Time Series Regression Models Research Articles

Rising trend of dentist-prescribed antibiotics in Brazil: 2011-2021.

To describe the trends in antibiotic prescribing by dental practitioners and to investigate the relationship between these trends and some factors of public oral health services in Minas Gerais (MG), Brazil. This was a time-series analysis of antibiotics prescribed by dental practitioners between January 2011 and December 2021. The outcome variables were number of defined daily doses (DDD) and DDD/1000 population/year in a sample of cities in MG. Covariates were public oral healthcare factors, such as coverage, estimates of dental procedures, and frequency of dental pain. Linear time-series regression models were used to examine trends and the influence of covariates on antibiotic prescribing. Overall, the number of prescriptions increased by 334.69% between 2011 and 2021, with amoxicillin being the most commonly prescribed drug (78.53%). The number of DDD for all antibiotics increased from 17,147.13 to 77,346.67 and the average DDD/1000 inhabitants/year was 126.66 (SD: 130.28). The linear time-series regression model showed that for each one-year increase, the average log DDD/1000 inhabitants increased by 0.35 (standard error = 0.07, p < 0.001). No covariates were found to be associated with the outcome. In Minas Gerais, Brazil, a significant upward trend was observed in the number of prescriptions and the number of DDD of antibiotics prescribed by dental practitioners. No influence of factors related to public oral healthcare services on the outcome was observed, thereby emphasizing the need for further research on factors influencing medication use in dental practice.

Association between temperature and occupational injuries in Spain: The role of contextual factors in workers’ adaptation

BackgroundExtensive evidence links both cold and hot temperatures to an increased incidence of occupational injuries. Contextual modifiers of the temperature-injury association have been scarcely researched. The present study addresses temporal and spatial variations to identify factors associated with (mal)adaptation to heat and cold among Spanish workers. MethodsWe assessed the association between daily mean temperature and work injuries using quasi-Poisson time-series regression models in 48 Spanish provinces over the period 1988–2019, with comparative analyses with census and economic data for the sub-periods 1989–1993, 1999–2003, 2009–2013 and 2015–2019. We explored the spatial and spatiotemporal modification of the association by demographic and socioeconomic variables via cross-sectional and longitudinal meta-regressions. Findings:We found an increased risk of work-injuries by 4 % [95 % CI: 3 %-6 %] and 12 % [95 % CI: 10 %-13 %], for the 1st and 99th percentiles of temperature, respectively, for period 1988–2019. Heat had a greater overall impact than cold, and the groups more vulnerable to heat were male workers, under 35 years, and working in agriculture, construction and hostelry. Vulnerability to heat was highest in the earliest sub-period, while vulnerability to cold rose during periods of both economic expansion and recession. High educational attainment emerged as a protective factor during the warm months in the cross-sectional meta-regressions. ConclusionsOur findings suggest an adaptation of Spanish workers to high temperatures over time. However, preventive measures are needed for traditionally exposed workers (agriculture and construction), non-traditionally vulnerable sectors (hostelry), and young, male, and less educated workers during warm months. For cold vulnerability, targeted measures should focus on women, the elderly, and tertiary service workers, especially in colder regions. Addressing temperature vulnerability would enhance worker safety, reduce injuries, and yield economic benefits.

Effect of Temperature and Precipitation on Acute Appendicitis Incidence in Seoul: A Time Series Regression Analysis.

This study aimed to investigate the relationship between meteorological factors, specifically temperature and precipitation, and the incidence of appendicitis in Seoul, South Korea. Using data from the National Health Insurance Service spanning 2010-2020, the study analyzed 165,077 appendicitis cases in Seoul. Time series regression modeling with distributed-lag non-linear models was employed. Regarding acute appendicitis and daily average temperature, the incidence rate ratio (IRR) showed an increasing trend from approximately - 10°C to 10°C. At temperatures above 10°C, the increase was more gradual. The IRR approached a value close to 1 at temperatures below - 10°C and above 30°C. Both total and complicated appendicitis exhibited similar trends. Increased precipitation was negatively associated with the incidence of total acute appendicitis around the 50mm/day range, but not with complicated appendicitis. The findings suggest that environmental factors, especially temperature, may play a role in the occurrence of appendicitis. This research underscores the potential health implications of global climate change and the need for further studies to understand the broader impacts of environmental changes on various diseases.

A thirty-year time series analyses identifies coherence between oscillations in Anthrax outbreaks and El Niño in Karnataka, India

Anthrax is an economically important zoonotic disease affecting both livestock and humans. The disease is caused by a spore forming bacterium, Bacillus anthracis, and is considered endemic to the state of Karnataka, India. It is critical to quantify the role of climatic factors in determining the temporal pattern of anthrax outbreaks, so that reliable forecasting models can be developed. These models will aid in establishing public health surveillance and guide strategic vaccination programs, which will reduce the economic loss to farmers, and prevent the spill-over of anthrax from livestock to humans. In this study, correlation and coherence between time series of anthrax outbreaks in livestock (1987–2016) and meteorological variables and Sea Surface Temperature anomalies (SST) were identified using a combination of cross-correlation analyses, spectral analyses (wavelets and empirical mode decomposition) and further quantified using a Bayesian time series regression model accounting for temporal autocorrelation. Monthly numbers of anthrax outbreaks were positively associated with a lagged effect of rainfall and wet day frequency. Long-term periodicity in anthrax outbreaks (approximately 6–8 years) was coherent with the periodicity in SST anomalies and outbreak numbers increased with decrease in SST anomalies. These findings will be useful in planning long-term anthrax prevention and control strategies in Karnataka state of India.

Predicting GDP with Machine Learning Technique

This paper proposes a method for reducing model errors in regressions when modelling macroeconomic variables by using machine learning algorithms and traditional time series regression models. In this paper, machine learning models are subjected to repeated k-fold cross validation and hyperparameter tuning. The linear model uses repeated k-fold cross validation, on the other hand, the traditional time series model Mixed Data Sampling Auto Regressive Distribution Lag model is run without repeated k-fold cross validation and hyperparameter tuning. The results show that integrating repeated k-fold cross validation with hyperparameter tuning increases the overall performance of machine learning algorithms and each model records the average outcome from all folds and runs. These findings demonstrate how machine learning models outperform the traditional time series model.

2020 CMS prior authorization for hospital outpatient departments: Associated surgical volume impact

BackgroundPrior authorization is common for privately administered Medicare Advantage plans but is rarely used for surgical care when considering publicly administered plans. A 2020 Centers for Medicare and Medicaid services (CMS) policy, CMS-1717-FC, requires prior authorization for Medicare Fee-for-Service beneficiaries undergoing select procedures (blepharoplasty, abdominoplasty, botulinum toxin injection, rhinoplasty, and vein ablation) in hospital outpatient departments. The impact of this policy on surgical volume at hospital outpatient departments and shifts in care to ambulatory surgery centers is unknown. MethodsThis study used a segmented interrupted time series and pre-post logistic regression model. This study was a retrospective cohort study using data from the Healthcare Cost and Utilization Project state ambulatory surgery database and state inpatient database. ResultsFrom 2016 through 2021, a total of 272,879 patients underwent the affected procedures. Pre–CMS-1717-FC, a trend of decreasing hospital outpatient department utilization was found for Medicare Fee-for-Service beneficiaries (–10.82, 95% confidence interval: –18.32 to –3.33, P = .01). In the post-implementation period, no change in the rate of decreasing hospital outpatient department utilization was found for Medicare Fee-for-Service beneficiaries (–3.45, 95% confidence interval: –36.15 to 29.25, P = .83). In the pre-policy period, Medicare Fee-for-Service beneficiaries were 46% less likely to use freestanding ambulatory surgery centers but 27% less likely to use hospital-owned ambulatory surgery centers. ConclusionCMS-1717-FC was not associated with significant changes in hospital outpatient department volume beyond baseline trends. Policy aiming to right-size prior authorization for these procedures and considering site-of-service will balance the need to ensure medical necessity while constraining costs.

Housing Price-Vacancy Dynamics—An Empirical Study of the Hong Kong Housing Market

This study uses time series regression models and dynamic panel models of five-class housing to investigate the dynamics of the housing price-vacancy relationship in Hong Kong, offering insights distinct from previous cross-sectional analyses that take new housing completions as a supply proxy, without considering vacant homes as a source of housing supply. Two major contributions emerge: first, the results support the hypothesis that housing vacancies exert a negative impact on housing prices, holding other factors constant. Second, new builds supply is found to have a positive effect on housing prices, which is in line with many previous studies, but it contradicts the prediction. The results challenge the use of land supply or new housing completions as the proxy of housing supply and put forward a novel suggestion of including vacant homes in the housing price analysis. Advanced approaches to collecting housing vacancy data are also discussed. These findings have significant implications for policymakers, urban planners, and real estate investors, providing valuable insights for crafting targeted interventions and informing investment decisions. This is one of the first time series and dynamic panel analyses of housing vacancy’s effect on prices.

Positive time series regression models: theoretical and computational aspects

Positive time series regression models: theoretical and computational aspects

Assessing two decades of landscape greenness in relation to temperature and precipitation in a tropical dry forest of Northwestern Mexico

Canopy greenness is an indicator of ecosystem primary productivity, which is often limited by temperature and precipitation. Changes in vegetation greening have been reported mostly at global scales. However, we still have a poor understanding of vegetation greening patterns and drivers for major vegetation types, such as the tropical dry forest, one of the most extensive vegetation types in Mexico. Here, we analyze two decades of interannual variation in greenness and its relationship to temperature and precipitation in the northmost neotropical dry forest occurring in Northwestern Mexico. We constructed time-series linear regression models using standardized anomalies with z-scores (i.e., standard deviations away from the long-term mean) for the Normalized Difference Vegetation Index (NDVI) and the climate data from 2001 to 2021. Our best models indicate both temperature and precipitation exert positive effects on vegetation greenness, particularly from a lagged effect perspective, as retained predictors were the accumulated precipitation of two monsoon (summer) seasons and previous year mean temperature. The lowest levels of landscape greenness seem connected to prolonged droughts and extreme frost events. In fact, a switch from negative to positive NDVI anomalies was observed in the years following the February 2011 extreme frost that affected much of North America, including northern Mexico. Notable, under the stricter statistical criterion of −1.7 ≥ z score ≥ 1.7, only climatic variables presented very-extreme anomalies and these were not necessarily linked to a very-extreme response in landscape greenness. However, considering the criterion of −1.3 ≥ z score ≥ 1.3, we identified several extreme NDVI anomalies and their corresponding climatic anomaly. Therefore, more flexible statistical criteria might reveal climate extremes of ecological and social relevance. Our overall findings have implications for forest and climate risk management, particularly as extreme climatic anomalies are expected to continue increasing in light of climate change.

Forecasting changes in the demographic situation in the Republic of Sakha (Yakutia)

Subject. This article discusses the issues related to the population of the Republic of Sakha (Yakutia). Objectives. The article aims to predict the demographic situation in the Republic of Sakha (Yakutia) on the basis of socio-economic factors. Methods. For the study, we used a statistical analysis, time series regression modeling, and the forecasting method. Results. Based on regression modeling of time series, eleven factors influencing the change in the population of the Republic of Sakha (Yakutia) were identified. The article presents two developed forecasts of changes in the demographic situation. Conclusions. The development of industry, science, and improving the quality of life of the population contribute to the improvement of the demographic situation in the Republic of Sakha (Yakutia). The results obtained can be used to develop a comprehensive development programme for the Republic of Sakha (Yakutia).

In-Person and Teleconsultation Services at a National Hospital in Peru: Time Series Analysis of General and Psychiatric Care Amid the COVID-19 Pandemic.

The COVID-19 pandemic led to a global reduction in health care accessibility for both infected and noninfected patients, posing a particular burden on those with chronic conditions, including mental health issues. Peru experienced significant devastation from the pandemic, resulting in a collapsed health care system and leading to the world's highest per capita mortality rate as a result of COVID-19. Understanding the trends in health care utilization, particularly in mental health care, is crucial for informing pandemic response efforts and guiding future recovery strategies. This study aims to analyze the trends of outpatient medical and psychiatric consultations during the COVID-19 pandemic in a national hospital in Peru. This observational study was conducted at a national hospital in Lima, Peru. We analyzed data on user care across all services, including psychiatric services, from May 2019 to December 2022. The data were calculated for users served per month, including the number of users seen monthly in mental health services. Sociodemographic variables such as sex (female or male), age (≥0 years), type of medical appointment (regular or additional), and modality of care (in-person or teleconsultations) were taken into account. An interrupted time series regression model was conducted to assess the number of outpatient medical and psychiatric consultations. Subgroup analyses were performed based on service modality, including overall consultations, telemonitoring/teleconsultations only, or face-to-face only, for all service users and for mental health service users. A total of 1,515,439 participants were included, with females comprising 275,444/484,994 (56.80%) of the samples. Only 345,605/1,515,439 (22.81%) visits involved telemedicine. The total monthly outpatient visits were significantly reduced compared with the expected projection (P<.001) at the beginning of the pandemic, followed by a later monthly increment of 298.7 users. Face-to-face interventions experienced a significant reduction at the beginning of the pandemic (P<.001), gradually recovering in the following months. By contrast, telemedicine use initially increased but subsequently declined toward the end of the pandemic. A similar trend was observed in mental health units. During the pandemic years, health care utilization in both general and psychiatric services experienced a significant decrease, particularly at the beginning of the pandemic (March 2020). However, no significant trends were observed in either case throughout the pandemic period. Telemedicine consultations witnessed a significant increase overall during this period, particularly among mental health users.

Did the increase in birth grants result in higher births? Evidence from the Czech demographic and population policy change

PurposeThis research contributes to a better understanding of the effects of public demographic and population policy. The study investigates whether a considerable increase in birth grants in 2006 in the Czech Republic resulted in a higher number of births.Design/methodology/approachTo quantify the effects, we work with the administrative, statistical daily data (1993–2016) on the number of births and use time series analysis tools. In particular, we estimate a time series regression model via the ordinary least squares (OLS) technique with robust standard errors, testing placebo effects and the differences between the trend-forecasted values in the absence of the grant change.FindingsOur results, spread over several time windows covering up to a ten-year period after the intervention, show that the intervention significantly and positively affected the number of births in the post-intervention periods. The evidence obtained from the econometric analysis of the time series clearly shows that after the demographic and population policy change, there was an average increase of 17–19 births daily when the data was analysed up to 2016. Therefore, the conclusion is that the policy intervention led to expected and desired policy outcomes.Originality/valueThe study provides the stakeholders and policymakers with an experience of the public policy aiming to support fertility, which has reached its expectations.

Estimating the Temporal Impacts of Nearshore Fisheries on Coastal Ocean-Sourced Waste Accumulation in South Korea Using Stepwise Regression

Fishing activities have been recognized as one of the primary contributors to marine environmental pollution. Studies have been conducted on the impact of fishing activities on the accumulation of marine debris, but most of these studies have been conducted at specific points in time. This study collected marine debris data over four years in the coastal area of Korea. Data on the magnitude of nearshore fishing activities during the same period were collected and analyzed. Regression models were constructed to explore the impact of nearshore fishing activities on coastal waste accumulation over time. This research aimed to understand the influence of nearshore fishing activities on the accumulation of ocean-sourced coastal waste, leading to the development of a time series regression model. The results indicated that time series models have substantially more explanatory power compared to conventional models, emphasizing the importance of temporal considerations in quantifying the relationship between fishing activities and coastal litter over time.

Severe outcomes among adults with TB during COVID-19.

The COVID-19 pandemic prompted strict public health measures to reduce SARS-CoV-2 transmission, potentially interrupting TB programmes in the Western Cape, South Africa. We conducted a retrospective cohort study, estimating changes in new TB case rates and risk of death during TB-specific admissions within 6 months of TB first evidence, during the pre-pandemic (1 January 2019-26 March 2020) and after the implementation of public health and social measures (PHSM) periods (26 March 2020-30 September 2021), based on PHSM strictness. We used interrupted time series and logistic regression models to adjust for key characteristics. We found an average 22% reduction (95% CI 19-25) in monthly TB cases during the entire PHSM implementation period. Additionally, the risk of death during TB-specific admissions increased, with the adjusted odds ratio ranging across PHSM levels from 1.36 (95% CI 1.17-1.57) on Level 1 to 1.44 (95% CI 1.16-1.79) on Level 2 compared with the pre-pandemic period. There was a decline in the number of diagnosed TB cases and an increased risk of severe outcomes from 26 March 2020 to 30 September 2021 in the Western Cape. TB programme recovery strategies must be prioritised, and TB management programmes must be integrated into future pandemic responses.

The impact of economic instability on credit concessions: the case of the Covid-19 pandemic

Purpose: This research aims to analyze the impacts of the economic instability caused by Covid-19 on the volume of credit issuance in Brazil. Methodology: The research methodology employed time series regression models in the Brazilian market and was based on historical values of macroeconomic variables. The study period spans from February 2013 to April 2023, divided into pre-pandemic (02/2013-02/2020) and pandemic (03/2020-04/2023) periods. Results: It was observed that the impact of the Covid-19 pandemic on credit issuance volume was minor when controlling for other macroeconomic characteristics. There are indications that only the year 2020 exhibited more significant variations, with the average during the 2020 to 2023 period being not so different from the pre-pandemic period. Contributions of the Study: This study suggests that the crisis had only a temporary effect and that the economic measures taken during this period may have contributed to the recovery, indicating preparation for future pandemics that experts expect to occur.

Post-Pandemic Growth in 9-1-1 Paramedic Calls and Emergency Department Transports Surpasses Pre-Pandemic Rates in the COVID-19 Era: Implications for Paramedic Resource Planning

Objectives The COVID-19 pandemic led to a decline in emergency department (ED) visits and a subsequent return to baseline pre-pandemic levels. It is unclear if this trend extended to paramedic services and if patient cohorts accessing paramedics changed. We examined trends and associations between paramedic utilization (9-1-1 calls and ED transports) and the COVID-19 timeframe. Methods We conducted a retrospective cross-sectional study using paramedic call data from the Hamilton Paramedic Services from January 2016 to December 2023. We included all 9-1-1 calls where paramedics responded to an incident, excluding paramedic interfacility transfers. We calculated lines of best fit for the pre-pandemic period (January 2016 to January 2020) and compared their predictions to the actual volumes in the post-pandemic period (May 2021 to December 2023). We used an interrupted time series regression model to determine the association between pandemic timeframes (pre-, during-, post-COVID-19) and paramedic utilization (9-1-1 calls and ED transports), while testing for annual seasonality. Results During the study timeframe, 577,278 calls for paramedics were received and 413,491 (71.6%) were transported to the ED. Post-pandemic, 9-1-1 calls exceeded predicted pre-pandemic levels by 1,298 per month, while ED transports exceeded by 543 per month. The pandemic significantly reduced monthly 9-1-1 calls (-588.2, 95% CI −928.8 to −247.5) and ED transports (-677.3, 95% CI −927.0 to −427.5). Post-pandemic, there was a significant and sustained resurgence in monthly 9-1-1 calls (1,208.0, 95% CI 822.1 to 1,593.9) and ED transports (868.8, 95% CI 585.8 to 1,151.7). Both models exhibited seasonal variations. Conclusions Post-pandemic, 9-1-1-initiated paramedic calls experienced a substantial increase, surpassing pre-pandemic growth rates. ED transports returned to pre-pandemic levels but with a steeper and continuous pattern of growth. The resurgence in paramedic 9-1-1 calls and ED transports post-COVID-19 emphasizes an urgent necessity to expedite development of new care models that address how paramedics respond to 9-1-1 calls and transport to overcrowded EDs.

Movement for Black Lives at Work? Racial Justice Spillover, Labor Organization, and New Worker Militancy

ABSTRACT Emerging from a confluence of events – including Black Lives Matter Movement (BLM) – racial consciousness has intensified in recent years. In the wake of a long declining labor movement, there has also been a surprising upsurge in labor militancy. Are these two currents systematically related? Situating our analysis in the sociological literature on social movement spillover, we ask if there is evidence of racial justice spillover into and stimulating workplace militancy. Employing all known strikes and worker protests during 2021 and 2022, we find: (a) racial justice demands did, indeed, appear as part of workplace militancy during this period; (b) racial justice demands were voiced in both worker protests and strikes launched most heavily by unions but also by alt-labor/SMOs and by unorganized workers; (c) weekly time-series regression models indicate that BLM demonstrations stimulated the frequency of workplace protests and strikes with racial justice demands which, in turn, stimulated wider worker militancy over other issues; and (d) interdependency analysis of worker actions reveals that unionized worker protests served as a key catalyst driving other forms of worker militancy with racial justice demands. We discuss the implications of our findings for social movement spillover theory and for moving racial justice and labor movements forward.

Health and Economic Benefits of Complying With the World Health Organization Air Quality Guidelines for Particulate Matter in Nine Major Latin American Cities.

This study aims to estimate the short-term preventable mortality and associated economic costs of complying with the World Health Organization (WHO) air quality guidelines (AQGs) limit values for PM10 and PM2.5 in nine major Latin American cities. We estimated city-specific PM-mortality associations using time-series regression models and calculated the attributable mortality fraction. Next, we used the value of statistical life to calculate the economic benefits of complying with the WHO AQGs limit values. In most cities, PM concentrations exceeded the WHO AQGs limit values more than 90% of the days. PM10 was found to be associated with an average excess mortality of 1.88% with concentrations above WHO AQGs limit values, while for PM2.5 it was 1.05%. The associated annual economic costs varied widely, between US$ 19.5 million to 3,386.9 million for PM10, and US$ 196.3 million to 2,209.6 million for PM2.5. Our findings suggest that there is an urgent need for policymakers to develop interventions to achieve sustainable air quality improvements in Latin America. Complying with the WHO AQGs limit values for PM10 and PM2.5 in Latin American cities would substantially benefits for urban populations.

Impact of the COVID-19 pandemic on cervical cancer screening participation, abnormal cytology prevalence and screening interval in Catalonia.

The COVID-19 pandemic led to a national lockdown and the interruption of all cancer preventive services, including cervical cancer screening. We aimed to assess the COVID-19 pandemic impact on opportunistic screening participation, abnormal cytology (ASCUS+) prevalence and screening interval in 2020 and 2021 within the Public Health System of Catalonia, Spain. Individual data on cytology and HPV testing of women aged 25-65 from 2014 to 2021 were retrieved from the Information System for Primary Care Services (SISAP). Time-series regression models were used to estimate expected screening participation and abnormal cytology prevalence in 2020 and 2021. The impact was determined by comparing observed and expected values (ratios). Additionally, changes in screening interval trends between 2014 and 2021 were assessed by fitting a Piecewise linear regression model. Cervical cancer screening participation decreased by 38.8% and 2.2% in 2020 and 2021, respectively, with the most significant impact on participation (-96.1%) occurring in April 2020. Among older women, participation was lower, and it took longer to recover. Abnormal cytology prevalence was 1.4 times higher than expected in 2020 and 2021, with variations by age (range=1.1-1.5). From June 2020 onwards, the screening interval trend significantly changed from an increase of 0.59 to 3.57 months per year, resulting in a median time of 48 months by December 2021. During the pandemic, fewer women have participated in cervical cancer screening, abnormal cytology prevalence has increased, and the screening interval is more prolonged than before. The potential cervical cancer lifetime risk implications highlight the need for organized HPV-based screening.

Major Pancreatic Resection Increases Bone Mineral Density Loss, Osteoporosis, and Fractures.

To assess whether long-term survivors of pancreatic surgery show increased risk to develop impaired bone mineral density, osteoporosis, and vitamin D deficiency. Pancreatic resection poses a risk for malabsorption of fat-soluble vitamins and other micronutrients essential for bone mineralization. Here, we evaluated the long-term effects of pancreatic resection on bone mineral density (BMD) and its clinical sequelae. This was a two-pronged analysis of post-pancreatectomy patients with a follow-up period greater than 3 years comprising (1) a large, propensity score-matched, cohort study based on a multinational federated research network (FRN) and (2) a retrospective single institution review of clinical and radiographic patient data. In the FRN analysis, an initial cohort of 8,423 post-pancreatectomy patients were identified and propensity score-matched with normal controls. The primary endpoint was the 10-year risk of developing osteoporotic pathological fractures and secondary endpoints included diagnosis of osteoporosis, vitamin-D deficiency, and related therapies. The single institution retrospective analysis identified 224 patients who underwent pancreatic resection between 2005 and 2019. BMD was quantified in CT images acquired before and after surgery. BMD trends and related factors were assessed in a time-series mixed effect linear regression model. A total of 8,080 propensity score-matched pairs were included in the FRN analysis. The analysis revealed a 2.4-fold increase in pathological fractures (P<0.0001) and 1.4-1.5 fold increase in osteoporosis/osteomalacia (P<0.0001) and vitamin-D deficiency (P<0.0001) in post-pancreatectomy patients. Vitamin-D supplements were more common in the pancreatectomy group (OR 1.4, 95% CI 1.28-1.53, P<0.0001), as were specific osteoporosis/osteomalacia treatments such as calcitonin, denosumab, romosozumab, abaloparatide, and teriparatide (OR 2.24, 95%CI 1.69-2.95, P<0.0001). Retrospective analysis of CT imaging revealed that BMD declined more rapidly following pancreatic resection compared to normal historical controls (P=0.015). Older age, pancreatic cancer, and pancreaticoduodenectomy were associated with increased rates of BMD loss (P<0.05, each). After pancreatic resection, patients are at higher risk for BMD loss and subsequent fractures. As the cohort of pancreatic resection survivorship grows, attention will need to be paid to focused prevention efforts to reduce BMD loss, osteoporosis, and fractures in these vulnerable patients, with specific attention to the pancreatic cancer population.