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Interrupted time series (ITS) is a powerful and increasingly popular design for evaluating public health and health service interventions. The design involves analyzing trends in the outcome of interest and estimating the change in trend following an intervention relative to the counterfactual (the expected ongoing trend if the intervention had not occurred). There are two key components to modeling this effect: first, defining the counterfactual; second, defining the type of effect that the intervention is expected to have on the outcome, known as the impact model. The counterfactual is defined by extrapolating the underlying trends observed before the intervention to the postintervention period. In doing this, authors must consider the preintervention period that will be included, any time-varying confounders, whether trends may vary within different subgroups of the population and whether trends are linear or nonlinear. Defining the impact model involves specifying the parameters that model the intervention, including for instance whether to allow for an abrupt level change or a gradual slope change, whether to allow for a lag before any effect on the outcome, whether to allow a transition period during which the intervention is being implemented, and whether a ceiling or floor effect might be expected. Inappropriate model specification can bias the results of an ITS analysis and using a model that is not closely tailored to the intervention or testing multiple models increases the risk of false positives being detected. It is important that authors use substantive knowledge to customize their ITS model a priori to the intervention and outcome under study. Where there is uncertainty in model specification, authors should consider using separate data sources to define the intervention, running limited sensitivity analyses or undertaking initial exploratory studies.

Background Despite the considerable and growing body of knowledge translation (KT) literature, there are few methodologies sufficiently detailed to guide an integrated KT research approach for a population health study. This paper argues for a clearly articulated collaborative KT approach to be embedded within the research design from the outset. Discussion Population health studies are complex in their own right, and strategies to engage the local community in adopting new interventions are often fraught with considerable challenges. In order to maximise the impact of population health research, more explicit KT strategies need to be developed from the outset. We present four propositions, arising from our work in developing a KT framework for a population health study. These cover the need for an explicit theory-informed conceptual framework; formalizing collaborative approaches within the design; making explicit the roles of both the stakeholders and the researchers; and clarifying what counts as evidence. From our deliberations on these propositions, our own co-creating (co-KT) Framework emerged in which KT is defined as both a theoretical and practical framework for actioning the intent of researchers and communities to co-create, refine, implement and evaluate the impact of new knowledge that is sensitive to the context (values, norms and tacit knowledge) where it is generated and used. The co-KT Framework has five steps. These include initial contact and framing the issue; refining and testing knowledge; interpreting, contextualising and adapting knowledge to the local context; implementing and evaluating; and finally, the embedding and translating of new knowledge into practice. Summary Although descriptions of how to incorporate KT into research designs are increasing, current theoretical and operational frameworks do not generally span a holistic process from knowledge co-creation to knowledge application and implementation within one project. Population health studies may have greater health impact when KT is incorporated early and explicitly into the research design. This, we argue, will require that particular attention be paid to collaborative approaches, stakeholder identification and engagement, the nature and sources of evidence used, and the role of the research team working with the local study community.

The increasing diversity of population of the United States presents many challenges to conducting health research that is representative and informative. Dispersion and accessibility issues can increase logistical costs; populations for which it is difficult to obtain adequate sample size are also likely to be expensive to study. Hence, even if it is technically feasible to study a small population, it may not be easy to obtain the funding to do so. In order to address the issues associated with improving health research of small populations, the National Academies of Sciences, Engineering, and Medicine convened a workshop in January 2018. Participants considered ways of addressing the challenges of conducting epidemiological studies or intervention research with small population groups, including alternative study designs, innovative methodologies for data collection, and innovative statistical techniques for analysis.

A unique and self-contained resource, Handbook of Health Survey Methods presents techniques necessary for confronting challenges that are specific to health survey research. The handbook guides readers through the development of sample designs, data collection procedures, and analytic methods for studies aimed at gathering health information on general and targeted populations.

A new semiparametric approach to model-based small area prediction for counts is proposed and used for estimating the average number of visits to physicians for Health Districts in Central Italy. The proposed small area predictor can be viewed as an outlier robust alternative to the more commonly used empirical plug-in predictor that is based on a Poisson generalized linear mixed model with Gaussian random effects. Results from the real data application and from a simulation experiment confirm that the proposed small area predictor has good robustness properties and in some cases can be more efficient than alternative small area approaches.

Introduction Since 2001, the Health Resources and Services Administration’s Maternal and Child Health Bureau (HRSA MCHB) has funded and directed the National Survey of Children’s Health (NSCH) and the National Survey of Children with Special Health Care Needs (NS-CSHCN), unique sources of national and state-level data on child health and health care. Between 2012 and 2015, HRSA MCHB redesigned the surveys, combining content into a single survey, and shifting from a periodic interviewer-assisted telephone survey to an annual self-administered web/paper-based survey utilizing an address-based sampling frame. Methods The U.S. Census Bureau fielded the redesigned NSCH using a random sample of addresses drawn from the Census Master Address File, supplemented with a unique administrative flag to identify households most likely to include children. Data were collected June 2016–February 2017 using a multi-mode design, encouraging web-based responses while allowing for paper mail-in responses. A parent/caregiver knowledgeable about the child’s health completed an age-appropriate questionnaire. Experiments on incentives, branding, and contact strategies were conducted. Results Data were released in September 2017. The final sample size was 50,212 children; the overall weighted response rate was 40.7%. Comparison of 2016 estimates to those from previous survey iterations are not appropriate due to sampling and mode changes. Discussion The NSCH remains an invaluable data source for key measures of child health and attendant health care system, family, and community factors. The redesigned survey extended the utility of this resource while seeking a balance between previous strengths and innovations now possible.

Recent estimates suggesting that over half of Alzheimer's disease burden worldwide might be attributed to potentially modifiable risk factors do not take into account risk-factor non-independence. We aimed to provide specific estimates of preventive potential by accounting for the association between risk factors. Using relative risks from existing meta-analyses, we estimated the population-attributable risk (PAR) of Alzheimer's disease worldwide and in the USA, Europe, and the UK for seven potentially modifiable risk factors that have consistent evidence of an association with the disease (diabetes, midlife hypertension, midlife obesity, physical inactivity, depression, smoking, and low educational attainment). The combined PAR associated with the risk factors was calculated using data from the Health Survey for England 2006 to estimate and adjust for the association between risk factors. The potential of risk factor reduction was assessed by examining the combined effect of relative reductions of 10% and 20% per decade for each of the seven risk factors on projections for Alzheimer's disease cases to 2050. Worldwide, the highest estimated PAR was for low educational attainment (19·1%, 95% CI 12·3–25·6). The highest estimated PAR was for physical inactivity in the USA (21·0%, 95% CI 5·8–36·6), Europe (20·3%, 5·6–35·6), and the UK (21·8%, 6·1–37·7). Assuming independence, the combined worldwide PAR for the seven risk factors was 49·4% (95% CI 25·7–68·4), which equates to 16·8 million attributable cases (95% CI 8·7–23·2 million) of 33·9 million cases. However, after adjustment for the association between the risk factors, the estimate reduced to 28·2% (95% CI 14·2–41·5), which equates to 9·6 million attributable cases (95% CI 4·8–14·1 million) of 33·9 million cases. Combined PAR estimates were about 30% for the USA, Europe, and the UK. Assuming a causal relation and intervention at the correct age for prevention, relative reductions of 10% per decade in the prevalence of each of the seven risk factors could reduce the prevalence of Alzheimer's disease in 2050 by 8·3% worldwide. After accounting for non-independence between risk factors, around a third of Alzheimer's diseases cases worldwide might be attributable to potentially modifiable risk factors. Alzheimer's disease incidence might be reduced through improved access to education and use of effective methods targeted at reducing the prevalence of vascular risk factors (eg, physical inactivity, smoking, midlife hypertension, midlife obesity, and diabetes) and depression. National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care for Cambridgeshire and Peterborough.

ObjectivesIn the first wave of the COVID-19 pandemic, social isolation, school/child care closures and employment instability have created unprecedented conditions for families raising children at home. This study describes the mental health impacts of the COVID-19 pandemic on families with children in Canada.Design, setting and participantsThis descriptive study used a nationally representative, cross-sectional survey of adults living in Canada (n=3000) to examine the mental health impacts of the COVID-19 pandemic. Outcomes among parents with children <18 years old living at home (n=618) were compared with the rest of the sample. Data were collected via an online survey between 14 May to 29 May 2020.Outcome measuresParticipants reported on changes to their mental health since the onset of the pandemic and sources of stress, emotional responses, substance use patterns and suicidality/self-harm. Additionally, parents identified changes in their interactions with their children, impacts on their children’s mental health and sources of support accessed.Results44.3% of parents with children <18 years living at home reported worse mental health as a result of the COVID-19 pandemic compared with 35.6% of respondents without children <18 living at home, χ2 (1, n=3000)=16.2, p<0.001. More parents compared with the rest of the sample reported increased alcohol consumption (27.7% vs 16.1%, χ2 (1, n=3000)=43.8, p<0.001), suicidal thoughts/feelings (8.3% vs 5.2%, χ2 (1, n=3000)=8.0, p=0.005) and stress about being safe from physical/emotional domestic violence (11.5% vs 7.9%, χ2 (1, n=3000)=8.1, p=0.005). 24.8% (95% CI 21.4 to 28.4) of parents reported their children’s mental health had worsened since the pandemic. Parents also reported more frequent negative as well as positive interactions with their children due to the pandemic (eg, more conflicts, 22.2% (95% CI 19.0 to 25.7); increased feelings of closeness, 49.7% (95% CI 45.7 to 53.7)).ConclusionsThis study identifies that families with children <18 at home have experienced deteriorated mental health due to the pandemic. Population-level responses are required to adequately respond to families’ diverse needs and mitigate the potential for widening health and social inequities for parents and children.

Background The most commonly used self-reported health question asks people to rate their general health from excellent to poor . This is one of the Patient-Reported Outcomes Measurement Information System (PROMIS) global health items. Four other items are used for scoring on the PROMIS global physical health scale. Because the single item is used on the majority of large national health surveys in the U.S., it is useful to construct scores that can be compared to U.S. general population norms. Objective To estimate the PROMIS global physical health scale score from the responses to the single excellent to poor self-rated health question for use in public health surveillance, research, and clinical assessment. Design A cross-sectional survey of 21,133 individuals, weighted to be representative of the U.S. general population. Participants The PROMIS items were administered via a Web-based survey to 19,601 persons in a national panel and 1,532 subjects from PROMIS research sites. The average age of individuals in the sample was 53 years, 52 % were female, 80 % were non-Hispanic white, and 19 % had a high school degree or lower level of education. Main outcome measures PROMIS global physical health scale. Key results The product–moment correlation of the single item with the PROMIS global physical health scale score was 0.81. The estimated scale score based on responses to the single item ranged from 29 ( poor self-rated health, 2.1 SDs worse than the general population mean) to 62 (excellent self-rated health, 1.2 SDs better than the general population mean) on a T-score metric (mean of 50). Conclusions This item can be used to estimate scores for the PROMIS global physical health scale for use in monitoring population health and achieving public health objectives. The item may also be used for individual assessment, but its reliability (0.52) is lower than that of the PROMIS global health scale (0.81).