COVID-19 vaccine prioritization based on district classification in Yogyakarta Province, Indonesia

Due to limited availability, Indonesia’s coronavirus disease 2019 (COVID-19) vaccination will be done in 4 stages until herd immunity has been reached. Yogyakarta, an education and tourist destination, needs to get a specific, spatial estimation of the exact need for COVID-19 vaccination without delay. This study sheds light on identifying which districts should be prioritized at each vaccination phase. Secondary data collected from provincial, and county-level statistical agencies were quantitatively calculated by the Z-Score method. The results indicate that the first phase of vaccination should prioritize Pengasih and Sentolo districts in Kulon Progo Regency, which have a large number of health workers; the districts of Depok, Banguntapan, Piyungan, Sewon, Wonosari, Gamping, Mlati and Ngaglik should be done in the second phase based on the fact that these districts have many public service officials as well as elderly people; Umbulharjo and Depok districts will be approached in the third phase since they have more vulnerable groups and facilities that may promote COVID- 19 transmission during their daily activities; while the fourth phase should focus on the districts of Banguntapan, Sewon, Kasihan, Gamping, Mlati, Depok, and Ngaglik due to the intensity of COVID-19 clusters discovered there. Overall, vaccination would be given the priority in the districts with the largest number of people in need, i.e., public service officers, elderly people and those likely to be exposed to the coronavirus causing COVID-19.

Freedom of Information and Personal Confidentiality in Spatial COVID-19 Data

We draw attention to how, in the name of protecting the confidentiality of personal data, national statistical agencies have limited public access to spatial data on COVID-19. We also draw attention to large disparities in the way that access has been limited. In doing so, we distinguish between absolute confidentiality in which the probability of detection is 1, relative confidentiality where this probability is less than 1, and collective confidentiality, which refers to the probability of detection of at least one person. In spatial data, the probability of personal detection is less than 1, and the probability of collective detection varies directly with this probability and COVID-19 morbidity. Statistical agencies have been concerned with relative and collective confidentiality, which they implement using the techniques of truncation, where spatial data are not made public for zones with small populations, and censoring, where exact data are not made public for zones where morbidity is small. Granular spatial data are essential for epidemiological research into COVID-19. We argue that in their reluctance to make these data available to the public, data security officers (DSO) have unreasonably prioritized data protection over freedom of information. We also argue that by attaching importance to relative and collective confidentiality, they have over-indulged in data truncation and censoring. We highlight the need for legislation concerning relative and collective confidentiality, and regulation of DSO practices regarding data truncation and censoring.

Fertility Projections in a European Context: A Survey of Current Practices among Statistical Agencies

Projection studies have often focused on mortality and, more recently, migration. Fertility is less studied, although even small changes can have significant repercussions for the size and age structure of future populations. Across Europe, there is no consensus on how fertility is best projected. In this article, we identify different approaches used to project fertility among statistical agencies in Europe and provide an assessment of the different approaches according to the producers themselves. Data were collected using a mixed-method approach. First, European statistical agencies answered a questionnaire regarding fertility projection practices. Second, an in-depth review of select countries was performed. Most agencies combine formal models with expert opinion. While many attempt to maximise the use of relevant inputs, there is more variation in the detail of outputs, with some agencies unable to account for changing age patterns. In a context of limited resources, most are satisfied with their approaches, though some are assessing alternative methodologies to improve accuracy and increase transparency. This study highlights the diversity of approaches used in fertility projections across Europe. Such knowledge may be useful to statistical agencies as they consider, test and implement different approaches, perhaps in collaboration with other agencies and the wider scientific community.

A Researcher’s Guide to the Labour Force Survey: Its Evolution and the Choice of Public Use versus Master Files

To balance researchers’ need for detailed information with respondents’ confidentiality concerns, statistical agencies such as Statistics Canada commonly offer two versions of the same dataset: a public use file that is readily available and a master file with richer information but to which access is restricted. This article examines the choice of using public use versus master files of the Labour Force Survey (LFS). The article also provides researchers with a unified source of LFS information, including a thorough discussion of the structure of the LFS and its implication for research, such as the creation of mini-panels.

How Leaders of Arm’s Length Agencies Respond to External Threats: A Strategic-Performative Analysis

Researchers have recently shown increasing interest in how leaders of agencies respond to external threats. This article extends Katharine Dommett and Chris Skelcher’s strategic-relational analysis of agency leaders’ responses to exogenous threats. It focuses on the role of dramatic performances and impression management in agencies’ strategic responses. Interviews with senior officials in statistical agencies in Britain and Canada were used to assess the strategic-performative model. Agencies are better able to defend their functions and autonomy when they undertake effective dramatic performances to shape external stakeholders’ impressions. These findings further our understanding of the mechanisms that influence the legitimacy and autonomy of public agencies.

Six things to know about the homicides of doctors: a review of 30 years from Italy

Background Healthcare workers have a 16 times greater risk of suffering workplace violence than workers in other sectors and around 50% experience workplace violence in the course of their career. The objective of this study is to explore the characteristics and circumstances of work-related killings of doctors. Methods Work-related homicides of doctors over the period 1988–2019 were identified retrospectively through the Italian national statistical agencies. Variables such as perpetrator, motive and location of the crime were obtained through forensic psychiatric work. After classification, the absolute and percent values of the main characteristics of the homicides were calculated. Results Over the period considered, 21 doctors were killed in Italy in connection with their professional activity. In 52% (n = 11) of cases, the killer was one of the doctor’s patients, in 29% (n = 6) of cases it was a patient’s relative, in 19% (n = 4) an occasional patient (first consultation). The location of the homicide was a community clinic in 48% (n = 10) of cases, the street in 19% (n = 4) of cases, the doctor’s home in 14% (n = 3), the hospital in 14% (n = 3) and the patient’s home in 5% (n = 1). In 57% (n = 12) of cases the perpetrator was not affected by any mental disorders. The motive for the homicide was revenge in 66.7% (n = 14) of cases; in 28.6% (n = 6) the revenge was preceded by stalking. Conclusions Doctors should be aware that the risk of being killed is not limited to hospital settings and that their patients’ family members might also pose a threat to them.

A Diagnostic for Seasonality Based Upon Polynomial Roots of ARMA Models

Methodology for seasonality diagnostics is extremely important for statistical agencies, because such tools are necessary for making decisions whether to seasonally adjust a given series, and whether such an adjustment is adequate. This methodology must be statistical, in order to furnish quantification of Type I and II errors, and also to provide understanding about the requisite assumptions. We connect the concept of seasonality to a mathematical definition regarding the oscillatory character of the moving average (MA) representation coefficients, and define a new seasonality diagnostic based on autoregressive (AR) roots. The diagnostic is able to assess different forms of seasonality: dynamic versus stable, of arbitrary seasonal periods, for both raw data and seasonally adjusted data. An extension of the AR diagnostic to an MA diagnostic allows for the detection of over-adjustment. Joint asymptotic results are provided for the diagnostics as they are applied to multiple seasonal frequencies, allowing for a global test of seasonality. We illustrate the method through simulation studies and several empirical examples.

Deaths from ‘diseases of despair’ in Britain: comparing suicide, alcohol-related and drug-related mortality for birth cohorts in Scotland, England and Wales, and selected cities

BackgroundThe contribution of increasing numbers of deaths from suicide, alcohol-related and drug-related causes to changes in overall mortality rates has been highlighted in various countries. In Scotland, particular vulnerable cohorts have been shown to be most at risk; however, it is unclear to what extent this applies elsewhere in Britain. The aim here was to compare mortality rates for different birth cohorts between Scotland and England and Wales (E&W), including key cities.MethodsMortality and population data (1981–2017) for Scotland, E&W and 10 cities were obtained from national statistical agencies. Ten-year birth cohorts and cohort-specific mortality rates (by age of death, sex, cause) were derived and compared between countries and cities.ResultsSimilarities were observed between countries and cities in terms of peak ages of death, and the cohorts with the highest death rates. However, cohort-specific rates were notably higher in Scotland, particularly for alcohol-related and drug-related deaths. Across countries and cities, those born in 1965–1974 and 1975–1984 had the highest drug-related mortality rates (peak age at death: 30–34 years); the 1965–1974 birth cohort also had the highest male suicide rate (peak age: 40–44 years). For alcohol-related causes, the highest rates were among earlier cohorts (1935–1944, 1945–1954, 1955–1964)—peak age 60–64 years.ConclusionsThe overall similarities suggest common underlying influences across Britain; however, their effects have been greatest in Scotland, confirming greater vulnerability among that population. In addressing the socioeconomic drivers of deaths from these causes, the cohorts identified here as being at greatest risk require particular attention.