Emerging Themes in Epidemiology最新文献

Background: Through a nationally representative household survey in Afghanistan, we conducted an operational study in two relatively secure provinces comparing effectiveness of computer-aided personal interviewing (CAPI) with paper-and-pencil interviewing (PAPI).

Methods: In Panjshir and Parwan provinces, household survey data were collected using paper questionnaires in 15 clusters, and OpenDataKit (ODK) software on electronic tablets in 15 other clusters. Added value was evaluated from three perspectives: efficient implementation, data quality, and acceptability. Efficiency was measured through financial expenditures and time stamped data. Data quality was measured by examining completeness. Acceptability was studied through focus group discussions with survey staff.

Results: Survey costs were 68% more expensive in CAPI clusters compared to PAPI clusters, due primarily to the upfront one-time investment for survey programming. Enumerators spent significantly less time administering surveys in CAPI cluster households (248 min survey time) compared to PAPI (289 min), for an average savings of 41 min per household (95% CI 25-55). CAPI offered a savings of 87 days for data management over PAPI. Among 49 tracer variables (meaning responses were required from all respondents), small differences were observed between PAPI and CAPI. 2.2% of the cleaned dataset's tracer data points were missing in CAPI surveys (1216/ 56,073 data points), compared to 3.2% in PAPI surveys (1953/ 60,675 data points). In pre-cleaned datasets, 3.9% of tracer data points were missing in CAPI surveys (2151/ 55,092 data points) compared to 3.2% in PAPI surveys (1924/ 60,113 data points). Enumerators from Panjsher and Parwan preferred CAPI over PAPI due to time savings, user-friendliness, improved data security, and less conspicuity when traveling; however approximately half of enumerators trained from all 34 provinces reported feeling unsafe due to Taliban presence. Community and household respondent skepticism could be resolved by enumerator reassurance. Enumerators shared that in the future, they prefer collecting data using CAPI when possible.

Conclusions: CAPI offers clear gains in efficiency over PAPI for data collection and management time, although costs are relatively comparable even without the programming investment. However, serious field staff concerns around Taliban threats and general insecurity mean that CAPI should only be conducted in relatively secure areas.

Respiratory syncytial virus (RSV)-the most common viral cause of bronchiolitis-is a significant cause of serious illness among young children between the ages of 0-5 years and is especially concerning in the first year of life. Globally, RSV is a common cause of childhood acute lower respiratory illness (ALRI) and a major cause of hospital admissions in young children and infants and represents a substantial burden for health-care systems. This burden is strongly felt as there are currently no effective preventative options that are available for all infants. However, a renaissance in RSV prevention strategies is unfolding, with several new prophylactic options such as monoclonal antibodies and maternal vaccinations that are soon to be available. A key concern is that health decision makers and systems may not be ready to take full advantage of forthcoming technological innovations. A multi-stakeholder approach is necessary to bridge data gaps to fully utilise upcoming options. Knowledge must be made available at multiple levels to ensure that parents and doctors are aware of preventative options, but also to ensure that stakeholders and policymakers are given the necessary information to best advise implementation strategies.

Background: Women's empowerment is a multidimensional construct which varies by context. These variations make it challenging to have a concrete definition that can be measured quantitatively. Having a standard composite measure of empowerment at the individual and country level would help to assess how countries are progressing in efforts to achieve gender equality (SDG 5), enable standardization across and within settings and guide the formulation of policies and interventions. The aim of this study was to develop a women's empowerment index for Tanzania and to assess its evolution across three demographic and health surveys from 2004 to 2016.

Results: Women's empowerment in Tanzania was categorized into six distinct domains namely; attitudes towards violence, decision making, social independence, age at critical life events, access to healthcare, and property ownership. The internal reliability of this six-domain model was shown to be acceptable by a Cronbach's α value of 0.658. The fit statistics of the root mean squared error of approximation (0.05), the comparative fit index (0.93), and the standardized root mean squared residual (0.04) indicated good internal validity. The structure of women's empowerment was observed to have remained relatively constant across three Tanzanian demographic and health surveys.

Conclusions: The use of factor analysis in this research has shown that women's empowerment in Tanzania is a six-domain construct that has remained relatively constant over the past ten years. This could be a stepping stone to reducing ambiguity in conceptualizing and operationalizing empowerment and expanding its applications in empirical research to study different women related outcomes in Tanzania.

This paper introduces the Blinder-Oaxaca decomposition method to be applied in explaining inequality in health outcome across any two groups. In order to understand every aspect of the inequality, multiple regression model can be used in a way to decompose the inequality into contributing factors. The method can therefore be indicated to what extent of the difference in mean predicted outcome between two groups is due to differences in the levels of observable characteristics (acceptable and fair). Assuming the identical characteristics in the two groups, the remaining inequality can be due to differential effects of the characteristics, maybe discrimination, and unobserved factors that not included in the model. Thus, using the decomposition methods can identify the contribution of each particular factor in moderating the current inequality. Accordingly, more detailed information can be provided for policy-makers, especially concerning modifiable factors. The method is theoretically described in detail and schematically presented. In the following, some criticisms of the model are reviewed, and several statistical commands are represented for performing the method, as well. Furthermore, the application of it in the health inequality with an applied example is presented.

Background: The use of meta-analysis to aggregate the results of multiple studies has increased dramatically over the last 40 years. For homogeneous meta-analysis, the Mantel-Haenszel technique has typically been utilized. In such meta-analyses, the effect size across the contributing studies of the meta-analysis differs only by statistical error. If homogeneity cannot be assumed or established, the most popular technique developed to date is the inverse-variance DerSimonian and Laird (DL) technique (DerSimonian and Laird, in Control Clin Trials 7(3):177-88, 1986). However, both of these techniques are based on large sample, asymptotic assumptions. At best, they are approximations especially when the number of cases observed in any cell of the corresponding contingency tables is small.

Results: This research develops an exact, non-parametric test for evaluating statistical significance and a related method for estimating effect size in the meta-analysis of k 2 × 2 tables for any level of heterogeneity as an alternative to the asymptotic techniques. Monte Carlo simulations show that even for large values of heterogeneity, the Enhanced Bernoulli Technique (EBT) is far superior at maintaining the pre-specified level of Type I Error than the DL technique. A fully tested implementation in the R statistical language is freely available from the author. In addition, a second related exact test for estimating the Effect Size was developed and is also freely available.

Conclusions: This research has developed two exact tests for the meta-analysis of dichotomous, categorical data. The EBT technique was strongly superior to the DL technique in maintaining a pre-specified level of Type I Error even at extremely high levels of heterogeneity. As shown, the DL technique demonstrated many large violations of this level. Given the various biases towards finding statistical significance prevalent in epidemiology today, a strong focus on maintaining a pre-specified level of Type I Error would seem critical. In addition, a related exact method for estimating the Effect Size was developed.

Today's most troublesome population health challenges are often driven by social and environmental determinants, which are difficult to model using traditional epidemiological methods. We agree with those who have argued for the wider adoption of agent-based modelling (ABM) in taking on these challenges. However, while ABM has been used occasionally in population health, we argue that for ABM to be most effective in the field it should be used as a means for answering questions normally inaccessible to the traditional epidemiological toolkit. In an effort to clearly illustrate the utility of ABM for population health research, and to clear up persistent misunderstandings regarding the method's conceptual underpinnings, we offer a detailed presentation of the core concepts of complex systems theory, and summarise why simulations are essential to the study of complex systems. We then examine the current state of the art in ABM for population health, and propose they are well-suited for the study of the 'wicked' problems in population health, and could make significant contributions to theory and intervention development in these areas.

Background: Health surveys provide a rich array of information but on relatively small numbers of individuals and evidence suggests that they are becoming less representative as response levels fall. Routinely collected administrative data offer more extensive population coverage but typically comprise fewer health topics. We explore whether data combination and multiple imputation of health variables from survey data is a simple and robust way of generating these variables in the general population.

Methods: We use the UK Integrated Household Survey and the English 2011 population census both of which included self-rated general health. Setting aside the census self-rated health data we multiply imputed self-rated health responses for the census using the survey data and compared these with the actual census results in 576 unique groups defined by age, sex, housing tenure and geographic region.

Results: Compared with original census data across the groups, multiply imputed proportions of bad or very bad self-rated health were not a markedly better fit than those simply derived from the survey proportions.

Conclusion: While multiple imputation may have the potential to augment population data with information from surveys, further testing and refinement is required.

Quality assurance is one of the most important aspects of an epidemiological study, as its validity is largely determined by data quality. The mounting success of quality management in the industrial sector caused a rapid spread throughout manufacturing industries and beyond. Yet, little has been published so far on quality assurance in epidemiology. In this article we review three models for quality assurance (Juran, Donabedian and ISO 9000) and showcase how these can be brought together in one intuitive, systematic and flexible approach to quality assurance in epidemiology. The resulting Open Quality approach refers back to the three processes identified by Juran (planning, control and verification). During the planning stage, we propose a subdivision of the study process in a set of steps and a definition of quality attributes corresponding to activities in that step as suggested by the ISO approach. We refer to the Donabedian model to determine the level at which the control/monitoring should take place-structure, processes or outcomes. Along with an overview of the Open Quality approach we propose an Open Quality tool to support the definition of quality attributes, failure modes, preventive strategies, verification activities, and corrective actions, which form the backbone of the Open Quality approach.

Background: Social media have in recent years challenged the way in which research questions are formulated in epidemiology and medicine, and in particular when it comes to women's health. They have contributed to the emergence of 'new' public health topics (e.g. gynaecological and obstetric violence, long-Covid), the unearthing of testimonials of medical injustice, and in some cases, the creation of new evidence and changes in medical practice.

Main text: From a theoretical and methodological perspective, we observe two powerful mechanisms at play on social media, which can facilitate the implementation of feminist epidemiological research and address so-called anti-feminist bias: social media as a 'third' space and the power of groups. Social media posts can be seen as inhabiting a third space, akin to what is said off the record or in-between doors, at the end of a therapy session. Researchers somehow miss the opportunity to use the third spaces that people occupy. Similarly, another existing space that researchers are seldom interested in are peer-groups. Peer-groups are the ideal terrain to generate bottom-up research priorities. To some extent, their on-line versions provide a safe and emancipatory space, accessible, transnational, and inclusive. We would argue that this could bring feminist epidemiology to scale.

Conclusion: Given the emancipatory power of social media, we propose recommendations and practical implications for leveraging the potential of online-sourced feminist epidemiology at different stages of the research process (from design to dissemination), and for increasing synergies between researchers and the community. We emphasise that attention should be paid to patriarchal sociocultural contexts and power dynamics, the mitigation of risks for political recuperation and stigmatisation, and the co-production of respectful discourse on studied populations.