Epidemiology最新文献

Rigid prespecification can be impractical for noninterventional studies using secondary datasets, where data-driven flexibility is often required. Using target trial emulations comparing immunomodulator treatments for COVID-19, we piloted an adaptive strategy that accommodates warranted mid-course refinements within a prespecified framework. Our preregistered protocol outlined an initial study plan along with predetermined diagnostic thresholds and contingencies. Implementation proceeded through sequential phases, allowing researcher decisions to be guided by prespecified criteria under varying degrees of blinding to results. The adaptive approach led to alterations in the underlying target trial and to the analysis plan used for emulation, strengthening the plausibility of causal assumptions and improving the relevance of findings. During the initial baseline phase, indicated contingencies included sample restrictions, redefining treatments from class-level to product-specific comparisons, a revised propensity score model, and weight truncation. In the subsequent postbaseline phase, diagnostic checks triggered a modified causal contrast, inverse probability of censor weighting to address noncompliance, cause-specific hazard estimation to contextualize competing events, and additional reporting of hazard ratios for progressively truncated follow-up periods. For a secondary study objective, the adaptive framework allowed for some iterative attempts to improve validity while providing a clear stopping point. Similar approaches could lend transparent structure to the process of learning what causal questions the data are equipped to support. Beyond guarding against researcher bias, prespecification of adaptive protocols may promote more robust designs by encouraging investigators to be explicit about their assumptions, strategies for interrogating those assumptions, and specific criteria for determining when and how deviations may be required.

Background: We estimated the extent to which the risk of developing opioid use disorder or overdose over 15 months of follow-up would be affected by applying prescription opioid dose and duration reductions to subsets of newly diagnosed musculoskeletal pain patients, defined in terms of the "riskiness" level of their initial opioid prescription.

Methods: We studied a cohort of nonpregnant Medicaid patients, aged 19-63 years, without cancer nor on palliative care, who were opioid-naive, newly diagnosed with musculoskeletal pain, and were prescribed an opioid within 3 months from the diagnosis date (N = 324,389). We applied a novel statistical approach to estimate the effects of local modified treatment policies (a generalization of the average treatment effect on the treated). Specifically, we estimated the expected difference in risk of developing opioid use disorder or opioid overdose by sequential 3-month follow-ups among patients with different levels of opioid prescribing had those patients had their prescription opioid dose and/or duration decreased by 20% versus no hypothetical intervention, and had they remained uncensored.

Results: We estimated clinically modest effects on absolute opioid use disorder risk when universally reducing opioid prescription dose and duration by 20% across the cohort. In contrast, we estimated much larger, clinically relevant reductions in absolute risk of one percentage point or greater when assessing the localized effects of: (1) a 20% reduction in dose among individuals with doses ≥90 morphine milligram (mg) equivalents, (2) a 20% reduction in days supplied among individuals with >30 days supplied, and (3) 20% reductions in both dose and duration among those with ≥50 morphine mg equivalents and >7 days supplied.

Conclusions: We estimate that reductions in opioid prescribing may have a limited impact on the risk of opioid use disorder when applied broadly but possibly meaningful reductions in risk when applied to those with riskier prescriptions.

Objectives: When ground ambulances are busy with any task, delays are likely for concurrent emergencies. Whereas time-critical conditions are affected by delays, general impacts remain unclear. We aimed to assess how delayed ambulance response due to busy ambulances affects risk of death and use of hospital services.

Methods: We studied individuals with out-of-hospital emergencies that precipitated a call to the medical emergency number in Central Norway from 2013 to 2022. Emergency service and hospital data were linked to assess subsequent death and hospitalizations. We addressed potential bias by multivariable adjustment and a natural experiment: For emergencies that occurred in the same area at similar times, we compared outcomes for patients with differences in busy ambulances to analyze delays in response that were arguably unrelated to prioritization due to the patient severity.

Results: Among 239,320 acute emergencies, 4.1% of patients died within 7 days. An interquartile range of variation in the probability a busy ambulance was associated with a 2.9-minute delay (95% confidence interval [CI] = 2.8, 3.0). Overall, a 5-minute delay was associated with a risk difference of 0.10 percentage points in the risk of death (95% CI = -0.17, 0.36) and 1.24 for hospitalization (95% CI = 0.59, 1.94). The cost of hospital treatment within 1 year increased by 616 euros (95% CI = 183, 1069).

Conclusion: While we found no substantial increase in the overall risk of death associated with delayed ambulance response, the observed rise in hospital costs suggests a potential increase in morbidity.

Background: Major lower limb amputation is a devastating but preventable complication of peripheral artery disease. It is unclear whether racial and ethnic and rural differences in amputation rates are due to clinical, hospital, or structural factors.

Methods: We included all peripheral artery disease hospitalizations of patients ≥40 years old between 2017 and 2019 in Florida, Georgia, Maryland, Mississippi, or New York (HCUP State Inpatient Databases). We estimated the expected number of amputations using three models: (1) unadjusted, (2) adjusted for clinical factors, and (3) adjusted for clinical factors, hospital factors, and social determinants of health using least absolute shrinkage and selection operator (LASSO). We calculated and compared observed-to-expected ratios and quantified the role of these factors in amputation rates.

Results: Overall, 1,577,061 hospitalizations (990,152 unique patients) and 21,233 major lower limb amputations (1.4%) were included. After accounting for clinical differences, we observed amputation disparities among rural Black, Hispanic, Native American, and White patients and nonrural Black and Native American patients. After accounting for hospital factors and social determinants of health, disparities were no longer present among rural White adults (0.93, 95% confidence interval [CI]: 0.77, 1.09); however, disparities persisted among rural Black (1.26, 95% CI: 1.01, 1.51), Hispanic (1.50, 95% CI: 0.89, 2.12), and Native American patients (1.13, 95% CI: 0.68, 1.58) and nonrural Black (1.12, 95% CI: 1.09, 1.15) and Native American (1.15, 95% CI: 0.86, 1.44) patients.

Conclusion: Clinical factors did not fully explain differences in amputation rates, and hospital factors and social determinants of health did not fully explain disparities. These findings provide additional evidence that implicit bias is associated with amputation disparities.

Background: Hepatitis C virus (HCV) infection is a public health concern, with people living with opioid use disorder having a higher risk of infection. Despite the cooccurrence of HCV and opioid use disorder, little is known about the treatment patterns for the disorder in this population. This study characterized opioid agonist therapy adherence trajectories over 15 months following opioid agonist therapy initiation among people living with HCV and opioid use disorder and described the baseline characteristics of the patients within distinct opioid agonist therapy adherence trajectories.

Methods: We used Merative MarketScan healthcare claims data from 2015 to 2019 to identify distinct medication treatment adherence trajectories via growth mixture modeling among 5,495 people who initiated opioid agonist therapy for opioid use disorder and were living with HCV.

Results: Our models identified three distinct opioid agonist therapy adherence trajectories over the 15 months of follow-up. We named these trajectories rapidly declining opioid agonist therapy adherence (class 1; N = 1,904; 35%), steadily declining opioid agonist therapy adherence (class 2; N = 2,150; 39%), and consistently high opioid agonist therapy adherence (N = 1,441; 26%). People in the consistently high adherence group were older, more likely to be women (vs. men), White (vs. Black), had HCV direct-acting antiviral treatment during the baseline period, and had the lowest prevalence of nonopioid substance use diagnoses.

Conclusions: These results may inform support for populations with elevated baseline risk of low opioid agonist therapy adherence during follow-up.

Simulation studies are used to evaluate and compare the properties of statistical methods in controlled experimental settings. In most cases, performing a simulation study requires knowledge of the true value of the parameter, or estimand, of interest. However, in many simulation designs, the true value of the estimand is difficult to compute analytically. Here, we illustrate the use of Monte Carlo integration to compute true estimand values in simple and more complex simulation designs. We provide general pseudocode that can be replicated in any software program of choice to demonstrate key principles in using Monte Carlo integration in two scenarios: a simple three-variable simulation where interest lies in the marginally adjusted odds ratio and a more complex causal mediation analysis where interest lies in the controlled direct effect in the presence of mediator-outcome confounders affected by the exposure. We discuss general strategies that can be used to minimize Monte Carlo error and to serve as checks on the simulation program to avoid coding errors. R programming code is provided illustrating the application of our pseudocode in these settings.

Social contact studies are used in infectious disease epidemiology to infer a contact matrix , having the mean number of contacts between individuals of different age groups as elements. However, does not capture the (often large) variation in the number of contacts within each age group, information is also available in social contact studies. Here, we include such variation by separating each age group into two halves: the socially active (having many contacts) and the socially less active (having fewer contacts). The extended contact matrix and its associated epidemic model show that acknowledging variation in social activity within age groups has a substantial impact on the basic reproduction number, , and the final fraction getting infected if the epidemic takes off, . In fact, variation in social activity is more important for data fitting than allowing for different age groups. A difficulty with variation in social activity, however, is that social contact studies typically lack information on whether mixing with respect to social activity is assortative (when socially active mainly have contact with other socially active individuals) or not. Our analysis shows that accounting for variation in social activity improves model predictability, yielding more accurate expressions for and irrespective of whether such mixing is assortative, but different assumptions on assortativity give rather different outputs. Future social contact studies should, therefore, also try to infer the degree of assortativity (with respect to social activity) between peers and their contacts.

Background: In this article, we test the hypothesis that SARS-CoV-2 infection and the COVID-19 pandemic period had stronger adverse implications for perinatal outcomes among marginalized racial and ethnic groups in California.

Methods: We used California birth certificates and hospital data from 2019 to 2021 to estimate marginal risk differences for SARS-CoV-2 infection and the COVID-19 pandemic period in relation to perinatal outcomes for Asian, Black, Hispanic, Multiracial, and White pregnant people using targeted maximum likelihood estimation.

Results: Among 849,401 deliveries, there were racial and ethnic disparities in the burden of SARS-CoV-2 infection and perinatal outcomes and in the magnitudes of risk associated with SARS-CoV-2 infection and the COVID-19 pandemic. Hispanic pregnant people had the highest incidence of SARS-CoV-2 infection. Asian and Black pregnant people had the greatest marginal risk differences for multiple outcomes, particularly outcomes already disproportionately experienced by these groups.

Conclusions: Risks from SARS-CoV-2 infection and the COVID-19 pandemic period on perinatal outcomes were disproportionately experienced by marginalized racial and ethnic groups. Differential burdens of infection and larger risks experienced with pandemic exposures were associated with worse perinatal outcomes for Asian, Black, and Hispanic pregnant people in California compared with those for White pregnant people.