Toward utilization of data for program management and evaluation: quality assessment of five years of health management information system data in Rwanda

Nisingizwe, Marie Paul; Iyer, Hari S.; Gashayija, Modeste; Hirschhorn, Lisa R.; Amoroso, Cheryl; Wilson, Randy; Rubyutsa, Eric; Gaju, Eric; Basinga, Paulin; Muhire, Andrew; Binagwaho, Agnès; Hedt‐Gauthier, Bethany

doi:10.3402/gha.v7.25829

Cited by 66 publications

(76 citation statements)

References 17 publications

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“…These results are probably due to the increase of data demand that accompanies RBF implementation. Similar results were found earlier in Rwanda where the quality of health information improved with RBF implementation [18].…”

Section: Quality Of the Data Collection Processsupporting

confidence: 79%

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Effects of Results Based Financing Models on Data Quality Improvement in Benin on 2014

Salami¹,

Ouendo²,

Fayomi³

2016

ujph

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The performances generated by results based financing (RBF) raise questions about the quality of data used to measure them. This study was carried out to evaluate the RBF contribution to the quality improvement of maternal and child health data. Sixty seven health facilities are sampled randomly in three strata (RBF_PRPSS, RBF_PASS, No_ RBF) gathering health zones in Benin. The strata comparison with maternal and child health data for the first halves 2014 and 2011 revealed that timeliness and completeness have improved in the strata exposed to results based financing compared to unexposed (p <0.05). Compared to No_RBF stratum, accuracy deteriorated in RBF_PRPSS stratum, with a 52% decrease in the proportion of concordance reports between 2011 and 2014. In 2014, recorded discrepancies are largely under-reported in the RBF_PRPSS stratum, and over-reported in RBF_PASS stratum. The accuracy of National Health Information and Management System (SNIGS) data compared to RBF's validated data was low, verification factors varying between 30% and 97%. In total, the data quality was globally poor in most areas in Benin and it is not improved by the results based financing models implemented between 2011 and 2014. There is a need to target data quality in RBF models and to use SNIGS as a unique system of reporting.

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Section: Quality Of the Data Collection Processsupporting

confidence: 79%

“…This improvement is significant compared to the findings of Glèlè et al [12]. Completeness level in the RBF strata is similar to that seen in Rwanda where the RBF has already been scaled nationwide [18].…”

Section: Quality Of the Data Collection Processsupporting

confidence: 42%

Effects of Results Based Financing Models on Data Quality Improvement in Benin on 2014

Salami¹,

Ouendo²,

Fayomi³

2016

ujph

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“…We obtained national numbers of respiratory hospitalizations from the Rwanda Health Management Information System (HMIS), which collects aggregated and de‐identified data on number of admission by syndrome from all public hospitals in Rwanda13, 14 from January 2012 through December 2014.…”

Section: Methodsmentioning

confidence: 99%

The national burden of influenza‐associated severe acute respiratory illness hospitalization in Rwanda, 2012‐2014

Nyamusore

Rukelibuga

Mutagoma

et al. 2017

Influenza Resp Viruses

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BackgroundEstimates of influenza‐associated hospitalization are severely limited in low‐ and middle‐income countries, especially in Africa.ObjectivesTo estimate the national number of influenza‐associated severe acute respiratory illness (SARI) hospitalization in Rwanda.MethodsWe multiplied the influenza virus detection rate from influenza surveillance conducted at 6 sentinel hospitals by the national number of respiratory hospitalization obtained from passive surveillance after adjusting for underreporting and reclassification of any respiratory hospitalizations as SARI during 2012‐2014. The population at risk was obtained from projections of the 2012 census. Bootstrapping was used for the calculation of confidence intervals (CI) to account for the uncertainty associated with all levels of adjustment. Rates were expressed per 100 000 population. A sensitivity analysis using a different estimation approach was also conducted.Results SARI cases accounted for 70.6% (9759/13 813) of respiratory admissions at selected hospitals: 77.2% (6783/8786) and 59.2% (2976/5028) among individuals aged <5 and ≥5 years, respectively. Overall, among SARI cases tested, the influenza virus detection rate was 6.3% (190/3022): 5.7% (127/2220) and 7.8% (63/802) among individuals aged <5 and ≥5 years, respectively. The estimated mean annual national number of influenza‐associated SARI hospitalizations was 3663 (95% CI: 2930‐4395—rate: 34.7; 95% CI: 25.4‐47.7): 2637 (95% CI: 2110‐3164—rate: 168.7; 95% CI: 135.0‐202.4) among children aged <5 years and 1026 (95% CI: 821‐1231—rate: 11.3; 95% CI: 9.0‐13.6) among individuals aged ≥5 years. The estimates obtained from both approaches were not statistically different (overlapping CIs).ConclusionsThe burden of influenza‐associated SARI hospitalizations was substantial and was highest among children aged <5 years.

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“…However, in spite of the advances, it remains a challenge to ensure that data provided through HMIS are used in countries, especially at the peripheral levels of the health sector (Nicol et al, ; Sahay, Sundararaman, & Braa, ; Wickremasinghe et al, ). This lack of use is often linked to poor data quality (Braa & Sahay, ; Nisingizwe et al, ), to a lack of the skills needed to work with data (Braa, Heywood, & Sahay, ; Kumar, Gotz, Nutley, & Smith, ) or to HMIS mainly being set up as a tool to support the registration of data rather than its dissemination (Madon, Krishna, & Michael, ). While making these links is not wrong, it does tend to neglect the fact that data do not represent a single driver of action, which in turns means adopting a data‐centric approach to social change.…”

Section: Theoretical Lensmentioning

confidence: 99%

Plans and “off‐plan activities”: Exploring the roles of data and situated action in health planning in Burkina Faso

Rasmussen

2018

E J Info Sys Dev Countries

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Implementation of health management information systems (HMIS) has gone hand in hand with a growing global demand for high-quality data. While this has sustained HMIS implementation, it has also promoted a data-centric assumption that better data leads to better action on health. An example is health managers envisaged using HMIS data to make action plans for health service delivery. The paper draws on an in-depth study of practices of health data use in Burkina Faso to explore this relationship further. In this context, deviation from the agreed plan is not seen as problematic. Local action plans are often overruled because of requirements of higher levels, unexpected developments in diseases, or sudden provision of fresh funding. Health managers organize work under these uncertain circumstances through situated action. The concept of "off-plan activities" is used to handle unforeseen events. Mobile phones are used extensively to make and remake plans. It is argued that the findings invite us to consider a less datacentric approach to the role of HMIS data in health service delivery, where HMIS data are seen less as a tool to discipline local practices than as one element of the dynamic practices undertaken by individuals in the organization of daily work. KEYWORDSdata use, HMIS, planning, situated action, uncertainty 1 | INTRODUCTION District health managers in Burkina Faso are in charge of the day-to-day functioning of health districts, which involves making plans for the delivery of health services. However, their work comprises a whole range of functions. They work in the field, in clinics, and hospitals, where they carry out supervision, training, and vaccination campaigns, and where they can exchange information directly with health workers. They work in meeting rooms, where they present and discuss progress reports and strategies. Furthermore, they work in offices, where they engage in meetings, finalize reports, and sign off a wealth of papers, ranging from medical prescriptions via annual action plans to displacement permits. These offices are usually welcoming spaces with comfortable chairs and air-conditioning. Their walls are seldom bare, perhaps displaying key district health statistics, clinical guidelines, photos of an official visit to the district, or certificates that the health district manager has been awarded. One item, always on display, is a large institutional calendar with a standard display of each month of the year in columns and spiced up with photos and headlines about larger donor-driven projects. One day, I was studying the landscape of these office walls in more detail than usual, as the district manager I interviewed had to take a long phone call. It struck me that these calendars were almost always empty. I started to make note of this when I visited other offices and noticed that, although there were calendars everywhere, they had hardly any notes on them, if any at all. Why would you put a calendar on your wall if you were not going to use it? Maybe, they were simply used to t...

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Toward utilization of data for program management and evaluation: quality assessment of five years of health management information system data in Rwanda

Cited by 66 publications

References 17 publications

Effects of Results Based Financing Models on Data Quality Improvement in Benin on 2014

Effects of Results Based Financing Models on Data Quality Improvement in Benin on 2014

The national burden of influenza‐associated severe acute respiratory illness hospitalization in Rwanda, 2012‐2014

Plans and “off‐plan activities”: Exploring the roles of data and situated action in health planning in Burkina Faso

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