In the past the health service has been overly focused on commissioning for price and volume rather than quality and outcome. There was too much emphasis on treating illness rather than its prevention. Health inequalities have also been worsening and in England the rich can still expect to live for longer than the poor. Now is a new era for public health. The government is committed to closing the gap between the most advantaged and the least advantaged parts of society (GBDH, 2010a; GBDH, 2010b; GBDH 2010c; GBDH, 2010d). Health needs assessment (HNA) is a vital tool in this process because it targets services and support towards the most disadvantaged. It is a method for examining the health needs of a population leading to agreed priorities and resource allocation in order to improve public health (Hooper and Longworth, 1998).
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The purpose of this assignment is to undertake an HNA for the community that I currently work in as part of my Specialist Community Public Health Nurse (SCPHN) qualification in health visiting. In fact HNA is a standard of proficiency in order to gain professional registration (NMC, 2004). Recent evidence has increasingly demonstrated that the first few years of life greatly influence future health, wealth and happiness (Tickle, 2011; Field, 2010; Marmot, 2010). The involvement of health visitors during this period is vital as they are experts in public health. The Health Visitor Implementation Plan (2011) aims to expand the health visiting service with an extra 4,200 health visitors to be in post by 2015. This will support the Government’s commitment to improve health outcomes by developing an understanding of the health needs of children, families and communities enabling the commission of services that are based on need.
The five-step approach to HNA developed by Cavanagh and Chadwick (HDA, 2005) will be used as a framework for this assignment because it is simple, robust, flexible, and has been tested over several years. This five step process is based on the model outlined by Hooper and Longworth (2002). Due to time constraints and lack of resources only steps one to three of the five steps of HNA will be undertaken: step 1 (my community), step 2 (identifying health priorities) and step 3 (assessing a health priority for action).
All of the data used within this assignment is within the public domain however effort has been made to protect identity.
Step 1 – My Community
According to Cavanagh and Chadwick (2005) the community for HNA can be identified as those sharing: a geographic location like a housing estate; a setting such as a school, prison or workplace; a social experience like ethnicity or sexuality; or an experience of a particular condition for example mental illness or diabetes. The community has also been defined as ‘a group of people who share an interest, a neighbourhood, or a common set of circumstances. They may or may not acknowledge membership of a particular community’ (Smithies and Adams, 1990). It is the common interest of people that is particularly significant for public health. This is because even though people operate as individuals, they may share characteristics or needs that can be assessed at a community level (Brocklehurst, 2004). However Naidoo and Wills (2000) state that individuals may be a part of different communities at various points in their lives rather than belonging to a single community. The community which is the focus of this HNA is a geographical area. It has been chosen because it is attached to a GP practice from which a team of health visitors are based and most work is with clients within this community. It consists of two wards in the borough of North Tyneside. They will be referred to as ward A and ward B and will be compared to regional and national data available.
Step 2 – Identifying Health Priorities
This section will focus on the identification of the health priorities of my community by considering the factors which may affect health conditions. These factors can be grouped into five categories: biological, social, economic, environmental and lifestyle.
Biological
The population of wards A and B are shown in appendix 1.1 using data from the census of 2001. Census data can be extremely useful however it has significant limitations. Its data rapidly goes out of date and only provides a picture of the UK population decennially. For example, wards A and B have experienced population change within the past ten years due to new housing developments therefore the 2001 census data may now be unrepresentative. For this reason a population estimate (appendix 1.2) based on expected births and deaths is often a valuable tool but must be used with caution as it is only a guide to what may happen if past trends continue. From the data it is clear that this borough has an ageing population but it is also notable that the percentage of the population under 5 years in both wards A and B is significantly higher than the regional and national percentages. This perhaps underlines the importance of the role of the health visitor in these wards.
Appendix 1.3 illustrates that the combined male and female life expectancy for wards A and B are significantly lower than the North Tyneside and national figures. It is particularly alarming to note that some of those in ward A could potentially expect to live for seven years less than some living merely a few miles away in other parts of North Tyneside. The poor life expectancy of wards A and B may be due to the condition of their general health. These wards have worse general health than North Tyneside and England (appendix 1.4). That is, a greater percentage of those in wards A and B report poor health compared with locally and nationally. However Sen (2002) argues that there are complications in the self assessment of health because a person’s own understanding of their health may differ from that of the healthcare professional. Therefore additional statistics should be used to assess health status.
The prevalence of long-term health conditions in wards A and B as reported by the Quality Outcomes Framework (QOF) is also shown in appendix 1.5. Ward A has a higher prevalence of conditions such as coronary heart disease (CHD), diabetes, chronic obstructive pulmonary disease (COPD) and cancer all of which contribute to lower life expectancy. Indeed CHD is the biggest cause of preventable death in England (British Heart Foundation, 2010). In contrast ward B has a similar prevalence of long-term conditions to that of the national. However data from within the QOF has several limitations. For example QOF was not originally designed as a research tool and its data is not externally validated. It has also been suggested that QOF data may be more favourably presented with the aim of maximising practice income (Ashworth et al., 2008) yet Doran et al. (2011) found that financial incentives had little impact upon the data. Further limitations include that prevalence data is not standardised for age and sex and that many patients appear simultaneously on more than one disease register (Ashworth et al., 2008).
Mental health was seen as having equal importance to physical health for the first time in 2010 (GBDH 2010c). It was recognised that inequality contributes to mental health and in turn mental health can cause further inequality. Consequently tackling mental health is now a key priority for the Government (GBDH, 2011b). It is difficult to determine the exact prevalence of mental health disorders but there are indicators to reflect the situation in North Tyneside (appendix 1.6). For example benefit claims for mental health disorders along with hospital admissions due to self harm and mortality rates due to suicide are significantly greater in North Tyneside than England.
Social
The Marmot Review (2010) emphasised the correlation between lower social position and poor health. The social grade of those living in wards A and B is shown in appendix 2.1. Almost a quarter of those living in these wards are of lowest social grade compared with just 16% of people nationally. Marmot called for action to reduce social gradients in order to improve the health of communities like those living in wards A and B.
Appendix 2.2 shows the measure of deprivation for wards A and B from the 2001 census. It is clear that both wards rank as some of most deprived in the country but as discussed earlier the census data is extremely out of date. After the 2001 census local super output areas (LSOAs) were created to improve reporting of small area statistics as it was thought that wards vary too much in size (ONS, 2011). The English Indices of Deprivation 2010 ranked LSOAs according to their deprivation level. It has undergone a range of procedures to assure its quality as well as being externally validated. According to the index North Tyneside is ranked as 113 out of 326 boroughs in England and is one of the least deprived areas in the North East. However within the borough there are pockets of extreme deprivation which fall into the 10% most deprived areas in England (ONS, 2011). It is difficult to determine ward level deprivation using LSOAs because they do not fit exactly into ward boundaries. Nevertheless appendix 2.3 shows estimates of the deprivation levels of wards A and B using a best fit geographical alignment combined with averaging the LSOA scores. It shows that these wards have been ranked as the most deprived wards in North Tyneside (North Tyneside Council, 2011). It has long been known that there is a relationship between deprivation and poor health (Marmot, 2010). In a social context this may be because a more deprived community is more likely to offer health risks such as higher crime rates (appendix 2.4) and poor housing.
Research has shown that poor housing is associated with greater risk of cardiovascular disease, respiratory disease and mental health conditions. The poorest communities are often made up from estates of mostly socially rented housing (Marmot, 2010). Indeed appendix 2.5 shows that wards A and B have a significantly higher percentage of people living in socially rented accommodation than regionally and nationally. Those who live in social housing have been found to have increased unemployment rates, poor health and disability than the rest of the population (Clarke et al., 2008). There is also evidence to suggest that children living in social housing have a greater risk of disadvantage in adult life (Feinstein et al., 2008, Harker, 2006). Further, poor housing conditions like overcrowding can influence health. Appendix 2.6 illustrates the increased problem of overcrowding in wards A and B relative to North Tyneside and England.
Economic
Social gradient in communities is also affected by patterns of employment. Appendix 3.1 shows employment levels in wards A and B and it is clear that the percentage unemployed is significantly higher in these wards than in the rest of North Tyneside and England. Evidence suggests that the unemployed have considerably increased health risks including higher incidence of limiting long term conditions and mental health problems (Thomas et al., 2005; Gallo et al., 2006). In addition Jin et al. (1997) demonstrated a relationship between unemployment and decreased life expectancy although this research is dated. Perhaps it could be said that the poorer health and decreased life expectancy of wards A and B previously discussed may be linked to their high levels of unemployment. Physical and mental health is also affected by low paid, poor quality employment. Appendix 3.2 illustrates that the percentage of those in elementary, low level employment is greater in wards A and B than regionally and nationally. There are also less people working in managerial and professional roles in these wards. Further, those with few or no qualifications have the highest rates of unemployment and poor quality employment (Marmot, 2010). Appendix 3.3 clearly shows that wards A and B have a significantly greater proportion of people with no formal qualifications than figures for North Tyneside, North East and England.
There is a well established link between income and poor health because those with lower incomes cannot buy items that maintain health and have to buy cheaper goods that could elevate health risks (Marmot, 2010). Appendix 3.4 shows that the average weekly income is less in wards A and B than the average for the North East. Unfortunately there is no data available for North Tyneside or England to enable comparison.
The data discussed above forms a picture of the economic factors that influence health in my community. The data is from the census 2001 and as previously stated it is ten years out of date. Consequently a greatly significant limitation of the data is that it will not reflect changes caused by the recent economic downturn. Therefore wards A and B could currently have worsened levels of unemployment and income however this cannot be confirmed until the results of the 2011 census are published.
Environmental
An important factor in reducing health inequality is creating an environment where people can live healthily. Those who live near areas of green space such as parks can have improved health and wellbeing (Croucher et al., 2007). Green space may also encourage social integration, physical activity and improve quality of air. Appendix 4.1 shows a decreased percentage of green space areas in wards A and B compared to the borough.
Another contributing factor to the creation of a healthy living environment is reducing cold housing. The cold is thought to be the main cause of extra deaths each year during the winter (Marmot, 2010). It is clear that the ability to afford to keep a warm home is crucial in the prevention of these deaths. Appendix 4.2 shows the percentage of households with central heating in wards A and B. Ward B has a significantly lower percentage of households with central heating than regionally and nationally. This data is again out of date and will not reflect recent rises in fuel costs. In November 2008 the increased price of fuel caused fuel poverty in more than half of single pensioners and two thirds of workless households (Bradshaw et al., 2008). This is important to note considering the ageing population of North Tyneside and the high unemployment levels of wards A and B.
Finally appendix 4.3 shows information regarding car ownership. Wards A and B have a lower percentage of households without a car than North Tyneside and England. In fact the percentage of those with no car in these wards is double that of England. Transport is vital because it enables access to employment, education, services and social networks (GBDT, 2004). Transport also has an impact on health inequalities when considering deaths from road traffic accidents (RTAs). RTAs are thought to be particularly high among children who live in the most deprived areas in England (GBDT, 2009). However appendix 4.4 shows that the rate of injuries and deaths from RTAs is much lower in North Tyneside compared with England although data at ward level is unavailable.
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Lifestyle
Lifestyle choices have a huge impact on health. England has one of the highest obesity rates in Europe (WHO, 2012). It is linked with increased risk of conditions such as diabetes, cancer and mental health problems (GBDH 2011c). The percentage of obese adults in North Tyneside is significantly higher than the national average and there are less healthy eating adults locally than nationally (appendix 5.1). This data is from the Health Survey for England (HSE) and is based on a sample of the population therefore estimates are subject to sampling error. In contrast to the findings above the Active People Survey found that the percentage of physically active adults in North Tyneside is greater than that of England (appendix 5.1) but this data also has several limitations. Firstly it is not age standardised and it is likely that those who are younger undertake the recommended levels of physical activity. Secondly the survey is self reported so may be subject to responder bias. Finally the data does not include active recreation such as housework or active transport.
There is great concern over the trends for childhood obesity in England and more than 20% of children are overweight or obese by the age of 3 (Rudolph, 2009). Appendix 5.2 contains data from the National Child Measurement Programme (NCMP) which shows that wards A and B have higher percentages of obese children in year 6 than nationally. But the NCMP has a considerably low participation level therefore it is likely that some prevalence of childhood obesity figures are underestimated. Indeed there were a much lower number of children measured in the North East than any other region. There may also be an element of selection bias particularly with the year 6s where those who do not participate are those most likely to be obese. These limitations must be addressed in order to improve accuracy of the data.
Smoking is the single greatest preventable cause of illness and premature death in England (GBDH 2011) but 1 in 5 adults remain smokers (Robinson and Bugler 2010). Appendix 5.3 shows that the prevalence of smokers is greater in North Tyneside than England. This data could be affected by responder bias as it is self reported and therefore lead to underestimation of the prevalence of smoking.
Regular heavy drinking has caused a huge increase in liver disease and is currently the fifth biggest cause of death in England (GBDH 2011). Appendix 5.4 illustrates that the rate of alcohol related hospital admissions in North Tyneside is much higher than the national average. It also shows that the rate of alcohol specific hospital stays for those under 18 in the borough is double that of England. In addition 33% of people were found to binge drink in North Tyneside compared with just 20% in England. Clearly harm from alcohol is a huge concern for the borough.
Appendix 5.5 demonstrates the higher rate of under 18 conceptions in North Tyneside than England. Teenage pregnancy is a major social concern as teenage mothers are at increased risk of poverty, poor health and lower educational attainment. They are also considerably less likely to breastfeed and access services (DfE, 2006). The evidence also shows that children born to teenagers have greater chance of experiencing a range of negative outcomes later in life (GBDH, 2008).
Breastfeeding has a huge positive impact on the health of both mother and baby (Wilson et al., 1998; Horta et al., 2007; Quigley et al., 2012). But for the past fifty years the UK has had some of the lowest rates of breastfeeding in the world (WHO, 2010) even though UK policy clearly promotes breastfeeding (GBDH, 2003; NICE, 2008; GBDH, 2012). Breastfeeding is a huge factor in promoting public health and reducing health inequalities as there is increasing recognition that women from lower socio-economic groups have decreased rates of breastfeeding. Indeed appendix 5.6 shows breastfeeding statistics for North Tyneside and it is evident that both breastfeeding initiation and prevalence at 6-8 weeks are significantly decreased in the borough compared with nationally. It would be interesting to compare with ward level data however this is currently unavailable. The data is considered accurate however there remain some limitations. For example the initiation data is susceptible to observer and measurement bias because it based on observation by the midwives or nurses who record the data and interpret whether or not breastfeeding has been initiated. Similarly the number of infants who are totally or partially breastfed at the 6-8 week check is also based on observation so the same bias may arise. The method of data collection also assumes that all infants whose breastfeeding status is unknown are not breastfed resulting in underestimation of its prevalence. Even so it is obvious that low breastfeeding rates are of significant concern for the borough.
Step 3
This section will focus on the identification of a health need for action. The concept of need in relation to HNA can be discussed using the frequently quoted taxonomy of need by Bradshaw (1972) which considers:
Normative need – perceptions of what professionals, experts or commissioners define as need based on available data.
Felt need – perceptions of what the profiled population feel that they need.
Expressed need – demand of the profiled population or felt need turned into action.
Comparative need – the need found by those who receive a service.
When selecting a priority for action HNA should balance these different needs (Thurtle, 2008; Cavanagh and Chadwick 2005). Therefore a significant limitation of this HNA is that only normative need is taken into account as only quantitative research is used. The incorporation of qualitative research would address felt, expressed and comparative need and greatly strengthen this HNA.
It is evident from step 2 that North Tyneside has many health needs. Those of highest priority appear to be mental health, adult and childhood obesity, smoking, alcohol intake, teenage conceptions and breastfeeding. As an aspiring health visitor the priority that if addressed could have the greatest impact and changeability in my community is breastfeeding.
As discussed previously breastfeeding is supported by much evidence for the short and long term health benefits for both mother and baby (UNICEF, 2012). The government recognises the importance of improving initiation and prevalence of breastfeeding and it has been included in the Public Health Outcomes Framework 2013-2015 to encourage the prioritisation of local breastfeeding support. Yet as illustrated in step 2 North Tyneside has extremely poor rates of breastfeeding. There are also more teenage mothers in the borough and wards A and B are areas of extreme deprivation. Research has found that females under 20 demonstrate the lowest incidence of breastfeeding (Infant Feeding Survey, 2010) and that there is a relationship between low rates of breastfeeding and socioeconomic deprivation (Dyson et al., 2010). Current services to promote breastfeeding in North Tyneside include breastfeeding support groups and a breastfeeding coordinator who visits the homes of breastfeeding mothers to provide one on one support. However the support groups are based mainly in more affluent areas and the coordinator has a massive caseload often failing to see many struggling mothers.
Perhaps the supporting and influencing of disadvantaged younger mothers would be easier if the NHS embraced the technology that these people use on a daily basis. The proposed action of this HNA is to use social media to engage with hard to reach mothers to provide breastfeeding information and support. Social media is a modern, convenient and cost effective method of communication. Research from OFCOM (2012) showed that in the past year 50% of adults used the internet to access social networking sites such as Facebook and Twitter. In addition social networking has now overtaken text messaging as the most used method of communication among 16-24 year olds in the last two years. A study for the NHS Confederation (2012) recommended that health organisations should act immediately to avoid falling behind and to use social media to become ‘communitarians’ that is, to engage, listen, respond and support communities. There is a significant lack of literature concerning social media and the NHS but Hawker (2010) suggests that some health organisations are starting to become more digitally connected. Still it is clear that a vast amount of further research in this area must be undertaken.
In conclusion this HNA has identified breastfeeding as an urgent priority for intervention in my community. The proposed action is to take advantage of social media opportunities in order to engage with young or disadvantaged mothers who require breastfeeding support and advice. Indeed the Health Visitor Implementation Plan (2011) called for more innovative approaches to the profession. Incorporating such a change into health visiting practice would of course be a huge challenge that would require planning, funding and training of staff. But now is the time to develop a new service vision and to embrace these opportunities. This will establish health visiting as a central part of community health, working with families to improve health equity and life chances.
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