Barriers to eHealth adoption in routine antenatal care practices: Perspectives of expectant mothers in Uganda - A qualitative study using the unified theory of acceptance and use of technology model

Current empirical evidence suggests that successful adoption of eHealth systems improves maternal health outcomes, yet there are still existing gaps in adopting such systems in Uganda. Service delivery in maternal health is operating in a spectrum of inadequacy, hence eHealth adoption cannot ensue. This study set out to explore the challenges that impede eHealth adoption in women's routine antenatal care practices in Uganda. A qualitative approach using semi-structured interviews was employed to document challenges. These challenges were classified based on a unified theory of acceptance and use of technology constructs. One hundred and fifteen expectant mothers, aged between 18 and 49 years, who spoke either English or Luganda were included in the study that took place between January to May 2019. Thematic analysis using template analysis was adopted to analyse qualitative responses. Challenges were categorised based on five principal unified theories of acceptance and use of technology constructs namely: performance expectancy, effort expectancy, social influence, facilitating conditions and behavioural intention. Facilitating conditions had more influence on technology acceptance and adoption than the other four constructs. Specifically, the lack of training prior to using the system, technical support, computers and smart phones had a downhill effect on adoption. Subsequently, the cost of data services, internet intermittency, and the lack of systems that bridge the gap between mothers and health providers further hindered technology uptake. In conclusion, strategies such as co-development, training end-users, garnering support at the national and hospital levels should be advocated to improve user acceptance of technology.

A Systems Dynamics Approach to Understanding the Determinants of Antenatal Care Utilization in Low-and Middle-Income Countries

There has been low adherence of antenatal care utilization (ANC) in low and middle-income countries (LMIC) despite its associated negative outcome on women and their unborn babies. Although several studies have examined ANC, the majority focus on isolated aspects and do not explore the holistic approach to understand its dynamics. The system dynamics approach provides a deeper understanding of the phenomenon by examining the underlying factors, causes, effects, feedback, and delays. This study aimed at understanding factors that influence ANC utilization using the system's dynamics approach. An interpretive systematic review to establish multifaceted and context-specific processes was done between May and November 2019. Data from 24 articles were synthesized and used to build causal loop diagrams, which were validated through focus group discussions and interviews with stakeholders. Results revealed human resource numbers and welfare, awareness campaigns, peer support groups, and community-based engagement as key leverage points towards ANC improvement.

E-Screening for Prenatal Depression in Kampala, Uganda Using the Edinburgh Postnatal Depression Scale: Survey Results

Background

Perinatal depression remains a substantial public health challenge, often overlooked or incorrectly diagnosed in numerous low-income nations.

Objective

The goal of this study was to establish statistical baselines for the prevalence of perinatal depression in Kampala and understand its relationship with key demographic variables.

Methods

We employed an Android-based implementation of the Edinburgh Postnatal Depression Scale (EPDS) to survey 12,913 women recruited from 7 government health facilities located in Kampala, Uganda. We used the standard EPDS cutoff, which classifies women with total scores above 13 as possibly depressed and those below 13 as not depressed. The χ2 test of independence was used to determine the most influential categorical variables. We further analyzed the most influential categorical variable using odds ratios. For continuous variables such as age and the weeks of gestation, we performed a simple correlation analysis.

Results

We found that 21.5% (2783/12,913, 95% CI 20.8%-22.3%) were possibly depressed. Respondents' relationship category was found to be the most influential variable (χ21=806.9, P<.001; Cramer's V=0.25), indicating a small effect size. Among quantitative variables, we found a weak negative correlation between respondents' age and the total EPDS score (r=-0.11, P<.001). Similarly, a weak negative correlation was also observed between the total EPDS score and the number of previous children of the respondent (r=-0.07, P<.001). Moreover, a weak positive correlation was noted between weeks of gestation and the total EPDS score (r=0.02, P=.05).

Conclusions

This study shows that demographic factors such as spousal employment category, age, and relationship status have an influence on the respondents' EPDS scores. These variables may serve as proxies for latent factors such as financial stability and emotional support.

A water quality dataset of levels of metal, nutrient and anions in sample water points from sixteen selected urban and rural districts of Uganda

This dataset highlights some of the water quality issues in Uganda. The rationale for collecting the water samples was to test and ascertain the level and source of contamination. A total of one hundred and eighty five samples were collected from sixteen districts. At each water point, a sample was collected using a sterile plastic container, which was pre-rinsed with the water to be sampled. Water samples were drawn from protected and unprotected springs, shallow wells, taps, rain tanks, water reservoirs, open and hand dug wells and boreholes and immediately transported on ice to the National Water Quality Reference Laboratory for analysis. At the laboratory, a BWB flame photometer, Ethylenediamine tetraacetic acid (EDTA) titration and gallery plus-thermos fisher discreet analyzer were used to analyze metal, nutrient and anion elements. On-site testing of dissolved oxygen, pH, electrical conductivity and turbidity was done using a water data sonde. This data can be used to draw comparative analyses of water quality issues in rural and urban districts and help in identifying the factors that influence water quality variations. The data can further be used for trend analysis and identifying long-term patterns whilst providing insights into pollution sources and the impact of environmental and climate change. Consequently, mathematical and machine learning models can use this data together with other parameters to predict the changes in water quality which information is essential for policy and decisions making. This data can be used by environmental scientists to draw insights into the health of the aquatic biodiversity; geospatial analysts to ascertain proximal water contaminants; public health specialists to analyze pathogens leading to water-borne diseases; water chemists to study the source and cause of water pollution; data scientists to perform predictive and descriptive analyses; and policy makers to formulate laws and regulations.