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.

A two-arm cluster-randomized controlled trial (CRCT) to evaluate the effect of mCDMSI on neonatal mortality was implemented in 16 districts in the Eastern Region of Ghana [20]. Each of the 16 districts formed one cluster in this study. The intervention period lasted for 18 months. The study site was the Eastern Region of Ghana, the third most populous region in Ghana (Fig. 1) [30]. The region is divided into twenty-one [21] geographic local administrative units called districts. At the start of intervention implementation, there were a total of 250 health facilities i.e. Community-based Health Planning and Services (CHPS) compounds and maternity homes, Health centres (HCs), and hospitals in the Eastern Region. At the primary health care level, the CHPS, HCs and maternity homes provide services including neonatal healthcare services to the various communities and refer cases to the hospitals. The Eastern Region ranks fourth in terms of high neonatal mortality rate (NMR) in Ghana [31]. The NMR for the region in 2014 was 30 per 1000 live births [31]. Clusters participating in randomized controlled trial to evaluate the effect of an mHealth clinical decision-making intervention on neonatal mortality in Ghana. The inclusion criteria for cluster selection for the CRCT included the following: i) District located in the Eastern Region of Ghana ii) Expected deliveries of ≥ 1100/year for the year 2014 for a district iii) Both District Health Management Team and the District Hospital Management Team agree to participate in the study iv) Health facilities within the district should have conducted at least one (1) delivery in the year 2014. The exclusion criteria for our study were: i) District location outside the Eastern Region ii) Expected deliveries of < 1100/year for the year 2014 for a district iii) The District Health Management Team and the Hospital Management Team disagreeing to participate in the study iv) Health facilities within the districts not conducting at least one (1) delivery during the year 2014. The year 2014 was selected as the baseline year as the most current data pertaining to deliveries (births) at the time of commencement of the study was for that year. The protocol for this study has been published previously [20]. As data analyzed in this study was obtained from Ghana's national institutional health database, informed consent of patients for this study was not applicable. Consent to utilize data from the national institutional health database and to conduct this study was obtained from the Regional Health Directorate, Eastern Region, Ghana. The study was approved by the Ghana Health Service Ethics Review Committee (Reference: GHS-ERC: 10/09/14), and was registered at clinicaltrials.gov {"type":"clinical-trial","attrs":{"text":"NCT02468310","term_id":"NCT02468310"}}NCT02468310 and Pan African Clinical Trials Registry PACTR20151200109073. Out of the twenty-one eligible districts in the Eastern Region, seventeen districts fulfilled the inclusion and exclusion criteria for the CRCT. The regional capital was excluded from the selection process to avoid selection bias as its regional hospital is the highest referral point in the region. Sixteen clusters were therefore randomized into 8 intervention and 8 control clusters (Fig. 2). Cluster-randomization was preferred over individual randomization to avoid contamination both at the health professional and client levels, which may occur as a result of social interaction. A randomization scheme of permuted blocks was used to randomize the 16 districts equally to the two-armed program (control and intervention). The randomization scheme consisted of a sequence of blocks such that each block contained a pre-specified number of treatment assignments in random order. The purpose of this was so that the randomization scheme was balanced at the completion of each block. Randomization was performed by an independent data analyst in order to achieve comparability and avoid selection bias. Within the randomized clusters, all health facilities that conducted deliveries in the year preceding the start of the intervention (2014) were recruited into this study. Due to the nature of this intervention, masking was not feasible. Trial flow-chart of cluster randomized controlled trial to assess the effect of an mHealth clinical decision making tool on neonatal mortality in Ghana. This study was designed as a superiority trial with neonatal mortality as the primary outcome. To detect a 30% decline in neonatal mortality at a power of 80%, a significance level of 0.05 (two-tailed test), with a fixed number of 8 clusters in each arm of the study and intra-cluster correlation coefficient for neonatal mortality of 0.0007256 [32], approximately 1065 patients in each of the 16 clusters was needed [20]. Participation in this study was at the cluster level. The impact of the intervention was measured by extracting data about deliveries that occurred in health facilities in the clusters recruited in this study. Data was extracted from the district health information management system-2 (DHIMS-2) database. The DHIMS-2 is a data recording, collection, collation and analysis tool that hosts the entire national institutional health data of Ghana [20]. Data in the DHIMS-2 comes from mainly public health facilities and a few private ones. The DHIMS-2 has been shown to provide reliable estimates of measures in some studies [33], [34], however, other studies have reported incomplete entries for certain variables in the database [35]. In the DHIMS-2, data of clients or patients who seek health services in a health facility is captured either in aggregate per health facility (e.g., hospital ‘A' had 20 deliveries), or as individual level data of all patients who were treated in each health facility. Individual level data is however, limited to clients who are seen and treated in hospitals. With regard to this study, data that was available in the DHIMS-2 and captured as aggregate per health facility were the number of neonatal deaths and the number of deliveries. Detailed information regarding each delivery captured in the DHIMS-2 was limited to hospital deliveries, and further limited to peri-partum maternal data (e.g., age, parity, and duration of pregnancy etc.). Thus, detailed information about babies delivered e.g., Apgar scores, weight and gender could not be obtained from the DHIMS-2. For each delivery that occurred in a hospital, there was no data that linked the detailed maternal delivery information to neonatal deaths that occurred in each health facility. Given these limitations with the DHIMS-2, we extracted data regarding incidence of neonatal mortality and deliveries per health facility and individual records of peri-partum characteristics of women who delivered in hospitals in the study clusters for the 18-month intervention period (August 2015 to January 2017), from the database. Fig. 3 illustrates the data structure for this study. Due to technical challenges with data entry and extraction from the DHIMS-2, seven hospitals agreed and captured the individual records of women who delivered in their facilities on excel spreadsheets that were given to the project team for analysis. The data entry in such situations was done by the hospital health information officers responsible for entering that data into the DHIMS-2 and the data was validated by the head of the health information unit in these hospitals. Thus data analyzed in this study is a combination of data already captured in the DHIMS-2 at the time of data analysis and, facility level data that may or may not be presently captured in the DHIMS-2. There were 8 private hospitals in total in this study; only one contributed individual level data into the database for analysis. Data sources and structure for cluster randomized controlled trial evaluating the effect of a clinical decision-making intervention in the Eastern Region of Ghana. The research team collected baseline data regarding the number of doctors and midwives at post in each health facility and the location of health facilities. We classified health facilities into two groups of remote and non-remote areas based on access. Remote facilities were located either more than 30 min' walk, or more that 15 min motor-bike ride from the main district township, and had poor road access (uneven and untarred roads overcrowded with weeds and shrubs) leading to them [36]. Non-remote health facilities were located either within 30 min' walk, or 15 min motor-bike ride from the main district township, and had good road access leading to them. Data concerning the use of the USSD protocols during intervention implementation was extracted from the database of the telecommunication company that provided support for the intervention (Vodafone Ghana). Four intervention clusters were of interest in this study for 2 reasons; i. they shared boundaries with non-study clusters that did not have hospitals and/or, ii. they recorded high neonatal mortalities. In Ghana, address systems are not well established. To enable us analyze the addresses of women who delivered in hospitals within these clusters, the district health management team (DHMT) in each cluster was tasked to identify addresses within and outside their district from a list of addresses captured as addresses in their district in the DHIMS-2. The DHMT run the day-to-day health activities within a district, traveling to every corner of their districts; they are therefore a good resource with regard to identification of names of locations within a district that may not be formally documented. The primary outcome measure estimated in this study was institutional neonatal mortality which included deaths of babies admitted from birth and those (re)admitted from home. Utilization of the mCDMSI for clinical decision-making was estimated as a secondary outcome. For this study neonatal mortality was defined as death of a new-born occurring from birth up to the 28th day of life [37]. In Ghana, the expulsion of a product of conception before 28 completed weeks of gestation is considered an abortion. We therefore limited our analysis to pregnancies of gestation 28 completed weeks or more. We performed an intention-to-treat analysis at cluster level. We assessed the peri-partum characteristics of the women who delivered in hospitals during the intervention period to identify possible imbalance in characteristics of these women and their pregnancies in the study arms. We limited our analysis of peri-partum characteristics of women delivering in health facilities to pregnancies of women in the reproductive age group of 15 to 44 years [38] as the excluded ages formed < 1% of available data. Potential sources of imbalance in the study arms i.e., age, parity, duration of pregnancy were summarized and expressed as means or medians, while insurance status and education level of women were expressed as numbers and percentages (Table 3). Differences in distributions of these potential confounders between the intervention and control arms where assessed using t-tests or Wilcoxon rank-sum tests and chi-square tests where appropriate. We calculated the proportion of remotely located health facilities, the number of deliveries per midwife and, number of deliveries per doctor per cluster to assess cluster level imbalance in the study arms. Characteristics of women delivering in hospitals in CRCT clusters during the intervention period. We defined our denominator for neonatal mortality rate as ‘number of deliveries’ as we could only obtain information regarding peri-partum conditions of pregnancies that resulted in deliveries from the DHIMS-2. We estimated neonatal mortality as the number of neonatal deaths per the number of deliveries occurring in each cluster. We estimated the neonatal mortality per cluster during the one year proceeding the intervention period (prior risk of neonatal mortality) and analyzed the trend in neonatal mortality in the clusters during the intervention period. We estimated the effect of the intervention using a grouped binomial logistic regression with a random intercept per cluster specifying the Laplacian approximation to correct for the clustered design and estimated the intra-cluster correlation. We adjusted for the prior risk of neonatal mortality per cluster in analysis. The effect of the intervention compared with the control group was expressed with odds ratios (with 95% CI and p-values), which, given the low risk of the outcome, may be interpreted as relative risks. Additional analysis of addresses of women who delivered in hospitals in four intervention clusters (clusters B, C, F and H) was performed to assess the proportion of deliveries within a cluster that were actually deliveries by women who lived within a specified cluster. We further analyzed the correlations between the number of USSD requests (maternal and neonatal requests combined) and the number of deliveries per cluster; the number of neonatal USSD requests and the number of neonatal deaths using Spearman correlation as a proxy for the extent to which the intervention was utilized in decision-making. All analyses were two-tailed with a significance level 0 ∙ 05, and were performed in Stata version 13 [39].

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