UNLABELLEDThe prevalence of chronic pain is well described in various parts of the world; primarily in Western societies such as Europe, America and Australia. Little is known of the prevalence of chronic pain within Asia or Southeast Asia. In view of the cultural and genetic variation in pain causation, manifestation and reporting, the findings of previous studies cannot be translated to Asian countries. Prevalence studies needed to be carried out to quantify the magnitude and impact of chronic pain within Asian countries to properly allocate precious health funds to deal with this important healthcare issue. We report the findings of the prevalence study within one Asian country: Singapore.

OBJECTIVETo determine the prevalence and impact of chronic pain in adult Singaporeans.

MATERIALS AND METHODSTwo sets of questionnaires were designed. The first, a screening questionnaire, to identify the prevalence of chronic pain, and should there be chronic pain; the second, a detailed questionnaire was administered, to characterise the features and the impact of pain. A cross-sectional sampling of Singapore adults were achieved using a computer-based multi-step random sampling of listed telephones numbers. The questionnaires were administered via telephone by a trained interviewer with the aid of a computer-assisted telephone interview system.

RESULTSA total of 4141 screening and 400 detailed questionnaires were completed. The prevalence of chronic pain, defined as pain of at least 3 months' duration over the last 6 months was 8.7% (n = 359). There was a higher prevalence in females (10.9%) and with increasing age. In particular, pain prevalence increased steeply beyond the age of 65 years old. There was a significant impact on work and daily function of those with chronic pain.

CONCLUSIONThough the prevalence of chronic pain was marginally lower compared other studies, the impact of pain was just as significant. In a rapidly ageing population such as Singapore, chronic pain is an important emerging healthcare problem which will likely exert increasing toll on the existing social infrastructure within the next 5 to 10 years.

Absenteeism ; Adolescent ; Adult ; Aged ; Aged, 80 and over ; Cross-Sectional Studies ; Female ; Humans ; Income ; Male ; Middle Aged ; Pain, Intractable ; epidemiology ; physiopathology ; Singapore ; epidemiology ; Surveys and Questionnaires ; Young Adult

Bone and Bones/diagnostic imaging* ; Breast/diagnostic imaging* ; Breast Neoplasms/diagnostic imaging* ; Female ; Humans

INTRODUCTION: Singapore's enhanced surveillance programme for COVID-19 identifies and isolates hospitalised patients with acute respiratory symptoms to prevent nosocomial spread. We developed risk prediction models to identify patients with low risk for COVID-19 from this cohort of hospitalised patients with acute respiratory symptoms. METHODS: This was a single-centre retrospective observational study. Patients admitted to our institution's respiratory surveillance wards from 10 February to 30 April 2020 contributed data for analysis. Prediction models for COVID-19 were derived from a training cohort using variables based on demographics, clinical symptoms, exposure risks and blood investigations fitted into logistic regression models. The derived prediction models were subsequently validated on a test cohort. RESULTS: Of the 1,228 patients analysed, 52 (4.2%) were diagnosed with COVID-19. Two prediction models were derived, the first based on age, presence of sore throat, dormitory residence, blood haemoglobin level (Hb), and total white blood cell counts (TW), and the second based on presence of headache, contact with infective patients, Hb and TW. Both models had good diagnostic performance with areas under the receiver operating characteristic curve of 0.934 and 0.866, respectively. Risk score cut-offs of 0.6 for Model 1 and 0.2 for Model 2 had 100% sensitivity, allowing identification of patients with low risk for COVID-19. Limiting COVID-19 screening to only elevated-risk patients reduced the number of isolation days for surveillance patients by up to 41.7% and COVID-19 swab testing by up to 41.0%. CONCLUSION Prediction models derived from our study were able to identify patients at low risk for COVID-19 and rationalise resource utilisation.
Humans ; COVID-19/epidemiology* ; SARS-CoV-2 ; Hospitalization ; Logistic Models ; Retrospective Studies ; Hemoglobins