Abstract
BackgroundMonitoring medically certified causes of death is essential to shape national health policies, track progress to Sustainable Development Goals, and gauge responses to epidemic and pandemic disease. The combination of electronic health information systems with new methods for data quality monitoring can facilitate quality assessments and help target quality improvement. Since 2015, Tanzania has been upgrading its Civil Registration and Vital Statistics system including efforts to improve the availability and quality of mortality data.MethodsWe used a computer application (ANACONDA v4.01) to assess the quality of medical certification of cause of death (MCCD) and ICD-10 coding for the underlying cause of death for 155,461 deaths from health facilities from 2014 to 2018. From 2018 to 2019, we continued quality analysis for 2690 deaths in one large administrative region 9 months before, and 9 months following MCCD quality improvement interventions. Interventions addressed governance, training, process, and practice. We assessed changes in the levels, distributions, and nature of unusable and insufficiently specified codes, and how these influenced estimates of the leading causes of death.Results9.7% of expected annual deaths in Tanzania obtained a medically certified cause of death. Of these, 52% of MCCD ICD-10 codes were usable for health policy and planning, with no significant improvement over 5 years. Of certified deaths, 25% had unusable codes, 17% had insufficiently specified codes, and 6% were undetermined causes. Comparing the before and after intervention periods in one Region, codes usable for public health policy purposes improved from 48 to 65% within 1 year and the resulting distortions in the top twenty cause-specific mortality fractions due to unusable causes reduced from 27.4 to 13.5%.ConclusionData from less than 5% of annual deaths in Tanzania are usable for informing policy. For deaths with medical certification, errors were prevalent in almost half. This constrains capacity to monitor the 15 SDG indicators that require cause-specific mortality. Sustainable quality assurance mechanisms and interventions can result in rapid improvements in the quality of medically certified causes of death. ANACONDA provides an effective means for evaluation of such changes and helps target interventions to remaining weaknesses.
Highlights
Monitoring medically certified causes of death is essential to shape national health policies, track progress to Sustainable Development Goals, and gauge responses to epidemic and pandemic disease
Completeness of cause of death recording based on medical certificates of death The proportion of deaths with an medical certification of cause of death (MCCD) at national level, calculated using the annual crude death rate per annual national mainland population shows an average of 9.7% of the estimated number of annual deaths in Tanzania received an ICD-10 code of any quality
ICD-10 code ranges Our ANACONDA national analyses for the years 2014– 2018 inclusive showed an average of 151 distinct ICD-10 codes used per year
Summary
Monitoring medically certified causes of death is essential to shape national health policies, track progress to Sustainable Development Goals, and gauge responses to epidemic and pandemic disease. Since 2015, Tanzania has been upgrading its Civil Registration and Vital Statistics system including efforts to improve the availability and quality of mortality data. Under-five mortality in Tanzania declined from 141 to 67 per 1000 live births, crude death rate declined from 14 to 6.5 deaths per 1000 and life expectancy at birth increased from 53 to 68 years) [4] These changes coincide with changes in both the cause mix and cause-specific fractions of the major causes of death. Monitoring mortality and cause of death is essential to shape national health policies, to track progress to the 2030 Sustainable Development Goals (SDGs), and to gauge responses to pandemic disease. Civil Registration and Vital Statistics (CRVS) systems are key instruments for such data [10]
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