Segmented Linear Regression Models Research Articles

Correlation between vitamin D levels and blood pressure in elderly hypertensive patients with osteoporosis

ObjectivesThe association between vitamin D and blood pressure in elderly patients with hypertension complicated by osteoporosis remains unclear. The objective of this study is to explore whether vitamin D deficiency contributes to elevated blood pressure in elderly individuals with both hypertension and osteoporosis.MethodsThis study represents a single-center retrospective observational investigation carried out at the Zhongshan Hospital Affiliated to Xiamen University. Ambulatory blood pressure, bone density, vitamin D levels, and additional laboratory parameters were collected upon admission. The association between vitamin D and ambulatory blood pressure outcomes was assessed using Spearman correlation tests and partial correlation analyses. The relationship between vitamin D and changes in blood pressure was analyzed through Generalized Additive Models, and threshold analysis was conducted to explore potential thresholds.Results139 patients with newly diagnosed osteoporosis were consecutively included (mean age 73 years, 84.9% female). There is a negative correlation between 25-(OH) D3 and 24 h mean systolic blood pressure (mSBP), diurnal mSBP, nocturnal mSBP, maximum SBP, respectively. The results of the generalized additive model analysis show that there is a nonlinear relationship between 25-(OH) D3 and 24 h mSBP, diurnal mSBP, nocturnal mSBP, respectively. After determining the critical point of 25-(OH) D3 as 42 nmol/L, a segmented linear regression model was used to calculate the effect size and 95% confidence interval on both sides of the critical point. When 25-(OH) D3 is ≤42 nmol/L, it significantly negatively correlates with 24 h, diurnal, and nocturnal mean SBP. Conversely, when 25-(OH) D3 exceeds 42 nmol/L, there is no statistically significant association with 24 h, diurnal, or nocturnal mSBP.ConclusionThere was a significant negative correlation between vitamin D levels and blood pressure levels in elderly patients with hypertension and osteoporosis.

The impact of the English national health inequalities strategy on inequalities in mortality at age 65: a time-trend analysis.

During the 1997-2010 Labour government, several policies were implemented to narrow health inequalities as part of a national health inequalities strategy. Many of these policies are likely to have had a disproportionately large impact on people aged 65 and over. We aimed to understand the association between the health inequalities strategy period and inequalities in mortality at age 65-69. We use population at risk and mortality data covering 1991-2019 to calculate mortality rate at age 65-69 at the Local Authority level. We use the 2019 Index of Multiple Deprivation to examine geographical inequalities. We employ segmented linear regression models with marginal spline terms for the strategy period and interact these with an indicator of deprivation to understand how inequalities changed before, during and after the strategy. The reporting of this study adheres to STROBE guidelines. Mortality rates in each deprivation quintile improved continuously throughout the period of study. Prior to the programme (1991-9) there was no significant change in absolute inequalities. However, during the strategy (2000-10) there was a significant decrease in absolute inequalities of -9.66 (-17.48 to -1.84). The period following the strategy (2011-19) was associated with a significant increase in absolute inequalities of 12.84 (6.60 to 19.08). Our results were robust to a range of sensitivity tests. The English health inequalities strategy was associated with a significant reduction in absolute inequality in mortality age 65-69. Future strategies to address inequalities in ageing populations may benefit from adopting a similar approach.

Relationship between glycated hemoglobin levels at admission and chronic post-stroke fatigue in patients with acute ischemic stroke

BackgroundChronic Post-Stroke Fatigue (PSF) is a common and persistent complications among ischemic stroke survivors. The serum glycated hemoglobin (HbA1c) level, as it is known has emerged as a critical risk factor for Acute Ischemic Stroke (AIS) and post-stroke cognitive and emotional impairment. However, no studies have been conducted on the link between HbA1c and PSF. Therefore, this study aims to estimate the relationship between HbA1c and PSF in the chronic phase. MethodsA longitudinal study was conducted on 559 patients diagnosed with their first AIS episode and admitted to Suining Central Hospital within three days after onset. All patients were examined for serum HbA1c, blood glucose levels and routine blood biochemical indicators at admission. The Fatigue Severity Scale (FSS) was employed to assess fatigue symptoms at six months post-stroke. Multivariate logistic regression and smooth curve fitting were used to analyze the relationship between admission HbA1c, blood glucose levels, discharge blood glucose and PSF, and the predictive value of HbA1c on PSF was assessed using a segmented linear regression model. Results189(33.8 %)of the 559 patients included in the study, reported PSF at six-month follow-up. Compared with the non-PSF group, the PSF group displayed significantly higher levels of HbA1c (7.8 ± 3.0 vs 6.5 ± 2.0 %, P < 0.001), admission blood glucose (7.8 ± 3.8 vs 7.1 ± 3.5 mmol/L, P = 0.041), and discharge blood glucose (6.3 ± 1.6 vs 5.8 ± 1.2 mmol/L, P < 0.001). The dose-response relationship among admission HbA1c, blood glucose, discharge blood glucose and PSF showed that HbA1c level is positively and non-linearly related to the risk of PSF. A linear positive correlation is noted between PSF and discharge blood glucose levels, while no significant correlation was observed for the blood glucose levels upon admission. ConclusionsHigher HbA1c levels at admission were independently associated with the risk of chronic PSF, the correlation between blood glucose and PSF showed significant variability, HbA1c may serve as a more stable risk factor in predicting the occurrence of chronic PSF and long-term active glycemic management may have a favorable impact on chronic PSF after AIS.

Philadelphia Beverage Tax's Impact on Beverage Ad Expenditures and Number of Ads Purchased

IntroductionOn January 1, 2017, Philadelphia implemented a beverage excise tax. The study's objective was to determine whether beverage advertising expenditures and the number of beverage ads purchased changed in Philadelphia compared to Baltimore because of this tax. MethodsMonthly beverage ad expenditures and the number of beverage ads purchased by brand from January 2016 through December 2019 were obtained. Ads were coded as being for taxed or not taxed beverages and analyzed in 2023. The primary outcomes were quarterly taxed beverage ad expenditures and number of ads purchased. A controlled interrupted time series design on segmented linear regression models was used. Models (aggregated and stratified by internet, spot TV, and local radio) compared whether levels and trends in the outcomes changed from pre- to post-tax in Philadelphia compared to Baltimore. ResultsThere were no significant differences in taxed beverage advertising expenditures between Philadelphia and Baltimore for trends pre-tax, at implementation, or post-tax. There were 0.13 (95% CI: -0.25, -0.003) fewer quarterly taxed beverage ads purchased per 100 households in Philadelphia versus Baltimore at baseline. Among internet advertising, there were 0.42 (95% CI: -0.77, -0.06) fewer quarterly taxed beverage ads purchased per 100 households in Philadelphia versus Baltimore immediately post-tax. For spot TV ads, the percentage of taxed beverages ads purchased per quarter was greater at baseline in Philadelphia by 28.0 percentage points (95% CI: 1.9, 54.1). ConclusionsThis study found little evidence of changes in mass media advertising on the examined platforms between 2016 - 2019 due to the Philadelphia beverage tax.

Impacts of national volume-based drug procurement policy on the utilization and costs of antihypertensive drugs in a Chinese medicine hospital: an interrupted time series analysis of 5138 patients.

Objectives: The study aimed to estimate the effects of National Volume-based Drug Procurement (NVBP) policy on drug utilization and medical expenditures of hypertension patients in public medical institutions in mainland China. Methods: This study used patient-level data based on electronic health records retrieved from the hospital information system of Nanjing Hospital of Chinese Medicine. Data on patients with hypertension who received care at this institution between 2016 and 2021 was used for analysis. Segmented linear regression models incorporating Interrupted Time Series (ITS) analysis were adopted to examine the effects of NVBP policy on drug utilization and health expenditures of eligible patients. Drug utilization volume and health expenditures were the primary outcomes used to assess the policy effects, and were measured using the prescription proportion of each drug class and the overall per-encounter treatment costs. Results: After the implementation of NVBP policy, the volume of non-winning drugs decreased from 54.42% to 36.25% for outpatient care and from 35.62% to 15.65% for inpatient care. The ITS analysis showed that the volume of bid-winning drugs in outpatient and inpatient settings increased by 9.55% (p < 0.001) and 6.31% (p < 0.001), respectively. The volume changes in non-volume based purchased (non-VBP) drugs differed between outpatients and inpatients. The proportion of non-VBP drugs immediately increased by 5.34% (p = 0.002) overall, and showed an upward trend in the outpatient setting specially (p < 0.001) during the post-intervention period. However, no significant differences were observed in the proportion of non-VBP drugs in inpatient setting (p > 0.05) in term of level change (p > 0.05) or trend change (p > 0.05). The average per-visit expenditures of outpatients across all drug groups exhibited an upward trend (p < 0.05) post policy intervention. In addition, a similar increase in the overall costs for chemical drugs were observed in inpatient settings (coefficient = 2,599.54, p = 0.036), with no statistically significant differences in the regression slope and level (p = 0.814). Conclusion: The usage proportion of bid-winning drugs increased significantly post policy intervention, indicating greater use of bid-winning drugs and the corresponding substitution of non-winning hypertensive drugs. Drug expenditures for outpatients and health expenditures per visit for inpatients also exhibited an upward trend, suggesting the importance of enhanced drug use management in Traditional Chinese Medicine hospital settings.

The natural history of insomnia: evaluating illness severity from acute to chronic insomnia; is the first the worst?

The 3P and 4P models represent illness severity over the course of insomnia disorder. The 3P model suggests that illness severity is worst during acute onset. The 4P model suggests that illness severity crescendos with chronicity. The present analysis from an archival dataset assesses illness severity with new onset illness (i.e. from good sleep [GS] to acute insomnia [AI] to chronic insomnia [CI]). Illness severity is quantified in terms of total wake time (TWT). GSs (N = 934) were followed up to 1 year with digital sleep diaries, and classified as GS, AI, or CI. Data for CIs were anchored to the first of 14 days with insomnia so that day-to-day TWT was represented prior to and following AI onset. A similar graphic (+/-acute onset) was constructed for number of days per week with insomnia. GS data were temporally matched to CI data. Segmented linear mixed regression models were applied to examine the change in slopes in the AI-to-CI period compared to GS-to-AI period. Twenty-three individuals transitioned to AI and then CI. Average TWT rose during the first 2 weeks of AI onset (b = 1.8, SE = 0.57, p = 0.001) and was then stable for 3 months (b = -0.02, SE = 0.04, p = 0.53). Average number of affected days was stable from AI to CI (b = 0.0005, SE = 0.002, p = 0.81). That is, while there was week-to-week variability in the number of days affected, no linear trend was evident. In our sample of CIs, primarily with middle insomnia, the average severity and number of affected days were worst with the onset of AI (worst is first) and stable thereafter.

Malaria seroepidemiology in very low transmission settings in the Peruvian Amazon

Despite progress towards malaria reduction in Peru, measuring exposure in low transmission areas is crucial for achieving elimination. This study focuses on two very low transmission areas in Loreto (Peruvian Amazon) and aims to determine the relationship between malaria exposure and proximity to health facilities. Individual data was collected from 38 villages in Indiana and Belen, including geo-referenced households and blood samples for microscopy, PCR and serological analysis. A segmented linear regression model identified significant changes in seropositivity trends among different age groups. Local Getis-Ord Gi* statistic revealed clusters of households with high (hotspots) or low (coldspots) seropositivity rates. Findings from 4000 individuals showed a seropositivity level of 2.5% (95%CI: 2.0%-3.0%) for P. falciparum and 7.8% (95%CI: 7.0%-8.7%) for P. vivax, indicating recent or historical exposure. The segmented regression showed exposure reductions in the 40–50 age group (β1 = 0.043, p = 0.003) for P. vivax and the 50–60 age group (β1 = 0.005, p = 0.010) for P. falciparum. Long and extreme distance villages from Regional Hospital of Loreto exhibited higher malaria exposure compared to proximate and medium distance villages (p < 0.001). This study showed the seropositivity of malaria in two very low transmission areas and confirmed the spatial pattern of hotspots as villages become more distant.

Sliding Scale Insulin Use in Nursing Homes Before and After Onset of the COVID-19 Pandemic

ObjectiveTo characterize sliding-scale insulin (SSI) use in US nursing homes (NHs) before and after the COVID-19 pandemic. DesignCross-sectional study. Setting and ParticipantsA total of 129,829 US NH residents on SSI (01/2018-06/2022) across 12 NH chains with a common electronic health record system. MethodsAmong all residents with at least 1 administration of SSI documented in the electronic medication administration record, we described resident demographics, frequency of SSI monotherapy vs combination therapy with another diabetes medication, number of daily capillary blood glucose readings (“fingersticks”), and hypoglycemia (capillary blood glucose <70 mg/dL) and hyperglycemia after first SSI use. We used interrupted time series analysis (ITS) with segmented linear regression models to examine whether the monthly prevalence of SSI use changed at and after the onset of the COVID-19 pandemic (March 2020). ResultsThere were 129,829 unique NH residents with SSI use [51% women, average age 71.3 (SD 11.7) years]. Of these, 36% of residents received SSI monotherapy and 64% received SSI combination therapy. Residents on SSI received an average of 3.96 (SD 1.41) fingersticks per day. Overall, 26% of SSI users experienced a hypoglycemic event within 30 days of the first SSI dose. The ITS analysis identified a step decrease in the rate of SSI use following the onset of the COVID-19 pandemic (43 fewer SSI users per 1000 insulin users) but no change in overall trend over time from before the onset of the pandemic. Conclusions and ImplicationsSSI use and fingerstick burden are high in NH residents. Hypoglycemia occurred commonly among residents on SSI. Future research should compare the safety and effectiveness of SSI monotherapy vs other diabetes medication regimens to guide person-centered prescribing decisions in NHs.

Correlation between the Systemic Immunoinflammatory Index and Platelet–Lymphocyte Ratio in Patients with Adenomyosis

Background. The chronic inflammatory immune response is a significant factor in the pathogenesis of benign gynecological diseases. The systemic immunoinflammatory index (SII) and the platelet-to-lymphocyte ratio (PLR) are commonly available biomarkers of inflammation. However, evidence of the relationship between SII and PLR in patients with adenomyosis is limited. This study aimed to investigate the relationship between SII and PLR in patients with adenomyosis. Methods. This cross-sectional study included 483 patients with adenomyosis who were first diagnosed at our institution between January 2019 and December 2021. Basic patient clinical information and inflammatory factors were collected for univariate analysis, smoothed curve fitting, and multivariate segmented linear regression. Results. The results of the univariate analysis showed a significant positive correlation between PLR levels and SII (P&lt;0.001). In addition, a nonlinear relationship between PLR and SII was tested using a smoothed curve fit after adjusting for potential confounders. Multiple segmented linear regression models showed a significant relationship between SII and PLR in both SII &lt; 1,326.47 (β 0.14, 95% CI: 0.12, 0.16; P&lt;0.0001) and &gt;1,326.47 (β 0.02, 95% CI: −0.01, 0.05; P = 0.2461). Conclusions. In conclusion, this study showed a nonlinear relationship between SII and PLR in patients with uterine adenomyosis. An increase in serum PLR levels correlates with an increase in SII before SII levels reach an inflection point.

Machine learning approach for core permeability prediction from well logs in Sandstone Reservoir, Mediterranean Sea, Egypt

Precise reservoir description is crucial for the proper evaluation of reservoirs. The ability to predict permeability is key to successful reservoir characterization. Although obtaining permeability values through a reservoir is crucial, this is not a simple task. This requires a significant amount of time and money. Numerous methods use the core correlations documented in the literature. Well-log information that offers ways to determine permeability. In this study, we describe a machine learning-based permeability prediction method. With log and core (data), Generalized Additive Models (GAMs) allow for the expansion of the forecasts for(uncored)wells.It also considers the characteristics of the field to ensure a better comprehension of reservoirs, reservoir rock characteristics, and geological variations. We demonstrate the utility of generalized additive models (GAMs), a non-parametric regression-based technique, to account for nonlinear trends in seven wells located in the (WDDM) concession where data analysis is performed on the collected information to evaluate the model's performance. Both the linear and nonlinear functions were used to train the data. The findings of the analysis demonstrate that GAMs outperform segmented linear regression models when the trend is nonlinear, but they also demonstrate their effectiveness when the trend is linear. The GAMs used five wells for training and two blind wells for testing. The GAMs with all five wells used as input, were found to perform best in predicting permeability for the shaly sandstone, with coefficients of determination (Pseudo-R2) of approximately 0.98 and 0.82 for the training and blind data sets, respectively.

Antibiotic Resistance of Haemophilus influenzae in Nasopharyngeal Carriage of Children with Acute Otitis Media and in Middle Ear Fluid from Otorrhea.

Haemophilus influenzae (Hi) is one of the leading bacteria implicated in childhood acute otitis media (AOM). Recent concerns have been raised about the emergence of Hi-resistant strains. We aimed to analyze the evolution of β-lactam resistance to Hi among strains isolated from nasopharyngeal carriage in children with AOM and in mild ear fluid (MEF) after the spontaneous perforation of the tympanic membrane (SPTM) in France. In this national ambulatory-based cohort study over 16 years, we analyzed the rate of Hi nasopharyngeal carriage and the proportion of β-lactam-resistant Hi strains over time using a segmented linear regression model. Among the 13,865 children (median [IQR] age, 12.7 [9.3-17.3] months; 7400 [53.4%] male) with AOM included from November 2006 to July 2022, Hi was isolated in 7311 (52.7%) children by nasopharyngeal sampling. The proportion of β-lactamase-producing and β-lactamase-negative, ampicillin-resistant (BLNAR) Hi strains in nasopharyngeal carriage remained stable during the study period. Among the 783 children (median [IQR] age, 20 [12.3-37.8] months; 409 [52.2%] male) with SPTM included from October 2015 to July 2022, Hi was isolated in 177 (22.6%) cases by MEF sampling. The proportions of β-lactamase-producing and BLNAR Hi strains did not significantly differ between nasopharyngeal (17.6% and 8.8%, respectively) and MEF (12.6% and 7.4%) samples. Accordingly, amoxicillin remains a valid recommendation as the first-line drug for AOM in France.

Ecological drought monitoring of Inner Mongolia vegetation growing season based on kernel temperature vegetation drought index (kTVDI).

Ecological drought monitoring is important for regional status assessment and protection of water resources. In this study, we constructed a new ecological drought index, the kernel temperature vegetation drought index (kTVDI), by using the kernel normalized vegetation index (kNDVI) to improve the temperature vegetation drought index (TVDI) in Inner Mongolia. We further analyzed the spatial and temporal distribution of ecological drought in Inner Mongolia during 2000-2022 and the future trend of ecological drought by using segmented linear regression model, Theil-Sen median, Mann-Kendall test, and Hurst index. The results showed that kTVDI performed better in monitoring ecological drought than TVDI. From 2000 to 2022, kTVDI showed a decreasing trend in the growing season in Inner Mongolia, but the change was not significant, and a sudden change occurred in 2016, and the wetting trend after the sudden change was more obvious. During the study period, ecological drought in 23.6% of the areas of Inner Mongolia showed an aggravating trend, and ecological drought was alleviated in 46.5% of the area. In the future, ecological drought would be exacerbated in the eastern part but alleviated in the central and western parts of Inner Mongolia.

Investigating whether the inclusion of humid heat metrics improves estimates of AC penetration rates: a case study of Southern California

Global cooling capacity is expected to triple by 2050, as rising temperatures and humidity levels intensify the heat stress that populations experience. Although air conditioning (AC) is a key adaptation tool for reducing exposure to extreme heat, we currently have a limited understanding of patterns of AC ownership. Developing high resolution estimates of AC ownership is critical for identifying communities vulnerable to extreme heat and for informing future electricity system investments as increases in cooling demand will exacerbate strain placed on aging power systems. In this study, we utilize a segmented linear regression model to identify AC ownership across Southern California by investigating the relationship between daily household electricity usage and a variety of humid heat metrics (HHMs) for ~160000 homes. We hypothesize that AC penetration rate estimates, i.e. the percentage of homes in a defined area that have AC, can be improved by considering indices that incorporate humidity as well as temperature. We run the model for each household with each unique heat metric for the years 2015 and 2016 and compare differences in AC ownership estimates at the census tract level. In total, 81% of the households were identified as having AC by at least one heat metric while 69% of the homes were determined to have AC with a consensus across all five of the heat metrics. Regression results also showed that the r 2 values for the dry bulb temperature (DBT) (0.39) regression were either comparable to or higher than the r 2 values for HHMs (0.15–0.40). Our results suggest that using a combination of heat metrics can increase confidence in AC penetration rate estimates, but using DBT alone produces similar estimates to other HHMs, which are often more difficult to access, individually. Future work should investigate these results in regions with high humidity.

Effect estimates can be accurately calculated with data digitally extracted from interrupted time series graphs.

Interrupted time series (ITS) studies are frequently used to examine the impact of population-level interventions or exposures. Systematic reviews with meta-analyses including ITS designs may inform public health and policy decision-making. Re-analysis of ITS may be required for inclusion in meta-analysis. While publications of ITS rarely provide raw data for re-analysis, graphs are often included, from which time series data can be digitally extracted. However, the accuracy of effect estimates calculated from data digitally extracted from ITS graphs is currently unknown. Forty-three ITS with available datasets and time series graphs were included. Time series data from each graph was extracted by four researchers using digital data extraction software. Data extraction errors were analysed. Segmented linear regression models were fitted to the extracted and provided datasets, from which estimates of immediate level and slope change (and associated statistics) were calculated and compared across the datasets. Although there were some data extraction errors of time points, primarily due to complications in the original graphs, they did not translate into important differences in estimates of interruption effects (and associated statistics). Using digital data extraction to obtain data from ITS graphs should be considered in reviews including ITS. Including these studies in meta-analyses, even with slight inaccuracy, is likely to outweigh the loss of information from non-inclusion.

Experience of a single healthcare system with screening mammography before and after COVID-19 shutdown

PurposeTo evaluate COVID-19's longitudinal impact on screening mammography volume trends. MethodsHIPAA-compliant, IRB-approved, single institution, retrospective study of screening mammogram volumes before (10/21/2016–3/16/2020) and greater than two years after (6/17/2020-11/30/2022) a state-mandated COVID-19 shutdown (3/17/2020–6/16/2020) were reviewed. A segmented quasi-poisson linear regression model adjusting for seasonality and network and regional population growth compared volume trends before and after the shutdown of each variable: age, race, language, financial source, risk factor for severe COVID-19, and examination location. ResultsAdjusted model demonstrated an overall increase of 65 screening mammograms per month before versus a persistent decrease of 5 mammograms per month for >2 years after the shutdown (p < 0.0001). In subgroup analysis, downward volume trends were noted in all age groups <70 years (age < 50: +9/month before vs. −7/month after shutdown; age 50–60: +17 vs. −7; and age 60–70: +21 vs. −2; all p < 0.001), those identifying as White (+55 vs. −8, p < 0.0001) and Black (+4 vs. +1, p = 0.009), all financial sources (Medicare: +22 vs. −3, p < 0.0001; Medicaid: +5 vs. +2, p = 0.006; private insurance/self-pay: +38 vs. −4, p < 0.0001), women with at least one risk factor for severe COVID-19 (+30 vs. −48, p < 0.0001), and screening mammograms performed at a hospital-based location (+48 vs. −14, p = 0.0001). ConclusionThe screening mammogram volume trend more than two years after the COVID-19 shutdown has continued to decline for most patient populations. Findings highlight the need to identify additional areas for education and outreach.

The association between antibiotics administration and outcomes in men with chemotherapy-naive metastatic castration-resistant prostate cancer: Secondary analysis of two randomized clinical trials.

e17054 Background: The associations of antibiotics with overall survival (OS) and radiographic progression-free survival (rPFS) in trials in chemotherapy-naive metastatic castration-resistant prostate cancer remain unclear. Methods: COU-AA-302 (NCT00887198, n = 1088) and ACIS (NCT02257736, n = 982) patients were stratified by antibiotics administration. Since there was evidence of a threshold phenomenon, the association of duration of antibiotics administration and OS was assessed using a segmented linear regression model. Kaplan–Meier survival analysis was used to compare OS and rPFS. Hazard ratios were calculated using a multivariable Cox proportional-hazard model. Results: Using segmented linear regression models, we selected ≥30 days as the threshold for antibiotics administration in clinical trials. In COU-AA-302 Abiraterone + Prednisone (AAP) subgroup, antibiotics administration was associated with prolonged OS (50.4 vs. 33.8 months, HR0.57, 95%CI 0.38–0.84, P = 0.004) and prolonged rPFS (27.7 vs. 12.8 months, P = 0.0095). In COU-AA-302 Placebo + Prednisone (PLA) subgroup, antibiotics administration was also associated with prolonged OS (50.4 vs. 29.0 months, HR0.41, 95%CI 0.26–0.63, P &lt; 0.001) and prolonged rPFS (16.46 vs. 5.72 months, P &lt; 0.001). Antibiotics administration also improved patients’ outcomes in ACIS. In ACIS Apalutamide + Abiraterone + Prednisolone (AAAP) subgroup, antibiotics administration was associated with prolonged OS (43.3 vs. 27.3 months, HR0.68, 95%CI 0.54–0.85, P &lt; 0.001) and prolonged rPFS (30.5 vs. 18.2 months, P &lt; 0.001). In ACIS Placebo + Abiraterone + Prednisolone (PAAP) subgroup, antibiotics administration was also associated with prolonged OS (45.4 vs. 30.2 months, HR0.62, 95%CI 0.50–0.78, P &lt; 0.001) and prolonged rPFS (23.4 vs. 13.4 months, P &lt; 0.001). Conclusions: Within the limitations of post-hoc sub-analyses, antibiotics administration is associated with prolonged OS and rPFS in men with chemotherapy-naive metastatic castration-resistant prostate cancer. Further pharmacological researches and prospective clinical trials are needed to increase the indications for these drugs to benefit prostate cancer patients.

0319 Evaluating Illness Severity from Acute to Chronic Insomnia: Is the First the Worst?

Abstract Introduction The Spielman 3P and Perlis 4P models represent illness severity over the course of insomnia disorder. The 3P model suggests that illness severity is worst when subjects experience acute illness (as compared to the subchronic and chronic phases of the disorder). The 4P model suggests that illness severity crescendos with chronicity. The present analysis uses an archival data set to assess illness severity with new onset illness (i.e.,from Good Sleep [GS] to Acute Insomnia [AI] and from AI to Chronic Insomnia [CI]). Illness severity is quantified in terms of Total Wake Time (TWT). Number of affected days/week is a tabulation of nights/week with clinical insomnia symptoms. Methods Subjects were recruited as GSs(N=911), followed up to 1-year with digital sleep diaries, and were classified as GS, AI, or CI based on standardized criteria. Data for CI subjects were anchored to the 1st of 14 days with insomnia so that day-to-day TWT, SL, WASO, and EMA were represented for ~100 days prior-to-and-following AI onset. A similar graphic (+/-Acute onset) was constructed for number of days per week with insomnia. GS data were matched to CI subjects dates (i.e., the data were temporally tethered). Segmented linear mixed regression models were applied to examine the change in slopes in the AI-to-CI period compared to GS and AI period. Results 883 subjects remained as GSs (Mage=53.1±10.8years;35.1%female;83.0%white), while 28 indi-viduals transitioned to AI and then CI (Mage=53.6±12.6years;39.3%female;89.3%white). Average TWT rose over the course of the first 2 weeks from the AI onset(b=1.6,SE=0.52minutes,p=0.002) and then was stable for 3 months(b=-0.004,SE=0.03,p=0.91). This worsening of sleep continuity was almost entirely related to increased WASO. Average number of affected days was found to be stable from AI to CI (b=0.002,SE=0.002,p=0.20). That is, while there was week-to-week variability in the number of days affected, no linear trend was evident. Conclusion These data suggest that, in our sample of CIs with primarily middle insomnia, the average severity and number of affected days was worst with the onset of AI (worst is first) and stable thereafter. Whether this temporal trend applies equally to new onset Initial and Late insomnia remains to be determined. Analyses ongoing. Support (if any) R01AG041783

Temporal trend, high-risk spatial and spatiotemporal clustering of leprosy indicators in Brazil: A 20-year ecological and population-based study.

Leprosy still represents a public health concern in Brazil. The country is the only one in America not to reach the global goal of leprosy disease control. Hence, this study aimed to assess the temporal, spatial and space-time patterns of leprosy cases in Brazil of the 20-year time series 2001-2020. An ecological and population-based analysis was carried out, applying temporal and spatial techniques, and using the detection coefficient of sociodemographic and clinical-epidemiological variables of leprosy new cases in the 5570 municipalities of Brazil. Temporal trends were assessed using a segmented linear regression model. For spatial analysis, global and local Moran indexes were applied, and space-time scan statistics was used to identify risk clusters. The mean detection coefficient was 19.36/100,000 inhabitants, with a higher occurrence among men (21.29/100,000 inhabitants) and in the 60-69 age group (36.31/100,000). A decreasing temporal trend was observed in the country (annual percentage change: -5.20% per year). The North and Midwest regions were the most affected, exhibiting municipalities with a high/high standard, and with the highest annual percentage increase of multibacillary (MB) cases. Leprosy has a heterogeneous distribution throughout Brazil, but with high-risk spatiotemporal clusters, mainly located in the North and Midwest regions. Although Brazil has shown a decreasing temporal trend during the past 20 years, the country is still classified as highly endemic for leprosy, showing an increase in the proportion of new MB cases over the years.

Impact of the value driven outcomes program among cataract surgery patients in Singapore: an interrupted time series analysis

BackgroundHealthcare cost is increasing rapidly in Singapore. Moving towards a value-based healthcare framework enables a sustainable health system. The National University Hospital (NUH) implemented the Value Driven Outcome (VDO) Program for cataract surgery due to its high volume and cost variability. We aimed to evaluate the association between VDO program implementation and costs and quality outcomes for cataract surgery in NUH.MethodsWe conducted an interrupted time-series analysis for cataract surgery episodes between January 2015 and December 2018. Using segmented linear regression models, we estimate the changes in levels and trends of cost and quality outcomes post-program implementation. We adjusted for autoregression and various confounders.ResultsFollowing VDO program implementation, the total cost of cataract surgery had a significantly decreased by $327.23 (95% CI: -$421.04 to -$233.43; p < 0.01) and the trend significantly decreased by $13.75 per month (95% CI: -$23.19 to -$4.30 per month; p < 0.01). There was a small improvement in the combined quality outcome score (0.028, 95% CI: 0.016 to 0.040; p < 0.01), but the trend remained unchanged.ConclusionThe VDO program was associated with a reduction in cost without compromising on quality outcomes. The program provides a structured methodology to measure performances, and through these data, initiatives were implemented to improve value. There are benefits to providing a data reporting system to physicians to understand actual care costs and quality outcomes achieved by individual patients with defined clinical conditions.

Anticipating adaptation: tracking the impact of planned and unplanned adaptations during the implementation of a complex population-based genomic screening program.

In 2021, the Medical University of South Carolina (MUSC) launched In Our DNA SC. This large-scale initiative will screen 100,000 individuals in South Carolina for three preventable hereditary conditions that impact approximately two million people in the USA but often go undetected. In anticipation of inevitable changes to the delivery of this complex initiative, we developed an approach to track and assess the impact of evaluate adaptations made during the pilot phase of program implementation. We used a modified version of the Framework for Reporting Adaptations and Modification-Enhanced (FRAME) and Adaptations to code adaptations made during the 3-month pilot phase of In Our DNA SC. Adaptations were documented in real-time using a REDCap database. We used segmented linear regression models to independently test three hypotheses about the impact of adaptations on program reach (rate of enrollment in the program, rate of messages viewed) and implementation (rate of samples collected) 7 days pre- and post-adaptation. Effectiveness was assessed using qualitative observations. Ten adaptations occurred during the pilot phase of program implementation. Most adaptations (60%) were designed to increase the number and type of patient contacted (reach). Adaptations were primarily made based on knowledge and experience (40%) or from quality improvement data (30%). Of the three adaptations designed to increase reach, shortening the recruitment message potential patients received significantly increased the average rate of invitations viewed by 7.3% (p = 0.0106). There was no effect of adaptations on implementation (number of DNA samples collected). Qualitative findings support improvement in effectiveness of the intervention after shortening the consent form and short-term positive impact on uptake of the intervention as measured by team member's participation. Our approach to tracking adaptations of In Our DNA SC allowed our team to quantify the utility of modifications, make decisions about pursuing the adaptation, and understand consequences of the change. Streamlining tools for tracking and responding to adaptations can help monitor the incremental impact of interventions to support continued learning and problem solving for complex interventions being delivered in health systems based on real-time data.