Reflections on tomorrow’s menu : evidence for inpatient meal preferences at a large tertiary public hospital

There are limited studies that describe patient meal preferences in hospital; however this data is critical to develop menus that address satisfaction and nutrition whilst balancing resources. This quality study aimed to determine preferences for meals and snacks to inform a comprehensive menu revision in a large (929 bed) tertiary public hospital. The method was based on Vivanti et al. (2008) with data collected by two final year dietetic students. The first survey comprised 72 questions, achieved a response rate of 68% (n = 192), with the second more focused at 47 questions achieving a higher response rate of 93% (n = 212). Findings showed over half the patients reporting poor or less than normal appetite, 20% describing taste issues, over a third with a LOS >7 days, a third with a MST _ 2 and less than half eating only from the general menu. Soup then toast was most frequently reported as eaten at home when unwell, and whilst most reported not missing any foods when in hospital (25%), steak was most commonly missed. Hot breakfasts were desired by the majority (63%), with over half preferring toast (even if cold). In relation to snacks, nearly half (48%) wanted something more substantial than tea/coffee/biscuits, with sandwiches (54%) and soup (33%) being suggested. Sandwiches at the evening meal were not popular (6%). Difficulties with using cutlery and meal size selection were identified as issues. Findings from this study had high utility and supported a collaborative and evidenced based approach to a successful major menu change for the hospital.

Let them eat steak : food preferences of patients in a tertiary public hospital

Information on foods patients like and dislike is the essential basis for planning menus which are acceptable to patients and promote adequate consumption. The aim of this study was to obtain quantitative data on the food preferences of inpatients at a large metropolitan public hospital for use in menu planning. Methodology was based on a study by Williams et al (1988), and included additional questions about appetite and taste changes. The survey used a 9 point hedonic scale to rate foods listed in random order and was modified to incorporate more contemporary foods than those used in the originalWilliams study. Surveys were conducted by final year University of Queensland dietetics students on Food Service Practicum at the Royal Brisbane and Women’s Hospital (929 beds) in 2012. The first survey (220 questions, n = 157) had a response rate of 61%. The second included more sandwich fillings and salads (231 questions, n = 219, response rate 67%). Total number surveyed was 376. Results showed the most preferred foods were roast potato, grilled steak, ice cream, fresh strawberries, roast lamb, roast beef, grapes and banana. The least preferred foods were grapefruit, soybeans, lentils, sardines, prune juice and grapefruit juice. Patients who reported taste changes (10%) had similar food preferences to those who didn’t report taste changes. Patients who reported poor/very poor appetite (10%) generally scored foods lower than those who reported OK (22%), good/very good appetite (65%). The results of this study informed planning for a new patient menu at the RBWH in December 2012.

Functional independence following subacute inpatient rehabilitation was not affected by age among older patients recovering from cardiac-related hospital admissions

Introduction Older people recovering from cardiac events requiring an acute hospital admission may experience a decline in physical function limiting their ability to return home to their previous accommodation. Subacute inpatient rehabilitation therapies have potential to assist recovery of physical functioning. However, it is unknown whether age influences the length of stay or physical functioning at discharge from subacute inpatient rehabilitation for this population. Objectives This study examined the outcomes of a cohort of older patients recovering from a cardiac event requiring hospitalisation to investigate the association between age and physical function at discharge, as well as age and length of rehabilitation stay. Methods Participants included 145 consecutive inpatient admissions to a subacute geriatric assessment and rehabilitation unit with a cardiac condition as their primary reason for hospital admission. Participants were required to complete a multi-disciplinary physical functioning assessment within 72 hours of admission to the unit, and again within 72 hours prior to discharge from the unit. The primary outcome measure was the Functional Independence Measure motor score. Demographic and clinical information, including length of stay and discharge destination, were also recorded. Results A total n=126 (87%) participants, with a mean (standard deviation) age of 79 (10) years, had both assessments completed and were included in analyses. Participants who had passed away (n=4, 3%), or did not have both assessments completed per protocol were excluded from analyses. Discharge destinations included home (n=101, 80%), residential aged care (n=17, 13%) and another hospital (n=8, 6%). The (median, interquartile range) Functional Independence Measure motor score was higher at discharge (79, 71 to 84) than admission (61, 48 to 71); z=7.75 p<0.001. Age was not associated with Functional Independence Measure motor score at discharge (t= -0.18, p=0.86), or length of stay in the rehabilitation unit (t= -0.52, 0.60). Conclusion Any perception that age may be associated with longer lengths of stay and reduced physical function outcomes among patients with cardiac conditions admitted for subacute inpatient rehabilitation for older adults is not supported data from this investigation. Older age should not be considered a disincentive when considering the suitability of patients with cardiac diagnoses for this type of inpatient rehabilitation or their potential physical functioning outcome.

The lag effects and vulnerabilities of temperature effects on cardiovascular disease mortality in a subtropical climate zone in China

This research quantifies the lag effects and vulnerabilities of temperature effects on cardiovascular disease in Changsha—a subtropical climate zone of China. A Poisson regression model within a distributed lag nonlinear models framework was used to examine the lag effects of cold- and heat-related CVD mortality. The lag effect for heat-related CVD mortality was just 0–3 days. In contrast, we observed a statistically significant association with 10–25 lag days for cold-related CVD mortality. Low temperatures with 0–2 lag days increased the mortality risk for those ≥65 years and females. For all ages, the cumulative effects of cold-related CVD mortality was 6.6% (95% CI: 5.2%–8.2%) for 30 lag days while that of heat-related CVD mortality was 4.9% (95% CI: 2.0%–7.9%) for 3 lag days. We found that in Changsha city, the lag effect of hot temperatures is short while the lag effect of cold temperatures is long. Females and older people were more sensitive to extreme hot and cold temperatures than males and younger people.

Mortality following hip arthroplasty—inappropriate use of National Joint Registry (NJR) data

Mortality following hip arthroplasty is affected by a large number of confounding variables each of which must be considered to enable valid interpretation. Relevant variables available from the 2011 NJR data set were included in the Cox model. Mortality rates in hip arthroplasty patients were lower than in the age-matched population across all hip types. Age at surgery, ASA grade, diagnosis, gender, provider type, hip type and lead surgeon grade all had a significant effect on mortality. Schemper's statistic showed that only 18.98% of the variation in mortality was explained by the variables available in the NJR data set. It is inappropriate to use NJR data to study an outcome affected by a multitude of confounding variables when these cannot be adequately accounted for in the available data set.

Calculate excess mortality during heatwaves using Hilbert-Huang transform algorithm

Background Heatwaves could cause the population excess death numbers to be ranged from tens to thousands within a couple of weeks in a local area. An excess mortality due to a special event (e.g., a heatwave or an epidemic outbreak) is estimated by subtracting the mortality figure under ‘normal’ conditions from the historical daily mortality records. The calculation of the excess mortality is a scientific challenge because of the stochastic temporal pattern of the daily mortality data which is characterised by (a) the long-term changing mean levels (i.e., non-stationarity); (b) the non-linear temperature-mortality association. The Hilbert-Huang Transform (HHT) algorithm is a novel method originally developed for analysing the non-linear and non-stationary time series data in the field of signal processing, however, it has not been applied in public health research. This paper aimed to demonstrate the applicability and strength of the HHT algorithm in analysing health data. Methods Special R functions were developed to implement the HHT algorithm to decompose the daily mortality time series into trend and non-trend components in terms of the underlying physical mechanism. The excess mortality is calculated directly from the resulting non-trend component series. Results The Brisbane (Queensland, Australia) and the Chicago (United States) daily mortality time series data were utilized for calculating the excess mortality associated with heatwaves. The HHT algorithm estimated 62 excess deaths related to the February 2004 Brisbane heatwave. To calculate the excess mortality associated with the July 1995 Chicago heatwave, the HHT algorithm needed to handle the mode mixing issue. The HHT algorithm estimated 510 excess deaths for the 1995 Chicago heatwave event. To exemplify potential applications, the HHT decomposition results were used as the input data for a subsequent regression analysis, using the Brisbane data, to investigate the association between excess mortality and different risk factors. Conclusions The HHT algorithm is a novel and powerful analytical tool in time series data analysis. It has a real potential to have a wide range of applications in public health research because of its ability to decompose a nonlinear and non-stationary time series into trend and non-trend components consistently and efficiently.

Discerning the timing and cause of historical mortality events in modern Porites from the Great Barrier Reef

The life history strategies of massive Porites corals make them a valuable resource not only as key providers of reef structure, but also as recorders of past environmental change. Yet recent documented evidence of an unprecedented increase in the frequency of mortality in Porites warrants investigation into the history of mortality and associated drivers. To achieve this, both an accurate chronology and an understanding of the life history strategies of Porites are necessary. Sixty-two individual Uranium–Thorium (U–Th) dates from 50 dead massive Porites colonies from the central inshore region of the Great Barrier Reef (GBR) revealed the timing of mortality to have occurred predominantly over two main periods from 1989.2 ± 4.1 to 2001.4 ± 4.1, and from 2006.4 ± 1.8 to 2008.4 ± 2.2 A.D., with a small number of colonies dating earlier. Overall, the peak ages of mortality are significantly correlated with maximum sea-surface temperature anomalies. Despite potential sampling bias, the frequency of mortality increased dramatically post-1980. These observations are similar to the results reported for the Southern South China Sea. High resolution measurements of Sr/Ca and Mg/Ca obtained from a well preserved sample that died in 1994.6 ± 2.3 revealed that the time of death occurred at the peak of sea surface temperatures (SST) during the austral summer. In contrast, Sr/Ca and Mg/Ca analysis in two colonies dated to 2006.9 ± 3.0 and 2008.3 ± 2.0, suggest that both died after the austral winter. An increase in Sr/Ca ratios and the presence of low Mg-calcite cements (as determined by SEM and elemental ratio analysis) in one of the colonies was attributed to stressful conditions that may have persisted for some time prior to mortality. For both colonies, however, the timing of mortality coincides with the 4th and 6th largest flood events reported for the Burdekin River in the past 60 years, implying that factors associated with terrestrial runoff may have been responsible for mortality. Our results show that a combination of U–Th and elemental ratio geochemistry can potentially be used to precisely and accurately determine the timing and season of mortality in modern massive Porites corals. For reefs where long-term monitoring data are absent, the ability to reconstruct historical events in coral communities may prove useful to reef managers by providing some baseline knowledge on disturbance history and associated drivers.

Validation of a framework for measuring hospital disaster resilience using factor analysis

Hospital disaster resilience can be defined as “the ability of hospitals to resist, absorb, and respond to the shock of disasters while maintaining and surging essential health services, and then to recover to its original state or adapt to a new one.” This article aims to provide a framework which can be used to comprehensively measure hospital disaster resilience. An evaluation framework for assessing hospital resilience was initially proposed through a systematic literature review and Modified-Delphi consultation. Eight key domains were identified: hospital safety, command, communication and cooperation system, disaster plan, resource stockpile, staff capability, disaster training and drills, emergency services and surge capability, and recovery and adaptation. The data for this study were collected from 41 tertiary hospitals in Shandong Province in China, using a specially designed questionnaire. Factor analysis was conducted to determine the underpinning structure of the framework. It identified a four-factor structure of hospital resilience, namely, emergency medical response capability (F1), disaster management mechanisms (F2), hospital infrastructural safety (F3), and disaster resources (F4). These factors displayed good internal consistency. The overall level of hospital disaster resilience (F) was calculated using the scoring model: F = 0.615F1 + 0.202F2 + 0.103F3 + 0.080F4. This validated framework provides a new way to operationalise the concept of hospital resilience, and it is also a foundation for the further development of the measurement instrument in future studies.

Evaluation of public hospital performance in Khanh Hoa Province : Vietnam in connection with patient and staff satisfaction surveys

This research aimed to develop a framework for performance evaluation of public hospitals in Vietnam that is culturally, socially, and politically appropriate. The research included both qualitative and quantitative methods and identified and validated novel instruments to measure patient satisfaction and job satisfaction of hospital staff and to determine a set of hospital indicators that reflect the quality of hospital performance. New models for understanding the determinants of patient and staff satisfaction were developed along with a new performance indicator framework for hospital performance. These instruments will now be applied to the evaluation of hospital services in Khanh Hoa Province, permitting longer term evaluation of their effectiveness in changing system wide performance and satisfaction.

Survival models of community tenure and length of hospital stay for the seriously mentally ill : a 10-year perspective

Objective: To examine the effects of personal and community characteristics, specifically race and rurality, on lengths of state psychiatric hospital and community stays using maximum likelihood survival analysis with a special emphasis on change over a ten year period of time. Data Sources: We used the administrative data of the Virginia Department of Mental Health, Mental Retardation, and Substance Abuse Services (DMHMRSAS) from 1982-1991 and the Area Resources File (ARF). Given these two sources, we constructed a history file for each individual who entered the state psychiatric system over the ten year period. Histories included demographic, treatment, and community characteristics. Study Design: We used a longitudinal, population-based design with maximum likelihood estimation of survival models. We presented a random effects model with unobserved heterogeneity that was independent of observed covariates. The key dependent variables were lengths of inpatient stay and subsequent length of community stay. Explanatory variables measured personal, diagnostic, and community characteristics, as well as controls for calendar time. Data Collection: This study used secondary, administrative, and health planning data. Principal Findings: African-American clients leave the community more quickly than whites. After controlling for other characteristics, however, race does not affect hospital length of stay. Rurality does not affect length of community stays once other personal and community characteristics are controlled for. However, people from rural areas have longer hospital stays even after controlling for personal and community characteristics. The effects of time are significantly smaller than expected. Diagnostic composition effects and a decrease in the rate of first inpatient admissions explain part of this reduced impact of time. We also find strong evidence for the existence of unobserved heterogeneity in both types of stays and adjust for this in our final models. Conclusions: Our results show that information on client characteristics available from inpatient stay records is useful in predicting not only the length of inpatient stay but also the length of the subsequent community stay. This information can be used to target increased discharge planning for those at risk of more rapid readmission to inpatient care. Correlation across observed and unobserved factors affecting length of stay has significant effects on the measurement of relationships between individual factors and lengths of stay. Thus, it is important to control for both observed and unobserved factors in estimation.

Sequential monitoring of hospital adverse events when control charts fail : the example of fall injuries in hospitals

Objective To evaluate methods for monitoring monthly aggregated hospital adverse event data that display clustering, non-linear trends and possible autocorrelation. Design Retrospective audit. Setting The Northern Hospital, Melbourne, Australia. Participants 171,059 patients admitted between January 2001 and December 2006. Measurements The analysis is illustrated with 72 months of patient fall injury data using a modified Shewhart U control chart, and charts derived from a quasi-Poisson generalised linear model (GLM) and a generalised additive mixed model (GAMM) that included an approximate upper control limit. Results The data were overdispersed and displayed a downward trend and possible autocorrelation. The downward trend was followed by a predictable period after December 2003. The GLM-estimated incidence rate ratio was 0.98 (95% CI 0.98 to 0.99) per month. The GAMM-fitted count fell from 12.67 (95% CI 10.05 to 15.97) in January 2001 to 5.23 (95% CI 3.82 to 7.15) in December 2006 (p<0.001). The corresponding values for the GLM were 11.9 and 3.94. Residual plots suggested that the GLM underestimated the rate at the beginning and end of the series and overestimated it in the middle. The data suggested a more rapid rate fall before 2004 and a steady state thereafter, a pattern reflected in the GAMM chart. The approximate upper two-sigma equivalent control limit in the GLM and GAMM charts identified 2 months that showed possible special-cause variation. Conclusion Charts based on GAMM analysis are a suitable alternative to Shewhart U control charts with these data.

Parent's socio-economic status and social support as risks for child mortality : consideration of health equity in The Gambia

A matched case-control study of mortality to children under age five was conducted to consider associations with parents' socio-economic status and social support in the Farafenni Demographic Surveillance Site (DSS). Cases and controls were selected from Farafenni DSS, matched on date of birth, and parents were interviewed about personal resources and social networks. Parents with the lowest personal socio-economic status and social support were identified. Multivariate multinomial regression was used to consider whether the children of these parents were at increased risk of either infant or 1-4 mortality, in separate models using either parents' characteristics. There was no benefit found for higher SES or better social support with respect to child mortality. Children of fathers who had the poorest social support had lower 1-4 mortality risk (OR=0.52, p=0.037). Given that socio-economic status was not associated with child mortality, it seems unlikely that the explanation for the link between father's social support and mortality is linked to resource availability. Explanations for the risk effect of father's social ties may lie in decision-making around health maintenance and health care for children.

Developing an evaluation framework for hospital disaster resilience : tertiary hospitals of Shandong Province, China

This thesis provided a definition and conceptual framework for hospital disaster resilience; it used a mixed-method, including an empirical study in tertiary hospitals of Shandong Province in China, to devise an assessment instrument for measuring hospital resilience. The instrument is the first of its type and will allow hospitals to measure their resilience levels. The concept of disaster resilience has gained prominence in the light of the increased impact of various disasters. The notion of resilience encompasses the qualities that enable the organisation or community to resist, respond to, and recover from the impact of disasters. Hospital resilience is essential as it provides 'lifeline' services which minimize disaster impact. This thesis has provided a framework and instrument to evaluate the level of hospital resilience. Such an instrument could be used to better understand hospital resilience, and also as a decision-support tool for its promoting strategies and policies.

Progress and challenges of disaster health management in China : a scoping review

Background Despite the importance of an effective health system response to various disasters, relevant research is still in its infancy, especially in middle- and low-income countries. Objective This paper provides an overview of the status of disaster health management in China, with its aim to promote the effectiveness of the health response for reducing disaster-related mortality and morbidity. Design A scoping review method was used to address the recent progress of and challenges to disaster health management in China. Major health electronic databases were searched to identify English and Chinese literature that were relevant to the research aims. Results The review found that since 2003 considerable progress has been achieved in the health disaster response system in China. However, there remain challenges that hinder effective health disaster responses, including low standards of disaster-resistant infrastructure safety, the lack of specific disaster plans, poor emergency coordination between hospitals, lack of portable diagnostic equipment and underdeveloped triage skills, surge capacity, and psychological interventions. Additional challenges include the fragmentation of the emergency health service system, a lack of specific legislation for emergencies, disparities in the distribution of funding, and inadequate cost-effective considerations for disaster rescue. Conclusions One solution identified to address these challenges appears to be through corresponding policy strategies at multiple levels (e.g. community, hospital, and healthcare system level).

Assessment of the mortality displacement in temperature-related deaths in Brisbane, Australia

This thesis is a population-based ecological study designed to investigate the issue of mortality displacement (or "harvesting" effect) in the assessment of temperature-related deaths in Brisbane, Australia. It examines the temperature impacts on mortality, and assesses the harvesting effects on the temperature–related deaths. This study contributes to the knowledge base of understanding the temperature-mortality relationship and assists in formulating and evaluating public health intervention strategies within the context of climate change.