Estimating effects of time-varying exposures on mortality risk

Administrative databases have become an increasingly popular data source for population-based health research. We explore how mortality risk is associated with some health service utilization process via linked administrative data. A generalized Cox regression model is proposed using a time-dependent stratification variable to summarize lifetime service utilization. Recognizing the service utilization over time as an internal covariate in the survival analysis, conventional likelihood methods are inapplicable. We present an estimating function based procedure for estimating model parameters, and provide a testing procedure for updating the stratification levels. The proposed approach is examined both asymptotically and numerically via simulation. We motivate and illustrate the proposed approach using an on-going program pertaining to opioid agonist treatment (OAT) management for individuals identified with opioid use disorders. Our analysis of the OAT data indicates that the OAT effect on mortality risk decreases in successive OAT attempts, in which two risk classes based on an individual's treatment episode number are established: one with 1–3 OAT episodes, and the other with 4+ OAT episodes.</p

Additional file 1: of Geographic variation in the costs of medical care for people living with HIV in British Columbia, Canada

This supplement contains the data plotted in Figs. 1, 2 and 3 of the manuscript, as well as results from sensitivity analysis. It also includes a map of Health Service Delivery Areas in British Columbia. (DOCX 532 kb

Incremental costs per quality-adjusted life year gained: strategies A vs. Standard of Care.

<p>ICER = (Cost_{strategy i}−Cost_{current standard})/(QALY_{strategy i}−QALY_{current standard}); D: Dominant - Lower cost, higher QALYs in comparison to usual care; DT: Dominated - higher cost, lower QALYs in comparison to usual care. * Threshold for attaining 50% clinical protection for HIV/AIDS patients.</p

Decision analytic model.

<p>All nodes following vaccine response are repeated each month throughout the initial 12 months of the model duration; therefore patients not suffering fatal ILI or death due to other causes may transition from HIV viral load suppression to non-suppression, and subsequently face differential risk of ILI at each month. The probability of ILI is summed across each of the three strains of influenza assessed in CTN-237.</p

Mean of the probabilities of ILI and 95% credibility interval for each strategy by baseline pVL.

<p>Strategy A: single standard dose+single standard dose booster; Strategy B: double dose+double dose booster; Strategy C: single standard dose+no booster; Strategy D: standard of care.</p

Model parameters.

<p>ILI: Influenza like illness; PY: Person-year; HRQoL: Health-related quality of life;</p><p>*Among pre-treated patients.</p><p>**Drawn from 2000/2001 estimates among a healthy elderly population <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0027059#pone.0027059-Sullivan1" target="_blank">[20]</a>.</p>#<p>Unsuppressed patients were treated with one new drug; percentage of patients transitioning within a one-year period.</p><p>***Derived from baseline HUI3 scores of CTN-237 participants.</p>##<p>In 2009$CDN. Included the costs of Derived from 5000 bootstrap re-samples of N = 31 ILI events captured in the CTN-237 database.</p

Results of one-way sensitivity analyses.

<p>*Threshold for attaining 50% clinical protection for individuals with HIV.</p

Monthly distribution of the probability of ILI.

<p>Weekly influenza surveillance report form CDC <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0027059#pone.0027059-Myliwska1" target="_blank">[22]</a>. 2008–2009 influenza season, week 39 ending October 3, 2009. Data shows only seasonal influenza and pandemic strain, 2009 influenza A (H1N1) virus, has been omitted.</p

Probability of HAI titre improvement^*: results from 1^st-stage analysis.

<p>*HAI Titre improvement was defined as HAI titre ever being greater than 1∶10 during follow-up assessments.</p

Cost-effectiveness acceptability curves.

<p>Cost-effectiveness acceptability curves.</p