FMOH) of Ethiopia, regional health bureaus (RHBs) and IHI Ethiopia selected one woreda in each of the four most populous agrarian regions of Ethiopia to introduce the improvement collaborative approach. The woredas were purposefully selected in consultation with the Federal Ministry of Health (FMoH) of Ethiopia and regional health bureaus (RHBs) based on pre-set criteria, including high maternal and perinatal deaths, high level of leadership commitment to improve the service and the absence of other partner organizations working on quality improvement project. All facilities in each woreda were included into a collaborative. This study includes results from the first three collaboratives introduced simultaneously at twenty health facilities in: Lemu Bilbilu (8), Tanqua Abergele (6) and Duguna Fango (6) woredas of Oromia, Tigray and Southern Nations and Nationalities People’s region respectively.
The overall structure of the improvement collaboratives is summarized in
Each month the improvement collaboratives measured implementation progress using a number of core measures, including four sourced from HMIS data described in
Core Measures Name | Definition | Data Source |
---|---|---|
Antenatal Care 1 | Number of pregnant women who had at least one antenatal care visit during their pregnancy. | National Health Management Information System (HMIS) |
Antenatal Care 4 | Number of pregnant women who had four or more antenatal care visits during their pregnancy. | National Health Management Information System (HMIS) |
Syphilis Screening | Number of pregnant women tested for syphilis | National Health Management Information System (HMIS) |
Skilled Birth | Number of births attended by skilled health personnel | National Health Management Information System (HMIS) |
Post-natal care 48 hours | Number of women who attended post-natal care at least once within 48 hours after delivery. | National Health Management Information System (HMIS) |
To understand how data accuracy changed from the pre-intervention period to intervention period to the post-Intervention period, we followed the WHO data review methodology [
Monthly, from May 2016 to December 2018, for each selected core measure, experienced IHI senior project officers collected data from the archive of HMIS reports at each facility.
Experienced IHI senior project officers undertook an audit by repeating the data collection, from May 2016 to December 2018, from standard antenatal care and delivery registers, developed by the Federal Ministry of Health (FMOH) of Ethiopia. This approach resulted in a monthly audited value for the selected core measures.
For each selected core measure, a monthly verification factor was calculated by dividing the recounted value by the original HMIS report value. A verification factor of 0.9–1.1 is considered “accurate”; <0.9 is considered over reported and a value > 1.1 as under reported [
We used
There are no fixed limits regarding the number of data points for interrupted time series study, as the power depends on various other factors including distribution of data points before and after the intervention, variability within the data, strength of effect and the presence of confounding effects such as seasonality [
This research is part of a broader evaluation study that was reviewed and approved by Ethiopian Public Health Association (EPHA) Scientific and Ethical Review Committee. A letter of support was obtained from IHI Ethiopia project office.
All health facilities in the three improvement collaboratives (20); Lemu Bilbilu, Tanqua Abergele and Duguna Fango provide antenatal care services including the first to fourth visits, Syphilis screening and skilled birth attendance (institutional delivery). Monthly HMIS reports and registers were also available and complete at all the health facilities in the improvement collaboratives; 3 primary hospitals and 17 health centers. All health facilities also reported their performances monthly to their respective woreda health offices.
Data personnel, HMIS officers, were available at all hospitals and 13 (76.5%) health centers. Training on data quality was given to all HMIS officers at hospitals and 3 (17.6%) health center staffs including HMIS officers. Data quality was checked monthly by lot quality assurance sampling (LQAS) method at all the hospitals and 15 (88.2%) of the health centers. As HMIS officers and relevant staff participated in the improvement collaboratives, they all received training on data quality.
The mean & standard deviation of verification factors for core measure for each woreda over time period are summarized in
Mean (standard deviation) | |||
---|---|---|---|
Baseline May 2016 to Oct 2016 | Intervention Nov 2016 to Dec 2017 | Post-Intervention Jan 2018 to Dec 2018 | |
Lemu Bilbilu | 0.842 (0.123) | 1.012 (0.043) | 0.897 (0.133) |
Duguna Fango | 0.794 (0.082) | 0.972 (0.039) | 0.963 (0.048) |
Tanqua Abergele | 1 (0) | 0.983 (0.062) | 0.971 (0.099) |
Overall | 0.817 (0.077) | 0.991 (0.018) | 0.923 (0.078) |
Lemu Bilbilu | 0.589 (0.131) | 0.866 (0.171) | 0.946 (0.147) |
Duguna Fango | 0.486 (0.173) | 0.842 (0.148) | 0.982 (0.052) |
Tanqua Abergele | 1 (0) | 0.990 (0.063) | 1.029 (0.069) |
Overall | 0.549 (0.116) | 0.851 (0.142) | 0.964 (0.081) |
Lemu Bilbilu | 0.664 (0.442) | 0.875 (0.235) | 1.058 (0.114) |
Duguna Fango | 0.472 (0.141) | 0.933 (0.089) | 0.932 (0.101) |
Tanqua Abergele | 1.045 (0.070) | 0.993 (0.014) | 0.982 (0.059) |
Overall | 0.531 (0.195) | 0.899 (0.154) | 0.993 (0.067) |
Lemu Bilbilu | 0.917 (0.031) |
0.979 (0.049) | 1.024 (0.072) |
Duguna Fango | 1.001 (0.040) | 0.961 (0.047) | 0.989 (0.019) |
Tanqua Abergele | 1 (0) | 0.984 (0.078) | 1.008 (0.039) |
Overall | 0.917 (0.031) | 0.975 (0.032) | 1.007 (0.032) |
For each time-series analysis, applying the Durbin-Watson test, suggested there was no autocorrelation in for all the verification factor measures. Additionally, we found no seasonality in the data. The results of the time series analysis are shown in
Baseline May 2016 to Oct 2016 | Intervention Nov 2016 to Dec 2017 | Post-Intervention Jan 2018 to Dec 2018 | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Constant | Slope | Constant | Slope | Constant | Slope | |||||||
Coefficient (95% CI) | P | Coefficient (95% CI) | P | Coefficient (95% CI) | P | Coefficient (95% CI) | P | Coefficient (95% CI) | P | Coefficient (95% CI) | P | |
Lemu Bilbilu | 0.842 | <0.001 | - | - | 0.170 | 0.002 | - | - | -0.116 | 0.01 | - | - |
(0.761, 0.923) | (0.073, 0.267) | (-0.194, -0.038) | ||||||||||
Duguna Fango | 0.794 | <0.001 | - | - | 0.173 | <0.001 | - | - | - | - | - | - |
(0.753, 0.836) | (0.128, 0.219) | |||||||||||
Lemu Bilbilu | 0.589 | <0.001 | - | - | - | - | 0.037 | <0.001 | 0.358 | <0.001 | - | - |
(0.513, 0.664) | (0.026, 0.048) | (0.258, 0.458) | ||||||||||
Duguna Fango | 0.486 | <0.001 | - | - | 0.149 | 0.044 | 0.028 | <0.001 | 0.349 | <0.001 | - | - |
(0.405, 0.567) | (0.01, 0.287) | (0.015, 0.041) | (0.223, 0.474) | |||||||||
Lemu Bilbilu | 0.917 | <0.001 | - | - | 0.083 | 0.004 | - | - | - | - | - | - |
(0.869, 0.965) | (0.03, 0.136) | |||||||||||
Duguna Fango | 1.008 | <0.001 | - | - | -0.034 | 0.064 | - | - | - | - | - | - |
(0.977, 1.039) | (-0.069, 0.001) | |||||||||||
Lemu Bilbilu | 0.664 | <0.001 | - | - | 0.296 | 0.018 | - | - | - | - | - | - |
(0.454, 0.873) | (0.064, 0.528) | |||||||||||
Duguna Fango | 0.472 | <0.001 | - | - | 0.460 | <0.001 | - | - | - | - | - | - |
(0.39, 0.554) | (0.369, 0.552) |
CI = Confidence Interval, P = P-value.
The verification factor for all four measures averaged 1 for all three time periods at Tanqua Abergele. Thus, we conducted the time series analysis only in Lemu Bilbilu and Duguna Fango.
For Antenatal Care 1 (
For Antenatal Care 4 (
For Syphilis Screening (
For Skilled Birth Attendance (
This data quality improvement initiative, embedded within a wider set of improvement collaboratives, significantly improved accuracy of antenatal care visits and syphilis screening data within these improvement collaboratives in Ethiopia. The finding is comparable to a similar embedded data quality improvement initiative in South Africa [
Data quality improvement activities focusing on training, regular audits, monthly review and feedback have been successfully applied in other settings, without being embedded in a wider care improvement initiative. For example, in the United Republic of Tanzania [
At baseline, a significant proportion of health facilities over reported syphilis screening (50%) and pregnant women that received antenatal care four visit (60%) as compared to pregnant women that received antenatal care first visit (35%) and Skilled Birth Attendance (15%). The finding is consistent with recent studies in south west [
A limitation of this study is the use of data in a limited number of health facilities (20) and a lack of data on factors associated with data accuracy at baseline. The lack of 8 time points before intervention, limited the statistical power of the study [
Despite these limitations, the improvement in data quality observed in this study is encouraging, it suggests a similar approach of embedding data quality improvement efforts within a wider initiative where participants experience how the data can be used to improve care more broadly, could improve the quality of the data needed for decision-making and resource allocation in other public health programs.
This study reports a simple, practical approach to improving the quality of public health information, both locally at health facility and in a woreda health information system. This data quality intervention improved both data accuracy on antenatal care1, antenatal care4 and syphilis screening in this study. Further research is needed to assess the effectiveness of similar data quality improvement approaches prospectively on a large scale.
We would like to thank Institute for HealthCare Improvement (IHI), Ethiopia project office, Addis Ababa, Ethiopia for unreserved cooperation in providing data for this study and time for data analysis & manuscript writing.
PONE-D-19-32598
Effect of data quality improvement intervention on health management information system data accuracy: an interrupted time series analysis
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Reviewer #1: Thank you very much for giving an opportunity to review the present manuscript. This study sets out to describe the extent to which the accuracy of the HMIS data was improved during the improvement collaborative embedded data quality improvement initiative, as measured using the World Health Organization (WHO) data quality review methodology in Ethiopia. They found accurate data across all time periods in one region (Tanqua Abergele) while in two of the remaining three regions (Lem Bilbilu and Duguna Fango), data quality improved for all core metrics over time.
The manuscript needs some improvement in mainly the methods and results sections as a number of pages and lines should were not well described in the manuscript.
[Materials and methods]
1. Line 85 - The authors indicated that “one woreda in each of the four most populous agrarian regions of Ethiopia to introduce the improvement collaborative approach” but how the woreda were selected was not described.
2. Measures
Table 1: The authors definition for Antenatal Care 1 and Antenatal Care 4 were not that clear. I suggest the authors defined Antenatal Care 1 as “Number of pregnant women who had at least one antenatal care visit during their pregnancy”.
131 Data management and analysis
3. The authors did not mention any of the descriptive statistics used in this section.
4. What also informed the authors’ choice of median (inter-quartile range) instead of mean and standard deviation as the measure of central tendency and dispersion in the results section. If a test of normality was done, indicate the specific test statistic used.
5. 143-144: The authors described the selection of data used as consecutive sampling but go further to indicate that all data available during the study period was used. This is an indication of a census and not a sample as described by the authors.
6. What informed the authors choice of the slope change impact model used?
7. Did the authors assessed serial autocorrelation, non-stationarity and seasonality?
8. How was the fitness of the final selected model assessed? The authors should provide information on that.
9. Scatter plots of the various core measures over time should be added to help visualize the distribution of the data.
10. What was the level of significance used?
Results
11. 157-158 The authors mentioned nothing about what happened to the health centres without data personels and the 82.4% of health center staffs who were not trained. If nothing was done for them, if nothing was done for them won’t it affect the expected results?
12. An overall verification factor for the whole 3 regions will be important to be added in Table 2.
13. Verification factor for the intervention and post intervention period combined will be very informative since the impact of the intervention is not expected to take longer to be released.
14. Overall time series analysis for the verification factor for the core measures for the whole 2 regions combined will be important to be added in Table 3.
15. Time series analysis for the verification factor for the core measures for the intervention and post intervention periods combined will be very informative since the impact of the intervention is not expected to take longer to be released
16. Foot notes should be added to Tables to explain abbreviations used (CI, P).
17. The interpretations for the result should preside the tables.
18. The interpretation of Table 3 results was poorly done . it was explained in only line 178
19. Nothing was mentioned on the significance of the changes observed.
Conclusion
20. The conclusion should be rewritten to be based on the findings of the study.
Reviewer #2: This paper is may be interesting but very difficult to understanding for reader. Actually I can not understand the objectives of this paper. even i think the findings of this paper may be not sound. i think advanced statistical analysis may be upgrade the quality of this paper.
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Reviewer#1
[Materials & Methods]
1. Line 85 – The authors indicated that “one woreda in each of the four most populous agrarian regions of Ethiopia to introduce the improvement collaborative approach” but how the woreda were selected was not described.
We have added details of the Woreda selection (line 88).
2. Measures
Table1: The authors definition for antenatal care1 and antenatal care4 were not that clear. I suggest that authors defined antenatal care1 as “Number of pregnant women who had at least one antenatal care visit during their pregnancy”.
Thank you, we have redefined Antenatal care1 and antenatal care4 as per the suggestion.
131: Data management and analysis
3. The authors didn’t mention any of the descriptive statistics used in this section
We have added details of the descriptive statistics used in the data management and analysis section (line 138).
4. What also informed the authors’ choice of median (inter-quartile range) instead of mean and standard deviation as the measure of central tendency and dispersion in the result section. If a test of normality was done, indicate the specific test statistic used.
As we used mean for the time series analysis, we replaced all median (inter-quartile range) throughout the paper with mean (standard deviation).
5. 143-144: The authors described the selection of data used as consecutive sampling but go further to indicate that all data available during the study period was used. This is an indication of a census and not a sample as described by the authors.
We have clarified this description in the Sample Size section.
6. What informed the authors choice of the slope change impact model used?
The study referred to in the introduction, set in South Africa by Mphatswe W et al (Bull World Health Organ. 2012) indicated data accuracy changed shortly after the data quality intervention. This led us to hypothesize that a similar finding may occur in the current study, and thus we applied a model that would allow us to detect changes in the slope and overall average (step change) after the introduction of the data quality improvement efforts in the current study.
7. Did the authors assessed serial autocorrelation, non-stationarity and seasonality?
Yes, we checked autocorrelation using Durbin-Watson d-statistic and it is zero (0) suggesting the outcomes are independent. We found no non-stationarity and seasonality. We have added details of this into the results section (line 179).
8. How was the fitness of the final selected model assessed? The authors should provide information on that.
We have added details to the results section describing how we used standard approaches to assess fitness, using residual plots and examination of autocorrelation and seasonality, as described in #7 above.
9. Scatter plots of the various core measures over time should be added to help visualize the distribution of the data.
We have updated the Figure 2, to show both the individual data points and the fitted models.
10. What was the level of significance used?
We used a significance level of 0.05 and have added this to the methods section.
Results
11. 157-158 The authors mentioned nothing about what happened to the health centers without data personnels and the 82.4% of health center staffs who were not trained. If nothing was done for them, won’t it affect the expected results?
We have clarified this section in the text. The data quality improvement activities were embedded within a wider improvement collaborative. Part of the underlying theory of an improvement collaborative is that attendees will take their learning back to their home facilities and apply it with local staff.
12. An overall verification factor for the whole 3regions will be important to be added in Table2.
We have added the overall verification factor to Table 2
13. Verification factor for the intervention and post intervention period combined will be very informative since the impact of the intervention is not expected to take longer to be released.
The data quality improvement initiative was embedded within a wider quality improvement collaborative. As such the data quality improvement activities were introduced and built upon over time and ended when the wider improvement collaborative was completed. The post intervention period is thus a different period to the intervention period allowing us to assess sustainability. Consequently, we believe it is important to keep the intervention and post-intervention periods separate.
14. Overall time series analysis for the verification factor for the core measures for the whole 2regions combined will be important to be added in Table3.
Quality improvement initiatives are reliant on local engagement with activities, and on local context. This leads to the timing of improvement occurring, or not, to often differ from one setting to another. Consequently, we choose to focus of the time series analysis on the two Woredas separately, rather than combine them, which can risk to important variation being lost. As such, we argue strongly not to combine the results across the two Woredas, and to focus on learning from the individual woredas.
15. Time series analysis for the verification factor for the core measures for the intervention and post intervention periods combined will be very informative since the impact of the intervention is not expected to take longer to be released.
As described in our response to #13 above, we believe it is important to keep the intervention and post-intervention phases separate.
16. Foot notes should be added to tables to explain abbreviations used (CI, P)
We have added the foot notes as suggested.
17. The interpretations for the result should preside the tables.
We have re-positioned the tables so the description of the results comes before them.
18. The interpretation of table3 results was poorly done. It was explained in only line 178.
We have updated the wording to this section to better describe the analysis.
19. Nothing was mentioned on the significance of the changes observed.
We have included reference to the relevant p-values in the results section.
Conclusion
20. The conclusion should be rewritten to be based on the findings of the study.
We have re-written the conclusion to be more closely based on the findings of the study (line 258).
Reviewer#2: This paper may be interesting but very difficult to understand for the reader. Actually, I can’t understand the objectives of this paper, even I think the findings of this paper may be not sound. I think advanced statistical analysis may upgrade the quality of this paper.
Based on the responses to Reviewer #1, we believe this paper is now clearer, and is statistically sound.
Submitted filename:
Effect of data quality improvement intervention on health management information system data accuracy: An interrupted time series analysis
PONE-D-19-32598R1
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PONE-D-19-32598R1
Effect of data quality improvement intervention on health management information system data accuracy: An interrupted time series analysis.
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