scholarly journals Self-healing and Underreporting of Cases of Visceral Leishmaniasis in Bihar, India: A Mathematical Modeling-based Study

2019 ◽  
Author(s):  
Olivia Prosper ◽  
Swati DebRoy ◽  
Austin Mishoe ◽  
Cesar Montalvo ◽  
Niyamat Ali Siddiqui ◽  
...  
Keyword(s):  
Public Health ◽  
Health Care ◽  
Visceral Leishmaniasis ◽  
The State ◽  
Data Sets ◽  
Surveillance Systems ◽  
Self Healing ◽  
Data Set ◽  
True Incidence ◽  
Government Surveillance

Background: Underreporting of Visceral Leishmaniasis (VL) in India remains a problem to public health controls. Effective and reliable surveillance systems are critical for monitoring disease outbreaks and public health control programs. However, in India, government surveillance systems are affected by levels of scarcity in resources and therefore, uncertainty surrounds the true incidence of asymptomatic and clinical cases, affecting morbidity and mortality rates. The State of Bihar alone contributes up to the 40\% of the worldwide VL cases. The inefficiency of surveillance systems occurs because of multiple reasons including delay in seeking health care, accessing non-authentic health care clinics, and existence of significant asymptomatic self healing infectious cases. This results in a failure of the system to adequately report true transmission rates and number of symptomatic cases that have sought medical advice (thus, high underreporting of cases). Objectives and Methods: There are several methods to estimate the extent of underreporting in the surveillance system. In this research, we use a mathematical dynamic model and two different types of data sets, namely, monthly incidence for 2003-2005 and yearly incidence from 2006-2012 from the Bihar's 21 most VL affected districts out of its 38 districts. The goals of the study are to estimate critical metrics to measure level of transmission and to evaluate the estimation process between the two data sets and 21 districts. In particularly, our focus is on (i) estimating infection transmission potential, underreporting level in incidence and proportion of self-healing cases, (ii) quantifying reproduction number of the$R_0$, and (iii) comparing underreporting incidence levels and proportion of self-healing cases between the two periods 2003-2005 and 2006-2012 and between 21 districts. Results: Our research suggests that the number of asymptomatic individuals in the population who eventually self-heal may have a significant effect on the dynamics of VL spread. The estimated mean self-healing proportion (out of all infected) is found to be $\sim 0.6$ with only 7 out of 21 affected districts having self-healing proportion less than $0.5$ for both data sets. The estimated mean underreporting level is at least $64$\% for the state of Bihar. The estimates of the basic reproduction numbers obtained are similar in magnitude for most of the districts, being in the range of (0.88, 2.79) and (0.98, 1.01) for 2003-2005 and 2006-2012, respectively. Conclusions: The estimates for the two types (monthly and yearly) of temporal data suggest that monthly data are better for estimation if less number of data points are available, however, in general, using such data set results in larger variances in parameters as compared to estimates obtained through aggregated yearly data. Estimated values of transmission related metrics are lower than those obtained from earlier analyses in the literature, and the implications of this for VL control are discussed. The spatial heterogeneity in these control metrics increases the risk of epidemics and makes the control strategies more complex.

scholarly journals Novel Tools Supporting Knowledge Translation for Public Health Practice in British Columbia, Canada

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Joanne Stares ◽  
Jenny Sutherland
Keyword(s):  
Public Health ◽  
Health Care ◽  
Air Quality ◽  
Public Health Practice ◽  
Secondary Data ◽  
Data Sets ◽  
Health Practice ◽  
Notifiable Diseases ◽  
Health Practitioners ◽  
Public Health Practitioners

ABSTRACT ObjectivesUnderlying the delivery of services by the universal Canadian health care system are a number of rich secondary administrative health data sets which contain information on persons who are registered for care and details on their contacts with the system. These datasets are powerful sources of information for investigation of non-notifiable diseases and as an adjunct to traditional communicable disease surveillance. However, there are gaps between public health practitioners, access to these data, and access to experts in the use of these secondary data. The data linkage requires in-depth knowledge of these data including usages, limitations and data quality issues and also the skills to extract data to support secondary usage. OLAP reports have been developed to support operation needs but not on advanced analytics reports for surveillance and cohort study. To fill these gaps, we developed a set of web-based modular, parameterized, extraction and reporting tools for the purpose of: 1) decreasing the time and resources necessary to fill general secondary data requests for public health audiences; 2) quickly providing information from descriptive analysis of secondary data to public health practitioners; 3) informing the development of data feeds for continued enhanced surveillance or further data access requests; 4) assisting in preliminary stages of epidemiological investigations of non-notifiable diseases; and, 5) facilitating access to information from secondary data for evidence-based decision making in public health. ApproachWe intend to present these tools by case study of their application to small area analysis of secondary data in the context of air quality concerns. Data sources include individuals registered for health care coverage in BC, hospital separations, physician consultations, chronic disease registries, and drugs dispensation. Data sets contain complete information from 1992. Data were extracted and analyzed to describe the occurrence of health service utilization for cardiovascular and respiratory morbidity. Analysis was undertaken for BC residents in areas identified by local public health as priorities for monitoring. Health outcomes were directly standardized by age and compared to provincial trends by use of the comparative morbidity figure. ResultsResults will include descriptive epidemiological analysis of secondary data relating to respiratory and cardiovascular morbidity in the context of air quality concerns, summary of next steps, as well as an assessment of tool performance. ConclusionsWhere adopted tools such as these can make information from secondary data more accessible to support public health practice, particularly in regions with low analytical or epidemiological capacity.

scholarly journals Health system financing paradigm in the state of São Paulo

2019 ◽  
Vol 53 ◽  
pp. 39
Author(s):  
Adilson Soares
Keyword(s):  
Public Health ◽  
Health Care ◽  
Sao Paulo ◽  
The State ◽  
São Paulo ◽  
High Complexity ◽  
Primary Health ◽  
Capita Spending ◽  
Per Capita ◽  
The City

OBJECTIVE: To analyze the allocation of financial resources in the Brazilian Unified Health System (SUS) in the state of São Paulo by level of care, health region, source of funds and level of government. METHODS: This is an exploratory study based on 2014 data extracted from the Public Health Budget Database, presented in absolute terms, relative terms and per capita. RESULTS: In 2014, R$52.1 bi were spent on public health, 58.0% having corresponded to the expenditures of the municipalities and 42.0% to those of the state government. Regional per capita spending varied from R$561.75 to R$824.85. As for the per capita spending on primary health care, which represented 37.5% of the municipalities’ total expenditure, the lowest value was found in the city of São Paulo and the highest, in Araçatuba. Campinas had the highest per capita expenditure on medium and high complexity care, while Presidente Prudente had the lowest. The highest regional percentage of the current net revenue spent on health was verified in Registro, and the lowest, in the city of São Paulo. CONCLUSIONS: The paradigm of the health sector’s financing in São Paulo revealed that the expenditure on primary health care, level elected by health policy as strategic because it depends on coordination and integral health care in the attention networks, was not considered a priority in relation to the expenditure with the medium and high complexity, exposing the iniquities in the state’s regions.

scholarly journals A Novel Approach to Data Driven Pandemic Recovery: The Pandemic Recovery Acceleration Model

2020 ◽  
Author(s):  
Jeffrey P Gold ◽  
Christopher Wichman ◽  
Kenneth Bayles ◽  
Ali S Khan ◽  
Christopher Kratochvil ◽  
...  
Keyword(s):  
Public Health ◽  
Health Care ◽  
Real Time ◽  
Local Community ◽  
The State ◽  
Data Driven ◽  
Real Time Analysis ◽  
Novel Approach ◽  
Acceleration Model ◽  
Care Systems

A data driven approach to guide the global, regional and local pandemic recovery planning is key to the safety, efficacy and sustainability of all pandemic recovery efforts. The Pandemic Recovery Acceleration Model (PRAM) analytic tool was developed and implemented state wide in Nebraska to allow health officials, public officials, industry leaders and community leaders to capture a real time snapshot of how the COVID-19 pandemic is affecting their local community, a region or the state and use this novel lens to aid in making key mitigation and recovery decisions. This is done by using six commonly available metrics that are monitored daily across the state describing the pandemic impact: number of new cases, percent positive tests, deaths, occupied hospital beds, occupied intensive care beds and utilized ventilators, all directly related to confirmed COVID-19 patients. Nebraska is separated into six Health Care Coalitions based on geography, public health and medical care systems. The PRAM aggregates the data for each of these geographic regions based on disease prevalence acceleration and health care resource utilization acceleration, producing real time analysis of the acceleration of change for each metric individually and also combined into a single weighted index, the PRAM Recovery Index. These indices are then shared daily with the state leadership, coalition leaders and public health directors and also tracked over time, aiding in real time regional and statewide decisions of resource allocation and the extent of use of comprehensive non-pharmacologic interventions.

scholarly journals Modified Deep Neural Networks for Dog Breeds Identification

2018 ◽  
Author(s):  
Aydin Ayanzadeh ◽  
Sahand Vahidnia
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
The State ◽  
Fine Tuning ◽  
Test Accuracy ◽  
Data Sets ◽  
Data Set

In this paper, we leverage state of the art models on Imagenet data-sets. We use the pre-trained model and learned weighs to extract the feature from the Dog breeds identification data-set. Afterwards, we applied fine-tuning and dataaugmentation to increase the performance of our test accuracy in classification of dog breeds datasets. The performance of the proposed approaches are compared with the state of the art models of Image-Net datasets such as ResNet-50, DenseNet-121, DenseNet-169 and GoogleNet. we achieved 89.66% , 85.37% 84.01% and 82.08% test accuracy respectively which shows thesuperior performance of proposed method to the previous works on Stanford dog breeds datasets.

scholarly journals Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study

10.2196/22624 ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. e22624 ◽  
Author(s):  
Ranganathan Chandrasekaran ◽  
Vikalp Mehta ◽  
Tejali Valkunde ◽  
Evangelos Moustakas
Keyword(s):  
Health Care ◽  
English Language ◽  
Latent Dirichlet Allocation ◽  
Data Sets ◽  
Data Set ◽  
Time Period ◽  
Combining Data ◽  
Social Media Platforms ◽  
And Control ◽  
The Impact

Background With restrictions on movement and stay-at-home orders in place due to the COVID-19 pandemic, social media platforms such as Twitter have become an outlet for users to express their concerns, opinions, and feelings about the pandemic. Individuals, health agencies, and governments are using Twitter to communicate about COVID-19. Objective The aims of this study were to examine key themes and topics of English-language COVID-19–related tweets posted by individuals and to explore the trends and variations in how the COVID-19–related tweets, key topics, and associated sentiments changed over a period of time from before to after the disease was declared a pandemic. Methods Building on the emergent stream of studies examining COVID-19–related tweets in English, we performed a temporal assessment covering the time period from January 1 to May 9, 2020, and examined variations in tweet topics and sentiment scores to uncover key trends. Combining data from two publicly available COVID-19 tweet data sets with those obtained in our own search, we compiled a data set of 13.9 million English-language COVID-19–related tweets posted by individuals. We use guided latent Dirichlet allocation (LDA) to infer themes and topics underlying the tweets, and we used VADER (Valence Aware Dictionary and sEntiment Reasoner) sentiment analysis to compute sentiment scores and examine weekly trends for 17 weeks. Results Topic modeling yielded 26 topics, which were grouped into 10 broader themes underlying the COVID-19–related tweets. Of the 13,937,906 examined tweets, 2,858,316 (20.51%) were about the impact of COVID-19 on the economy and markets, followed by spread and growth in cases (2,154,065, 15.45%), treatment and recovery (1,831,339, 13.14%), impact on the health care sector (1,588,499, 11.40%), and governments response (1,559,591, 11.19%). Average compound sentiment scores were found to be negative throughout the examined time period for the topics of spread and growth of cases, symptoms, racism, source of the outbreak, and political impact of COVID-19. In contrast, we saw a reversal of sentiments from negative to positive for prevention, impact on the economy and markets, government response, impact on the health care industry, and treatment and recovery. Conclusions Identification of dominant themes, topics, sentiments, and changing trends in tweets about the COVID-19 pandemic can help governments, health care agencies, and policy makers frame appropriate responses to prevent and control the spread of the pandemic.

scholarly journals Real-time surveillance for chronic conditions in Massachusetts using EHR data

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Noelle Cocoros ◽  
John Menchaca ◽  
Michael Klompas
Keyword(s):  
Public Health ◽  
Type 2 Diabetes ◽  
Electronic Health Record ◽  
Chronic Diseases ◽  
The State ◽  
Community Level ◽  
Health Record ◽  
Surveillance Systems ◽  
Electronic Health

ObjectiveTo assess the feasibility of tracking the prevalence of chronicconditions at the state and community level over time using MDPHnet,a distributed network for querying electronic health record systemsIntroductionPublic health agencies and researchers have traditionally reliedon the Behavioral Risk Factor Surveillance System (BRFSS) andsimilar tools for surveillance of non-reportable conditions. Thesetools are valuable but the data are delayed by more than a year,limited in scope, and based only on participant self-report. Thesecharacteristics limit the utility of traditional surveillance systems forprogram monitoring and impact assessments. Automated surveillanceusing electronic health record (EHR) data has the potential to increasethe efficiency, breadth, accuracy, and timeliness of surveillance. Wesought to assess the feasibility and utility of public health surveillancefor chronic diseases using EHR data using MDPHnet. MDPHnet isa distributed data network that allows the Massachusetts Departmentof Public Health to query participating practices’ EHR data for thepurposes of public health surveillance (www.esphealth.org). Practicesretain the ability to approve queries on a case-by-case basis and thenetwork is updated daily.MethodsWe queried the quarterly prevalence of pediatric asthma, smoking,type 2 diabetes, obesity, overweight, and hypertension statewideand in 9 Massachusetts communities between January 1, 2012 andJuly 1, 2016. We selected these 9 communities because they wereparticipating in a state-funded initiative to decrease the prevalenceof one or more of these conditions. Conditions were defined usingalgorithms based upon vital signs, diagnosis codes, laboratorymeasures, prescriptions, and self-reported smoking status. Eligiblepatients were those with at least 1 encounter of any kind within the2 years preceding the start of each quarter. Results were adjusted forage, sex, and race / ethnicity using the 2010 Massachusetts censusdata.ResultsSurveillance data were available for 1.2 million people overall,approximately 20% of the state population. Coverage varied bycommunity with >28% coverage for 7 of the communities and11% coverage in the eighth. The ninth community had only 2%coverage and was dropped from further analyses. The race / ethnicitydistribution in MDPHnet data was comparable to census datastatewide and in most communities. Queries for all six conditionssuccessfully executed across the network for all time periods ofinterest. The prevalence of asthma among children under 10 yrs rosefrom 12% in January 2012 to 13% in July 2016. Current smoking inadults age≥20 rose from 14% in 2013 to 16% in 2016 (we excludedresults from 2012 due to changes in documentation propelled by theintroduction of meaningful use criteria). This is comparable to the15% rate of smoking per BRFSS in 20141. Obesity among adultsincreased slightly from 22% to 24% during the study period, resultsnearly identical to the most recent BRFSS results for Massachusetts(23% in 2014 and 24% in 2015)2. The prevalence of each conditionvaried widely across the communities under study. For example, forthe third quarter of 2016, the prevalence of asthma among childrenunder 10 ranged from 5% to 23% depending on the community,the prevalence of smoking among adults ranged from 11% to 35%,and the prevalence of type 2 diabetes among adults ranged from7% to 14%. We also examined differences in disease estimates byrace / ethnicity. Substantial racial / ethnic differences were evidentfor type 2 diabetes among adults, with whites having the lowestprevalence at 7% and blacks having the highest at 12% in the thirdquarter of 2016; this trend was consistent over the study period.ConclusionsOur study demonstrates that MDPHnet can provide theMassachusetts Department of Public Health with timely population-level estimates of chronic diseases for numerous conditions at boththe state and community level. MDPHnet surveillance providesprevalence estimates that align well with BRFSS and other traditionalsurveillance sources but is able to make surveillance more timelyand more efficient with more geographical specificity compared totraditional surveillance systems. Our ability to generate real-timetime-series data supports the use of MDPHnet as a source for project/program evaluation.

scholarly journals Comparing Findings From a Friends of Cancer Research Exploratory Analysis of Real-World End Points With the Cancer Analysis System in England

2021 ◽  
pp. 1155-1168
Author(s):  
Pia Horvat ◽  
Christen M. Gray ◽  
Alexandrina Lambova ◽  
Jennifer B. Christian ◽  
Laura Lasiter ◽  
...  
Keyword(s):  
Health Care ◽  
Cancer Research ◽  
Real World ◽  
Pilot Project ◽  
Data Sets ◽  
Patient Privacy ◽  
Data Set ◽  
End Points ◽  
Analysis System ◽  
Cas A

PURPOSE This study compared real-world end points extracted from the Cancer Analysis System (CAS), a national cancer registry with linkage to national mortality and other health care databases in England, with those from diverse US oncology data sources, including electronic health care records, insurance claims, unstructured medical charts, or a combination, that participated in the Friends of Cancer Research Real-World Evidence Pilot Project 1.0. Consistency between data sets and between real-world overall survival (rwOS) was assessed in patients with immunotherapy-treated advanced non–small-cell lung cancer (aNSCLC). PATIENTS AND METHODS Patients with aNSCLC, diagnosed between January 2013 and December 2017, who initiated treatment with approved programmed death ligand-1 (PD-[L]1) inhibitors until March 2018 were included. Real-world end points, including rwOS and real-world time to treatment discontinuation (rwTTD), were assessed using Kaplan-Meier analysis. A synthetic data set, Simulacrum, on the basis of conditional random sampling of the CAS data was used to develop and refine analysis scripts while protecting patient privacy. RESULTS Characteristics (age, sex, and histology) of the 2,035 patients with immunotherapy-treated aNSCLC included in the CAS study were broadly comparable with US data sets. In CAS, a higher proportion (46.7%) of patients received a PD-(L)1 inhibitor in the first line than in US data sets (18%-30%). Median rwOS (11.4 months; 95% CI, 10.4 to 12.7) and rwTTD (4.9 months; 95% CI, 4.7 to 5.1) were within the range of US-based data sets (rwOS, 8.6-13.5 months; rwTTD, 3.2-7.0 months). CONCLUSION The CAS findings were consistent with those from US-based oncology data sets. Such consistency is important for regulatory decision making. Differences observed between data sets may be explained by variation in health care settings, such as the timing of PD-(L)1 approval and reimbursement, and data capture.

scholarly journals Use of artificial intelligence for public health surveillance: a case study to develop a machine Learning-algorithm to estimate the incidence of diabetes mellitus in France

2021 ◽  
Vol 79 (1) ◽  
Author(s):  
Romana Haneef ◽  
Sofiane Kab ◽  
Rok Hrzic ◽  
Sonsoles Fuentes ◽  
Sandrine Fosse-Edorh ◽  
...  
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Test Data ◽  
Public Health Surveillance ◽  
Health Surveillance ◽  
Data Sets ◽  
Data Set ◽  
Linear Discriminant ◽  
Final Data ◽  
Selection Of

Abstract Background The use of machine learning techniques is increasing in healthcare which allows to estimate and predict health outcomes from large administrative data sets more efficiently. The main objective of this study was to develop a generic machine learning (ML) algorithm to estimate the incidence of diabetes based on the number of reimbursements over the last 2 years. Methods We selected a final data set from a population-based epidemiological cohort (i.e., CONSTANCES) linked with French National Health Database (i.e., SNDS). To develop this algorithm, we adopted a supervised ML approach. Following steps were performed: i. selection of final data set, ii. target definition, iii. Coding variables for a given window of time, iv. split final data into training and test data sets, v. variables selection, vi. training model, vii. Validation of model with test data set and viii. Selection of the model. We used the area under the receiver operating characteristic curve (AUC) to select the best algorithm. Results The final data set used to develop the algorithm included 44,659 participants from CONSTANCES. Out of 3468 variables from SNDS linked to CONSTANCES cohort were coded, 23 variables were selected to train different algorithms. The final algorithm to estimate the incidence of diabetes was a Linear Discriminant Analysis model based on number of reimbursements of selected variables related to biological tests, drugs, medical acts and hospitalization without a procedure over the last 2 years. This algorithm has a sensitivity of 62%, a specificity of 67% and an accuracy of 67% [95% CI: 0.66–0.68]. Conclusions Supervised ML is an innovative tool for the development of new methods to exploit large health administrative databases. In context of InfAct project, we have developed and applied the first time a generic ML-algorithm to estimate the incidence of diabetes for public health surveillance. The ML-algorithm we have developed, has a moderate performance. The next step is to apply this algorithm on SNDS to estimate the incidence of type 2 diabetes cases. More research is needed to apply various MLTs to estimate the incidence of various health conditions.

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