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Prevalence and documentation of malnutrition in hospitals: a case study in a large private hospital setting.

Abstract

Objectives: To determine the prevalence of malnutrition and whether the malnourished participants were being identified and documented as malnourished. To evaluate the impact of poor documentation on financial reimbursement to the hospital.

Subjects: Three hundred and twenty-four inpatients from a total of 690 randomly selected patients consented to participate in the study.

Design and setting: Subjective Global Assessment (SGA) was used to assess the nutritional status of inpatients. There were 1906 patients were admitted over a three-month period. Of these, 1860 were eligible and 690 were randomly selected from computer generated ward lists. The referral rate for nutrition intervention of malnourished participants was determined by viewing the patient medical records retrospectively. The Australian National Diagnostic Related Group (AN-DRG) of the malnourished subjects, not documented in the medical record as malnourished, were redetermined with the addition of the malnutrition code. The potential shortfall in financial reimbursement to the hospital was calculated by subtracting the average costing based on original AN-DRGs from the average costing based on the revised AN-DRGs.

Main outcome measures: Prevalence of malnutrition, levels of malnourished patients identified and documented, revenue losses under case payment system.

Statistical analyses: Logistic regression analyses were used to evaluate group differences in sex across SGA categories and to investigate predictors of referral versus non referral. Analysis of variance was used to evaluate group differences in age across SGA categories.

Results: One hundred and twenty-seven (42.3%) of the 324 subjects were malnourished. Only one of 137 malnourished patients was documented as malnourished in the medical records and only 21 (15.3%) were referred for nutrition intervention. The inclusion of the malnutrition code to the AN-DRG of the identified malnourished patients highlighted a shortfall of $125 311 in reimbursements to the hospital.

Conclusions: The degree of malnutrition in this hospital is similar to that found internationally. Malnourished patients are not being identified using the current referral method. Failure to flag malnourished patients requiring nutrition intervention potentially impacts on length of stay, hospital costs and patient outcomes and ultimately results in a shortfall for case payment funded institutions.

Key words: diagnostic related groups, subjective global assessment, reimbursement, malnutrition, prevalence, hospitalisation, casemix, nutritional status

(Nutr Diet 2005;62:41-47)

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Introduction

There has been little recent change to prevalence rates of malnutrition in hospitals (1-3) yet it often remains unrecognised and uncorrected (1,4-8) despite first being acknowledged over 35 years ago (1,9-11). It is well established that malnutrition results in an increased risk of morbidity and mortality (12-16) with subsequent increases in hospitalisation costs and length of stay (17-19). Despite the evidence, effective outcomes are often complicated and difficult to achieve. Malnutrition is infrequently diagnosed (1,6,8,11,17,20-23) or documented in medical notes (1,17,20,21,24) and administrative changes can compound the problem. Staff cuts and budget constraints can impinge upon the ability to provide a flexible, appropriate and timely meal service. This is a powerful combination for patients fasting for tests, or who have nausea, depression or feeding difficulties (1,4,19).

Implementation of a screening tool provides the means to identify malnutrition. Failure or lack of documentation can result in inappropriate acuity downgrades and reduced payments (25). Hospitals are critically dependent on the malnutrition diagnosis and documentation by medical practitioners under a case payment environment (23,26). The shortfall resulting from the omission of malnutrition documentation can amount to millions of dollars as recent studies have suggested (2,20,23,24). Addition of a malnutrition code has the potential to change the patient's Australian National DRG (AN-DRG), allocating a greater average cost to the hospital for the patient's episode of care. Ferguson et al. (20) projects financial reimbursement of AUD $1.67 million when malnutrition is coded as the sole comorbidity.

Overseas and Australian studies indicate malnutrition prevalence ranges from 4% (20) to 68% (27). The wide variation depends partly on the different criteria used to diagnose malnutrition and the methods of assessment such as biochemical and anthropometric data (1,28) or SGA (12). Difficulties comparing malnutrition prevalence rates are aptly acknowledged by Kelly et al. (1). Consequently, malnutrition prevalence rates from the literature cannot be applied to predicting malnutrition in the hospital.

Despite the large number of studies indicating the prevalence of malnutrition in hospitalised patients, few studies until very recently have been done in Australia, and even fewer in the private hospital setting. This provided the impetus to conduct a study to investigate what is happening in an inpatient population. The validated SGA tool was selected for this study as the most appropriate tool based on the evidence of reliability and reproducibility (12,13,29,30).

The purpose of this study was to determine the prevalence of malnutrition at a large private hospital and to determine whether malnourished patients were being identified and documented as malnourished. This data was then used to determine the potential financial shortfall to the hospital under a case payment system should the at risk subjects fail to have been identified and documented as malnourished.

Methods and subjects

The case study was conducted on a not-for-profit private hospital with 228 acute care beds. The main specialities are general surgery, cardiology, neurosurgery, orthopaedics and urology. Patients excluded from the study were intensive care and day surgery patients, those too ill to participate, patients under 18 years of age and those who could not speak English where an interpreter could not be accessed. Patients eligible for the study were randomly selected from computer generated ward lists using Cbord software (Cbord Group Inc, Sydney, Cbord Diet Office System version 5.0.04).

During the period of the study (August-October 1999) 1906 patients were admitted to the hospital. There were 1868 patients who were eligible to be included at the commencement of the study. Of these, 690 patients were randomly selected for inclusion in the study. Data were collected on 324 consenting patients, representing 17.3% of the eligible hospital population during the study period. There were 297 subjects who declined to participate or were not available at the time of data collection, 48 subjects had been discharged before data was collected, seven were too sick to participate and 14 subjects were identified at the bedside as being of non-English speaking background and met the exclusion criteria so were removed from the study.

Two clinical dietitians, proficient in the use of the Subjective Global Assessment (SGA) tool collected data from the 324 consenting participants. In 100% of cases there was agreement that subjects were well nourished and severely malnourished, and for the moderately malnourished subjects, there was consensus in 90% of the cases. The SGA data collected was recorded on a standardised SGA questionnaire. SGA is a valid and reliable assessment tool (12,13,15,20,29) and is based on the patient's medical history (weight change, dietary intake change, gastrointestinal symptoms, functional capacity) and physical examination (muscle mass, subcutaneous fat, presence of ascites and oedema) (30). The application of this validated prognostic tool divides patients into three groups:

SGA-A: Patient is well nourished

SGA-B: Patient is moderately/mildly malnourished

SGA-C: Patient is severely malnourished

Using the SGA tool, malnutrition occurs when there is a greater than 10% continuing weight loss, continuing loss of subcutaneous fat and muscle, poor appetite, suboptimal intake, excess nutrient losses and loss of functional ability. The prevalence of malnutrition was determined by calculating the frequency of categories SGA-B and SGA-C. The rate of referral to Dietetic Services for nutrition intervention for malnourished patients (SGA-B and SGA-C) was determined retrospectively by viewing the patient order screen on the computerised medical records system (deLacy Patient Information System, 1993) to see if a referral had been generated.

Statistical analysis was performed with SPSS version 6.1.3 (SPSS Inc, Chicago, version SPSS 6.1.3 1995) statistics package. Analysis of variance was used to evaluate group differences in age across SGA categories. Logistic regression was used to evaluate group differences in gender across SGA categories. Logistic regression was also used to investigate the predictors of referral (versus non-referral): two comparisons were constructed, contrasting mild/moderately malnourished subjects (SGA-B) with well nourished subjects (SGA-A), and contrasting severely malnourished subjects (SGA-C) with well nourished subjects (SGA-A). Comparison of means of the study sample against the eligible patient sample was achieved by use of unpaired t tests.

The computerised and paper medical records were reviewed retrospectively to determine whether malnutrition had been documented. The data was then used to determine the percentage of malnourished patients identified in the study but not documented as malnourished in the medical records. The AN-DRGs for the malnourished subjects (SGA-B and SGA-C) identified were obtained from the medical record department. Patients' AN-DRGs were assigned by the medical record department according to patients' diagnoses and procedures using a grouping software package, 3M Health Information Systems Australian National Diagnosis Related Groups Grouper Version 3.1, (3M Australia Pty Ltd and Commonwealth of Australia, 1996) to determine their Australian National Diagnostic Related Group (AN-DRG).

The AN-DRGs of the malnourished patients (SGA-B and SGA-C) identified in the study, but not in the medical records, were redetermined with the addition of the appropriate malnutrition codes by the medical record department using 3M Health Information Systems Australian National Diagnosis Related Groups Grouper Version 3.1 software. Unspecified severe protein-energy malnutrition was assigned for SGA-C and (E43) moderate protein-energy malnutrition (E44.0) was assigned for SGA-B. The NSW Health Acute Care Costs (NSW Health, Sydney, Acute Care Performance Indicators by Hospital Type and AN-DRG version 3.1, 1996/97) were used to determine the average cost per episode of care for the original AN-DRGs and the revised AN-DRGs. The potential shortfall in financial reimbursement to the hospital was determined by subtracting the average costs based on the original AN-DRG from the average costing based on the revised AN-DRG.

The number of patients whose AN-DRG changed with the addition of the malnutrition code were recorded using the malnourished patients identified in the study. The percentage of malnourished patients identified in the study was extrapolated to the entire patient population, for a three-month period and annually. These figures were used to predict the number of malnourished patients whose AN-DRGs would be altered and a projection was made using the NSW Health Acute Care Costs to obtain the annual potential financial shortfall to the hospital.

The hospital in 1999 received DRG-based case payments from one major health fund for patients whose AN-DRG fell into the joint replacement AN-DRGs 404-407. Calculations previously described above were made for malnourished patients identified in the study who fall into the joint replacement AN-DRGs. Three-month and annual projections were calculated to determine the financial shortfall to the hospital under this case payment system.

Approval for the study was obtained from the hospital's ethics committee and informed consent was obtained from patients.

Results

The mean age of the study sample was 66.8 [+ or -] 14.8 years compared with 65.1 [+ or -] 25.4 for the eligible patient population. There was no significant difference between the age of the subjects compared with the eligible patient population (P = 0.2). Forty-nine percent of the subjects were male and 51% female compared with 52% male and 48% female in the eligible patient population. There were no significant differences in other measured variables between the study sample and the eligible patient population.

One hundred and thirty-seven of the 324 subjects (42.3%) were malnourished. Of these, 118 (36.4%) subjects were mild to moderately malnourished (SGA-B) and 19 (5.9%) subjects were severely malnourished (SGA-C). There was a significant difference in age between the SGA groups Logistic regression indicated that the proportion of male subjects did not vary significantly across the three groups ([X.sub.(2).sup.2] = 3.54; P = 0.1703) as seen in Table 1. Thus age was a potential confounder, while sex might still confound the results.

Of the 137 malnourished patients, only 21 (15.3%) patients had been referred for nutrition intervention. Thirteen of the 118 (11%) mild to moderately malnourished patients (SGA-B) had been referred for nutrition intervention. Eight of the 19 (42%) severely malnourished patients (SGA-C) were referred for nutrition intervention.

We firstly predicted referral odds with a logistic regression model having four predictors: two comparisons--for mild/moderately malnourished (SGA-B) and for severely malnourished (SGA-C), with well nourished subjects (SGA-A) as the 'control' group, and with age and sex as covariates. We found the latter two had nonsignificant partial betas (age, P = 0.9478, sex P = 0.1233), so we fit the model predicting referral from the two comparisons.

The odds of referral among the three groups were: well nourished (SGA-A)--11/176=6%; mild/moderate (SGA-B)--13/10=12%; severe (SGA-C)--8/11=73%. The estimated odds ratio (and 95% confidence interval; CI) for the first comparison was 1.98 (0.86-4.58, P = 0.110). That is, the odds of referral for subjects in the mild/moderately malnourished group was about twice the odds of referral for the well nourished group. The CI, however, included unity, indicating that this estimate could be the result of a purely chance fluctuation. The estimated odds ratio for the comparison of severe and well groups was 11.64 (3.89, 34.78), indicating that referral among the severely malnourished is far more likely than among well subjects. Only one out of 137 malnourished patients were documented as malnourished in the paper or computerised medical records. This patient was classified as SGA-C, severely malnourished.

Inclusion of the malnutrition codes (E43 and E44.0) to the original medical record coding and regrouping of the record changed the AN-DRG in 30 out of 137 (21.9%) of the malnourished subjects to an AN-DRG with complication or comorbidity. This is demonstrated in Table 2. The remaining 107 out of 137 (78.1%) malnourished subjects were already in an AN-DRG with a complication or comorbidity code or the AN-DRG did not have a complication or comorbidity split, so the AN-DRG did not change. Using the NSW Health Acute Care Costs 1996/97, the malnutrition code added to the coding of the malnourished patients, changed the AN-DRG and resulted potentially in $AUD125 311 additional reimbursement for these 137 patients.

A total of 137 of the 324 subjects (42.3%) were malnourished. With the patient population of 1906 over the study period, it is predicted that 806 patients may be malnourished for any three-month period and 3224 patients malnourished over the year. With 21.9% (30 out of 137) patients having malnutrition as the complication or sole comorbidity, we predict each of these patients potentially attracts an additional AUD$4177 in reimbursement. For the year, this extrapolates to an annual potential increase in reimbursement of AUD$2 948 962.

Of the patients with an AN-DRG in the range 404-407 which currently qualify for case payments with one of the major health funds, nine

of the 14 patients (64.3%) had changes to the AN-DRG with the addition of the malnutrition code. This made a difference in AUD of $26 938 in reimbursement to the hospital under a case payment arrangement as seen in Table 3. The remaining five malnourished patients were already in an AN-DRG with a complication or comorbidity code or the AN-DRG did not have a complication or comorbidity split, so the AN-DRG did not change.

With the patient population of 1906 over the study period, 82 patients with an AN-DRG in the range 404-407 would be malnourished for any three-month period and 328 over the year. With 64.3% (nine out of 14) patients having malnutrition as the sole comorbidity or complication, we predicted each of these patients would potentially attract AUD$2993 in reimbursement. This extrapolates to AUD$158 629 for any three-month period and AUD$634 516 for the year under a case payment system.

Discussion

The high prevalence of malnutrition in hospitals highlighted in recently published papers (20,31) suggests that malnutrition occurs in Australia to an extent comparable with overseas studies (6,32,33). Our finding of malnutrition in 42.3% of the study sample is similar to that found in some major teaching hospitals (31,34) and most likely reflects the high acuity of patients in the case study hospital. In 1999, the study hospital had an average acute casemix weight of 1.61, comparable with a tertiary referral hospital. This is the NSW Health Department calculated average acute casemix weight.

The prevalence of malnutrition found in the hospital under study is likely to be an underestimation as some patients at high risk of malnutrition were excluded from the study as they were too ill to participate or were in intensive care. It is emphasised that the financial reimbursements to this hospital were estimated values, based on the current malnutrition codes and tools in use. There are limitations pertaining to lack of definitions of under-nutrition and absence of cut-off values for variables used to measure nutritional status (39). Swails et al. (2) referred to the shortcomings of the ICD-9-CM malnutrition diagnosis codes and the same could be said about the ICD-10-AM codes, which are based on malnutrition in developing countries. These are the only malnutrition codes available and are currently in use nationally and internationally for classifying adult malnutrition in hospitalised patients, and in determining financial reimbursements (20,24) despite their relevance being questionable.

The prevalence of severe malnutrition in the hospital (6%) fell within the range indicated in previous studies overseas and in Australia (1,3,20,35). Age differed significantly (while sex did not) across the three malnutrition groups, indicating that age was associated with nutritional status in the study sample. However when both age and sex were fitted as predictors of referral together with the malnutrition groups, there was no association.

Referral rates for malnourished patients were poor overall. While the severely malnourished patients were more likely to be referred, the rates remained at less than half. This result is not surprising, as severely malnourished patients are easier to identify. The cost weighting and financial reimbursement estimates based on the NSW Health Acute Care Costs remained unchanged when different malnutrition codes were assigned because both codes caused the patients to group to the applicable 'with CC' DRG. The addition of any malnutrition code was sufficient to cause the patient to group to a 'with complication/comorbidity (CC)' DRG, except where the patient was already in a 'with CC' DRG or where there was no CC split for the DRG. SGA-B patients were assigned the malnutrition code E44.0 and SGA-C were assigned the malnutrition code E43. In each case where the patient's DRG changed as a result of the addition of a malnutrition code, this occurred regardless of whether code E44.0 or code E43 was assigned. Both malnutrition codes caused the patient to group to the applicable 'with CC' DRG.

The documentation of malnutrition was minimal. Only one in 137 malnourished patients was documented as malnourished in the medical record by a medical practitioner. Similar results are demonstrated in overseas and Australian studies (17,20,21). We suggest doctors may fail to document malnutrition in the medical notes due to difficulties encountered when interpreting the existing malnutrition definitions specified in the ICD-9-CM (36) and ICD-10-AM tabular list of diseases (37). These definitions are designed principally in relation to clinical syndromes of primary protein-energy malnutrition seen in paediatric populations in developing countries and are not easily applied to the hospitalised adult population in industrialised countries (2). It is possible doctors interpret malnutrition to be a pre-existing comorbidity or an expected comorbidity resulting from the patient's hospital admission.

Under the hospital's case payment arrangement, full reimbursement was not realised for patients with an AN-DRG in the range 404-407, based on the NSW Health Acute Care Costs. The potential shortfall to the hospital of AUD$26 938 was not recognised because the 14 malnourished subjects with AN-DRGs 404-407 were not identified or documented as malnourished, so could not be assigned a malnutrition code. Annually, the potential revenue loss of AUD$634 516 cannot be ignored. Identifying and managing malnutrition is probably the best attainable practice. A recent study has shown that early and intensive nutrition intervention appears to be beneficial in minimising weight loss and deterioration of nutritional status in ambulatory oncology patients (38). Failure to identify and document malnourished patients is an important issue, not only because it compromises best practice of care, but because it has the potential to incur a significant revenue deficit to Australian hospitals under a case payment system. No practitioner would debate that patient care is the most important issue, but it cannot be ignored that a significant dollar shortfall may also occur through lack of identification, documentation and therefore coding of malnutrition in the medical record.

No single assessment tool or measure has been accepted as a gold standard. We selected SGA as the most appropriate valid assessment tool because it incorporated a range of clinical criteria for assessing nutritional status (30), and did not emphasise one measure compared to a reference population which is no longer relevant (1,28).

Conclusion

Dietitians at some Australian hospitals already have malnutrition screening programs in place and use SGA to identify malnutrition. The National Centre for Classification in Health recognises malnutrition diagnosis for coding purposes by a dietitian if verified by the primary treating clinician. Dietitians can support hospitals in maximising reimbursement under a case payment system by documenting malnutrition.

The degree of malnutrition identified in the case study hospital was similar to that seen internationally and in local major teaching hospitals. Failure to adequately recognise and document its existence would have a financial impact on the hospital. We consider there is an underlying global complacency towards malnutrition within hospital culture because it is regarded as an expected occurrence among hospitalised patients and it is difficult to diagnose routinely. A set of criteria approved by each hospital for diagnosing malnutrition and a preadmission screening program would facilitate recognition, diagnosis, documentation and treatment. A multidisciplinary team approach that includes hospital administrators, medical practitioners and clinical dietitians is needed to assess reimbursement opportunities to maximise revenue. Until this occurs, it is unlikely that the levels of documentation will reflect the true prevalence of malnutrition in hospitals and allow these facilities to fully realise their revenue.
Table 1. Average age and degree of malnutrition

 Mean
SGA category (a) n (b) age sd (c) % Male

A 187 62.1 14.3 45
B 118 69.4 14.6 56
C 19 68.9 15.6 47

(a) SGA category: A = well nourished; B=mild/moderately
malnourished; C=severely malnourished.
(b) n = number of subjects.
(c) sd = standard deviation.

Table 2. Variations in AN-DRG payment with the inclusion of malnutrition
codes E43 and E44.0

 Original Original
Patient SGA category (a) DRG (b) Revised DRG (c) payment (d)

 72 B 568 567 6021
 19 B 24 23 9865
 22 B 24 23 9865
 31 B 306 305 8327
125 C 311 310 5679
137 C 161 160 11196
 6 B 416 414 5012
 13 B 416 414 5012
 18 B 416 414 5012
 58 B 416 414 5012
 87 B 416 414 5012
 89 B 416 414 5012
 54 B 512 511 3296
 83 B 794 793 2661
 2 B 405 404 12000
 8 B 405 404 12000
 33 B 405 404 12000
 70 B 405 404 12000
112 B 405 404 12000
114 B 405 404 12000
117 B 405 404 12000
105 B 309 308 8529
104 B 38 37 4478
 14 B 407 406 11413
 37 B 407 406 11413
 32 B 455 454 2368
 93 B 455 454 2368
116 B 576 575 3042
 35 B 349 348 1405
 57 B 349 348 1405

 Revised Difference
Patient payment (e) payment (f)

 72 14359 8338
 19 17355 7490
 22 17355 7490
 31 15359 7032
125 12247 6568
137 17073 5877
 6 10716 5704
 13 10716 5704
 18 10716 5704
 58 10716 5704
 87 10716 5704
 89 10716 5704
 54 7922 4626
 83 7124 4463
 2 15240 3240
 8 15240 3240
 33 15240 3240
 70 15240 3240
112 15240 3240
114 15240 3240
117 15240 3240
105 11470 2941
104 6981 2503
 14 13542 2129
 37 13542 2129
 32 3930 1562
 93 3930 1562
116 4415 1373
 35 2567 1162
 57 2567 1162
Total 125311

(a) SGA category: A = well nourished; B=mild/moderately malnourished;
C=severely malnourished.
(b) Original DRG: assigned to the patient by the medical records
department; DRG=diagnostic related group.
(c) Revised DRG results from the inclusion of malnutrition codes with
the original DRG of the malnourished patients. The malnutrition codes
are E43=unspecified severe protein energy malnutrition, assigned to
SGA-C; malnutrition code E44.0=moderate protein energy malnutrition,
assigned to SGA-B.
(d) Original payment expressed as $AUD; payment for that DRG category
using the NSW Health Acute Care Costs 1996/97; AUD=Australian Dollars.
(e) Revised payment expressed as $AUD; payment based on regrouping of
the DRG with the addition of the malnutrition codes (E43 for SGA-C and
E44.0 for SGA-B); AUD=Australian Dollars.
(f) Revised payment minus the original payment.

Table 3. Financial reimbursement under the current DRG case payment
system

 Original Original
Patient SGA category (a) DRG (b) Revised DRG (c) payment (d)

 1 B 405 404 12000
 2 B 405 404 12000
 5 B 405 404 12000
 9 B 405 404 12000
11 B 405 404 12000
13 B 405 404 12000
14 B 405 404 12000
 3 B 407 406 11413
 7 B 407 406 11413
 4 B 406 406 13542
 6 B 404 404 15240
 8 B 406 406 13542
10 B 406 406 13542
12 B 404 404 15240

 Revised
Patient payment (e) Difference payment (f)

 1 15240 3240
 2 15240 3240
 5 15240 3240
 9 15240 3240
11 15240 3240
13 15240 3240
14 15240 3240
 3 13542 2129
 7 13542 2129
 4 13542 0
 6 15240 0
 8 13542 0
10 13542 0
12 15240 0
Total 26938

(a) SGA category: A=well nourished; B=mild/moderately malnourished;
C=severely malnourished.
(b) Original DRG: assigned to the patient by the medical records
department; DRG=diagnostic related group.
(c) Revised DRG results from the inclusion of malnutrition codes with
the original DRG of the malnourished patients. The malnutrition codes
are E43=unspecified severe protein energy malnutrition, assigned to
SGA-C; malnutrition code E44.0=moderate protein energy malnutrition,
assigned to SGA-B.
(d) Original payment expressed as $AUD; payment for that DRG category
using the NSW Health Acute Care Costs 1996/97; AUD=Australian Dollars.
(e) Revised payment expressed as $AUD; payment based on regrouping of
the DRG with the addition of the malnutrition codes (E43 for SGA-C and
E44.0 for SGA-B); AUD=Australian Dollars.
(f) Revised payment minus the original payment.


Acknowledgments

Assistance provided by Kathryn Tucker, Gina Martin and Archana Gulvady is gratefully acknowledged. We also would like to thank Gabrielle Challis.

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38. Isenring EA, Capra S, Bauer J. Nutrition intervention is beneficial in oncology outpatients receiving radiotherapy to the gastrointestinal or head and neck area. Br J Cancer 2004 August 2;91:447-52.

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St Vincent's Private Hospital, Sydney

C. Lazarus, BSc, MNutrDiet (Syd), Food Service Nutrition Manager

J. Hamlyn, GradDipNutrDiet (Syd), Clinical Dietitian

Correspondence: J. Hamlyn, St Vincent's Private Hospital, Locked Bag 5, Darlinghurst NSW 2010. Email: jhamlyn@stvincents.com.au

Conception, design, acquisition of data, analysis and interpretation of results, manuscript by C. Lazarus and acquisition of data, analysis and interpretation of results, manuscript by J. Hamlyn.
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Title Annotation:Original research
Author:Hamlyn, Jenny
Publication:Nutrition & Dietetics: The Journal of the Dietitians Association of Australia
Geographic Code:8AUST
Date:Mar 1, 2005
Words:5458
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