Neighborhood clustering of non-communicable diseases: results from a community-based study in Northern Tanzania

Non-communicable diseases (NCDs) are a growing global epidemic that disproportionately affect low- and middle-income countries (LMICs) [1]. In sub-Saharan Africa, they are now one of the leading causes of death among adults, and in order to begin to address this burden, high quality community-based epidemiological studies from the region are urgently needed [2‐5]. Additionally, outcomes-related research either through observational cohort studies or randomized-controlled trials (RCTs) will be an important component of the public health response moving forward [6].

Nonetheless, many challenges exist in carrying out these studies. Poor infrastructure and a lack of resources in many of the sub-Saharan African countries limit rigorous studies, in part due to inadequate methodological capabilities. Physical addresses, phonebooks, and reliable census data are often unavailable for many populations in the region which means that representative community-based samples often require labor-intensive, prospective household surveys. In this context, cluster-designed sampling methods offer an efficient, practical, and cost-effective means of obtaining a representative sample from the population of interest [7, 8].

However, studies that use cluster sampling methods require extra considerations in their design and analyses, and cluster-designed studies in sub-Saharan Africa continue to inadequately address many of these considerations [9]. Because study participants or households are drawn from clusters, which serve as the primary sampling unit, they can demonstrate more homogeneity than would otherwise be expected from a simple, random sample. For NCDs, similar lifestyles, environmental risks, economic stress, and genetic backgrounds may all increase homogeneity within clusters, and consequently, this increased homogeneity within clusters, or intra-cluster correlation (ICC), can significantly affect the precision of population parameter estimates [10, 11]. The ICC is typically quantified by the ICC coefficient, and although the ICC coefficient can be calculated post hoc during the analysis stage, this method may not be preferable or ethical in many sub-Saharan African settings due to cost and limited resources. Accounting for the design effect beforehand allows for more accurate estimations of sample size, budget requirements, and logistical needs; however, for NCD-related research, few ICCs have been reported in the region [9, 10].

The Comprehensive Kidney Disease Assessment for Risk Factors, epidemiology, Knowledge, and Attitudes (CKD-AFRiKA) study is an ongoing project in northern Tanzania with the goal of understanding and addressing the health burden of chronic kidney disease (CKD) and CKD-related NCDs. As part of the study, we conducted a cluster-designed, community-based epidemiologic survey. In the design stage, we were unable to identify any comparable ICCs for health outcomes related to CKD or CKD-related NCDs, and we had to extrapolate them from data derived from high-income settings. To fill this gap, we report here the observed intra-cluster correlations for multiple NCD-related factors from a community-based, sub-Saharan African setting [12].

Between January 2014 and June 2014, we enrolled 481 participants from 346 households from a total of 37 sampling areas (30 urban and 7 rural) within 29 neighborhoods (23 Urban and 6 rural) (Table 1). These 29 neighborhoods were located within 18 wards (13 urban and 5 rural). The mean age was 46.9 years (SD 15.1). The household non-response rate was 15.0 %. Men (p < 0.001) and adults 18–39 years old (p = 0.001) were more likely to be non-responders. The median neighborhood cluster size was 13.0 participants (IQR 9–21), and neighborhood cluster size ranged from 6 to 49 participants (Appendix 2).

The majority of participants lived in an urban setting (n = 370; 77.0 %), were women (n = 358; 74.4 %), ethnically Chagga (n = 288; 59.9 %), and had obtained a primary school level of education (n = 349; 72.6 %), and most participants were occupied as farmers or daily wage-earners (n = 199; 41.4 %) (Table 1). Many participants reported an ongoing use of alcohol (n = 198; 41.2 %) and many reported a history of malaria (n = 427; 88.8 %), diabetes (n = 61; 12.7 %), or hypertension (n = 134; 28.0 %). Few reported a history of stroke, heart disease, tuberculosis, hepatitis, HIV/AIDS, COPD/asthma, cancer or kidney disease. From our assessment of NCD-related health outcomes, 149 participants (31.0 %) had hypertension, 138 (28.7 %) were obese, 57 (11.9 %) had CKD, and 129 (26.8 %) had glucose impairment of which 84 (17.5 %) had pre-diabetes and 45 (9.4 %) had diabetes.

Clustering varied across neighborhoods and differed by urban or rural setting. Overall ICC coefficients ranged from 0.00 to 0.125 with a mean value of 0.30 (SD 0.033) (Table 2). In the rural setting, ICC coefficients ranged from 0.000 to 0.331, and in the urban setting, ICC coefficients ranged from 0.000 to 0.109. Ongoing alcohol use exhibited the strongest neighborhood clustering (ρ = 0.125), which was most prominent in rural neighborhoods (ρ = 0.331). Ongoing tobacco use exhibited modest neighborhood clustering in both rural (ρ = 0.022) and urban settings (ρ = 0.042). Neighborhood clustering of self-reported medical histories was most significant for diabetes (ρ = 0.045), hypertension (ρ = 0.100), HIV (ρ = 0.054), and CKD (ρ = 0.020).

Among the NCDs, neighborhood clustering varied with ρ ranging from 0.000 to 0.075. Hypertension (ρ = 0.075) exhibited the strongest clustering within neighborhoods followed by CKD (ρ = 0.440), obesity (ρ = 0.040), and glucose impairment (ρ = 0.039) (Fig. 1). Among those with glucose impairment, neighborhood clustering was more significant for pre-diabetes (ρ = 0.031) than for diabetes (ρ = 0.000). Neighborhood clustering for physical and laboratory measurements paralleled the NCD outcomes. Both systolic (ρ = 0.064) and diastolic (ρ = 0.056) blood pressures exhibited strong neighborhood clustering. Clustering for albuminuria was modest (ρ = 0.038), but it accounted for most of the neighborhood clustering observed for CKD when compared to serum creatinine or eGFR measurements. Similar to obesity and glucose impairment, clustering of BMI was more significant in urban neighborhoods (ρ = 0.049) while clustering of HbA1C was more significant in rural neighborhoods (ρ = 0.025).

In northern Tanzania, prevalence of NCDs, including hypertension, CKD, obesity, and glucose impairment, exhibited clustering by neighborhood. This clustering varied across urban and rural settings, and for NCD prevalence, it was most significant for hypertension and CKD. Based on the ICC coefficients that we observed, cluster-designed studies examining NCDs in the region should account for the design effect on precision or variance caused by clustering. In a region where the NCD burden is quickly growing, these results will be valuable in designing such studies, including cluster RCTs [5, 12, 23].

The urban and rural differences in neighborhood clustering of NCDs may highlight important environmental and lifestyle risk factors for the development of hypertension, glucose impairment, obesity, and CKD. The neighborhood clustering for hypertension and glucose impairment was most pronounced in the rural settings where families tend to remain more environmentally clustered, share meals, and work in similar agricultural jobs which may all contribute to the development of such NCDs that are known to be highly associated with lifestyle [24‐26]. On the other hand, obesity and CKD were most clustered in the urban neighborhoods. For obesity, this urban clustering highlights the importance that urban lifestyles, which may be clustered within neighborhoods on the basis of socioeconomic status, transportation, or occupation, play in the development of obesity. In the context of CKD, living in an urban setting has been shown to be a significant risk factor, yet specific etiologies associated with the urban environment remain unknown [12]. The clustering of CKD within urban neighborhoods that we observed may be important in highlighting causes of CKD, and it further stresses that public health efforts targeting CKD must take a broad approach that includes urban planning with sanitation improvement, safe drinking water, pollution reduction, and infection control.

Among all measured variables, ongoing alcohol use, hypertension, a self-reported history of hypertension, and a self-reported history of HIV were most highly correlated among cluster-sampled individuals, and the latter two variables may reflect an increased awareness and/or prevalence of these conditions within certain neighborhoods. In northern Tanzania, alcohol is commonly homemade and shared among households which may in part explain the significant clustering that we observed.

To our knowledge, this is the first community-based, household-level survey to report on the neighborhood clustering of NCDs in East Africa. As such, these are the first ICC coefficients reported for hypertension, CKD, obesity, and glucose impairment in the region, and compared to reports of ICC coefficients in high-income countries there are significant differences in several of the physical and laboratory variables [27‐29]. Because we also measured clustering in both an urban and rural settings we were able to demonstrate important differences which may help inform future studies examining the demographic transition of NCDs in sub-Saharan Africa where rapid urbanization is occurring [30].

Despite these strengths, we also noted a few limitations. Caution must be taken when applying these estimates to other populations and settings. Although the paucity of data currently available for NCD-related measurements and outcomes may make these results useful to researchers more broadly across the region, differences in prevalence and risk factors for NCDs, particularly those that are geographic or environmental-based, mean that even NCDs can cluster at different rates within villages, neighborhoods, or households. Additionally, although we used sample-balancing approaches to address potential non-response bias, the effect of participant non-response upon these estimates is not fully known. Finally, some results, such as self-reported medical history, rely upon the subjective response of individual participants, and as such, they may be prone to recall or response bias.

In conclusion, we have reported on the observed neighborhood clustering for several NCDs from a community-based study in northern Tanzania. The neighborhood clustering, which varied by urban or rural setting, was substantial enough to contribute to a design effect for NCD outcomes including hypertension, CKD, obesity, and glucose impairment, and it may also highlight NCD risk factors that vary by setting. These results may help inform the design of future community-based studies or randomized controlled trials examining NCDs in the region particularly those that use cluster-sampling methods.