Prenatal diagnosis lowers neonatal cardiac care costs in resource-limited settings

2022 ◽  
pp. 1-7
Author(s):  
Balu Vaidyanathan ◽  
Karthika Rani ◽  
Farooq Kunde ◽  
Stephy Thomas ◽  
Abish Sudhakar ◽  
...  
Keyword(s):  
Prenatal Diagnosis ◽  
Study Groups ◽  
Cardiac Care ◽  
Resource Limited Settings ◽  
Resource Limited ◽  
Income Ratio ◽  
Modifiable Factor ◽  
The Impact ◽  
Care Costs ◽  
Limited Setting

Abstract Background: Prenatal diagnosis of critical CHDs and planned peripartum care is an emerging concept in resource-limited settings. Objective: To report the impact of prenatal diagnosis and planned peripartum care on costs of neonatal cardiac care in a resource-limited setting. Methods: Prospective study (October 2019 to October 2020). Consecutive neonates undergoing surgery or catheter-based interventions included. Patients were divided into prenatal (prenatal diagnosis) and post-natal (diagnosis after birth) groups. Costs of cardiac care (total, direct, and indirect) and health expenses to income ratio were compared between study groups; factors impacting costs were analysed. Results: A total of 105 neonates were included, including 33 in prenatal group. Seventy-seven neonates (73.3%) underwent surgical procedures while the rest needed catheter-based interventions. Total costs were 16.2% lower in the prenatal group (p = 0.008). Direct costs were significantly lower in the prenatal group (18%; p = 0.02), especially in neonates undergoing surgery (20.4% lower; p = 0.001). Health expenses to income ratio was also significantly lower in the prenatal group (2.04 (1.03–2.66) versus post-natal:2.58 (1.55–5.63), p = 0.01);, particularly in patients undergoing surgery (prenatal: 1.58 (1.03–2.66) vs. post-natal: 2.99 (1.91–6.02); p = 0.002). Prenatal diagnosis emerged as the only modifiable factor impacting costs on multivariate analysis. Conclusion: Prenatal diagnosis and planned peripartum care of critical CHD is feasible in resource-limited settings and is associated with significantly lower costs of neonatal cardiac care. The dual benefit of improved clinical outcomes and lower costs of cardiac care should encourage policymakers in resource-limited settings towards developing more prenatal cardiac services.

scholarly journals Evaluating clinical outcomes of routinely delivered task-shared care for depression in rural Haiti

2021 ◽  
Vol 8 ◽  
Author(s):  
Alexandra L. Rose ◽  
Ryan McBain ◽  
Jesse Wilson ◽  
Sarah F. Coleman ◽  
Emmanuel Mathieu ◽  
...  
Keyword(s):  
Mental Health ◽  
Clinical Outcomes ◽  
Positive Impact ◽  
International Health ◽  
Routine Care ◽  
Care Organization ◽  
Depression Care ◽  
Resource Limited Settings ◽  
Resource Limited ◽  
The Impact

Abstract Background There is a growing literature in support of the effectiveness of task-shared mental health interventions in resource-limited settings globally. However, despite evidence that effect sizes are greater in research studies than actual care, the literature is sparse on the impact of such interventions as delivered in routine care. In this paper, we examine the clinical outcomes of routine depression care in a task-shared mental health system established in rural Haiti by the international health care organization Partners In Health, in collaboration with the Haitian Ministry of Health, following the 2010 earthquake. Methods For patients seeking depression care betw|een January 2016 and December 2019, we conducted mixed-effects longitudinal regression to quantify the effect of depression visit dose on symptoms, incorporating interaction effects to examine the relationship between baseline severity and dose. Results 306 patients attended 2052 visits. Each visit was associated with an average reduction of 1.11 in depression score (range 0–39), controlling for sex, age, and days in treatment (95% CI −1.478 to −0.91; p < 0.001). Patients with more severe symptoms experienced greater improvement as a function of visits (p = 0.04). Psychotherapy was provided less frequently and medication more often than expected for patients with moderate symptoms. Conclusions Our findings support the potential positive impact of scaling up routine mental health services in low- and middle-income countries, despite greater than expected variability in service provision, as well as the importance of understanding potential barriers and facilitators to care as they occur in resource-limited settings.

Point-of-Care Ultrasound: A Useful Diagnostic Tool in Africa

Praxis ◽  
2020 ◽  
Vol 109 (8) ◽  
pp. 608-614
Author(s):  
Omary Ngome ◽  
Martin Rohacek
Keyword(s):  
Point Of Care ◽  
Clinical Work ◽  
Traumatic Shock ◽  
Clinical Findings ◽  
Diagnostic Tools ◽  
Point Of Care Ultrasound ◽  
Resource Limited Settings ◽  
Resource Limited ◽  
The Impact ◽  
Work Up

Abstract. In resource limited settings with limited tests and diagnostic tools, most of diagnoses are based on clinical findings, and patients are managed empirically, e.g. with anti-tuberculosis drugs. This article aims at describing the use of point-of-care ultrasound in diagnosing the most important conditions in Africa, in addition to clinical work-up. Different protocols exist for the diagnosis of trauma-related disorders, tuberculosis, schistosomiasis, thromboembolism, causes of dyspnea, and non- traumatic shock. Point-of-care ultrasound might be a beneficial tool in Africa, aiding diagnostics and management of patients with these conditions. However, studies must be done to assess the impact of point-of-care ultrasound on mortality.

scholarly journals Towards Continuing Interprofessional Education: Interaction patterns of health professionals in a resource-limited setting

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0253491
Author(s):  
Champion N. Nyoni ◽  
Cecilna Grobler ◽  
Yvonne Botma
Keyword(s):  
Communication Networks ◽  
Professional Practice ◽  
Health Professionals ◽  
Interprofessional Education ◽  
Common Language ◽  
Interaction Patterns ◽  
Resource Limited Settings ◽  
Resource Limited ◽  
Limited Setting ◽  
Education Initiatives

There are challenges related to collaboration among health professionals in resource-limited settings. Continuing Interprofessional Education initiatives grounded on workplace dynamics, structure and the prevailing attitudes and biases of targeted health professionals may be a vehicle to develop collaboration among health professionals. Workplace dynamics are revealed as health professionals interact. We argue that insights into the interaction patterns of health professionals in the workplace could provide guidance for improving the design and value of CIPE initiative. The study was conducted through rapid ethnography and data were collected from non-participant observations. The data were transcribed and analysed through an inductive iterative process. Appropriate ethical principles were applied throughout the study. Three themes emerged namely “Formed professional identities influencing interprofessional interaction”, “Diversity in communication networks and approaches” and “Professional practice and care in resource limited contexts”. This study revealed poor interaction patterns among health professionals within the workplace. These poor interaction patterns were catalyzed by the pervasive professional hierarchy, the protracted health professional shortages, limited understanding of professional roles and the lack of a common language of communication among the health professionals. Several recommendations were made regarding the design and development of Continuing Interprofessional Education initiatives for resource-limited settings.

scholarly journals Impact of National and Regional Lockdowns on Growth of COVID-19 Cases in COVID-Hotspot City of Pune in Western India: A Real-World Data Analysis

2021 ◽  
Author(s):  
Vidya Mave ◽  
Arsh Shaikh ◽  
Joy Merwin Monteiro ◽  
Prasad Bogam ◽  
Bhalchandra S Pujari ◽  
...  
Keyword(s):  
Real World ◽  
Urban Region ◽  
Western India ◽  
Real World Data ◽  
Resource Limited Settings ◽  
World Data ◽  
Resource Limited ◽  
Incident Cases ◽  
The Impact ◽  
Geospatial Mapping

Background Real-world data assessing the impact of lockdowns on COVID-19 cases remain limited from resource-limited settings. We examined growth of incident confirmed COVID-19 cases before, during and after lockdowns in Pune, a city in western India with 3.1 million population that reported the largest COVID-19 burden at the peak of the pandemic. Methods Using anonymized individual-level data captured by Pune`s public health surveillance program between February 1st and September 15th 2020, we assessed weekly incident COVID-19 cases, infection rates, and epidemic curves by lockdown status (overall and by sex, age, and population density) and modelled the natural epidemic using the 9-compartmental model INDSCI-SIM. Effect of lockdown on incident cases was assessed using multilevel Poisson regression. We used geospatial mapping to characterize regional spread. Findings Of 241,629 persons tested for SARS-CoV-2, the COVID-19 disease rate was 267.0 (95% CI 265.3,268.8) per 1000 persons. Epidemic curves and geospatial mapping showed delayed peak of the cases by approximately 8 weeks during the lockdowns as compared to modelled natural epidemic. Compared to a subsequent unlocking period, incident COVID-19 cases 43% lower (IRR 0.57, 95% CI 0.53, 0.62) during India`s nationwide lockdown and 22% (IRR 0.78, 95% CI 0.73, 0.84) during Pune`s regional lockdown and was uniform across age groups and population densities. Conclusion Lockdowns slowed the growth of COVID-19 cases in population dense, urban region in India. Additional analysis from rural and semi-rural regions of India and other resource-limited settings are needed.

scholarly journals The Impact of Artificial Intelligence on Health Equity in Oncology: A Scoping Review

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 4934-4934
Author(s):  
Paul Istasy ◽  
Wen Shen Lee ◽  
Alla Iansavitchene ◽  
Ross Upshur ◽  
Bekim Sadikovic ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Health Equity ◽  
Scoping Review ◽  
Clinical Decision Making ◽  
Ethical Issues ◽  
Clinical Decision ◽  
Resource Limited Settings ◽  
Resource Limited ◽  
The Impact

Abstract Introduction: The expanding use of Artificial Intelligence (AI) in hematology and oncology research and practice creates an urgent need to consider the potential impact of these technologies on health equity at both local and global levels. Fairness and equity are issues of growing concern in AI ethics, raising problems ranging from bias in datasets and algorithms to disparities in access to technology. The impact of AI on health equity in oncology, however, remains underexplored. We conducted a scoping review to characterize, evaluate, and identify gaps in the existing literature on AI applications in oncology and their implications for health equity in cancer care. Methodology: We performed a systematic literature search of MEDLINE (Ovid) and EMBASE from January 1, 2000 to March 28, 2021 using key terms for AI, health equity, and cancer. Our search was restricted to English language abstracts with no restrictions by publication type. Two reviewers screened a total of 9519 abstracts, and 321 met inclusion criteria for full-text review. 247 were included in the final analysis after assessment by three reviewers. Studies were analysed descriptively, by location, type of cancer and AI application, as well as thematically, based on issues pertaining to health equity in oncology. Results: Of the 247 studies included in our analysis, 150 (60.7%) were based in North America, 57 (23.0%) in Asia, 29 (11.7%) in Europe, 5 (2.1%) in Central/South America, 4 (1.6%) in Oceania, and 2 (0.9%) in Africa. 71 (28.6%) were reviews and commentaries, and 176 were (71.3%) clinical studies. 25 (10.1%) focused on AI applications in screening, 42 (17.0%) in diagnostics, 46 (18.6%) in prognostication, and 7 (2.9%) in treatment. 40 (16.3%) used AI as a tool for clinical/epidemiological research and 87 (35.2%) discussed multiple applications of AI. A diverse range of cancers were represented in the studies, including hematologic malignancies. Our scoping review identified three overarching themes in the literature, which largely focused on how AI might improve health equity in oncology. These included: (1) the potential for AI reduce disparities by improving access to health services in resource-limited settings through applications such as low-cost cancer screening technologies and decision support systems; (2) the ability of AI to mitigate bias in clinical decision-making through algorithms that alert clinicians to potential sources of bias thereby allowing for more equitable and individualized care; (3) the use of AI as a research tool to identify disparities in cancer outcomes based on factors such as race, gender and socioeconomic status, and thus inform health policy. While most of the literature emphasized the positive impact of AI in oncology, there was only limited discussion of AI's potential adverse effects on health equity . Despite engaging with the use of AI in resource-limited settings, ethical issues surrounding data extraction and AI trials in low-resource settings were infrequently raised. Similarly, AI's potential to reinforce bias and widen disparities in cancer care was under-examined despite engagement with related topics of bias in clinical decision-making. Conclusion: The overwhelming majority of the literature identified by our scoping review highlights the benefits of AI applications in oncology, including its potential to improve access to care in low-resource settings, mitigate bias in clinical decision-making, and identify disparities in cancer outcomes. However, AI's potential negative impacts on health equity in oncology remain underexplored: ethical issues arising from deploying AI technologies in low-resources settings, and issues of bias in datasets and algorithms were infrequently discussed in articles dealing with related themes. Disclosures No relevant conflicts of interest to declare.

The impact of digital technologies on point-of-care diagnostics in resource-limited settings

2018 ◽  
Vol 18 (4) ◽  
pp. 385-397 ◽  
Author(s):  
Natasha Gous ◽  
Debrah I. Boeras ◽  
Ben Cheng ◽  
Jeff Takle ◽  
Brad Cunningham ◽  
...  
Keyword(s):  
Point Of Care ◽  
Digital Technologies ◽  
Resource Limited Settings ◽  
Resource Limited ◽  
Point Of Care Diagnostics ◽  
The Impact
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