Patterns of non-communicable disease and injury risk factors in Kenyan adult population: a cluster analysis

Non-communicable diseases (NCDs) cause more deaths globally than all other causes combined together [1, 2]. In 2012, about 38 million people died from NCDs, and the number of deaths is projected to reach 52 million by the year 2030 [1–5]. Cardiovascular diseases (CVDs), cancers, chronic respiratory diseases and diabetes comprise 80% of NCDs. The shift in the global burden of disease from communicable diseases to NCDs is attributed to population growth and the increased average age of the world’s population, combined with the decreasing age-, sex- and cause-specific death rates [6].

NCDs are caused by multiple risk factors which interact in a complex way [7]. Many of the risk factors for NCDs are related to lifestyle and are therefore modifiable. These modifiable risk factors include physical inactivity, low fruit and vegetable intake, unhealthy diet and high cholesterol intake. Physiological risk factors for NCDs include overweight and obesity [7, 8].

Achieving the 25*25 target, which is the reduction of premature mortality from four main NCDs—cardiovascular diseases, chronic respiratory diseases, cancers, and diabetes—by 25% from 2010 levels by 2025 [7] will very much depend on achieving the risk factor target on the key risk factors for NCDs (tobacco and alcohol use, salt intake, obesity, and raised blood pressure and glucose) [7].

As the risk factors for many of the common NCDs are shared, the likelhood of their co-occurrence is high. Thus, studies of single risk factors or prevalence of individual risk factors will miss the complex interaction among the risk factors. For a better understanding of risk profiles of a population, the whole set of risk factors should be considered. Hence, there is a need for approaches that consider common risk factors together to describe risk profile of the population.

This study sought to investigate patterns of NCD risk factors, hence profiles of the Kenyan population based on the clustering of these risk factors. Different segments of the population experience, or are exposed to different risk factors and therefore have different risk profiles, and will require targeted approaches and interventions in mitigating these risk factors for the prevention of NCDs.

The Kenya 2015 STEPS survey was a cross-sectional household survey that was carried out in Kenya from April to June 2015,targeting individuals aged between 18 and 69 years. The survey used the fifth national sample surveys and evaluation programme (NASSEP V) sampling frame from the Kenya National Bureau of Statistics, developed using the enumeration areas generated from the 2009 Kenya population and Housing census. The sample size was determined to be 6000 to allow for national estimates as per sex and residence (rural or urban).

A three stage cluster sample design was used. In the first stage, 200 clusters (100 urban and 100 rural) were selected. In the second stage, a uniform sample of 30 households from the listed households in each cluster, while in the third stage, one individual was randomly selected from all eligible listed household members.

The STEPS survey is the first countrywide population based NCD survey in Kenya and has provided important insights into the burden of NCD and injury risk profiles of both rural and urban populations. Cluster analysis was employed to determine patterns of NCD and injury risks and this segmented the population into five heterogeneous NCD risk clusters and four injury risk clusters. Two of the NCD risk clusters named fat lovers (23%) and harmful users (7%) demonstrated patterns consistent with three known behavioural NCD risk factors- unhealthy diet, tobacco smoking and harmful use of alcohol, and two NCD risk clusters referred to as the obese (26%) and the hypertensive (14%) fell in the physiological NCD risk group. One cluster had no extreme NCD risk. However, in all clusters fruit and vegetables consumption was way below the recommended five servings per day and physical inactivity was not common.

These findings are consistent with literature from rural and urban settings in Kenya highlighting that the burden of NCDs is driven by all the known behavioural and physiological NCD risk factors but not physical inactivity [14–16]. Recent publications from other countries in East Africa have revealed similar findings of dietary habits characterised by poor consumption of fruits and vegetables and a high consumption of fats and carbohydrate amidst adequate physical activity [17, 18], a pattern typical of an early phase of nutrition transition [19].

Our study has identified distinct population groups with prevalent NCD risk factors for targeted interventions. It is interesting to note that the smallest NCD risk cluster represents tobacco consumption, harmful alcohol consumption and excessive salt use. The lower frequency of harmful alcohol use and tobacco smoking may be a reflection of the relative success in the development and implementation of policies addressing the WHO “best buy” interventions for NCD prevention. These policies should ideally include measures to reduce common NCD risk factors such as tobacco use, unhealthy diet, physical inactivity and the harmful use of alcohol – that would deliver the greatest benefit in reducing population level risks in a cost-effective manner [20].

A recent NCD prevention policy review for Kenya revealed a fairly better formulated tobacco control policy addressing all WHO “best buy” interventions such as tax increases, bans on tobacco advertising, and warnings on the dangers of tobacco; a weak alcoholic drinks control act (ADCA) addressing some of the “best buy” interventions including taxation and restriction to alcohol access; and a deficient food and nutrition policy not adequately addressing “best buy” interventions for unhealthy diet [21]. Although physical activity policies are not given priority, no cluster emerged with physical inactivity as the main risk factor because most people are active through work and travel other than recreation [22].

For injuries, 62% of the population was classified into two high risk injury clusters referred to as the defiant (36%) for not using seatbelts and jaywalkers (26%) because of inappropriate road crossing. The remaining two clusters which were low risk included helmet users (33%) and the compliant (5%) who used belts consistently and crossed roads appropriately. A recent survey conducted in five regional referral hospitals in Kenya showed that road traffic accidents were the most common injury among patients admitted in the emergency department and this is consistent with the clustering of risk factors at population level in this study [23]. Two other studies in Kenya have also revealed that among road traffic injuries, passengers in public transport vehicles followed by pedestrians were most involved [24, 25]. These accidents could have occurred because of non-compliance with belt use or jaywalking (inappropriate road crossing) reported in our study.

Identification of demographic characteristics associated with NCD risk clusters and the injury risk clusters is essential for programming successful primary preventive measures. We therefore profiled the NCD and injury risk clusters to inform differentiated prevention and care services. The factors that stood out as independent predictors of NCD risk clusters were; age, gender, education, wealth and living arrangements. Hypertension, harmful use of alcohol or salt and tobacco smoking, and obesity increased with age while fat consumption reduced with age. Men were more likely to be hypertensive, harmful users and fat lovers, while women were more likely to be obese.

The gender and age association with NCD risk has been well established before in Kenya [14]. An interesting finding in relation to age is the high consumption of fats by younger people. This may be explained by growing westernization of diet that young people are quickly adapting to and it is often observed in the early phase of nutrition transition characterized by a high consumption of fats, sweeteners and inadequate fruit intake as in the fat lovers’ cluster that was dominated by young people this study [19]. Shopping in supermarkets in Kenya is increasing and making in-roads beyond the richer consumers to lower-income groups in smaller towns with up to 56% of the customers in supermarkets reported to be from low income groups [26]. This has implications on the food choices of young people.

Education has an additional benefit in reducing NCD risk as illustrated in our study by the increase in the proportion of those in the hopeful cluster with education, however obesity increased with education. Likewise, wealth was associated with a reduction in NCD risk due to less harmful use of alcohol, salt and reduced tobacco smoking, less fat consumption but obesity also increased with wealth. Education influences health behaviors and attitudes and consequently, lifestyle through exposure to relevant health information and comprehension of the information [27]. The increase in obesity by education and wealth may be a result of increased exposure to advertisements by the food industry that has the potential to change food choices among the educated and wealthy who can afford to buy these foods.

It was also interesting to note that when three or more people shared a household, they were less likely to engage in high consumption of salt, fat, harmful consumption of alcohol and tobacco smoking. This may be largely attributed to a social audit by other household members checking on each other’s lifestyle and eating habits. For the same reason, the married are less likely to smoke or consume alcohol. Personal social networks have been reported to be associated with compliance to good health promoting behaviors [28].

Surprisingly no difference in NCD risk profile was observed between rural and urban residents contrary to studies showing that urbanicity is associated several NCD risk factors in India and Philippines [29, 30]. A recent study in rural Uganda also showed that increasing urbanicity was associated with an increase in lifestyle risk factors particularly physical inactivity, low fruit and vegetable consumption and high body mass index [31]. The common feature among these studies was the use of a multi-component scale to accurately define urbanicity even among villages considered to be rural and they found marked variation in levels of urbanicity across the villages, largely attributable to differences in economic activity, civil infrastructure, and availability of educational and healthcare services. Studies that loosely defined villages as urban or rural based on demarcation by national statistical bureaus as in this study have found no difference in NCD risk profiles among rural and urban populations, especially for hypertension [17, 18]. This suggests that even within rural populations social inequalities may exist which are often missed by the statistical bureaus because their classification of communities into rural and urban centers may not capture all the urbanicity scale components.

Regarding injuries, age, education and wealth improved compliant behaviors such as use of belts and helmets, and reduced defiant behaviors meaning as people get older or more educated or wealthier they become more responsible and tend to follow injury risk prevention measures. Education mediates comprehension of information such as written traffic rules or through an early exposure to a teaching curriculum in schools that includes traffic rules. It is worthwhile to mention that on the contrary jaywalking did not reduce with education, age, or wealth, but was instead seen to increase. A systematic review of literature on road traffic injuries in Kenya revealed that road traffic injuries have increased by four fold in three decades and up to 75% of the causalities are young adults aged 18–44 years, 80% of deaths are accounted for by pedestrians and passengers [25]. The traffic rules and enforcement seem to pay little attention to pedestrians. Most times the pedestrians break traffic rules and are not apprehended but instead treated as the victim of accidents. Public awareness about road safety especially for passengers and pedestrians is limited, thus the high risk of injuries among these groups.

The findings of this study have important implication for policy, practice and research. The identified clusters can guide where NCD policies and strategies need to focus. The resulting clusters would also be useful in the planning, implementation and evaluation of segmented approach to the prevention and control of NCDs. Similarly, future research projects could use these clusters to further explore the various characteristics associated with NCD profiles of the population of Kenya.

In conclusion, this nationally representative survey reveals interesting patterns of NCD and injury risk clusters generated through K-medians cluster analysis which is a popular form of cluster analysis due to its simplicity of implementation, ability to partition large data sets, and ease in interpretation of its cluster solution and tolerance of outliers [32, 33]. This analysis has provided a holistic view of patterns of risk at population level for decision-makers to target populations with appropriate interventions. The main population groups to be prioritized for targeted NCD prevention interventions include; those with unhealthy diet (young fat lovers), the obese and hypertensive (older, wealthy and educated, men) and harmful users of alcohol, salt and tobacco (unmarried, older, living alone). When designing NCD preventive interventions rural populations should also be considered. Since Kenya is in the early stage of epidemiological transition, there is a window of opportunity to implement primordial NCD prevention measures to curtail the growing NCD epidemic. There is need for a multi-sectoral action to strengthen policies and implementation of programs with a focus on tackling unhealthy diet, prevention and management of hypertension and obesity. Strengthening the existing policies for tobacco and alcohol control to further reduce the current frequency of consumption and the experiences of developing these policies should inform the design of robust nutrition policies.

For injuries, there is need to design targeted messaging for road safety measures particularly for young, poor and uneducated people. Clear guidelines on safety measures for pedestrians and general public awareness on traffic guidelines for pedestrians are needed. Lastly, enhanced enforcement of traffic laws for pedestrians and passengers in public transport will be crucial in reducing road traffic injuries.