scholarly journals Dynamic Panel Estimate–Based Health Surveillance of SARS-CoV-2 Infection Rates to Inform Public Health Policy: Model Development and Validation (Preprint)

2020 ◽  
Author(s):  
James Francis Oehmke ◽  
Theresa B Oehmke ◽  
Lauren Nadya Singh ◽  
Lori Ann Post
Keyword(s):  
Public Health ◽  
Measurement Error ◽  
The United States ◽  
Statistical Technique ◽  
Health Surveillance ◽  
Dynamic Panel ◽  
Chi Square ◽  
Data Contamination ◽  
Persistence Rate ◽  
Chi Square Test

BACKGROUND SARS-CoV-2, the novel coronavirus that causes COVID-19, is a global pandemic with higher mortality and morbidity than any other virus in the last 100 years. Without public health surveillance, policy makers cannot know where and how the disease is accelerating, decelerating, and shifting. Unfortunately, existing models of COVID-19 contagion rely on parameters such as the basic reproduction number and use static statistical methods that do not capture all the relevant dynamics needed for surveillance. Existing surveillance methods use data that are subject to significant measurement error and other contaminants. OBJECTIVE The aim of this study is to provide a proof of concept of the creation of surveillance metrics that correct for measurement error and data contamination to determine when it is safe to ease pandemic restrictions. We applied state-of-the-art statistical modeling to existing internet data to derive the best available estimates of the state-level dynamics of COVID-19 infection in the United States. METHODS Dynamic panel data (DPD) models were estimated with the Arellano-Bond estimator using the generalized method of moments. This statistical technique enables control of various deficiencies in a data set. The validity of the model and statistical technique was tested. RESULTS A Wald chi-square test of the explanatory power of the statistical approach indicated that it is valid (χ<sup>2</sup><sub>10</sub>=1489.84, <i>P</i>&lt;.001), and a Sargan chi-square test indicated that the model identification is valid (χ<sup>2</sup><sub>946</sub>=935.52, <i>P</i>=.59). The 7-day persistence rate for the week of June 27 to July 3 was 0.5188 (<i>P</i>&lt;.001), meaning that every 10,000 new cases in the prior week were associated with 5188 cases 7 days later. For the week of July 4 to 10, the 7-day persistence rate increased by 0.2691 (<i>P</i>=.003), indicating that every 10,000 new cases in the prior week were associated with 7879 new cases 7 days later. Applied to the reported number of cases, these results indicate an increase of almost 100 additional new cases per day per state for the week of July 4-10. This signifies an increase in the reproduction parameter in the contagion models and corroborates the hypothesis that economic reopening without applying best public health practices is associated with a resurgence of the pandemic. CONCLUSIONS DPD models successfully correct for measurement error and data contamination and are useful to derive surveillance metrics. The opening of America involves two certainties: the country will be COVID-19–free only when there is an effective vaccine, and the “social” end of the pandemic will occur before the “medical” end. Therefore, improved surveillance metrics are needed to inform leaders of how to open sections of the United States more safely. DPD models can inform this reopening in combination with the extraction of COVID-19 data from existing websites.

scholarly journals Dynamic Panel Estimate–Based Health Surveillance of SARS-CoV-2 Infection Rates to Inform Public Health Policy: Model Development and Validation

10.2196/20924 ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. e20924 ◽  
Author(s):  
James Francis Oehmke ◽  
Theresa B Oehmke ◽  
Lauren Nadya Singh ◽  
Lori Ann Post
Keyword(s):  
Public Health ◽  
Measurement Error ◽  
The United States ◽  
Statistical Technique ◽  
Health Surveillance ◽  
Dynamic Panel ◽  
Chi Square ◽  
Data Contamination ◽  
Persistence Rate ◽  
Chi Square Test

Background SARS-CoV-2, the novel coronavirus that causes COVID-19, is a global pandemic with higher mortality and morbidity than any other virus in the last 100 years. Without public health surveillance, policy makers cannot know where and how the disease is accelerating, decelerating, and shifting. Unfortunately, existing models of COVID-19 contagion rely on parameters such as the basic reproduction number and use static statistical methods that do not capture all the relevant dynamics needed for surveillance. Existing surveillance methods use data that are subject to significant measurement error and other contaminants. Objective The aim of this study is to provide a proof of concept of the creation of surveillance metrics that correct for measurement error and data contamination to determine when it is safe to ease pandemic restrictions. We applied state-of-the-art statistical modeling to existing internet data to derive the best available estimates of the state-level dynamics of COVID-19 infection in the United States. Methods Dynamic panel data (DPD) models were estimated with the Arellano-Bond estimator using the generalized method of moments. This statistical technique enables control of various deficiencies in a data set. The validity of the model and statistical technique was tested. Results A Wald chi-square test of the explanatory power of the statistical approach indicated that it is valid (χ210=1489.84, P<.001), and a Sargan chi-square test indicated that the model identification is valid (χ2946=935.52, P=.59). The 7-day persistence rate for the week of June 27 to July 3 was 0.5188 (P<.001), meaning that every 10,000 new cases in the prior week were associated with 5188 cases 7 days later. For the week of July 4 to 10, the 7-day persistence rate increased by 0.2691 (P=.003), indicating that every 10,000 new cases in the prior week were associated with 7879 new cases 7 days later. Applied to the reported number of cases, these results indicate an increase of almost 100 additional new cases per day per state for the week of July 4-10. This signifies an increase in the reproduction parameter in the contagion models and corroborates the hypothesis that economic reopening without applying best public health practices is associated with a resurgence of the pandemic. Conclusions DPD models successfully correct for measurement error and data contamination and are useful to derive surveillance metrics. The opening of America involves two certainties: the country will be COVID-19–free only when there is an effective vaccine, and the “social” end of the pandemic will occur before the “medical” end. Therefore, improved surveillance metrics are needed to inform leaders of how to open sections of the United States more safely. DPD models can inform this reopening in combination with the extraction of COVID-19 data from existing websites.

scholarly journals XGBoost-Based Framework for Smoking-Induced Noncommunicable Disease Prediction

2020 ◽  
Vol 17 (18) ◽  
pp. 6513
Author(s):  
Khishigsuren Davagdorj ◽  
Van Huy Pham ◽  
Nipon Theera-Umpon ◽  
Keun Ho Ryu
Keyword(s):  
Public Health ◽  
The United States ◽  
Noncommunicable Disease ◽  
Noncommunicable Diseases ◽  
Gradient Boosting ◽  
Chi Square ◽  
Proposed Model ◽  
Extreme Gradient Boosting ◽  
Chi Square Test ◽  
Interpretable Models

Smoking-induced noncommunicable diseases (SiNCDs) have become a significant threat to public health and cause of death globally. In the last decade, numerous studies have been proposed using artificial intelligence techniques to predict the risk of developing SiNCDs. However, determining the most significant features and developing interpretable models are rather challenging in such systems. In this study, we propose an efficient extreme gradient boosting (XGBoost) based framework incorporated with the hybrid feature selection (HFS) method for SiNCDs prediction among the general population in South Korea and the United States. Initially, HFS is performed in three stages: (I) significant features are selected by t-test and chi-square test; (II) multicollinearity analysis serves to obtain dissimilar features; (III) final selection of best representative features is done based on least absolute shrinkage and selection operator (LASSO). Then, selected features are fed into the XGBoost predictive model. The experimental results show that our proposed model outperforms several existing baseline models. In addition, the proposed model also provides important features in order to enhance the interpretability of the SiNCDs prediction model. Consequently, the XGBoost based framework is expected to contribute for early diagnosis and prevention of the SiNCDs in public health concerns.

scholarly journals Dynamic Panel Surveillance of COVID-19 Transmission in the United States to Inform Health Policy: Observational Statistical Study (Preprint)

2020 ◽  
Author(s):  
James Francis Oehmke ◽  
Charles B Moss ◽  
Lauren Nadya Singh ◽  
Theresa Bristol Oehmke ◽  
Lori Ann Post
Keyword(s):  
Public Health ◽  
United States ◽  
State Government ◽  
Public Health Practice ◽  
Generalized Method Of Moments ◽  
The United States ◽  
Statistical Technique ◽  
Dynamic Panel Data ◽  
Transmission Model ◽  
Dynamic Panel

BACKGROUND The Great COVID-19 Shutdown aimed to eliminate or slow the spread of SARS-CoV-2, the virus that causes COVID-19. The United States has no national policy, leaving states to independently implement public health guidelines that are predicated on a sustained decline in COVID-19 cases. Operationalization of “sustained decline” varies by state and county. Existing models of COVID-19 transmission rely on parameters such as case estimates or R<sub>0</sub> and are dependent on intensive data collection efforts. Static statistical models do not capture all of the relevant dynamics required to measure sustained declines. Moreover, existing COVID-19 models use data that are subject to significant measurement error and contamination. OBJECTIVE This study will generate novel metrics of speed, acceleration, jerk, and 7-day lag in the speed of COVID-19 transmission using state government tallies of SARS-CoV-2 infections, including state-level dynamics of SARS-CoV-2 infections. This study provides the prototype for a global surveillance system to inform public health practice, including novel standardized metrics of COVID-19 transmission, for use in combination with traditional surveillance tools. METHODS Dynamic panel data models were estimated with the Arellano-Bond estimator using the generalized method of moments. This statistical technique allows for the control of a variety of deficiencies in the existing data. Tests of the validity of the model and statistical techniques were applied. RESULTS The statistical approach was validated based on the regression results, which determined recent changes in the pattern of infection. During the weeks of August 17-23 and August 24-30, 2020, there were substantial regional differences in the evolution of the US pandemic. Census regions 1 and 2 were relatively quiet with a small but significant persistence effect that remained relatively unchanged from the prior 2 weeks. Census region 3 was sensitive to the number of tests administered, with a high constant rate of cases. A weekly special analysis showed that these results were driven by states with a high number of positive test reports from universities. Census region 4 had a high constant number of cases and a significantly increased persistence effect during the week of August 24-30. This change represents an increase in the transmission model R value for that week and is consistent with a re-emergence of the pandemic. CONCLUSIONS Reopening the United States comes with three certainties: (1) the “social” end of the pandemic and reopening are going to occur before the “medical” end even while the pandemic is growing. We need improved standardized surveillance techniques to inform leaders when it is safe to open sections of the country; (2) varying public health policies and guidelines unnecessarily result in varying degrees of transmission and outbreaks; and (3) even those states most successful in containing the pandemic continue to see a small but constant stream of new cases daily.

scholarly journals Dynamic Panel Surveillance of COVID-19 Transmission in the United States to Inform Health Policy: Observational Statistical Study

10.2196/21955 ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. e21955 ◽  
Author(s):  
James Francis Oehmke ◽  
Charles B Moss ◽  
Lauren Nadya Singh ◽  
Theresa Bristol Oehmke ◽  
Lori Ann Post
Keyword(s):  
Public Health ◽  
United States ◽  
State Government ◽  
Public Health Practice ◽  
Generalized Method Of Moments ◽  
The United States ◽  
Statistical Technique ◽  
Dynamic Panel Data ◽  
Transmission Model ◽  
Dynamic Panel

Background The Great COVID-19 Shutdown aimed to eliminate or slow the spread of SARS-CoV-2, the virus that causes COVID-19. The United States has no national policy, leaving states to independently implement public health guidelines that are predicated on a sustained decline in COVID-19 cases. Operationalization of “sustained decline” varies by state and county. Existing models of COVID-19 transmission rely on parameters such as case estimates or R0 and are dependent on intensive data collection efforts. Static statistical models do not capture all of the relevant dynamics required to measure sustained declines. Moreover, existing COVID-19 models use data that are subject to significant measurement error and contamination. Objective This study will generate novel metrics of speed, acceleration, jerk, and 7-day lag in the speed of COVID-19 transmission using state government tallies of SARS-CoV-2 infections, including state-level dynamics of SARS-CoV-2 infections. This study provides the prototype for a global surveillance system to inform public health practice, including novel standardized metrics of COVID-19 transmission, for use in combination with traditional surveillance tools. Methods Dynamic panel data models were estimated with the Arellano-Bond estimator using the generalized method of moments. This statistical technique allows for the control of a variety of deficiencies in the existing data. Tests of the validity of the model and statistical techniques were applied. Results The statistical approach was validated based on the regression results, which determined recent changes in the pattern of infection. During the weeks of August 17-23 and August 24-30, 2020, there were substantial regional differences in the evolution of the US pandemic. Census regions 1 and 2 were relatively quiet with a small but significant persistence effect that remained relatively unchanged from the prior 2 weeks. Census region 3 was sensitive to the number of tests administered, with a high constant rate of cases. A weekly special analysis showed that these results were driven by states with a high number of positive test reports from universities. Census region 4 had a high constant number of cases and a significantly increased persistence effect during the week of August 24-30. This change represents an increase in the transmission model R value for that week and is consistent with a re-emergence of the pandemic. Conclusions Reopening the United States comes with three certainties: (1) the “social” end of the pandemic and reopening are going to occur before the “medical” end even while the pandemic is growing. We need improved standardized surveillance techniques to inform leaders when it is safe to open sections of the country; (2) varying public health policies and guidelines unnecessarily result in varying degrees of transmission and outbreaks; and (3) even those states most successful in containing the pandemic continue to see a small but constant stream of new cases daily.

Disaggregation of Latina/o Child and Adult Health Data: A Systematic Review of Public Health Surveillance Surveys in the United States

2021 ◽  
Vol 40 (1) ◽  
pp. 61-79
Author(s):  
Carmela Alcántara ◽  
Shakira F. Suglia ◽  
Irene Perez Ibarra ◽  
A. Louise Falzon ◽  
Elliot McCullough ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Systematic Review ◽  
Public Health Surveillance ◽  
The United States ◽  
Health Data ◽  
Health Surveillance ◽  
Adult Health

Abstract 177: Credentials of Staff Performing Echocardiography in the United States: Insights from the Intersocietal Accreditation Commission

2014 ◽  
Vol 7 (suppl_1) ◽  
Author(s):  
Matthew W Parker ◽  
Diana Sobieraj ◽  
Mary Beth Farrell ◽  
Craig I Coleman
Keyword(s):  
United States ◽  
Cardiovascular Diseases ◽  
The United States ◽  
Chi Square ◽  
Volume Number ◽  
National Board ◽  
Formal Level ◽  
Chi Square Test ◽  
Level 3 ◽  
Medical Directors

Background: Little has been published on the practice of echocardiography (echo) in the United States. We used the Intersocietal Accreditation Commission-Echocardiography (IAC-Echo) applications database to describe the personnel in echo laboratories seeking accreditation. Methods: We used de-identified data provided on IAC-Echo applications to characterize facilities by hospital association, census region, annual volume, number of sites, previous accreditation, and numbers of physicians and sonographers as well as National Board of Echocardiography (NBE) testamur status of physicians and registered credential status of sonographers. We categorized Medical Directors by board certification in cardiovascular diseases, internal medicine, other specialty, or none. Medical Director echo training could be formal Level 2 or 3 or experiential by ≥3 years of practice. Frequencies, means, and medians were compared between groups using the chi-square test, t-test, or Mann Whitney test, respectively. Results: From 2011 to 2013, 1926 echo labs representing 10618 physicians and 6870 sonographers applied for IAC-Echo accreditation or re-accreditation. The majority of medical directors were board certified in cardiovascular diseases and 34.1% of medical directors and 27.2% of staff physicians held NBE testamur status; 79.5% of sonographers held registered credentials. Most echo labs were in the Northeast or South census regions, have an average of 1.75 sites, and are based outside of hospitals (Table). Compared to nonhospital echo labs, medical directors of hospital-based echo labs were more likely to be Level 3 trained (19.8% versus 30.8%, p<0.01) and be NBE testamurs (28.9% versus 45.6%, p<0.01). Markers of echo lab size, region, previous accreditation, and credentialed sonographers were associated with accreditation versus delay decisions; there was a trend toward accreditation among facilities with NBE medical directors. Conclusion: Among facilities seeking IAC-Echo accreditation, the minority of echo physicians hold NBE testamur status. Hospital and nonhospital facilities are different in the credentials of their personnel.

Abstract 56: Acute Stroke Reperfusion Treatment Rates at NIH StrokeNET Hospitals

Stroke ◽  
2015 ◽  
Vol 46 (suppl_1) ◽  
Author(s):  
Opeolu Adeoye ◽  
Dawn Kleindorfer
Keyword(s):  
Clinical Trials ◽  
High Volume ◽  
The United States ◽  
Primary Diagnosis ◽  
Categorical Variables ◽  
Phase 3 ◽  
Chi Square ◽  
Successful Recruitment ◽  
Chi Square Test ◽  
Reperfusion Phase

Background: In 2013, the NIH Stroke Trials Network (StrokeNET) was established to maximize efficiencies in stroke clinical trials. Successful recruitment in future trials was required for participating sites. A high volume of cases treated is a surrogate for the potential to recruit. Among Medicare-eligible acute ischemic stroke (AIS) cases, we estimated the IV rt-PA and endovascular embolectomy treatment rates at StrokeNET Regional Coordinating Centers and their partner hospitals compared with non-StrokeNET hospitals in the United States (US). Methods: We used demographics and IV rt-PA and embolectomy rates in the 2013 Medicare Provider and Analysis Review (MEDPAR) dataset. ICD-9 codes 433.xx, 434.xx and 436 identified AIS cases. ICD-9 code 99.10 defined rt-PA treatment and ICD-9 code 39.74 defined embolectomy. Demographics and treatment rates at StrokeNET and non-StrokeNET sites were compared using t-test for proportions and Chi-square test for categorical variables as appropriate. Results: Of 386,157 AIS primary diagnosis discharges, 5.1% received IV rt-PA and 0.8% had embolectomy (Table). By June 6, 2014, StrokeNET comprised 247 acute care hospitals that discharged 48,946 (13%) out of 386,157 AIS cases. rt-PA (7.4% vs 4.8%) and embolectomy (1.9% vs 0.6%) treatment rates were higher at StrokeNET hospitals. In 2013, 36% of StrokeNET hospitals treated more than 20 AIS cases with rt-PA or embolectomy compared with 6% of non-StrokeNET hospitals (P<0.0001).Conclusions StrokeNET hospitals treat more AIS cases with acute reperfusion therapies. Thus, StrokeNET could successfully recruit in acute reperfusion clinical trials depending on study size, capture of eligible patients and the number of competing trials. We likely underestimated treatment rates due to not accounting for drip-and-ship and non-Medicare cases. To further enhance enrollments in large acute reperfusion phase 3 trials, partnership with high volume non-StrokeNET hospitals may be warranted.

HUBUNGAN TINGKAT PENGETAHUAN MENGENAI DIET HIPERTENSI TERHADAP KEJADIAN HIPERTENSI PADA LANSIA DI PUSKESMAS BALOI PERMAI BATAM KOTA

2020 ◽  
Vol 9 (1) ◽  
pp. 50-60
Author(s):  
Brain Guntoro ◽  
Kasih Purwati
Keyword(s):  
Public Health ◽  
Health Center ◽  
The Elderly ◽  
P Value ◽  
Chi Square ◽  
Cross Sectional ◽  
Public Health Center ◽  
Chi Square Test ◽  
History Of ◽  
Level Of Knowledge

Hypertension is one of the number one causes of death and disability in the world. Hypertension contributes nearly 9.4 million deaths from cardiovascular disease each year. Hypertension can cause undesirable effects, it needs good handling, one of them is by doing a hypertension diet. To carry out a hypertension diet requires knowledge, lack of knowledge can increase risk factors for hypertension. This study aims to determine the relationship of the level of knowledge about hypertension diet to the incidence of hypertension in the elderly at the Baloi Permai Public Health Center Batam City. This research method is an analytic observational with a cross-sectional approach conducted at the Baloi Permai Public Health Center Batam City 2018. Sampling technique is a total sampling with a sample of 64 people in 2018 determined by inclusion and exclusion criteria. The results of the study were analyzed with frequency distribution and then tested with the Chi-square test. Based on the results of this study indicate that of the 64 respondents found elderly who have a good level of knowledge are 41 people (64.1%), 48 people (75.0%) have an age range between 60-70 years. 27 people (42.2%) elderly have the last high school education and 40 people (62.5%) have jobs as entrepreneurs. Elderly people who have normal blood pressure are 40 people (62.5%), and those affected by hypertension are 24 people (37.5%). The elderly who have a family history of hypertension is 21 people (32.8%) and those who do not have a history of hypertension are 43 people (67.2%). Chi-Square Test analysis results show the significance value p = 0.009. This number is significant because the p-value is smaller than the significance level (α) ≤ 5% (0.05), so H0 is rejected and Ha is accepted. Therefore it can be concluded that there is a significant relationship about the level of knowledge about the hypertension diet to the incidence of hypertension in the elderly. From the results of this study it was concluded that there was a relationship between the level of knowledge about the hypertension diet and the incidence of hypertension in the elderly at the Baloi Permai Public Health Center Batam City in 2016.

scholarly journals An audit of US FDA warning letters issued to sponsors, institutional review boards and investigators over a six-year period

2021 ◽  
pp. 01-06
Author(s):  
Unnati Saxena ◽  
Debdipta Bose ◽  
Shruti Saha ◽  
Nithya J Gogtay ◽  
Urmila M Thatte
Keyword(s):  
Adverse Drug Events ◽  
The United States ◽  
Institutional Review Boards ◽  
Chi Square ◽  
Institutional Review ◽  
United States Food ◽  
Post Marketing ◽  
Chi Square Test ◽  
Us Fda ◽  
Review Boards

The present audit was carried out with the objective of evaluating warning letters (WLs) issued to trial sponsors, clinical investigators and institutional review boards (IRBs) by the United States Food and Drug Administration during a six-year period and compare it with two similar earlier audits. WLs were reviewed and classified as per stakeholders and further categorised as per predefined violation themes. The chi-square test was performed for trend analysis of WLs. A total of 62 WLs were issued to the three stakeholders. The maximum number of WLs were issued to the clinical investigators (36/62, 58.06%), followed by sponsors (19/62, 30.64%), and least to the IRBs (7/62, 11.29%). Among sponsors, lack of standard operating procedures for the monitoring, receipt, evaluation and reporting of post-marketing adverse drug events was the most common violation theme (8/19, 42.1%). Among clinical investigators, deviation from investigational plan was the most common violation theme (31/36, 86.11%.). For IRBs, inadequate documentation was the most common violation theme (6/7, 85.71%). We saw an overall reduction in the number of WLs issued to the stakeholders. Thus, we identified multiple areas on which each stakeholder should work for improvement.

Structural Transphobia, Homophobia, and Biphobia in Public Health Practice: The Example of COVID-19 Surveillance

2021 ◽  
pp. e1-e7
Author(s):  
Randall L. Sell ◽  
Elise I. Krims
Keyword(s):  
Public Health ◽  
United States ◽  
Race And Ethnicity ◽  
Public Health Surveillance ◽  
Public Health Practice ◽  
The United States ◽  
Health Surveillance ◽  
Essential Information ◽  
And Gender ◽  
Public Health Surveillance System

Public health surveillance can have profound impacts on the health of populations, with COVID-19 surveillance offering an illuminating example. Surveillance surrounding COVID-19 testing, confirmed cases, and deaths has provided essential information to public health professionals about how to minimize morbidity and mortality. In the United States, surveillance has also pointed out how populations, on the basis of geography, age, and race and ethnicity, are being impacted disproportionately, allowing targeted intervention and evaluation. However, COVID-19 surveillance has also highlighted how the public health surveillance system fails some communities, including sexual and gender minorities. This failure has come about because of the haphazard and disorganized way disease reporting data are collected, analyzed, and reported in the United States, and the structural homophobia, transphobia, and biphobia acting within these systems. We provide recommendations for addressing these concerns after examining experiences collecting race data in COVID-19 surveillance and attempts in Pennsylvania and California to incorporate sexual orientation and gender identity variables into their pandemic surveillance efforts. (Am J Public Health. Published online ahead of print June 10, 2021: e1–e7. https://doi.org/10.2105/AJPH.2021.3062727 )

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