A study of cardiovascular risk in overweight and obese people in England

Abstract

Objectives: To report current levels of obesity and associated cardiac risk using routinely collected primary care computer data. Methods: 67 practices took part in an educational intervention to improve computer data quality and care in cardiovascular disease. Data were extracted from 435 102 general practice computer records. 64.3% (229 108/362 861) of people age 15 y and older had a body mass index (BMI) recording or a valid height and weight record that enabled BMI to be derived. Data about cardiovascular disease and risk factors were also extracted. The prevalence of disease and the control of risk factors in the overweight and obese population were compared with those of normal body weight. Results: 56.8% of men and 69.3% of women aged over 15 y had a BMI record. 22% of men and 32.3% of women aged 15 to 24 y were overweight or obese; rising each decade to a peak of 65.6% of men and 57.5% of women aged 55 to 64 y. Thereafter, the proportion who were overweight or obese declined. The prevalence of ischaemic heart disease, diabetes mellitus and hypertension rose with increasing levels of obesity; their prevalence in those who are moderately obese was between two and three times that of the general population. Systolic and diastolic blood pressure, blood glucose even in non-diabetics, cholesterol and triglycerides were all elevated in the overweight and obese population.

Conclusion: Based on the recorded data over half of men and nearly half of women are overweight or obese. They have increased cardiovascular risk, which is not adequately controlled by current practice.

• Cross-sectional studies
• information systems
• medical informatics
• medical records systems (computerized)
• obesity
• prevalence
• risk factors

Introduction

Obesity in England is thought to be expanding at a faster rate than in other parts of Europe, and was estimated in 1998 to have cost the health service at least £½ billion per year in direct costs, about 1.5% of total NHS expenditure; with a further £2 billion in indirect costs [1]. Obesity is defined as a condition where excessive adipose tissue is present to the extent that health may be impaired [2]. Although it has been argued that waist circumference may better reflect adipose tissue deposits [3], body mass index (BMI, weight in kilograms divided by height in metres squared) is the most used, and useful, to define obesity [4]. Overweight or “pre-obesity” is defined as a BMI of 25 to 29.9 kg/m² and obesity as 30 kg/m² or over [2]. Obesity is subdivided into “moderate” or class 1, where BMI is 30 to 34.9 kg/m², “severe” or class 2, where BMI is 35 to 39.9 kg/m², and “morbid”, where BMI is 40 kg/m² or more [2], [5]. Obesity is associated with non-insulin-dependent diabetes, cardiovascular disease and hypertension; it is also associated with other non-cardiovascular diseases, though these were not examined in this study [6]. BMI peaks in the sixth or seventh decade, and it has been speculated that obese people with co-morbidities or complications do not reach very old age [7].

The Primary Care Data Quality Programme (PCDQ) is an educational intervention which improves general practice data quality through a series of targeted disease-focused programmes. The largest of these is its cardiovascular programme [8]. One question that often arises is whether an association between obesity and cardiovascular risk is apparent in routinely collected general practice data; what proportion of those with cardiovascular disease are obese; and how well are their risk factors managed. We therefore re-analysed the data from pilot practices visited between November 2003 and February 2004, to provide baseline data against which practices could compare their own data.

Methods

This study was carried out in 67 general practices, with a total registered population of 435 102 (mean practice size 6494, range 1890–14 701). They were all participants in the PCDQ programme. The PCDQ programme works with individual practices and localities, extracting anonymized computer data from participating practices. English general practice is highly computerized, so data are readily available. The data are aggregated, analysed and fed back. Practitioners draw their own conclusions about where data quality might be improved, and where there is scope to improve data quality and the quality of care. One of the principal learning opportunities created is the Data Quality Workshop, which ideally runs as a half-day session. After an initial plenary, practitioners work in small groups to decide what action might be taken as a result of the data presented.

The practices were drawn from existing PCDQ programmes in three English regions, with the intention of forming an overview of the national prevalence. Thirty-two practices (43% of the sample) were in London (average list size of 5830), 21 (32% of sample) were located in the south east (average list 6641) and 14 (25% of the sample) from the north of England (average list 7792). The male-to-female ratio was 49.5:50.5, a slightly higher proportion of men compared to the national average of 48.7:51.3 [9]. There were also more people in the first half of their working life than the national average: 1.17 times the number of 35- to 45-y-old males, and 1.10 times more females. There is a corresponding reduction in the number of children aged 0–15 y. A population pyramid shows how the study population compared with the English population (Figure 1).

Figure 1.  Population pyramid comparing the study population with the English 2001 population.

The data were collected between November 2003 and March 2004. We extracted the latest data recorded prior to the collection date. We did this with the written consent of the practices. The practices were participating in the PCDQ programme [8], and had volunteered to test an enhanced version of the programme. Generally, localities took part in the programme because they wished to improve the cholesterol management as well as the general cardiovascular health of their population with coronary heart disease [8]. These practices did not have a particular interest in obesity, and joined the programme on a locality basis. Enhancements were being made because the existing programme did not cover all the cardiovascular indicators in the new general practice contract [10], and participants were keen to receive more feedback about their rate of progress towards its financially incentivized targets. During this period, a data collector visited each practice and collected information on demography, BMI and height and weight recording, cardiovascular diagnosis, and risk factors from their computer systems using the health query language MIQUEST (Morbidity Information and Export Syntax, Box 1) [11]. The use of the MIQUEST query tool allowed each practice's data to be extracted in a consistent fashion. We collected clinical data which had been recorded as structured or coded data within the practice computer system. We did not collect narrative text. The data were aggregated in a customized Microsoft Access database to produce a single file for analysis purposes.

Box 1. Dataset.

Demographic details	MIQUEST unique ID, age and sex
Diagnostic data	Coronary heart disease, hypertension, diabetes, transient ischaemic attack (TIA) or stroke, peripheral vascular disease
Cardiovascular risk factors	Blood pressure, body mass index (BMI), height, weight, smoking status, cholesterol level, LDL cholesterol, HDL cholesterol, triglycerides, blood sugar
Drug treatment	Aspirin, statins, other lipid-lowering agents

Only anonymized data were collected from the practices using MIQUEST. The data extracted consisted of the date that the data were recorded and the code used, usually a read code, e.g. “12/2/2004, 1371” means that the “never smoked tobacco” code was recorded for that patient on 12 February 2004. Where there was an associated numeric date, code and numeral were extracted, e.g. “12/2/2004, 44p6, 3.1” means that 3.1 mmol/l LDL cholesterol was recorded on 12 February 2004.

The data had to be extensively cleaned and processed [12]. Data reliability was tested by careful examination of the population denominator, the diagnostic and other clinical data due to be used in the study [13], [14]. We adopted a pragmatic approach to this study and included practices with a recorded prevalence of coronary heart disease of over 2%, who also had at least 50% of patients with a cholesterol measure of whom at least 50% were prescribed a statin. We knew from experience gained in a 2.4 million-person study that a combination of these three indicators suggested that they were engaged in diagnostic coding, electronic recording of pathology test results and computerized prescribing [15].

We calculated the prevalence of overweight and obese people based on their BMI or, where this was absent, from their height and weight. We standardized the prevalence using the European standard population, because this has now become the standard comparator used within the UK health service [16]. We also looked at the prevalence of cardiovascular co-morbidities and risk factors by gender and age. We analysed in 10-y bands from age 15 y upwards, grouping together the population over 85 y.

We then formulated audit criteria (Box 2) based upon best practice guidance [17], [18], in conjunction with representatives of the practices involved. Their purpose was to see what proportion of the population we could calculate BMI for and look at their level of cardiovascular disease and risk. Whether or not there was a causal link, practitioners wanted to know how much more likely overweight and obese people were to have cardiovascular disease and risk factors. It was agreed that control of blood pressure and lipid management in the overweight and obese would be compared using the standards set out in the UK National Service Framework for Coronary Heart Disease [19]. The proportion of patients “above target” was to be identified, irrespective of being on treatment or not. We also extracted blood glucose information; data in practice computer systems are not flagged to indicate whether they were random or fasting samples.

Box 2. Audit criteria.

1. Data quality–-the recording of valid or derived BMI

The proportion of the population with a valid BMI recording The proportion of the population with a valid height and weight who can have their BMI derived

2. Prevalence of overweight and obese people by age band and sex

The prevalence of overweight and obese people The standardized prevalence to enable comparison with other studies The prevalence of category of BMI of overweight (BMI 25–29.9 kg/m^2), moderately obese (BMI 30–34.9 kg/m^2), severely obese (BMI 35–39.9 kg/m^2) and morbidly obese (BMI ≥ 40 kg/m^2)

3. Prevalence of cardiovascular disease in overweight and obese people compared with the rates in the population overall

The percentage of people with ischaemic heart disease, diabetes and hypertension Comparison by age and gender Numbers of patient with more than one cardiovascular disease

4. Data quality—the recording of cardiovascular risk factors

The proportion of the population with BP, blood glucose, cholesterol, triglycerides and smoking records by age band and gender The rate of recording in those with a BMI recording, and the rate of recording for overweight and obese people (BMI ≥ 25 kg/m^2)

5. Values of cardiovascular risk factors

The mean values for the quantitative risk factors listed above (BP, glucose (in non-diabetics), cholesterol and triglycerides) by age band and gender Comparison of the general population with overweight and obese people

6. Smoking habits in the overweight and obese

The proportion of smokers, ex-smokers and people who never smoked in the general population compared with the obese and overweight

7. Management of hypertension and dyslipidaemia

The percentage of people who are overweight and obese who have raised blood pressure (using >160 and >145 mmHg systolic, and >90 and >85 mmHg diastolic BP thresholds) The percentage of people who are overweight and obese who are dyslipidaemic (examining raised cholesterol ≥ 5mmol/l and LDL cholesterol ≥ 3 mmol/l.)

Data were analysed in SPSS version 12. An independent samples t-test was used to test whether the difference in the means between normal and overweight and obese for continuous variables was statistically significant and the Pearson χ² test to compare the distribution of categorical variables. Mean and standard deviation (SD) are used to describe normally distributed variables, and the standard error of the mean (SEM) and confidence intervals (CI) to describe the precision of the population parameters.

Results

BMI was extensively recorded in the adult computerized medical record. Nearly three-quarters of women between 25 and 85 y had a recorded BMI. There were lower levels of data recording for men (56.5% of over 15 y, compared with 69% of women). Fifty-five per cent of 25- to 35-y-old men had a BMI recorded and the level of recording rose with increasing age, reaching just under three-quarters by age 85. Relatively few BMIs had to be derived from height and weight other than in the 15- to 25-year-old age group.

Nearly half (47.7%) of the standardized population was overweight or obese; the proportion was 55% based on the raw data. Thirty-nine per cent of men were overweight compared with only 29% of women. However, 17.2% of women were obese compared with only 15.5% of men. Men and women had comparable proportions who were moderately obese (BMI 30 to 34.9 kg/m²) and there were nearly twice as many severely (BMI 35 to 39.9 kg/m²) and morbidly obese (BMI ≥ 40 kg/m²) women as men. In men, the proportions who were overweight or obese peaked in one of two decades: either between 45 and 54 y, or 55 and 64 y. In females, the comparable peaks came 10 y later: either between 55 and 64 y, or 65 and 74 y (Table I).

Table I.  Percentage of people with a BMI recording who were overweight and obese. Raw data and data standardized to the European population.

	Overweight + obese Cases Raw% Standardised% (Std)			Obese Cases Raw% Standardised% (Std)			Overweight	Moderately Obese	Severely Obesity	Morbidly obesity
BMI (kg/m^2)	No >25	Raw% >25	Std >25	No >30	Raw% >30	Std% >30	Raw 25–29	Raw 30–34	Raw 35–39	Raw ≥40
Males
15–24	1327	19.4	22.0	379	5.5	6.3	13.8	3.9	1.2	0.4
25–34	6892	39.9	38.9	1737	10.1	9.8	29.8	7.4	1.8	0.9
35–44	12462	56.7	45.7	3584	16.3	13.1	40.4	12.1	3.0	1.2
45–54	11098	64.3	67.7	3602	20.9	22.0	43.4	15.4	4.1	1.4
55–64	10917	68.1	65.6	3665	22.9	22.0	45.3	17.3	4.0	1.6
65–74	8323	67.8	61.2	2493	20.3	18.3	47.5	16.2	3.3	0.8
75–84	4654	60.0	36.9	1103	14.2	8.8	45.8	12.0	1.9	0.4
>85	883	47.1	34.5	156	8.3	6.1	38.8	7.5	0.7	0.1
Total	56556	55.8	52.6	16719	16.5	15.5	39.3	12.5	2.9	1.0
Females
15–24	2745	26.8	32.3	943	9.2	11.1	17.6	6.2	2.1	0.8
25–34	8232	35.3	34.7	3037	13.0	12.8	22.3	8.2	3.2	1.7
35–44	11456	43.5	38.0	4671	17.8	15.5	25.8	10.7	4.5	2.6
45–54	10063	50.3	56.6	4314	21.6	24.3	28.7	13.2	5.4	3.0
55–64	10858	58.1	57.5	4509	24.1	23.9	34.0	15.4	5.9	2.8
65–74	8865	62.6	52.7	3548	25.1	21.1	37.6	17.1	5.7	2.3
75–84	6186	56.0	24.3	2186	19.8	8.6	36.2	14.6	3.9	1.3
>85	1834	45.6	13.8	532	13.2	4.0	32.4	10.5	2.0	0.8
Total	60239	47.1	43.8	23740	18.6	17.2	28.6	12.0	4.4	2.2
Population
Total	116795	51.0	47.7	40459	17.7	16.5	33.3	12.2	3.8	1.7

Std: standardized to the European standard population; raw: raw prevalence from practice data.

The mean BMI for males over 15 was 26.0 (SD 4.63, SEM 0.146) kg/m² and for women was 25.7 (SD 5.51, SEM 0.154) kg/m². The peak in the mean BMI mirrors the peak proportions of overweight and obese patients. The mean BMI for men aged 15 to 24 was 21.7 kg/m² rising to 27.3 kg/m² between 55 and 64 y of age, then falling off again. For females, the corresponding figures were 23 kg/m², rising to 27.12 kg/m² one decade later (age 65 to 74).

Ischaemic heart disease (IHD), diabetes mellitus and hypertension were all more prevalent with increasing obesity (Table II). These differences were all highly significant (t-test, p<0.001). However, the prevalence of IHD fell in men with morbid obesity, and in females with severe and morbid obesity. IHD and diabetes were more prevalent in overweight and obese men, but hypertension was more prevalent in females. The mean BMI for women with diabetes is 29.9 (SEM 0.086, SD 6.47) kg/m² and for men 28.7 (SEM 0.063, SD 5.19) kg/m²; there was almost no gender difference in BMI in IHD and hypertension.

Table II.  Change in the prevalence of ischaemic heart disease, diabetes and hypertension with increasing obesity.

	Ischaemic heart disease					Diabetes mellitus					Hypertension
Level of Obesity	Pop	Over weight	Mod	Severe	Morbid	Pop	Over Weight	Mod	Severe	Morbid	Pop	Over Weight	Mod	Severe	Morbid
BMI (kg/m^2)	All	25–29	30–34	35–39	≥40	All	25–29	30–34	35–39	≥40	All	25–29	30–34	35–39	≥40
Male
n	9037	3650	1474	367	81	7376	2838	1571	566	213	10136	4019	1766	497	175
15–24	0.0	0.0	0.0	0.0	0.0	0.4	1.8	0.7	2.4	4.2	0.1	0.1	0.0	0.0	0.0
25–34	0.1	0.1	0.2	0.3	0.0	0.6	0.7	1.2	3.9	7.5	0.4	0.7	1.3	3.6	2.7
35–44	0.4	0.7	1.2	2.4	0.4	1.5	2.4	3.3	9.1	7.8	1.5	2.0	5.2	5.8	7.4
45–54	2.4	3.3	5.4	9.0	8.9	3.9	4.9	10.3	19.4	24.5	4.9	6.7	11.0	17.9	20.7
55–64	7.8	10.3	14.7	18.4	13.4	7.1	8.5	17.2	26.3	32.0	10.1	13.3	18.1	25.5	26.9
65–74	18.1	22.8	27.2	26.2	19.4	12.4	15.5	23.1	35.0	33.3	17.2	20.9	26.5	29.1	30.1
75–84	24.4	29.4	32.2	37.9	20.0	12.5	16.0	24.7	26.9	36.7	20.7	26.4	28.8	24.1	20.0
>85	22.4	29.2	35.0	50.0	50.0	9.4	14.6	20.0	35.7	0.0	17.0	25.2	15.0	28.6	50.0
All ≥15	5.1	9.2	11.6	12.4	7.8	4.1	7.1	12.4	19.1	20.4	5.7	10.1	13.9	16.7	16.8
P	–	<0.001	<0.001	<0.001	n.s.	–	<0.001	<0.001	<0.001	<0.001	–	<0.001	<0.001	<0.001	<0.001
Female
n	6385	1975	1070	358	160	6176	1845	1369	701	424	13468	4038	2233	926	451
15–24	0.0	0.1	0.2	0.0	0.0	0.4	0.9	0.2	0.9	1.2	0.2	0.3	0.8	1.4	4.7
25–34	0.1	0.0	0.1	0.0	0.3	0.5	0.7	1.3	2.2	2.3	0.6	0.9	1.5	2.0	4.9
35–44	0.2	0.2	0.5	0.5	0.4	1.2	1.4	3.0	4.2	8.5	1.7	1.9	3.8	6.6	7.6
45–54	0.9	1.1	2.0	2.6	3.9	2.7	3.1	6.4	11.7	15.7	4.6	5.8	8.3	13.2	15.7
55–64	3.8	5.0	6.7	7.8	8.0	4.9	5.1	10.6	17.2	25.8	11.1	12.9	18.9	22.4	24.6
65–74	9.4	11.1	14.7	17.0	15.9	8.9	10.5	16.5	26.6	25.3	18.6	22.0	27.6	31.0	28.1
75–84	14.8	18.0	22.2	20.9	23.4	9.1	11.9	19.8	22.5	28.3	24.6	28.8	32.2	38.0	37.2
>85	15.7	20.7	22.3	16.5	19.4	7.5	12.4	15.9	10.1	12.9	21.1	29.3	32.9	36.7	25.8
All ≥15	3.5	5.4	7.0	6.4	5.8	3.3	5.1	8.9	12.5	15.3	7.3	11.1	14.6	16.5	16.2
P	–	<0.001	<0.001	<0.001	<0.001	–	<0.001	<0.001	<0.001	<0.001	–	<0.001	<0.001	<0.001	<0.001
Population
All ≥15	4.3	7.4	9.1	8.4	6.3	3.7	6.1	10.5	14.7	16.7	6.5	10.6	14.3	16.6	16.4
P	–	<0.001	<0.001	<0.001	n.s.	–	<0.001	<0.001	<0.001	<0.001	–	<0.001	<0.001	<0.001	<0.001

Pop: whole population; mod: moderately obese; severe: severely obese; morbid: morbidly obese.

All >15 is the population over 15 y.

The proportion of people with one co-morbidity increased with age and level of obesity and the same pattern was repeated with two, three and four co-morbidities (Table III). (Diagnosis of stroke or transient ischaemic attacks and peripheral vascular disease were included with IHD, diabetes and hypertension as possible co-morbidities.) In most of the sections of Table III there is a fall in the proportion of patients with morbidities in the over-85s with severe or morbid obesity.

Table III.  Percentage of each age band and level of obesity for people with one to four co-morbidities.

No of co-morbidities	One co-morbidity				Two co-morbidities				Three co-morbidities				Four co-morbidities
Level of obesity	Over weight	Mod	Severe	Morbid	Over Weight	Mod	Severe	Morbid	Over Weight	Mod	Severe	Morbid	Over Weight	Mod	Severe	Morbid
BMI (kg/m^2)	25–29	30–34	35–39	≥40	25–29	30–34	35–39	≥40	25–29	30–34	35–39	≥40	25–29	30–34	35–39	≥40
n	9276	11867	5517	2736	2420	3352	1810	956	491	755	386	209	65	112	59	25
15–24	0.8	1.6	1.1	2.9	0	0	0	0.2	0	0	0	0	0	0	0	0
25–34	1.0	1.6	2.9	5.6	0.03	0	0.09	0.4	0	0.01	0	0	0	0	0	0
35–44	2.1	4.2	6.9	12.1	0.2	0.3	0.9	1.1	0.01	0.01	0.0	0.2	0	0	0	0
45–54	5.8	10.8	15.9	22.4	0.5	1.2	2.9	7.1	0.02	0.2	0.3	0.8	0.01	0.01	0.09	0
55–64	12.9	19.9	25.8	31.9	2.2	4.5	7.7	11.0	0.4	0.7	1.5	2.7	0.0	0.1	0.1	0.3
65–74	24.0	31.1	35.2	35.6	6.6	10.1	14.2	17.5	1.3	2.3	3.2	4.2	0.3	0.3	0.5	0.6
75–84	31.7	35.7	37.2	37.8	12.2	14.2	18.2	19.8	2.7	3.9	4.7	5.5	0.3	0.6	0.9	0.8
>85	33.6	34.7	42.5	37.3	13.4	16.4	14.2	17.5	3.2	4.0	4.1	3.2	0.4	0.7	0.5	1.6
% ≥15	8.3	15.5	19.7	22.0	2.2	4.39	6.5	7.7	0.4	1.0	1.4	1.7	0.1	0.1	0.2	0.2

Pop: whole population; mod: moderately obese; severe: severely obese; morbid: morbidly obese; % ≥15: population over 15 y.

The completeness of cardiovascular risk factor data was increased in those with a BMI record and higher still in people who were overweight or obese. Data were more complete in females than in males and with increasing age. The quality of risk factors recording was higher for overweight and obese people compared to data quality levels in the rest of the population. Data recording for smoking and blood pressure was better for both men and women between 25 and 85 y in overweight and obese people. Cholesterol and triglyceride were also better recorded in the obese and overweight; the data quality levels peaked in the 65 to 75 age group for both men and women.

Systolic and diastolic blood pressure, glucose (in non-diabetics), cholesterol, and triglycerides were generally higher in those who were overweight and obese. All these variables were higher in men than women and generally increased with age, with the difference between the non-overweight and obese populations being significant (t-test, p<0.001.) The exceptions were diastolic blood pressure, which fell in people over age 65 y and cholesterol in obese men, which was not statistically significantly different from the non-obese (see Table IV).

Table IV.  Mean values of risk factors.

Age	Systolic blood pressure			Diastolic Blood pressure			Glucose in non-diabetics			Cholesterol			Triglycerides
	Pop	BMI ≥ 25	BMI ≥ 30	Pop	BMI ≥ 25	BMI ≥ 30	Pop	BMI ≥ 25	BMI ≥ 30	Pop	BMI ≥ 25	BMI ≥ 30	Pop	BMI ≥ 25	BMI ≥ 30
Males
15–24	117	126	129	70.2	75.2	77.6	4.9	4.9	5.2	4.4	4.8	5.4	1.3	1.9	2.8
25–34	124	127	132	75.1	77.4	80.6	5.0	5.0	5.1	5.0	5.2	5.3	1.7	2.0	2.1
35–44	127	129	133	78.6	80.3	83.4	5.1	5.2	5.3	5.4	5.5	5.5	2.0	2.1	2.3
45–54	131	133	137	81.3	82.7	84.6	5.3	5.4	5.5	5.5	5.5	5.4	2.0	2.1	2.3
55–64	136	138	141	81.7	82.6	84.0	5.4	5.5	5.6	5.3	5.3	5.2	1.8	2.0	2.1
65–74	140	141	143	80.3	80.6	80.8	5.5	5.5	5.6	5.0	5.0	4.9	1.7	1.8	1.9
75–84	143	144	144	78.1	78.2	78.2	5.5	5.5	5.8	4.9	4.9	4.8	1.5	1.6	1.8
85 +	143	144	144	77.1	77.5	79.0	5.6	5.8	5.8	4.9	4.9	5.0	1.4	1.6	1.9
≥15	131	135	138	78.6	80.6	82.6	5.3	5.4	5.5	5.2	5.2	5.2	1.8	1.9	2.1
SD	18	17	17	10.1	9.8	10.0	1.1	1.0	1.0	1.1	1.1	1.1	1.2	1.2	1.4
t-test, p		<0.001	<0.001		<0.001	<0.001		<0.001	<0.001		<0.001	n.s.		<0.001	<0.001
Female
15–24	114	119	122	70.0	72.9	75.2	4.7	4.8	5.0	4.6	4.7	4.8	1.3	1.5	1.5
25–34	116	121	124	72.4	75.2	77.7	4.8	4.9	4.9	4.9	5.0	5.2	1.3	1.5	1.6
35–44	119	123	127	74.8	77.5	80.0	4.9	5.0	5.1	5.1	5.2	5.3	1.3	1.5	1.6
45–54	127	131	134	78.2	80.5	82.4	5.1	5.2	5.3	5.4	5.4	5.5	1.5	1.6	1.8
55–64	135	137	140	80.0	81.3	82.6	5.2	5.2	5.3	5.7	5.7	5.6	1.6	1.7	1.8
65–74	141	143	145	79.9	80.3	80.6	5.3	5.3	5.4	5.6	5.5	5.4	1.6	1.7	1.7
75–84	146	147	148	79.1	79.2	79.6	5.4	5.5	5.6	5.6	5.5	5.4	1.5	1.7	1.8
85 +	147	148	149	78.7	79.4	80.3	5.7	5.7	5.7	5.6	5.7	5.5	1.5	1.7	1.7
≥15	127	133	135	76.3	78.8	80.5	5.1	5.2	5.3	5.5	5.5	5.5	1.5	1.7	1.8
SD	20	19	19	10.1	9.8	9.8	1.0	1.0	1.0	1.1	1.1	1.1	0.9	1.0	0.9
t-test, p		<0.001	<0.001		<0.001	<0.001		<0.001	<0.001		<0.001	<0.01		<0.001	<0.001

Pop: whole population.

A higher proportion of overweight and obese patients was recorded as smokers than those with a normal body weight. However, this may be accounted for because the data for overweight and obese patients was much more complete. The peak decade for male smoking was 34 to 45, and for females 25 to 34 (Figure 2).

Figure 2.  Proportion of obese people who smoked compared with overweight people and the general population.

The proportion of overweight and obese people above targets for lipids and blood pressure, set out for cardiovascular health [10], [19], were generally two to three times greater than in those of normal BMI. The differences in blood pressure were highly significant (χ² test, p<0.001), the changes in cholesterol and LDL cholesterol much less so, particularly in males (Table V).

Table V.  The proportion of overweight and obese people above national targets for lipid and blood pressure management.

BMI (kg/m^2)	<25		≥25		Comparing proportion above and below threshold	<30		≥30		Comparing proportion above and below threshold
	%> target	n	%> target	n		%> target	n	%> target	n
Males					χ^2					χ^2
Cholesterol = 5 mmol/l	10.8	4881	21.8	12309	p=0.002	15.3	13050	25.6	4140	n.s.
LDL-C = 3 mmol/l	4.6	2083	9.8	5539	n.s.	6.8	5763	11.5	1859	n.s.
Systolic BP > 160	2.7	1196	5.5	3093	p<0.001	3.6	3115	7.2	1174	p<0.001
Systolic BP > 145	10.8	4878	21.1	11922	p<0.001	14.7	12526	26.4	4274	p<0.001
Diastolic BP > 90	3.9	1772	10.0	5669	p<0.001	6.0	5135	14.2	2306	p<0.001
Diastolic BP > 85	12.3	5539	26.0	14729	p<0.001	17.5	14901	33.1	5367	p<0.001
Females					χ^2					χ^2
Cholesterol = 5 mmol/l	11.0	7475	21.7	13075	p<0.001	14.1	14812	24.6	5738	n.s.
LDL-C ≥ 3 mmol/l	4.2	2820	9.5	5697	p=0.054	5.7	5977	10.9	2540	p=0.054
Systolic BP > 160	3.2	2148	6.2	3755	p<0.001	4.1	4245	7.1	1658	p<0.001
Systolic BP > 145	10.6	7179	20.9	12585	p<0.001	13.5	14184	24.0	5580	p<0.001
Diastolic BP > 90	3.2	2173	7.8	4671	p<0.001	4.3	4480	10.2	2364	p<0.001
Diastolic BP > 85	10.1	6859	21.0	12657	p<0.001	12.9	13465	26.0	6051	p<0.001

Discussion

Principal findings

Where data are recorded, over half of men and nearly half of women were overweight or obese. A greater proportion of men were overweight, whereas more women were obese. Obesity peaked between 45 and 55 in men, and 10 y later in women, after which it began to fall. Cardiovascular morbidity, diabetes, hypertension and cardiovascular risk factors were all more common with increasing obesity. Our data suggest that smoking was not increased in overweight or obese people; though computer data were more complete in these groups.

Implications for practices

The data demonstrate that, despite current levels of treatment, cardiovascular risk was increased and risk factor control worse in these groups of people. General practitioners and primary healthcare teams need to treat their risk factors more aggressively.

Limitations of the study

The population did not precisely match the national population. Practices generally joined on a locality basis, following the advice of their local primary care organization. Therefore, whilst there was good representation of different sizes of practices, this study population is weighted towards the more affluent south of England.

The data collected were not exhaustive. We did not search for obesity diagnosis codes, or drugs for obesity, we simply extracted or derived BMI. Heights in general practice may not be completely accurate; often they are reported verbally by patients. Elderly people lose vertical height.

Only 63.1% of the total population had a BMI record. Overall the overweight and obese had higher levels of data recording. This better data quality may be accounted for by their increased rates of attendance [20]; though it is impossible to do more than speculate about missing data.

No account was taken of people taking medication to control their blood pressure or cholesterol. It is possible that, if these patients were analysed separately, the rise in blood pressure and lipids may have been even greater in the overweight and obese groups. A survey of this type does not demonstrate a causal link between obesity and cardiovascular risk.

Comparison with the literature

Primary care professionals face the challenge of learning how to manage cardiovascular risk in overweight and obese individuals as well as they do the non-obese. This is recognized to be a problem across Europe [21]. Exercise campaigns have been found to increase physical activity, but only have a marginal effect on body weight [22]. Dietary advice from GPs has been found to be more effective than that from dieticians, but the effect is small [23]. GPs may be unsure how to give nutritional advice most effectively [24]. A trial is currently being conducted to see if Internet support might improve obesity management [25].

These data suggest that the epidemic in obesity predicted by the National Audit Office for the UK appears not to have occurred [1]. The data are consistent with those reported in other studies: the male/female ratio and age-related peaks are similar [5], [7], [26]. The associations with diabetes, heart disease and hypertension [27] have also been reported [6], [28]; these data illustrate that these problems remained unaddressed. Obesity has been identified as an important predictor of prevalence of diabetes [29] and the extent of glycaemic control [30].

Call for further research

Further research is needed to understand whether the decline in obesity with advancing age, and the fall in the prevalence of IHD in those with morbid obesity, is due to increased mortality of overweight and obese people with risk factors, i.e. “survival bias”. Practitioners need to know whether tackling the obesity or the cardiovascular risk (or both) is the most appropriate strategy. We also need to understand why obese patients are not having their modifiable risk factors managed as well as the non-obese. Is this because of a lack or resources, knowledge, or some other cause?

Conclusion

Obesity is associated with increased cardiovascular morbidity and risk. Despite this being known, modifiable cardiovascular risk factors remain suboptimally controlled in this group of patients compared with the general population. Primary care needs effective strategies to address this unmet need.

The participating practices are acknowledged for their participation in the PCDQ programme. The Primary Care Data Quality (PCDQ) coronary heart disease programme is funded by an unconditional educational grant from MSD.