Person, Place, and Time: Monitoring the Geographical and Temporal Context of Public Health Policies

2012 ◽  
Author(s):  
Michael Chaiton
Keyword(s):  
Public Health ◽  
Health Policies ◽  
Temporal Context ◽  
Public Health Policies

scholarly journals Population simulations of COVID-19 outbreaks provide tools for risk assessment and continuity planning

JAMIA Open ◽  
2021 ◽  
Author(s):  
Bo Peng ◽  
Rowland W Pettit ◽  
Christopher I Amos
Keyword(s):  
Public Health ◽  
Risk Assessment ◽  
Emergency Response ◽  
Robustness Analysis ◽  
Health Policies ◽  
Command Line ◽  
Public Health Policies ◽  
Continuity Planning ◽  
Quantitative Estimates ◽  
Community Infection

Abstract Objectives We developed COVID-19 Outbreak Simulator (https://ictr.github.io/covid19-outbreak-simulator/) to quantitatively estimate the effectiveness of preventative and interventive measures to prevent and battle COVID-19 outbreaks for specific populations. Materials and methods Our simulator simulates the entire course of infection and transmission of the virus among individuals in heterogeneous populations, subject to operations and influences, such as quarantine, testing, social distancing, and community infection. It provides command-line and Jupyter notebook interfaces and a plugin system for user-defined operations. Results The simulator provides quantitative estimates for COVID-19 outbreaks in a variety of scenarios and assists the development of public health policies, risk-reduction operations, and emergency response plans. Discussion Our simulator is powerful, flexible, and customizable, although successful applications require realistic estimation and robustness analysis of population-specific parameters. Conclusion Risk assessment and continuity planning for COVID-19 outbreaks are crucial for the continued operation of many organizations. Our simulator will be continuously expanded to meet this need.

scholarly journals Characterization of death from colorectal cancer in Mato Grosso State/Legal Amazon/Brazil, 2000-2016

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
R S Caló ◽  
B S N Souza ◽  
N D Galvão ◽  
R A G Souza ◽  
J C S Oliveira ◽  
...  
Keyword(s):  
Public Health ◽  
Colorectal Cancer ◽  
International Classification Of Diseases ◽  
Health Policies ◽  
Rectosigmoid Junction ◽  
Mato Grosso ◽  
Public Health Policies ◽  
Goods And Services ◽  
Classification Of Diseases ◽  
Using Data

Abstract Background Colorectal cancer has been one of the cancers that most contributed to mortality, in both sexes in the world. In Brazil, cancer is among the top five causes of death and colorectal cancer is ranked on the fifth position. Of the Federative Units belonging to the Legal Amazon, Mato Grosso stands out for the higher adjusted incidence of colorectal cancer for both sexes. Thus, the objective is to characterize deaths from colorectal cancer, according to sociodemographic variables in Mato Grosso from 2000 to 2016. Methods A descriptive study was carried out, using data from the Mortality Information System, made available by the Department of Health of the Mato Grosso State. Deaths of all ages were selected, whose basic cause was identified by the codes from the International Classification of Diseases: (C.18) colon cancer, (C.19) rectosigmoid junction cancer, (C.20) rectal cancer or (C.21) anus cancer. Results Between 2000 and 2016, 31,607 deaths from cancer were registered. Of these, 1,750 (5.6%) were due to colorectal cancer. An increased number of deaths was observed at the end of the period, with a variation from 46 deaths in 2000 from 173 in 2016. Highest frequency was verified in men (51.3%), people aged 60 years or older (59.7%), black (54.6%), married (52.3%) and those with primary education (55.2%). According to Brazilian occupation classification options or those answers filled out on the death certificate, highest frequency were for “Retired” (26.2%), “Housewife” (23.1%), Agricultural/Forestry and Fisheries” (11.3%) and “Production of Industrial Goods and Services” (10.3%). Conclusions This study evidenced the increased number of deaths due to colorectal cancer in Mato Grosso State, and identified priority groups for interventions through public health policies which should include screening and early diagnosis to cope with the disease. Key messages Evidenced the increased number of deaths due to colorectal cancer in Mato Grosso State. Identified priority groups for interventions through public health policies.

Epidemiological surveillance and performance assessment: the two roles of health indicators during the COVID-19 pandemic

2021 ◽  
Vol 17 (2) ◽  
pp. 186-203
Author(s):  
Nathan Genicot
Keyword(s):  
Public Health ◽  
Performance Assessment ◽  
Health Indicators ◽  
Health Policies ◽  
Epidemiological Surveillance ◽  
Public Health Policies ◽  
History Of ◽  
And Performance ◽  
The One ◽  
Massive Development

AbstractThe COVID-19 pandemic has given rise to the massive development and use of health indicators. Drawing on the history of international public health and of the management of infectious disease, this paper attempts to show that the normative power acquired by metrics during the pandemic can be understood in light of two rationales: epidemiological surveillance and performance assessment. On the one hand, indicators are established to evaluate and rank countries’ responses to the outbreak; on the other, the evolution of indicators has a direct influence on the content of public health policies. Although quantitative data are an absolute necessity for coping with such disasters, it is critical to bear in mind the inherent partiality and precarity of the information provided by health indicators. Given the growing importance of normative quantitative devices during the pandemic, and assuming that their influence is unlikely to decrease in the future, they call for close scrutiny.

The effects of public health policies on health inequalities: a review of reviews

The Lancet ◽  
2017 ◽  
Vol 390 ◽  
pp. S12 ◽  
Author(s):  
Katie Thomson ◽  
Frances Hillier-Brown ◽  
Adam Todd ◽  
Courtney McNamara ◽  
Tim Huijits ◽  
...  
Keyword(s):  
Public Health ◽  
Health Inequalities ◽  
Health Policies ◽  
Public Health Policies ◽  
Review Of Reviews

scholarly journals Public Health Policies in the Akyem Abuakwa of Ghana (1850-1957)

2020 ◽  
Vol 20 (2) ◽  
pp. 129-157
Author(s):  
Samuel Adu Gyamfi ◽  
Phinehas Asiamah ◽  
Benjamin Dompreh Darkwa ◽  
Lucky Tomdi
Keyword(s):  
Public Health ◽  
Academic Research ◽  
Health Policies ◽  
Historical Study ◽  
Colonial Era ◽  
Public Health Policies ◽  
Health History ◽  
Documentary Sources ◽  
The People ◽  
The Impact

Abstract Akyem Abuakwa is one of the largest states of the Akan ethnic group in Ghana. Notwithstanding its size and important contribution to Ghana’s development, historians have paid little attention in doing academic research on the health history of the people. Using a qualitative method of research, this paper does a historical study on public health policies in Akyem Abuakwa from the 1850s to 1957. We utilised documentary and non-documentary sources to discuss the various public health policies implemented in Akyem Abuakwa from the pre-colonial era to the colonial era. We examined the impact of the policies on the people of Akyem Abuakwa and the various challenges faced by the British colonial administration in their quest to implement public health policies.

scholarly journals NeMoR: a New Method Based on Data-Driven for Neonatal Mortality Rate Forecasting

2021 ◽  
Author(s):  
Carlos Eduardo Beluzo ◽  
Luciana Correia Alves ◽  
Natália Martins Arruda ◽  
Cátia Sepetauskas ◽  
Everton Silva ◽  
...  
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Neonatal Mortality ◽  
Regression Models ◽  
Mortality Rates ◽  
Data Driven ◽  
Health Policies ◽  
Neonatal Mortality Rate ◽  
Policy Makers ◽  
Public Health Policies

ABSTRACTReduction in child mortality is one of the United Nations Sustainable Development Goals for 2030. In Brazil, despite recent reduction in child mortality in the last decades, the neonatal mortality is a persistent problem and it is associated with the quality of prenatal, childbirth care and social-environmental factors. In a proper health system, the effect of some of these factors could be minimized by the appropriate number of newborn intensive care units, number of health care units, number of neonatal incubators and even by the correct level of instruction of mothers, which can lead to a proper care along the prenatal period. With the intent of providing knowledge resources for planning public health policies focused on neonatal mortality reduction, we propose a new data-driven machine leaning method for Neonatal Mortality Rate forecasting called NeMoR, which predicts neonatal mortality rates for 4 months ahead, using NeoDeathForecast, a monthly base time series dataset composed by these factors and by neonatal mortality rates history (2006-2016), having 57,816 samples, for all 438 Brazilian administrative health regions. In order to build the model, Extra-Tree, XGBoost Regressor, Gradient Boosting Regressor and Lasso machine learning regression models were evaluated and a hyperparameters search was also performed as a fine tune step. The method has been validated using São Paulo city data, mainly because of data quality. On the better configuration the method predicted the neonatal mortality rates with a Mean Square Error lower than 0.18. Besides that, the forecast results may be useful as it provides a way for policy makers to anticipate trends on neonatal mortality rates curves, an important resource for planning public health policies.Graphical AbstractHighlightsProposition of a new data-driven approach for neonatal mortality rate forecast, which provides a way for policy-makers to anticipate trends on neonatal mortality rates curves, making a better planning of health policies focused on NMR reduction possible;a method for NMR forecasting with a MSE lower than 0.18;an extensive evaluation of different Machine Learning (ML) regression models, as well as hyperparameters search, which accounts for the last stage in NeMoR;a new time series database for NMR prediction problems;a new features projection space for NMR forecasting problems, which considerably reduces errors in NRM prediction.

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