|Year : 2021 | Volume
| Issue : 3 | Page : 115-121
Spatial distribution and control status of hypertension in urban field practice area of a tertiary medical care institution of South India: A cross-sectional analytical study
Namrata Kharat, Parthibane Sivanantham, G Dinesh Kumar, James T Devasia, Sitanshu Sekhar Kar
Department of Preventive and Social Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
|Date of Submission||16-May-2021|
|Date of Acceptance||17-Sep-2021|
|Date of Web Publication||22-Nov-2021|
Dr. Sitanshu Sekhar Kar
Department of Preventive and Social Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry
Source of Support: None, Conflict of Interest: None
Background: Hypertension is a global public health issue. Geographic information systems (GIS) are increasingly being used by health-care systems as an emerging tool to address the public health burden of hypertension.
Objective: The objective of the study is to describe the geographic distribution of adults with known hypertension residing in the urban field practice area of a tertiary care institution and to assess the factors associated with its control status.
Materials and Methods: We conducted a cross-sectional analytical study in an urban health center (UHC) with adults with hypertension (n = 343) seeking care from the NCD clinic of UHC and private clinics were included. Geo-coding was done (n = 343) using digital GPS device by house-to-house visit and average of the three blood pressure recordings using digital sphygmomanometer taken for assessing control status (n = 277) of hypertension. A structured questionnaire was used to collect sociodemographic, risk factors distribution, and medication adherence. Geospatial analysis was done using QGIS 3.0, ArcGIS 10.2 and SPSS version 22 (IBM Corp. Armonk, NY, USA) was used for statistical analysis.
Results: The geographic distribution showed clusters and hotspots in the study area. Of the 277 study participants, 57.4% (51.6–63.5) had blood pressure under control and 41% were male. Patients with age ≥60 years (prevalence ratios [PR]: 1.2, 95% CI: 1–1.6), with no comorbidity (PR: 1.3, 95% CI: 1–1.7), high medicine adherence (PR: 7.6, 95% CI: 3.9–14.6) were independent factors associated with control status.
Conclusion: The study identified the clustering and hotspot areas of known patients with hypertension. Around three-fifth of known hypertensives had their blood pressure under control.
Keywords: Blood pressure, control status, geographic information system, hypertension
|How to cite this article:|
Kharat N, Sivanantham P, Kumar G D, Devasia JT, Kar SS. Spatial distribution and control status of hypertension in urban field practice area of a tertiary medical care institution of South India: A cross-sectional analytical study. Int J Non-Commun Dis 2021;6:115-21
|How to cite this URL:|
Kharat N, Sivanantham P, Kumar G D, Devasia JT, Kar SS. Spatial distribution and control status of hypertension in urban field practice area of a tertiary medical care institution of South India: A cross-sectional analytical study. Int J Non-Commun Dis [serial online] 2021 [cited 2021 Nov 27];6:115-21. Available from: https://www.ijncd.org/text.asp?2021/6/3/115/330907
| Introduction|| |
Around 972 million (26.4%) people worldwide had hypertension in 2000 and this is expected to rise to 1.56 billion in 2025. Around 80% of the cardiovascular deaths occur in low and middle income countries.
The prevalence of hypertension in India is 29.8% and the control of blood pressure was 10.7% and 20.2% for rural and urban Indians, respectively. Out of one-third adults with hypertension (33.3%) in Puducherry, less than half were aware of their hypertension status, and of them, only three-fourths were on medications.
Over the years, emerging technologies such as M-Health, telemedicine, telemonitoring, virtual clinics, artificial intelligence, and geographic information system (GIS) have empowered health systems as well as patients improve the management and control of hypertension. In the spatial technologies front, GISs are an emerging tool used by health-care systems to address the public health burden of noncommunicable diseases by disseminating crucial information related to the spatial distribution of patients residence, factors affecting the prevalence of disease, and other information in a defined geographical area which needs additional implementation.
Assessing spatial distribution of patients with hypertension would help identifying high prevalent areas of patients with hypertension in the urban field practice area. This would help to offer targeted interventions to reduce the burden of uncontrolled hypertension. In this context, we aim to describe the geographic distribution of patients with hypertension and assess their control status and associated factors.
| Materials and Methods|| |
A community-based cross-sectional analytical study was conducted in the urban field practice area operating under a tertiary care teaching hospital in Southern India. It caters to population of approximately 8000 in four urban wards, Kurusukuppam (807 households), Chinnayapuram (248 households), Vazhaikulam (559 households), and Vaithikuppam (195 households).
Recruitment of participants
A total of 488 adults (≥18 years) diagnosed with hypertension seeking care from NCD clinic (n = 233) and private practitioners (n = 265) were listed in the morbidity register and as per a previous study in the same area. One-fifth (101, 20.7%) patients could not be contacted even after two household visits. Known hypertensives who had shifted (19, 3.9%) or were dead (25, 5.1%) were also excluded from the study. Informed written consent was obtained from 343 physician diagnosed patients.
Geospatial distribution of patients
The data were collected over a period of 2 months (September–October 2019) by house-to-house visit. The place of residence was geocoded using a GPS, Garmin Oregon 550 and a unique GPS id was given on the GPS device. Analysis was conducted using Esri ArcGIS 10.2. (ESRI Redlands, CA, USA). Using ArcGIS software 10.2, spatial analysis such as the cluster-outlier analysis, hotspot analysis, and generation of a heatmap was done using QGIS 3.0 software (QGIS Development Team. CA, USA).
Assessment of control status
Assuming the proportion of controlled hypertension status to be 23.6% (3) with 5% precision and alpha error of 5% (95% confidence interval [CI]), 277 patients was calculated using OpenEpi version 3.03 software. (OpenEpi. Atlanta, GA, USA). Stratified random sampling was conducted to recruit study participants for control status assessment. We measured three blood pressure reading at a 5-min interval from the participants, and the average of the three readings was considered for analysis. If blood pressure was ≥150/90 in patients aged more than 60 years and ≥140/90 in patients aged <60 years, they were considered to have their blood pressure “not under control” as per Joint National Committee on hypertension (JNC8) guidelines.
We collected information on sociodemographic characteristics, medication adherence Morisky Medication-Taking Adherence Scale-MMAS (4-item), social capital: integration questionnaire (SC-IQ), and average of the three blood pressure readings was considered final and responses were administered by the investigator with help in local language from fellow colleagues. Patient's privacy and confidentiality was maintained. Data were collected using Epicollect5.
The sociodemographic factors, medication adherence, and social integration were summarized as proportions with 95% CI and their association with hypertension control status was described using Chi-square test and unadjusted prevalence ratio with 95% CI was calculated. P <0.05 was considered statistically significant.
This study was approved by the Institute Ethics Committee (JIP/IEC/2019/0291).
| Results|| |
Around 343 patients were tracked to their individual household and were geocoded for each patient. There were 156 (45.5%), 82 (23.8%), 83 (24.2%), and 22 (6.5%) patients residing in the wards Kurusukuppam, Vazhaikulam, Chinnaiyapuram, and Vaithikkuppam, respectively.
Spatial autocorrelation of residence of patients with hypertension
The spatial autocorrelation report for individuals with hypertension showed that the Moran's I was significant [Figure 1], implying that residence of patients with hypertension was clustered in the study area of Vazhaikulam, Chinnayapuram, and Kurusukuppam. This was validated by the positive Z score and P value below 0.05.
|Figure 1: Spatial autocorrelation report of patients with hypertension in urban field practice area of JIUHC|
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In this study, we determined clusters of high and low value. A cluster map was obtained of high values (High-High-denoted by red color) with areas of clusters identified in Kumaran Street and Mariamman Koil Street in Vazhaikulam; Akkasamy Madam Street in Chinnayapuram; Murugesa gramini thottam in Kurusukuppam. The distribution of the hypertension cases in the urban field practice area as follows [Figure 2].
|Figure 2: Map depicting clusters and outliers of patients with hypertension in urban field practice area of JIUHC (n = 277)|
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Hypertension hotspots of P value = 0.01 (99% confidence) show that the hotspots observed are caused by presence of clustering of patients in that area. The study identified these hotspots in Goubert Pada Salai (1), R.K. Thottam (2), Municipal Quarters (3), Akkasamy madam Street (4), M.G. Thottam (5) areas [Figure 3].
|Figure 3: Map showing hotspots of patient's residence in the urban field practice area of JIUHC (n = 277)|
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The density analysis [Figure 4] of the urban field practice area of JIPMER Urban Health Centre (JIUHC) showed the distribution of patients with hypertension in map generated by QGIS 3.0 software based on the number of patients with hypertension present in the area. High prevalence is indicated by red (more than 33 patients with hypertension) and low prevalence is indicated by yellow (<8 patients with hypertension).
|Figure 4: Density analysis map showing patients with hypertension in the urban field practice area of JIUHC (n = 277)|
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Control status and its associated factors
Out of the 343 study participants, 277 (80.7%) patients with hypertension were assessed for hypertension control. About 163 (58.8%, 95% CI: 52.7–65) belonged to 60 years or above and a higher proportion were females 163 (58.8%, 95% CI: 53.1–64.6). Majority of the patients with hypertension had no co-existing conditions 159 (57.4%, 95% CI: 51.6–63.2) [Table 1]. About 154 (55.6%, 95% CI: 49.8–61.4) had high adherence to antihypertensive medication and 83 (30%, 95% CI: 24.5–35.4) patients had no social exclusion [Table 2]. Patients with hypertension who had their blood pressure under control were 159 (57.4%, 95% CI: 51.6–63.5).
|Table 1: Sociodemographic characteristics of patients with hypertension (n=277)|
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|Table 2: Medicine adherence and social exclusion of patients with hypertension (n=277)|
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Around five-eighth (103, 63%) patients with hypertension who belonged to age 60 years or above had their blood pressure under control. About 101 (62%) female participants had their blood pressure under control. Age category of 60 years and above, primary education, patients with no comorbidity, high medicine adherence; no, low, and moderate social exclusion were significantly associated with controlled status of hypertension [Table 3] and [Table 4].
|Table 3: Association of sociodemographic factors and hypertension control status among patients with hypertension (n=277)|
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|Table 4: Association of medicine adherence, social integration and hypertension control status among patients with hypertension|
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| Discussion|| |
This community-based cross-sectional analytical study was conducted to describe the geographic distribution and assess the control status of patients with hypertension. Out of the total study participants enrolled for the study, 343 were geocoded and 277 were interviewed and assessed for control status. Clustering of cases and hotspot areas were seen in study area. Majority of the patients with hypertension (57.4%) had their blood pressure under control. Old age (age above 60 years), high medication adherence, and no to low social exclusion were significantly associated with controlled status of hypertension.
In this study, clustering of hypertension cases and hotspot areas were found in the areas of Chinnayapuram, Kurusukuppam, and Vazhaikulam which was statistically significant (Moran's index = 0.0748). A study in Thailand assessed prevalence of hypertension by showing clustering of hypertension cases and hotspot areas. The study showed statistically significant clustering of hypertension cases and hotspot areas during 3 years from 2005 to 2007. Both these studies demonstrated the presence clustering of patients with hypertension in urban areas by showing positive Moran's index.
In this study, the density analysis was used for showing density-wise presence of the patients with hypertension as high prevalence was indicated by red and low prevalence was indicated by light yellow in the JIUHC urban field practice area. Another study used heat maps that showed the prevalence of hypertension, diabetes, and current smoking variation in blacks and whites on the map of United States using the logistic regression model. High prevalence was indicated by red while low prevalence was indicated by blue. This helped to give a glance view of prevalent areas of hypertension presently to be focused for future interventions. This study collected data based on the present registry records and included all known participants. Both studies showed the presence of hotspots present for hypertension which shows that phenomena of clustering of hypertension is present. Therefore, public health system can utilize this tool for assessing the hotspots for their population so that targeted interventions can be given.
Several studies from southern states of India reported control status of hypertension ranging between 30.6% and 45.9%.,, Studies from northern India also reported lower prevalence of control status of hypertension., The proportion of controlled hypertension in this study is 57.4%.The control status is higher in this study as compared to other studies in the southern part of India. Although the control status observed in this current study is higher, the variation could be due to difference in topography, treatment seeking behavior, and cultural practices across various places. However, the control status of hypertension is less across Indian setting. This requires further studies on finding out why people were not able to manage their blood pressure.
Participants equal to or above the age of 60 years showed significant association with controlled hypertension in our study. This may be due to the increased awareness and longer duration of hypertension in this age group motivating them to remain adherent to the treatment and their proportion in this study as compared to patients with hypertension aged below 60 years.
55.5% patients had high medicine adherence to antihypertensive medication. Whereas, the study on determinants of patient's adherence to hypertension medication in Kancheepuram district in Tamil Nadu showed overall 24.1% adherence. We considered a patient adherent to medication by adding a score of yes and no responses to all four questions whereas in the other study, the patient was considered adherent if the response was yes to all four questions of Morisky's medication adherence scale-4 item. This showed the variations in the presence of adherence in both the studies.
In a neighborhood social cohesion and prevalence of hypertension study in South Asian population, women with high social cohesions had 46% reduced odds of having hypertension as compared to those with low social cohesion. Social integration has been hypothesized to influence health by encouraging social inclusion, stress effects are buffered and lowered and helps to impart positive behavioral change. In this study, 73.5% patients with no social exclusion showed controlled hypertension as compared to 29.5% patients with high social exclusion showed controlled hypertension status. Significant association was found between no, low, and moderate social exclusion with achieving controlled blood pressure.
It is a community-based study which shows concurrent evidence that would exist in both public and private health-care sector in the known population. There was only a single investigator for interview and blood pressure measurement that could help to avoid interobserver bias. The present study has some limitations. The study results are not generalizable for geographic distribution as it is unique to the local setting in the location context. Moreover, missing data for 101 patients would not give overall representation of results.
There is a requirement to know the possible reason of why people were not able to manage their blood pressure and to view what works for the patients who had their blood pressure under control. Hence, it is important to conduct need assessment for health services at different levels of health care, leverage existing counseling services and integrate M-Health for service delivery. The health providers could emphasize on standard management guidelines for targeting blood pressure control through various pharmacologic and nonpharmacologic approaches. Furthermore, spatial analysis tool could be used to focus interventions to the hotspot areas by doing community-based health promotion, interactive workshops in prevalent areas and by advising and ensuring execution of lifestyle modification like increase of physical activity in groups.
| Conclusions|| |
In conclusion, clustering of patients with hypertension and hotspot areas were seen which showed prevalence of cases in the four wards. This will assist in reallocating resources based on concurrent data to reduce the burden of uncontrolled blood pressure.
We acknowledge the support of JIPMER administration for supporting us with the intramural grant. We thank participants for helping and their support during this study. We also thank Dr. Anjali C, Dr. Neema Joseph for their support in the conduct of the study.
Financial support and sponsorship
This study was financially supported by Intramural grant, JIPMER.
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4]
[Table 1], [Table 2], [Table 3], [Table 4]