The Global Crisis of Parkinson’s Disease: Epidemiology and Risk Factors
Bibliographic record
Abstract
Parkinson’s disease is the second most prevalent neurodegenerative disorder, encompassing sufferers from all races worldwide. With countries around the world transitioning further along their demographics, many developing poor and middle-income countries are falling behind with the required healthcare, education and resources needed to meet the needs of Parkinson’s disease patients. We reviewed how demographic transition trends are affecting worldwide Parkinson’s disease incidence and prevalence, evaluated the effects of poverty on Parkinson’s disease management, reviewed current global initiatives to support Parkinson’s disease patients, and proposed factors for the prevention of Parkinson’s disease crises in the near future. Parkinson’s disease prevalence is increasing due to old age and higher life expectancy. North Americans have higher Parkinson’s disease prevalence than Asian and African populations. Parkinson’s disease is most prevalent amongst Caucasians in North American and European populations and amongst blacks in African populations. Important Parkinson’s disease risk factors include insecticide and heavy metal exposure, welding, antipsychotic medications, and LRRK2 gene mutations. The association of Parkinson’s disease and poverty showcases lack of knowledge of Parkinson’s disease diagnosis, predominance of care for more pervasive illnesses, limited healthcare facilities, inadequate or no access to care from specialists, and increases in Parkinson’s disease-related illnesses. Cost of care can lock up a significant portion of annual income since health insurance may not cover all expenses. These alarming situations may lead to a global Parkinson’s crisis. Thus, efforts need to be made to increase the number of training programs for educating caregivers, patients and Parkinson’s disease professionals to raise awareness and provide better healthcare and drug and treatment facilities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".