How Do You Know Which Health Care Effectiveness Research You Can Trust? A Guide to Study Design for the Perplexed

Rapid recognition and optimal management of hemophilia in the emergency department: A quality improvement project

Objectives

This study aimed to assess the effectiveness of a continuous quality improvement initiative at the University of Florida Health Physicians practice in reducing the time to administer factor replacement therapy (FRT) for hemophilia patients presenting with bleeding in the emergency department (ED).

Methods

The study, a quasi-experimental, interventional design, was conducted between January 2020 and January 2023. The intervention, implemented in September 2021, involved training ED physicians, creating a specialized medication order set within the electronic health record (EHR), and a rapid triage system. The effectiveness was measured by comparing the time from ED arrival to factor administration before and after the intervention and benchmarking it against the National Bleeding Disorders Foundation's Medical and Scientific Advisory Council (MASAC)-recommended 1-hour timeline for factor administration. An interrupted time series (ITS) analysis with a generalized least squares model assessed the intervention's impact.

Results

A total of 43 ED visits (22 pre-intervention and 21 post-intervention) were recorded. Post-intervention, the average time from ED arrival to factor administration decreased from 5.63 to 3.15 hours. There was no significant increase (27% vs. 29%) in the patients receiving factor within 1-hour of ED arrival. The ITS analysis predicted a 20-hour reduction in the average quarterly time to administer factor by the end of the study, an 84% decrease.

Conclusions

The quality improvement program decreased the time to administer FRT for patients with hemophilia in the ED. However, the majority of patients did not achieve the 1-hour MASAC-recommended timeline for factor administration after ED arrival.

The effect of power stretchers on occupational injury rates in an urban emergency medical services system

BACKGROUND

To examine occupational injury rates in a dual-response emergency medical services (EMS) system before and after implementation of a power-lift stretcher system.

METHODS

The seasonally-adjusted occupational injury rate was estimated relative to medical call volume (per 1000 calls) and workers (per 100 FTEs) from 2009 to 2019, and stratified by severity (lost-time, healthcare only), role (EMS, FIRE) and type (patient-handling). Power-lift stretchers were adopted between 2013 and 2015. Preinjury versus postinjury rates were compared using binomial tests. Interrupted time series (ITS) analysis was used to estimate the trend and change in injuries related to patient-handling, with occupational illnesses serving as control.

RESULTS

Binomial tests revealed varied results, with reductions in the injury rate per 1000 calls (-14.0%) and increases in the rate per 100 FTEs (+14.1%); rates also differed by EMS role and injury severity. ITS analysis demonstrated substantial reductions in patient-handling injuries following implementation of power-lift stretchers, both in the injury rate per 1000 calls (-50.4%) and per 100 FTEs (-46.6%), specifically among individuals deployed on the ambulance. Injury rates were slightly elevated during the winter months (+0.8 per 100 FTEs) and lower during spring (-0.5 per 100 FTEs).

CONCLUSIONS

These results support the implementation of power-lift stretchers for injury prevention in EMS systems and demonstrate advantages of ITS analysis when data span long preintervention and postintervention periods.

COVID-19 pandemic and initiation of treatment for atrial fibrillation: a nationwide analysis of claims data

BACKGROUND

The COVID-19 pandemic profoundly disrupted the delivery of medical care. It remains unclear whether individuals diagnosed with new onset disease during the pandemic were less likely to initiate treatments after diagnosis. We sought to evaluate changes in the treatment initiation of patients newly diagnosed with atrial fibrillation (AF) after the onset of the COVID-19 pandemic.

METHODS

In this retrospective cohort study, we identified individuals with incident AF from 01/01/2016-09/30/2021 using Optum's de-identified Clinformatics® Data Mart Database. The primary outcome was initiation of oral anticoagulation (OAC) within 30 days of AF diagnosis. Secondary outcomes included initiation of OAC within 180 days of diagnosis, initiation of warfarin, direct oral anticoagulants (DOACs), rhythm control medications and electrical cardioversion within 30 days of diagnosis. We constructed interrupted time series analyses to examine changes in the outcomes following the onset of the pandemic.

RESULTS

A total of 573,524 patients (age 73.0 ± 10.9 years) were included in the study. There were no significant changes in the initiation of OAC, DOAC, and rhythm control medications associated with the onset of the pandemic. There was a significant decrease in initiation of electrical cardioversion associated with the onset of the pandemic. The rate of electronic cardioversion within 30 days of diagnosis decreased by 4.9% per 1,000 patients after the onset of the pandemic and decreased by about 35% in April 2020, compared to April 2019, from 5.53% to 3.58%.

CONCLUSION

The COVID-19 pandemic did not affect the OAC initiation within 30 days of AF diagnosis but was associated with a decline in the provision of procedures for patients newly diagnosed with AF.

Evaluation of statistical methods used to meta-analyse results from interrupted time series studies: A simulation study

Interrupted time series (ITS) are often meta-analysed to inform public health and policy decisions but examination of the statistical methods for ITS analysis and meta-analysis in this context is limited. We simulated meta-analyses of ITS studies with continuous outcome data, analysed the studies using segmented linear regression with two estimation methods [ordinary least squares (OLS) and restricted maximum likelihood (REML)], and meta-analysed the immediate level- and slope-change effect estimates using fixed-effect and (multiple) random-effects meta-analysis methods. Simulation design parameters included varying series length; magnitude of lag-1 autocorrelation; magnitude of level- and slope-changes; number of included studies; and, effect size heterogeneity. All meta-analysis methods yielded unbiased estimates of the interruption effects. All random effects meta-analysis methods yielded coverage close to the nominal level, irrespective of the ITS analysis method used and other design parameters. However, heterogeneity was frequently overestimated in scenarios where the ITS study standard errors were underestimated, which occurred for short series or when the ITS analysis method did not appropriately account for autocorrelation. The performance of meta-analysis methods depends on the design and analysis of the included ITS studies. Although all random effects methods performed well in terms of coverage, irrespective of the ITS analysis method, we recommend the use of effect estimates calculated from ITS methods that adjust for autocorrelation when possible. Doing so will likely to lead to more accurate estimates of the heterogeneity variance.

Trends in alcohol-impaired crashes in California, 2016 to 2021: A time series analysis for alcohol involvement and crash distribution among demographic subgroups

BACKGROUND

In 2020, the COVID-19 pandemic and control measures changed alcohol consumption in the United States (US) and globally. Before the pandemic, alcohol-impaired crashes contributed to approximately one-third of all road traffic crash injuries and fatalities nationally. We examined the impact of the COVID-19 pandemic on crashes and examined differences in alcohol-involved crashes across various subgroups.

METHODS

The University of California Berkeley Transportation Injury Mapping Systems provided information on all crashes reported to the California Highway Patrol from January 1, 2016 through December 31, 2021. Using autoregressive integrated moving average (ARIMA) models applied to weekly time series data, we estimated the effect of California's first mandatory statewide shelter-in-place order (March 19, 2020) on crashes per 100,000 population. We also examined crash subgroups according to crash severity, sex, race/ethnicity, age, and alcohol involvement.

RESULTS

In California, the mean crash rate per week before the pandemic (January 1, 2016-March 18, 2020) was 9.5 crashes per 100,000 population, and 10.3% of those were alcohol-involved. After the initiation of the COVID-19 stay-at-home order, the percentage of crashes that were alcohol-involved rose to 12.7%. Overall, the crash rate across California decreased significantly (-4.6 crashes per 100,000; 95% CI: -5.3, -3.9), including across all examined subgroups, with the greatest decrease among the least severe crashes. However, there was a 2.3% absolute increase in the proportion of crashes that were alcohol-involved (0.02 crashes per 100,000; 95% CI: 0.02, 0.03).

CONCLUSIONS

The initiation of a COVID-19 stay-at-home ordinance in California was associated with a substantial decrease in overall crash rates. While crashes have returned to pre-pandemic levels, alcohol-involved crashes remain elevated. The initiation of the stay-at-home order significantly increased alcohol-impaired driving, which has remained elevated.

The interactions of SARS-CoV-2 with cocirculating pathogens: Epidemiological implications and current knowledge gaps

Despite the availability of effective vaccines, the persistence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) suggests that cocirculation with other pathogens and resulting multiepidemics (of, for example, COVID-19 and influenza) may become increasingly frequent. To better forecast and control the risk of such multiepidemics, it is essential to elucidate the potential interactions of SARS-CoV-2 with other pathogens; these interactions, however, remain poorly defined. Here, we aimed to review the current body of evidence about SARS-CoV-2 interactions. Our review is structured in four parts. To study pathogen interactions in a systematic and comprehensive way, we first developed a general framework to capture their major components: sign (either negative for antagonistic interactions or positive for synergistic interactions), strength (i.e., magnitude of the interaction), symmetry (describing whether the interaction depends on the order of infection of interacting pathogens), duration (describing whether the interaction is short-lived or long-lived), and mechanism (e.g., whether interaction modifies susceptibility to infection, transmissibility of infection, or severity of disease). Second, we reviewed the experimental evidence from animal models about SARS-CoV-2 interactions. Of the 14 studies identified, 11 focused on the outcomes of coinfection with nonattenuated influenza A viruses (IAVs), and 3 with other pathogens. The 11 studies on IAV used different designs and animal models (ferrets, hamsters, and mice) but generally demonstrated that coinfection increased disease severity compared with either monoinfection. By contrast, the effect of coinfection on the viral load of either virus was variable and inconsistent across studies. Third, we reviewed the epidemiological evidence about SARS-CoV-2 interactions in human populations. Although numerous studies were identified, only a few were specifically designed to infer interaction, and many were prone to multiple biases, including confounding. Nevertheless, their results suggested that influenza and pneumococcal conjugate vaccinations were associated with a reduced risk of SARS-CoV-2 infection. Finally, fourth, we formulated simple transmission models of SARS-CoV-2 cocirculation with an epidemic viral pathogen or an endemic bacterial pathogen, showing how they can naturally incorporate the proposed framework. More generally, we argue that such models, when designed with an integrative and multidisciplinary perspective, will be invaluable tools to resolve the substantial uncertainties that remain about SARS-CoV-2 interactions.

Evaluation of reticuloruminal temperature for the prediction of clinical mastitis in dairy cows challenged with Streptococcus uberis

Automated monitoring devices have become increasingly utilized in the dairy industry, especially for monitoring or predicting disease status. While multiple automated monitoring devices have been developed for the prediction of clinical mastitis (CM), limitations in performance or applicability remain. The aims of this study were to (1) detect variations in reticuloruminal temperature (RRT) relative to an experimental intramammary challenge with Streptococcus uberis and (2) evaluate alerts generated automatically based on variation in RRT to predict initial signs of CM in the challenged cows based on severity of clinical signs and the concentration of bacteria (cfu/mL) in the infected quarter separately. Clinically healthy Holstein cows without a history of CM in the 60 d before the experiment (n = 37, parity 1 to 5, ≥120 d in milk) were included if they were microbiologically negative and had a somatic cell count under 200,000 cells/mL based on screening of quarter milk samples 1 wk before challenge. Each cow received an intra-reticuloruminal automated monitoring device before the trial and was challenged with 2,000 cfu of Strep. uberis 0140J in 1 rear quarter. Based on interrupted time series analysis, intramammary challenge with Strep. uberis increased RRT by 0.54°C [95% confidence interval (CI): 0.41, 0.66] at 24 h after the challenge, which remained elevated until the end of the study. Alerts based on RRT correctly classified 78.3% (95% CI: 65.8, 87.9) of first occurrences of CM at least 24 h in advance, with a sensitivity of 70.0% (95% CI: 50.6, 85.3) and a specificity of 86.7% (95% CI: 69.3, 96.2). The accuracy of CM for a given severity score was 90.9% (95% CI: 70.8, 98.9) for mild cases, 85.2% (95% CI: 72.9, 93.4) for moderate cases, and 92.9% (95% CI: 66.1, 99.8) for severe cases. Test characteristics of the RRT alerts to predict initial signs of CM improved substantially after bacterial count in the challenged quarter reached 5.0 log₁₀ cfu/mL, reaching a sensitivity of 73.5% (95% CI: 55.6, 87.1) and a specificity of 87.5% (95% CI: 71.0, 96.5). Overall, the results of this study indicated that RRT was affected by the intramammary challenge with Strep. uberis and the RRT-generated alerts had similar accuracy as reported for other sensors and algorithms. Further research that includes natural infections with other pathogens as well as different variations in RRT to determine CM status is warranted.

Evaluating the impact of universal varicella vaccination among preschool-aged children in Qingdao, China: An interrupted time-series analysis

Varicella is a contagious disease of children. Qingdao administrated free one-dose and free two-dose universal varicella vaccination schedules in 2013 and 2016 for preschool children. The effectiveness of the vaccination was analyzed in this study. Monthly varicella incidence data of 1-6 years old children during 2007-2020 were obtained from the Qingdao Infectious Disease Reporting Information Management System. We applied Interrupted time series and segmented regression analyses to assess changes in varicella incidence at the beginning of each month and average monthly changes during the vaccination. The vaccination was associated with a reduction of 32.7% in varicella morbidity on average during the 8-year intervention, there is a statistically significant difference between the voluntary period and free vaccination period (χ² = 290.80,P < 0.001). Immediately after the free one-dose vaccination implementation in 2013 and free two-dose vaccination implementation in 2016, varicella incidence decreased by 0.135 cases per 100 000 population (P < 0.001) and increased by 1.189 cases per 100 000 population (P = 0.039), respectively, the results were statistically significant. There were significant declining trends in varicella incidence after free vaccination: 0.135(P < 0.001) and 0.055 (P = 0.025) per month in 2013.7-2016.6 and 2016.7-2020.12, respectively. This study shows a further decaying trend of varicella incidence based on the impact of free two-dose vaccination. It is necessary to prolong free two-dose universal varicella vaccination to strengthen the immune barrier of preschool children sequentially.

Distributed lag interrupted time series model for unclear intervention timing: effect of a statement of emergency during COVID-19 pandemic

Background

Interrupted time series (ITS) analysis has become a popular design to evaluate the effects of health interventions. However, the most common formulation for ITS, the linear segmented regression, is not always adequate, especially when the timing of the intervention is unclear. In this study, we propose a new model to overcome this limitation.

Methods

We propose a new ITS model, ARIMAITS-DL, that combines (1) the Autoregressive Integrated Moving Average (ARIMA) model and (2) distributed lag functional terms. The ARIMA technique allows us to model autocorrelation, which is frequently observed in time series data, and the decaying cumulative effect of the intervention. By contrast, the distributed lag functional terms represent the idea that the intervention effect does not start at a fixed time point but is distributed over a certain interval (thus, the intervention timing seems unclear). We discuss how to select the distribution of the effect, the model construction process, diagnosing the model fitting, and interpreting the results. Further, our model is implemented as an example of a statement of emergency (SoE) during the coronavirus disease 2019 pandemic in Japan.

Results

We illustrate the ARIMAITS-DL model with some practical distributed lag terms to examine the effect of the SoE on human mobility in Japan. We confirm that the SoE was successful in reducing the movement of people (15.0–16.0% reduction in Tokyo), at least between February 20 and May 19, 2020. We also provide the R code for other researchers to easily replicate our method.

Conclusions

Our model, ARIMAITS-DL, is a useful tool as it can account for the unclear intervention timing and distributed lag effect with autocorrelation and allows for flexible modeling of different types of impacts such as uniformly or normally distributed impact over time.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12874-022-01662-1).

The effect of clinical pharmacists' intervention in adverse drug reaction reporting: a retrospective analysis with a 9-year interrupted time series

Background

In China, 85.4% of adverse drug reactions (ADRs) are spontaneously reported by healthcare facilities. As a result, many ADRs are not reported due to lack of mandatory reporting requirements. As healthcare professionals, clinical pharmacists (CPhs) serve as a bridge between clinical work and medication and ensure rational drug use. In China, A team of CPhs implemented an intervention for ADRs reporting, with the goal of improving the number of ADRs reports, the number of unreported ADRs, and the standardized reporting rate.

Methods

On June 01, 2015, a team of CPhs implemented an intervention for ADRs reporting at a Grade A, Class 3 hospital in China. The drug review catalogue (DRC) was used to screen physician orders for having visible symptoms of ADRs across departments, pooled the ADRs, and submitted them to the Center for Advanced Drug Monitoring (CNCAM). We retrospectively analysed the effect of a CPhs ADRs reporting intervention on the number of clinical ADRs reports, the number of unreported ADRs, and the standardized reporting rate over a 9-year period by interrupted time series (ITS). The method was implemented at the hospital on June 1, 2015, and a segmented regression model was used to analyse the data from January 1, 2010, to December 31, 2019.

Results

After the CPhs ADRs reporting intervention, the number of inpatient ADRs reports submitted to the CNCAM immediately increased by approximately 63 (62.658, P < 0.01) and then decreased by approximately 1 (0.701, P = 0.000151 < 0.01) per month afterward; the number of unreported ADRs was immediately reduced by approximately 44 (44.091, P < 0.01) and remained largely unchanged over time (P > 0.05); the standardized ADRs reporting rate per month immediately increased by 63.634% (P < 0.01) and remained largely unchanged over time (P > 0.05).

Conclusion

The CPhs ADRs reporting intervention had an immediate effect on improving ADRs reporting, which highlights the severity of ADRs underreporting in Chinese hospitals. The method is practical and should be used more widely in clinical practice. For example, the method can adjust and establish a DRC catalog that meets the actual situation of the implementing hospital based on the hospital's drug use habits and has the characteristics of good adaptability. However, it does have some limitations; for example, it may be difficult to detect early ADRs without visible symptoms.

Changes in Acute Myocardial Infarction, Stroke, and Heart Failure Hospitalizations During COVID-19 Pandemic in Tuscany-An Interrupted Time Series Study

Objectives: We evaluate the impact of the COVID-19 pandemic on unplanned hospitalization rates for patients without COVID-19, including their length of stay, and in-hospital mortality, overall, and for acute myocardial infarction (AMI), stroke, and heart failure in the Tuscany region of Italy. Methods: We carried out a population-based controlled interrupted time series study using segmented linear regression with an autoregressive error term based on admissions data from all public hospitals in Tuscany. The primary outcome measure was weekly hospitalization rates; secondary outcomes included length of stay, and in-hospital mortality. Results: The implementation of the pandemic-related mitigation measures and fear of infection was associated with large decreases in inpatient hospitalization rates overall (-182 [-234, -130]), unplanned hospitalization (-39 [-51, -26]), and for AMI (-1.32 [-1.98, -0.66]), stroke (-1.51 [-2.56, -0.44]), and heart failure (-8.7 [-11.1, -6.3]). Average length of stay and percent in-hospital mortality for select acute medical conditions did not change significantly. Conclusion: In Tuscany, Italy, the COVID-19 pandemic was associated with large reductions in hospitalization rates overall, as well as for heart failure, and the time sensitive conditions of AMI and stroke during the months January to July 2020.

Internal medicine physician embedded in an orthopedic service in a level 1 hospital: clinical impact

BACKGROUND

The aim of our study was to evaluate the impact of an internist physician specialized in diabetes, appointed as an in-house physician in the orthopedic wards, on improving clinical outcomes and in particular 30-day mortality.

METHODS

We analyzed a cohort of patients hospitalized more than 24 h in the orthopedic service. The analyses included a comparative analysis between the pre- and post-intervention time periods and an interrupted time series (ITS) analysis, which were conducted in stratification to three populations: whole population, patients with at least one chronic disease and/or older than 75 years of age and patients diagnosed with diabetes. The primary outcome was 30-day mortality following the hospitalization.

RESULTS

A total of 11,546 patients were included in the study, of which 19% (2212) were hospitalized in the post intervention period. Although in the comparative analysis there was no significant change in 30-day mortality, in the ITS there was a decrease in the mortality trend during the post intervention period in the entire and chronic disease/elderly populations, compared to no change during the pre-intervention period: a post-intervention slope of - 0.14(p value < 0.001) and  - 0.11(p value = 0.03), respectively. Additionally, we found decrease in length of stay, increase in transfers to the internal medicine department with a negative trend, increase in HbA1c testing during the hospitalization and changes in diabetes drugs administration.

CONCLUSION

The presence of an internist in the orthopedic wards is associated with health care improvement; decrease in the 30-day mortality trend, decrease in length of stay, increase in HbA1c testing during the hospitalization and an increase in diabetes drugs administration.

Innovation in the pediatric electronic health record to realize a more effective platform

Commercial electronic health records (EHRs) were first developed to automate business processes. As EHRs developed, design principles focused on transferring existing paper-based documentation to comparable electronic forms. In addition, a strong industry focus on adult healthcare settings and quality measures has limited attention and resources for high priority EHR functionality needed for the unique health care of children. The objective of this paper is to provide a review of innovation in the EHR, that includes a variety of established and emerging technologies that may help realize a more effective EHR in child health settings. A more effective EHR would serve as an electronic hub. Existing EHR infrastructure could provide the foundation upon which new technologies and approaches branch and extend, enabling more rapid and customizable innovation to better meet shifting stakeholder and end-user needs. Among many areas for improvement, key goals of innovation could include technology that relieves ambulatory primary care clinician documentation burden, identifies needs, and supports improved care coordination and outcomes, focused on the following key areas: identification of child and family care needs, decision support, documentation, care coordination, and family communication.

A Multidisciplinary Perspective of Ultra-Processed Foods and Associated Food Processing Technologies: A View of the Sustainable Road Ahead

Ultra-processed foods (UPFs) are negatively perceived by part of the scientific community, the public, and policymakers alike, to the extent they are sometimes referred to as not "real food". Many observational surveys have linked consumption of UPFs to adverse health outcomes. This narrative synthesis and scientific reappraisal of available evidence aims to: (i) critically evaluate UPF-related scientific literature on diet and disease and identify possible research gaps or biases in the interpretation of data; (ii) emphasize the innovative potential of various processing technologies that can lead to modifications of the food matrix with beneficial health effects; (iii) highlight the possible links between processing, sustainability and circular economy through the valorisation of by-products; and (iv) delineate the conceptual parameters of new paradigms in food evaluation and classification systems. Although greater consumption of UPFs has been associated with obesity, unfavorable cardiometabolic risk factor profiles, and increased risk for non-communicable diseases, whether specific food processing techniques leading to ultra-processed formulations are responsible for the observed links between UPFs and various health outcomes remains elusive and far from being understood. Evolving technologies can be used in the context of sustainable valorisation of food processing by-products to create novel, low-cost UPFs with improved nutritional value and health potential. New paradigms of food evaluation and assessment should be funded and developed on several novel pillars-enginomics, signalling, and precision nutrition-taking advantage of available digital technologies and artificial intelligence. Research is needed to generate required scientific knowledge to either expand the current or create new food evaluation and classification systems, incorporating processing aspects that may have a significant impact on health and wellness, together with factors related to the personalization of foods and diets, while not neglecting recycling and sustainability aspects. The complexity and the predicted immense size of these tasks calls for open innovation mentality and a new mindset promoting multidisciplinary collaborations and partnerships between academia and industry.

The use of segmented regression for evaluation of an interrupted time series study involving complex intervention: the CaPSAI project experience

An interrupted time series with a parallel control group (ITS-CG) design is a powerful quasi-experimental design commonly used to evaluate the effectiveness of an intervention, on accelerating uptake of useful public health products, and can be used in the presence of regularly collected data. This paper illustrates how a segmented Poisson model that utilizes general estimating equations (GEE) can be used for the ITS-CG study design to evaluate the effectiveness of a complex social accountability intervention on the level and rate of uptake of modern contraception. The intervention was gradually rolled-out over time to targeted intervention communities in Ghana and Tanzania, with control communities receiving standard of care, as per national guidelines. Two ITS GEE segmented regression models are proposed for evaluating of the uptake. The first, a two-segmented model, fits the data collected during pre-intervention and post-intervention excluding that collected during intervention roll-out. The second, a three-segmented model, fits all data including that collected during the roll-out. A much simpler difference-in-difference (DID) GEE Poisson regression model is also illustrated. Mathematical formulation of both ITS-segmented Poisson models and that of the DID Poisson model, interpretation and significance of resulting regression parameters, and accounting for different sources of variation and lags in intervention effect are respectively discussed. Strengths and limitations of these models are highlighted. Segmented ITS modelling remains valuable for studying the effect of intervention interruptions whether gradual changes, over time, in the level or trend in uptake of public health practices are attributed by the introduced intervention.

Trial Registration: The Australian New Zealand Clinical Trials registry.

Trial registration number: ACTRN12619000378123.

Trial Registration date: 11-March-2019.

The Impact of China's Lockdown Policy on the Incidence of COVID-19: An Interrupted Time Series Analysis

Background

Policy changes are often necessary to contain the detrimental impact of epidemics such as those brought about by coronavirus disease (COVID-19). In the earlier phases of the emergence of COVID-19, China was the first to impose strict restrictions on movement (lockdown) on January 23rd, 2020. A strategy whose effectiveness in curtailing COVID-19 was yet to be determined. We, therefore, sought to study the impact of the lockdown in reducing the incidence of COVID-19.

Methods

Daily cases of COVID-19 that occurred in China which were registered between January 12th and March 30th, 2020, were extracted from the Johns Hopkins CSSE team COVID-19 ArcGIS® dashboards. Daily cases reported were used as data points in the series. Two interrupted series models were run: one with an interruption point of 23 January 2020 (model 1) and the other with a 14-day deferred interruption point of 6th February (model 2). For both models, the magnitude of change (before and after) and linear trend analyses were measured, and β-coefficients reported with 95% confidence interval (CI) for the precision.

Results

Seventy-eight data points were used in the analysis. There was an 11% versus a 163% increase in daily cases in models 1 and 2, respectively, in the preintervention periods (p ≤ 0.001). Comparing the period immediately following the intervention points to the counterfactual, there was a daily increase of 2,746% (p < 0.001) versus a decline of 207% (p = 0.802) in model 2. However, in both scenarios, there was a statistically significant drop in the daily cases predicted for this data and beyond when comparing the preintervention periods and postintervention periods (p < 0.001).

Conclusion

There was a significant decrease the COVID-19 daily cases reported in China following the institution of a lockdown, and therefore, lockdown may be used to curtail the burden of COVID-19.

Effectiveness of an insurance enrollment support tool on insurance rates and cancer prevention in community health centers: a quasi-experimental study

Background

Following the ACA, millions of people gained Medicaid insurance. Most electronic health record (EHR) tools to date provide clinical-decision support and tracking of clinical biomarkers, we developed an EHR tool to support community health center (CHC) staff in assisting patients with health insurance enrollment documents and tracking insurance application steps. The objective of this study was to test the effectiveness of the health insurance support tool in (1) assisting uninsured patients gaining insurance coverage, (2) ensuring insurance continuity for patients with Medicaid insurance (preventing coverage gaps between visits); and (3) improving receipt of cancer preventive care.

Methods

In this quasi-experimental study, twenty-three clinics received the intervention (EHR-based insurance support tool) and were matched to 23 comparison clinics. CHCs were recruited from the OCHIN network. EHR data were linked to Medicaid enrollment data. The primary outcomes were rates of uninsured and Medicaid visits. The secondary outcomes were receipt of recommended breast, cervical, and colorectal cancer screenings. A comparative interrupted time-series using Poisson generalized estimated equation (GEE) modeling was performed to evaluate the effectiveness of the EHR-based tool on the primary and secondary outcomes.

Results

Immediately following implementation of the enrollment tool, the uninsured visit rate decreased by 21.0% (Adjusted Rate Ratio [RR] = 0.790, 95% CI = 0.621–1.005, p = .055) while Medicaid-insured visits increased by 4.5% (ARR = 1.045, 95% CI = 1.013–1.079) in the intervention group relative to comparison group. Cervical cancer preventive ratio increased 5.0% (ARR = 1.050, 95% CI = 1.009–1.093) immediately following implementation of the enrollment tool in the intervention group relative to comparison group. Among patients with a tool use, 81% were enrolled in Medicaid 12 months after tool use. For the 19% who were never enrolled in Medicaid following tool use, most were uninsured (44%) at the time of tool use.

Conclusions

A health insurance support tool embedded within the EHR can effectively support clinic staff in assisting patients in maintaining their Medicaid coverage. Such tools may also have an indirect impact on evidence-based practice interventions, such as cancer screening.

Trial registration

This study was retrospectively registered on February 4th, 2015 with Clinicaltrials.gov (#NCT02355262). The registry record can be found at https://www.clinicaltrials.gov/ct2/show/NCT02355262.

Trend changes of national zolpidem users and exposure cases after FDA drug safety communications

PURPOSE

To evaluate the impact of FDA's 2013 zolpidem Drug Safety Communications (DSCs), which recommended lowering the initial dose to mitigate drowsiness, on national estimates of zolpidem users and zolpidem exposure cases.

METHODS

We analyzed trend changes of national zolpidem users from the IQVIA Total Patient Tracker (TPT) and zolpidem exposure cases reported to the National Poison Data System (NPDS), 2009-2018. To control for time varying confounding, the adjusted trends were analyzed using simple and controlled interrupted time series (ITS). We also adjusted for seasonal changes. Three sedating antidepressants were used together as a control.

RESULTS

The national estimates of high-dose zolpidem users in TPT decreased significantly in the month immediately post-DSC; the absolute level decrease was -12.51 (95% CI: -14.12, -10.89) per 10 000 U.S. population relative to sedating antidepressants. The trend continuously decreased post-DSC, resulting in a 59% overall decrease by the end of the study period. There was a larger decrease in high-dose zolpidem use in females than in males. There was a level decrease of zolpidem exposure cases in the NPDS immediately post-DSC, -0.37 absolute decline (95% CI, -0.53, -0.20) per 10 000 national zolpidem users; or -1.33 absolute decline (95% CI, -1.54, -1.13) per 1000 total NPDS exposure cases relative to sedating antidepressants. Similar patterns were observed for cases reporting drowsiness. The results from the single ITS and controlled ITS were similar.

CONCLUSIONS

Zolpidem users and exposure cases decreased significantly post-DSC, suggesting practitioners and patients became aware of and responded to the zolpidem DSCs.

Examining the Potential Relationship Between Multidisciplinary Team Meetings and Patient Survival in Pediatric Oncology Settings: A Systematic Review

The organization of multidisciplinary team meetings (MTMs) has become standard practice in pediatric oncology and is widely felt to improve communication, knowledge, and patient care. Although the impact of MTMs on survival in adult oncology has been extensively researched, the potential benefits of survival for pediatric cancer patients are still unclear. This systematic review aimed to examine the impact of MTMs on survival in pediatric oncology settings. Relevant studies were identified by searching MEDLINE/PubMed, EMBASE, and the Cochrane Library databases up to January 2020, resulting in 325 unique records. After the title/abstract and full-text screening, 5 studies were included. All of the included studies (one prospective and 4 retrospective cohort studies) described a difference in overall or event-free survival when comparing patients who were discussed in MTMs with non-MTM patients. This association was statistically significant in 3 studies. The quality of the studies was strongly affected by their design. Because of the small number of studies in combination with high clinical and methodological heterogeneity, this review was unable to definitively assert a causal relationship between MTMs and survival in pediatric cancer patients. Further research is needed to explore this relationship and allow cost-benefit analyses, so that time and resources are optimally spent to deliver the best possible care to childhood cancer patients.

Reflection on modern methods: a common error in the segmented regression parameterization of interrupted time-series analyses

Interrupted time-series (ITS) designs are a robust and increasingly popular non-randomized study design for strong causal inference in the evaluation of public health interventions. One of the most common techniques for model parameterization in the analysis of ITS designs is segmented regression, which uses a series of indicators and linear terms to represent the level and trend of the time-series before and after an intervention. In this article, we highlight an important error often presented in tutorials and published peer-reviewed papers using segmented regression parameterization for the analyses of ITS designs. We show that researchers cannot simply use the product between their calendar time variable and the indicator variable indicating pre- versus post-intervention time periods to represent the post-intervention linear segment. If researchers use this often-presented parameterization, they will get an erroneous result for the level change in their time-series. We show that researchers must take care to use the product between their intervention variable and the time elapsed since the start of the intervention, rather than the time since the beginning of their study. Thus, the second linear segment of the time-series indexing the post-intervention level and trend should be zero before intervention implementation and begin by counting from zero, rather than counting from the time elapsed since the beginning of the study. We hope that this article can clarify segmented regression parameterization for the analysis of ITS designs and help researchers avoid confusing and erroneous results in the level changes of their time-series.

Birds of a feather flock together: Comparing controlled pre-post designs

OBJECTIVE

To formalize comparative interrupted time series (CITS) using the potential outcomes framework; compare two version of CITS-a standard linear version and one that adds postperiod group-by-time parameters-to two versions of difference-in-differences (DID)-a standard version with time fixed effects and one that adds group-specific pretrends; and reanalyze three previously published papers using these models.

DATA SOURCES

Outcome data for reanalyses come from two counties' jail booking and release data, Medicaid prescription drug rebate data from the Centers for Medicare and Medicaid Services (CMS), and acute hepatitis C incidence from the Centers for Disease Control and Prevention.

STUDY DESIGN

DID and CITS were compared using potential outcomes, and reanalyses were conducted using the four described pre-post study designs.

DATA COLLECTION/EXTRACTION METHODS

Data from county jails were provided by sheriffs. Data from CMS are publicly available. Data for the third reanalysis were provided by the authors of the original study.

PRINCIPAL FINDINGS

Though written differently and preferred by different research communities, the general version of CITS and DID with group-specific pretrends are the same: they yield the same counterfactuals and identify the same treatment effects. In a reanalysis with evidence of divergent preperiod trends, failing to account for this in standard DID led to an 84% smaller effect estimate than the more flexible models. In a second reanalysis with evidence of nonlinear outcome trends, failing to account for this in linear CITS led to a 28% smaller effect estimate than the more flexible models.

CONCLUSION

We recommend detailing a causal model for treatment selection and outcome generation and the required counterfactuals before choosing an analytical approach. The more flexible versions of DID and CITS can accommodate features often found in real data, namely, nonlinearities and divergent preperiod outcome trends.

Impact of COAP on access to primary care in Ceará and Mato Grosso do Sul through the analysis of interrupted time series

OBJECTIVE

To analyze the impact of implementing the Contrato Organizativo de Ação Pública (COAP - Public Action Organizational Contract) on the expansion of access to primary care in the states of Ceará and Mato Grosso do Sul.

METHODS

We used the interrupted time series method to analyze the effect of COAP on primary care coverage (PCCov) and on avoidable hospitalization rates. To analyze the effects of increased PCCov on avoidable hospitalizations, we used non-segmented time series models.

RESULTS

The results showed that implementing COAP had a positive impact on increased coverage in both cases, with did not happen in the control states. However, this impact was not reflected in the decrease in hospitalizations due to primary care sensitive conditions (HPCSC) or for acute preventable causes. When we analyzed the effects of the increase in PCCov on avoidable hospitalizations between 2009 and 2016, we observed that coverage had a positive impact on the decrease in the rate of HPCSC only in Ceará, although hospitalizations have a significant trend to decrease in time both in this state and in Mato Grosso do Sul, except for acute respiratory infections.

CONCLUSIONS

The COAP continues to be the regulatory instrument of regionalization force, however, the results obtained by adhering to it in the expansion of primary care in Ceará and Mato Grosso do Sul makes us question whether the contractual model, as predicted, is the best instrument for advancing regionalization in the Brazilian Unified Health System.

Over half of clinical practice guidelines use non-systematic methods to inform recommendations: A methods study

INTRODUCTION

Assessing the process used to synthesize the evidence in clinical practice guidelines enables users to determine the trustworthiness of the recommendations. Clinicians are increasingly dependent on guidelines to keep up with vast quantities of medical literature, and guidelines are followed to avoid malpractice suits. We aimed to assess whether systematic methods were used when synthesizing the evidence for guidelines; and to determine the type of review cited in support of recommendations.

METHODS

Guidelines published in 2017 and 2018 were retrieved from the TRIP and Epistemonikos databases. We randomly sorted and sequentially screened clinical guidelines on all topics to select the first 50 that met our inclusion criteria. Our primary outcomes were the number of guidelines using either a systematic or non-systematic process to gather, assess, and synthesise evidence; and the numbers of recommendations within guidelines based on different types of evidence synthesis (systematic or non-systematic reviews). If a review was cited, we looked for evidence that it was critically appraised, and recorded which quality assessment tool was used. Finally, we examined the relation between the use of the GRADE approach, systematic review process, and type of funder.

RESULTS

Of the 50 guidelines, 17 (34%) systematically synthesised the evidence to inform recommendations. These 17 guidelines clearly reported their objectives and eligibility criteria, conducted comprehensive search strategies, and assessed the quality of the studies. Of the 29/50 guidelines that included reviews, 6 (21%) assessed the risk of bias of the review. The quality of primary studies was reported in 30/50 (60%) guidelines.

CONCLUSIONS

High quality, systematic review products provide the best available evidence to inform guideline recommendations. Using non-systematic methods compromises the validity and reliability of the evidence used to inform guideline recommendations, leading to potentially misleading and untrustworthy results.

Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions

BACKGROUND

Interrupted time series analysis is increasingly used to evaluate the impact of large-scale health interventions. While segmented regression is a common approach, it is not always adequate, especially in the presence of seasonality and autocorrelation. An Autoregressive Integrated Moving Average (ARIMA) model is an alternative method that can accommodate these issues.

METHODS

We describe the underlying theory behind ARIMA models and how they can be used to evaluate population-level interventions, such as the introduction of health policies. We discuss how to select the shape of the impact, the model selection process, transfer functions, checking model fit, and interpretation of findings. We also provide R and SAS code to replicate our results.

RESULTS

We illustrate ARIMA modelling using the example of a policy intervention to reduce inappropriate prescribing. In January 2014, the Australian government eliminated prescription refills for the 25 mg tablet strength of quetiapine, an antipsychotic, to deter its prescribing for non-approved indications. We examine the impact of this policy intervention on dispensing of quetiapine using dispensing claims data.

CONCLUSIONS

ARIMA modelling is a useful tool to evaluate the impact of large-scale interventions when other approaches are not suitable, as it can account for underlying trends, autocorrelation and seasonality and allows for flexible modelling of different types of impacts.

Evaluation of a Pilot Vancomycin Precision Dosing Advisory Service on Target Exposure Attainment Using an Interrupted Time Series Analysis

This study evaluated the ability of a pilot therapeutic drug monitoring (TDM) Advisory Service to facilitate vancomycin therapeutic target attainment within a real-world clinical setting. The Service provided area under the concentration-time curve (AUC)-guided vancomycin dose recommendations, using Bayesian forecasting software and clinical expertise, to prescribers at an Australian hospital. A retrospective audit of intravenous vancomycin therapy (> 48 hours) in adults (≥ 18 years old) was undertaken over a 54-month period to evaluate attainment of established vancomycin pharmacokinetic/pharmacodynamic targets (AUC over 24 hours / minimum inhibitory concentration: 400-600) before (36-month period) and after (18-month period) Service implementation. Interrupted time series analysis was employed to evaluate monthly measures of the median proportion of therapy spent within the target range. Indices of time to target attainment were also assessed before and after Service implementation. The final cohort comprised 1,142 courses of vancomycin (816 patients); 835 courses (596 patients) and 307 courses (220 patients) administered before and after Service implementation, respectively. Prior to piloting the Service, the median proportion of time in the target range was 40.1% (95% CI, 34.3-46.0%); this increased by 10.4% (95% CI, 1.2-19.6%, P = 0.03) after the Service, and was sustained throughout the post-Service evaluation period. Post-Service target attainment at 48-72 hours after initiation of therapy was increased (7.8%, 95% CI, 1.3-14.3%, P = 0.02). The findings of this study provide evidence that a consultative TDM Service can facilitate attainment of vancomycin therapeutic targets; however, optimization of the Service may further improve the use of vancomycin.

An examination of relationships between cannabis legalization and fatal motor vehicle and pedestrian-involved crashes

OBJECTIVE

While attention has been given to how legalization of recreational cannabis affects traffic crash rates, there was been limited research on how cannabis affects pedestrians involved in traffic crashes. This study examined the association between cannabis legalization (medical, recreational use, and recreational sales) and fatal motor vehicle crash rates (both pedestrian-involved and total fatal crashes).

METHODS

We used crash data from the Fatality Analysis Reporting System (FARS) to calculate monthly rates of fatal motor vehicle crashes and fatal pedestrian-involved crashes per 100,000 people from 1991 to 2018. Changes in monthly crash rates in three states that had legalized cannabis (Colorado, Washington, and Oregon) were compared to matched control states using segmented regression with autoregressive terms.

RESULTS

We found no significant differences in pedestrian-involved fatal motor vehicle crashes between legalized cannabis states and control states following medical or recreational cannabis legalization. Washington and Oregon saw immediate decreases in all fatal crashes (-4.15 and -6.60) following medical cannabis legalization. Colorado showed an increase in trend for all fatal crashes after recreational cannabis legalization and the beginning of sales (0.15 and 0.18 monthly fatal crashes per 100,000 people).

CONCLUSIONS

Overall findings do not suggest an elevated risk of total or pedestrian-involved fatal motor vehicle crashes associated with cannabis legalization.

Association of an Acute Myocardial Infarction Readmission-Reduction Program With Mortality and Readmission

BACKGROUND

Reducing hospital readmission after acute myocardial infarction (AMI) has the potential to both improve quality and reduce costs. As such, readmission after AMI has been a target of financial penalties through Medicare. However, substantial concern exists about potential adverse effects and efficacious readmission-reduction strategies are not well validated.

METHODS AND RESULTS

We started an AMI readmissions reduction program in November 2017. Between July 2016 and February 2019, hospital billing data were queried to detect all inpatient hospitalizations at the Massachusetts General Hospital for AMI. Thirty-day readmission was identified through hospital billing data, and mortality was extracted from our electronic health record. The data set was merged with claims data for patients in accountable care organizations to detect readmission at other hospitals. We performed segmented linear regression, adjusting for secular trend and case mix, to assess the independent association of our program on both outcome variables. After inclusion and exclusion criteria were applied, the study population included 2020 patients. The overall 30-day readmission rate was higher before the intervention than after the intervention (15.5% versus 10.7%, P=0.002). The overall 30-day mortality rate was similar in both time periods (1.8% versus 1.4%, P=0.457). The program was associated with initial reduction in 30-day readmission (-9.8%, P=0.0002) and 30-day mortality (-2.6%, P=0.041). The program did not change trend in 30-day readmission (+0.19% readmissions/mo, P=0.554) and trend in 30-day mortality (-0.21% deaths/mo, P=0.119).

CONCLUSIONS

An AMI readmissions reduction program that increases outpatient and emergency department (ED) access to cardiology care is associated with reduced 30-day readmission and 30-day mortality. Similar statistical techniques can be used to conduct a rigorous, mechanistic program evaluation of other quality improvement initiatives.

The effect of preauthorization and prospective audit and feedback system on oral antimicrobial prescription for outpatients at a children's hospital in Japan

Antimicrobial stewardship programs (ASP) for oral antibiotics is still uncommon, despite the fact that oral antibiotics prescription accounts for 90% of total antibiotic consumption in developed countries. We introduced preauthorization and prospective audit and feedback (PAF) system on broad-spectrum oral antimicrobials as a part of ASP intervention from October 2015 in a tertiary children's hospital in Japan. Antimicrobial consumption and cost of targeted oral antimicrobials decreased from 11.1 days of therapy (DOT) per 1000 outpatient visits and 860,040 yen ($ 7167: 1 $ = 120 yen) to 1.9 DOT per 1000 outpatient visits and 142,200 yen ($ 1185) annually, respectively (p < 0.001). Interrupted time-series analysis showed that prescriptions for targeted antimicrobials decreased rapidly after initiation of preauthorization (p < 0.001). Prescriptions for non-targeted oral antimicrobial increased temporarily (p < 0.001), but a decreasing trend was found after the initiation (p < 0.001). In pre-intervention period, the main indications for using targeted antimicrobials were upper and lower respiratory infection, urinary tract infections, prophylaxis for medical procedures and otitis media, but only 21.4% of them were appropriate prescription. The appropriate prescription rate of post -intervention period increased to 58.5%. During the study period, the susceptibility pattern of major bacteria to these antimicrobials did not change. In conclusion, introduction of the preauthorization and PAF for selected oral antimicrobials decreased the antimicrobial use of both targeted and non-targeted antimicrobials. This intervention may be an effective method of ASP for other children's hospitals.

Digital Mammography Stereotactic Biopsy versus Digital Breast Tomosynthesis-guided Biopsy: Differences in Biopsy Targets, Pathologic Results, and Discordance Rates

Background Digital breast tomosynthesis (DBT)-guided biopsy is increasingly used in practice. It is important to know expected changes in biopsy targets, pathologic results, and discordance rates. Purpose To compare biopsy target types, pathologic results, and discordance rates for 2 years preceding and 2 years following implementation of DBT-guided biopsy. Materials and Methods All 9-gauge vacuum-assisted core biopsies from a single tertiary breast center that used digital mammography (DM) stereotactic guidance from 2013 to 2015 and DBT-guided biopsy from 2015 to 2017 were retrospectively reviewed. All mammographic examinations were performed with DBT. Patient demographics, biopsy target type, pathologic reports, surgical excision specimens when available, breast density, and imaging follow-up results were recorded. Biopsy targets and discordance rates between radiologic and pathologic examinations were compared between the two biopsy groups. Generalized mixed modeling was used to examine results before and after DBT-guided biopsy. Results A total of 1313 women underwent 1405 breast biopsies: 643 by using DM (August 2013 to July 2015) (median age, 56 years; interquartile range, 49-66 years) and 762 by using DBT (August 2015 to July 2017) (median age, 58 years; interquartile range, 50-67 years), (P = .58). Calcifications were the most common biopsy target for both groups, constituting 89.9% (578 of 643) of DM-guided biopsies and 71.1% (542 of 762) of DBT-guided biopsies (P = .03). The rate of architectural distortion biopsies was 2.0% (13 of 643) with DM-guided biopsy and 17.7% (135 of 762) with DBT-guided biopsy (P = .01). Although overall malignancy rate was similar for DM-guided biopsy (27.8% [179 of 643]) and DBT-guided biopsy (24.8% [191 of 762], P = .54), DBT-guided biopsy helped identify a similar percentage of invasive malignancies (37.4% [72 of 191] vs 29.0% [52 of 179] at DM P = .35), but more radial sclerosing lesions (8.3% [95% confidence interval {CI}: 6.0, 10.0] vs 1.7% [95% CI: 1.0, 3.1]) (P = .01). The discordance rate was 1.4% (95% CI: 1.0, 2.7) with DM-guided biopsy and 4.5% (95% CI: 3.2, 6.3) with DBT-guided biopsy (P = .01). Of the 34 discordant DBT-guided biopsies, 30 were architectural distortions. Conclusion With the transition to digital breast tomosynthesis-guided biopsy, more architectural distortions were biopsied, more radial sclerosing lesions were identified, and more discordance existed in radiologic and pathologic examinations, with a similar percentage of carcinomas diagnosed. © RSNA, 2020 Online supplemental material is available for this article.

Reprint of: Blood Sugar Regulation for Cardiovascular Health Promotion and Disease Prevention: JACC Health Promotion Series

The primary objective of this study was to analyze the most up-to-date evidence regarding whether and how blood sugar regulation influences cardiovascular health promotion and disease prevention by carrying out an umbrella review. Three separate, systematic literature searches identified 2,343 papers in total. Overall, 44 studies were included for data extraction and analysis. The included systematic reviews and meta-analyses published between January 1, 2016, and December 31, 2017, were of good to very good quality (median Overview Quality Assessment Questionnaire score = 17). Identified evidence suggests that cardiovascular disease (CVD) prevention services should consider regulation of blood glucose as a key target for intervention. Furthermore, the recommendations for effective intervention and service development/training described here for prevention of CVD should be adopted into evidence-based practice guidelines. Multidisciplinary teams should be formed to deliver multicomponent interventions in community-based settings. There may be substantial opportunities for integrating CVD and diabetes prevention services.

Blood Sugar Regulation for Cardiovascular Health Promotion and Disease Prevention: JACC Health Promotion Series

The primary objective of this study was to analyze the most up-to-date evidence regarding whether and how blood sugar regulation influences cardiovascular health promotion and disease prevention by carrying out an umbrella review. Three separate, systematic literature searches identified 2,343 papers in total. Overall, 44 studies were included for data extraction and analysis. The included systematic reviews and meta-analyses published between January 1, 2016, and December 31, 2017, were of good to very good quality (median Overview Quality Assessment Questionnaire score = 17). Identified evidence suggests that cardiovascular disease (CVD) prevention services should consider regulation of blood glucose as a key target for intervention. Furthermore, the recommendations for effective intervention and service development/training described here for prevention of CVD should be adopted into evidence-based practice guidelines. Multidisciplinary teams should be formed to deliver multicomponent interventions in community-based settings. There may be substantial opportunities for integrating CVD and diabetes prevention services.

Comparison of iodine status pre- and post-mandatory iodine fortification of bread in South Australia: a population study using newborn thyroid-stimulating hormone concentration as a marker

Objective:

The present study aimed to evaluate the effect of mandatory iodine fortification of bread on the iodine status of South Australian populations using newborn thyroid-stimulating hormone (TSH) concentration as a marker.

Design:

The study used an interrupted time-series design.

Setting:

TSH data collected between 2005 and 2016 (n 211 033) were extracted from the routine newborn screening programme in South Australia for analysis. Iodine deficiency is indicated when more than 3 % of newborns have TSH > 5 mIU/l.

Participants:

Newborns were classified into three groups: the pre-fortification group (those born before October 2009); the transition group (born between October 2009 and June 2010); and the post-fortification group (born after June 2010).

Results:

The percentage of newborns with TSH > 5 mIU/l was 5·1, 6·2 and 4·6 % in the pre-fortification, transition and post-fortification groups, respectively. Based on a segmented regression model, newborns in the post-fortification period had a 10 % lower risk of having TSH > 5 mIU/l than newborns in the pre-fortification group (incidence rate ratio (IRR) = 0·90; 95 % CI 0·87, 0·94), while newborns in the transitional period had a 22 % higher risk of having TSH > 5 mIU/l compared with newborns in the pre-fortification period (IRR = 1·22; 95 % CI 1·13, 1·31).

Conclusions:

Using TSH as a marker, South Australia would be classified as mild iodine deficiency post-fortification in contrast to iodine sufficiency using median urinary iodine concentration as a population marker. Re-evaluation of the current TSH criteria to define iodine status in populations is warranted in this context.

Impact of removal and restriction of me-too medicines in a hospital drug formulary on in- and outpatient drug prescriptions: interrupted time series design with comparison group

BACKGROUND

The study covered in- and out-of-hospital care in a region in north-western Spain. The intervention evaluated took the form of a change in the hospital drugs formulary. Before the intervention, the formulary contained four of the five low molecular weight heparins (LMWHs) marketed in Spain. The intervention consisted of withdrawing two LMWHs (bemiparin and dalteparin) from the formulary and restricting the use of another (tinzaparin), leaving only enoxaparin as an unrestricted prescription LMWH. Accordingly, the aim of this study was to evaluate the effect on in- and outpatient drug prescriptions of removing and restricting the use of several LMWHs in a hospital drugs formulary.

METHODS

We used a natural, before-after, quasi-experimental design with a control group and monthly data from January 2011 to December 2016. Based on data drawn from official Public Health Service sources, the following dependent variables were extracted: defined daily doses (DDD) per 1000 inhabitants per day (DDD/TID), DDD per 100 stays per day, and expenditure per DDD.

RESULTS

The two compounds that were removed from the formulary registered an immediate decrease at both an intra- and out-of-hospital level (66.6% and 55.6% for bemiparin and 73.0% and 92.2% for dalteparin, respectively); similarly, the compound that was restricted also registered an immediate decrease (36.1% and 9.0% at the in- and outpatient levels, respectively); in contrast, the remaining LMWH (enoxaparin) registered an immediate, significant increase at both levels (44.9% and 32.6%, respectively). The intervention led to an immediate reduction of 6.8% and a change in trend in out-of-hospital cost/DDD; it also avoided an expenditure of €477,317.1 in the 21 months following the intervention.

CONCLUSIONS

The results indicate that changes made in a hospital drugs formulary towards more efficient medications may lead to better use of pharmacotherapeutic resources in its health catchment area.

Effects of mandatory prescription drug monitoring program (PDMP) use laws on prescriber registration and use and on risky prescribing

BACKGROUND

Comprehensive mandatory use laws for prescription drug monitoring programs (PDMPs) have been implemented in a number of states to help address the opioid overdose epidemic. These laws may reduce opioid-related overdose deaths by increasing prescribers' use of PDMPs and reducing high-risk prescribing behaviors.

METHODS

We used state PDMP data to examine the effect of these mandates on prescriber registration, use of the PDMP, and on prescription-based measures of patient risk in three states-Kentucky, Ohio, and West Virginia-that implemented mandates between 2010 and 2015. We conducted comparative interrupted time series analyses to examine changes in outcome measures after the implementation of mandates in the mandate states compared to control states.

RESULTS

Mandatory use laws increased prescriber registration and utilization of the PDMP in the mandate states compared to controls. The multiple provider episode rate, rate of opioid prescribing, rate of overlapping opioid prescriptions, and rate of overlapping opioid/benzodiazepine prescriptions decreased in Kentucky and Ohio. Nevertheless, the magnitude of changes in these measures varied among mandates states.

CONCLUSIONS

These findings indicate that PDMP mandates have the potential to reduce risky opioid prescribing practices. Variation in the laws may explain why the effectiveness varied between states.

The impact of the 2016 flood event in Anhui Province, China on infectious diarrhea disease: An interrupted time-series study

Climate change may bring more frequent and severe floods which will heighten public health problems, including an increased risk of infectious diarrhea in susceptible populations. Affected by heavy rainfall and an El Niño event, a destructive flood occurred in Anhui province, China on 18th June 2016. This study investigates the impact of this severe flood on infectious diarrhea at both city-level and provincial level, and further to identify modifying factor. We obtained information on infectious diarrheal cases during 2013-2017 from the National Disease Surveillance System. An interrupted time-series design was used to estimate effects of the flood event on diarrhea in 16 cities. Then we applied a meta-analysis to estimate the area-level pooled effects of the flood in both flooded areas and non-flooded areas. Finally, a meta-regression was applied to determine whether proximity to flood was a predictor of city-level risks. Stratified analyses by gender and age group were also conducted for flooded areas. A significant increase in infectious diarrhea risk (RR = 1.11, 95% CI: 1.01, 1.23) after the flood event was found in flooded area with variation in risks across cities, while there was no increase in non-flooded areas. Diarrheal risks post-flood was progressively higher in cities with greater proximity to the Yangtze River. Children aged 5-14 were at highest risk of diarrhea post-flood in the flooded areas. Our study provides strong evidence that the 2016 severe flood significantly increased infectious diarrheal risk in exposed populations. Local public health agencies are advised to develop intervention programs to prevent and control infectious diarrhea risk when a major flood occurs, especially in areas close to water bodies and among vulnerable populations.

Impact of legislation to reduce the drink-drive limit on road traffic accidents and alcohol consumption in Scotland: a natural experiment study

Background

It is widely recognised that drink driving is a leading cause of road traffic accidents (RTAs). There is evidence that changing the drink-drive limit from a blood alcohol concentration of 0.08 to 0.05 g/dl is effective in reducing RTAs. Scotland changed the blood alcohol concentration limit to 0.05 g/dl on 5 December 2014.

Aims

To assess whether or not the numbers and rates of RTAs and per capita alcohol consumption in Scotland were reduced because of the 2014 drink-drive legislation. To assess whether or not the 2014 change in legislation provided good value for money.

Design

A natural experimental, quantitative study. The control group was England and Wales, that is, the other countries in Great Britain, where the drink-drive legislation remained unchanged.

Setting

Great Britain.

Participants

The entire population of Scotland, England and Wales for the period of January 2013–December 2016.

Intervention

The change to drink-drive legislation in Scotland.

Outcome measures

The counts and rates of RTAs; and per capita alcohol consumption.

Methods

For the numbers and rates of RTAs (both traffic flow and population denominators were used), and separately for the intervention and control trial groups, negative binomial regression models were fitted to panel data sets to test for a change in outcome level after the new 2014 legislation was in place. To obtain a ‘difference-in-differences’ (DiD)-type measure of effect, an interaction term between the intervention group indicator and the binary covariate for indicating pre and post change in legislation (‘pseudo’-change for the control group) was assessed. For off- and on-trade per capita alcohol sales, and separately for the intervention and control trial group, seasonal autoregressive integrated moving average error models were fitted to the relevant time series.

Results

The change to drink-drive legislation was associated with a 2% relative decrease in RTAs in Scotland [relative risk (RR) 0.98, 95% CI 0.91 to 1.04; p = 0.53]. However, the pseudo-change in legislation was associated with a 5% decrease in RTAs in England and Wales (RR 0.95, 95% CI 0.90 to 1.00; p = 0.05). For RTA rates, with traffic flow as the denominator, the DiD-type estimate indicated a 7% increase in rates for Scotland relative to England and Wales (unadjusted RR 1.07, 95% CI 0.98 to 1.17; p = 0.1). The change to drink-drive legislation was associated with a 0.3% relative decrease in per capita off-trade sales (–0.3%, 95% CI –1.7% to 1.1%; p = 0.71) and a 0.7% decrease in per capita on-trade sales (–0.7%, 95% CI –0.8% to –0.5%; p < 0.001).

Conclusion

The change to drink-drive legislation in Scotland in December 2014 did not have the expected effect of reducing RTAs in the country, and nor did it change alcohol drinking levels in Scotland. This main finding for RTAs was unexpected and the research has shown that a lack of enforcement is the most likely reason for legislation failure.

Future work

Investigations into how the public interpret and act on changes in drink-drive legislation would be welcome, as would research into whether or not previous change in drink-drive legislation effects on RTAs in other jurisdictions are associated with the level of enforcement that took place.

Trial registration

Current Controlled Trials ISRCTN38602189.

Funding

This project was funded by the National Institute for Health Research (NIHR) Public Health Research programme and will be published in full in Public Health Research; Vol. 7, No. 12. See the NIHR Journals Library website for further project information.

Examining the impact of WHO's Focused Antenatal Care policy on early access, underutilisation and quality of antenatal care services in Malawi: a retrospective study

Background

A variety of antenatal care models have been implemented in low and middle-income countries over the past decades, as proposed by the World Health Organisation (WHO). One such model is the 2001 Focused Antenatal Care (FANC) programme. FANC recommended a minimum of four visits for women with uncomplicated pregnancies and emphasised quality of care to improve both maternal and neonatal outcomes. Malawi adopted FANC in 2003, however, up to now no study has been done to analyse the model’s performance with regards to antenatal care service quality and utilisation patterns.

Methods

The paper is based on data pooled from three comparable nationally representative Malawi Demographic and Health Survey (MDHS) datasets (2000, 2004 and 2010). The DHS collects data on demographics, socio-economic indicators, antenatal care, and the fertility history of reproductive women aged between 15 and 49. We pooled a sample of 8545 women who had a live birth in the last 5 years prior to each survey. We measure the impact of FANC on early access to care, underutilisation of care and quality of care with interrupted time series analysis. This method enables us to track changes in both levels and the trends of our outcome variables.

Results

We find that FANC is associated with earlier access to care. However, it has also been associated with unintended increases in underutilisation. We see no change in the quality of ANC services.

Conclusion

In light of the WHO 2016 ANC guidelines, which recommend an increase of visits to eight, these results are important. Given that we find underutilisation when the benchmark is set at four visits, eight visits are unlikely to be feasible in low-resource settings.

Evaluation of intensified dengue control measures with interrupted time series analysis in the Panadura Medical Officer of Health division in Sri Lanka: a case study and cost-effectiveness analysis

BACKGROUND

Dengue has become a major public health problem in Sri Lanka with a considerable economic burden. As a response, in June, 2014, the Ministry of Health initiated a proactive vector control programme in partnership with military and police forces, known as the Civil-Military Cooperation (CIMIC) programme, that was targeted at high-risk Medical Officer of Health (MOH) divisions in the country. Evaluating the effectiveness and cost-effectiveness of population-level interventions is essential to guide public health planning and resource allocation decisions, particularly in resource-limited health-care settings.

METHODS

Using an interrupted time series design with a non-linear extension, we evaluated the impact of vector control interventions from June 22, 2014, to Dec 29, 2016, in Panadura, a high-risk MOH division in Western Province, Sri Lanka. We used dengue notification and larval survey data to estimate the reduction in Breteau index and dengue incidence before and after the intervention using two separate models, adjusting for time-varying confounding variables (ie, rainfall, temperature, and the Oceanic Niño Index). We also assessed the cost and cost-effectiveness of the CIMIC programme from the perspective of the National Dengue Control Unit under the scenarios of different levels of hospitalisation of dengue cases (low [25%], medium [50%], and high [75%]) in terms of cost per disability-adjusted life-year averted (DALY).

FINDINGS

Vector control interventions had a significant impact on combined Breteau index (relative risk reduction 0·43, 95% CI 0·26 to 0·70) and on dengue incidence (0·43, 0·28 to 0·67), the latter becoming prominent 2 months after the intervention onset. The mean number of averted dengue cases was estimated at 2192 (95% CI 1741 to 2643), and the total cost of the CIMIC programme at 2016 US$271 615. Personnel costs accounted for about 89% of the total cost. In the base-case scenario of moderate level of hospitalisation, the CIMIC programme was cost-saving with a probability of 70% under both the lowest ($453) and highest ($1686) cost-effectiveness thresholds, resulting in a net saving of $20 247 (95% CI -57 266 to 97 790) and averting 176 DALYs (133 to 226), leading to a cost of -$98 (-497 to 395) per DALY averted. This was also the case for the scenario with high hospitalisation levels (cost per DALY averted -$512, 95% CI -872 to -115) but with a higher probability of 99%. In the scenario with low hospitalisation levels (cost per DALY averted $690, 143 to 1379), although the CIMIC programme was cost-ineffective at the lowest threshold with a probability of 77%, it was cost-effective at the highest threshold with a probability of 99%.

INTERPRETATION

This study suggests that communities affected by dengue can benefit from investments in vector control if interventions are implemented rigorously and coordinated well across sectors. By doing so, it is possible to reduce the disease and economic burden of dengue in endemic settings.

FUNDING

None.

Evaluation of the impact that the changes in tuberculosis treatment implemented in Brazil in 2009 have had on disease control in the country

Objective:

To analyze the impact that the 2009 changes in tuberculosis treatment in Brazil had on the rates of cure, tuberculosis recurrence, mortality, treatment abandonment, and multidrug-resistant tuberculosis (MDR-TB).

Methods:

An ordinary least squares regression model was used in order to perform an interrupted time series analysis of secondary data collected from the Brazilian Tuberculosis Case Registry Database for the period between January of 2003 and December of 2014.

Results:

The 2009 changes in tuberculosis treatment in Brazil were found to have no association with reductions in the total number of cases (β = 2.17; 95% CI: −3.80 to 8.14; p = 0.47) and in the number of new cases (β = −0.97; 95% CI: −5.89 to 3.94; p = 0.70), as well as having no association with treatment abandonment rates (β = 0.40; 95% CI: −1.12 to 1.93; p = 0.60). The changes in tuberculosis treatment also showed a trend toward an association with decreased cure rates (β = −4.14; 95% CI: −8.63 to 0.34; p = 0.07), as well as an association with increased mortality from pulmonary tuberculosis (β = 0.77; 95% CI: 0.16 to 1.38; p = 0.01). Although there was a significant increase in MDR-TB before and after the changes (p < 0.0001), there was no association between the intervention (i.e., the changes in tuberculosis treatment) and the increase in MDR-TB cases.

Conclusions:

The changes in tuberculosis treatment were unable to contain the decrease in cure rates, the increase in treatment abandonment rates, and the increase in MDR-TB rates, being associated with increased mortality from pulmonary tuberculosis during the study period.

Keywords: Tuberculosis, pulmonary/epidemiology; Tuberculosis, pulmonary/drug therapy; Tuberculosis, pulmonary/mortality; Interrupted time series analysis; Drug resistance, multiple; Drug compounding.

The use of controls in interrupted time series studies of public health interventions

Interrupted time series analysis differs from most other intervention study designs in that it involves a before-after comparison within a single population, rather than a comparison with a control group. This has the advantage that selection bias and confounding due to between-group differences are limited. However, the basic interrupted time series design cannot exclude confounding due to co-interventions or other events occurring around the time of the intervention. One approach to minimizse potential confounding from such simultaneous events is to add a control series so that there is both a before-after comparison and an intervention-control group comparison. A range of different types of controls can be used with interrupted time series designs, each of which has associated strengths and limitations. Researchers undertaking controlled interrupted time series studies should carefully consider a priori what confounding events may exist and whether different controls can exclude these or if they could introduce new sources of bias to the study. A prudent approach to the design, analysis and interpretation of controlled interrupted time series studies is required to ensure that valid information on the effectiveness of health interventions can be ascertained.

Advancing Health Policy and Program Research in Diabetes: Findings from the Natural Experiments for Translation in Diabetes (NEXT-D) Network

PURPOSE OF REVIEW

To advance our understanding of the impacts of policies and programs aimed at improving detection, engagement, prevention, and clinical diabetes management in the USA, we synthesized findings from a network of studies that used natural experiments to evaluate diabetes health policies and programs.

FINDINGS

Studies from the Natural EXperiments for Translation in Diabetes (NEXT-D) network used rigorous longitudinal quasi-experimental study designs (e.g., interrupted time series) and analytical methods (e.g., difference-in-differences) to augment causal inference. Investigators partnered with health system stakeholders to evaluate whether glucose testing rates changed from before-to-after clinic interventions (e.g., integrating electronic screening decision prompts in New York City) or employer programs (e.g., targeted messaging and waiving copayments for at-risk employees). Other studies examined participation and behavior change in low- (e.g., wellness coaching) or high-intensity lifestyle modification programs (e.g., diabetes prevention program-like interventions) offered by payers or employers. Lastly, studies assessed how employer health insurance benefits impacted healthcare utilization, adherence, and outcomes among people with diabetes. NEXT-D demonstrated that low-intensity interventions to facilitate glucose testing and enhance engagement in lifestyle modification were associated with small improvements in weight but large improvements in screening and testing when supported by electronic health record-based decision-support. Regarding high-intensity diabetes prevention program-like lifestyle programs offered by payers or employers, enrollment was modest and led to weight loss and marginally lower short-term health expenditures. Health plans that incentivize patient behaviors were associated with increases in medication adherence. Meanwhile, shifting patients to high-deductible health plans was associated with no change in medication use and preventive screenings, but patients with diabetes delayed accessing healthcare for acute complications (e.g., cellulitis). Findings were more pronounced among lower-income patients, who experienced increased rates and acuity of emergency department visits for diabetes complications and other high-severity conditions. Findings from NEXT-D studies provide informative data that can guide programs and policies to facilitate detection, prevention, and treatment of diabetes in practice.