Publications by authors named "Hannah Brown Amoakoh"

8 Publications

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

Pregnancy Hypertens 2022 Mar 4;27:123-130. Epub 2022 Jan 4.

Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Postbus 85500, 3508 GA Utrecht, The Netherlands.

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.
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http://dx.doi.org/10.1016/j.preghy.2021.12.015DOI Listing
March 2022

Synergies and fragmentation in country level policy and program agenda setting, formulation and implementation for Global Health agendas: a case study of health security, universal health coverage, and health promotion in Ghana and Sierra Leone.

BMC Health Serv Res 2021 May 20;21(1):476. Epub 2021 May 20.

Non-Communicable Diseases Control Program, Ghana Health Service, P.O.Box KB 493, Korlebu, Accra, Ghana.

Background: Global health agendas have in common the goal of contributing to population health outcome improvement. In theory therefore, whenever possible, country level policy and program agenda setting, formulation and implementation towards their attainment should be synergistic such that efforts towards one agenda promote efforts towards the other agendas. Observation suggests that this is not what happens in practice. Potential synergies are often unrealized and fragmentation is not uncommon. In this paper we present findings from an exploration of how and why synergies and fragmentation occur in country level policy and program agenda setting, formulation and implementation for the global health agendas of Universal Health Coverage (UHC), Health Security (HS) and Health Promotion (HP) in Ghana and Sierra Leone. Our study design was a two country case study. Data collection involved document reviews and Key Informant interviews with national and sub-national level decision makers in both countries between July and December 2019. Additionally, in Ghana a stakeholder workshop in December 2019 was used to validate the draft analysis and conclusions.

Results: National and global context, country health systems leadership and structure including resources were drivers of synergies and fragmentation. How global as well as country level actors mobilized power and exercised agency in policy and program agenda setting and implementation processes within country were also important drivers.

Conclusions: There is potential in both countries to pull towards synergies and push against fragmentation in agenda setting, formulation and implementation of global health agendas despite the resource and other structural constraints. It however requires political and bureaucratic prioritization of synergies, as well as skilled leadership. It also requires considerable mobilization of country level actor exercise of agency to counter sometimes daunting contextual, systems and structural constraints.
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http://dx.doi.org/10.1186/s12913-021-06500-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8134803PMC
May 2021

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

BMC Pediatr 2020 11 27;20(1):534. Epub 2020 Nov 27.

University of Health and Allied Sciences, Ho, Ghana.

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.
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http://dx.doi.org/10.1186/s12887-020-02378-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7694934PMC
November 2020

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

EClinicalMedicine 2019 Jul 4;12:31-42. Epub 2019 Jul 4.

University of Allied Sciences, Ho, Ghana.

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 ( and Pan African Clinical Trials Registry (

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.
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http://dx.doi.org/10.1016/j.eclinm.2019.05.010DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6677648PMC
July 2019

Using Mobile Health to Support Clinical Decision-Making to Improve Maternal and Neonatal Health Outcomes in Ghana: Insights of Frontline Health Worker Information Needs.

JMIR Mhealth Uhealth 2019 05 24;7(5):e12879. Epub 2019 May 24.

Centre for Malaria Research, University of Health and Allied Sciences, Ho, Ghana.

Background: Developing and maintaining resilient health systems in low-resource settings like Ghana requires innovative approaches that adapt technology to context to improve health outcomes. One such innovation was a mobile health (mHealth) clinical decision-making support system (mCDMSS) that utilized text messaging (short message service, SMS) of standard emergency maternal and neonatal protocols via an unstructured supplementary service data (USSD) on request of the health care providers. This mCDMSS was implemented in a cluster randomized controlled trial (CRCT) in the Eastern Region of Ghana.

Objective: This study aimed to analyze the pattern of requests made to the USSD by health workers (HWs). We assessed the relationship between requests made to the USSD and types of maternal and neonatal morbidities reported in health facilities (HFs).

Methods: For clusters in the intervention arm of the CRCT, all requests to the USSD during the 18-month intervention period were extracted from a remote server, and maternal and neonatal health outcomes of interest were obtained from the District Health Information System of Ghana. Chi-square and Fisher exact tests were used to compare the proportion and type of requests made to the USSD by cluster, facility type, and location; whether phones accessing the intervention were shared facility phones or individual-use phones (type-of-phone); or whether protocols were accessed during the day or at night (time-of-day). Trends in requests made were analyzed over 3 6-month periods. The relationship between requests made and the number of cases reported in HFs was assessed using Spearman correlation.

Results: In total, 5329 requests from 72 (97%) participating HFs were made to the intervention. The average number of requests made per cluster was 667. Requests declined from the first to the third 6-month period (44.96% [2396/5329], 39.82% [2122/5329], and 15.22% [811/5329], respectively). Maternal conditions accounted for the majority of requests made (66.35% [3536/5329]). The most frequently accessed maternal conditions were postpartum hemorrhage (25.23% [892/3536]), other conditions (17.82% [630/3536]), and hypertension (16.49% [583/3536]), whereas the most frequently accessed neonatal conditions were prematurity (20.08% [360/1793]), sepsis (15.45% [277/1793]), and resuscitation (13.78% [247/1793]). Requests made to the mCDMSS varied significantly by cluster, type of request (maternal or neonatal), facility type and its location, type-of-phone, and time-of-day at 6-month interval (P<.001 for each variable). Trends in maternal and neonatal requests showed varying significance over each 6-month interval. Only asphyxia and sepsis cases showed significant correlations with the number of requests made (r=0.44 and r=0.79; P<.001 and P=.03, respectively).

Conclusions: There were variations in the pattern of requests made to the mCDMSS over time. Detailed information regarding the use of the mCDMSS provides insight into the information needs of HWs for decision-making and an opportunity to focus support for HW training and ultimately improved maternal and neonatal health.
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http://dx.doi.org/10.2196/12879DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6555115PMC
May 2019

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

BMJ Glob Health 2019 8;4(2):e001153. Epub 2019 Mar 8.

Ghana Health Service, Research and Development Division, Dodowa Research Centre, Accra, Ghana.

Introduction: Despite increasing use of mHealth interventions, there remains limited documentation of 'how and why' they are used and therefore the explanatory mechanisms behind observed effects on beneficiary health outcomes. We explored '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.
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http://dx.doi.org/10.1136/bmjgh-2018-001153DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6441261PMC
March 2019

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.

Trials 2017 04 4;18(1):157. Epub 2017 Apr 4.

School of Public Health, University of Ghana, Legon, PO Box LG13, Accra, Ghana.

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.
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http://dx.doi.org/10.1186/s13063-017-1897-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5379695PMC
April 2017

Household cost of out-patient treatment of Buruli ulcer in Ghana: a case study of Obom in Ga South Municipality.

BMC Health Serv Res 2013 Dec 5;13:507. Epub 2013 Dec 5.

Department of Health Policy, Planning & Management, School of Public Health, College of Health Sciences, University of Ghana, P, O, Box LG13, Legon, Accra, Ghana.

Background: The economic burden of diseases has become increasingly relevant to policy makers as healthcare expenditure keep rising in the face of limited and competing resources. Buruli ulcer (BU), a neglected but treatable tropical disease caused by Mycobacterium ulcerans, the only known environmental mycobacterium is capable of causing long term disability when left untreated. However, most BU studies have tended to focused on its bacteriology, epidemiology, entomology and other social determinants to the neglect of its economic evaluation. This paper reports estimated the household economic costs of BU and describe the intangible cost suffered by BU patients in an endemic area.

Methods: Retrospective one year cost data was used. A total of 63 confirmed BU cases were randomly sampled for the study. Economic cost and cost burden of BU were estimated. Sensitivity analysis was conducted to test the robustness of the cost estimates. Intangible cost measured stigmatization, pain, functional limitation and social isolation of children.

Results: The annual total household economic cost was US$35,915.98, of which about 65% was cost incurred by children with a mean cost of US$521.04. The mean annual household cost was US$570.09. The direct cost was 96% of the total cost. Non-medical cost accounts for about 97% of the direct cost with a mean cost of US$529.27. The mean medical cost was US$18.94. The main cost drivers of the household costs were transportation (78%) and food (12%). Caregivers and adult patients lost a total of 535 productive days seeking care, which gives an indirect cost valued at US$1,378.67 with a mean of US$21.88. A total of 365 school days (about 1 year) were lost by 19 BU patients (mean, 19.2 days). Functional loss and pain were low, and stigma rated moderate. Most children suffering from BU (84%) were socially isolated.

Conclusion: Household cost burden of out-patient BU ulcer treatment was high. Household cost of BU is therefore essential in the design of its intervention. BU afflicted children experience social isolation.
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http://dx.doi.org/10.1186/1472-6963-13-507DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4029088PMC
December 2013
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