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Using data from patient interactions in primary care for population level chronic disease surveillance: The Sentinel Practices Data Sourcing (SPDS) project

  • Abhijeet Ghosh1Email author,
  • Karen E Charlton2,
  • Lisa Girdo1 and
  • Marijka Batterham3
BMC Public Health201414:557

https://doi.org/10.1186/1471-2458-14-557

Received: 12 March 2014

Accepted: 23 May 2014

Published: 5 June 2014

Abstract

Background

Population health planning within a health district requires current information on health profiles of the target population. Information obtained during primary care interactions may provide a valuable surveillance system for chronic disease burden. The Sentinel Practices Data Sourcing project aimed to establish a sentinel site surveillance system to obtain a region-specific estimate of the prevalence of chronic diseases and mental health disorders within the Illawarra-Shoalhaven region of New South Wales, Australia.

Methods

In September 2013, de-identified information for all patient interactions within the preceding 24 months was extracted and collated using a computerised chronic disease management program that has been designed for desktop application (Pen Computer Systems Clinical Audit Tool: (PCS CAT)). Collated patient data included information on all diagnosed pathologies and mental health indicators, clinical variables such as anthropometric measures, and patient demographic variables such as age, sex, geographical location of residence and indigenous status. Age-standardised prevalence of selected health conditions was calculated.

Results

Of the 52 general practices within the 6 major Statistical Local Areas (SLAs) of the health district that met the inclusion criteria, 17 consented to participate in the study, yielding data on n = 152,767 patients, and representing 39.7% of the regional population. Higher than national average estimates were found for the age-adjusted prevalence of chronic diseases such as obesity/overweight (65.9% vs 63.4%), hypertension (11.9% vs 10.4%) and anxiety disorders (5.0% vs 3.8%), but a lower than national average age-adjusted prevalence of asthma (8.0% vs 10.2%) was also identified.

Conclusions

This proof-of-concept study has demonstrated that the scope of data collected during patient visits to their general practitioners (GPs), facilitated through the Medicare-funded primary health care system in Australia, provides an opportunity for monitoring of chronic disease prevalence and its associated risk factors at the local level. Selection of sentinel sites that are representative of the population being served will facilitate an accurate and region-specific system for the purpose of population health planning at the primary care level.

Keywords

Sentinel sites Surveillance Primary care General practice Morbidity data

Background

Valid data on morbidity, at the regional level, is essential for planning of primary healthcare services that are specifically tailored to the needs, demands and requirements of the local population. The currently available peer reviewed literature indicates multiple avenues of population health surveillance including national surveys, administrative data and electronic health records. National surveys are surveillance methods that have commonly been used [1, 2], but that demonstrate multiple shortcomings. Prevalence estimates obtained from national and state level surveys are based on self-reported measures, provided by respondents of these surveys, the accuracy of which is questionable [2]. Secondly, surveys do not include all aspects of health, and therefore do not provide a full and accurate picture of health [3], resulting in a lack of generalisability to smaller regional populations [4]. Additionally, different subgroups of the population may demonstrate different response rates to surveys, which impacts on the generalisability of the survey data to the general population [5, 6]. Surveys, however, provide prevalence rates comparable to routinely collected clinical administrative data [13], indicating that clinical administrative data is a potential avenue for surveillance. The use of information obtained from administrative data, including primary care medical records (data collected during general practitioner (GP) visits), physician billing, specialist visits, pharmacy data (prescription dispensation) and hospital data (inpatient/outpatient information); has been shown to provide reliable and valid prevalence estimates of chronic disease conditions [13, 7, 8].

Clinical administrative data is widely available [1] and its collection and reporting systems are currently in place in both primary care and tertiary levels of care [9]. Further, administrative data is validated by the clinical judgment of medical practitioners and may be generalisable to smaller/regional populations [10]. Patient data that is entered into electronic medical recording software at the point of contact with primary health care practitioners is often supported by diagnostic testing and clinical examination, and is thus likely to be more valid than self-reported health information [10]. The peer-reviewed literature hence provides a vast amount of current evidence on the effectiveness of utilising administrative and/or primary care data for population health surveillance; however, there exists a current lack of such data based disease monitoring models in Australia.

The Sentinel Practices Data Sourcing (SPDS) project aimed to implement a sentinel site surveillance system within the Illawarra-Shoalhaven region of the state of New South Wales (NSW) in Australia to obtain a region-specific prevalence of chronic diseases and mental health disorders through the use of patient data obtained during primary care patient interactions. A pre-tested method of data extraction [10] was used, aimed at informing the population health planning within health service catchments of regional Australia.

Methods

The study was conducted in the Illawarra-Shoalhaven region of the state of NSW in Australia (Figure 1). Within NSW there are 15 Local Health Districts which are responsible for the acute, sub-acute and tertiary care service delivery in the state through the public hospital system; and 17 Medicare Locals which are responsible for the primary healthcare planning and delivery for their constituent regions [10]. Unlike other regions, the geographical catchment boundary of the Illawarra-Shoalhaven Local Health District (ISLHD) is the same as that covered by a single Medicare Local namely the Illawarra-Shoalhaven Medicare Local (ISML); which places the region in a unique and advantageous position in terms of planning and implementing a chronic disease surveillance system. Additionally the region has a diverse socio-economic profile and has pockets of both higher and lower socio-economic disadvantage, comparing the Index of Relative Socio-Economic Disadvantage (IRSD) scores between the region and for Australia as a whole (Figure 2). IRSD is a composite summary measure constructed by the Australian Bureau of Statistics (ABS) for all regions in Australia and is a based on income, educational attainment, employment status, occupation type, family structure, dwellings, house ownership, marital status and ethnicity [11].
Figure 1

Illawarra-Shoalhaven region of NSW and its Statistical Local Areas (SLAs) on the map of Australia.

Figure 2

Socioeconomic snapshot of the Illawarra-Shoalhaven region: Index of Relative Socio-Economic Disadvantage (IRSD) scores compared with NSW state and Australian national averages. *Illawarra-Shoalhaven score is a population weighted average of individual SLA scores.

The study undertook secondary analysis of administrative data through extraction of de-identified clinical patient information and the project was rolled out in 4 phases: -

Phase 1: Practice recruitment

The study aimed to recruit 12–18 practices within the Illawarra-Shoalhaven region based on the Statistical Local Area (SLA) geography and the demographic profile of the catchment. Eligible practices were identified by requesting the Illawarra-Shoalhaven Medicare Local (ISML) for a list of the region’s practices that fulfilled the following inclusion criteria (n = 52):

location in one of the 7 SLAs that represent the Illawarra-Shoalhaven region;

multiple (more than 1) GPs working at the practice site (solo practitioner sites are likely to have smaller patient numbers);

employment of either more than one full-time GP or more than two part-time GPs (i.e. who work for at least 20 hours a week);

additional criteria: -

  • installation of a clinical auditing software package on desktop software or a desire to install and use the Pen Computer Systems (PCS) Clinical Audit Tool: ™(PCS CAT) (multiple licensing for PCS CAT has been procured by ISML and is therefore freely available to all general practices within its catchment); and

  • a willingness to provide de-identified practice data extracts to the researchers for surveillance purposes.

Seventeen general practices in the catchment volunteered and consented to participate in the study (response rate = 33%). Only two electronic medical record software packages were being used by participating practices in the study, either Medical Director™ (n = 8) or Best Practice™ (n = 9).

Phase 2: Data cleansing and enhancement of data accuracy

Recruited practices undertook comprehensive “Data Cleansing” training to understand the usage and the various functionalities of PCS CAT, and to update and clean the data stored in their clinical systems. With the consent of the primary GPs and managers within each practice, the “Data Cleansing” training was conducted by the researchers. The cleaning of practice records improves searches (in both the practice electronic medical record software program and the PCS CAT), to identify patients with particular conditions and thus to target health research and patient management activities. This data cleaning process allowed the complete patient database that had been entered during GP consultations to be identified when searching for specific variables. The data cleansing phase of the study was conducted using the data maintenance utility tools which are available within both the GP electronic medical record software programs used in the study. Data cleansing included: -

  • encouraging all practice staff to use the ‘drop down box functionality’ of their clinical software to define and code all medical diagnoses and other sections of the patient record;

  • strictly avoiding free text entries in all sections of the patient record;

  • finding all identifiable free text non-coded past medical history items, and either linking them to appropriate coded items or replacing them with the correct coded item; and

  • coding all inactive patients as ‘Inactive’ (an ‘active patient’ is one who has attended the practice three or more times in the past two years as defined in the Royal Australian College of General Practice: Standards for general practices [12]).

Phase 3: Data collection

Patient data that had been de-identified by practice employees was extracted to a database. Data items extracted from general practice clinical systems included: demography (population by age and sex and population geography including postcodes and suburbs), chronic disease surveillance items (hypertension, type 2 diabetes mellitus, depression, anxiety, COPD, asthma, congestive heart disease, stroke, osteoarthritis, osteoporosis, high Body Mass Index (BMI) – overweight and obese), and Medicare Benefits Schedule (MBS) items uptake relevant to primary care services for GP and other non-referred attendances. A cleaned, de-identified PCS CAT data extract was performed in September 2013 for all recruited practices which included all information obtained from patient interactions in the preceding 24 months for all diagnosed pathologies, clinical variables such as anthropometric measures, and patient demographic information such as age, sex, geographical location of residence (postcodes and suburbs) and indigenous status.

Phase 4: Data collation and analysis

The research team collated all extracted data, cross-matched residential suburb and postcode information with health and clinical information using de-identified unique link ID tags, converted all resultant information into usable database formats, and then analysed the datasets using Microsoft Excel (V2007: Microsoft Corporation, Redmond Washington, USA). The final datasets hence included clinical diagnosis and patient demographic information as entered by GPs within each participating practice. Basic epidemiological measures, including age-specific prevalence and total prevalence were calculated for all major disease conditions. The prevalence figures were compared against comparable indicators reported for the same age groups by the Australian Health Survey (AHS) 2011–12 conducted by the ABS [13].

The age-specific disease prevalence figures obtained from the study sample and the estimated national prevalence figures reported by the AHS 2011–12 were then age-standardised using the 2011 estimated resident population of Australia [14]. Comparisons across age-standardised prevalence were conducted for all major chronic conditions that the SPDS project is targeting for regular surveillance namely; obesity, overweight, diabetes mellitus, hypertension, asthma, mental health disorders such as clinically diagnosed depression and anxiety disorder, coronary heart disease, stroke, and chronic bone diseases such as osteoarthritis and osteoporosis. Both Microsoft Excel (V2007: Microsoft Corporation, Redmond Washington, USA) and the PCS CAT tool (v.3.1: pencs.com.au) were used for graphical illustration of demographic data and age-specific disease prevalence.

The study undertook secondary analysis of administrative data through extraction of de-identified clinical patient information. The study was performed with the approval of the Human Research Ethics Committee (Health and Medical) of the University of Wollongong (HE 12/447). Written informed consent was not obtained from individual patients due to the retrospective nature of the study design, however all data was exclusively extracted and de-identified by trained practice clinical staff only.

Results

The number of patients that had visited the 17 general practices within the previous 24 months (September 2011 to September 2013) was 164,435 (152,767 from within the Illawarra-Shoalhaven and 11,668 from outside of the catchment). The Illawarra-Shoalhaven catchment sample of 152,767 included 70,103 men, 82,506 women and 158 without an identified gender.

While 144 patients did not have their age recorded, the median age for the study sample (n = 152,767) was 39 years (IQR = 20 – 58 years). Adults aged 20–24 years comprised the largest age group at 7% of the total sample, followed by the 40–44 year old age group (6.8%), and 5–9 year old children (6.6%). Older adults aged 65 years and above comprised 18.2% of the sample. The population pyramid of the study sample along with the comparison with the population structure of the 2011 estimated resident population of the Illawarra-Shoalhaven catchment is shown in Figure 3. The proportion of the local residential population of the SLAs that consulted the study practices during the study period is shown in Table 1[14]. The majority of the study sample (92.9%) were found to reside within the Illawarra-Shoalhaven catchment SLAs.
Figure 3

Population pyramid comparisons: study sample and the 2011 resident population of the Illawarra-Shoalhaven catchment.

Table 1

Proportion of local population that had consulted the 17 general practices during the previous 24 months (September 2011 to September 2013)

Illawarra-Shoalhaven SLAs

No. of patients from the SLA within the sample

Proportion of the sample from the SLA (%)

Estimated Resident Population of the SLA, 2011*

Proportion of the SLA population included in the sample (%)

Jervis Bay Territory

6

0.0

387

1.6

Kiama (A)

19769

12.9

20832

94.9

Shellharbour (C)

44971

29.4

66054

68.1

Shoalhaven (C) - Pt A

15347

10.0

34444

44.6

Shoalhaven (C) - Pt B

26000

17.0

61599

42.2

Wollongong (C) - Inner

23351

15.3

104601

22.3

Wollongong (C) Bal

23323

15.3

96614

24.1

Total ^

152767

100

384531

39.7

*ABS [14].

^Total number of patients in the data extract = 164,435; n = 152,767 after excluding patients residing outside the Illawarra-Shoalhaven SLAs and removing patients with geographic data inaccuracies.

The age-specific population and disease counts within the study sample for major chronic conditions and high BMI are shown in Tables 2 and 3 respectively, while the crude and age-standardised prevalence comparisons of the sample and the Australian national estimates [13] are shown in Tables 4 and 5 respectively. Overall the study sample population exhibits figures higher than Australian averages for the age-standardised prevalence of chronic conditions such as anxiety, cancer, hypertension, obesity and overweight/obesity (Figure 4). An illustration of age-specific burden of disease (Figure 5) indicates that prevalence (non-age standardised) of asthma and mental health conditions (depression and anxiety) are significantly higher amongst younger age groups compared to older adults.
Table 2

Age-specific population and chronic disease counts within the study sample during the last 24 months (September 2011 to September 2013)

SLA

Age

Denominator (n)

Type 2 Diabetes

CHD

Stroke

Asthma

COPD

Osteoarthritis

Osteoporosis

Depression

Anxiety

Cancer

Hypertension

Jervis Bay Territory

0-14

3

0

0

0

0

0

0

0

0

0

0

0

15-24

0

0

0

0

0

0

0

0

0

0

0

0

25-34

0

0

0

0

0

0

0

0

0

0

0

0

35-44

2

0

0

0

0

0

0

0

0

0

0

0

45-54

0

0

0

0

0

0

0

0

0

0

0

0

55-64

1

0

1

0

0

0

0

0

0

0

0

0

65-74

0

0

0

0

0

0

0

0

0

0

0

0

75 & Above

0

0

0

0

0

0

0

0

0

0

0

0

Total ^

6

0

1

0

0

0

0

0

0

0

0

0

Kiama (A)

0-14

3348

0

0

0

262

0

0

0

6

20

1

0

15-24

2572

1

0

1

190

0

0

2

118

57

1

5

25-34

2138

0

0

2

137

1

4

1

115

80

3

13

35-44

2450

12

5

6

147

4

30

3

191

94

8

115

45-54

2558

39

18

15

143

13

96

19

218

104

24

286

55-64

2751

124

100

26

180

29

267

80

227

113

59

691

65-74

2094

203

152

53

134

72

355

145

152

71

128

844

75 & Above

1844

209

284

126

137

132

437

329

147

63

122

980

Total ^

19755

588

559

229

1330

251

1189

579

1174

602

346

2934

Shellharbour (C)

0-14

9359

0

1

1

1028

1

1

0

31

64

2

5

15-24

6826

0

0

1

598

1

7

2

483

334

3

19

25-34

6381

10

4

4

543

8

39

4

714

478

5

55

35-44

6191

33

15

13

530

33

131

6

816

511

16

245

45-54

5378

106

64

32

490

85

336

34

767

434

26

627

55-64

4340

168

158

50

456

161

629

126

598

323

68

1055

65-74

3328

254

329

99

338

233

841

236

447

298

94

1276

75 & Above

3156

302

638

215

343

405

1146

575

567

256

117

1504

Total ^

44959

873

1209

415

4326

927

3130

983

4423

2698

331

4786

Shoalhaven (C) - Pt A

0-14

2865

0

0

0

236

0

0

0

1

16

0

0

15-24

2027

1

0

2

271

0

0

1

133

85

1

3

25-34

1774

9

0

2

172

0

3

3

178

101

1

27

35-44

1794

18

3

5

159

5

26

4

267

138

10

97

45-54

1930

84

32

23

169

29

94

19

280

102

19

310

55-64

1928

190

108

39

194

56

269

62

261

93

50

642

65-74

1554

265

167

68

147

104

394

128

200

99

108

760

75 & Above

1457

238

318

153

139

140

541

286

167

109

106

863

Total ^

15329

805

628

292

1487

334

1327

503

1487

743

295

2702

Shoalhaven (C) - Pt B

0-14

4344

0

0

0

307

3

0

2

5

15

0

2

15-24

2970

0

0

0

302

0

0

0

129

86

2

4

25-34

2486

3

1

0

188

2

5

3

249

94

10

39

35-44

3032

26

7

5

186

1

44

5

366

163

18

119

45-54

3486

78

45

20

201

29

178

27

410

173

72

429

55-64

3767

222

134

28

191

81

430

110

403

168

171

921

65-74

3297

403

323

86

221

149

736

200

299

152

291

1414

75 & Above

2576

356

418

133

164

160

786

314

238

107

351

1292

Total ^

25958

1088

928

272

1760

425

2179

661

2099

958

915

4220

Wollongong (C) - Inner

0-14

3914

0

0

3

339

1

0

0

3

14

3

1

15-24

2926

2

0

1

221

1

0

1

125

123

2

2

25-34

3095

4

0

2

205

1

2

2

202

158

0

39

35-44

3305

29

9

4

180

9

26

9

307

200

20

100

45-54

3070

79

28

12

184

26

99

43

316

203

52

344

55-64

2713

158

119

21

179

53

281

141

253

189

98

638

65-74

2082

267

199

55

138

95

376

216

196

134

145

790

75 & Above

2203

362

403

136

157

173

614

454

169

115

230

1186

Total ^

23308

901

758

234

1603

359

1398

866

1571

1136

550

3100

Wollongong (C) Bal

0-14

4411

0

0

1

370

9

2

0

6

24

0

0

15-24

3141

1

1

3

270

17

4

0

193

167

2

11

25-34

3121

6

0

6

236

13

7

5

379

269

4

33

35-44

3080

23

13

7

208

35

47

7

461

311

12

174

45-54

2850

96

49

18

186

86

162

28

402

263

40

440

55-64

2489

199

143

43

161

137

380

105

319

209

78

780

65-74

2036

236

216

89

134

229

538

177

210

156

129

888

75 & Above

2180

290

420

189

127

284

729

403

254

150

207

1173

Total ^

23308

851

842

356

1692

810

1869

725

2224

1549

472

3499

Entire Sample

0-14

28244

0

1

5

2542

14

3

2

52

153

6

8

15-24

20462

5

1

8

1852

19

11

6

1181

852

11

44

25-34

18995

32

5

16

1481

25

60

18

1837

1180

23

206

35-44

19854

141

52

40

1410

87

304

34

2408

1417

84

850

45-54

19272

482

236

120

1373

268

965

170

2393

1279

233

2436

55-64

17989

1061

763

207

1361

517

2256

624

2061

1095

524

4727

65-74

14391

1628

1386

450

1112

882

3240

1102

1504

910

895

5972

75 & Above

13416

1757

2481

952

1067

1294

4253

2361

1542

800

1133

6998

Total ^

152623

5106

4925

1798

12198

3106

11092

4317

12978

7686

2909

21241

^n = 152,767 but 144 patients without a recorded age were excluded, leaving n = 152,623 patients. All region stratified totals also exclude patients without a recorded age.

Table 3

Age-specific population and high BMI counts within the study sample during the last 24 months (September 2011 to September 2013)

SLA~

Age

Quantified BMI*: Denominator (n) [proportion of sample population (%)]

Obese

Overweight/obese

Kiama (A)

18-24

367 [19.7]

42

101

25-34

366 [17.1]

75

178

35-44

513 [20.9]

164

336

45-54

782 [30.6]

283

557

55-64

979 [35.6]

372

782

65-74

958 [45.7]

354

726

75 & Above

1125 [61]

275

735

Total ^

5090 [32.4]

1565

3415

Shellharbour (C)

18-24

510 [10.4]

132

257

25-34

754 [11.8]

277

465

35-44

778 [12.6]

366

602

45-54

983 [18.3]

445

779

55-64

832 [19.2]

367

689

65-74

744 [22.4]

333

610

75 & Above

775 [24.6]

228

524

Total ^

5376 [16.0]

2148

3926

Shoalhaven (C) - Pt A

18-24

528 [37.5]

90

187

25-34

596 [33.6]

200

345

35-44

696 [38.8]

277

507

45-54

987 [51.1]

417

751

55-64

1119 [58]

470

892

65-74

993 [63.9]

417

798

75 & Above

1004 [68.9]

261

676

Total ^

5923 [50.0]

2132

4156

Shoalhaven (C) - Pt B

18-24

468 [21.9]

55

135

25-34

524 [21.1]

147

296

35-44

823 [27.1]

252

541

45-54

1258 [36.1]

414

889

55-64

1492 [39.6]

517

1102

65-74

1641 [49.8]

625

1290

75 & Above

1481 [57.5]

376

990

Total ^

7687 [37.0]

2386

5243

Wollongong (C) - Inner

18-24

583 [26.1]

73

167

25-34

729 [23.6]

161

355

35-44

1048 [31.7]

295

681

45-54

1179 [38.4]

414

846

55-64

1042 [38.4]

372

782

65-74

870 [41.8]

355

688

75 & Above

1138 [51.7]

322

746

Total ^

6589 [35.2]

1992

4265

Wollongong (C) Bal

18-24

441 [19.1]

87

181

25-34

634 [20.3]

252

422

35-44

812 [26.4]

373

622

45-54

1099 [38.6]

476

850

55-64

970 [39]

467

804

65-74

891 [43.8]

424

714

75 & Above

1195 [54.8]

350

811

Total ^

6042 [33.4]

2429

4404

Entire sample

18-24

2897 [19.5]

479

1028

25-34

3603 [19]

1112

2061

35-44

4670 [23.5]

1727

3289

45-54

6288 [32.6]

2449

4672

55-64

6434 [35.8]

2565

5051

65-74

6097 [42.4]

2508

4826

75 & Above

6718 [50.1]

1812

4482

Total ^

36707 [30.9]

12652

25409

*Only includes patients with both height and weight recorded.

 ~ No patients identified in Jervis Bay Territory for this indicator.

^All region stratified totals also exclude patients without a recorded age.

Table 4

Crude prevalence proportions of chronic conditions in the study sample compared to Australian national averages

Chronic Disease/Conditions (as defined and entered into electronic records by GP)

Crude prevalence in sample (%) (95% CI)

Australian crude disease prevalence (AHS 2011–12~) (%) (95% CI)

Type 2 Diabetes

3.3 (3.26 - 3.44)

3.4 (3.09 - 3.71)

CHD

3.2 (3.14 - 3.32)

4.7 (4.39 - 5.01)

Stroke

1.2 (1.12 - 1.23)

1.1 (0.94 - 1.26)

Asthma

8.0 (7.86 - 8.13)

10.2 (9.58 - 10.82)

COPD

2.0 (1.96 - 2.11)

2.4 (2.09 - 2.71)

Osteoarthritis

7.3 (7.14 - 7.4)

8.3 (7.84 - 8.76)

Osteoporosis

2.8 (2.75 - 2.91)

3.3 (3.03 - 3.57)

Depression

8.5 (8.36 - 8.64)

9.7 (9.19 - 10.21)

Anxiety

5.0 (4.93 - 5.15)

3.8 (3.45 - 4.15)

Cancer

1.9 (1.84 - 1.97)

1.5 (1.29 - 1.71)

Hypertension

13.9 (13.74 - 14.09)

10.2 (9.72 - 10.68)

Obesity (BMI ≥ 30)*

34.5 (33.98 - 34.95)

28.3 (27.36 - 29.24)

Overweight or obese (BMI ≥ 25)*

69.2 (68.75 - 69.69)

63.4 (62.28 - 64.52)

*Adults Only.

~ABS [13].

Bold font indicates a higher prevalence than national estimates in the study sample.

Table 5

Age-standardised prevalence of chronic conditions in the study sample compared to Australian national averages

Chronic Disease/Conditions (as defined and entered into electronic records by GP)

Age-standardised disease prevalence in sample (%)

Australian age-standardised disease prevalence (AHS 2011–12~) (%)

Type 2 Diabetes

2.8

3.4

CHD

2.6

4.9

Stroke

0.9

1.1

Asthma

8.0

10.2

COPD

1.7

2.4

Osteoarthritis

6.1

8.4

Osteoporosis

2.2

3.4

Depression

8.4

9.7

Anxiety

5.0

3.8

Cancer

1.6

1.5

Hypertension

11.9

10.4

Obesity (BMI ≥ 30)*

33.6

28.3

Overweight or obese (BMI ≥ 25)*

65.9

63.4

*Adults Only.

~ABS [13].

Bold font indicates a higher prevalence than national estimates in the study sample.

Figure 4

Comparisons of age-standardised chronic disease prevalence between the study sample and Australian national averages. *Includes adults only.

Figure 5

Age-specific prevalence of chronic diseases within the study sample.

Discussion

In Australia, nationally representative data is available through the National Health Surveys (NHS) conducted by the Australian Bureau of Statistics (ABS) [15] and regionally through state surveys such as the annual New South Wales Population Health Survey [16]; however, extrapolations of these data to smaller geographical areas such as Local Government Areas (LGAs) and/or small area geographic regions within LGAs such as SLAs and suburbs is limited [10].

In 2011–12, the percentage of adults who saw a GP in the preceding 12 months varied across Australia ranging from 74% to 86%, with the Illawarra-Shoalhaven catchment recording the highest percentage nationally [17]. This indicates that extraction of patient and GP interactions over a 24 month period has the potential to include health information of almost the entire resident population of catchment regions such as the Illawarra-Shoalhaven; however, not all SLAs were evenly represented in terms of coverage in this study.

All general practice clinical and electronic medical record softwares utilise one of the several nationally validated health coding and medical classification systems such as SNOMED-CT, DOCLE, PYEFINCH and ICPC2+ [10]. These medical vocabularies enable recording of nationally/internationally recognised coded disease diagnosis, which also assists in maintaining accurate and consistent primary care clinical data that can be extracted and analysed [10]. Additionally, recent introduction of the Personally Controlled Electronic Health Records (PCEHR) in Australia, further requires general practices to “work towards recording the majority of diagnoses for active patients electronically” [18]. This enables accurate recording and easy identification of medical conditions and hence provides an opportunity for successful public health and chronic disease surveillance. However, Aizpuru et al. [19] suggest that chronic disease data from electronic health records provide a lower prevalence of conditions, as compared to health surveys because actions taken by physicians are often not recorded leading to cases being missed out. The limitations of primary care practice based data collection have also been illustrated in systematic reviews. Common problems include inconsistent diagnostic coding vocabulary of different clinical systems [20] and errors in data entry and recording [7, 21]. Major barriers faced by general practice staff in this regard include difficulties with clinical coding of diagnoses; complexities with software applications; preference for entering free text rather than the pre-coded options; inadequate skills in information technology; time constraints; poor motivation and low prioritisation of data entry compared to other clinical duties; inconsistency of data entry; coding of a condition in order to justify choice of prescribed treatment; and the additional burden of including laboratory test results in patient records, as well as a need to enter a diagnosis, even in the early preclinical stages of the disease (Attard E, Ghosh A, Charlton K: Barriers faced by general practice staff in maintaining clean primary care databases: a systematic review, unpublished).

Studies conducted in Canada [22, 23], Italy [24] and the UK [25] have demonstrated methods to improve the quality and accuracy of practice-based disease surveillance models. Keshavjee et al. [22] trained and employed ‘data managers’ in an attempt to standardise disease coding and de-identify patient information. Similarly, Griever et al. [23] employed a trained data entry clerk to check missing or incorrectly coded records. Cricelli et al. [24] trained GPs, themselves, in data entry and use of standard software. Pearson et al. [25] provided initial training and updates to all doctors and practice staff and carried out validation procedures such as verification of clinical coding, checking for rare diagnoses and those made outside the usual age and sex parameters through random validation visits to all participating practices.

A number of limitations to the study need consideration. The SPDS study identified various common data entry errors, including misspelt suburb names or postcodes that did not match the suburb entry, missing geographic information (postcodes and suburbs), missing values for age and sex, incorrect/mismatched entries within data entry fields such as height inserted in the weight field and/or vice-versa, and missing entries for weight and/or height measurements. While geospatial analysis of disease patterns is highly useful to target services towards areas of need [26], the SPDS data has highlighted difficulties in obtaining consistent information on patient residential postcodes and suburbs, including missing entries and mismatched entries, for example, a record with suburb of the Kiama (A) SLA and postcode of 3000 which is the incorrect postcode for this suburb. It was often unclear which variable to change in order to resolve this inconsistency and hence led to the deletion of such records from the analysis. Data quality and accuracy discrepancies required immense post-extraction data cleaning/editing efforts by the researchers which is vital to improve data linkage quality [27]. It is therefore imperative to undertake further research and technological innovation into improving utility and interface functionality of practice clinical desktop systems and creation of valid and easy to use advanced data aggregation systems which could vastly improve the processes of primary care clinical data extraction and modelling resulting in furthermore accurate prevalence estimation.

Both international literature and Australian evidence identifies a higher reported prevalence of overweight and/or obesity within primary care settings [28, 29]. It has been argued that obese patients are more likely than healthy or underweight patients to visit their physician and also more likely to be weighed and measured by practice staff and clinicians. This results in lower population denominators for obesity and overweight, as also seen in our study (Table 3), and arguably higher prevalence figures. This is another limitation of the proposed method of surveillance.

The seventeen general practices recruited from the major SLAs within the Illawarra-Shoalhaven region include approximately 40% of the resident population of the catchment area but generalisability of the findings to the general population of the Illawarra-Shoalhaven region cannot be assumed. Additionally there was a clear coverage disparity between the 7 Illawarra-Shoalhaven SLAs with high representation of Kiama (A) and Shellharbour (C) residents, moderate representation of the Shoalhaven (C) - Pt A and Shoalhaven (C) - Pt B residents and low proportional share of Wollongong (C) Inner and Wollongong (C) Bal SLAs within the study sample (Table 1). This can be attributed to the recruitment of practices that voluntarily consented to participate rather than routine surveillance as such. Thus, disease prevalence estimates drawn from the study sample may not be representative of the true population disease status for the region.

Another limitation to the study is that it only investigated the interaction between one extraction tool (the PCS CAT) and two general practice electronic medical record (EMR) software systems (Best Practice™ and Medical Director™). Although these are the most commonly used systems in Australia, the findings cannot be extrapolated to other systems. Additionally the validity of a PCS CAT extract has not been completely investigated. While the tool is co-developed by the Royal Australian College of General Practice (RACGP), the peak body of general practice in Australia and is advocated by them as an integrated product aimed at improving the way patient information can be used to better inform decisions in both clinical and business settings [30]; to date there has not been any empirical validation of the PCS CAT as a general practice data extraction tool. Further research into validation of the PCS CAT extract and the assessment of its agreement with manual data review/audit is required. A final limitation is that we only included data that could potentially be extracted from the electronic medical record software programs. While the data cleansing phase of the study focused heavily on avoidance of any free text entered into medical or clinical notes by GPs and practice staff; if a practitioner still made free text entries rather than using the codable sections of the record, then neither the extraction tool nor our manual case record reviews/audits would be able to detect those cases.

Despite these limitations the SPDS study has significant implications for public health planning, primary health care delivery and epidemiological research. Apart from ongoing chronic disease surveillance, the study methodology and protocol also has the potential to provide evidence-based direction to population health planning strategies aimed at addressing the local health needs of regional areas of Australia. The most recently reported planning documents for the Illawarra-Shoalhaven region of NSW, both from the Local Health District level [31] and the Medicare Local level [32], illustrate disease rates and health status indicators drawn from statistically modelled estimates from the 2006–07 Australian National Health Surveys. These figures are significantly outdated for planning purposes in 2014 and their generalisability for regional and smaller area disease prevalence and health status is questionable [10]. The proposed surveillance system also provides opportunity for monitoring trends in chronic disease prevalence across regular time intervals and promotes the engagement of general practice staff and clinicians in maintaining primary care clinical data quality and accuracy. The inclusion of a larger number of sentinel sites that are generalisable to the population being served would provide an accurate and region-specific system for the purposes of population health planning at the primary care level in order to improve the overall health of the community.

Conclusion

This study has demonstrated that extraction of patient clinical data from general practice settings is both a feasible and valid method to obtain a region-specific estimate of the prevalence of chronic diseases and mental health disorders within regional NSW, Australia. General practices that agreed to participate were included in the study, however further sampling methodology is required to identify which sentinel sites would provide an accurate and truly representative surveillance system. Technological updates/changes to general practice clinical software systems are recommended to improve functionality and data quality within general practice databases. Drop down menus with fixed nationally recognised lists of suburb names, cross matched with correct geographical concordance postcode and state information is currently lacking within the general practice clinical software systems. Additionally, making age, sex, postcode and suburb mandatory fields for creating a new patient record could eliminate the issue of missing data for these essential socio-demographic variables. Investment in computer skills and data entry training for general practice staff and advancements in data aggregation instruments are essential to improve quality of clinical data and their collection methods for effective utilisation by researchers and population health planners for surveillance purposes. Annually obtained chronic disease prevalence figures through the surveillance methodology implemented by the SPDS project, could provide more updated and granular health information for prompt health service planning.

Abbreviations

GPs: 

General practitioners

ISML: 

Illawarra-Shoalhaven Medicare local

ABS: 

Australian Bureau of Statistics

LGAs: 

Local Government Areas

SLAs: 

Statistical Local Areas

MLs: 

Medicare Locals

NHS: 

National Health Surveys

ISLHD: 

Illawarra Shoalhaven Local Health District

PHIDU: 

Public Health Information Development Unit

NSW: 

New South Wales

PCS CAT: 

Pen Computer Systems Clinical Audit Tool

SNOMED-CT: 

Systematized Nomenclature of Medicine Clinical Term

DOCLE: 

Doctor Command Language

ICPC2+: 

International Classification for Primary Care 2+

AHS: 

Australian Health Survey

MBS: 

Medicare Benefits Schedule.

Declarations

Acknowledgements

The authors would like to acknowledge the support provided by the clinical as well as administrative staff at all participating general practices. Their inputs and enthusiasm in database cleansing and providing an accurate, de-identified data extract was invaluable.

The authors would also like to acknowledge the financial support from the Grand Pacific Health Ltd and its funding body the Australian Government Department of Health.

Authors’ Affiliations

(1)
Grand Pacific Health Ltd. trading as Illawarra-Shoalhaven Medicare Local (ISML)
(2)
School of Medicine, Faculty of Science, Medicine & Health, University of Wollongong (UOW)
(3)
Statistical Consulting Centre, National Institute of Applied Statistics Research Australia, University of Wollongong (UOW)

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Copyright

© Ghosh et al.; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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