The economic benefits of increased levels of nursing care in the hospital setting


  • Diane E. Twigg PhD RN RM,

    Professor of Nursing and Head of School, Corresponding author
    1. Sir Charles Gairdner Hospital, Perth, Western Australia, Australia
    • School of Nursing and Midwifery, Edith Cowan University, Joondalup, Western Australia, Australia
    Search for more papers by this author
  • Elizabeth A. Geelhoed PhD MPH BEc,

    1. School of Population Health, The University of Western Australia, Perth, Australia
    Search for more papers by this author
  • Alexandra P. Bremner BSc(Hons) PhD,

    Assistant Professor
    1. School of Population Health, The University of Western Australia, Perth, Australia
    Search for more papers by this author
  • Christine M. Duffield PhD RN

    Professor and Associate Dean (Research)
    1. Director Centre for Health Services Management, Faculty of Nursing, Midwifery and Health, University of Technology, Sydney, Sydney, New South Wales, Australia
    Search for more papers by this author

Correspondence to D.E. Twigg: e-mail:



To assess the economic impact of increased nursing hours of care on health outcomes in adult teaching hospitals in Perth, Western Australia.


Advancing technology and increased availability of treatment interventions are increasing demand for health care while the downturn in world economies has increased demand for greater efficiency. Nurse managers must balance nurse staffing to optimize care and provide efficiencies.


This longitudinal study involved the retrospective analysis of a cohort of multi-day stay patients admitted to adult teaching hospitals.


Hospital morbidity and staffing data from September 2000 until June 2004, obtained in 2010 from a previous study, were used to analyse nursing-sensitive outcomes pre- and post-implementation of the Nurse Hours per Patient Day staffing method, which remains in place today. The cost of the intervention comprised increased nursing hours following implementation of the staffing method.


The number of nursing-sensitive outcomes was 1357 less than expected post-implementation and included 155 fewer ‘failure to rescue’ events. The 1202 other nursing-sensitive outcomes prevented were ‘surgical wound infection’, ‘pulmonary failure’, ‘ulcer, gastritis’, ‘upper gastrointestinal bleed’, and ‘cardiac arrest’. One outcome, pneumonia, showed an increase of 493. Analysis of life years gained was based on the failure to rescue events prevented and the total life years gained was 1088. The cost per life year gained was AUD$8907.


The implementation of the Nurse Hours per Patient Day staffing method was cost-effective when compared with thresholds of interventions commonly accepted in Australia.

What is already known about the topic?

  • Higher nurse staffing levels and a richer skill mix have been associated with improved patient outcomes.
  • Internationally, improved nurse staffing levels have been associated with economic benefits.
  • The available evidence does not examine the economic impact of increased nursing hours in the Nurse Hours per Patient Day staffing method.

What this paper adds

  • Increased nursing hours in acute hospitals resulting from using the Nurse Hours per Patient Day staffing method was considered cost-effective when using accepted thresholds for life years gained.
  • The Nurse Hours per Patient Day staffing method was associated with avoidance of specific nursing-sensitive outcomes, which demonstrates parallel improvements in the quality of care.

Implications for practice and/or policy

  • Investment in increased nursing hours via the Nurse Hours per Patient Day staffing method has proven a cost-effective initiative with clinical benefits.
  • Further research is needed to better the cost-specific nursing-sensitive outcomes and determine the economic benefits of nurse staffing changes at ward level.


This article discusses the economic benefits of increased levels of nursing care and reports the findings of a study that assessed the cost-effectiveness of increased nursing care on health outcomes. It builds on previous analysis (Twigg et al. 2011, 2012) by examining the cost-effectiveness of the staffing method and by ‘incorporating a more complex individual measure of patient risk aggregated by hospital’ (Twigg et al. 2011). To date an economic evaluation has not been undertaken in an Australian setting.


The recent downturn in world economies has increased pressure on public and private health services to increase efficiency in an environment where advancing technology and increased availability of treatment interventions are increasing the demand for health care. Seventy-two percent of the recurrent cost per ‘case-mix-adjusted’ separation is staff related (medical and non-medical labour) (Australian Institute for Health & Welfare 2010) and as nursing is the largest workforce in health, nurse managers are increasingly forced to make difficult decisions. Nurse managers must decide the number and mix of nursing staff needed to optimize safe patient care within the limitations of budgetary constraints (Twigg & Duffield 2009). In a recent report, the Australian Nursing Federation (2009) observed that excessive workloads are common in the Australian healthcare setting. Nursing workloads and patient outcomes are inextricably linked (Aiken et al. 2002). Simply put: ‘If there are not enough nurses, the workload for each nurse is increased’ (Australian Nursing Federation 2009). Inadequate time reduces nurses' ability to deliver adequate patient care and forces nurses to leave work undone which directly has an impact on patient outcomes (Kalisch 2006, Duffield et al. 2011).

Higher nurse staffing levels and a richer skill mix [a higher proportion of registered nurse (RN) hours] have been linked with improved patient outcomes in many studies (Aiken et al. 2003, 2002, b, Rafferty et al. 2007, Tourangeau et al. 2007, Kane et al. 2007). Fifteen states and one district in the USA have enacted regulations or legislation aimed at improving nurse staffing. California was the first state to do so in 1999 and numerous studies about the impact of these changes have been undertaken (Donaldson & Shapiro 2010). A synthesis of these studies found that the nurse-to-patient ratio fell and the nursing hours per patient day increased. However, the authors did not establish any significant impact on patient safety indicators (Donaldson & Shapiro 2010) although they noted that adverse outcomes did not increase despite the case-mix index suggesting a sicker patient group. On the other hand, Aiken et al. (2010) found the mandated ratios in California were associated with lower mortality when compared with two states (Pennsylvania and New Jersey) without legislation. The continuous (24 hour 7 days a week) surveillance provided by RNs is key to early detection and prompt intervention for deteriorating patients (Aiken et al. 2002, b, Estabrooks et al. 2005). Nurses also have the capacity to proactively minimize adverse events and subsequent negative patient outcomes (Aiken et al. 2003). This function, however, depends on adequate nurse staffing levels in terms of both the volume of nursing and the mix of nurses (Aiken et al. 2003, Needleman et al. 2011).

Two Australian studies found similar results (Duffield et al. 2011, Twigg et al. 2011). The first study, undertaken in New South Wales found a higher proportion of RNs was associated with a statistically significant decrease in pressure ulcers, gastrointestinal bleeding, sepsis, shock, physiologic/metabolic derangement, pulmonary failure, and failure to rescue (Duffield et al. 2011). The same study found increased rates of deep vein thrombosis with improved skill mix (Duffield et al. 2011). The second study, undertaken in Western Australia (WA) over 4 years, evaluated implementation of the Nurse Hours per Patient Day (NHPPD) staffing method (Twigg et al. 2011). Twigg et al. (2011) found decreased rates of nine nursing-sensitive outcomes (NSOs), including mortality, at hospital level and significant decreased rates of five NSOs at ward level, following implementation of NHPPD.

This research evidence has put hospitals on notice to implement appropriate nurse staffing levels and a better skill mix (Clarke & Aiken 2006) as illustrated by the mandated staffing changes described previously. However, budgetary constraints and the labour market often limit the ability of hospitals to implement higher levels of nurse staffing and administrators have expressed concerns about the cost implications (Needleman & Buerhaus 2003). In response, several papers modelled the potential impact of fewer or additional nursing hours, given the association with NSOs. Many have argued that significant financial savings are to be gained by improving nurse-to-patient ratios (Rothberg et al. 2005, Needleman et al. 2006, Newbold 2008). Needleman et al. (2006) used data from the landmark 2002 study of 799 hospitals to argue the economic and social case for increasing nurse staffing levels. They found improving the RN mix (higher proportion of RN hours) to the 75th percentile while maintaining the total hours of care resulted in significant cost savings via reductions in length of stay and/or adverse outcomes. Although increasing total hours of care (RNs and licensed practical nurses) to the 75th percentile produced a larger reduction in length of stay, improvements in adverse outcomes were not so great and did not offset the increased hours of care. Needleman et al. (2006) estimated 6700 inpatient deaths could be avoided by increasing nursing staffing, mostly by a richer RN mix.

Newbold (2008) used production theory techniques to suggest staffing profiles that maximized patient outcomes and minimized costs. Reinterpreting Aiken et al.'s (2003) data, Newbold (2008) suggested increasing the number of graduate RNs as a percentage of the workforce was the most cost-effective way to improve patient outcomes. Thungjaroenkul et al. (2007) found that the proportion of RNs (skill mix) was inversely related to costs. More recently, Weiss et al. (2011) found that units with higher RN non-overtime staffing had lower odds of readmission. Their projected total savings was $409·59 per hospitalized patient per standard deviation increase in RN non-overtime staffing. For the 16 units studied, this represented US$11·64 million total savings.

Staffing at the nurse-to-patient level has also been examined from the context of a patient safety intervention (Rothberg et al. 2005). Rothberg et al. estimated that decreasing the patient-to-nurse ratio from 8:1–4:1 would reduce patient mortality and cost US$136,000 per life saved. This cost compares favourably to, for example, thrombolytic therapy in acute myocardial infarction at US$182,000 per life saved (Catillo et al. 1997 cited in Rothberg et al. 2005) or routine cervical cancer screening at US$432,000 per life saved (Charny et al. 1987 cited in Rothberg et al. 2005). This is supported by another study (Dall et al. 2009) that found the economic value of each additional full time RN ranged from US$58,100 to US$62,500 because of an associated reduction in nosocomial complications and therefore reduced medical costs. These analyses (Rothberg et al. 2005, Needleman et al. 2006, Dall et al. 2009, Weiss et al. 2011) indicate there is also an economic argument to improve nurse staffing.

The study


The aim of this study was to assess the cost-effectiveness of increased nursing hours of care on health outcomes of patients in adult tertiary teaching hospitals following a direction from the Australian Industrial Relations Commission to implement the Nurse Hours per Patient Day staffing method (Australian Industrial Relations Commission 2002). Specifically, the study:

  • Assessed the net cost of intervention by comparing the costs of increased nursing staff with the savings in terms of reduced nursing-sensitive outcomes.
  • Evaluated the cost per life year gained of increased nursing hours on mortality outcomes.

Data were obtained in 2010 from a previous Australian study (Twigg et al. 2011) that demonstrated an association between improved health outcomes and increased hours of nurse staffing following implementation of the NHPPD staffing method (Twigg & Duffield 2009). The study was set in Perth, the capital city of WA, which is the largest state in Australia. The population of WA was 2,317,100 in 2010, with over 1·2 million residing in metropolitan Perth (Australian Bureau of Statistics 2010). The three metropolitan adult tertiary teaching hospitals have a total of 1449 beds, which give ‘a comprehensive range of clinical services including trauma, emergency (except obstetrics), critical care, and acute medical and surgical services' (Twigg et al. 2011). For the purposes of this study we have assumed that when the observed number of NSOs varied from the expected number, the primary reason for the difference was the NHPPD, however, other factors may also have contributed.


This longitudinal study involved the retrospective analysis of a cohort of all multi-day stay patients (medical and surgical) admitted to the three teaching hospitals for more than 24 hours from September 2000–June 2004.


Data comprised 22 months prior to implementation of the NHPPD staffing method (pre-implementation), 6 months transition (data not included), and 22 months following implementation of the NHPPD staffing method (post-implementation). Rates of 13 NSOs were calculated using the hospital morbidity data associated with each of these admissions. NSOs were defined as a ‘variable patient or family caregiver state, condition, or perception responsive to nursing intervention' (Mass et al. 1996, Johnson & Lass 1997, Irvine et al. 1998). The specific NSOs included in this study were based on the Needleman et al. (2002) study and comprised: central nervous system complications, deep vein thrombosis/pulmonary embolus, pressure ulcers, gastrointestinal bleeding, pneumonia, sepsis, shock/cardiac arrest, urinary tract infection, failure to rescue, physiologic/metabolic derangement, pulmonary failure, wound infections, and mortality.

The sample also included nursing hours in the three adult tertiary hospitals' NHPPD wards. In Australia, an RN is defined as a nurse who is on the register maintained by the Nursing and Midwifery Board of Australia (NMBA) to practise nursing. Currently, RN education is a minimum 3-year degree from a tertiary education institution (Australian Institute for Health & Welfare 2008). Australia's Enrolled Nurses (ENs) are also registered by the NMBA. Their minimum educational requirement is a 1-year diploma from a higher education institution (Australian Institute for Health & Welfare 2008). ENs are similar to the US and Canadian licensed practical or vocational nurses, who undertake a 12–18 month training programme emphasizing technical tasks and skills (Page 2004).

Data collection

Patient data were sourced from patient discharge abstracts from September 2000–June 2004 extracted from the hospitals' morbidity systems. Data were identified for inclusion based on the process described in Tourangeau and Tu (2003) and were used to develop the risk adjustment model.

Ethical considerations

This study was granted Research Ethics Committee approval by the Human Research Ethics Committee of Edith Cowan University and the Human Research Ethics Committee of the study hospitals.

Data analysis

All analyses were conducted using PASW version 18 Release 18·0·2.

Pre- and post-implementation comparisons

Comparisons of patient characteristics pre- and post-implementation were undertaken using chi-squared tests (gender, indigenous status, country of birth, season of admission, referral source, major diagnostic category, care type) and two sample independent t-tests (age, DRG cost weight).

Individual patient risk adjustment

For each of the 13 categorical NSOs, a multivariable logistic regression model was fitted to the pre-NHPPD intervention data. The models were adjusted for the following patient and admission characteristics: age, gender, age gender interaction, indigenous status, country of birth, season of admission, referral source, major diagnostic category, care type, and Diagnostic Related Group cost weight. These models were applied to patients in the postimplementation period and the predicted probabilities from these models were used to calculate the expected frequency of each NSO post-implementation. The difference between the expected and observed frequencies of each NSO for the post-implementation period was calculated and the significance of this difference was tested using chi-squared analysis. The only NSOs included in the economic analysis were those that demonstrated statistical significance (< 0·004, based on the Bonferroni correction for multiple comparisons to reduce the probability of false positives; i.e. testing 13 outcomes 0·05/13 = 0·0038) (Hair et al. 2010 p. 437).

Measurement of costs – nursing variables

The cost of the intervention comprised increased hours of nursing staff following implementation of the NHPPD staffing method. Staff records (n = 140,060) were used to collect nursing hours over the study period. Total numbers of nursing hours provided by RNs and ENs were collected for the pre-implementation period (22 months) and the post-implementation period (22 months). Hourly rates for RNs and ENs were based on total annual salaries (including on-costs such as annual leave) and an average 40 hour working week. Staff data were sourced from the Department of Health Western Australia Human Resource Data Warehouse. Nursing variables included in the database were skill mix percent, total nursing hours, and total RN hours. Only productive hours (nursing hours of care excluding annual leave sick leave and workers compensation) were included. Three adult acute hospitals, 52 wards, and the associated nurse hours for each ward were included. The hourly cost was based on the average nursing costs for each hospital.

Cost savings were based on the net reduction in NSOs (refer to Table 1 for listing of NSOs.) The cost of NSOs prevented was taken as an average cost and based on a published cost of an adverse event for a multiple day admission corrected for age and comorbidity (Ehsani et al. 2006). All costs were referenced to a single calendar year using health index deflators.

Table 1. Nursing-sensitive outcomes observed and expected frequencies
Nursing-sensitive outcomePre/Post-interventionn =Observed number of outcome (frequency)Expected number of outcome (frequency)Difference between expected and observed frequenciesP value
  1. Pearson chi-squared tests were used to determine differences between expected and observed frequencies.

  2. a

    Analysis completed on surgical patients only.

CNS complicationsPost108,224489486−3Increase0·923
Surgical wound infectionPost43,7498571002145Decrease0·001
Pulmonary failurePost43,749398571173Decrease<0·001
Urinary tract infectionPost108,22449065039133Decrease0·172
Pressure ulcerPost108,224885778−107Increase0·008
Deep vein thrombosisPost108,2246226286Decrease0·864
Ulcer/gastritis/UGI bleedPost108,2248271368541Decrease<0·001
Physiologic/metabolic derangementPost43,7491319134425Decrease0·623
Shock/cardiac arrestPost108,224303646343Decrease<0·001
Failure to rescuePost108,22411601315155Decrease0·002

Measurement of NSOs and cost-effectiveness

Nursing-sensitive outcomes were assessed, as previously described. The outcome for life years gained was based on pre- and post-intervention differences in ‘failure to rescue’. Future life years gained were discounted at 3% to reflect time preference, that is, benefits sustained currently have greater value than those in the future. The cost of the intervention as described above was compared with the net number of NSOs averted to establish the total net cost, which was compared with the net number of discounted life years gained to establish the cost per life year gained.

Validity and reliability

This study used data previously collected by hospitals in WA and recorded in their hospital morbidity databases. Although secondary data from medical records may be subject to coding error, validation studies confirm the accuracy and reliability of WA hospital morbidity data (Brameld et al. 1999, Teng et al. 2008). For example, Teng et al. (2008) found the positive predictive value of case-mix coding of heart failure as the principal diagnosis was 99·5% when compared with the medical chart diagnosis. Sensitivity analysis was used to validate the robustness of the cost-effectiveness ratio by testing levels of uncertainty in the analysis.


Patient demographics

Characteristics of the patient population were similar across the three hospitals and were consistent for both the pre- and post-implementation periods of analysis (patient population 107,253 compared with 107,026). While there was a significant difference in age (60·3 pre- and 60·8 post-implementation) (t-test P < 0·001), this difference was not considered clinically relevant. The increase in Diagnostic Related Group cost weight between the pre- and post-implementation period was also significant (t-test P < 0·001), suggesting increased patient complexity post-implementation (Table 2).

Table 2. Patient demographics for pre- and post-intervention
 GenderMean age (years)Diagnostic related group cost weight
  1. a

    t-test P < 0·001.


Nursing hours

Nursing hours increased by 590,568 hours in the post-implementation period, comprising 409,987 more RN hours and 180,580 more EN hours. Agency hours, which included RN and EN hours, reduced by 21,333 hours (refer Table 3). Across all hospitals the skill mix [RNs/(RNs + ENs)] changed very little, but decreased slightly from 87% pre-implementation to 85% post-implementation. Hence, cost-effectiveness was calculated assuming no change in skill mix and based on costs incurred and life years gained.

Table 3. Nursing hours by pre/post-intervention in all hospitals
 RN hoursOther hoursAgency hoursaTotal hours
  1. a

    Agency hours were excluded in the analysis of nursing hours.



The total number of NSOs prevented was 1357 including 155 ‘failure to rescue’ events and 1202 other NSOs comprising ‘surgical wound infection’, ‘pulmonary failure’, ‘ulcer, gastritis, upper gastrointestinal bleed’, and ‘cardiac arrest’ (refer to Table 4). One NSO, pneumonia, showed an increase of 493. Net cost was estimated based on 1202 NSOs averted (savings) and 493 NSOs having incurred an additional cost. Other NSOs did not demonstrate difference at the 0·004 significance level (Table 1).

Table 4. Summary of nursing-sensitive outcomes prevented
Nursing-sensitive outcomeNumber of nursing-sensitive outcomes prevented
Surgical wound infection145
Pulmonary failure173
Ulcer, gastritis, upper gastrointestinal bleed541
Shock, cardiac arrest343
Failure to rescue155

Analysis of life years gained was based on the 155 failure to rescue events prevented post-intervention. The average age of all inpatients who experienced a failure to rescue event was 73·8 years and the average life expectancy for Australians was 81·5 years in 2008 (OECD 2011), therefore the total life years gained was 1240. To adjust for future benefits (time preference), life years were discounted at 3%, so that total life years gained became 1088 years.

Total nursing hours increased by 590,568 hours (refer to Table 3); costing AUD$16,833,392 based on proportional contribution of RNs and EN average salary costs. As previously reported (Twigg & Duffield 2009), when the staffing method was introduced the increases were achieved by increasing nursing numbers rather than a reliance on agency nurses or overtime. The cost per adverse event was AUD$10,074 (Ehsani et al. 2006) and the total cost averted was AUD$7,142,466 (for four NSOs averted and one NSO increased), leading to a net intervention cost of AUD$9,690,926. The cost per life year gained was AUD$8907.

Sensitivity analysis

Our cost of the NSO prevented was taken from published work and corrected for age and comorbidity, however, sequelae of adverse events frequently depend on the original cause of admission and we were unable to validate this figure directly. If we underestimated the cost of an adverse event by 50%, (assume NSO cost of AUD$15,000) then the cost per life year gained becomes AUD$5697. Conversely, if we overestimated the cost of a NSO by approximately the same amount so that the cost was AUD$5000, then the cost per life year gained becomes AUD$12,213 (discounted).

Our cost-effectiveness ratio may overestimate in that not all NSOs occur in different patients and the cost per NSO prevented is potentially less when more than one NSO occurs in the same patient. To test the impact of repeat events we analysed frequency data to estimate the number of events in the same individual. The data suggest that up to 25% of events occurred in the same individual. If only 75% of NSOs prevented are considered to lead to resource savings, then cost per life year gained becomes AUD$14,064 (discounted), suggesting that the result is only moderately sensitive to several repeat events in the same individual. When analysis included only NSOs prevented (i.e. excluding the increased pneumonia events), the net cost of the intervention became AUD$12,108,948 and the cost per life year gained was AUD$4324.


A reasonable threshold for cost-effectiveness in Australia is $30–60,000 per life year gained (Eichler et al. 2004), hence the implementation of the NHPPD staffing method was cost-effective under all scenarios. These results are in keeping with the findings in the literature (Rothberg et al. 2005, Dall et al. 2009) and suggest increasing nurse staffing is a cost-effective patient-safety intervention. Furthermore, these results fall within the cost-effectiveness thresholds of the USA, the UK, and Sweden suggesting broader application that of Australia (Eichler et al. 2004). In addition, the implementation of the NHPPD staffing method was associated with the avoidance of 1202 other NSOs (surgical wound infection, pulmonary failure, ulcer, gastritis, upper gastrointestinal bleed, and cardiac arrest) demonstrating parallel improvements in the quality of care. The significant increase in the pneumonia rates is an anomaly that cannot be easily explained. Pneumonia is susceptible to severe fluctuations according to influenza prevalence but we were unable to ascertain whether this was the cause for the increase. However, one of the three hospital's senior managers advised that a focus on coding pneumonia as a complication had occurred during the study period (T. Basile, personal communication, 2012) suggesting that the increase in pneumonia may have been related to a change in data capture. These results suggest the increased expenditure on nursing salaries was justified from a cost-effective threshold even though the business case for increased nurse staffing could not be made on the basis of cost savings. That is, the intervention is a cost-effective expenditure compared with other accepted health interventions although financial returns in averted illness do not exceed the financial investment in nurse salaries. This raises the question: What is the community prepared to pay for quality health care (Needleman et al. 2006)?


There were some limitations to this study. Agency hours were not included in the analysis as they represented <10% of all RN and EN nursing hours. The study was unable to control for variation in the staffing levels of other disciplines or for variation across hospitals, which may have masked benefits since they were averaged across hospitals. The study did not take into account changes in treatments or medications that may also have contributed to changes in NSOs. The study was also unable to control for secular trends, however, over the study period health services were relatively static as a major review and planning process for the future of health services was underway (Health Reform Committee 2004). The data did not have sufficient detail to undertake a probabilistic sensitivity analysis, however, elementary sensitivity analysis was undertaken to determine the effect of variation in the variables. Finally, an average cost for NSOs was used as costing data on specific nurse sensitivity outcomes was unavailable.


This study demonstrates that the investment in the increased nursing hours associated with the implementation of the NHPPD staffing method has been a cost-effective initiative based on the accepted Australian threshold. The findings of this study are timely as the Council of Australian Governments has established Health Workforce Australia to examine several matters including a National Training Plan with a goal of self-sufficiency in the supply of doctors, nurses, and midwives by 2025 (Health Workforce Australia 2012). Better costing of specific NSOs would strengthen future research examining the economic benefits of changes in staffing methods and hours of care. In addition, this study has focused on the changes in NSOs across adult acute hospitals in WA. Staffing decisions occur at the ward level and larger national studies examining the economic benefits of staffing changes at a detailed ward level would further refine the evidence to support the allocation of scarce nursing resources.


The authors thank Judith Pugh PhD RN, from the School of Nursing and Midwifery at Edith Cowan University, for proof reading and administrative support in manuscript preparation.


This research was funded by an Edith Cowan University Collaborative Grant with industry partners Sir Charles Gairdner Hospital Nursing Service and the Western Australian Department of Health, Nursing and Midwifery Office. The funding amount was AU$37,899.

Conflict of interest

No conflict of interest has been declared by the authors.

Author contributions

All authors meet at least one of the following criteria (recommended by the ICMJE: and have agreed on the final version:

  • substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data;
  • drafting the article or revising it critically for important intellectual content.