A pandemia de covid-19 e a morbidade hospitalar por transtorno mental e comportamental no Brasil: uma análise de série temporal interrompida, janeiro de 2008 a julho de 2021 / La pandemia de covid-19 y la morbilidad hospitalaria por trastornos mentales y del comportamiento en Brasil: un análisis de series temporales interrompidas, de enero de 2008 a julio de 2021 / The covid-19 pandemic and hospital morbidity due to mental and behavioral disorders in Brazil: an interrupted time series analysis, from January 2008 to July 2021

Objective: to analyze records of hospitalizations due to mental and behavioral disorders before and after the beginning of the covid-19 pandemic in Brazil, from January 2008 to July 2021. Methods: this was a descriptive ecological interrupted time series study, using secondary data retrieved from the Brazilian National Health System Hospital Information System; a time series analysis of hospitalizations was conducted based on a population-weighted Poisson regression model; relative risk (RR) and respective 95% confidence intervals (95%CI) were calculated. Results: we identified 6,329,088 hospitalizations due to mental and behavioral disorders; hospitalization rates showed an 8% decrease (RR = 0.92; 95%CI 0.91;0.92) after the start of the pandemic, compared to the pre-pandemic period. Conclusion: the pandemic changed the trend of hospitalizations due to mental and behavioral disorders in Brazil; the drop observed in the period is evidence that the pandemic affected the mental health care network.

Objetivo: analizar las hospitalizaciones por trastornos mentales y del comportamiento antes y después del inicio de la pandemia de covid-19 en Brasil, desde enero 2008 hasta julio 2021. Método: estudio ecológico descriptivo de series temporales interrumpidas, con datos registrados en el Sistema de Informações Hospitalares del Sistema Único de Saúde; se realizó un análisis de series temporales de hospitalizaciones basado en modelo de regresión de Poisson, ponderado por la población; calculado el riesgo relativo (RR), con intervalo de confianza del (IC95%). Resultados: se identificaron 6.329.088 hospitalizaciones por trastornos mentales y del comportamiento; las tasas de hospitalización mostraron disminución del 8% (RR = 0,92; IC95% 0,91;0,92) tras el inicio de la pandemia, en relación con el periodo prepandémico. Conclusión: la pandemia cambió la tendencia de hospitalizaciones por trastornos mentales y del comportamiento en Brasil; la caída observada en el período evidencia que la pandemia afectó la cadena asistencial estructurada para la salud mental.

Objetivo: analisar as internações por transtorno mental e comportamental, antes e após o início da pandemia de covid-19 no Brasil, de janeiro de 2008 a julho de 2021. Métodos: estudo ecológico descritivo de série temporal interrompida, com dados registrados no Sistema de Informações Hospitalares do Sistema Único de Saúde (SIH/SUS); realizada análise da série temporal das internações baseada em um modelo de regressão de Poisson, ponderado pela população; calculado o risco relativo (RR), com intervalo de confiança de 95% (IC95%). Resultados: foram identificadas 6.329.088 internações por transtornos mentais e comportamentais; as taxas de internação apresentaram um decréscimo de 8% (RR = 0,92; IC95% 0,91;0,92) após o início da pandemia, em relação ao período pré-pandemia. Conclusão: a pandemia modificou a tendência das internações por transtornos mentais e comportamentais no Brasil; a queda observada no período é evidência de que a pandemia afetou a cadeia de cuidado estruturada para saúde mental.

Impact of the COVID-19 pandemic on neonatal admissions in a tertiary children's hospital in southwest China: An interrupted time-series study.

BACKGROUND: The unprecedented coronavirus disease 2019 (COVID-19) pandemic has caused millions of infections worldwide and represents a significant challenge facing modern health care systems. This study was conducted to investigate the impact of lockdown measures in a tertiary Children's Hospital in southwest China, which might be used to predict long-term effects related to health-seeking behavior of parents/caregivers. METHODS: This study included newborns enrolled over a span of 86 weeks between January 4, 2019, and August 27, 2020. We designated two time periods for analysis purposes: a stable pre-COVID period(55 weeks between January 4, 2019, and January 23, 2020) and a COVID-impacted period (31 weeks between January 24, 2020, and August 27, 2020). An interrupted time-series analysis was employed to compare changes and trends in hospital admissions and disease spectra before and after the period of nonpharmaceutical interventions (NPIs). Furthermore, this study was conducted to evaluate whether the health-seeking behavior of parents/caregivers was influenced by pandemic factors. RESULTS: Overall, 16,640 infants were admitted to the neonatology department during the pre-COVID period (n = 12,082) and the COVID-impacted period (n = 4,558). The per week neonatal admissions consistently decreased following the first days of NPIs (January 24, 2020). The average weekly admission rates of 220/week pre-COVID period and 147/week COVID-impacted period. There was an evident decrease in the volume of admissions for all disease spectra after the intervention, whereas the decrease of patients complaining about pathological jaundice-related conditions was statistically significant (p<0.05). In the COVID-impacted period, the percentage of patients who suffered from respiratory system diseases, neonatal encephalopathy, and infectious diseases decreased, while the percentage of pathological jaundice-related conditions and gastrointestinal system diseases increased. The neonatal mortality rates (NMRs) increased by 8.7% during the COVID-impacted period compared with the pre-COVID period. CONCLUSIONS: In summary, there was a significant decline in neonatal admissions in a tertiary care hospital during the COVID-19 Pandemic and the associated NPIs. Additionally, this situation had a remarkable impact on disease spectra and health-seeking behavior of parents/caregivers. We, therefore, advise continuing follow-ups and monitoring the main health indicators in vulnerable populations affected by this Pandemic over time.

Observational management of penetrating occult pneumothoraces: Outcomes and risk factors for interval tube thoracostomy placement.

BACKGROUND: Guidelines for penetrating occult pneumothoraces (OPTXs) are based on blunt injury. Further understanding of penetrating OPTX pathophysiology is needed. In observational management of penetrating OPTX, we hypothesized that specific clinical and radiographic features may be associated with interval tube thoracostomy (TT) placement. Our aims were to (1) describe OPTX occurrence in penetrating chest injury, (2) determine the rate of interval TT placement in observational management and clinical outcomes compared with immediate TT placement, and (3) describe risk factors associated with failure of observational management. METHODS: Penetrating OPTX patients presenting to our level 1 trauma center from 2004 to 2019 were reviewed. Occult pneumothorax was defined as a pneumothorax on chest computed tomography but not on chest radiograph. Patient groups included immediate TT placement versus observation. Clinical outcomes compared were TT duration and complications, need for additional thoracic procedures, length of stay (LOS), and disposition. Clinical and radiographic factors associated with interval TT placement were determined by multivariable regression. RESULTS: Of 629 penetrating pneumothorax patients, 103 (16%) presented with OPTX. Thirty-eight patients underwent immediate TT placement, and 65 were observed. Twelve observed patients (18%) needed interval TT placement. Regardless of initial management strategy, TT placement was associated with longer LOS and more chest radiographs. Chest injury complications and outcomes were similar. Factors associated with increased odds of interval TT placement included Chest Abbreviated Injury Scale score of ≥4 (adjusted odds ratio [aOR], 7.38 [95% confidence interval, 1.43-37.95), positive pressure ventilation (aOR, 7.74 [1.07-56.06]), concurrent hemothorax (aOR, 6.17 [1.08-35.24]), and retained bullet fragment (aOR, 11.62 [1.40-96.62]) (all p < 0.05). CONCLUSION: The majority of patients with penetrating OPTX can be successfully observed with improved clinical outcomes (LOS, avoidance of TT complications, reduced radiation). Interval TT intervention was not associated with risk for adverse outcomes. In patients undergoing observation, specific clinical factors (chest injury severity, ventilation) and imaging features (hemothorax, retained bullet) are associated with increased odds for interval TT placement, suggesting need for heightened awareness in these patients. LEVEL OF EVIDENCE: Prognostic, level IV.

Decline of Influenza and Respiratory Viruses With COVID-19 Public Health Measures: Alberta, Canada.

OBJECTIVE: To determine the incidence of influenza and noninfluenza respiratory viruses (NIRVs) pre-/post-implementation of public health measures aimed to decrease coronavirus disease 2019 (COVID-19) transmission using population-based surveillance data. We hypothesized that such measures could reduce the burden of respiratory viruses (RVs) transmitting via the same routes. PATIENTS AND METHODS: An interrupted time-series analysis of RV surveillance data in Alberta, Canada, from May 2017 to July 2020 was conducted. The burden of influenza and NIRVs before and after intervention initiation at week 11 was compared. The analysis was adjusted for seasonality, overdispersion, and autocorrelation. RESULTS: During the study period, an average of 708 and 4056 weekly respiratory multiplex molecular panels were conducted pre-/post-intervention, respectively. We found significant reductions in test positivity rates in the postintervention period for influenza (-94.3%; 95% CI, -93.8 to 97.4%; P<.001) and all NIRVs (-76.5%; 95% CI, -77.3 to -75.8%; P<.001) in the crude model, and -86.2% (95% CI, -91.5 to -77.4%: P<.001) and -75% (95% CI, -79.7 to -69.3%; P<.001), respectively, in the adjusted models. Subanalyses for individual viruses showed significant decreases in respiratory syncytial virus, human metapneumovirus, enterovirus/rhinovirus, and parainfluenza. For non-severe acute respiratory coronavirus 2 human coronaviruses, the decline was not statistically significant after adjustment (-22.3%; 95% CI, -49.3 to +19%, P=.246). CONCLUSION: The implementation of COVID-19 public health measures likely resulted in reduced transmission of common RVs. Although drastic lockdowns are unlikely to be required given widespread COVID-19 vaccination, targeted implementation of such measures can lower RV disease burden. Studies to evaluate relative contributions of individual interventions are warranted.

Respiratory virus surveillance in Canada during the COVID-19 pandemic: An epidemiological analysis of the effectiveness of pandemic-related public health measures in reducing seasonal respiratory viruses test positivity.

BACKGROUND: Various public health measures have been implemented globally to counter the coronavirus disease 2019 (COVID-19) pandemic. The purpose of this study was to evaluate respiratory virus surveillance data to determine the effectiveness of such interventions in reducing transmission of seasonal respiratory viruses. METHOD: We retrospectively analysed data from the Respiratory Virus Detection Surveillance System in Canada, before and during the COVID-19 pandemic, by interrupted time series regression. RESULTS: The national level of infection with seasonal respiratory viruses, which generally does not necessitate quarantine or contact screening, was greatly reduced after Canada imposed physical distancing and other quarantine measures. The 2019-2020 influenza season ended earlier than it did in the previous year. The influenza virus was replaced by rhinovirus/enterovirus or parainfluenza virus in the previous year, with the overall test positivity remaining at approximately 35%. However, during the 2019-2020 post-influenza period, the overall test positivity of respiratory viruses during the COVID-19 was still low (7.2%). Moreover, the 2020-2021 influenza season had not occurred by the end of February 2021. CONCLUSION: Respiratory virus surveillance data may provide real-world evidence of the effectiveness of implemented public health interventions during the current and future pandemics.

Creating effective interrupted time series graphs: Review and recommendations.

INTRODUCTION: Interrupted Time Series (ITS) studies may be used to assess the impact of an interruption, such as an intervention or exposure. The data from such studies are particularly amenable to visual display and, when clearly depicted, can readily show the short- and long-term impact of an interruption. Further, well-constructed graphs allow data to be extracted using digitizing software, which can facilitate their inclusion in systematic reviews and meta-analyses. AIM: We provide recommendations for graphing ITS data, examine the properties of plots presented in ITS studies, and provide examples employing our recommendations. METHODS AND RESULTS: Graphing recommendations from seminal data visualization resources were adapted for use with ITS studies. The adapted recommendations cover plotting of data points, trend lines, interruptions, additional lines and general graph components. We assessed whether 217 graphs from recently published (2013-2017) ITS studies met our recommendations and found that 130 graphs (60%) had clearly distinct data points, 100 (46%) had trend lines, and 161 (74%) had a clearly defined interruption. Accurate data extraction (requiring distinct points that align with axis tick marks and labels that allow the points to be interpreted) was possible in only 72 (33%) graphs. CONCLUSION: We found that many ITS graphs did not meet our recommendations and could be improved with simple changes. Our proposed recommendations aim to achieve greater standardization and improvement in the display of ITS data, and facilitate re-use of the data in systematic reviews and meta-analyses.

Parental Awareness of Youth Tobacco Use and the Role of Household Tobacco Rules in Use Prevention.

BACKGROUND AND OBJECTIVES: Noncigarette tobacco use is increasing. In this study, we reexamined (1) parental knowledge or suspicion of their children's tobacco use and (2) associations of household tobacco-free rules with youth initiation. METHODS: Participants were youth (aged 12-17) in waves 1 to 4 (2013-2018) of the Population Assessment of Tobacco and Health Study. A pseudo cross-sectional time-series analysis (N = 23 170) was used to examine parent or guardian knowledge or suspicion of their child's tobacco use according to youth-reported use categories: cigarette only, electronic cigarette only, smokeless tobacco only, noncigarette combustible only, and poly use. A longitudinal analysis among wave 1 never users (n = 8994) was used to examine rules barring tobacco inside the home and whether parents talked with youth about not using tobacco as predictors of youth tobacco initiation after 1 to 3 years. Survey-weighted multivariable models were adjusted for tobacco use risk factors. RESULTS: In all waves, parents or guardians much less often knew or suspected that their children used tobacco if youth only reported use of electronic cigarettes, noncigarette combustible products, or smokeless tobacco compared with cigarettes. Youth tobacco initiation was lower when youth and parents agreed that rules prohibited all tobacco use throughout the home (1-year adjusted odds ratio: 0.74; 95% confidence interval: 0.59-0.94) but not when parents talked with youth about tobacco (adjusted odds ratio: 1.08; 95% confidence interval: 0.94-1.23). CONCLUSIONS: Many parents are unaware of their children's noncigarette tobacco use. Setting expectations for tobacco-free environments appears more effective at preventing youth tobacco initiation than parents advising children not to use tobacco.

Modeling analysis of COVID-19 based on morbidity data in Anhui, China.

Since the first case of coronavirus disease (COVID-19) in Wuhan Hubei, China, was reported in December 2019, COVID-19 has spread rapidly across the country and overseas. The first case in Anhui, a province of China, was reported on January 10, 2020. In the field of infectious diseases, modeling, evaluating and predicting the rate of disease transmission is very important for epidemic prevention and control. Different intervention measures have been implemented starting from different time nodes in the country and Anhui, the epidemic may be divided into three stages for January 10 to February 11, 2020, namely. We adopted interrupted time series method and develop an SEI/QR model to analyse the data. Our results displayed that the lockdown of Wuhan implemented on January 23, 2020 reduced the contact rate of epidemic transmission in Anhui province by 48.37%, and centralized quarantine management policy for close contacts in Anhui reduced the contact rate by an additional 36.97%. At the same time, the estimated basic reproduction number gradually decreased from the initial 2.9764 to 0.8667 and then to 0.5725. We conclude that the Wuhan lockdown and the centralized quarantine management policy in Anhui played a crucial role in the timely and effective mitigation of the epidemic in Anhui. One merit of this work is the adoption of morbidity data which may reflect the epidemic more accurately and promptly. Our estimated parameters are largely in line with the World Health Organization estimates and previous studies.

Effect of data quality improvement intervention on health management information system data accuracy: An interrupted time series analysis.

BACKGROUND: As part of a partnership between the Institute for Healthcare Improvement and the Ethiopian Federal Ministry of Health, woreda-based quality improvement collaboratives took place between November 2016 and December 2017 aiming to accelerate reduction of maternal and neonatal mortality in Lemu Bilbilu, Tanqua Abergele and Duguna Fango woredas. Before starting the collaboratives, assessments found inaccuracies in core measures obtained from Health Management Information System reports. METHODS AND RESULTS: Building on the quality improvement collaborative design, data quality improvement activities were added and we used the World Health Organization review methodology to drive a verification factor for the core measures of number of pregnant women that received their first antenatal care visit, number of pregnant women that received antenatal care on at least four visits, number of pregnant women tested for syphilis and number of births attended by skilled health personnel. Impact of the data quality improvement was assessed using interrupted time series analysis. We found accurate data across all time periods for Tanqua Abergele. In Lemu Bilbilu and Duguna Fango, data quality improved for all core metrics over time. In Duguna Fango, the verification factor for number of pregnant women that received their first antenatal care visit improved from 0.794 (95%CI 0.753, 0.836; p<0.001) pre-intervention by 0.173 (95%CI 0.128, 0.219; p<0.001) during the collaborative; and the verification factor for number of pregnant women tested for syphilis improved from 0.472 (95%CI 0.390, 0.554; p<0.001) pre-intervention by 0.460 (95%CI 0.369, 0.552; p<0.001) during the collaborative. In Lemu Bilbilu, the verification factor for number of pregnant women receiving a fourth antenatal visit rose from 0.589 (95%CI 0.513, 0.664; p<0.001) at baseline by 0.358 (95%CI 0.258, 0.458; p<0.001) post-intervention; and skilled birth attendance rose from 0.917 (95%CI 0.869, 0.965) at baseline by 0.083 (95%CI 0.030, 0.136; p<0.001) during the collaborative. CONCLUSIONS: A Data quality improvement initiative embedded within woreda clinical improvement collaborative improved accuracy of data used to monitor maternal and newborn health services in Ethiopia.

Using extremal events to characterize noisy time series.

Experimental time series provide an informative window into the underlying dynamical system, and the timing of the extrema of a time series (or its derivative) contains information about its structure. However, the time series often contain significant measurement errors. We describe a method for characterizing a time series for any assumed level of measurement error [Formula: see text] by a sequence of intervals, each of which is guaranteed to contain an extremum for any function that [Formula: see text]-approximates the time series. Based on the merge tree of a continuous function, we define a new object called the normalized branch decomposition, which allows us to compute intervals for any level [Formula: see text]. We show that there is a well-defined total order on these intervals for a single time series, and that it is naturally extended to a partial order across a collection of time series comprising a dataset. We use the order of the extracted intervals in two applications. First, the partial order describing a single dataset can be used to pattern match against switching model output (Cummins et al. in SIAM J Appl Dyn Syst 17(2):1589-1616, 2018), which allows the rejection of a network model. Second, the comparison between graph distances of the partial orders of different datasets can be used to quantify similarity between biological replicates.

A reliable time-series method for predicting arthritic disease outcomes: New step from regression toward a nonlinear artificial intelligence method.

BACKGROUND AND OBJECTIVE: The interrupted time-series (ITS) concept is performed using linear regression to evaluate the impact of policy changes in public health at a specific time. Objectives of this study were to verify, with an artificial intelligence-based nonlinear approach, if the estimation of ITS data could be facilitated, in addition to providing a computationally explicit equation. METHODS: Dataset were from a study of Hawley et al. (2018) in which they evaluated the impact of UK National Institute for Health and Care Excellence (NICE) approval of tumor necrosis factor inhibitor therapies on the incidence of total hip (THR) and knee (TKR) replacement in rheumatoid arthritis patients. We used the newly developed Generalized Structure Group Method of Data Handling (GS-GMDH) model, a nonlinear method, for the prediction of THR and TKR incidence in the abovementioned population. RESULTS: In contrast to linear regression, the GS-GMDH yields for both THR and TKR prediction values that almost fitted with the measured ones. These models demonstrated a low mean absolute relative error (0.10 and 0.09 respectively) and high correlation coefficient values (0.98 and 0.78). The GS-GMDH model for THR demonstrated 6.4/1000 person years (PYs) at the mid-point of the linear regression line post-NICE, whereas at the same point linear regression is 4.12/1000 PYs, a difference of around 35%. Similarly for the TKR, the linear regression to the datasets post-NICE was 9.05/1000 PYs, which is lower by about 27% than the GS-GMDH values of 12.47/1000 PYs. Importantly, with the GS-GMDH models, there is no need to identify the change point and intervention lag time as they simulate ITS continually throughout modelling. CONCLUSIONS: The results demonstrate that in the medical field, when looking at the estimation of the impact of a new drug using ITS, a nonlinear GS-GMDH method could be used as a better alternative to regression-based methods data processing. In addition to yielding more accurate predictions and requiring less time-consuming experimental measurements, this nonlinear method addresses, for the first time, one of the most challenging tasks in ITS modelling, i.e. avoiding the need to identify the change point and intervention lag time.

Methodology and reporting characteristics of studies using interrupted time series design in healthcare.

BACKGROUND: Randomised controlled trials (RCTs) are considered the gold standard when evaluating the causal effects of healthcare interventions. When RCTs cannot be used (e.g. ethically difficult), the interrupted time series (ITS) design is a possible alternative. ITS is one of the strongest quasi-experimental designs. The aim of this methodological study was to describe how ITS designs were being used, the design characteristics, and reporting in the healthcare setting. METHODS: We searched MEDLINE for reports of ITS designs published in 2015 which had a minimum of two data points collected pre-intervention and one post-intervention. There was no restriction on participants, language of study, or type of outcome. Data were summarised using appropriate summary statistics. RESULTS: One hundred and sixteen studies were included in the study. Interventions evaluated were mainly programs 41 (35%) and policies 32 (28%). Data were usually collected at monthly intervals, 74 (64%). Of the 115 studies that reported an analysis, the most common method was segmented regression (78%), 55% considered autocorrelation, and only seven reported a sample size calculation. Estimation of intervention effects were reported as change in slope (84%) and change in level (70%) and 21% reported long-term change in levels. CONCLUSIONS: This methodological study identified problems in the reporting of design features and results of ITS studies, and highlights the need for future work in the development of formal reporting guidelines and methodological work.

Environmental interventions to reduce the consumption of sugar-sweetened beverages and their effects on health.

BACKGROUND: Frequent consumption of excess amounts of sugar-sweetened beverages (SSB) is a risk factor for obesity, type 2 diabetes, cardiovascular disease and dental caries. Environmental interventions, i.e. interventions that alter the physical or social environment in which individuals make beverage choices, have been advocated as a means to reduce the consumption of SSB. OBJECTIVES: To assess the effects of environmental interventions (excluding taxation) on the consumption of sugar-sweetened beverages and sugar-sweetened milk, diet-related anthropometric measures and health outcomes, and on any reported unintended consequences or adverse outcomes. SEARCH METHODS: We searched 11 general, specialist and regional databases from inception to 24 January 2018. We also searched trial registers, reference lists and citations, scanned websites of relevant organisations, and contacted study authors. SELECTION CRITERIA: We included studies on interventions implemented at an environmental level, reporting effects on direct or indirect measures of SSB intake, diet-related anthropometric measures and health outcomes, or any reported adverse outcome. We included randomised controlled trials (RCTs), non-randomised controlled trials (NRCTs), controlled before-after (CBA) and interrupted-time-series (ITS) studies, implemented in real-world settings with a combined length of intervention and follow-up of at least 12 weeks and at least 20 individuals in each of the intervention and control groups. We excluded studies in which participants were administered SSB as part of clinical trials, and multicomponent interventions which did not report SSB-specific outcome data. We excluded studies on the taxation of SSB, as these are the subject of a separate Cochrane Review. DATA COLLECTION AND ANALYSIS: Two review authors independently screened studies for inclusion, extracted data and assessed the risks of bias of included studies. We classified interventions according to the NOURISHING framework, and synthesised results narratively and conducted meta-analyses for two outcomes relating to two intervention types. We assessed our confidence in the certainty of effect estimates with the GRADE framework as very low, low, moderate or high, and presented 'Summary of findings' tables. MAIN RESULTS: We identified 14,488 unique records, and assessed 1030 in full text for eligibility. We found 58 studies meeting our inclusion criteria, including 22 RCTs, 3 NRCTs, 14 CBA studies, and 19 ITS studies, with a total of 1,180,096 participants. The median length of follow-up was 10 months. The studies included children, teenagers and adults, and were implemented in a variety of settings, including schools, retailing and food service establishments. We judged most studies to be at high or unclear risk of bias in at least one domain, and most studies used non-randomised designs. The studies examine a broad range of interventions, and we present results for these separately.Labelling interventions (8 studies): We found moderate-certainty evidence that traffic-light labelling is associated with decreasing sales of SSBs, and low-certainty evidence that nutritional rating score labelling is associated with decreasing sales of SSBs. For menu-board calorie labelling reported effects on SSB sales varied.Nutrition standards in public institutions (16 studies): We found low-certainty evidence that reduced availability of SSBs in schools is associated with decreased SSB consumption. We found very low-certainty evidence that improved availability of drinking water in schools and school fruit programmes are associated with decreased SSB consumption. Reported associations between improved availability of drinking water in schools and student body weight varied.Economic tools (7 studies): We found moderate-certainty evidence that price increases on SSBs are associated with decreasing SSB sales. For price discounts on low-calorie beverages reported effects on SSB sales varied.Whole food supply interventions (3 studies): Reported associations between voluntary industry initiatives to improve the whole food supply and SSB sales varied.Retail and food service interventions (7 studies): We found low-certainty evidence that healthier default beverages in children's menus in chain restaurants are associated with decreasing SSB sales, and moderate-certainty evidence that in-store promotion of healthier beverages in supermarkets is associated with decreasing SSB sales. We found very low-certainty evidence that urban planning restrictions on new fast-food restaurants and restrictions on the number of stores selling SSBs in remote communities are associated with decreasing SSB sales. Reported associations between promotion of healthier beverages in vending machines and SSB intake or sales varied.Intersectoral approaches (8 studies): We found moderate-certainty evidence that government food benefit programmes with restrictions on purchasing SSBs are associated with decreased SSB intake. For unrestricted food benefit programmes reported effects varied. We found moderate-certainty evidence that multicomponent community campaigns focused on SSBs are associated with decreasing SSB sales. Reported associations between trade and investment liberalisation and SSB sales varied.Home-based interventions (7 studies): We found moderate-certainty evidence that improved availability of low-calorie beverages in the home environment is associated with decreased SSB intake, and high-certainty evidence that it is associated with decreased body weight among adolescents with overweight or obesity and a high baseline consumption of SSBs.Adverse outcomes reported by studies, which may occur in some circumstances, included negative effects on revenue, compensatory SSB consumption outside school when the availability of SSBs in schools is reduced, reduced milk intake, stakeholder discontent, and increased total energy content of grocery purchases with price discounts on low-calorie beverages, among others. The certainty of evidence on adverse outcomes was low to very low for most outcomes.We analysed interventions targeting sugar-sweetened milk separately, and found low- to moderate-certainty evidence that emoticon labelling and small prizes for the selection of healthier beverages in elementary school cafeterias are associated with decreased consumption of sugar-sweetened milk. We found low-certainty evidence that improved placement of plain milk in school cafeterias is not associated with decreasing sugar-sweetened milk consumption. AUTHORS' CONCLUSIONS: The evidence included in this review indicates that effective, scalable interventions addressing SSB consumption at a population level exist. Implementation should be accompanied by high-quality evaluations using appropriate study designs, with a particular focus on the long-term effects of approaches suitable for large-scale implementation.

Segmented Linear Regression Models for Assessing Change in Retrospective Studies in Healthcare.

INTRODUCTION: In retrospective studies, the effect of a given intervention is usually evaluated by using statistical tests to compare data from before and after the intervention. A problem with this approach is that the presence of underlying trends can lead to incorrect conclusions. This study aimed to develop a rigorous mathematical method to analyse temporal variation and overcome these limitations. METHODS: We evaluated hip fracture outcomes (time to surgery, length of stay, and mortality) from a total of 2777 patients between April 2011 and September 2016, before and after the introduction of a dedicated hip fracture unit (HFU). We developed a novel modelling method that fits progressively more complex linear sections to the time series using least squares regression. The method was used to model the periods before implementation, after implementation, and of the whole study period, comparing goodness of fit using F-tests. RESULTS: The proposed method offered reliable descriptions of the temporal evolution of the time series and augmented conclusions that were reached by mere group comparisons. Reductions in time to surgery, length of stay, and mortality rates that group comparisons would have credited to the hip fracture unit appeared to be due to unrelated underlying trends. CONCLUSION: Temporal analysis using segmented linear regression models can reveal secular trends and is a valuable tool to evaluate interventions in retrospective studies.

Do cardiovascular disease prevention programs in northern Sweden impact on population health? An interrupted time series analysis.

BACKGROUND: Cardiovascular disease (CVD) is the main cause of morbidity and mortality in Sweden. This study aims to assess the impact of a CVD intervention implemented in 1993 in northern Sweden on the reduction of premature ischemic heart disease (IHD) morbidity and mortality in women and men during the period 1987-2013. METHODS: An ecological controlled interrupted time series design, with pre-intervention period defined as 1987-1993 and post-intervention period 1994-2013 was carried out. For each year, IHD events, stratified by sex, were retrieved from national registers. RESULTS: Impressive reductions on IHD premature morbidity and mortality were observed to a similar degree in both the intervention county and the other comparison counties across the last 27 years. Significant differences in the pre-post intervention trends indicating the intervention group had smaller reductions than expected from its pre-intervention trend and the trend of control counties were found among men for both IHD morbidity and mortality. A similar pattern was observed among women but without significant differences. CONCLUSIONS: Taken together, the data do not support that the intervention has contributed to an additional reduction on IHD morbidity and mortality, above and beyond that which is already seen in neighbouring counties without similar programs.

Does rural health system reform aimed at improving access to primary health care affect hospitalization rates? An interrupted time series analysis of national policy reforms in Iran.

OBJECTIVES: To evaluate the effects of rural health insurance and family physician reforms on hospitalization rates in Iran. METHODS: An interrupted time series analysis of national monthly hospitalization rates in Iran (2003-2014), starting from two years before the intervention. Segmented regression analysis was used to assess the effects of the reforms on hospitalization rates. RESULTS: The analyses showed that hospitalization rates increased one year after the initiation of the reforms: 1.55 (95% CI: 1.24-1.86) additional hospitalizations per 1000 rural inhabitants per month ('immediate effect'). This increase was followed by a further gradual increase of 0.034 per 1000 inhabitants per month (95% CI: 0.02-0.04). The gradual monthly increase continued for two years after the reforms. The higher hospitalization rates were maintained in the following years. We observed a significant increase in hospitalization rates at a national level in rural areas that continued for over 10 years after the policy implementation. CONCLUSION: Primary health care reforms are often proposed for their efficiency outcomes (i.e. reduction in costs and use of hospitals) as well as their impact on improving health outcomes. We demonstrated that in populations with unmet needs, such reforms are likely to substantially increase hospitalization rates. This is an important consideration for successful design and implementation of interventions aimed at achieving universal health coverage in low- and middle-income countries.

Assessing health care interventions via an interrupted time series model: Study power and design considerations.

The delivery and assessment of quality health care is complex with many interacting and interdependent components. In terms of research design and statistical analysis, this complexity and interdependency makes it difficult to assess the true impact of interventions designed to improve patient health care outcomes. Interrupted time series (ITS) is a quasi-experimental design developed for inferring the effectiveness of a health policy intervention while accounting for temporal dependence within a single system or unit. Current standardized ITS methods do not simultaneously analyze data for several units nor are there methods to test for the existence of a change point and to assess statistical power for study planning purposes in this context. To address this limitation, we propose the "Robust Multiple ITS" (R-MITS) model, appropriate for multiunit ITS data, that allows for inference regarding the estimation of a global change point across units in the presence of a potentially lagged (or anticipatory) treatment effect. Under the R-MITS model, one can formally test for the existence of a change point and estimate the time delay between the formal intervention implementation and the over-all-unit intervention effect. We conducted empirical simulation studies to assess the type one error rate of the testing procedure, power for detecting specified change-point alternatives, and accuracy of the proposed estimating methodology. R-MITS is illustrated by analyzing patient satisfaction data from a hospital that implemented and evaluated a new care delivery model in multiple units.

Technology Delivered Interventions for Depression and Anxiety in Children and Adolescents: A Systematic Review and Meta-analysis.

Depression and anxiety are common during adolescence. Whilst effective interventions are available treatment services are limited resulting in many adolescents being unable to access effective help. Delivering mental health interventions via technology, such as computers or the internet, offers one potential way to increase access to psychological treatment. The aim of this systematic review and meta-analysis was to update previous work and investigate the current evidence for the effect of technology delivered interventions for children and adolescents (aged up to 18 years) with depression and anxiety. A systematic search of eight electronic databases identified 34 randomized controlled trials involving 3113 children and young people aged 6-18. The trials evaluated computerized and internet cognitive behavior therapy programs (CBT: n = 17), computer-delivered attention bias modification programs (ABM: n = 8) cognitive bias modification programs (CBM: n = 3) and other interventions (n = 6). Our results demonstrated a small effect in favor of technology delivered interventions compared to a waiting list control group: g = 0.45 [95% CI 0.29, 0.60] p < 0.001. CBT interventions yielded a medium effect size (n = 17, g = 0.66 [95% CI 0.42-0.90] p < 0.001). ABM interventions yielded a small effect size (n = 8, g = 0.41 [95%CI 0.08-0.73] p < 0.01). CBM and 'other' interventions failed to demonstrate a significant benefit over control groups. Type of control condition, problem severity, therapeutic support, parental support, and continuation of other ongoing treatment significantly influenced effect sizes. Our findings suggest there is a benefit in using CBT based technology delivered interventions where access to traditional psychotherapies is limited or delayed.

Increase in Reported Cholera Cases in Haiti Following Hurricane Matthew: An Interrupted Time Series Model.

Matthew, a category 4 hurricane, struck Haiti on October 4, 2016, causing widespread flooding and damage to buildings and crops, and resulted in many deaths. The damage caused by Matthew raised concerns of increased cholera transmission particularly in Sud and Grand'Anse departments, regions which were hit most heavily by the storm. To evaluate the change in reported cholera cases following Hurricane Matthew on reported cholera cases, we used interrupted time series regression models of daily reported cholera cases, controlling for the impact of both rainfall, following a 4-week lag, and seasonality, from 2013 through 2016. Our results indicate a significant increase in reported cholera cases after Matthew, suggesting that the storm resulted in an immediate surge in suspect cases, and a decline in reported cholera cases in the 46-day post-storm period, after controlling for rainfall and seasonality. Regression models stratified by the department indicate that the impact of the hurricane was regional, with larger surges in the two most highly storm-affected departments: Sud and Grand'Anse. These models were able to provide input to the Ministry of Health in Haiti on the national and regional impact of Hurricane Matthew and, with further development, could provide the flexibility of use in other emergency situations. This article highlights the need for continued cholera prevention and control efforts, particularly in the wake of natural disasters such as hurricanes, and the continued need for intensive cholera surveillance nationally.