Comparison of statistical methods for recurrent event analysis using pediatrics asthma data

Associations between air pollution and the risk of first admission and multiple readmissions for cardiovascular diseases

OBJECTIVES

We aimed to investigate the associations between air pollutants and the risk of admission and multiple readmission events for cardiovascular disease (CVD).

METHODS

A total of 285 009 participants free of CVD at baseline from the UK Biobank were included in this analysis. Four major cardiovascular admission events were identified during the follow-up: chronic ischaemic heart disease (CIHD), cerebrovascular disease, atrial fibrillation and heart failure. We used Prentice, Williams and Peterson-Total Time model to examine the association between ambient air pollution and first admission, as well as multiple readmissions for these CVDs.

RESULTS

During a median follow-up of 12 years, 17 176 (6.03%) participants were hospitalised with CVDs, and 6203 (36.11%) patients with CVD had subsequent readmission events for CVDs. We observed significant associations between air pollution and both first admission and readmission for CVDs, with generally stronger associations on readmission for cardiovascular events. For example, the adjusted HRs for the first admission and subsequent readmission for cerebrovascular disease were 1.130 (95% CI 1.070 to 1.194) and 1.270 (95% CI 1.137 to 1.418) for each IQR increase of particulate matter with a diameter ≤2.5 µm. The corresponding HRs for CIHD were 1.060 (95% CI 1.008 to 1.114) and 1.120 (95% CI 1.070 to 1.171). Sex stratified analyses showed that the associations were generally more pronounced among females than males.

CONCLUSION

This study provides evidence that ambient air pollutants might play an important role in both first admission and readmission for cardiovascular events. In addition, patients with pre-existing CVDs may be more vulnerable to air pollution compared with healthy population.

Previous psychiatric hospitalizations as risk factors for single and multiple future alcohol-related hospitalizations in patients with alcohol use disorders

BACKGROUND AND AIMS

People with alcohol use disorder (AUD) often have co-occurring psychiatric conditions. The association between psychiatric conditions and AUD relapse has not yet been fully explored. This study aimed to quantify different psychiatric comorbidities as risk factors for first and multiple AUD rehospitalizations in patients already hospitalized once for AUD.

METHODS

We used a nation-wide routine health-care database in Scotland, UK, between 2010 and 2019. Individuals with a first hospitalization for AUD (codes F10.0-9 in the ICD-10 codes) were checked for previous hospitalizations where the main or co-occurring cause was a psychiatric condition (any other F0-F99 code in ICD-10). The final cohort included 23 529 patients, 18 620 of whom did not have a history of any other psychiatric comorbidity. First, individuals with a history of any previous psychiatric hospitalization were grouped and compared with those without on the basis of time to AUD rehospitalization. Then, individuals with different histories of psychiatric hospitalization were compared with each other. Cox and Prentice, Williams and Peterson gap-time models were used for single and multiple AUD rehospitalizations, respectively.

RESULTS

The AUD rehospitalization rate in individuals with a previous psychiatric hospitalization was 8% higher compared with those without [hazard ratio (HR) = 1.08, 95% confidence interval (CI) = 1.01-1.14]. The difference in rehospitalization rate reduced following the first rehospitalization (HR at second rehospitalization from first: 0.95, 95% CI = 0.87-1.04 and HR at third rehospitalization from second: 0.94, 95% CI = 0.84-1.07). Mood disorders and neurotic, stress-related and somatoform disorders were associated with a 54% (HR = 1.54, 95% CI = 1.38-1.72) and 39% (HR = 1.39, 95% CI = 1.17-1.66) increase in the risk of a first AUD rehospitalization. Other conditions, such as disorders due to psychoactive substance use or schizophrenia, were associated with decreases in future AUD rehospitalization (HR = 0.89, 95% CI = 0.82-0.97 and HR = 0.82, 95% CI = 0.58-1.16, respectively).

CONCLUSIONS

Patients with AUD appear to have different rates of AUD rehospitalization based on different co-occurring psychiatric conditions. Addiction-related characteristics may be more relevant risk indicators for multiple AUD readmission than psychiatric comorbidities.

Exploring factors jointly associated with recurrent relapse and dropout of methadone maintenance treatment clients in Guangdong, China: A retrospective cohort study

BACKGROUND

Clients receiving methadone maintenance treatment (MMT) may experience multiple relapses, which may lead to dropout. However, previous studies mostly only explored the influencing factors of relapse or dropout separately. In this study, we investigated the influencing factors jointly associated with both recurrent relapse and dropout.

METHODS

This study was conducted in 16 MMT clinics in Guangdong, China. Data on the clients' demographic characteristics; drug-related behavior; HIV, HCV, and urine morphine test results; and daily methadone doses were collected. The outcomes were time to recurrent relapse and time to dropout. A joint frailty model was used to explore factors jointly associated with recurrent relapse and dropout. The hazard ratios (HRs) of the covariates in the multivariable model were adjusted, with HR > 1 indicating a faster time to recurrent relapse and dropout.

RESULTS

Among 1539 clients, 39.5% had ≥ 2 relapse events, and 90.8% of the clients dropped out. A high attendance rate was protective for recurrent relapse (HR_50-80%=0.63, 95% CI: 0.51, 0.79; HR_>80%=0.35, 95% CI: 0.28, 0.44) and dropout (HR_50-80%=0.56, 95% CI: 0.47, 0.68; HR_>80%=0.27, 95% CI: 0.23, 0.33). Being married (HR=0.80, 95% CI: 0.67, 0.96), having a fair relationship with family (HR=0.79, 95% CI: 0.67, 0.93) and drug injection (HR=0.80, 95% CI: 0.67, 0.96) were also protective for recurrent relapse. Female (HR=0.64, 95% CI: 0.50, 0.82) and a high methadone dose (≥60 ml/day; HR=0.79, 95% CI: 0.65, 0.96) were protective for dropout, and a longer traveling time to the clinic (>60 min; HR=1.59, 95% CI: 1.20, 2.10) was associated with an increased risk of dropout.

CONCLUSIONS

It is common for clients to experience recurrent relapse and dropout. The findings suggest that MMT clinics should provide holistic interventions and appropriate methadone doses for clients to improve treatment compliance.

Adoption of the Website and Mobile App of a Preventive Health Program Across Neighborhoods With Different Socioeconomic Conditions in the Netherlands: Longitudinal Study

Background

Socioeconomic disparities in the adoption of preventive health programs represent a well-known challenge, with programs delivered via the web serving as a potential solution. The preventive health program examined in this study is a large-scale, open-access web-based platform operating in the Netherlands, which aims to improve the health behaviors and wellness of its participants.

Objective

This study aims to examine the differences in the adoption of the website and mobile app of a web-based preventive health program across socioeconomic groups.

Methods

The 83,466 participants in this longitudinal, nonexperimental study were individuals who had signed up for the health program between July 2012 and September 2019. The rate of program adoption per delivery means was estimated using the Prentice, Williams, and Peterson Gap–Time model, with the measure of neighborhood socioeconomic status (NSES) used to distinguish between population segments with different socioeconomic characteristics. Registration to the health program was voluntary and free, and not within a controlled study setting, allowing the observation of the true rate of adoption.

Results

The estimation results indicate that program adoption across socioeconomic groups varies depending on the program’s delivery means. For the website, higher NSES groups have a higher likelihood of program adoption compared with the lowest NSES group (hazard ratio 1.03, 95% CI 1.01-1.05). For the mobile app, the opposite holds: higher NSES groups have a lower likelihood of program adoption compared with the lowest NSES group (hazard ratio 0.94, 95% CI 0.91-0.97).

Conclusions

Promoting preventive health programs using mobile apps can help to increase program adoption among the lowest socioeconomic segments. Given the increasing use of mobile phones among disadvantaged population groups, structuring future health interventions to include mobile apps as means of delivery can support the stride toward diminishing health disparities.