Smoking and finances: baseline characteristics of low income daily smokers in the FISCALS cohort

Insights from linking police domestic abuse data and health data in South Wales, UK: a linked routine data analysis using decision tree classification

BACKGROUND

Exposure to domestic abuse can lead to long-term negative impacts on the victim's physical and psychological wellbeing. The 1998 Crime and Disorder Act requires agencies to collaborate on crime reduction strategies, including data sharing. Although data sharing is feasible for individuals, rarely are whole-agency data linked. This study aimed to examine the knowledge obtained by integrating information from police and health-care datasets through data linkage and analyse associated risk factor clusters.

METHODS

This retrospective cohort study analyses data from residents of South Wales who were victims of domestic abuse resulting in a Public Protection Notification (PPN) submission between Aug 12, 2015 and March 31, 2020. The study links these data with the victims' health records, collated within the Secure Anonymised Information Linkage databank, to examine factors associated with the outcome of an Emergency Department attendance, emergency hospital admission, or death within 12 months of the PPN submission. To assess the time to outcome for domestic abuse victims after the index PPN submission, we used Kaplan-Meier survival analysis. We used multivariable Cox regression models to identify which factors contributed the highest risk of experiencing an outcome after the index PPN submission. Finally, we created decision trees to describe specific groups of individuals who are at risk of experiencing a domestic abuse incident and subsequent outcome.

FINDINGS

After excluding individuals with multiple PPN records, duplicates, and records with a poor matching score or missing fields, the resulting clean dataset consisted of 8709 domestic abuse victims, of whom 6257 (71·8%) were female. Within a year of a domestic abuse incident, 3650 (41·9%) individuals had an outcome. Factors associated with experiencing an outcome within 12 months of the PPN included younger victim age (hazard ratio 1·183 [95% CI 1·053-1·329], p=0·0048), further PPN submissions after the initial referral (1·383 [1·295-1·476]; p<0·0001), injury at the scene (1·484 [1·368-1·609]; p<0·0001), assessed high risk (1·600 [1·444-1·773]; p<0·0001), referral to other agencies (1·518 [1·358-1·697]; p<0·0001), history of violence (1·229 [1·134-1·333]; p<0·0001), attempted strangulation (1·311 [1·148-1·497]; p<0·0001), and pregnancy (1·372 [1·142-1·648]; p=0·0007). Health-care data before the index PPN established that previous Emergency Department and hospital admissions, smoking, smoking cessation advice, obstetric codes, and prescription of antidepressants and antibiotics were associated with having a future outcome following a domestic abuse incident.

INTERPRETATION

The results indicate that vulnerable individuals are detectable in multiple datasets before and after involvement of the police. Operationalising these findings could reduce police callouts and future Emergency Department or hospital admissions, and improve outcomes for those who are vulnerable. Strategies include querying previous Emergency Department and hospital admissions, giving a high-risk assessment for a pregnant victim, and facilitating data linkage to identify vulnerable individuals.

FUNDING

National Institute for Health Research.

Age-Related Interactions on Key Theoretical Determinants of Smoking Cessation: Findings from the ITC Four Country Smoking and Vaping Surveys (2016-2020)

BACKGROUND

This paper explores whether plans to quit, wanting to quit, and quit efficacy add predictive value over measures of habit strength and dependence in making quit attempts and/or attaining smoking abstinence.

AIMS AND METHODS

We used three waves of the International Tobacco Control (ITC) Four Country Smoking and Vaping Survey conducted in 2016, 2018, and 2020. Baseline daily smokers (N = 6710) who provided data for at least one wave-to-wave transition (W1 to W2, N = 3511 or W2 to W3, N = 3199) and provided outcome data at the next wave (follow-up) formed the analytic sample. Generalized estimating equations (GEE) logistic regression analyses examined predictors of quit attempts and abstinence at follow-up (1- and 6-month sustained abstinence).

RESULTS

Wanting and planning to quit were significantly positively associated with making quit attempts, but negatively associated with smoking abstinence. A significant interaction between the Heaviness of Smoking Index and age warranted an age-stratified analysis for both abstinence outcomes. Lower HSI predicted abstinence in only the younger smokers Motivation and plans to quit were positively associated with abstinence in younger smokers, but surprisingly were negatively associated with abstinence in older smokers. Quit efficacy was associated with abstinence in the older, but not the younger smokers.

CONCLUSIONS

Models of smoking abstinence are significantly improved by including motivational predictors of smoking. Age was an important moderator of the association between abstinence for both dependence and motivational variables.

IMPLICATIONS

The findings from this large cohort study indicate there are age-related differences in predictors of smoking abstinence but not quit attempts. These associations may reflect differential experiences of older and younger cohorts of smokers, which may have implications for interventions to motivate and assist smokers in quitting.

Financial Hardship, Motivation to Quit and Post-Quit Spending Plans among Low-Income Smokers Enrolled in a Smoking Cessation Trial

Background:

Tobacco spending may exacerbate financial hardship in low-income populations by using funds that could go toward essentials. This study examined post-quit spending plans among low-income smokers and whether financial hardship was positively associated with motivation to quit in the sample.

Methods:

We analyzed data from the baseline survey of a randomized controlled trial testing novel a smoking cessation intervention for low-income smokers in New York City (N = 410). Linear regression was used to examine the relationship between financial distress, food insecurity, smoking-induced deprivation (SID) and motivation to quit (measured on a 0-10 scale). We performed summative content analyses of open-ended survey questions to identify the most common plans among participants with and without SID for how to use their tobacco money after quitting.

Results:

Participants had an average level of motivation to quit of 7.7 (SD = 2.5). Motivation to quit was not significantly related to having high financial distress or food insecurity (P > .05), but participants reporting SID had significantly lower levels of motivation to quit than those without SID (M = 7.4 versus 7.9, P = .04). Overall, participants expressed an interest in three main types of spending for after they quit: Purchases, Activities, and Savings/Investing, which could be further conceptualized as spending on Oneself or Family, and on Needs or Rewards. The top three spending plans among participants with and without SID were travel, clothing and savings. There were three needs-based spending plans unique to a small number of participants with SID: housing, health care and education.

Conclusions:

Financial distress and food insecurity did not enhance overall motivation to quit, while smokers with SID were less motivated to quit. Most low-income smokers, including those with SID, did not plan to use their tobacco money on household essentials after quitting.

Are Australian smokers with mental illness receiving adequate smoking cessation and harm reduction information?

Provision of smoking cessation support in the form of advice and information is central to increasing quit rates, including among people with mental illness (MI), who have 3-5 times higher odds of smoking than those without MI. This study investigated the extent and perceived utility of quit smoking advice and information available to Australian smokers with MI through face-to-face, semi-structured, in-depth interviews with 29 current smokers with MI. Qualitative analysis identified four major sources of quit smoking advice and information: (i) mental health practitioners; (ii) Quitline; (iii) social networks; and (iv) Internet and media. All identified sources, including formal sources (mental health practitioners and Quitline), were perceived as providing inadequate information about quitting smoking, particularly regarding optimal usage of nicotine replacement therapy (NRT). Social networks emerged as a substantial source of quit smoking advice and information, especially for nontraditional methods such as vaping. Participants showed high interest in receiving support from peer-led smoking cessation groups. A minority of participants reported that they had received quit smoking information from Internet and media; this was largely restricted to negative reports about e-cigarettes and short advertisements for nicotine replacement therapy. Our findings suggest that more can be done to provide smokers with MI with practical smoking cessation advice and support. Comprehensive information resources tailored for smokers with MI should be developed and disseminated via multiple pathways. We also recommend a number of policy and practice reforms to promote smoking cessation among those with MI.