INTRODUCTION

In New Zealand good information about diabetes andits complications is limited.1 This stems in part fromincomplete hospital discharge and mortality records,particularly for the complications of diabetes.2,3 Thecollation of an agreed national diabetes dataset providesbetter information about diabetes and facilitates:

(1) Monitoring of progress towards achievingdiabetes related goals and targets on a provider andgeographical basis, both nationally and regionally.

(2) Benchmarking, that is, comparisons of keyclinical measures between providers, geographicalareas and with other international benchmarkingservices with feedback to relevant parties.

(3) Identification of possible reasons for clinicalgoals not being achieved and to support the develop-ment of local and national solutions for any problemsidentified.

(4) Development of prevention strategies.Two major diabetes datasets have been published:

that of the British Diabetic Association in the UnitedKingdom4 and that of Diabcare in Europe.5 In NewZealand, established datasets have included those of theDiabetes Care Support Service in South and WestAuckland,6 the Royal New Zealand College of GeneralPractitioners, the Otago Diabetes Team project and theChristchurch Diabetes Centre. While these datasets aresimilar, they are not the same. Hence any analysis ofthese combined data is likely to be difficult. This is

currently the situation in Britain.7 For this reason anationally consistent diabetes dataset is preferred.

We describe the development of a recommendedminimum diabetes dataset for New Zealand.

METHODS

In 1997, the Diabetes Information Group, a subcom-mittee of the New Zealand Society for the Study ofDiabetes (NZSSD), reviewed datasets from the BritishDiabetic Association, Diabcare in Europe and theDiabetes Care Support Service in South Auckland. A minimum diabetes dataset focusing on monitoringdiabetes outcomes and applicable to electronic andpaper medical record systems was defined in collabor-ation with Diabcare from Europe. In November 1997,Government funding was approved for a joint diabeteshealth information project involving the SouthAuckland Diabetes Project, Otago Diabetes Teamproject, Comprehensive Health Services Ltd and theMangere Health Resources Trust. This project wascalled the Diabetes Health Information Project(DHIP). The DHIP included defining a national min-imum diabetes dataset, developing guidelines for themanagement of core aspects of diabetes care, describ-ing current regional diabetes information initiatives inNew Zealand and developing recommendations for aNational Diabetes Benchmarking and InformationService. The DHIP team collaborated with theNZSSD Diabetes Information Group to determine,finalise and gain widespread agreement on arecommended minimum diabetes dataset for NewZealand.

J. Qual. Clin. Practice (2000) 20, 44–50

The development of a national agreed minimum diabetesdataset for New Zealand

DAVID SIMMONS,1,* FRACP, MD, K COPPELL,1 FRNZCGP, PL DRURY,2 FRCP, MD1Diabetes Health Information Project, New Zealand, 2Diabetes Information Group, New ZealandSociety for the Study of Diabetes, Auckland

Abstract The development of a minimum diabetes dataset (MDD) for monitoring diabetes in New Zealandwas intended to facilitate diabetes quality initiatives. Existing published datasets were reviewed and a draft MDDfor New Zealand was distributed to all 147 specialist, general practice and relevant community groups. Datadefinitions were either identical or compatible with other datasets and dataset items included if there were at leastsix supportive replies. All groups were followed up by letter and telephone. A total of 26 (18%) replies were received.Comments were reviewed and the recommended MDD finalised. There was agreement that this dataset wouldbe embedded into the software of at least three commercially available patient management systems. In conclusion,developing an agreed national MDD was difficult, in spite of its potential utility for local, regional and nationalcollation of diabetes data allowing those involved to generate a picture of diabetes and its outcomes.

Key words: audit; dataset; diabetes; New Zealand.

*Current address: Professor D Simmons, University ofMelbourne, Department of Rural Health, PO Box 6500,Shepparton, Victoria 3632, Australia.

In defining a recommended minimum diabetesdataset, ‘pop-up’ screen options for the purpose of dataentry were assumed. For paper audits, a clear choicefor circling discrete variables or writing in continuousvariables, such as blood pressure measurements, wasgiven. It was also considered necessary to ensure thedefinition of variables would maximise compatibilitywith other national (e.g. the National MinimumDataset8–10) and international (e.g. Diabcare) datasets.

Datasets

Three datasets were examined: the Diabcare dataset,the NZSSD Diabetes Information Group preliminarydraft dataset and the Diabetes Care Support Servicedataset. From these datasets, a comprehensive diabetesdataset was constructed. Other possible additionalvariables were discussed according to linkage withguidelines (e.g. serum potassium concentration aftercommencing angiotensin converting enzyme (ACE)inhibitor) and new knowledge (e.g. foot deformity asa risk factor for the diabetic foot). The draft compre-hensive dataset was divided into three parts for con-sideration:

(1) A minimum dataset including process (e.g.completed foot examination), intermediate (e.g.HbA1c) and outcome measures (e.g. end-stage renalfailure). This was essentially the NZSSD DiabetesInformation Group minimum dataset and was consid-ered to be most suitable for paper audit and bench-marking work with limited resources.

(2) A more comprehensive dataset that included theminimum dataset, and more process, intermediate andoutcome measures. This was considered more suitablefor electronic audit and detailed benchmarking work.

(3) Other variables that may be considered useful,for example, those factors increasingly consideredimportant such as callus and foot deformity. Thesecould be of use locally within a district or region.

Variable definition for electronic data collection

Each variable was defined by data name, descriptionand definition, field length, normal range and units.Where possible these definitions were made compatiblewith the Diabcare and British Diabetic Associationdatasets to enable future international benchmarking.A demographic header was also considered important.Definition of these variables followed the New ZealandHealth Information Services (NZHIS) format.8,9

Consultation process

The dataset and electronic variable definitions weredistributed to all diabetes services, general practitionergroups (mainly Independent Practitioner Associations)and other known relevant provider groups (e.g.

laboratories). In total 147 patient groups, primary care,specialist and district services were contacted. Contactdetails for inquiries and return addresses were provided.A second reminder letter was sent to those who hadnot replied within the timeline. Those who had notreplied were rung individually. All replies relating to theminimum dataset were forwarded to one team memberfor collation. For each data variable, the number ofreplies agreeing and disagreeing with that item beingincluded in the dataset were counted. Additional com-ments written by respondents were also reviewed. Thedata variables for electronic collection identified forinclusion were then cross-referenced with the Diabcareand NZHIS datasets to incorporate standard defin-itions, field structures, units and field names whereverpossible.

RESULTS

Comments were received from 26 (18%) respondents.During the consultation process, two further diabetesdatasets under development were identified; one forpaediatric care and one for diabetes in pregnancy care.The latter is being developed across Australasia by theAustralasian Diabetes in Pregnancy Society.

Items were included if there was agreement from atleast six replies. Appendix 1 shows the recommendedminimum diabetes dataset for New Zealand. Anadditional list of variables is included in Appendix 2.These variables had some support but insufficient tojustify inclusion on a national basis.

A number of issues were raised during the consul-tation process. A clear purpose for the collection of dia-betes data was considered important. One area stated‘Our view is the purpose for collecting diabetes data inthis setting is to principally monitor diabetes care pro-cesses and monitor outcomes, rather than to compareand contrast different modes of treatment. Thereforewe consider it is necessary to collect key indicators onlyas part of a minimum diabetes dataset.’ There wereother comments in relation to the size of the dataset.For example, one area stated ‘I am concerned that thedata elements outlined above, if all were collectedwould involve too much time’ and another stated ‘theminimum dataset looks like all we could cope with’.However, another comment was ‘the draft NZSSDminimum dataset is too minimal and some items fromthe additional datasets could be usefully included’. A further concern was that if the dataset was too big,then there was a risk that the data collected would beincomplete. One area replied ‘The database isextremely full, too full for general practice use, butwould certainly provide comprehensive data if there isa worker to fill in the data’. Another indicated ‘A lotof information is [already] accessible from clinicalrecords’.

NZ MINIMUM DIABETES DATASET 45

There was a clear need to address the conflictbetween detail in the outcome fields and repetitiveinclusion of negative fields for many cases. For example,some diabetic patients will have an eye event, and whenthey do, some detail such as the type of retinopathywould be needed. This was to be addressed by definingsubfields.

Two other areas where there was not a clearly agreedview were the details of treatment and reasons forhospitalisation. One respondent indicated that thereshould only be three pharmaceutical treatment fields(ACE inhibitors, lipid lowering drugs and antihyperten-sives), not the 12 eventually decided upon. Other areassuggested adding other treatments including treatmentfor obesity. Defining hospitalisation as either diabetesor non-diabetes related was a major issue with somesuggesting all hospitalisation events should be recorded,others only diabetes ketoacidosis, hyperglycaemichyperosmolar-non-ketotic coma or alcoholic acidosis.Three areas recommended excluding all hospitalisationdetails.

DISCUSSION

In New Zealand, good information about diabetes andthe complications of diabetes on a national basis islimited.11 In the Ministry of Health’s 1997 document,Strategies for the Prevention and Control of Diabetesin New Zealand, it was stated that ‘The establishmentof accurate baseline data is essential to enable themonitoring of these [New Zealand Declaration]targets’.1 Agreement through a consultative frameworkof a recommended diabetes minimum dataset for NewZealand is therefore a major achievement and a signifi-cant step towards obtaining much needed informationabout diabetes in New Zealand. The transparency andinclusivity of the process allow the dataset to be viewedwith confidence. However, the price of this inclusiveprocess was a low response rate, presumably becauseof existing workload, low priority and/or a belief thatothers would achieve an acceptable outcome. Althoughwe did not receive a high response rate, no groupsdisagreed with the concept of a national minimumdiabetes dataset.

In defining this minimum dataset, we tried to main-tain a focus on the main purpose for such a dataset,that is, to monitor diabetes in New Zealand. We alsoendeavoured to take the majority view on all data itemsin the dataset. Regions may elect to collect and collatethe optional data items, but we do not consider themto be a necessary part of the core diabetes dataset.

Because diabetes is such a complex disease and manydifferent health professionals are involved with diabeteshealth care, there are many other data items that differ-ent groups would like to see collected. However, thesize of the dataset had to be balanced against who

would be collecting the data and how the data wouldbe collected (manual or electronic). Manual collectionwould currently predominate, but in the future it maybe possible to readily collect and collate larger diabetesdatasets. While some respondents commented thateven the suggested minimum dataset seemed large,some areas may decide to collect additional items. This generally reflected the different perspectives of‘non-computerised’ and ‘computerised’ practitioners,respectively; a pattern previously described in Holland(D Ruwaard et al., unpubl. data). For these reasons,and the awareness of the importance of consistent data definitions to enable accurate data analysis, a consid-erably larger and more comprehensive diabetes datasetis to be defined. This challenge has been taken up bythe NZSSD Diabetes Information Group who are currently developing such an extended dataset throughthe Society and in collaboration with Diabcare inEurope. A more detailed dataset is available in Australia(S Colagiuri & JA Flack, unpubl. data).

The existence of groups developing Australasianpaediatric and diabetes in pregnancy minimumdiabetes datasets, along with the extended dataset, willcomplete the ability to review diabetes related infor-mation in a useful manner. It is important that thegroups involved maintain contact with those chargedwith the responsibility to maintain the dataset devel-oped by the NZSSD and Diabetes Health InformationProject.

Two issues now arise, that of how to maximise accessand use of the dataset and how to update the datasetas new knowledge about diabetic care is acquired andfuture relevant clinical measures are adopted.

There are several methods for increasing access toand use of the dataset and therefore standardisingdiabetes information collection. The first, that ofasking the major software vendors to incorporate the dataset into their systems, was undertaken duringthe Mangere Co-ordinated Diabetes Care Project.12

The three vendors present, Houston, Intrahealth andHealthtech all agreed to embed the dataset within theirsoftware. The second method is to incorporate theminimum diabetes dataset into the National MinimumDataset and an approach has been made to achieve thisend. The third method was to publish the dataset in apeer reviewed Medical Journal. The final method forwidespread use of the dataset was to distribute thedataset to all providers and funders, as well as othergroups and organisations involved with diabetes.

Regular reviewing and updating of the minimumdiabetes dataset will be necessary. Our view is that thisprocess needs to be undertaken as part of an ongoingdiabetes information strategy. The NZSSD–DiabetesInformation Group (DIG) is to continue to address thespecialist issues, but a national subcommittee ondiabetes information including representatives fromgeneral practice, specialist services and patients need

46 D SIMMONS ET AL.

to meet on at least an annual basis to review the dataset,its extension and variable deletion (e.g. fructosamine).The subcommittee should link in with other nationaldiabetes related activities (e.g. guideline revision,benchmarking oversight, progress review) and inter-national information initiatives (e.g. Diabcare). Ineffect, there needs to be a national taskforce of whichconsideration of diabetes related information strategiesshould be one activity.

ACKNOWLEDGEMENTS

The New Zealand government and the Health FundingAuthority funded this work. We are grateful to the othermembers of the Diabetes Health Information Project,the New Zealand Society for the Study of Diabetes,Diabetes Information Group, Greg Gamble of theDiabetes Projects Trust, Norbert Jersch of Medinet,Germany, Sue Wright, previously of Roche Diagnosticsand the many respondents to the questionnaire.

REFERENCES

1 Diabetes Prevention and Control. The Public Health Issues.The Background Paper. Public Health Group, Ministry ofHealth. Wellington, NZ, 1997.

2 Phillips DE & Mann JI. Performance/outcome measures forarea health board diabetes services. NZ. Med. J. 1991; 104:197–9.

3 Simmons D & Laughton SJ. Diabetes on the surgical wardsin an area with a high prevalence of diabetes. NZ. Med. J.1993; 106: 156–7.

4 Vaughan NJA & Home PD. for the diabetes audit workinggroup of the research unit of the Royal College of Physiciansand the British Diabetic Association. The UK diabetesdataset-a standard for information exchange. Diabetic Med.1995; 12: 712–17.

5 Piwernetz K, Home PD, Snorgaard O et al. Monitoring thetargets of the St Vincent Declaration and the implementationof quality management in diabetes care: The Diab Careinitiative. Diabetic Med. 1993; 10: 371–7.

6 Simmons D, Fleming C, Innes J, Cutfield R, Patel A &Wellingham J. The Diabetes Care Support Service forGeneral Practitioners in Auckland. NZ. Med. J. 1997; 110:48–50.

7 Roxburgh MA & Vaughan NJA. A UK Structure for Inter-District Benchmarking in Diabetes (UKDIABS). Diabetes,Nutrition Metabolism 1998; 11: 42.

8 New Zealand Health Information Service. A Guide to DataRequirements 1997/8. New Zealand Health InformationService, Wellington, 1997.

9 New Zealand Health Information Service. Data DictionaryAppendix. New Zealand Health Information Service,Wellington, 1997.

10 Patel A. General Practice Data Dictionary. Royal New ZealandCollege of General Practitioners, Wellington, 1995.

11 Wilson N, Mansoor O & Simmons D. Diabetes in NewZealand – better information needed. NZ. Med. J. 1999; 112:206–8.

12 Simmons D. Development of a Population Based DiabetesIntegrated Care Service to Be Piloted in Mangere. Report to theHealth Funding Authority. Auckland, 1998.

NZ MINIMUM DIABETES DATASET 47

Appendix 1. The recommended New Zealand minimum diabetes dataset

Field structure and type:

Minimum A 5 alpha; Field Dataset Field Comments and explanatory notes N 5 numeric Units name

IDENTIFIABLE To enable local identification/processing.DEMOGRAPHICS Not for export unless authorizedSurname Surname or family name compatible with Diabcare A30 – snameGiven name Given or forename compatible with Diabcare A30 – fnameAlias Other names including maiden name A30ANONYMOUSDEMOGRAPHICSand BASIC DATA

Minimum NHI National Health Index number A3/N4 – nhiMinimum Agency Health Agency code number with NZHIS agency A5 – agency

or practitioner NZMC codeMinimum Sex M, male; f, female; u, not known; k, not entered. A1 – sexMinimum Date of birth Date of birth Ddmmyyyy – dobMinimum Domicile Numerically coded: NZHIS code number for area N4 – domcMinimum Ethnic origin Numerically coded: Use NZHIS coding for ethnicity N2 – ethnMinimum Benefit 0, none; 1, community services card; 2, high user card; A1 – benefit

3, other; u, not known, k, not entered.

48 D SIMMONS ET AL.

Appendix 1. continued

Field structure and type;

Minimum A 5 alpha; Field Dataset Field Comments and explanatory notes N 5 numeric Units name

Minimum Status This allows for pruning of the database as patients die A2 – statusor move. Codes are: 1, newly diagnosed; 2, dead; 3, moved elsewhere but are known to be alive; 4, alive and in ongoing care; 5, defaulted or lost to follow up status; 6, transferred in; u, not known; k, not entered.

Minimum Period covered Not entered agreed prior to audit process beginning N2 – Rec_peri(in months, initially 12 months)

Minimum Date of Date Ddmmyyyy – rec_dateinformation

Minimum Type of diabetes 1, Type 1; 2, Type 2; 3, secondary; 4, gestational; A1 – dm_type5, genetic; 6, other known; u, unknown; k, not recorded.

Minimum Year of diagnosis Date as year, unknown 5 9999 yyyy – dm_yearMinimum Insulin treatment Date as year, unknown 5 9999 yyyy – Ins_year

since

EDUCATIONAL and RELATED FIELDSMinimum Self blood glucose Blood glucose self monitoring no. tests per week A2 – sm

monitoring calculated on a monthly basis (No.; u, unknown;k, not recorded.)

Minimum Hypoglycaemia Significant severe hypoglycaemia last 12 months A2 No. hypoas defined for DCCT (i.e. requiring 3rd party timesassistance). (No. reflecting number of episodes;u, unknown; k, not recorded.)

Minimum Smoker Currently smoking any tobacco material N1 – smoker1, No; 2, Yes; 3, past; 9, unknown.

DIETETIC FIELDSMinimum Height No shoes N3 cm heightMinimum Weight Dressed without shoes N3 Kg weightDerived BMI Derived from aboveDerived Change in weight Derived from above and from last record

since last measured

EYE FIELDSMinimum Date last retinal Date of last retinal examination, if only year known, Ddmmyyyy – retdate

examination yyyyMinimum Method for last Method of eye examination; 1, retinal camera; N1 retmeth

retinal examination 2, fundoscopy – opthalmologist; 3, fundoscopy –other specialist; 4, fundoscopy – GP; 5, fundoscopy –optometrist; 9, not known or not recorded.

Minimum Visual activity Corrected or pinhole visual acuity of left and right A5, A5 See vis_acu_1,eye by local method (snellen chart e.g. 6/20 or text vis_acu_rdecimals e.g. 0.5)

Minimum Retinopathy 1, Nil; 2, background; 3, pre-proliferative; N1 Retinaworst eye 4, proliferative; 5, maculopathy; 9, unknown.

Minimum Cataract 1, absent; 2, present; 9, unknown. N1 cataract

NEUROPATHY and PERIPHERAL VASCULAR FIELDSMinimum Foot sensory exam 1, normal; 2, abnormal; 9, unknown. N1 sensexamMinimum Ulcer left or right 1, No; 2, Yes; 3, past; 9, unknown. N1 ulcerMinimum Foot pulses 1, normal; 2, reduced/absent; 9, unknown. N1 fpulse

RENAL and CARDIOVASCULAR FIELDSMinimum Renal status up 1, Normal; 2, past microalbuminuria; 1N renstat

to now 3, nephropathy.

Minimum Hypertension 1, diagnosed; 2, not diagnosed; 3, not known. N1 ht

NZ MINIMUM DIABETES DATASET 49

Appendix 1. continued

Field structure and type;

Minimum A 5 alpha; Field Dataset Field Comments and explanatory notes N 5 numeric Units name

Minimum Systolic blood Sitting after 5 min taken in the right arm (mmHg) N3 bp_syspressure

Minimum Diastolic blood Sitting after 5 min taken in the right arm (mmHg) N3 bp_diapressure

Derived Cardiovascular risk Calculated from blood pressure, age, smoking history,total cholesterol, HDL cholesterol, presence of knownvascular disease. NB recent data indicates all diabeticpatients should be considered vasculopaths.

INVESTIGATION RESULTSMinimum Lab at which Ref range needs to be known for each lab. N4 hba1clab

HbA1c undertaken NZHIS lab code usedMinimum HbA1c Need to specify the assay type and normal range, N4 % hba1c

and keep the centre informed of changes. Two decimalplaces possible

Minimum Fructosamine This measure will be gradually replaced by HbA1c N4 mmol/L fructMinimum Albumin:creatinine Preferably done as an early morning sample. To two N6.2 mg/ uacr

ratio decimal places if possible mmolcreatinine

Minimum Albumin excretion From 24 hour urine sample N6.1 mg/day uaerate

Minimum Serum creatinine Expressed as mmol/L N4.2 mmol/L creaMinimum Lipid fasting 1, no; 2, yes; 9, not known. N1 fast

methodMinimum Total cholesterol Expressed to one decimal place (mmol/L) N4.1 mmol/L cholMinimum Triglycerides Fasting and expressed to one decimal place (mmol/L) N4.1 mol/L tgMinimum HDL-cholesterol Expressed to two decimal places (mmol/L) N4.2 mmol/L chol_hdlDerived TC:HDL ratio Derived from above

TREATMENTS and TARGETSMinimum Diabetes therapy: The options need to be presented as yes/no for each 4 3 N1 insulin

Insulin, modality since they may be used in a variety of susulphonylureas, combinations. The options can be presented as a pick mfmetformin, diet list on a pop-up screen with a tick box. The data format diet

should be as: 1, no; 2, yes; 9, unknown.Minimum Other relevant Options presented as for diabetes therapy. Options 12 3 N1 acei

therapies include: ACE, inhibitors; beta-blockers; calcium beta_blantagonist; alpha blocker; diuretic; nitrate; resin; ca_antfibrate; HMGCo-A reductase inhibitor; aspirin; alpha_blhormone replacement; corticosteroids. The data diureticformat should be as: 1, no; 2, yes; 9, unknown. nitrate

resinfibratestatinaspirinhrtsteroid

PROCESSES and Outcomes are new if since last audit, old if before lastOUTCOMES audit. Sub fields are only completed if the master field

indicates disease is/has been present.Minimum Hospitalisation Number of times; unknown 5 99 N2 hospital

since last auditSub Primary reason 1, No; 2, hyperglycaemia (e.g. HONK, DKA); N1 whyhosp

for hospitalisation 3, hypoglycaemia; 4, cardiac; 5, vascular; 6, foot;7, other; 8, unknown.

Minimum Cardiovascular 1, New; 2, old; 3, none, 9, unknown. N1 cvddisease present

Sub CHD-Angina 1, New; 2, old; 3, none; 9, unknown. N1 d_ang_p

50 D SIMMONS ET AL.

Appendix 1. continued

Field structure and type;

Minimum A 5 alpha; Field Dataset Field Comments and explanatory notes N 5 numeric Units name

Sub CHD-Myocardial 1, New; 2, old; 3, none; 9, unknown. N1 miinfarction

Sub CHD-Bypass/ 1, New; 2, old, 9, unknown. N1 cabgptcaPTCA

Sub CHD-Heart failure 1, New; 2, old; 3, none; 9, unknown. N1 hfSub PVD-Angioplasty 1, New; 2, old; 3, none; 9, unknown. N1 pvdangioSub PVD-Vascular 1, New; 2, old; 3, none; 9, unknown. N1 pvdsurg

surgerySub Cerebrovascular 1, CVA or TIA – new; 2, old; 3, none; 9, unknown. N1 cvatiaSub Leg amputation 0, None; 1, new-left; 2, new-right; 3, new-both; N1 sv_a_ank

above ankle 4, old-left; 5, old-right; 6, old-both, 9, unknown,for diabetes cause only.

Sub Leg amputation 0, None; 1, new-left; 2, new-right; 3, new-both; N1 sv_b_ankbelow ankle 4, old-left; 5, old-right; 6, old-both, 9, unknown,

for diabetes cause only.Minimum End stage renal Last treatment: 1, haemodialysis; 2, CAPD; N1 sv_dialy

failure treatment 3, transplant; 4, other; 5, none; 9, unknown.for diabetes sincelast audit

Minimum Eye event 1, New; 2, old; 3, none; 9, unknown. N1 eyeSub Laser treatment 1, New; 2, old; 3, none; 9, unknown. N1 laserSub Cataract extraction 1, New; 2, old; 3, none; 9, unknown. N1 cat_surgSub Vitrectomy/other 1, New; 2, old; 3, none; 9, unknown. N1 eye_surg

surgerySub Visual impairment 1, New; 2, old; 3, none; 9, unknown. N1 sv_blind

6/60 or lessSub Other eye event 1, New; 2, old; 3, none; 9, unknown. N1 eye_othMinimum Pregnancy outcome 0, No pregnancy; 1, normal; 2, miscarriage; N1 preg

since last audit 3, major malformations (cardiovascular or neuroskeletal abnormalities); 4, stillbirth; 5, ongoing;9, unknown.

Appendix 2. Fields supported by two or more replies

Impotence (4 areas) Hypo awareness (2 areas)Driving status (3 areas) Sick day education (2 areas)Medicalert status (3 areas) Activity assessment (2 areas)Foot callosities (3 areas) Foot deformity (2 areas)Last podiatric visit (2 areas) Frequency of HbA1c (2 areas)