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Using Planning & GIS (GeoMedicine) in Monitoring and Tracking Diabetes - Kuwait Case Study

Updated on October 7, 2017
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Independent Researcher has new vision and solutions for the Diabetes epidemic in the gulf

The Arabian Gulf Region
The Arabian Gulf Region | Source
Kuwait has the highest IGT Comparative Prevalence in the World
Kuwait has the highest IGT Comparative Prevalence in the World | Source

Gulf Region and the world's top 10 Countries in Diabetes

Saudi Arabia, Kuwait and Qatar are three countries of the world's top 10 countries with higher prevalence of Diabetes. These countries have very close socioeconomic Indicators specially, the indicator of (GDP) per capita and human development index (HDI) which are considered the main causes of the prevalence of Diabetes there which has reflected on the life style in this region (Obesity - Physical inactivity). The aim of this study is how we can scientifically verify the validity of these causes and how we can research in new solutions to cope with different challenges such as the lack of data at the country level.

Source
Source

The role of planning and GIS

Planning is a comprehensive and sensitive process. it needs follow up, support from the decision-makers and the effective participation from the various experts in order to reach to the comprehensive vision through a clear methodology of work in order to achieve the proper planning for monitoring and tracking Diabetes.

GIS system is the executive side and the analytical tool of the science of Geo-Medicine and it is the big container for health informatics. in addition to the other related information environmental , geographical , socioeconomic information..etc), GIS system needs valid data for health indicators ,social and environmental metrics that should match with the specifications of the GIS in order to reflect the reality of disease on the ground. In case of using data are collected randomly or incomplete or out of the rules of planning guidelines it means misleading results which lead to wasting time, money and efforts.

Introduction - Types of Diabetes

there are three main types of diabetes Type 1 , Type 2 , gestational diabetes .

WHO says :

  • The cause of type 1 diabetes is not known and it is not preventable with current knowledge.
  • Type 2 diabetes comprises 90% of people with diabetes around the world and is largely the result of excess body weight and physical inactivity. The risk factors are
  1. Age - being over age of 40 years
  2. Genetics - having a close relative with the condition (Parents, brothers and sisters)
  3. Weight – being overweight or obese
  4. Ethnicity – being of South Asian, Chinese, African-Caribbean or black African origin
  • Gestational diabetes is hypoglycemia with onset of first recognition during pregnancy

Women who have had Gestational diabetes during pregnancy also have a greater risk of developing diabetes in later life..

in addtion to

Impaired glucose tolerance (IGT) and impaired fasting glycaemia (IFG)

IGT or IFG are at high risk of progressing to type 2 diabetes if preventive steps were not taken.

Source
Source

Diabetes in Kuwait

Kuwait is one of the world’s top 10 countries with the highest prevalence (%) of diabetes. its ranking in 2013 was (9).

Kuwait is an ideal model for the oil exploration. Also it can be considered as ideal model for the scientific research in diabetes. The high prevalence (%) of Diabetes in Kuwait raises many questions for the researchers who research in the causes of the accelerated prevalence in Kuwait. there are many questions about causes of Diabetes, such as, are the causes of Diabetes are same in the other countries that have also high prevalence of Diabetes in their communities or the causes of the accelerated prevalence are different in each country and each region in the world !!.

this is the scope of the research.


The potential causes for Diabetes prevalence in Kuwait

In the last 25 years there were a big change in the environmental factors and socioeconomic indicators such as, the contamination, high standard of living, remarkable change in the pattern of nutrition for many people through increase fast food outlets and physical inactivity. therefore, marked increases in rates of overweight, obesity and all of these factors led to Diabetes. Also, there are Unknown causes for Diabetes still needs research may they are behind this abnormal prevalence of Diabetes.


The Required data for the GIS System (GeoMedicine) - Spatial Data

1- Updated geocoded addresses for patients.

2- Updated addresses for the residential buildings

3- Contamination raster maps & mobile towers locations.

4- locations of high voltage stations

Specifications of the required data

1- Diabetes Registry should match the real number of Patients. (including all health metrics which are related to Diabetes ).

2- Diabetes Registry should include addresses of patients.

3- Population and Socioeconomic data should be available at the different administrative levels (specifically at the Neighborhoods level and updated)



The common obstacles of the success of GeoMedicine

The obstacles of the success of Geo-Medicine are different from country to another country. these obstacles can be classified in general as follows:

1- Readiness of data in terms of quality, completion, the existence of spatial information in health data which are mandatory for data migration with the GIS system to represent the reality of disease.

2- Not taking in account the recommendations of planners.

3- The weakness of planning studies and the absence of future planning.

This is an examples for the obstacles of the success of Geo-Medicine as its shown in tables.

The potential missing information - Diabetes Registry

Spatial data
Total number of patients
Updated registry
Address of patient is Incomplete or missing
the sum of patients in the registry does not match or not close to the total number of patients who were diagnosed actually
not up to date registry with the new cases
Data without QC&QA

The potential missing information - Population and socioeconomic data

Population by Age groups
Statistical Surveys
The Census
Age groups statistics are missing at the administrative level
Incomplete parameters
Census of population is old
* Absence of valid data and data without QC&QA leads to misleading results

The monitoring system for Diabetes

It's not easy at all to establish integrated effective system for monitoring Diabetes and chronic Diseases . Because this kind of systems are classified as large projects (informatics infrastructure at the country level) which need effective cooperation between many governmental organizations (not only the Ministry of Health). The challenges for the establishment of such systems are many and vary widely from country to country. in this study, there is no available data for Diabetes registry in Kuwait so that, i will use hypothetical models in order to facilitate the understanding of the execution side of Geo-Medicine and to show the findings and benefits which can be gained by the GIS system.

Dr.John Snow
Dr.John Snow | Source
Dr.John Snow’s famous cholera analysis data in modern GIS formats
Dr.John Snow’s famous cholera analysis data in modern GIS formats | Source

Patients distribution on the Map (hypothetical models)

This Model illustrates patients' distribution based on their addresses. This is a hypothetical model for the Diabetes Registry and this model is supposed to be updated every year with the new patients of the coming years. Three models were created in attempt to match the realism of the current situation of Diabetes spatial distribution in Kuwait by using the total number of diabetics according to the IDF statistics for Kuwait. The aim of these scenarios is to show all the expected distribution patterns.

In these scenarios we will show 3 types of the expected distribution patterns. 1- the proportional distribution 2- Variance distribution 3- Statistical Inference

In the second and third scenarios we will see the hot spots and cold spots for patients (concentration) which raise a lot of new questions among researchers and open new horizons in research for new causes of Diabetes. exactly as Dr.Snow designed his model for cholera epidemic in London 1854.

The Work Methodology for Executing the Monitoring System of Diabetes

1- Detect building heights in the residential areas.

2- Prepare the Geocoded addresses (Address Locator) geocode patients on the map. .

3 - Diabetes data analysis using GIS analytical tools (Diabetics classification - Hot spots - Trends).

4- Model building , research in the association between different factors and high prevalence of Diabetes (the scientific research for the final outcomes analysis).


Residential Buildings Heights in Kuwait
Residential Buildings Heights in Kuwait | Source

1- Heights of residential buildings

Gathering data for the heights of residential buildings is so important to understand the pattern of Diabetes prevalence which expected to be concentrated vertically in the buildings with floors > 5 floors with number of patients (1-10). for the buildings < 3 floors, the prevalence expected to be visualized horizontally with number of patients (1-3) in each building. these expectations was calculated based on the total number of diabetics and total numbers of different kinds of the residential buildings.


2 - Patients geocoding - The big challenge

The geocoding process requires two types of information, reference data for creating an address locator (all addresses in Kuwait) and address data for matching (Patients addresses). reference data refers to a geographic information system (GIS) Spatial data which contain of the address attributes for all Kuwait addresses.

To geocode patients in their addresses on the map, it requires upgraded infrastructure data as possible for this important process. in order to make the geocoding processes more easy and flexible. which needs strong cooperation with ministry of health- MOH, The Public Authority for Civil Information PACI and Kuwait Municipality in order to make the success for this system.


Urban Areas in Kuwait
Urban Areas in Kuwait | Source

3 - Diabetes data analysis using GIS analytical tools

In Kuwait, people with Diabetes Type 2 are 407.530 (IDF) . the total number of the residential buildings are 152,268 buildings that means each residential building has 2,67 Patient if the distribution is equal without disparities (Theoretically). in this study we don't use the real Diabetes registry to produce a map reflects the actual reality of patients distribution in Kuwait. however, based on the total number of people with Diabetes Type 2 in Kuwait we can expect the potential scenarios that may could be close to the actual reality.

Adults Population in Kuwait (20-79)
Diabetes cases (20-79)
Residential Buildings
Percentage for each Residential Building
Diagnosed Diabetics Rate Per 1000 Population
2,293.740
407,530
152,268
2.67 Patient
177.6 / 1000
Data Source: IDF - 2013 , Central Statistical Bureau - 2012 (Kuwait). Diabetes Registry for Type 2 in Kuwait (IDF Statistics).

Classification of Residential Buildings

Villa
Apartment Building
Traditional Houses
103.555
26,843
21,870
Central Statistical Bureau - 2012 (Kuwait)

Geo-Medicine - The Elegant Industry for Nations

The model of monitoring diabetes which represents the reality of Diabetes on the ground and also represents the effective surveillance system should be built with respect to all the planning guidelines to reflect the actual real picture for the decision makers and for the epidemiology researchers to be the ideal model for monitoring diabetes.

This model should contains not only the addresses of patients but, all the health metrics which are related to Diabetes and this huge construction of data should built and updated based on clear methodology by the Geo-Medicine experts.

This model should be exist in every country in the world especially, the countries of the third world by applying set of procedures and standards for the quality of spatial data and health data to make successful integration between the health data and GIS data to avoid the misleading metrics of Diabetes in many of countries in the world.

4.1 Diabetes Modeling - Patients Distribution

** Objective :

1- To show how the diabetes registry how look like on the map in case of presence the Diabetes registry with full addresses and the presence of the accurate GIS infrastructure. (The Ideal model)

2- To show the expected scenarios for diabetes spatial distribution in the current situation (diabetes registry not available).

** Methods :

1- Data of the residential buildings in Kuwait were used ( it represents the addresses of patients).

2- 407.000 patients were distributed according to the vertical and horizontal sprawl of buildings in the urban areas .

The Probability to find concentration will be more in the buildings with many floors more than buildings with limited floors and that is very logical. the factor of building heights (population density) must be taken in account in the analysis to avoid the misleading conclusions.

3- 407.000 patients were geocoded (located) on the residential buildings and represented on the map by points based on specific criteria. These points were classified into 6 categories each category represents a range of patients number for each building as it's shown on the map.

** Findings :

1- Simulation the reality of Diabetes on the ground by modeling.

2- General perception for the Diabetes ideal model.

3- Facilitate the understanding of Geo-Medicine science.

4- Research in new potential causes for Diabetes from a new perspective according to the potential scenarios for the spatial distribution of diabetics.

** Conclusions :

Geocoding patients on the map is the ideal model for modeling diabetes in terms of :

1-Accuracy 2- effective surveillance 3- more understanding for researchers 4- reference to decision makers.

Also, in case of the sudden epidemics such as prevalence of Zika virus. this model can be considered as first line defense which can be used as crisis management system and the guide for the epidemiology researchers in spatially epidemic trapping.

** any other models rather than geocoding model where diabetes distribution at the health region level or at neighborhood level will be used in research only. It couldn't be adopted as early alarm system or crisis management system.

 Patients Distribution on the Map
Patients Distribution on the Map | Source
Patients Distribution by Geocoding -  The Ideal Model for the Early Alarm / Crisis management system and Scientific Research
Patients Distribution by Geocoding - The Ideal Model for the Early Alarm / Crisis management system and Scientific Research | Source

4.2 The Potential Scenarios - Jumping on the expected outcomes

In the absence of the Diabetes Registry with addresses and in the absence of updated and accurate GIS infrastructure data with geocoded addresses (The Ideal Model). this the common case in many countries but, that doesn't mean reason for stopping research.

** we can begin with assumptions and use the available data as follows **

Three hypothetical models were designed based on three expected scenarios.

The following data and expected scenarios are to facilitate the understanding the methodology of using Geo-Medicine in tracking the chronic diseases and to imagine how we can diagnose Diabetes causes from a new perspective.

Once again, the hypothetical models were not designed to reflect the reality of Diabetes . it's just an attempt to imagine all the expected disparities and to clarify the great role of planning in health sector.

What is the aim of patients distribution on the map....

For sure, it's not just for data visualization only as some of doctors think. it's to raise several questions for the different distribution patterns. there are certainly reasons behind the expected patterns of distribution.

There are three scenarios for the Diabetes distribution in Kuwait as follows:

1 - The first Scenario - Proportional Distribution

In this scenario it is supposed the diabetics density will be proportional to the population density, in this case, the conclusion will be as follows :

  • Patients are distributed equally and any apparent concentration in any health region or neighborhood is due to the population density.
  • By analyzing the equal distribution for diabetics on the map, there is no hot spot or cold spot at the level of neighborhoods.
  • Weights of Diabetes factors such as social factors and BMI factor are also proportional to population density .
  • any attempt to find hotspots / coldspots to confirm the association between presence of any environmental factors is completely excluded.

- But it remains the probability of presence potential environmental factors at the gulf region level from the surrounded countries or surrounded regions is the potential cause behind the accelerated prevalence of Diabetes in Kuwait for this scenario .

Equal Distribution
Equal Distribution | Source

2 - Second Scenario - Variance Distribution

In this scenario, i assumed spatial distribution for patients will be vary at the level of neighborhoods for each health region. unlike the distribution in the first scenario which was proportional to the population density . in this scenario patients density are not proportional with population density.

in this case conclusions will be as follows:

  • The distribution of diabetics is unequal in the neighborhoods which can be defined as hot spots for high concentration and cold spots for the low concentration of patients.
  • The disparities among neighborhoods.can be easily detected.
  • Analyzing the disparities of diabetics concentration strongly raises the probabilities of presence many factors such as socioeconomic factors, BMI factor and influential environmental factors behind this pattern of distribution.

Hot Spots/Cold Spots
Hot Spots/Cold Spots | Source

3 - Third Scenario - Statistical Inference

In this scenario which is considered the closest scenario to the reality by taking in account the registered new patients in each health region for the year 2006 for Kuwaiti people only. but, no data available for Non Kuwaiti people statistics and no data available at the neighborhood level also . the third scenario statistics was built by using the data in the table in below for the health regions level. the conclusions will be as follows:

  • The highest concentration found in Jahra and Farwaniya health regions.
  • The disparities will be among health regions and will not be observed among neighborhoods like scenario 2 because there is no data available at this level .
  • percentage of Kuwaiti people in Kuwait around 25% of total population and the registered new patients from the other nationalities (75% of total population) are unknown and those have the biggest weight in finding the real disparities among the health regions instead of existing disparities which was calculated based on the number of Kuwaiti people only .
  • Also, there is a probability of presence factors such as socioeconomic,BMI and influential environmental factors behind this pattern of distribution.


 The only available information
The only available information | Source
Disparities among Health Regions
Disparities among Health Regions | Source

Scenarios Evaluation

Criterias / Rates
Criteria Definition
Ideal Model
Scenario 1
Scenario 2
Scenario 3
Potentiality
The possibility of approaching reality for each scenario according to available information
5
1
2
3
Health Regions Disparities
the observed disparities among heath regions which can be realized
5
0
5
5
Neighborhoods Disparities
the observed disparities among neighborhoods which can be realized
5
0
5
0
Blocks Disparities
the observed disparities among blocks which can be realized
5
0
0
0
Socioeconomic Factors
Possibility to conclude presence socioeconomic factors
5
0
2
2
Environmental Factors
Possibility to conclude presence enviromental factors
5
1
2
2
Early alarm system/Surveillance system
Scalability Model
5
0
2
1
Total Criteria
Relative weights
5
0.28
2.5
1.85
Evaluation Scenarios that are built in the case of missing information in attempt to understand the different potential Diabetes geographic patterns that may match reality in Kuwait.

Research in the potential causes of Diabetes

Although all of these scenarios not reflect the reality of Diabetes spatial distribution but, all of these scenarios showed all the probabilities of the possible spatial patterns of Diabetes on the map regardless of which health region or neighborhood is highest or lowest.

These scenarios determined the trends of research in potential causes of Diabetes.

** In Scenario 1 which is so weak to represent the reality but, it leads us to research in regional factors which could be behind the high prevalence of Diabetes in Kuwait even if no disparities were detected at the level of health regions or neighborhoods.

** In Scenario 2 which is expected little more than scenario 1 to represent the reality in terms of the expected disparities in neighborhoods not in terms of which one of them specifically is highest or lowest but, Scenario 2 can lead us to take in account the cause of Diabetes in Kuwait can be shown clearly in the highest neighborhood which can be considered as inference indication of the cause of Diabetes by research in all the factors of neighborhoods with hotspot which are associated to Diabetes weather these factors are BMI factor , social factor or the proximity to environmental factors. therefore, we can compare all of these factors with the same factors in the other neighborhoods to know which one of these factor is responsible for the accelerated of Diabetes Prevalence.

** In Scenario 3 which is close to Scenario 2 and use the scale of health region level but, this scale will not give us accurate conclusion specially in tracking the environmental factors because there is a probability the concentration of diabetes could be high in one of the neighborhoods of region which reflect on the whole region. Therefore, the region may seems with high diabetes due to one of its high neighborhoods at the same time the rest of neighborhoods of region could be normal or low. so, the analysis at this level may leads to misleading conclusions.



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Conclusion

The Ideal Model/ (Data available at the patient level) which is built at the patient home level to show the highest level of details is the best model which can be absolutely matched with all the criteria which are related to scientific research and related to early alarm system for the sudden epidemics.

Scenario 2/ (Data available at the Neighborhood level). Scenario 2 has probability of occurrence and this is the best model (in case of the absence of the ideal model ) for Diabetes in terms of revealing Diabetes factors (scientific research).

Scenario 3/ (Data available at the Health region level) the only scenario which its model was implemented using real data and it was strong to make the probability of Scenario 1 occurrence is weak and gave us preliminary conclusion for the highest health regions with Diabetics but,at the same time this conclusion also is weak for the reasons were mentioned above.

Scenario 1/ although the occurrence of this scenario is weak in terms of the probability of presence the equal distribution for diabetics but, it remains potential and that push towards research in the large scale - the regional countries and the world.


Resources

1 - IDF - Report 2013

http://www.idf.org/sites/default/files/EN_6E_Atlas_Full_0.pdf

2- WHO - World Health Organization

http://www.wpro.who.int/mediacentre/factsheets/diabetes/en/

3- The cholera map that changed the world

http://www.theguardian.com/news/datablog/2013/mar/15/john-snow-cholera-map

4- Central Statistical Bureau - 2012 - Kuwait

http://www.csb.gov.kw/Socan_Statistic_EN.aspx?ID=19


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