28. PEOPLE WITH DIABETES INDICATOR
TABLE 1 – INDICATOR DESCRIPTION
| Information component | P4 Health Summary – Indicator No 28 |
| Subject category / domain(s) | Health and ill health in our community |
| Indicator name (* Indicator title in health profile) | Prevalence of recorded diabetes (“People with diabetes”) |
| PHO with lead responsibility | ERPHO |
| Date of PHO dataset creation | 20th December 2006 |
| Indicator definition | Diabetes prevalence (from QOF), persons, all ages, June 2006, per 100 resident population |
| Geography | England, GOR, Local Authority: Counties, County Districts, Metropolitan County Districts, Unitary Authorities, London Boroughs |
| Timeliness | Data is extracted from the QMAS system annually in June and published in QPID (quality and prevalence indicators database) in September-October each year. |
| Rationale:What this indicator purports to measure | Prevalence of diabetes |
| Rationale:Public Health Importance | Diabetes is a serious disease with serious consequences. It is the 5th leading cause of death globally and accounts for about 10% of NHS costs. The burden falls disproportionately on elderly and ethnic populations. We use the indicator in this context as a proxy for healthcare need and demand (a high prevalence of diabetes can indicate a less healthy population with higher service utilisation).The sequelae of diabetes include blindness, amputation, neuropathy, renal disease, heart disease and other complications. It is treatable and preventable. Important modifiable risk factors are obesity, diet and lack of physical activity. |
| Rationale: Purpose behind the inclusion of the indicator | To encourage better collection of the primary data to give more accurate estimates of disease prevalence.To monitor diabetes prevalenceTo emphasize the burden of diseaseTo encourage preventative action |
| Rationale:Policy relevance | Diabetes NSF |
| Interpretation: What a high / low level of indicator value means | A high value can indicate genuinely high prevalence and/or better detection and recording. Conversely a low value may indicate genuinely low prevalence or poor detection and recording. There is some evidence by comparing QOF data between 2004-5 and 2005-6 of all these e.g. large increases in prevalence in some practices; slight falls in other but the national average increased marginally and most practices had reasonably stable estimates suggesting that by and large that recording rates have stabilised. In many areas the levels of recorded diabetes are close to those predicted by the PBS model i.e. we believe the indicator to be a good estimate of actual prevalence. (See PBS diabetes prevalence modelhttp://www.yhpho.org.uk/viewResource.aspx?id=7 ). |
| Interpretation: Potential for error due to type of measurement method | See above. Also because recording is rewarded through QOF points there may be potential for “gaming”. There are a large number of codes used to record diabetes on GP systems which may lead to counting errors depending on how the data is extracted (see the QOF definitions for the codes used). There may also be potential biases in the attribution of practice populations to local authority areas but these are probably small. |
| Interpretation: Potential for error due to bias and confounding | There may be under-sampling of young people, ethnic populations and other vulnerable groups e.g. homeless, travellers in the numerator. |
| Confidence Intervals: Definition and purpose | A confidence interval is a range of values that is normally used to describe the uncertainty around a point estimate of a quantity, for example, a mortality rate. This uncertainty arises as factors influencing the indicator are subject to chance occurrences that are inherent in the world around us. These occurrences result in random fluctuations in the indicator value between different areas and time periods. In the case of indicators based on a sample of the population, uncertainty also arises from random differences between the sample and the population itself.The stated value should therefore be considered as only an estimate of the true or ‘underlying’ value. Confidence intervals quantify the uncertainty in this estimate and, generally speaking, describe how much different the point estimate could have been if the underlying conditions stayed the same, but chance had led to a different set of data. The wider is the confidence interval the greater is the uncertainty in the estimate.Confidence intervals are given with a stated probability level. In Health Profiles 2007 this is 95%, and so we say that there is a 95% probability that the interval covers the true value. The use of 95% is arbitrary but is conventional practice in medicine and public health. The confidence intervals have also been used to make comparisons against the national value. For this purpose the national value has been treated as an exact reference value rather than as an estimate and, under these conditions, the interval can be used to test whether the value is statistically significantly different to the national. If the interval includes the national value, the difference is not statistically significant and the value is shown on the health summary chart with a white symbol. If the interval does not include the national value, the difference is statistically significant and the value is shown on the health summary chart with a red or amber symbol depending on whether it is worse or better than the national value respectively. |
TABLE 2 – INDICATOR SPECIFICATION
| Indicator definition: Variable | Diabetes |
| Indicator definition: Statistic | Ratio of counts of recorded diabetes at practice level (from QOF) summed to local authorities to corresponding resident local authority population (point prevalence) |
| Indicator definition: Gender | Persons |
| Indicator definition: age group | All ages |
| Indicator definition: period | June 2006 |
| Indicator definition: scale | Per 100 resident population |
| Geography: geographies available for this indicator from other providers | SHA, new and old PCO; general practice. www.ic.nhs.uk |
| Dimensions of inequality: subgroup analyses of this dataset available from other providers | No (but could be grouped by deprivation score at practice level if scores are available). |
| Data extraction: Source | Numerator: The Information Centre for health and social care. Denominator: A special extract (mapping general practice populations to local authorities) of the National Strategic Tracing Service (NSTS) database commissioned from ATOS Origin (available on request)[The NSTS is part of connecting for health and is an amalgamation of the Exeter System and the NHS central register. It is due to close in July 2007 to be replaced by the Personal Demographic Service and other services as part of the Secondary Uses Service]. |
| Data extraction: source URL | www.ic,nhs.ukwww.connectingforhealth.nhs.uk/nsts |
| Data extraction: date | Numerator: QOF data downloaded in October 2006; Attribution table supplied December 2005Denominator: November 2006 |
| Numerator: definition | Patients registered with GP practices aged 17 and over at midnight on the 31st March 2006 with a coded diagnosis of diabetes on the 1st April 2005. (QOF DM1). The case definition of diabetes (with Read codes) can be found at:http://www.primarycarecontracting.nhs.uk/uploads/QOF/qof_bus_rules_v9/diabetes_ruleset_r4_v9.0.pdf |
| Numerator: source | Quality and Outcomes Framework (QOF).http://www.ic.nhs.uk/webfiles/QOF/2005-06/detailed%20results/QOF0506_Practices_Prevalence_Eastern.xlshttp://www.ic.nhs.uk/webfiles/QOF/2005-06/detailed%20results/QOF0506_Practices_Prevalence_NorthEast.xlshttp://www.ic.nhs.uk/webfiles/QOF/2005-06/detailed%20results/QOF0506_Practices_Prevalence_NorthWest.xlshttp://www.ic.nhs.uk/webfiles/QOF/2005-06/detailed%20results/QOF0506_Practices_Prevalence_London.xlshttp://www.ic.nhs.uk/webfiles/QOF/2005-06/detailed%20results/QOF0506_Practices_Prevalence_Southern.xls |
| Denominator: definition | Mid-year 2005 LA estimates (latest year available). We used a resident denominator rather than an apportioned registered population to avoid the list inflation in registered populations. We assumed the problems of list counting and inflation did not apply to the numerator (it is very unlikely that diabetics will be counted twice and there are QA checks in QOF to avoid this). |
| Denominator: source | Office for National Statistics (ONS). http://www.statistics.gov.uk/statbase/Product.asp?vlnk=14514&image.x=16&image.y=13 |
| Data quality: Accuracy and completeness | The data cover more than 99% of GP registered patients in England. There were 8,576 practices on the 2004/05 dataset. Users of data derived from QMAS should recognise that QMAS was established as a mechanism to support the calculation of practice QOF payments and not as a person based epidemiological tool. It is not a comprehensive source of data on quality of care in general practice, but it is potentially a rich and valuable source of such information, providing that the limitations of the data are acknowledged. See also QOF assessor validation reports available at: http://www.connectingforhealth.nhs.uk/delivery/programmes/qof/ |
TABLE 3 – INDICATOR TECHNICAL METHODS
| Numerator: extraction | Numerators extracted from downloaded spreadsheets by general practice. |
| Numerator: aggregation /allocation | We assigned counts of recorded to diabetes to give an estimated residentnumber of diabetics as follows:
*NB although we obtained a flow table for 2005-6 we were unable to use it in the HP2 calculations because it arrived after the data submission deadline so we took a pragmatic decision to use the previous years data. We have since re-calculated the estimates using 2005-6 flow tables (unpublished) and are comparing the estimates with those produced as described above. |
| Numerator data caveats | The allocation method may have incorrectly apportioned to LAs particularly for practice straddling LA boundaries. We have not found a satisfactory way of assessing this.There is no age adjustment so variations in prevalence need to be interpreted in the light of variations in age structures. (It may for example, be helpful to correlate the crude prevalence presented here with the proportion of the population over 65) |
| Denominator data caveats | Subject to limitations of ONS population estimation methods |
| Methods used to calculate indicator value | The indicator value is presented as a percentage although strictly is a ratio. There is no age adjustment. The calculation was performed as LA Count of diabetes/total LA population |
| Small Populations: How Isles of Scilly and City of London populations have been dealt with | We included City of London within Hackney and the Isles of Scilly in Penwith |
| Disclosure Control | Not relevant |
| Confidence Intervals calculation method | We assumed a Poisson distribution and calculated exact intervals using an Excel macro (see http://www.erpho.org.uk/download.aspx?urlid=12476&urlt=1&urlf=dsr%5Ffunnel2%2Exls ) (http://members.aol.com/johnp71/confint.xls |
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