Development and testing of a composite index to monitor the continuum of maternal health service delivery at provincial and district level in South Africa

The continuum of care is a recommended framework for comprehensive health service delivery for maternal health, and it integrates health system and social determinants of health. There is a current lack of knowledge on a measurement approach to monitor performance on the framework. In this study we aim to develop and test a composite index for assessing the maternal health continuum in a province in South Africa with the possibility of nationwide use

Building Analytic Capacity, Facilitating Partnerships, and Promoting Data Use in State Health Agencies: A Distance-Based Workforce Development Initiative Applied to Maternal and Child Health Epidemiology

The purpose of this article is to summarize the methodology, partnerships, and products developed as a result of a distance-based workforce development initiative to improve analytic capacity among maternal and child health (MCH) epidemiologists in state health agencies. This effort was initiated by the Centers for Disease Control’s MCH Epidemiology Program and faculty at the University of Illinois at Chicago to encourage and support the use of surveillance data by MCH epidemiologists and program staff in state agencies. Beginning in 2005, distance-based training in advanced analytic skills was provided to MCH epidemiologists. To support participants, this model of workforce development included: lectures about the practical application of innovative epidemiologic methods, development of multidisciplinary teams within and across agencies, and systematic, tailored technical assistance The goal of this initiative evolved to emphasize the direct application of advanced methods to the development of state data products using complex sample surveys, resulting in the articles published in this supplement to MCHJ. Innovative methods were applied by participating MCH epidemiologists, including regional analyses across geographies and datasets, multilevel analyses of state policies, and new indicator development. Support was provided for developing cross-state and regional partnerships and for developing and publishing the results of analytic projects. This collaboration was successful in building analytic capacity, facilitating partnerships and promoting surveillance data use to address state MCH priorities, and may have broader application beyond MCH epidemiology. In an era of decreasing resources, such partnership efforts between state and federal agencies and academia are essential for promoting effective data use

Ranked severe maternal morbidity index for population-level surveillance at delivery hospitalization based on hospital discharge data.

BackgroundSevere maternal morbidity (SMM) is broadly defined as an unexpected and potentially life-threatening event associated with labor and delivery. The Centers for Disease Control and Prevention (CDC) produced 21 different indicators based on International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) hospital diagnostic and procedure codes to identify cases of SMM.ObjectivesTo examine existing SMM indicators and determine which indicators identified the most in-hospital mortality at delivery hospitalization.MethodsData from the 1993-2015 and 2017-2019 Healthcare Cost and Utilization Project's National Inpatient Sample were used to report SMM indicator-specific prevalences, in-hospital mortality rates, and population attributable fractions (PAF) of mortality. We hierarchically ranked indicators by their overall PAF of in-hospital mortality. Predictive modeling determined if SMM prevalence remained comparable after transition to ICD-10-CM coding.ResultsThe study population consisted of 18,198,934 hospitalizations representing 87,864,173 US delivery hospitalizations. The 15 top ranked indicators identified 80% of in-hospital mortality; the proportion identified by the remaining indicators was negligible (2%). The top 15 indicators were: restoration of cardiac rhythm; cardiac arrest; mechanical ventilation; tracheostomy; amniotic fluid embolism; aneurysm; acute respiratory distress syndrome; acute myocardial infarction; shock; thromboembolism, pulmonary embolism; cerebrovascular disorders; sepsis; both DIC and blood transfusion; acute renal failure; and hysterectomy. The overall prevalence of the top 15 ranked SMM indicators (~22,000 SMM cases per year) was comparable after transition to ICD-10-CM coding.ConclusionsWe determined the 15 indicators that identified the most in-hospital mortality at delivery hospitalization in the US. Continued testing of SMM indicators can improve measurement and surveillance of the most severe maternal complications at the population level

Prevalence of severe maternal morbidity (SMM) per 10,000 delivery hospitalizations for (1) all 21 SMM indicators, (2) 20 SMM indicators (excluding blood transfusion), (3) blood transfusion alone, and (4) the top 15 ranked SMM indicators that identify the most in-hospital mortality, NIS, 1993–2019.

The blue, dotted line represents the 21 current SMM indicators developed by the Centers for Disease Control and Prevention (CDC). The green, dashed line represents the current indicator for blood transfusion alone. The red, solid line represents 20 SMM indicators (excluding blood transfusion). The purple dashed line represents the top 15 ranked SMM indicators that identify the most in-hospital mortality. Shaded areas represent the 95% confidence intervals for the regression-based SMM prevalence predictions, using 2010–2015 (ICD-9 coding) data, in 2017–2019 (ICD-10 coding). Interval colors correspond to the respective line colors. Data from the last quarter of 2015 and for the year 2016 were omitted because of changes in coding practices during the first year after ICD-10 coding implementation.</p

Observed and predicted prevalences for years 2017–2019 per 10,000 deliveries of the 15 highest ranked severe maternal morbidity (SMM) indicators for in-hospital mortality, Nationwide Inpatient Sample (weighted sample = 11,001,554, unweighted sample = 2,200,312).

Observed and predicted prevalences for years 2017–2019 per 10,000 deliveries of the 15 highest ranked severe maternal morbidity (SMM) indicators for in-hospital mortality, Nationwide Inpatient Sample (weighted sample = 11,001,554, unweighted sample = 2,200,312).</p

Results of the hierarchical population attributable fraction (hPAF) approach for severe maternal morbidity indicator selection, with the selection iteration (k) of each indicator, the cumulative hPAF, and the signal-to-noise ratio (SNR) selection criterion at iteration of selection.

Results of the hierarchical population attributable fraction (hPAF) approach for severe maternal morbidity indicator selection, with the selection iteration (k) of each indicator, the cumulative hPAF, and the signal-to-noise ratio (SNR) selection criterion at iteration of selection.</p

Characteristics of study population, Nationwide Inpatient Sample, 1993–2015 (weighted sample = 87,864,173, unweighted sample = 18,198,934).

Characteristics of study population, Nationwide Inpatient Sample, 1993–2015 (weighted sample = 87,864,173, unweighted sample = 18,198,934).</p

Prevalence, in-hospital mortality, population attributable fraction (PAF), and signal-to-noise ratio (SNR) for severe maternal morbidity indicators (SMM), Nationwide Inpatient Sample, 1993–2015 (weighted sample = 87,864,173, unweighted sample = 18,198,934).

Prevalence, in-hospital mortality, population attributable fraction (PAF), and signal-to-noise ratio (SNR) for severe maternal morbidity indicators (SMM), Nationwide Inpatient Sample, 1993–2015 (weighted sample = 87,864,173, unweighted sample = 18,198,934).</p

The signal to noise ratio: Formula for calculation.

The signal to noise ratio: Formula for calculation.</p