Importance of Public Transport Networks for Reconciling the Spatial Distribution of Dengue and the Association of Socio-Economic Factors with Dengue Risk in Bangkok, Thailand
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Design
2.2. Study Area
2.3. Data Sources
2.3.1. Dengue Incidence Data
2.3.2. Environmental Data
2.3.3. Socio-Economic Variables
- (1)
- (2)
- Immigration can impose a significant dengue risk due to lack of access to health care or proper housing conditions [31];
- (3)
- Agricultural areas generally have higher rainfall and humidity, lower abundance of Ae. aegypti but higher of Aedes albopictus [32];
- (4)
- Manual work sites such as construction sites are found to be potential areas of dengue clusters [33];
- (5)
- High population density is a known risk factor for dengue transmission [34];
- (6)
- Age will reflect differential exposure to DENV and subsequent level of acquired immunity and thus could be a potential confounder for dengue incidence [31];
- (7)
- (8)
- Shop houses usually have longer hours with windows and doors are open and thus provide easy entrance for mosquitoes [35];
- (9)
- (10)
- (11)
- Air conditioners promote indoor breeding sites and impact survival of Ae. aegypti mosquito through maintenance of clement temperatures;
- (12)
2.3.4. Intra-Urban Transport Networks Variables
2.4. Statistical Methods
2.4.1. Association Analyses
2.4.2. Spatial Analysis
3. Results
3.1. Impact of Meteorological Variables on the Dengue Incidence in Bangkok 2000–2013
3.2. Association of Socio-Economic Factors with Dengue Incidence
3.3. Spatial Analysis of Dengue Incidence
3.4. Impact of Transport and Distance
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Definition (as Per NSO and Own Analysis) | Referred to as |
---|---|
Demography | |
Proportion of population aged 0–4 years | Age 0–4 years |
Proportion of population aged 5–14 years | Age 5–14 years |
Proportion of population aged 15–24 years | Age 15–24 years |
Proportion of population aged 25–59 years | Age 25–59 years |
Proportion of population aged 60 years and above | Age above 60 |
Number of households (100s) | Number of households |
Total subdistrict area in km2 | Area |
% Area Built-up in km2 | Built-up area |
% Area with dense vegetation in km2 | Dense vegetation area |
% Area with low vegetation in km2 | Low vegetation area |
% Area with Roads in km2 | Road area |
% Area with water bodies in km2 | Waterbody area |
Population per household | Population per household |
Population density in km2 | Population density |
Education | |
Proportion of population aged 6 years and above who never studied | No education |
Proportion of population attending Primary school | Primary |
Proportion of population attending Secondary school | Secondary |
Proportion of population attending Undergraduate | Undergraduate |
Proportion of population attending Postgraduate | Postgraduate |
Nationality | |
Proportion of migrant population (abroad and Thai) who moved in last 5 years | Immigrants |
Types of occupation | |
Proportion of population engaged in agriculture, forest and fishing work | Agriculture |
Proportion of population involved in manual, construction, mining and network work | Manual work |
Household characteristics | |
Proportion of houses made up of cement or brick | Cement or brick houses |
Proportion of wooden houses | Wooden houses |
Proportion of households using ground water, well water | Ground water |
Proportion of households using rain water | Rain water |
Proportion of households with air conditioner | Air conditioner |
Proportion of shop house/row house/row homes | Shop houses |
Proportion of households with pit toilet or who defecate into river/canal | Pit toilet |
Models | Meteorological Variables | R2 |
---|---|---|
Model 1 | Mean Diurnal temperature Range +Year | 22.09 |
Model 2 | Lag 1_Mean Diurnal temperature Range + Year | 28.56 |
Model 3 | Lag 2_Mean Diurnal temperature Range + Year | 21.17 |
Model 4 | Lag 3_Mean Diurnal temperature Range + Year | 13.01 |
Model 5 | Max temperature + Year | 11.71 |
Model 6 | Lag 1_max temperature + Year | 9.06 |
Model 7 | Lag 2_max temperature + Year | 4.90 |
Model 8 | Lag 3_max temperature + Year | 9.86 |
Model 9 | Min temperature + Year | 6.01 |
Model 10 | Lag 1_min temperature + Year | 7.73 |
Model 11 | Lag 2_min temperature + Year | 13.21 |
Model 12 | Lag 3_ min temperature + Year | 21.19 |
Model 13 | Mean precipitation + Year | 8.45 |
Model 14 | Lag 1_ Mean precipitation + Year | 17.38 |
Model 15 | Lag 2_ Mean precipitation + Year | 16.66 |
Model 16 | Lag 3_ Mean precipitation + Year | 8.66 |
Model 17 | Max precipitation + Year | 3.2 |
Model 18 | Lag 1_Max precipitation + Year | 8.26 |
Model 19 | Lag 2_Max precipitation + Year | 8.14 |
Model 20 | Lag 3_Max precipitation + Year | 5.029 |
Model 21 | Mean temperature + Year | 6.68 |
Model 22 | Lag 1_mean temperature + Year | 5.37 |
Model 23 | Lag 2_mean temperature + Year | 7.82 |
Model 24 | Lag 3_mean temperature + Year | 16.34 |
Variable | Dry Season | Wet Season | Combined |
---|---|---|---|
Year | <0.001 | <0.001 | <0.001 |
Agriculture, Forest & fishing | 0.022 | 0.005 | 0.006 |
No education | 0.068 | 0.087 | 0.07 |
Primary | 0.303 | 0.244 | 0.262 |
Secondary | 0.221 | 0.274 | 0.252 |
Undergraduate | 0.721 | 0.436 | 0.510 |
Postgraduate | 0.991 | 0.967 | 0.984 |
Migrant Population | 0.165 | 0.024 | 0.04 |
Shop house | 0.009 | 0.006 | 0.005 |
House: Cement or brick | <0.001 | <0.001 | <0.001 |
House: Wood | <0.001 | <0.001 | <0.001 |
Air conditioning | 0.472 | 0.670 | 0.926 |
Groundwater, well | 0.071 | 0.193 | 0.126 |
Pit toilet | 0.636 | 0.261 | 0.353 |
Rain water | 0.045 | 0.013 | 0.014 |
Number of households | <0.001 | <0.001 | <0.001 |
Manual | 0.005 | 0.037 | 0.016 |
Population density | 0.301 | 0.822 | 0.682 |
Age 0–4 years | 0.716 | 0.834 | 0.754 |
Age 5–14 years | 0.267 | 0.077 | 0.099 |
Age 15–24 years | 0.147 | 0.126 | 0.122 |
Age 25–59 | 0.039 | 0.002 | 0.003 |
Age 60+ | <0.001 | <0.001 | <0.001 |
Pop per house | 0.089 | 0.032 | 0.033 |
Dense vegetation/km2 | 0.726 | 0.526 | 0.725 |
Low vegetation/km2 | 0.144 | 0.354 | 0.253 |
Road area/km2 | <0.001 | <0.001 | <0.001 |
Waterbody area/km2 | 0.349 | 0.260 | 0.262 |
Built-up area/km2 | <0.001 | <0.001 | <0.001 |
Season | Fixed Term | aRR | 95% CI Lower | 95% CI Upper | p Value |
---|---|---|---|---|---|
Wet | % No education | 1.048 | 1.016 | 1.082 | 0.004 |
Wet | % Cement houses | 1.007 | 1.003 | 1.011 | 0.006 |
Dry | %Ground water | 0.456 | 0.220 | 0.946 | 0.036 |
Dry | %Manual work | 1.008 | 1.002 | 1.014 | 0.018 |
Wet | Nb houses (100s) | 1.0019 | 1.0015 | 1.0023 | <0.001 |
Dry | Nb houses (100s) | 1.0016 | 1.0012 | 1.0020 | <0.001 |
Wet | lag 1 DTR | 0.835 | 0.757 | 0.921 | <0.001 |
Wet | lag 1 Mean monthly Rain | 1.016 | 1.012 | 1.020 | <0.001 |
Variables | Cluster | 2012 | 2013 | ||||
---|---|---|---|---|---|---|---|
Parameter Estimate | p Value | % Var Explained | Parameter Estimate | p Value | % Var Explained | ||
% No education | High–High | 1.265 | 0.023 | 7.80% | 9.596 | <0.001 | 67.70% |
Low–Low | −1.248 | <0.001 | −1.453 | 0.015 | |||
Low–High | −0.22 | 0.866 | 1.92 | 0.137 | |||
High–Low | NA | −7.66 | <0.001 | ||||
No cluster | Ref | Ref | |||||
%Cement house | High–High | 5.66 | 0.003 | 15.80% | −2.41 | 0.365 | 3.70% |
Low–Low | −1.22 | 0.31 | 5.98 | 0.007 | |||
Low–High | 21.34 | <0.001 | −5.26 | 0.271 | |||
High–Low | NA | 2.82 | 0.632 | ||||
No cluster | Ref | Ref | |||||
Nb houses (100s) | High–High | −69.3 | 0.188 | 6.60% | −409.1 | <0.001 | 30.80% |
Low–Low | 107.9 | 0.002 | −81.9 | 0.105 | |||
Low–High | 165 | 0.176 | −47 | 0.668 | |||
High–Low | NA | 184 | 0.176 | ||||
No cluster | Ref | Ref |
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Variables | aRR (95% CI) | p Value |
---|---|---|
Year | 1.03 (1.01–1.053) | 0.0043 |
DTR (Lag 1 month) | 0.81 (0.74–0.90) | <0.001 |
Maximum temperature | 0.92 (0.85–0.99) | 0.032 |
Mean precipitation (Lag 1 month) | 1.05 (1.03–1.09) | <0.001 |
Variable | Mean | SD |
---|---|---|
Demography | (%) or N | (%) |
Age 0–4 years | 3.77 | 1.65 |
Age 5–14 years | 9.22 | 2.97 |
Age 15–24 years | 16.58 | 4.46 |
Age 25–59 years | 59.17 | 4.62 |
Age above 60 | 11.27 | 3.90 |
Number of households (N 100s) | 179.3 | 186.3 |
Area (km2) | 9.89 | 11.95 |
Built-up Area (km2) | 0.38 | 0.40 |
Dense vegetation area (km2) | 0.97 | 2.01 |
Low vegetation area (km2) | 3.49 | 5.62 |
Road area (km2) | 0.38 | 0.40 |
Waterbody area (km2) | 0.81 | 3.89 |
Population per household | 3.2 | 1.02 |
Population density (km2) | 13,393 | 6324 |
Education | ||
No education | 4.82 | 2.17 |
Primary | 16.94 | 9.02 |
Secondary | 16.38 | 8.51 |
Undergraduate | 26.01 | 10.12 |
Postgraduate | 4.65 | 2.67 |
Nationality | ||
Immigrants | 9.96 | 8.02 |
Types of occupation | ||
Agriculture | 1.10 | 2.88 |
Manual work | 21.66 | 10.60 |
Household characteristics | ||
Cement or brick houses | 74.99 | 14.76 |
Wooden houses | 14.11 | 9.85 |
Shop houses | 29.42 | 22.85 |
Ground water | 0.05 | 0.10 |
Rain water | 1.27 | 5.87 |
Air conditioner | 46.21 | 13.89 |
Pit toilet | 2.44 | 2.54 |
Fixed Term | aRR | 95% Conf. Ints | Wald Statistic | p Value |
---|---|---|---|---|
%No education | 1.04 | 1.01–1.08 | 7.76 | 0.006 |
% Cement houses | 1.006 | 1.002–1.01 | 6.95 | 0.009 |
Nb houses (100s) | 1.0019 | 1.0015–1.0023 | 117.32 | <0.001 |
lag 1 DTR | 0.61 | 0.58–0.63 | 772.07 | <0.001 |
lag 1 Mean daily Rain | 1.051 | 1.045–1.058 | 319.12 | <0.001 |
Year 2013 (vs. 2012) | 1.88 | 1.77–2.00 | 403.66 | <0.001 |
Nb transport stops | 1.005 | 1.001–1.009 | 6.38 | 0.013 |
Variables | LISA Cluster | 2012 | 2013 | ||||
---|---|---|---|---|---|---|---|
Mean | SE | p Value | Mean | SE | p Value | ||
% No education | High–High | 7.40 | 0.67 | <0.001 | 8.16 | 0.86 | <0.001 |
Low–Low | 4.22 | 0.47 | 0.300 | 4.18 | 0.79 | 0.310 | |
Low–High | 6.21 | 0.69 | 0.092 | 5.89 | 1.06 | 0.170 | |
High–Low | 4.77 | 0.75 | 0.806 | ||||
No cluster | 4.61 | 0.18 | Ref | 4.64 | 0.17 | Ref | |
%Cement house | High–High | 81.12 | 3.87 | 0.055 | 79.04 | 6.18 | 0.258 |
Low–Low | 74.93 | 3.24 | 0.867 | 68.43 | 3.10 | 0.116 | |
Low–High | 69.29 | 9.79 | 0.501 | 82.20 | 4.06 | 0.311 | |
High–Low | 81.01 | 5.51 | 0.365 | ||||
No cluster | 74.52 | 1.33 | Ref | 74.84 | 1.32 | Ref | |
Nb houses (100s) | High–High | 34.29 | 6.88 | 0.007 | 40.86 | 10.98 | 0.023 |
Low–Low | 316.26 | 57.88 | <0.001 | 164.45 | 58.04 | 0.593 | |
Low–High | 49.42 | 16.12 | 0.165 | 44.37 | 13.73 | 0.076 | |
High–Low | 185.53 | 59.30 | 0.918 | ||||
No cluster | 173.12 | 14.58 | Ref | 194.17 | 16.77 | Ref | |
Public Transport Stops Density | High–High | 16.32 | 2.12 | <0.001 | 22.51 | 1.44 | 0.001 |
Low–Low | 4.20 | 0.63 | 0.237 | 7.74 | 0.89 | 0.032 | |
Low–High | 17.11 | 2.63 | 0.001 | 25.83 | 2.16 | <0.001 | |
High–Low | 8.42 | 2.63 | 0.255 | ||||
No cluster | 6.00 | 0.64 | Ref | 6.50 | 0.63 | Ref |
Transport Matrix | Distance Matrix | |||
---|---|---|---|---|
w/o | with | w/o | with | |
%No education | 0.047 2.2% | 0.081 36.9% | 0.047 2.1% | 0.072 73.1% |
%Cement houses | 0.0035 11.5% | 0.0045 36.9% | 0.0034 10.8% | −0.027 0.03% |
Nb houses (100s) | 0.0023 46.6% | 0.0023 18.9% | 0.002 48.1% | 0.0036 23.3% |
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Lefebvre, B.; Karki, R.; Misslin, R.; Nakhapakorn, K.; Daudé, E.; Paul, R.E. Importance of Public Transport Networks for Reconciling the Spatial Distribution of Dengue and the Association of Socio-Economic Factors with Dengue Risk in Bangkok, Thailand. Int. J. Environ. Res. Public Health 2022, 19, 10123. https://doi.org/10.3390/ijerph191610123
Lefebvre B, Karki R, Misslin R, Nakhapakorn K, Daudé E, Paul RE. Importance of Public Transport Networks for Reconciling the Spatial Distribution of Dengue and the Association of Socio-Economic Factors with Dengue Risk in Bangkok, Thailand. International Journal of Environmental Research and Public Health. 2022; 19(16):10123. https://doi.org/10.3390/ijerph191610123
Chicago/Turabian StyleLefebvre, Bertrand, Rojina Karki, Renaud Misslin, Kanchana Nakhapakorn, Eric Daudé, and Richard E. Paul. 2022. "Importance of Public Transport Networks for Reconciling the Spatial Distribution of Dengue and the Association of Socio-Economic Factors with Dengue Risk in Bangkok, Thailand" International Journal of Environmental Research and Public Health 19, no. 16: 10123. https://doi.org/10.3390/ijerph191610123
APA StyleLefebvre, B., Karki, R., Misslin, R., Nakhapakorn, K., Daudé, E., & Paul, R. E. (2022). Importance of Public Transport Networks for Reconciling the Spatial Distribution of Dengue and the Association of Socio-Economic Factors with Dengue Risk in Bangkok, Thailand. International Journal of Environmental Research and Public Health, 19(16), 10123. https://doi.org/10.3390/ijerph191610123