The objective of this study was to investigate the role of socio-economic and environmental factors in the pathogenesis of rheumatic heart disease in Ugandan patients.

This was a case control study in which rheumatic heart disease cases and normal controls aged 5–60 years were recruited and investigated for socioeconomic and environmental risk factors such as income status, employment status, distance from the nearest health centre, number of people per house and space area per person.

486 participants (243 cases and 243 controls) took part in the study. Average age was 32.37+/−14.6 years for cases and 35.75+/−12.6 years for controls. At univariate level, Cases tended to be more overcrowded than controls; 8.0+/−3.0 versus 6.0+/−3.0 persons per house. Controls were better spaced at 25.2 square feet versus 16.9 for cases. More controls than cases were employed; 45.3% versus 21.1%. Controls lived closer to health centers than the cases; 4.8+/−3.8 versus 3.3+/−12.9 kilometers. At multivariate level, the odds of rheumatic heart disease was 1.7 times higher for unemployment status (OR=1.7, 95% CI=1.05–8.19) and 1.3 times higher for overcrowding (OR=1.35, 95% CI=1.1–1.56). There was interaction between overcrowding and longer distance from the nearest health centre (OR=1.20, 95% CI=1.05–1.42).

This was an unmatched case control study in which 243 cases and 243 controls were recruited using systematic random sampling in Mulago National Referral Hospital.

Cases were subjects aged 5 to 60 years, diagnosed with RHD based on history of acute rheumatic fever, clinical examination finding of a murmur and standard echocardiographic criteria. Only cases living at the current location for the last five years were considered. Cases with possible and probable RHD were excluded from the study. Comparable eligibility criteria for controls included subjects aged 5 to 60 years with no previous history of RHD and had normal echocardiograms and living at the current location for the last five years were considered. Controls with raised antitreptolysin O titres on laboratory evaluation were excluded.

Echocardiography was done according to standard criteria [18] Briefly, Standard transthoracic echocardiography (GE, Vivid 8, Chicago, USA) was performed where 2D, M-mode, colour and doppler modalities were done with images taken from parasternal long axis, parasternal short axis, apical four chamber, apical five chamber and subcostal views. Mitral stenosis was diagnosed by two-dimensional echocardiographic and hemodynamic evidence, as well as characteristic mitral valve morphology, including thickened mitral leaflets, cusps, commissures, anterior mitral leaflet doming in the long-axis view, anterior motion of the posterior mitral leaflet on M-mode echocardiography. Mitral valve area was graded based on planimetry and pressure half time methods. Mitral stenosis was graded as mild for valve area between 1.6–2.0 cm2, moderate for valve area 1.1–1.5 cm2, and severe for valve area <1.0 cm2. Aortic stenosis was diagnosed based on the presence of commissural fusion of the aortic leaflets, possible increased echogenicity along the leaflet edges, and systolic doming of the aortic leaflets. Valve area was assessed using planimetry and classified as mild (vale area >1.5 cm2), moderate (valve area 1–1.5 cm2) and severe if valve area was <1 cm2. Mitral and aortic regurgitation were assessed using quantitative methods.

The following parameters were documented; age, gender, level of education, monthly earning (in Uganda Shillings and later converted to United States dollars), number of people per house, distance from the nearest health center (kilometers) and space area per person (square feet).

For standardization purposes, we used the modified Prasad classification of social status which divides individuals into 4 social strata including lower class (below a dollar a day) middle class, middle-high and highest class. [19] The Prasad classification was adapted to local conditions by converting the Indian Rupees to Uganda Shillings and to United States dollars. For study participants below 18 years, we used their mothers’ income as an estimate.

Distance from the nearest health centre was determined to assess access to health care. Previous studies in developing countries have showed that distance of 5 kilometers or less to a health unit predicted better access to health care and therefore uptake of prevention programs such as vaccination. [21], [22]

Data was double-entered and stored in EPI data version 3.0. (EpiData Association, Odense M, Denmark) Analysis was done using STATA 10.0 statistical package. (Stata Corporation, College Station, TX, USA).

With rheumatic heart disease status as an outcome (dependent) variable, the following were analyzed as independent variables; age (years), income level (Uganda Shillings/United States dollars), education status, number of people per house, space area per person (square feet), distance from the nearest health unit (kilometers) and employment status (yes/no).

A previous study in Soweto, South Africa found a higher prevalence of RHD among suburban dwellers, usually the lower socioeconomic class, who also tended to live in overcrowded residences, while a survey in Congo Kinshasa, found that children from overcrowded suburban families had a higher prevalence of RHD irrespective of income and social classes of their parents [4], [24] More recently, Jaine et al, in an ecological study in New Zealand assessed 1249 cases of ARF diagnosed between 1996 and 2005 found a positive and significant association between ARF and overcrowding, although this study was limited by its design [25], (it was not case controlled making it impossible to draw conclusions on their findings). Our study highlights the importance of overcrowding in the pathogenesis of RHD, especially in the developing world where rapid urbanization and poor housing predisposes suburban dwellers to overcrowding and consequently ARF and RHD. [26] In addition, the reemergence of RHD in the intermountain region of the United States [27], [28] and a high prevalence in Italian army recruits, both developed nations in the 1980s, had strong association with overcrowding, rather than poverty. Societies where living conditions have improved, have recorded substantial decline in the prevalence of RHD. For instance, the recent decline in RHD in China and other emerging economies is apparently linked to economic development which translated into improved living conditions. However, it is worth noting that the decline in RHD was also associated with a total change in house designs. [29] City authorities moved away from overcrowded communal accommodation to large, spacious accommodation.

To the best of our knowledge, this is the first study assessing overcrowding and its interaction with distance from the nearest health center as a risk factor for RHD. Findings from our study suggest that the effect of overcrowding on the risk of acquiring ARF/RHD increased with every kilometer increase from the nearest health center, although on its own, longer distance travelled from a health center appeared to be protective (OR=0.7, 95% CI=0.6–0.87). These observations suggest that irrespective of distance from the nearest health center, an individual’s risk of acquiring RHD starts to increase when their household becomes overcrowded. Previous studies in Kenya, Pakistan and Yemen showed that for populations at risk, shorter distance from the nearest health center correlated with better access to vaccination services, control of malaria and other infectious diseases. [30], [31], [32]. For overcrowded households, at high risk of streptococcal pharyngitis, distance to a health center would determine access to prophylactic medication such as penicillin. The delay in accessing care as a result of longer distances from the health facility further raises the issue of ability of RHD cases to comply and go for frequent benzathine penicillin injections, an important component of treatment given to patients with RHD.

Although RHD has generally been associated with poverty [26], [33], [34] a direct link between RHD and poverty has not been found. In our, study we found no significant difference in income between cases and controls at the multivariate level. This absence of a difference could be due to the fact that in Uganda, most respondents associate income to earning from formal employment. And yet majority of people are in the informal sector, making it difficult to ascertain their actual level of income. Furthermore, in developing countries, income is generally low across the board, and a threshold might not have been reached where difference in social classes leads to a difference in disease risk factors. This is consistent with findings by Steer et al, who in a systemic review of RHD prevalence in developing countries and the role of environmental factors, assessed 24 studies conducted between 1976 and 1999, and concluded that a threshold level where higher socio-economic status is associated with reduced prevalence of RHD has not been reached in developing countries. Therefore, differences in prevalence between socio-economic groups in one area cannot be detected. [11] Dobson et al in a well designed case controlled study of socioeconomic and environmental risk factors for RHD in 80 cases and 80 controls in Fiji, found a trend toward increased risk of RHD in association with poor-quality housing and lower socioeconomic status but only maternal unemployment reached statistical significance. This study was however limited by a small sample size. [35]

First, we were not able to visit all the households of the study participants to ascertain the state of overcrowding, because of time and resources limitation. Secondly, we determined overcrowding based on residence in an area for the last 5 years, it would have been beneficial to know housing status of an individual for the past 15 years as the disease pathogenesis is known to take five to fifteen years.

This study demonstrates that in Uganda, there is increased risk for RHD in association with overcrowding and unemployment. The effect of overcrowding on the risk of RHD seems to increase with every kilometer increase from a health center.