Limited access to neurological care leads to missed diagnoses of Parkinson's disease (PD), leaving many individuals unidentified and untreated. We trained a novel neural network-based fusion ...architecture to detect Parkinson's disease (PD) by analyzing features extracted from webcam recordings of three tasks: finger tapping, facial expression (smiling), and speech (uttering a sentence containing all letters of the alphabet). Additionally, the model incorporated Monte Carlo Dropout to improve prediction accuracy by considering uncertainties. The study participants (n = 845, 272 with PD) were randomly split into three sets: 60% for training, 20% for model selection (hyper-parameter tuning), and 20% for final performance evaluation. The dataset consists of 1102 sessions, each session containing videos of all three tasks. Our proposed model achieved significantly better accuracy, area under the ROC curve (AUROC), and sensitivity at non-inferior specificity compared to any single-task model. Withholding uncertain predictions further boosted the performance, achieving 88.0% (95% CI: 87.7% - 88.4%) accuracy, 93.0% (92.8% - 93.2%) AUROC, 79.3% (78.4% - 80.2%) sensitivity, and 92.6% (92.3% - 92.8%) specificity, at the expense of not being able to predict for 2.3% (2.0% - 2.6%) data. Further analysis suggests that the trained model does not exhibit any detectable bias across sex and ethnic subgroups and is most effective for individuals aged between 50 and 80. This accessible, low-cost approach requiring only an internet-enabled device with a webcam and microphone paves the way for convenient PD screening at home, particularly in regions with limited access to clinical specialists.
The COVID-19 pandemic has driven rapid, widespread adoption of telemedicine. The distribution of clinicians, long travel distances, and disability all limit access to care, especially for persons ...with Parkinson's disease. Telemedicine is not a panacea for all of these challenges but does offer advantages. These advantages can be summarized as the 5 C's: accessible care, increased convenience, enhanced comfort, greater confidentiality to patients and families, and now reduced risk of contagion. Telemedicine also has its limitations, including the inability to perform parts of the physical examination and inequitable access to the Internet and related technologies. Future models will deliver care to patients from a diverse set of specialties. These will include mental health specialists, physiotherapists, neurosurgeons, speech-language therapists, dieticians, social workers, and exercise coaches. Along with these new care models, digital therapeutics, defined as treatments delivered through software programs, are emerging. Telemedicine is now being introduced as a bridge to restart clinical trials and will increasingly become a normal part of future research studies. From this pandemic will be a wealth of new telemedicine approaches which will fundamentally change and improve care as well as research for individuals with Parkinson's disease.
BACKGROUND: Measurement of the global burden of disease with disability-adjusted life-years (DALYs) requires disability weights that quantify health losses for all non-fatal consequences of disease ...and injury. There has been extensive debate about a range of conceptual and methodological issues concerning the definition and measurement of these weights. Our primary objective was a comprehensive re-estimation of disability weights for the Global Burden of Disease Study 2010 through a large-scale empirical investigation in which judgments about health losses associated with many causes of disease and injury were elicited from the general public in diverse communities through a new, standardised approach. METHODS: We surveyed respondents in two ways: household surveys of adults aged 18 years or older (face-to-face interviews in Bangladesh, Indonesia, Peru, and Tanzania; telephone interviews in the USA) between Oct 28, 2009, and June 23, 2010; and an open-access web-based survey between July 26, 2010, and May 16, 2011. The surveys used paired comparison questions, in which respondents considered two hypothetical individuals with different, randomly selected health states and indicated which person they regarded as healthier. The web survey added questions about population health equivalence, which compared the overall health benefits of different life-saving or disease-prevention programmes. We analysed paired comparison responses with probit regression analysis on all 220 unique states in the study. We used results from the population health equivalence responses to anchor the results from the paired comparisons on the disability weight scale from 0 (implying no loss of health) to 1 (implying a health loss equivalent to death). Additionally, we compared new disability weights with those used in WHO's most recent update of the Global Burden of Disease Study for 2004. FINDINGS: 13 902 individuals participated in household surveys and 16 328 in the web survey. Analysis of paired comparison responses indicated a high degree of consistency across surveys: correlations between individual survey results and results from analysis of the pooled dataset were 0·9 or higher in all surveys except in Bangladesh (r=0·75). Most of the 220 disability weights were located on the mild end of the severity scale, with 58 (26%) having weights below 0·05. Five (11%) states had weights below 0·01, such as mild anaemia, mild hearing or vision loss, and secondary infertility. The health states with the highest disability weights were acute schizophrenia (0·76) and severe multiple sclerosis (0·71). We identified a broad pattern of agreement between the old and new weights (r=0·70), particularly in the moderate-to-severe range. However, in the mild range below 0·2, many states had significantly lower weights in our study than previously. INTERPRETATION: This study represents the most extensive empirical effort as yet to measure disability weights. By contrast with the popular hypothesis that disability assessments vary widely across samples with different cultural environments, we have reported strong evidence of highly consistent results. FUNDING: Bill & Melinda Gates Foundation.
BACKGROUND: Reliable and timely information on the leading causes of death in populations, and how these are changing, is a crucial input into health policy debates. In the Global Burden of Diseases, ...Injuries, and Risk Factors Study 2010 (GBD 2010), we aimed to estimate annual deaths for the world and 21 regions between 1980 and 2010 for 235 causes, with uncertainty intervals (UIs), separately by age and sex. METHODS: We attempted to identify all available data on causes of death for 187 countries from 1980 to 2010 from vital registration, verbal autopsy, mortality surveillance, censuses, surveys, hospitals, police records, and mortuaries. We assessed data quality for completeness, diagnostic accuracy, missing data, stochastic variations, and probable causes of death. We applied six different modelling strategies to estimate cause-specific mortality trends depending on the strength of the data. For 133 causes and three special aggregates we used the Cause of Death Ensemble model (CODEm) approach, which uses four families of statistical models testing a large set of different models using different permutations of covariates. Model ensembles were developed from these component models. We assessed model performance with rigorous out-of-sample testing of prediction error and the validity of 95% UIs. For 13 causes with low observed numbers of deaths, we developed negative binomial models with plausible covariates. For 27 causes for which death is rare, we modelled the higher level cause in the cause hierarchy of the GBD 2010 and then allocated deaths across component causes proportionately, estimated from all available data in the database. For selected causes (African trypanosomiasis, congenital syphilis, whooping cough, measles, typhoid and parathyroid, leishmaniasis, acute hepatitis E, and HIV/AIDS), we used natural history models based on information on incidence, prevalence, and case-fatality. We separately estimated cause fractions by aetiology for diarrhoea, lower respiratory infections, and meningitis, as well as disaggregations by subcause for chronic kidney disease, maternal disorders, cirrhosis, and liver cancer. For deaths due to collective violence and natural disasters, we used mortality shock regressions. For every cause, we estimated 95% UIs that captured both parameter estimation uncertainty and uncertainty due to model specification where CODEm was used. We constrained cause-specific fractions within every age-sex group to sum to total mortality based on draws from the uncertainty distributions. FINDINGS: In 2010, there were 52·8 million deaths globally. At the most aggregate level, communicable, maternal, neonatal, and nutritional causes were 24·9% of deaths worldwide in 2010, down from 15·9 million (34·1%) of 46·5 million in 1990. This decrease was largely due to decreases in mortality from diarrhoeal disease (from 2·5 to 1·4 million), lower respiratory infections (from 3·4 to 2·8 million), neonatal disorders (from 3·1 to 2·2 million), measles (from 0·63 to 0·13 million), and tetanus (from 0·27 to 0·06 million). Deaths from HIV/AIDS increased from 0·30 million in 1990 to 1·5 million in 2010, reaching a peak of 1·7 million in 2006. Malaria mortality also rose by an estimated 19·9% since 1990 to 1·17 million deaths in 2010. Tuberculosis killed 1·2 million people in 2010. Deaths from non-communicable diseases rose by just under 8 million between 1990 and 2010, accounting for two of every three deaths (34·5 million) worldwide by 2010. 8 million people died from cancer in 2010, 38% more than two decades ago; of these, 1·5 million (19%) were from trachea, bronchus, and lung cancer. Ischaemic heart disease and stroke collectively killed 12·9 million people in 2010, or one in four deaths worldwide, compared with one in five in 1990; 1·3 million deaths were due to diabetes, twice as many as in 1990. The fraction of global deaths due to injuries (5·1 million deaths) was marginally higher in 2010 (9·6%) compared with two decades earlier (8·8%). This was driven by a 46% rise in deaths worldwide due to road traffic accidents (1·3 million in 2010) and a rise in deaths from falls. Ischaemic heart disease, stroke, chronic obstructive pulmonary disease (COPD), lower respiratory infections, lung cancer, and HIV/AIDS were the leading causes of death in 2010. Ischaemic heart disease, lower respiratory infections, stroke, diarrhoeal disease, malaria, and HIV/AIDS were the leading causes of years of life lost due to premature mortality (YLLs) in 2010, similar to what was estimated for 1990, except for HIV/AIDS and preterm birth complications. YLLs from lower respiratory infections and diarrhoea decreased by 45–54% since 1990; ischaemic heart disease and stroke YLLs increased by 17–28%. Regional variations in leading causes of death were substantial. Communicable, maternal, neonatal, and nutritional causes still accounted for 76% of premature mortality in sub-Saharan Africa in 2010. Age standardised death rates from some key disorders rose (HIV/AIDS, Alzheimer's disease, diabetes mellitus, and chronic kidney disease in particular), but for most diseases, death rates fell in the past two decades; including major vascular diseases, COPD, most forms of cancer, liver cirrhosis, and maternal disorders. For other conditions, notably malaria, prostate cancer, and injuries, little change was noted. INTERPRETATION: Population growth, increased average age of the world's population, and largely decreasing age-specific, sex-specific, and cause-specific death rates combine to drive a broad shift from communicable, maternal, neonatal, and nutritional causes towards non-communicable diseases. Nevertheless, communicable, maternal, neonatal, and nutritional causes remain the dominant causes of YLLs in sub-Saharan Africa. Overlaid on this general pattern of the epidemiological transition, marked regional variation exists in many causes, such as interpersonal violence, suicide, liver cancer, diabetes, cirrhosis, Chagas disease, African trypanosomiasis, melanoma, and others. Regional heterogeneity highlights the importance of sound epidemiological assessments of the causes of death on a regular basis. FUNDING: Bill & Melinda Gates Foundation.
One of the most basic predictions of human capital theory is that life expectancy should impact human capital investment. Limited exogenous variation in life expectancy makes this difficult to test, ...especially in the contexts most relevant to the macroeconomic applications. We estimate the relationship between life expectancy and human capital investments using genetic variation in life expectancy driven by Huntington disease (HD), an inherited degenerative neurological disorder with large impacts on mortality. We compare investment levels for individuals who have ex ante identical risks of HD but learn (through early symptom development or genetic testing) that they do or do not carry the genetic mutation which causes the disease. We find strong qualitative support: individuals with more limited life expectancy complete less education and less job training. We estimate the elasticity of demand for college completion with respect to years of life expectancy of 0.40. This figure implies that differences in life expectancy explain about 10% of cross-country differences in college enrollment. Finally, we use smoking and cancer screening data to test the corollary that health capital is responsive to life expectancy.
We use novel data to study the decision to undergo genetic testing by individuals at risk for Huntington disease (HD), a hereditary neurological disorder that reduces healthy life expectancy to about ...age 50. Although genetic testing is perfectly predictive and carries little financial or time cost, less than 10% of at-risk individuals are tested prior to the onset of symptoms. Testing rates are higher for individuals with higher ex ante risk of carrying the genetic expansion for HD. Untested individuals express optimistic beliefs about their probability of having HD and make fertility, savings, labor supply, and other decisions as if they do not have HD, even though individuals with confirmed HD behave quite differently. We show that these facts are qualitatively consistent with a model of optimal expectations (Brunnermeier and Parker, 2005) and can be reconciled quantitatively in this model with reasonable parameter values. This model nests the neoclassical framework and, we argue, provides strong evidence rejecting the assumptions of that framework. Finally, we briefly develop policy implications.
Node view Abiola, Solomon O.; Portman, Eric; Kautz, Henry ...
Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers,
09/2015
Conference Proceeding
We present a real-time interface which allows for contact tracing using ubiquitous sensors present in the Node smartphone application on Android phones. The initial application seeks to aid in the ...prevention of infectious diseases in Lagos, Nigeria through installing the application on up to 100 smartphones. In our demo we demonstrate how anonymous user IDs can be interrogated from a real-time dataset. Using this information a public policy maker or health worker can identify infected or at risk individuals immediately. Furthermore, they can notify such individuals if they are at risk. Lastly, future work will allow us to use this information to model the spread of an infectious disease in real-time with geospatial resolution not readily available in typical infectious disease models.
Current clinimetrics assessment of Parkinson's disease (PD) is insensitive, episodic, subjective, and provider-centered. Ubiquitous technologies such as smartphones promise to fundamentally change PD ...assessments. To enable frequent remote assessment of PD tremor severity, here we present a 39-month smartphone research study in a real-world setting without supervision. More than 15,000 consented participants used the smartphone application, mPower, to perform self-administered active tasks. In the scope of this abstract, we developed an objective smartphone measure of PD tremor severity called mPower Tremor Scores (mPTS) using machine learning. Efficacy, and reliability of mPTS was further tested and validated in a separate cohort in the real world and in-clinic setting. This study demonstrates the utility of using structured activities with built-in smartphone sensors to measure PD tremor severity remotely and objectively in a real-world setting using more than 1100 participants.
Working Paper No. 15326 Individual, personalized genetic information is increasingly available, leading to the possibility of greater adverse selection over time, particularly in individual-payer ...insurance markets; this selection could impact the viability of these markets. We use data on individuals at risk for Huntington disease (HD), a degenerative neurological disorder with significant effects on morbidity, to estimate adverse selection in long-term care insurance. We find strong evidence of adverse selection: individuals who carry the HD genetic mutation are up to 5 times as likely as the general population to own long-term care insurance. We use these estimates to make predictions about the future of this market as genetic information increases. We argue that even relatively limited increases in genetic information may threaten the viability of private long-term care insurance.
PDF
