Research Article: Predicting resource-dependent maternal health outcomes at a referral hospital in Zanzibar using patient trajectories and mathematical modeling

Date Published: March 5, 2019

Publisher: Public Library of Science

Author(s): Devika Nadkarni, Avijit Minocha, Harshit Harpaldas, Grace Kim, Anuraag Gopaluni, Sara Gravelyn, Sarem Rashid, Anna Helfrich, Katie Clifford, Tanneke Herklots, Tarek Meguid, Benoit Jacod, Darash Desai, Muhammad H. Zaman, Sharon Mary Brownie.

http://doi.org/10.1371/journal.pone.0212753

Abstract

Poor intra-facility maternity care is a major contributor to maternal mortality in low- and middle-income countries. Close to 830 women die each day due to preventable maternal complications, partly due to the increasing number of women giving birth in health facilities that are not adequately resourced to manage growing patient populations. Barriers to adequate care during the ‘last mile’ of healthcare delivery are attributable to deficiencies at multiple levels: education, staff, medication, facilities, and delays in receiving care. Moreover, the scope and multi-scale interdependence of these factors make individual contributions of each challenging to analyze, particularly in settings where basic data registration is often lacking. To address this need, we have designed and implemented a novel systems-level and dynamic mathematical model that simulates the impact of hospital resource allocations on maternal mortality rates at Mnazi Mmoja Hospital (MMH), a referral hospital in Zanzibar, Tanzania. The purpose of this model is to provide a rigorous and flexible tool that enables hospital administrators and public health officials to quantitatively analyze the impact of resource constraints on patient outcomes within the maternity ward, and prioritize key areas for further human or capital investment. Currently, no such tool exists to assist administrators and policy makers with effective resource allocation and planning. This paper describes the structure and construct of the model, provides validation of the assumptions made with anonymized patient data and discusses the predictive capacity of our model. Application of the model to specific resource allocations, maternal treatment plans, and hospital loads at MMH indicates through quantitative results that medicine stocking schedules and staff allocations are key areas that can be addressed to reduce mortality by up to 5-fold. With data-driven evidence provided by the model, hospital staff, administration, and the local ministries of health can enact policy changes and implement targeted interventions to improve maternal health outcomes at MMH. While our model is able to determine specific gaps in resources and health care delivery specifically at MMH, the model should be viewed as an additional tool that may be used by other facilities seeking to analyze and improve maternal health outcomes in resource constrained environments.

Partial Text

Every day, close to 830 women die of preventable maternal complications. Nearly all of these maternal deaths occur in developing countries, the majority of which occur specifically in sub-Saharan Africa [1–3]. One in 16 women in these regions die in pregnancy or childbirth as a result of these complications– 175 times the maternal mortality risk of high income countries [4]. Poor intra-facility maternity care is becoming a major contributor to overall maternal mortality as an increasing number of women are persuaded to give birth at health facilities [5]. Barriers to adequate care during the ‘last mile’ of healthcare delivery are the result of deficiencies at multiple levels: education, staff, medication, facilities, and delays in receiving care. The contribution of each of these factors is difficult to analyze in settings where even basic data registration is lacking [6]. It is, however, crucial to understand how each factor contributes to a facility’s maternal health outcomes, as “more of everything” is not a viable strategy with limited financial resource availability,.

De-identified patient data spanning approximately six months was collected from medical records at Mnazi Mmoja Hospital. This research was approved by ZAMREC, the research authority in Zanzibar, Tanzania on July 18 2017; Protocol No. ZAMREC/0001/JUN/17. A stepwise, iterative, object-oriented program was developed to simulate the workflow and patient treatment process at the maternity ward at MMH. In order to ensure the algorithm accurately reflects a patient’s stay at the hospital and the resources used in treatment, it was developed in close collaboration with doctors, nurses, and clinical researchers at MMH. The model allows users to define resource allocations, patient load and incoming morbidity distributions over various shifts, as well as specific treatment plans based on patient status. A full list of key variables, classifications, and measures used throughout the model are summarized in Table 1. These factors are tracked and updated over a simulated duration of time, providing users with information on short-term and long-term maternal mortality impacts.

Our model provides a new and distinctive tool that allows hospital administrators and health officials to track the impact of resource limitations on mortality rates in local hospitals. Here, we demonstrate the application of this model on an analysis of the maternity ward at Mnazi Mmoja Hospital in Stone Town, Zanzibar, Tanzania. Unlike population level analyses that review resource uses as a whole, our simulation-based approach enables users to investigate how resource consumption is impacted by individual stochastic events involving patient admission rates and associated complications and severities at admission. Additionally, by tracking and treating individual patients, the model can help to understand the impact of cycles of successful and unsuccessful treatment due to limited resources and identify critical factors that may help prevent these cases from becoming fatalities. Simulations of three-month durations at MMH were conducted to understand the influence of three major factors: medicine restocking schedules, amount of staff on duty, and patient influx.

 

Source:

http://doi.org/10.1371/journal.pone.0212753

 

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