Can an mhealth clinical decision-making support system improve adherence to neonatal healthcare protocols in a low-resource setting?

BACKGROUND: This 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. METHODS: We 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. RESULTS: In 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. CONCLUSION: Whether 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

Strategies to improve interpersonal communication along the continuum of maternal and newborn care: A scoping review and narrative synthesis

Effective interpersonal communication is essential to provide respectful and quality maternal and newborn care (MNC). This scoping review mapped, categorized, and analysed strategies implemented to improve interpersonal communication within MNC up to 42 days after birth. Twelve bibliographic databases were searched for quantitative and qualitative studies that evaluated interventions to improve interpersonal communication between health workers and women, their partners or newborns' families. Eligible studies were published in English between January 1st 2000 and July 1st 2020. In addition, communication studies in reproduction related domains in sexual and reproductive health and rights were included. Data extracted included study design, study population, and details of the communication intervention. Communication strategies were analysed and categorized based on existing conceptualizations of communication goals and interpersonal communication processes. A total of 138 articles were included. These reported on 128 strategies to improve interpersonal communication and were conducted in Europe and North America (n = 85), Sub-Saharan Africa (n = 12), Australia and New Zealand (n = 10), Central and Southern Asia (n = 9), Latin America and the Caribbean (n = 6), Northern Africa and Western Asia (n = 4) and Eastern and South-Eastern Asia (n = 2). Strategies addressed three communication goals: facilitating exchange of information (n = 97), creating a good interpersonal relationship (n = 57), and/or enabling the inclusion of women and partners in the decision making (n = 41). Two main approaches to strengthen interpersonal communication were identified: training health workers (n = 74) and using tools (n = 63). Narrative analysis of these interventions led to an update of an existing communication framework. The categorization of different forms of interpersonal communication strategy can inform the design, implementation and evaluation of communication improvement strategies. While most interventions focused on information provision, incorporating other communication goals (building a relationship, inclusion of women and partners in decision making) could further improve the experience of care for women, their partners and the families of newborns

Improving neonatal health in low resource settings using mobile health technology

To attain the Sustainable Development Goal of ending preventable new-born deaths and reducing neonatal deaths to at least 12 per 1000 births, urgent attention is required in low- and middle-income countries which contribute the most to the global burden of neonatal deaths. Mobile health (mHealth) can potentially improve neonatal health outcomes. We designed and implemented an mHealth intervention that provided easy access to neonatal health guidelines for clinical decision-making for health workers in the Eastern Region of Ghana. We evaluated the impact of the intervention on institutional neonatal mortality and investigated the ‘how and why’ of the observed intervention effect. Sixteen districts in the Eastern Region were randomized into a 2-arm cluster-randomized controlled trial (8 intervention and 8 control clusters per arm) that assessed the impact of the intervention on neonatal mortality over an 18-month period. To understand the possible explanatory mechanism for the observed intervention effect, three sub-studies were undertaken. Firstly, we assessed utilization of the intervention and measured the correlation between the requests made and the incidence of deliveries and neonatal morbidities in the intervention clusters. Secondly, we assessed health worker adherence to neonatal protocols before and during the trial period using in-patient clinical records. Thirdly, to understand how and why the intervention was utilized as observed, we performed a single case study with each cluster as an embedded sub-unit of analysis using key informant interviews and focus group discussions with the intervention users, and manually analysed the data for themes. This thesis showed that health workers readily used the intervention to access neonatal health guidelines. The use of the intervention however, declined over time due to individual health worker, organizational, and technological factors as well as client perception of health worker intervention usage. During the trial period, there was a raise in neonatal deaths in both arms study arms. 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). The higher odds of neonatal death in the intervention arm is possibly due to unmeasured and unadjusted confounding due to limitations in the data structure of the national health database of Ghana, unintended use of the intervention and problems with births and deaths registration at the study sites. Many other neonatal health improvement programmes (unrelated to our mHealth intervention) were observed in the control arm compared to the intervention arm during the trial period. Adherence to neonatal health protocols improved in both study arms and this may be related to these other neonatal improvement programmes that took place particularly in the control arm clusters. Technological factors alone are unlikely to influence outcomes. This thesis highlights the importance of validating successful programmes and interventions in settings where they are to be implemented. Harmonized rather than fragmented efforts are needed to scale up mHealth interventions that have been proven to be effective in a given context

Improving neonatal health in low resource settings using mobile health technology

To attain the Sustainable Development Goal of ending preventable new-born deaths and reducing neonatal deaths to at least 12 per 1000 births, urgent attention is required in low- and middle-income countries which contribute the most to the global burden of neonatal deaths. Mobile health (mHealth) can potentially improve neonatal health outcomes. We designed and implemented an mHealth intervention that provided easy access to neonatal health guidelines for clinical decision-making for health workers in the Eastern Region of Ghana. We evaluated the impact of the intervention on institutional neonatal mortality and investigated the ‘how and why’ of the observed intervention effect. Sixteen districts in the Eastern Region were randomized into a 2-arm cluster-randomized controlled trial (8 intervention and 8 control clusters per arm) that assessed the impact of the intervention on neonatal mortality over an 18-month period. To understand the possible explanatory mechanism for the observed intervention effect, three sub-studies were undertaken. Firstly, we assessed utilization of the intervention and measured the correlation between the requests made and the incidence of deliveries and neonatal morbidities in the intervention clusters. Secondly, we assessed health worker adherence to neonatal protocols before and during the trial period using in-patient clinical records. Thirdly, to understand how and why the intervention was utilized as observed, we performed a single case study with each cluster as an embedded sub-unit of analysis using key informant interviews and focus group discussions with the intervention users, and manually analysed the data for themes. This thesis showed that health workers readily used the intervention to access neonatal health guidelines. The use of the intervention however, declined over time due to individual health worker, organizational, and technological factors as well as client perception of health worker intervention usage. During the trial period, there was a raise in neonatal deaths in both arms study arms. 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). The higher odds of neonatal death in the intervention arm is possibly due to unmeasured and unadjusted confounding due to limitations in the data structure of the national health database of Ghana, unintended use of the intervention and problems with births and deaths registration at the study sites. Many other neonatal health improvement programmes (unrelated to our mHealth intervention) were observed in the control arm compared to the intervention arm during the trial period. Adherence to neonatal health protocols improved in both study arms and this may be related to these other neonatal improvement programmes that took place particularly in the control arm clusters. Technological factors alone are unlikely to influence outcomes. This thesis highlights the importance of validating successful programmes and interventions in settings where they are to be implemented. Harmonized rather than fragmented efforts are needed to scale up mHealth interventions that have been proven to be effective in a given context

Diagnostic accuracy of urine dipstick tests for proteinuria in pregnant women suspected of preeclampsia: A systematic review and meta-analysis

OBJECTIVES: Dipstick tests are frequently used as bedside proteinuria tests to evaluate women suspected of preeclampsia and may inform diagnosis in low resource settings lacking laboratory facilities. This systematic review and meta-analysis aimed to (1) estimate the diagnostic accuracy of urine dipsticks in diagnosing proteinuria, (2) compare performance of different dipstick types and (3) estimate their related costs. METHODS: MEDLINE and EMBASE were searched up to August 1, 2020 for primary studies with cross-sectional diagnostic accuracy data on dipstick test(s) compared to a laboratory reference standard (24-hour protein ≥ 300 mg or protein-creatinine ratio ≥ 30 mg/mmol) in pregnant women ≥ 20 weeks of gestation suspected of preeclampsia. Risk of bias and applicability was assessed with QUADAS-2. Data were analysed using a bivariate model with hierarchical addition of covariates for subgroups. RESULTS: Nineteen studies were included. Protein-only dipsticks at 1 + threshold had a pooled sensitivity of 0.68 [95%CI: 0.57-0.77] and specificity of 0.85 [95% CI: 0.73-0.93] (n = 3700 urine samples, 18 studies). Higher specificity was found with automatedly (0.93 [95% CI: 0.82-0.98]) compared to visually (0.81 [95% CI: 0.65-0.91]) read dipsticks, whereas sensitivity was similar and costs were higher. The use of albumin-creatinine ratio (ACR) dipsticks was only reported in two studies and did not improve accuracy. Heterogeneity in study design and prevalence of preeclampsia amongst studies complicated interpretation of pooled estimates. CONCLUSION: Urine dipsticks performed poorly at excluding preeclampsia in hypertensive pregnant women. Further development of accurate and low-cost bedside proteinuria tests is warranted

The effect of a clinical decision-making mHealth support system on maternal and neonatal mortality and morbidity in Ghana: study protocol for a cluster randomized controlled trial

BACKGROUND: Mobile health (mHealth) presents one of the potential solutions to maximize health worker impact and efficiency in an effort to reach the Sustainable Development Goals 3.1 and 3.2, particularly in sub-Saharan African countries. Poor-quality clinical decision-making is known to be associated with poor pregnancy and birth outcomes. This study aims to assess the effect of a clinical decision-making support system (CDMSS) directed at frontline health care providers on neonatal and maternal health outcomes. METHODS/DESIGN: A cluster randomized controlled trial will be conducted in 16 eligible districts (clusters) in the Eastern Region of Ghana to assess the effect of an mHealth CDMSS for maternal and neonatal health care services on maternal and neonatal outcomes. The CDMSS intervention consists of an Unstructured Supplementary Service Data (USSD)-based text messaging of standard emergency obstetric and neonatal protocols to providers on their request. The primary outcome of the intervention is the incidence of institutional neonatal mortality. Outcomes will be assessed through an analysis of data on maternal and neonatal morbidity and mortality extracted from the District Health Information Management System-2 (DHIMS-2) and health facility-based records. The quality of maternal and neonatal health care will be assessed in two purposively selected clusters from each study arm. DISCUSSION: In this trial the effect of a mobile CDMSS on institutional maternal and neonatal health outcomes will be evaluated to generate evidence-based recommendations for the use of mobile CDMSS in Ghana and other West African countries. TRIAL REGISTRATION: ClinicalTrials.gov, identifier: NCT02468310 . Registered on 7 September 2015; Pan African Clinical Trials Registry, identifier: PACTR20151200109073 . Registered on 9 December 2015 retrospectively from trial start date

How and why front-line health workers (did not) use a multifaceted mHealth intervention to support maternal and neonatal healthcare decision-making in Ghana

Introduction Despite increasing use of mHealth interventions, there remains limited documentation of â € how and why' they are used and therefore the explanatory mechanisms behind observed effects on beneficiary health outcomes. We explored â € how and why' an mHealth intervention to support clinical decision-making by front-line providers of maternal and neonatal healthcare services in a low-resource setting was used. The intervention consisted of phone calls (voice calls), text messaging (short messaging service (SMS)), internet access (data) and access to emergency obstetric and neonatal protocols via an Unstructured Supplementary Service Data (USSD). It was delivered through individual-use and shared facility mobile phones with unique Subscriber Identification Module (SIM) cards networked in a Closed User Group. Methods A single case study with multiple embedded subunits of analysis within the context of a cluster randomised controlled trial of the impact of the intervention on neonatal health outcomes in the Eastern Region of Ghana was performed. We quantitatively analysed SIM card activity data for patterns of voice calls, SMS, data and USSD. We conducted key informant interviews and focus group discussions with intervention users and manually analysed the data for themes. Results Overall, the phones were predominantly used for voice calls (64%), followed by data (28%), SMS (5%) and USSD (2%), respectively. Over time, use of all intervention components declined. Qualitative analysis showed that individual health worker factors (demographics, personal and work-related needs, perceived timeliness of intervention, tacit knowledge), organisational factors (resource availability, information flow, availability, phone ownership), technological factors (attrition of phones, network quality) and client perception of health worker intervention usage explain the pattern of intervention use observed. Conclusion How and why the mHealth intervention was used (or not) went beyond the technology itself and was influenced by individual and context-specific factors. These must be taken into account in designing similar interventions to optimise effectiveness

How and why front-line health workers (did not) use a multifaceted mHealth intervention to support maternal and neonatal healthcare decision-making in Ghana

Introduction Despite increasing use of mHealth interventions, there remains limited documentation of â € how and why' they are used and therefore the explanatory mechanisms behind observed effects on beneficiary health outcomes. We explored â € how and why' an mHealth intervention to support clinical decision-making by front-line providers of maternal and neonatal healthcare services in a low-resource setting was used. The intervention consisted of phone calls (voice calls), text messaging (short messaging service (SMS)), internet access (data) and access to emergency obstetric and neonatal protocols via an Unstructured Supplementary Service Data (USSD). It was delivered through individual-use and shared facility mobile phones with unique Subscriber Identification Module (SIM) cards networked in a Closed User Group. Methods A single case study with multiple embedded subunits of analysis within the context of a cluster randomised controlled trial of the impact of the intervention on neonatal health outcomes in the Eastern Region of Ghana was performed. We quantitatively analysed SIM card activity data for patterns of voice calls, SMS, data and USSD. We conducted key informant interviews and focus group discussions with intervention users and manually analysed the data for themes. Results Overall, the phones were predominantly used for voice calls (64%), followed by data (28%), SMS (5%) and USSD (2%), respectively. Over time, use of all intervention components declined. Qualitative analysis showed that individual health worker factors (demographics, personal and work-related needs, perceived timeliness of intervention, tacit knowledge), organisational factors (resource availability, information flow, availability, phone ownership), technological factors (attrition of phones, network quality) and client perception of health worker intervention usage explain the pattern of intervention use observed. Conclusion How and why the mHealth intervention was used (or not) went beyond the technology itself and was influenced by individual and context-specific factors. These must be taken into account in designing similar interventions to optimise effectiveness

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

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