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Now showing 1 - 5 of 20102
  • Item
    Improving viral load monitoring coverage using a quality improvement approach in Blantyre Malawi
    (2021) Kamwendo, Angella Joy
    Background: Viral load (VL) testing coverage in individuals with HIV remains low particularly in resource limited countries despite recommendation by World Health Organization, and Malawi is no exception. A quality improvement (QI) approach was used to improve VL testing coverage from 27% to a target of 80% at an urban health facility in Malawi. Methods: A QI study employing a time-series quasi-experimental design with no comparison group was conducted at Chilomoni health centre in Blantyre from April 2020 to July 2020. A retrospective record review of 257 patient records from 8 weeks before the study was conducted to determine baseline VL testing coverage. Root cause identification and prioritization of low VL testing coverage was done using fish-bone tool and Pareto-chart respectively by healthcare providers. Change ideas were identified and prioritized using an effort-impact matrix by healthcare providers. Two change ideas; re-orienting ART providers on VL test order in EMR and dedicated ART provider to serve VL tested patients were implemented and tested in 5 Plan-Do-Study-Act (PDSA) cycles from the Model for Improvement (MFI), each lasting one week. The latter was tested, and adapted in 3 cycles, and eventually adopted for monitoring for another 5 weeks. VL testing coverage was tracked throughout the study using run charts and p-charts. Segmented regression analysis was also done to assess significance of the change in outcome. Results: VL testing coverage increased from 27% to 81% in the post-intervention period, with children aged up to 17 years experiencing the lowest VL testing coverage. A significant overall increase in the outcome was observed after implementation of interventions in the post intervention period (IRR 7.026; 95% confidence interval (CI) 1.484-33.263; P < 0. 014). However, change in children was insignificant. Conclusion: The MFI as a QI approach improved VL testing coverage through implementation of contextualized change ideas, although the results suggest children need tailored interventions. Future research should focus on evaluating sustainability of improved VL testing coverage at the health facility and assessing barriers to VL testing among children.
  • Item
    Barriers and facilitators of investigation coverage among index tuberculosis case contacts in rural Mbarara district, Uganda
    (2021) Tukamuhebwa, Paddy Mutungi
    Background: In 2012, the World Health Organization (WHO) recommended the investigation of contacts of index tuberculosis (TB) patients in low and middle-income countries (LMICs) as a strategy for accelerating TB case detection. Despite this WHO policy guidance, coverage of TB contact investigation in Uganda remains low nine years after adopting this recommendation. Studies identifying the barriers and facilitators to optimal TB contact investigation coverage have only been done in urban areas in Uganda and Kenya. Aim: To explore health providers perspectives of the factors that influence TB contact investigation coverage in three rural, primary health facilities (PHC) in Mbarara District, South Western Uganda. Methods: An exploratory qualitative study design was used to conduct semistructured interviews with health workers involved in the TB program at the district, health facility and community levels between February 2020 and July 2020. The sample included the district TB supervisors, health facility TB focal persons, nurses, clinical officers and Community Health Workers (CHWs). The data were taperecorded, transcribed and uploaded into MAXQDA for analysis. The analysis was done inductively using reflexive thematic analysis in six iterative steps: familiarization with the data, creating initial codes, theme search, reviewing the themes, developing theme definitions, and report writing. Five domains of the Consolidated Framework for Implementation Research; characteristics of the intervention, inner setting, outer setting, individuals involved and implementation process—were used to guide the development of semi-structured interviews and identification of barriers and facilitators of TB contact investigation. Results: Nineteen health workers, two district TB and supervisors, five clinical officers, five nurses, six community health workers and one laboratory staff participated in the research representing a 100% response rate. Intervention-related factors, healthcare system factors, and contextual factors were the three themes that emerged from this research. Health system barriers comprised inadequate or delayed funding, staff shortages, insufficient personal protective equipment and stock-out of GeneXpert cartridges. Contextual obstacles included rough terrain, poverty, and stigma associated with TB and COVID-19. Increased knowledge and understanding of the intervention, strong leadership and teamwork, and on-the-job training of health workers were all facilitators. All of the CFIR domains were important in examining the challenges and enablers of contact investigation coverage. Conclusion and recommendations: In this study, we were able to identify provider opinions on the barriers and facilitators of TB contact investigation coverage by using the Consolidated Framework for Implementation Research. The intervention-related, health system and contextual factors identified from this research may be used to guide the development of tailored interventions for rural hard to reach communities. To increase TB contact investigation coverage in rural areas, the National TB and Leprosy Program should advocate for increased funding to the TB program and address barriers in the flow of funds up the grass-root level. The government also should address the staffing gaps, strengthen the supply chain of essential suppliers and reinforce all the health system components. The Ministry of Health should also develop stigma reduction interventions and explore interventions for reaching out to hard to reach rural populations.
  • Item
    Factors associated with uptake of intermittent preventive treatment (IPTp-SP) for malaria among pregnant women aged 15 to 49 years in Nigeria, 2018.
    (2021) Kalu, Godwin Okeke
    Background Pregnancy-associated malaria is a leading public health threat that stances significant risks to pregnant women and neonates. This study aims to determine the prevalence of IPTp-SP uptake; and establish the factors associated with the uptake of any dose and optimal doses of IPTp-SP among pregnant women aged 15 to 49 years living in Nigeria, 2018. Methods The secondary data analysis used the 2018 Nigeria Demographic Health Survey (NDHS) dataset. The primary study chose 1389 clusters from a total of 74 strata formed from the urban and rural areas of the 36 States in Nigeria. Then, 30 households were selected from each cluster to form a sample size of 41,666 households. From the 41,666 households, 41,821 women aged 15 to 49 years were interviewed for the 2018 NDHS. Among the 41,821 women interviewed, only 12,742 with live birthstwo years before or during the NDHS were included in the analysis. Descriptive analysis was carried out to determine the prevalence of IPTp-SP uptake. Multivariable logistic regression was used to establish the factors associated with receiving IPTp-SP during pregnancy for adjusting possible confounding factors. The study looked at IPTp-SP uptake as two outcomes variables (uptake of any dose and optimal doses). Then, fitted a separate multivariable model for each outcome variable using a four-step approach for modelling survey data as recommended by Heeringa et al., 2017. Given the complex survey design, all analyses adjusted for sampling weight, stratification and clustering. The p-value of <0.05 was considered significant. Results The study included 12,742 women aged 15 to 49 years with live births living in Nigeria. The mean age ± SD of the selected women was 28.3 ± 6.7 years old. In 2018, the overall prevalence of any dose of IPTp-SP was 63.6% (95% CI:62.0–65.1), and optimal doses of IPTp-SP were 16.8% (95% CI:15.8–17.8) during pregnancy. Women aged 30 years or older had 31% increased odds to receive any IPTp-SP dose (cOR:1.31; 95% CI:1.08 - 1.58). And pregnant women in the Southwestern region were 50% less likely to initiate IPTp-SP therapy (aOR: 0.50; 95% CI:0.39 - 0.65). In addition, women in the wealthiest households whose husbands had secondary education predicted a four-fold increase in uptake of at least one IPTp-SP dose (aOR:4.17; 95% CI:1.11–8.85). Pregnant women in the poorer and richer households were 35% (aOR: 0.65; 95% CI:0.52– 0.81) and 19% (aOR:0.81; 95% CI:0.64–1.03) less likely to receive optimal doses of IPTp-SP respectively. Moreover, attending four or more ANC visits predicted a 58% higher odds of completing at least three doses of IPTp-SP during pregnancy (aOR:1.58; 95% CI:1.31–1.88). Conclusion The low prevalence of region-specific IPTp-SP uptake implies that most pregnant women in Nigeria remain at substantial risk of pregnancy-associated malaria. Therefore, stakeholders should explore context-specific strategies such as community ANC outreaches to improve the IPTp-SP coverage across the regions in Nigeria. Also, future research should explore the drivers of low uptake of optimal doses of IPTp-SP among pregnant women in South-West Nigeria.
  • Item
    Integration of cervical cancer prevention into other reproductive health services: factors influencing dissemination and adoption in the greater Accra region, Ghana
    (2021) Frempong, Helena Maame Ama
    INTRODUCTION: Cervical cancer is one of the most common cancers and the leading cause of cancer-related deaths in Ghana. There has been a national policy for the screening and treatment of cervical cancer in Ghana since 2005. However, coverage remains low, and the burden of the disease is increasing. There is a renewed effort to improve coverage by integrating cervical cancer screening and treatment into other reproductive services (ICP_RHS) by health facilities; however, health facilities have not adopted this innovation. AIM: To identify, from the perspective of health providers and managers, the barriers and facilitators of a) dissemination of the integration approach from policy level to implementers, and b) adoption by implementers of the integration of cervical cancer screening and treatment into reproductive health services at health facilities in the Greater Accra Region, Ghana. METHOD: This exploratory qualitative study was carried out in the Greater Accra Region, Ghana. Twenty-six semi-structured interviews were conducted with district officers, facility leaders and healthcare providers (doctors, nurses, and midwives). The conceptual framework that guided data collection and analysis was the consolidated framework for implementation research. Data were analysed using framework analysis. RESULTS: This study identified that facilitators of dissemination, among others, included the existence of change agents, effective communication processes and channels and good leadership and supportive management style and skills. Compatibility and perceived simplicity of the integrated approach, the enactment of a national policy or directive, perceived need and demand for ICP_RHS and availability of resources, among others, may influence organizational adoption. Factors that may affect individual adoption included training and coaching, access to protocol and guidelines and provider perceptions and beliefs. CONCLUSION: This study highlights the characteristics of the innovation, organization and individual providers that may influence dissemination and adoption of the integrated cervical screening and treatment innovation. Knowledge and understanding of these factors may be used as a guide in identifying strategies to counter barriers and enable effective dissemination and adoption of the innovation
  • Item
    Applying machine learning to classify disease status for selected notifiable medical conditions in South Africa.
    (2021) Erone, Innocent Lino
    Introduction There is a change in disease profiles. Environmental variabilities continue to alter morphological appearances of species necessitating enhancement in diagnostic methods used to detect diseases. The deterministic approaches applied in the current diagnosis methods for Malaria and COVID19 have presented challenges of low sensitivity and specificity. In this study, we described data structures and disease profiles for Malaria and COVID-19 surveillance data at the National Health Laboratory Services (NHLS), South Africa. We also explored the application of supervised Machine Learning (ML) to classify and predict clinical outcomes for Malaria and COVID-19. Methods The COVID-19 surveillance data comprised of 35,202 observations from a unit dataset. The Malaria data was made up of three files; a demographics file, a laboratory results file and a traveltreatment history file of which 40,094 observations were deduced. These datasets were divided into two portions, 75% for model specification and the 25% designated as out-of-sample testing. We compared three supervised ML classifiers: Support Vector Machine (SVM) the K-Nearest Neighbor (KNN) Random Forests (RF) with their variant novelty approaches Isolation Forest (iForest) and One-Class Support Vector Machines (OCSVM) to predict clinical outcomes for Malaria and COVID-19. To account for severe label imbalances, the data with majority class labels was under-sampled to obtain an equal class balance in the target. Novelty detection approaches with iForest and One-Class Support Vector Machines (OCSVM) were also used in classifying and predicting Malaria and COVID-19 clinical outcomes. Results Malaria surveillance data was characterized by large proportions of missing data for demographic, syndromic and environmental characteristics. Though complete, compared to Malaria, COVID-19 surveillance data did not follow tidy-data principles. In evaluating classifier predictive power using out-of-sample data with equal representation of clinical outcomes, RF yielded the best predictive power with Area Under Curve (AUC) scores (98%) from Malaria out-of-sample data accounting for distribution weight of clinical outcome. Though not comparable to scores from Malaria data, the RF still scored better than the SVM and KNN classifiers from out-of-sample evaluation over COVID-19 data. Generally, lower classifier performance was observed across all models when subjected to COVID-19 out-of-sample data, where the KNN classifier registered the highest number of false-positive results. There were significantly higher numbers of False-Negative predictions with the SVM classifiers compared to the RF and KNN. However, the RF performed slightly better in predicting True-Negative observations. By categorizing data with minority clinical outcome representation as outliers, OCSVM predicted more negative observation compared to the iForest. Conclusions This study showed the impact of data quality in disease surveillance with respect to predictive modeling for Malaria and COVID-19 medical conditions. The data were characterized by large proportions of incompleteness. Individual demographic characteristics reported and recorded signs and symptoms among other attributes that hold vital information for syndromic disease surveillance were lacking. While supervised ML classifiers performed well with Malaria out-ofsample data, the same methods produced suboptimal results with similar surveillance COVID-19 data. Future studies could explore unsupervised ML approaches on the same surveillance data.