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The effect of an mHealth clinical decision-making support system on neonatal mortality in a low resource setting: A cluster-randomized controlled trial.

ABSTRACT: Background:MHealth interventions promise to bridge gaps in clinical care but documentation of their effectiveness is limited. We evaluated the utilization and effect of an mhealth clinical decision-making support intervention that aimed to improve neonatal mortality in Ghana by providing access to emergency neonatal protocols for frontline health workers. Methods:In the Eastern Region of Ghana, sixteen districts were randomized into two study arms (8 intervention and 8 control clusters) in a cluster-randomized controlled trial. Institutional neonatal mortality data were extracted from the District Health Information System-2 during an 18-month intervention period. We performed an intention-to-treat analysis and estimated the effect of the intervention on institutional neonatal mortality (primary outcome measure) using grouped binomial logistic regression with a random intercept per cluster. This trial is registered at ClinicalTrials.gov (NCT02468310 ) and Pan African Clinical Trials Registry (PACTR20151200109073). Findings:There were 65,831 institutional deliveries and 348 institutional neonatal deaths during the study period. Overall, 47 ? 3% of deliveries and 56 ? 9% of neonatal deaths occurred in the intervention arm. During the intervention period, neonatal deaths increased from 4 ? 5 to 6 ? 4 deaths and, from 3 ? 9 to 4 ? 3 deaths per 1000 deliveries in the intervention arm and control arm respectively. The odds of neonatal death was 2?09 (95% CI (1 ? 00;4 ? 38); p?=?0 ? 051) times higher in the intervention arm compared to the control arm (adjusted odds ratio). The correlation between the number of protocol requests and the number of deliveries per intervention cluster was 0 ? 71 (p?=?0 ? 05). Interpretation:The higher risk of institutional neonatal death observed in intervention clusters may be due to problems with birth and death registration, unmeasured and unadjusted confounding, and unintended use of the intervention. The findings underpin the need for careful and rigorous evaluation of mHealth intervention implementation and effects. Funding:Netherlands Foundation for Scientific Research - WOTRO, Science for Global Development; Utrecht University.

SUBMITTER: Amoakoh HB

PROVIDER: S-EPMC6677648 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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The effect of an mHealth clinical decision-making support system on neonatal mortality in a low resource setting: A cluster-randomized controlled trial.

Amoakoh Hannah Brown HB Klipstein-Grobusch Kerstin K Agyepong Irene Akua IA Zuithoff Nicolaas P A NPA Amoakoh-Coleman Mary M Kayode Gbenga A GA Sarpong Charity C Reitsma Johannes B JB Grobbee Diederick E DE Ansah Evelyn K EK

EClinicalMedicine 20190704

<h4>Background</h4>MHealth interventions promise to bridge gaps in clinical care but documentation of their effectiveness is limited. We evaluated the utilization and effect of an mhealth clinical decision-making support intervention that aimed to improve neonatal mortality in Ghana by providing access to emergency neonatal protocols for frontline health workers.<h4>Methods</h4>In the Eastern Region of Ghana, sixteen districts were randomized into two study arms (8 intervention and 8 control clu ...[more]

PMID: 31388661

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Project description:BackgroundThis study assessed health workers' adherence to neonatal health protocols before and during the implementation of a mobile health (mHealth) clinical decision-making support system (mCDMSS) that sought to bridge access to neonatal health protocol gap in a low-resource setting.MethodsWe performed a cross-sectional document review within two purposively selected clusters (one poorly-resourced and one well-resourced), from each arm of a cluster-randomized trial at two different time points: before and during the trial. The total trial consisted of 16 clusters randomized into 8 intervention and 8 control clusters to assess the impact of an mCDMSS on neonatal mortality in Ghana. We evaluated health workers' adherence (expressed as percentages) to birth asphyxia, neonatal jaundice and cord sepsis protocols by reviewing medical records of neonatal in-patients using a checklist. Differences in adherence to neonatal health protocols within and between the study arms were assessed using Wilcoxon rank-sum and permutation tests for each morbidity type. In addition, we tracked concurrent neonatal health improvement activities in the clusters during the 18-month intervention period.ResultsIn the intervention arm, mean adherence was 35.2% (SD = 5.8%) and 43.6% (SD = 27.5%) for asphyxia; 25.0% (SD = 14.8%) and 39.3% (SD = 27.7%) for jaundice; 52.0% (SD = 11.0%) and 75.0% (SD = 21.2%) for cord sepsis protocols in the pre-intervention and intervention periods respectively. In the control arm, mean adherence was 52.9% (SD = 16.4%) and 74.5% (SD = 14.7%) for asphyxia; 45.1% (SD = 12.8%) and 64.6% (SD = 8.2%) for jaundice; 53.8% (SD = 16.0%) and 60.8% (SD = 11.7%) for cord sepsis protocols in the pre-intervention and intervention periods respectively. We observed nonsignificant improvement in protocol adherence in the intervention clusters but significant improvement in protocol adherence in the control clusters. There were 2 concurrent neonatal health improvement activities in the intervention clusters and over 12 in the control clusters during the intervention period.ConclusionWhether mHealth interventions can improve adherence to neonatal health protocols in low-resource settings cannot be ascertained by this study. Neonatal health improvement activities are however likely to improve protocol adherence. Future mHealth evaluations of protocol adherence must account for other concurrent interventions in study contexts.

Project description:BACKGROUND:Treatment intensity scores can predict mortality and estimate resource use. They may therefore be of interest for essential neonatal care in low resource settings where neonatal mortality remains high. We sought to systematically review neonatal treatment intensity scores to (1) assess the level of evidence on predictive performance in predicting clinical outcomes and estimating resource utilisation and (2) assess the applicability of the identified models to decision making for neonatal care in low resource settings. METHODS:We conducted a systematic search of PubMed, EMBASE (OVID), CINAHL, Global Health Library (Global index, WHO) and Google Scholar to identify studies published up until 21 December 2016. Included were all articles that used treatments as predictors in neonatal models. Individual studies were appraised using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). In addition, Grading of Recommendations Assessment, Development, and Evaluation (GRADE) was used as a guiding framework to assess certainty in the evidence for predicting outcomes across studies. RESULTS:Three thousand two hundred forty-nine articles were screened, of which ten articles were included in the review. All of the studies were conducted in neonatal intensive care units with sample sizes ranging from 22 to 9978, with a median of 163. Two articles reported model development, while eight reported external application of existing models to new populations. Meta-analysis was not possible due heterogeneity in the conduct and reporting of the identified studies. Discrimination as assessed by area under receiver operating characteristic curve was reported for in-hospital mortality, median 0.84 (range 0.75-0.96, three studies), early adverse outcome and late adverse outcome (0.78 and 0.59, respectively, one study). CONCLUSION:Existing neonatal treatment intensity models show promise in predicting mortality and morbidity. There is however low certainty in the evidence on their performance in essential neonatal care in low resource settings as all studies had methodological limitations and were conducted in intensive care. The approach may however be developed further for low resource settings like Kenya because treatment data may be easier to obtain compared to measures of physiological status. SYSTEMATIC REVIEW REGISTRATION:PROSPERO CRD42016034205.

Project description:BackgroundThe reality of finite resources has a real-world impact on a patient's ability to receive life-saving care in resource-poor settings. Blood for transfusion is an example of a scarce resource. Very few studies have looked at predictors of survival in patients requiring massive transfusion. We used data from a rural hospital in Kenya to develop a prediction model of survival among patients receiving massive transfusion.MethodsPatients who received five or more units of whole blood within 48 hours between 2004 and 2010 were identified from a blood registry in a rural hospital in Kenya. Presenting characteristics and in-hospital survival were collected from charts. Using stepwise selection, a logistic model was developed to predict who would survive with massive transfusion versus those who would die despite transfusion. An ROC curve was created from this model to quantify its predictive power.ResultsNinety-five patients with data available met inclusion criteria, and 74% survived to discharge. The number of units transfused was not a predictor of mortality, and no threshold for futility could be identified. Preliminary results suggest that initial blood pressure, lack of comorbidities, and indication for transfusion are the most important predictors of survival. The ROC curve derived from our model demonstrates an area under the curve (AUC) equal to 0.757, with optimism of 0.023 based on a bootstrap validation.ConclusionsThis study provides a framework for making prioritization decisions for the use of whole blood in the setting of massive bleeding. Our analysis demonstrated an overall survival rate for patients receiving massive transfusion that was higher than clinical perception. Our analysis also produced a preliminary model to predict survival in patients with massive bleeding. Prediction analyses can contribute to more efficient prioritization decisions; these decisions must also include other considerations such as equity, acceptability, affordability and sustainability.

Project description:The emergence of mobile technology offers new opportunities to improve clinical guideline adherence in resource-limited settings. We conducted a clinical pilot study in rural Bangladesh to evaluate the impact of a smartphone adaptation of the World Health Organization (WHO) diarrheal disease management guidelines, including a modality for age-based weight estimation. Software development was guided by end-user input and evaluated in a resource-limited district and sub-district hospital during the fall 2015 cholera season; both hospitals lacked scales which necessitated weight estimation. The study consisted of a 6 week pre-intervention and 6 week intervention period with a 10-day post-discharge follow-up. Standard of care was maintained throughout the study with the exception that admitting clinicians used the tool during the intervention. Inclusion criteria were patients two months of age and older with uncomplicated diarrheal disease. The primary outcome was adherence to guidelines for prescriptions of intravenous (IV) fluids, antibiotics and zinc. A total of 841 patients were enrolled (325 pre-intervention; 516 intervention). During the intervention, the proportion of prescriptions for IV fluids decreased at the district and sub-district hospitals (both p < 0.001) with risk ratios (RRs) of 0.5 and 0.2, respectively. However, when IV fluids were prescribed, the volume better adhered to recommendations. The proportion of prescriptions for the recommended antibiotic azithromycin increased (p < 0.001 district; p = 0.035 sub-district) with RRs of 6.9 (district) and 1.6 (sub-district) while prescriptions for other antibiotics decreased; zinc adherence increased. Limitations included an absence of a concurrent control group and no independent dehydration assessment during the pre-intervention. Despite limitations, opportunities were identified to improve clinical care, including better assessment, weight estimation, and fluid/ antibiotic selection. These findings demonstrate that a smartphone-based tool can improve guideline adherence. This study should serve as a catalyst for a randomized controlled trial to expand on the findings and address limitations.

Dataset Information

The effect of an mHealth clinical decision-making support system on neonatal mortality in a low resource setting: A cluster-randomized controlled trial.

Publications

The effect of an mHealth clinical decision-making support system on neonatal mortality in a low resource setting: A cluster-randomized controlled trial.

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