Measuring social vulnerability in communities and its association with leprosy burden through spatial intelligence in central West Brazil to guide strategic actions

Abstract

Background

It is known that leprosy is a socially determined disease, but most studies using spatial analysis have not considered the vulnerabilities present in these territories.

Objectives

To measure the association between social vulnerability and the burden of leprosy in the urban space of Cuiabá.

Methods

Ecological study, carried out in Cuiabá, Brazil. Diagnosed cases of leprosy were surveyed through the Notifiable Diseases Information System, from 2008 to 2018. The spatial scan statistics technique of leprosy cases per each Human Development Unit was applied. Social vulnerability was measured based on the Municipal Human Development Index (MHDI), education level and average per capita income. For the spatial correlation between vulnerability and leprosy, Global and local bivariate Moran’s index was used.

Results

8389 cases of leprosy were georeferenced, the majority being male (58%), 30% of cases were not evaluated for degree of physical disability. One of the spatial scan clusters had a relative risk (RR) of 6.93 (95% CI 6.49–7.4), and another had 1360 cases with RR 1.71 (95% CI 1.62–1.82). The bivariate global autocorrelation of Moran’s index for MHDI was 0.579, observing 1 High-High in the East region and 1 in South, for education the index was 0.429, 2 High-High in the East and 1 in the South, and 0.145 for average per capita income, 1 High-High in the East.

Conclusion

There was a spatial association between leprosy cases and territories with low MHDI, having a percentage of the population without schooling and/or with low income. The study advances knowledge by presenting characteristics of territories most affected by leprosy, verifying the spatial correlation of the disease with the recurrent socioeconomic characteristics in these territories.

Text box 1. Contributions to the literature
• The identification of places with a high number of leprosy cases helps in the development of strategies to minimize and control the transmission of leprosy.
• Social and health disparities in urban contexts provide information for more targeted and effective public policies.
• The identification of places with a high number of leprosy cases helps in the formulation of public policies to combat leprosy.

Introduction

Leprosy is a chronic infectious disease with a slow evolution characterized by high infectivity and low pathogenicity, causing severe neuropathies, high degrees of physical disability (DPD) and deformities [1, 2].

The interruption of transmission and elimination of the disease are at the heart of the “Global Leprosy Strategy 2021–2030 - Towards zero leprosy“ [3], and the primary actions for disease control should be directed towards increasing leprosy prevention, active case detection, controlling the disease and its complications, preventing new disabilities, combating stigma and ensuring that human rights are respected.

According to the United Nations [4], the reduction of leprosy diagnoses and the absence of disabilities and deformities are among the main strategies and commitments of the organization in accordance with the Sustainable Development Goals (SDGs) of the 2030 Agenda, for the 20th century.

Globally, 140,594 new cases were registered in 2021, translating into a detection rate of 17.83 cases per million people. Brazil, India and Indonesia continue to report more than 10,000 new cases each, being the three countries that most register the disease in the world [1].

In Brazil, with 71.44 new cases per 100,000 inhabitants in 2020, Mato Grosso had the highest overall detection rate, while its capital, Cuiabá, had a rate of 29.78 new cases per 100,000 inhabitants [5].

Considering the international and national epidemiological panorama of leprosy, investigating the epidemiology and distribution of the disease in territories can result in the expansion of control measures and interventions aimed at minimizing damage to those affected [2].

Since leprosy is a treatable disease with an essentially clinical diagnosis, it is necessary to take into account that the fact of the absence of a precise diagnosis, as well as its correct treatment, has as a corollary a social group that is in a vulnerable situation, which affects the understanding and behavior of those affected by the disease [6].

Studies that have employed spatial analysis are strategic for health surveillance monitoring, risk assessment, situational diagnosis and vulnerability identification, and as a result, they are increasingly important as tools for identifying transmission points of some diseases, as well as for examining the spatial heterogeneity of the distribution of health resources [7, 8].

As Brazil is a country of continental proportion, leprosy control is a major challenge. Among the regions of the country, the Midwest macro-region should be highlighted, which is one of the most problematic regions in terms of the disease burden [5]. A study carried out in the region showed that in the trienniums 2001–2003 and 2010–2012 there was a reduction in the disease, but there are geographic areas where leprosy control has not advanced and is far from being eliminated [9].

Determining the prevalence of leprosy and its spatial distribution has the effect of generating evidence to guide strategic actions in vulnerable areas. In the literature, there are studies that address the relationship between leprosy and space, its “spatialization” [10,11,12], however, most of the studies showed spatialization without showing connection with the vulnerabilities present in these territories.

In view of this, the importance and relevance provided by spatial intelligence to identify regions with the highest transmission of the disease and verify its relationship with vulnerabilities guided the present investigation. In the literature, social vulnerability is defined as a situation in which people are characterized by a condition of multidimensional deprivation that encompasses multiple aspects of life and exposes the population to different risks and dangers produced by natural, environmental, socioeconomic and epidemic factors [13, 14].

Given the above, the objective of the study is to measure the association of social vulnerability and leprosy burden in the urban space of Cuiabá - MT.

Methods

Study design and location

This is an ecological study [15], carried out in the city of Cuiabá, capital of the state of Mato Grosso, located in the Midwest Region of the country of Brazil, which has an area of 3,266,538 km2 and an estimated population in 2021 of 623,614,607,153 people [16].

The municipality has a high percentage of people with no schooling (4.56% for women and 4.79% for men), a life expectancy at birth of 75 years, Human Development Index (HDI) of 0.785 and GINI index of 0.59. It should be noted that only 53.52% of its territory has a sewage system and 98.12% water supply [5, 17]. The city has 63 Basic Health Units (UBS), four Polyclinics and two Emergency Care Units (UPA), distributed in four administrative regions: North, South, East and West [18, 19].

In Cuiabá there are reference services for procedures of medium and high complexity in relation to leprosy, namely: CERMAC (Sanitation Dermatology Services), Hospital Metropolitano (Hospital care for Leprosy surgery services), Julio Muller University Hospital (Hospital care for Ophthalmology reference), CRIDAC Center Specialized in Rehabilitation (CER III) and Regional Outpatient and Hospital Reference [20, 21].

Reference population, information sources and variables under study

The study population consisted of people diagnosed with leprosy, living in the urban area of Cuiabá and registered in the Notifiable Diseases Information System (SINAN), from 2008 to 2018. Data were obtained after approval by the Health Surveillance Service (SVS) from Cuiabá.

The variables selected from SINAN were: date of notification, date of diagnosis, date of birth, sex, education, operational classification, clinical form, DPD assessment at diagnosis and address of residence.

For the social vulnerability data, the Municipal Human Development Index (MHDI), average education level, and average per capita income were considered. These indicators were obtained from the Atlas of Human Development in Brazil [22] for all 96 Human Development Units (HDU) [4].

It is noteworthy that the HDUs are portions of homogeneous areas/units of analysis used to characterize the socioeconomic and development aspects of the Metropolitan Regions of Brazil, and it was the ecological unit of analysis used in this research, which provide a summarized portrait of intra-municipal spaces in Brazil using comparative data from the 2000 and 2010 censuses [4].

Data analysis

A descriptive analysis of the variables under investigation was carried out, with calculation of absolute and relative frequencies. For this analysis, the RStudio 4.2.0 software was used.

In the spatial analysis stage, initially, the leprosy cases were georeferenced from the geographic coordinates (latitude and longitude) of the residential addresses of the notifications, using the QGIS 3.14 software, with the elaboration of a file containing the locations of each case, represented by points. For this stage, cases whose addresses were located in rural areas, those without addresses and/or incomplete were excluded. The exclusion of cases from rural areas was due to the imprecise location of geographic coordinates in these areas.

To identify spatial clusters of leprosy occurrence in Cuiabá, the spatial scan statistics technique was applied [23]. Given the heterogeneous distribution of leprosy cases and the rarity of events relative to the population, the discrete Poisson model was used with the following parameters: Gini clusters, no geographic overlap of clusters, a maximum cluster size of 50% of the exposed population, circular-shaped clusters, and 999 Monte Carlo replications to assess the statistical significance of the identified clusters.

It is important to note that using a maximum cluster size of 50% of the exposed population is a common practice that seeks to balance sensitivity (ability to detect true clusters) and specificity (avoiding the detection of false clusters). Smaller proportions may not capture larger, significant clusters, while larger proportions may include unrelated areas, diluting the significance [24]. This value is often recommended in spatial scanning studies because it allows the detection of clusters of varying sizes without losing the ability to identify smaller, more localized clusters. Furthermore, a 50% threshold provides the flexibility to detect both small and large clusters, providing a comprehensive view of the spatial distribution of the disease. This is particularly useful in areas with significant variations in population density [24].

Regarding the choice of cluster shape, the circular shape ensures that the scan is uniform in all directions from a central point, which is useful for identifying clusters that may not have a specific orientation [24]. This is especially relevant in contexts where the spatial distribution of the disease does not follow a clear directional pattern. Furthermore, the use of circular clusters is a common practice in many spatial scanning studies, which facilitates the comparison of results with other studies and the replication of the methodology.

Furthermore, the spatial scanning technique was applied, controlling for the occurrence of leprosy cases based on the size of the HDU population. The analysis aimed to detect clusters of high and low relative risks (RR) with statistical significance (p < 0.05), and the confidence interval for each RR was subsequently calculated. The SaTScan software version 9.6 (https://www.satscan.org/) was used for this analysis.

To assess social vulnerability, it resorted to information contained in the Human Development Atlas, considering the HDU as an observation area [25]. Such variables comprise the entire complexity of the construct by Davino et al. [13].

To verify the spatial association between the leprosy detection rates and the social vulnerability variables per HDU in Cuiabá, the Global Moran I statistic, as well as the bivariate local Moran I statistic, was performed using the GeoDa software with Queen contiguity [26].

Given that leprosy is distributed heterogeneously and randomly in space, the local bivariate Moran’s index (I) statistic was also used and, according to this type of analysis, a local indicator of spatial autocorrelation (LISA) coefficient obtained in a multivariate context can be used [27]. Then, the bivariate local Moran’s I was calculated according to the equation:

$$\:{Ii}^{z1z2}={Z}_{li}W{Z}_{2i},$$

where WZ2i is the spatial lag of the standardized variable, Z2i only the neighbors of observation i, defined according to a matrix of spatial weights and included in the calculation.

It is important to mention that the calculation of the Moran Index was performed based on the leprosy rate, which is a standardized measure for the population of each HDU, and the social vulnerability variables. This means that the population size was considered when calculating the leprosy rate, allowing a more accurate analysis of the spatial autocorrelation of the disease. Using the rate, instead of absolute numbers, ensures that population variations between the different HDUs are taken into account, providing a more robust and reliable detection of the spatial patterns of leprosy.

This statistic indicates the degree of linear association, which can be positive or negative, between the value for a variable in a given region (i) and the mean of another variable in neighboring locations (j). Thus, it is possible to map the statistically significant values of the measurement probability, generating the so-called bivariate significance map of the local Moran [28].

Results

Table 1 presents the results of the clinical and epidemiological profile of the cases after excluding those who lived in rural areas and had incomplete or no address information. For the period from 2008 to 2018, 8,389 cases of leprosy were found in Cuiabá. There was a predominance of males (58%) and the age group of 15 to 59 years (75%). Regarding clinical characteristics, the majority of cases had incomplete primary education (44%), were diagnosed with multibacillary leprosy (73%), had the borderline clinical form (57%), and presented Degree 0 of disability at the time of diagnosis (40%) (Table 1).

Table 1 Clinical and social epidemiological characteristics of georeferenced cases diagnosed with leprosy, in an endemic municipality in Midwest Brazil (2008–2018)

Of the total number of cases notified during the study period (9739 cases), For the georeferencing of cases, from the total notified in Cuiabá in the period from 2008 to 2018 (9739 cases), residents of the rural area 885 (9%) and those with blank or incomplete addresses 21 (0.2%) were excluded. After georeferencing, 8389 cases were identified (86% of georeferencing) (Fig. 1).

Fig. 1

Cases of leprosy distributed by HDUs in Cuiabá-MT from 2008 to 2018

As for the results of the spatial scan (Figs. 2), 7 spatial clusters of leprosy occurrence were identified in Cuiabá with statistical significance, across all 96 HDUs of the municipality. Of the identified clusters, cluster 3 (RR:1.71; CI95%: 1.62–1.82) stands out with the highest number of cases (1,360 cases) with a population of 55,054 inhabitants, covering 13 UHDs. The highest RR was identified in cluster 1 (RR = 6.93; CI95%: − 6.49–7.4), with a population of 934 inhabitants, comprising one HDU.

In Fig. 2, the results of the spatial scan application are shown. Clusters 1 and 2 are located in the West and East regions, respectively, and each consists of only one HDU with the highest RR for leprosy. This is followed by a large cluster encompassing HDUs in the West and North regions (cluster 3). Finally, cluster 7, the one with the lowest RR, is located in the East and South regions of the city.

Fig. 2

Purely spatial scan of leprosy cases in, in Cuiabá-MT 2008–2018

With regards to the spatial correlation between the occurrence of leprosy and vulnerabilities, the results of the Moran Global Index are presented in Table 2. We observed a positive correlation between the MHDI, average per capita income and Schooling.

Table 2 Global bivariate autocorrelation of social vulnerability with the burden of leprosy in communities, Cuiabá 2008–2018

Figure 3 shows the correlations between leprosy detection rate and social vulnerabilities (education - Fig. 3A; average income - Fig. 3B and MHDI - Fig. 3C) identified using local Moran’s I. A similar pattern was identified for the three indicators analyzed, with the East region presenting HDUs with a high-high pattern, meaning that higher levels of education, average income and MHDI were associated with higher leprosy detection rates, while the South region presented HDUs with a low-low pattern (lower levels of education, average income and MHDI were associated with lower leprosy detection rates).

Fig. 3

Bivariate Cluster Map of the Spatial Correlation of Social Vulnerability and Leprosy Burden, Cuiabá 2008–2018

(A) Leprosy rate with education: Dark blue (1) Low-Low, dark red (3) High-high, (B) Leprosy rate with average per capita income: Light blue (1) Low-high, dark blue (1) Low-Low, dark red (1) High-high, (C) Leprosy rate with MHDI: Dark blue (1) Low-Low, dark red (2) High-high

Discussion

Leprosy proved to be quite problematic at the investigation site, with a high number of cases and a high percentage of DPD 1 and 2 at the time of diagnosis. According to the spatial analysis, the presence of clusters of risk was observed in Cuiabá, and a correlation between the disease and areas where there are low percentages of schooling, low HDI and low income was observed.

When checking the profile of the cases, it is identified that leprosy affects males proportionally more, a result corroborated by other investigations carried out in Brazil [29, 30] Among the explanations for this characteristic, the difficulty men have in accessing public health services, especially Primary Health Care (PHC) stands out [27, 31]. Among these difficulties is the conflict between the hours of operation of health units and the workday of the male population (who usually mention this obstacle) and the perception that men are less susceptible to the disease than women [32].

Regarding age, the range is between 15 and 59 years old (75%), which is already present in other studies in the literature [33, 34]. Another important marker is those under 15 years old where the state is hyperendemic and there was an increase in its detection rate during the years 2009 to 2018 according to the Ministry of Health [35], in terms of education, those with incomplete elementary education make up 44% of all investigated similar to another finding in the literature [29, 34].

Most cases of leprosy in Cuiabá had an operational classification of Multibacillary and a borderline clinical form. Regarding the DPD, a significant portion did not present changes at the time of diagnosis (40%), although 30% were not evaluated using the DPD during the diagnostic investigation. These results may indicate vulnerabilities in case care by health services, since a large portion of those affected by leprosy were not assessed for disabilities at the time of diagnosis, an assessment that is mandatory at the time of diagnosis [36].

In addition, individuals affected by the multibacillary forms of the disease are more likely to develop health problems [3]. When untreated, leprosy is extremely disabling in this population, which can affect performance in daily and work activities and lead to problems of social limitation and problems related to self-esteem. The presence of the disease is linked to stigma, especially when deformities are manifested [37].

The space scan statistics showed a heterogeneous distribution of leprosy in Cuiabá. As already seen in another study [38], locations with a higher risk of the disease are concentrated in regions characterized by high levels of demographic density, in addition to grouping populations with medium, medium-low and low income levels, in this case in the east and north of the city.

Regarding the bivariate Moran’s I statistic, it can be observed that, in the places where the highest demographic density values and the aforementioned income levels were found, High-High clusters were identified, overlapping with the space scan, mainly in the east region of Cuiabá. The municipal socioeconomic indicators present in these places deserve special attention when discussing communicable diseases, as they are linked to social and economic inequalities, favoring the occurrence of diseases such as leprosy [39].

In the present study, the positive correlation between the MHDI, average per capita income and schooling showed that areas are more likely to have leprosy. Such results are very valuable for directing care management strategies locally and improving treatment outcomes in problematic territories, which implies expanding efforts to assess the effectiveness of implementing actions to control and eliminate leprosy [40].

At first, the positive association between high socioeconomic indices and high rates of leprosy may seem counterintuitive, given that leprosy is a neglected disease and is generally associated with low rates of indicators. However, in areas with higher income and education, access to health services tends to be better. This may lead to more frequent and accurate diagnosis of leprosy, resulting in higher numbers of reported cases [41]. In addition, people from areas with higher income and education may have greater mobility, both socially and geographically. This may increase exposure to leprosy-endemic areas, resulting in a higher incidence of the disease [42].

It is also important to mention that in low-income areas, the stigma associated with leprosy may lead to underreporting of cases, since people may avoid seeking treatment due to fear of discrimination, which results in an apparent lower incidence of the disease [43]. Furthermore, areas with high leprosy rates may have populations that have migrated from endemic regions. Even if these areas have better sociodemographic indicators now, the population may still be suffering the consequences of past exposures [44].

Studies point out that education and effective communication in the community are fundamental, exploring experiences and perceptions, cultural beliefs, knowledge gaps, fears and family lifestyle, with efforts to understand, support, interact and increase maximum involvement in restoring self-confidence, reduce misconceptions and positively influence the perception of leprosy [40, 45, 46, 47].

More effective actions to overcome leprosy involve investments in health and different intervention approaches for the population, encompassing research and surveillance activities in the community or critical territories. It is worth highlighting the relevance of a model devoted to health promotion and prevention as a fundamental condition for reversing this reality [48] and the distribution of health resources and government financial support [40].

The present study has limitations, with emphasis on the secondary database used, which may contain inconsistent information regarding its quantity and quality, potentially ignored or incomplete data, regarding the completion of the leprosy clinical record form. Another limitation involves georeferencing, which allows identifying a certain number of locations in urban areas and is not accurate for rural areas. Finally, the ecological fallacy must be mentioned, whose interpretation of the data cannot be extended to the individual level.

Conclusion

Leprosy is an avoidable condition when there is sanitary responsibility to face the problem. However, it permeates responses from the health sector and is related to indicators of education, income and better living conditions, which must be articulated with intersectoral responses, reinforcement of social protection actions and the role of the social welfare state.

Another aspect to be reinforced is public policies guided by scientific evidence, which are essential for the advancement of equity and elimination of leprosy. Active case search actions can result in increased detection rates, in addition, search and follow-up of patient contacts should also be promoted to achieve zero detection, especially in endemic areas.