Applying time series modeling to assess the dynamics and forecast monthly reports of abuse, neglect and/or exploitation involving a vulnerable adult

Using administrative data from adult protective services: opportunities and considerations

As federal agencies support the development of data systems for adult protective services (APS), researchers and practitioners are increasingly using administrative data to study types of adult maltreatment, including self-neglect, and the systems that respond to them. To date, however, APS has worked with little guidance about how best to use these data. This situation has contributed to inconsistent findings and uneven quality of research. Based on the authors' experience in multiple states and using examples from research and practice, this paper reviews practical considerations related to organizing and analyzing APS administrative data. We address concerns related to time-limited, erroneous, and missing data, as well as defining metrics and accounting for the multilevel structure of data with repeated observations. Reviewing these methodological details can help APS practitioners strengthen quality assurance processes and improve research in this important area.

Modelling the Number of People per Physician, Nurse, and Midwives in Turkey in Terms of Reproductive Health Indicators

Health worker density and distribution is critical for a strong health system and therefore has been listed among 1 of the Sustainable Development Goal (SDG) targets. The present study aims to model the number of persons per physician, nurse, and midwives in Turkey until 2030 and to make estimates for better reproductive health outcomes. We used time series of people per physician, nurse, and midwife between the years 1928 and 2018. Estimates were obtained via the Box-Jenkins and Brown Exponential Smoothing Methods. The results of this study showed that both designed models provide a high diagnostic value to predict the number of person per doctor, nurse, and midwives. The goodness of fit criteria for both models was statistically significant. The results predict a slight decrease in the number of people per physician, a more significant decrease in the number of people per nurse, but no decrease in the number of people per midwives until 2030. We argue that there will not be much progress in reproductive health indicators if the health workforce progresses with the same trend in the coming years. We recommend decision-makers to re-consider the health workforce planning, especially in terms of the number of the person per nurses, for better reproductive health outcomes.