Category: Statistics

MSMR. 2024 Jun 20;31(6):34-42.

ABSTRACT

This report summarizes incidence rates and trends of sexually transmitted infections (STIs) from 2015 through 2023 among active component service members of the U.S. Armed Forces. The data compiled for this report are derived from the medical surveillance of chlamydia, gonorrhea, and syphilis as nationally notifiable diseases. Case data for 2 additional STIs, human papilloma virus (HPV) and genital herpes simplex virus (HSV), are also presented. The crude total case rates of chlamydia and gonorrhea initially rose by an average of 6.7% and 9.8% per year, respectively, until 2019. From 2020 onwards, rates steadily declined. By 2023, chlamydia rates had dropped by approximately 39%, while gonorrhea rates had fallen by more than 40% for female, and 19% for male, service members. Initially syphilis increased, on average, 10% annually from 2015 to 2019, then declined in 2020, but resumed its upward trend through 2023, nearly doubling the 2015 rate in 2023. The total crude annual incidence rates of genital HPV and HSV exhibited downward trends in general over the surveillance period, decreasing by 30.7% and 24.7%, respectively. Age- and gender-adjusted case rates for chlamydia, gonorrhea, and syphilis remain elevated within the U.S. Armed Forces compared to the general U.S. population, which may be due to factors that include mandatory STI screening, more complete reporting, incomplete adjustment for age distribution, and inequitable comparisons between the military active duty and general U.S. populations. Social restrictions enacted during the COVID-19 pandemic may have contributed to declines in true case rates and screening coverage.

PMID:38981080

MSMR. 2024 Jun 20;31(6):26-33.

NO ABSTRACT

PMID:38981072

MSMR. 2024 Jun 20;31(6):19-25.

NO ABSTRACT

PMID:38981071

Pain. 2024 Jul 9. doi: 10.1097/j.pain.0000000000003336. Online ahead of print.

ABSTRACT

Chronic pain is a serious and prevalent condition that can affect many facets of life. However, uncertainty remains regarding the strength of the association between chronic pain and death and whether the association is causal. We investigate the pain-mortality relationship using data from 19,971 participants aged 51+ years in the 1998 wave of the U.S. Health and Retirement Study. Propensity score matching and inverse probability weighting are combined with Cox proportional hazards models to investigate whether exposure to chronic pain (moderate or severe) has a causal effect on mortality over a 20-year follow-up period. Hazard ratios (HRs) with 95% confidence intervals (CIs) are reported. Before adjusting for confounding, we find a strong association between chronic pain and mortality (HR: 1.32, 95% CI: 1.26-1.38). After adjusting for confounding by sociodemographic and health variables using a range of propensity score methods, the estimated increase in mortality hazard caused by pain is more modest (5%-9%) and the results are often also compatible with no causal effect (95% CIs for HRs narrowly contain 1.0). This attenuation highlights the role of confounders of the pain-mortality relationship as potentially modifiable upstream risk factors for mortality. Posing the depressive symptoms variable as a mediator rather than a confounder of the pain-mortality relationship resulted in stronger evidence of a modest causal effect of pain on mortality (eg, HR: 1.08, 95% CI: 1.01-1.15). Future work is required to model exposure-confounder feedback loops and investigate the potentially cumulative causal effect of chronic pain at multiple time points on mortality.

PMID:38981067 | DOI:10.1097/j.pain.0000000000003336

MSMR. 2024 Jun 20;31(6):11-18.

NO ABSTRACT

PMID:38981065

MSMR. 2024 Jun 20;31(6):2-10.

NO ABSTRACT

PMID:38981057

Med Phys. 2024 Jul 9. doi: 10.1002/mp.17289. Online ahead of print.

ABSTRACT

BACKGROUND: A comprehensive collection of data on doses in adult computed tomography procedures in Australia has not been undertaken for some time. This is largely due to the effort involved in collecting the data required for calculating the population dose. This data collection effort can be greatly reduced, and the coverage increased, if the process can be automated without major changes to the workflow of the imaging facilities providing the data. Success would provide a tool to determine a truly national assessment of the dose incurred through diagnostic imaging in Australia.

PURPOSE: The aims of this study were to develop an automated tool to categorize electronic records of imaging procedures into a standardized set of broad procedure types, to validate the tool by applying it to data collected from nine facilities, and to assess the feasibility of applying the automated tool to compute population dose and determine the data manipulations required.

METHODS: A rule-based classifier was implemented capitalizing on semantic and clinical rules. The keyword list was initially built from 609 unique study descriptions. It was then refined using an additional 414 unique study descriptions. The classifier was then tested on an additional 1198 unique study descriptions. Input from a radiologist provided the ground truth for the refinement of the classifier.

RESULTS: From a sample of 238 139 studies containing 2794 unique study descriptions, the classifier correctly classified 2789 study types with only five misclassifications, demonstrating the feasibility of automating the process and the need for data pre-processing. Dose statistics for 21 categories were compiled using the 238 139 studies.

CONCLUSION: The classifier achieved excellent classification results using the testing data supplied by the facilities. However, since all data supplied were from public facilities, the performance of the classifier may be biased. The performance of the classifier is yet to be tested on a more representative mix of private and public facilities.

PMID:38981056 | DOI:10.1002/mp.17289

ESC Heart Fail. 2024 Jul 9. doi: 10.1002/ehf2.14918. Online ahead of print.

ABSTRACT

AIMS: Assessing the risk for HF rehospitalization is important for managing and treating patients with HF. To address this need, various risk prediction models have been developed. However, none of them used deep learning methods with real-world data. This study aimed to develop a deep learning-based prediction model for HF rehospitalization within 30, 90, and 365 days after acute HF (AHF) discharge.

METHODS AND RESULTS: We analysed the data of patients admitted due to AHF between January 2014 and January 2019 in a tertiary hospital. In performing deep learning-based predictive algorithms for HF rehospitalization, we use hyperbolic tangent activation layers followed by recurrent layers with gated recurrent units. To assess the readmission prediction, we used the AUC, precision, recall, specificity, and F1 measure. We applied the Shapley value to identify which features contributed to HF readmission. Twenty-two prognostic features exhibiting statistically significant associations with HF rehospitalization were identified, consisting of 6 time-independent and 16 time-dependent features. The AUC value shows moderate discrimination for predicting readmission within 30, 90, and 365 days of follow-up (FU) (AUC:0.63, 0.74, and 0.76, respectively). The features during the FU have a relatively higher contribution to HF rehospitalization than features from other time points.

CONCLUSIONS: Our deep learning-based model using real-world data could provide valid predictions of HF rehospitalization in 1 year follow-up. It can be easily utilized to guide appropriate interventions or care strategies for patients with HF. The closed monitoring and blood test in daily clinics are important for assessing the risk of HF rehospitalization.

PMID:38981003 | DOI:10.1002/ehf2.14918

Aust J Prim Health. 2024 Jul;30:PY24017. doi: 10.1071/PY24017.

ABSTRACT

Background Large datasets exist in Australia that make de-identified primary healthcare data extracted from clinical information systems available for research use. This study reviews these datasets for their capacity to provide insight into chronic disease care for Aboriginal and Torres Strait Islander peoples, and the extent to which the principles of Indigenous Data Sovereignty are reflected in data collection and governance arrangements. Methods Datasets were included if they collect primary healthcare clinical information system data, collect data nationally, and capture Aboriginal and Torres Strait Islander peoples. We searched PubMed and the public Internet for data providers meeting the inclusion criteria. We developed a framework to assess data providers across domains, including representativeness, usability, data quality, adherence with Indigenous Data Sovereignty and their capacity to provide insights into chronic disease. Datasets were assessed against the framework based on email interviews and publicly available information. Results We identified seven datasets. Only two datasets reported on chronic disease, collected data nationally and captured a substantial number of Aboriginal and Torres Strait Islander patients. No dataset was identified that captured a significant number of both mainstream general practice clinics and Aboriginal Community Controlled Health Organisations. Conclusions It is critical that more accurate, comprehensive and culturally meaningful Aboriginal and Torres Strait Islander healthcare data are collected. These improvements must be guided by the principles of Indigenous Data Sovereignty and Governance. Validated and appropriate chronic disease indicators for Aboriginal and Torres Strait Islander peoples must be developed, including indicators of social and cultural determinants of health.

PMID:38981000 | DOI:10.1071/PY24017

Transpl Infect Dis. 2024 Jul 9:e14337. doi: 10.1111/tid.14337. Online ahead of print.

ABSTRACT

BACKGROUND: Cytomegalovirus (CMV) is a driver of negative outcomes after lung transplant (LTX) and primary prophylaxis (PPX) with valganciclovir (VGC) is standard-of-care. VGC is associated with myelosuppression, prompting interest in letermovir (LTV).

METHODS: Adults receiving LTX between April 1, 2015, and July 30, 2022, at our institution were evaluated. Patients were excluded if low CMV risk (D-/R-), survived <90 days post-LTX, or transferred care before PPX withdrawal. Primary outcomes were leukopenia (white blood cell count [WBC] ≤ 3.0 × 10⁹/L), severe leukopenia (WBC ≤ 2.0 × 10⁹/L), and neutropenia (absolute neutrophil count ≤ 1500 cells/µL) requiring granulocyte-colony stimulating factor (GCSF) on PPX. Secondary outcomes included breakthrough CMV infection and post-PPX CMV infection.

RESULTS: 204 patients met inclusion criteria: 175 patients on VGC and 29 patients on LTV (after VGC conversion). Most patients received bilateral LTX (62.7%) with non-lymphocyte-depleting induction (96.6%) and moderate-risk serostatus (D+/R+, 48.5%). Patients transitioned from VGC to LTV after a mean of 178 days (SD 80.8 days) post-transplant. Patients on VGC experienced significantly more leukopenia (82.3% vs. 58.6%, p = 0.008), severe leukopenia (57.1% vs. 31.0%, p = 0.016), and neutropenia requiring GCSF (70.9% vs. 51.7%, p = 0.048). Breakthrough (5.7% vs. 3.4%, p = 0.955) and post-PPX (24.6% vs. 37.9%, p = 0.199) infections were similar. A subgroup analysis of patients with high-risk serostatus showed similar trends, though did not reach statistical significance.

CONCLUSIONS: In this single-center study, the incidence of leukopenia and neutropenia requiring GCSF were reduced with LTV compared to VGC. Breakthrough and post-PPX infections were not significantly different. This evidence suggests that LTV has comparable efficacy with reduced myelosuppression compared to VGC in LTX recipients, and may be an appropriate alternative for PPX.

PMID:38980979 | DOI:10.1111/tid.14337