Understanding the causes of missingness in primary care: a realist review.

BACKGROUND: Although missed appointments in healthcare have been an area of concern for policy, practice and research, the primary focus has been on reducing single 'situational' missed appointments to the benefit of services. Little attention has been paid to the causes and consequences of more 'enduring' multiple missed appointments in primary care and the role this has in producing health inequalities. METHODS: We conducted a realist review of the literature on multiple missed appointments to identify the causes of 'missingness.' We searched multiple databases, carried out iterative citation-tracking on key papers on the topic of missed appointments and identified papers through searches of grey literature. We synthesised evidence from 197 papers, drawing on the theoretical frameworks of candidacy and fundamental causation. RESULTS: Missingness is caused by an overlapping set of complex factors, including patients not identifying a need for an appointment or feeling it is 'for them'; appointments as sites of poor communication, power imbalance and relational threat; patients being exposed to competing demands, priorities and urgencies; issues of travel and mobility; and an absence of choice or flexibility in when, where and with whom appointments take place. CONCLUSIONS: Interventions to address missingness at policy and practice levels should be theoretically informed, tailored to patients experiencing missingness and their identified needs and barriers; be cognisant of causal domains at multiple levels and address as many as practical; and be designed to increase safety for those seeking care.

Implementation of a lifestyle and life-skills intervention to prevent weight-gain and cardiometabolic abnormalities in young people with first-episode psychosis as part of routine care: The Keeping the Body in Mind program.

OBJECTIVES: In 2013, a cluster-controlled pilot study found the 12-week Keeping the Body in Mind (KBIM) lifestyle and life skills intervention was able to prevent weight gain in a small sample of youth experiencing first-episode psychosis (FEP) with fewer than 4 weeks of antipsychotic exposure. This study aims to evaluate the effectiveness of KBIM as routine care on anthropometry and metabolic biochemistry in a larger sample of youth with FEP across three community mental health services. METHOD: This retrospective chart audit was conducted on youth with FEP, prescribed a therapeutic dose of antipsychotic medication, and who engaged with KBIM between 2015 and 2019. Primary outcomes were weight and waist circumference. Secondary outcomes were blood pressure, blood glucose, and blood lipids. Outcomes were collected in at baseline and at 12 weeks. Data on program engagement were obtained from the participant's medical file. RESULTS: One-hundred and eighty-two people met inclusion criteria, and up to 134 people had baseline and 12-week data on one or more outcome. Mean number of sessions attended was 11.1 (SD = 7.3). Increases in weight and waist circumference were limited to 1.5 kg (SD = 5.3, t(133) = 3.2, p = .002) and 0.7 cm (SD = 5.8, t(109) = 1.2, p = .23) respectively. Eighty-one percent of participants did not experience clinically significant weight gain (>7% of baseline weight). There were no significant changes in blood pressure or metabolic biochemistry. CONCLUSION: The prevention of substantial gains in weight and waist circumference observed in the initial pilot study was maintained with implementation of KBIM as part of routine clinical care for youth with FEP.

The influence of socioeconomic status on the association between unhealthy lifestyle factors and adverse health outcomes: a systematic review.

Background: Combinations of lifestyle factors (LFs) and socioeconomic status (SES) are independently associated with cardiovascular disease (CVD), cancer, and mortality. Less advantaged SES groups may be disproportionately vulnerable to unhealthy LFs but interactions between LFs and SES remain poorly understood. This review aimed to synthesise the available evidence for whether and how SES modifies associations between combinations of LFs and adverse health outcomes. Methods: Systematic review of studies that examine associations between combinations of >3 LFs (eg.smoking/physical activity/diet) and health outcomes and report data on SES (eg.income/education/poverty-index) influences on associations. Databases (PubMed/EMBASE/CINAHL), references, forward citations, and grey-literature were searched from inception to December 2021. Eligibility criteria were analyses of prospective adult cohorts that examined all-cause mortality or CVD/cancer mortality/incidence. Results: Six studies (n=42,467-399,537; 46.5-56.8 years old; 54.6-59.3% women) of five cohorts were included. All examined all-cause mortality; three assessed CVD/cancer outcomes. Four studies observed multiplicative interactions between LFs and SES, but in opposing directions. Two studies tested for additive interactions; interactions were observed in one cohort (UK Biobank) and not in another (National Health and Nutrition Examination Survey (NHANES)). All-cause mortality HRs (95% confidence intervals) for unhealthy LFs (versus healthy LFs) from the most advantaged SES groups ranged from 0.68 (0.32-1.45) to 4.17 (2.27-7.69). Equivalent estimates from the least advantaged ranged from 1.30 (1.13-1.50) to 4.00 (2.22-7.14). In 19 analyses (including sensitivity analyses) of joint associations between LFs, SES, and all-cause mortality, highest all-cause mortality was observed in the unhealthiest LF-least advantaged suggesting an additive effect. Conclusions: Limited and heterogenous literature suggests that the influence of SES on associations between combinations of unhealthy LFs and adverse health could be additive but remains unclear. Additional prospective analyses would help clarify whether SES modifies associations between combinations of unhealthy LFs and health outcomes. Registration: Protocol is registered with PROSPERO (CRD42020172588;25 June 2020).

Why is it difficult to implement e-health initiatives? A qualitative study

Material en inglés Why is it difficult to implement e-health initiatives? A qualitative study, que analiza las barreras y dificultades que se presentan para la implementación de estrategias eHealth en salud, Murray et al.