We are currently in the midst of a pandemic of SARS-COVID-19 that has spread and increased its reach geometrically in just 3 months. Different countries and states are employing multiple methods to decrease the spread of the virus and decrease its negative impact. The government of India also has taken steps to identify and trace all patients and their contacts. This requires immense input of manpower, finance and technological solutions. Models from India and all over the world act as guides in highlighting the advantages and pitfalls of this method. Models of South Korea, Taiwan and Singapore, where intensive contact tracing measures have been implemented have been successful in controlling the pandemic but have created issues of invasion of privacy. Most successful models, in the developing world have sought out to create a multi-disciplinary dedicated contact tracing team of roughly 2-3 contact tracers per 1000 population. It is important to set up a dedicated team for this so that the already stretched ASHA and other community health workers are not overburdened as more responsibilities might lead do decrease in quality. Such a team, which is sensitive to local customs and armed with basics of contact-tracing techniques, need not be highly educated. Technological solutions that keep user privacy as a priority and encourage transparent sharing of methodologies to ensure user privacy must be promoted. Solutions must ensure dissemination of information from trusted sources and self-monitoring of symptoms.
BackgroundThe decision to admit or refer a patient presenting with an obstetric emergency is extremely crucial. In rural India, such decisions are usually made by young physicians who are less experienced and often miss relevant data points required for appropriate decision making. In our setting, before the quality improvement (QI) initiative, this information was recorded on loose blank sheets (first information sheets (FIS)) where an initial clinical history, physical examination and investigations were recorded. The mean FIS completeness, at baseline, was 73.95% (1–5 January 2020) with none of the FIS being fully complete. Our objective was to increase the FIS completeness to >90% and to increase the number of FIS that were fully complete over a 9-month period.MethodsWith the help of a prioritisation matrix, the QI team decided to tackle the problem of incomplete FIS. The team then used fishbone analysis and identified that the main causes of incomplete FIS were that the interns did not know what to document and would often forget some data points. Change ideas to improve FIS completeness were implemented using Plan-Do-Study-Act (PDSA) cycles, and ultimately, a checklist (referred to as antenatal care (ANC) checklist) was implemented. The study was divided into six phases, and after every phase, a few FIS were conveniently sampled for completeness.ResultsFIS completeness improved to 86.34% (p<0.001) in the post implementation phase (1 Feb to 31 August 2020), and in this phase, 69.72% of the FIS were documented using the ANC checklist. The data points that saw the maximum improvement were relating to the physical examination.ConclusionThe use of ANC checklist increased FIS completeness. Interns with no prior clinical and QI experience can effectively lead and participate in QI initiatives. The ANC checklist is a scalable concept across similar healthcare settings in rural India.
Importance: Homelessness is a complex challenge with an estimated yearly economic burden of $6 billion in the United States. Mitigating homelessness requires an understanding of determinants of homelessness, their interaction with health factors, and quantification of impact. Objective: To investigate the health, social and policy factors influencing homelessness in a longitudinal integrative machine learning analysis. Data Sources and Study design: This retrospective longitudinal study integrated Global Burden of Disease (GBD), Health Inequality, and Housing and Urban Development (HUD) datasets for 3131 counties in the United States. We used the disease burden data of 2014, health inequalities data of 2001-2014, and homelessness count of 2015. Primary Outcome and Measurement Results: Homelessness, the burden of disease, health inequalities, economic policies, ethnic, social, and racial factors. Methods: Spearman rank correlation test was performed to check pairwise associations. A unified probabilistic model with temporal causality was fitted using a data-driven structure learning algorithm. The resulting associations adjusted for other variables in the network were quantified using network inference algorithms. Finally, counterfactual analysis was performed to quantify the potential impact of the learned interventions. Results: The total burden of homelessness was significantly (p<0.001) and positively associated with rates of HIV and hepatitis mortality. Inference from the unified probabilistic model indicated that a state with a high hepatitis mortality rate had a 9% higher homelessness. Further, the rate of rheumatic heart disease mortality had a 29% decrease with the provision of shelter in young adults experiencing homelessness (p<0.001). Finally, states with moderate tax progressivity had a mitigating effect on homelessness as compared to both high and low tax progressivity ( 2% and 5% respectively). We evaluated the counterfactual impact of policy interventions to provide more support to cancer patients to prevent homeless and provision of shelter to prevent rheumatic heart disease mortality in young adults experiencing homelessness. Conclusion and Relevance: Control of infectious diseases and the implementation of tax policies are critical interventions for the reduction of homelessness in the United States.
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