Table 4

Rural Laboratory Sample Transport Parameters

India

Kenya

Peru

South Africa

Thailand

Sources


Total Population

1,002,708,291

30,310,235

27,012,899

42,351,345

62,352,043

(1)

Women 35–39

32,872,209

660,717

867,291

1,420,154

2,557,418

(2)

Percent of Women Age 35

0.656

0.436

0.642

0.671

0.820

Land Area (sq km)

2,973,190

569,140

1,280,000

1,221,040

510,890

(1)

Rural Population (% of total)

72.343

66.631

27.232

43.132

80.171

(1)

Roads, total network (km)

3,319,644

63,941.5

72,900

362,099

64,600

(1)

Roads, paved (% of total roads)

45.7

12.1

12.8

20.3

97.5

(1)

Annual HPV DNA Samples processed by HPV Lab equivalent per year

21,600

21,600

21,600

21,600

21,600

Samples processed by Cytology Lab equivalent per year

28,800

28,800

28,800

28,800

28,800

Average speed on Paved Road (km/hr)

90

90

90

90

90

(3)

Average speed on Unpaved Road (km/hr)

45

45

45

45

45

(3)

Driver Yearly Salary (I$)

6,675.33

10,661.03

2,665.93

10,661.03

3,881.02

(4)

Work Hours Per Year

2,300

2,400

2,400

2,100

2,500

(4)

Gasoline Cost per km (I$)

0.12

0.12

0.1

0.12

0.11

(4)

Monthly Maintenance (I$)

250.65

250.65

250.65

250.65

250.65

(4)

Cost of Vehicle (I$)

19,935.33

19,935.33

19,935.33

19,935.33

19,935.33

(4)

Depreciation Annuity Factor

7.7861

7.7861

7.7861

7.7861

7.7861

(4)


Cervical Cytology Laboratory


Density of Screen Eligible Women (per sq km)

1.600

0.155

0.037

0.100

0.802

Lab Area (sq km)

17,994.517

186,148.471

780,578.700

286,901.656

35,895.338

Driving Length (km)

313.446

1,008.143

2,064.433

1,251.581

442.702

Driving Time (hrs)

5.374

21.048

42.940

24.990

5.042


HPV DNA Laboratory


Density of Screen Eligible Women (per sq km)

1.600

0.155

0.037

0.100

0.802

Lab Area (sq km)

13,495.888

13,9611.353

585,434.025

215,176.242

26,921.504

Driving Length (km)

271.452

873.078

1,787.851

1,083.901

383.391

Driving Time (hrs)

4.654

18.228

37.187

21.642

4.366


(1) World Bank's World Development Indicators; (2) US Census Bureau's International Data Base – country-specific estimates; (3) International Center for Tropical Agriculture and World Bank; (4) WHO-CHOICE

Goldhaber-Fiebert and Goldie Cost Effectiveness and Resource Allocation 2006 4:13   doi:10.1186/1478-7547-4-13

Open Data