Short questionnaire for Parkinson's disease as a screening instrument

The rise of Parkinson's disease is a global challenge, but efforts to tackle this must begin at a national level: a protocol for national digital screening and "eat, move, sleep" lifestyle interventions to prevent or slow the rise of non-communicable diseases in Thailand

The rising prevalence of Parkinson's disease (PD) globally presents a significant public health challenge for national healthcare systems, particularly in low-to-middle income countries, such as Thailand, which may have insufficient resources to meet these escalating healthcare needs. There are also many undiagnosed cases of early-stage PD, a period when therapeutic interventions would have the most value and least cost. The traditional "passive" approach, whereby clinicians wait for patients with symptomatic PD to seek treatment, is inadequate. Proactive, early identification of PD will allow timely therapeutic interventions, and digital health technologies can be scaled up in the identification and early diagnosis of cases. The Parkinson's disease risk survey (TCTR20231025005) aims to evaluate a digital population screening platform to identify undiagnosed PD cases in the Thai population. Recognizing the long prodromal phase of PD, the target demographic for screening is people aged ≥ 40 years, approximately 20 years before the usual emergence of motor symptoms. Thailand has a highly rated healthcare system with an established universal healthcare program for citizens, making it ideal for deploying a national screening program using digital technology. Designed by a multidisciplinary group of PD experts, the digital platform comprises a 20-item questionnaire about PD symptoms along with objective tests of eight digital markers: voice vowel, voice sentences, resting and postural tremor, alternate finger tapping, a "pinch-to-size" test, gait and balance, with performance recorded using a mobile application and smartphone's sensors. Machine learning tools use the collected data to identify subjects at risk of developing, or with early signs of, PD. This article describes the selection and validation of questionnaire items and digital markers, with results showing the chosen parameters and data analysis methods to be robust, reliable, and reproducible. This digital platform could serve as a model for similar screening strategies for other non-communicable diseases in Thailand.

Prevalence and associated comorbidities of restless legs syndrome (RLS): Data from a large population-based door-to-door survey on 19176 adults in Tehran, Iran

BACKGROUND

Discrepancies have been reported in the prevalence rate of restless legs syndrome (RLS) among different ethnic groups and geographic populations. Furthermore, there are disagreements on determinant factors and associated comorbidities of RLS. We aimed to estimate prevalence of RLS and investigate its associated comorbid conditions and risk factors in a large population-based door-to-door survey.

METHODS

Following a multistage random sampling from the households lived in 22 urban districts of Tehran, Iran, 19176 participants with ≥30 years of age were recruited. Trained surveyors filled study checklist consisting of baseline characteristics, risk factors and comorbidity profile and the International RLS Study Group (IRLSSG) diagnostic criteria through face-to-face interviews.

RESULTS

In total, 1580 individuals were positively screened for RLS resulting in a standardized prevalence rate of 60.0/1000. There was a gradual increase in RLS prevalence by advancing age, however, sex difference disappeared after adjustment. Parkinsonism [adjusted odds' ratio (adj-OR) = 7.4 (95% CI: 5.3-10.4)], peripheral neuropathy [adj-OR = 3.7 (95% CI: 3.3-4.1)], subjective cognitive impairment (SCI) [adj-OR = 3.1 (95% CI: 2.7-3.4)], acting out dreams [adj-OR = 2.8 (95% CI: 2.5-3.2)], hyposmia [adj-OR = 2.5 (95% CI: 2.2-2.9)], active smoking [adj-OR = 1.5 (95% CI: 1.3-1.9)] and additional number of cardiometabolic diseases associated with higher risk of RLS [adj-OR = 1.6 (95% CI: 1.2-2.3)].

CONCLUSION

Our findings showed that neuro-cognitive co-morbidities such as parkinsonism, peripheral neuropathy, SCI, acting out dreams and hyposmia as well as cardio-metabolic risk factors and diseases were independent determinants of RLS. It is recommended to screen individuals with either these comorbid conditions for RLS or the ones with RLS for the accompanying diseases.