To determine General Practice (GP) recording of carer status and the number of patients self-identifying as carers, while self-completing an automated check-in screen prior to a GP consultation.

A descriptive cross-sectional study.

11 GPs in the West Midlands, England. Recruitment commenced in September 2019 and concluded in January 2020.

All patients aged 10 years and over, self-completing an automated check-in screen, were invited to participate during a 3-week recruitment period.

The current coding of carers at participating GPs and the number of patients identifying themselves as a carer were primary outcome measures. Secondary outcome measures included the number of responses attained from automated check-in screens as a research data collection tool and whether carers felt supported in their carer role.

80.3% (n=9301) of patients self-completing an automated check-in screen participated in QUantifying the identification Of carers in general practice (STATUS QUO Study) (62.6% (n=5822) female, mean age 52.9 years (10–98 years, SD=20.3)). Prior to recruitment, the clinical code used to denote a carer was identified in 2.7% (n=2739) of medical records across the participating GPs.

10.1% (n=936) of participants identified themselves as a carer. They reported feeling supported with their own health and social care needs: always 19.3% (n=150), a lot of the time 13.2% (n=102), some of the time 40.8% (n=317) and never 26.7% (n=207).

Many more participants self-identified as a carer than were recorded on participating GP lists. Improvements in the recording of the population’s caring status need to be actioned, to ensure that supportive implementation strategies for carers are effectively received. Using automated check-in facilities for research continues to provide high participation rates.

To identify factors associated with malnutrition (undernutrition and overnutrition) and determine appropriate cut-off values for mid-arm circumference (MAC) and calf circumference (CC) among community-dwelling Indian older adults.

Data from the first wave of harmonised diagnostic assessment of dementia for Longitudinal Ageing Study in India (LASI-DAD) were used. Various sociodemographic factors, comorbidities, geriatric syndromes, childhood financial and health status were included. Anthropometric measurements included body mass index (BMI), MAC and CC.

Nationally representative cohort study including 36 Indian states and union territories.

4096 older adults aged >60 years from LASI DAD.

The outcome variable was BMI, categorised as low (2), normal (18.5–22.9 kg/m²) and high (>23 kg/m²). The cut-off values of MAC and CC were derived using ROC curve with BMI as the gold standard.

902 (weighted percentage 20.55%) had low BMI, 1742 (44.25%) had high BMI. Undernutrition was associated with age, wealth-quintile and impaired cognition, while overnutrition was associated with higher education, urban living and comorbidities such as hypertension, diabetes and chronic heart disease. For CC, the optimal lower and upper cut-offs for males were 28.1 cm and >31.5 cm, respectively, while for females, the corresponding values were 26 cm and >29 cm. Similarly, the optimal lower and upper cut-offs for MAC in males were 23.9 cm and >26.9 cm, and for females, they were 22.5 cm and >25 cm.

Our study identifies a high BMI prevalence, especially among females, individuals with higher education, urban residents and those with comorbidities. We establish gender-specific MAC and CC cut-off values with significant implications for healthcare, policy and research. Tailored interventions can address undernutrition and overnutrition in older adults, enhancing standardised nutritional assessment and well-being.