The economic consequences of malaria for households: a case-study in Nepal

MTH pdiiy ELSEVIER

Health

Policy 29 (1994) 209-227

The economic consequences of malaria for households: a case-study in Nepal Anne Mills Health Policy Unit, London School of Hygiene and Tropical Medicine. Keppel Street, London WC1 7HT, UK Received

21 March

1994, revision

received

19 April

1994, accepted

19 April

1994

Abstract

Increased attention has recently been paid to the impact of illness on the well-being of households in developing countries. This has been a particular theme in the case of malaria, but relatively little evidence is available on how households react to malaria and on its impact on expenditure and time allocation patterns. This paper reports the results of a study designed to investigate the economic consequences of malaria for households in Nepal. A household survey of malaria cases in two districts provided information on use of various sources of treatment, their cost to households, time lost by the person with malaria, the extent to which others inside or outside the household provided assistance with the normal work of the malaria patient, the time spent caring for a child with malaria and any financial losses associated with the malaria episode. Out-of-pocket expenditure on treatment differed greatly between the two districts, for reasons associated with the choice of public or private sources of treatment and the number of visits made per episode. The majority of households appeared to cope without great difficulty with the reduction in labour supply caused by a malaria episode, by drawing largely on the time of adult family members. Caution is advised in extrapolating the results to other situations, given the extent to which local factors are likely to influence the impact on households. Moreover, the findings relate to a situation where a malaria control programme is in place: a relatively greater impact per household would occur in the absence of control. However, it is argued that such surveys have value in informing health policy, particularly in relation to setting priorities and treatment policy. Keywords:

Economic impact; Health-seeking

0168-8510/94/$07.00

0

behaviour; Malaria; Nepal

1994 Elsevier Science Ireland

SSDI 0168-8510(94)00657-Z

Ltd. All rights reserved

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1. Introduction There has recently been a renewal of interest in the consequences of illness for the economic wellbeing of households in developing countries, with treatment costs and production losses particularly being emphasized [ 1,2]. However, it is remarkable how little empirical evidence exists of serious consequences for households resulting from illness [ 11.Partly this results from the methodological difficulties: the economic impact of illness is mediated by the response of households, which may both reduce and disguise the actual impact. Moreover, measures of infection, often the only indicators of illness available, can be a very poor indicator of a disease’s physical impact, and economists have had difficulty taking account of the various dimensions and degrees of severity of ill health in their econometric models. For example, in the case of many tropical diseases, individuals may be infected but without clinical symptoms, or infected but with differing levels of incapacity. A further difficulty for those wishing to use data on the economic impact of particular tropical diseases to influence policy-makers is that the impact is likely to vary considerably both between and within countries. This is particularly true for a disease such as malaria, where different parasite species result in clinical symptoms of differing severity, and different transmission patterns and intensity of transmission result in different levels of immunity of the population. It is thus difficult to extrapolate from a study done in one part of the world to other ecological, economic and epidemiological settings. The study reported here was designed to investigate the economic consequences of malaria for households in Nepal. The particular characteristics of malaria in Nepal (especially the predominance of the parasite species P. vivux) and the existence of a relatively well-organized malaria control programme meant that malaria resulted in a relatively brief episode of illness, and cases were scattered geographically. Estimated average malaria incidence at district level at the time of the field work (1984-85) ranged between 1.34 and 12.17 cases per 1000 people, with a national average of 4.22 [3]. These data are derived both from active case detection, involving field workers making monthly visits to houses to identify fever cases, and passive case detection, where patients seek care at health facilities; in both approaches infection is identified by laboratory examination. 2. Methods The standard methodological approach to studying economic impact as exemplilied by Conly [4] and Audibert [5], of taking a sample of households and obtaining data for a crop cycle in order to correlate incidence of malaria and economic activities and outputs, would have been very expensive in Nepal, given the low incidence and hence large sample size required, and would have run a high risk of producing inconclusive findings, given the variety of factors apart from malaria likely to affect the economic activities of subsistence farmers. Hence the study concentrated instead on gathering detailed information about household responses to malaria over a relatively short time period. The household was chosen as the unit of study because

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the local economy was based on subsistence farming, and the household rather than the individual was the relevant unit of economic activity. In order to be able to compare households with malaria and households without it, a case-control design was adopted. However, given the difficulties of inquiring about time use patterns, the study also sought the views of the malaria patient about the impact of malaria. Thus two comparisons were possible: of the case with the control; and of the case with and without malaria. Two small contrasting areas were chosen for the survey, one in the north of Dhanusa district and the other in Nawal Parasi district. Both were areas where malaria control had encountered difficulties, and hence it was expected that a reasonable number of cases would be available for study in an accessible geographical area. The areas differed physically: the Dhanusa area was in foothills close to forests, with largely subsistence farming. The Nawal Parasi area was in the flat plain (Terai), with large landowners mixed amongst subsistence farmers. The difficulties faced by malaria control were believed in Dhanusa to stem from transmission of malaria in the forest areas where farmers took cattle to graze, and in the Nawal Parasi area from ‘borrow pits’ created in the construction of irrigation canals. While cases in Dhanusa were almost entirely P. vivax, Nawal Parasi in the recent past had experienced a number of P. falciparum cases (P. falciparum tends to produce a more severe illness, with a greater degree of incapacity [6]). A questionnaire was used to obtain information on household economic characteristics and time allocation patterns, the period of incapacity caused by malaria and other illnesses, the amount of assistance required to cover the normal work of the sick person, treatment choices and their costs, and any financial losses the household experienced as a result of the malaria episode. The delinition of ‘work’ adopted was deliberately broad, to cover both work inside and outside the household, and followed the approach of Acharya and Bennett [7]. The conduct of the survey was contracted to New Era, a Nepali research organization. Local interviewers were recruited who spoke the local languages. Cases were detected either by the routine monthly house-to-house visits by malaria field workers (active case detection) or by the patient visiting the various official sources of treatment (passive case detection): malaria volunteers, malaria clinics, malaria offices and health posts and hospitals. The standard procedure was for a blood slide to be taken and presumptive treatment given. If slides were positive, patients were followed up and given a complete course of treatment. At the time of detection, cases were notified to the interviewers and an interview conducted as quickly as possible. A neighbourhood control of similar age and sex but without malaria was also interviewed. Both case and control were interviewed again two weeks later, and a third time if the patient had not recovered by tl second interview. The survey period was one year, with all cases detected in that period in the two areas to be interviewed. In Nawal Parasi, 286 cases were included in the survey, and in Dhanusa, 409. In the latter area, the interviewers were overwhelmed by cases in a remote part of the area, and omitted them if they were unable to conduct an interview within 2 weeks of case detection. The household characteristics of these cases were later recorded

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to check for any bias that might result from their omission: in most respects they were similar to included cases. This paper reports the findings of the survey for each district separately, since statistical analysis of the main economic characteristics of malaria patients indicated significant differences between districts. It considers the economic circumstances of households, malaria patient characteristics, use of treatment services, expenditure on treatment and travel, the impact on household productive time of both an adult and child malaria episode, and consequences of the malaria episode for household cash availability. Means and standard deviations are used to summarize the data, except where data are highly skewed, when the median is given in addition to the mean. Another paper [8] has analysed in depth the data on the effect of malaria on work time, taking the sample as a whole and using multivariate analysis of the case-control data to try to explain variations in work-time loss.

3. Results

3.1. The local economy The main occupation of patient household heads was own agriculture in both Dhanusa (66%) and Nawal Parasi (62%). The remaining household heads were predominantly wage labourers (27% Dhanusa, 19% Nawal Parasi). 38% of household heads had wage labour as their secondary occupation in Dhanusa, and 22% in Nawal Parasi. The great majority of households had registered land - 69% in Dhanusa and 86% in Nawal Parasi - and a slightly different proportion (76% and 85%) cultivated land (the difference between land registered and cultivated is accounted for by rented land). The mean registered land holding (of those with registered land) was 1.34 ha (median 1.O) in Dhanusa and 1.90 ha (median 0.99) in Nawal Parasi, and the mean cultivated land (of those cultivating land) was 1.52 ha (median 1.05) and 2.11 ha (median 1.12). Average household size was 5.57 (SD. 2.31) in Dhanusa and 7.40 (SD. 3.97) in Nawal Parasi. In the former area 90% of household heads had never been to school, and in the latter 77%. Households that experienced malaria were thus predominantly engaged in subsistence farming but with quite substantial wage labour activity. Data from control households provide a picture of time spent in the absence of malaria on daily activities, and relative responsibilities between the sexes for household maintenance and directly productive activities (Table 1 for Dhanusa and Table 2 for Nawal Parasi). This information was obtained by asking for a description of activities ‘yesterday’. The period of time spent in work activities was relatively long, at 8 to 11 h. There was a markedly different pattern of activities between men and women, with women spending the majority of their time in household maintenance and men in directly productive activities. Women were less likely than men to declare themselves as not working, except in the age group 55 and over, where consistently around 25% of men and women in both areas were not working.

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Table 1 Mean hours per day per person working or studying by age group and sex, Dhanusa Age group and sample size IO-19 (n = 229) 20-44 (n = 344) Hours % Hours %

45-54 (n = 70) Hours %

55+ (n = 45) Hours %

3.20 0.78 0.22 0.55 0.02 0.00 0.05 0.28 0.13 0.03 0.43 0.05 2.05 0.97

41.0 10.0 2.8 7.1 0.2 0.0 0.6 3.6 I.7 0.4 5.6 0.6 26.3 12.4

1.57 2.43 0.35 1.60 0.15 0.05 0.13 0.70 0.05 0.27 0.98 0.28 0.13 0.60

18.0 28.0 4.0 18.4 1.7 0.6 I.5 8.0 0.6 3.1 II.3 3.3 1.5 27.6

1.30 3.72 0.07 0.97 0.08 0.00 0.12 0.63 0.00 0.23 1.13 0.00 0.00 1.60

15.8 45.1 0.8 11.7 1.0 0.0 I.4 7.7 0.0 2.8 13.7 0.0 0.0 19.4

1.58 5.08 0.12 0.12 0.27 0.00 0.00 0.47 0.35 0.12 0.00 0.00 0.00 0.93

19.5 62.8 1.4 1.4 3.3 0.0 0.0 5.8 4.3 I.4 0.0 0.0 0.0 1I.5

4.78

61.3

6.53

75.1

6.65

80.6

7.17

88.5

7.80

100.0

8.70

100.0

8.25

100.0

8.10 100.0

25.8

24.6

Males, control households

I. Animal husbandry 2. Agriculture 3. Hunting/gathering 4. Fetching fuel 5. Manufacturing 6. Food processing 7. Construction 8. Domestic work 9. Child care 10. Trading 1I. Agriculture wage labour 12. Non-agricultural work 13. Study Total household maintenance (4, 6, 8, 91 Total directly productive (I, 2, 3, 5. 7, IO, II, 12) Total all work (includes 13) Percentage not working/studying

16.8

8.9

Age group and sample size IO-19 (n = 191) 20-44 (n = 370) Hours % Hours %

45-54 (n = 68) Hours %

55+ (n = 60) Hours %

3.03 0.13 0.03 0.20 0.05 0.57 0.00 2.87 0.75 0.00 0.12 0.07 0.62 4.38

36.0 1.6 0.4 2.4 0.6 6.7 0.0 34.0 8.9 0.0 1.4 0.8 7.3 52.0

I.10 0.23 0.02 0.28 0.07 0.87 0.12 5.38 1.03 0.07 0.33 0.10 0.00 7.57

II.5 2.4 0.2 3.0 0.7 9.0 1.2 56.1 10.8 0.7 3.5 1.0 0.0 78.8

1.25 0.38 0.00 0.10 0.23 1.02 0.00 4.70 0.83 0.00 0.38 0.07 0.00 6.65

13.9 4.3 0.0 I.1 2.6 II.3 0.0 52.4 9.3 0.0 4.3 0.7 0.0 74.2

0.93 0.15 0.00 0.23 0.00 0.50 0.00 5.07 0.73 0.07 0.08 0.12 0.00 6.53

II.8 1.9 0.0 3.0 0.0 6.3 0.0 64.3 9.3 0.8 I.1 1.5 0.0 82.9

3.43

40.7

2.03

21.2

2.32

25.8

1.35

17.1

8.43

100.0

9.60

100.0

8.97

100.0

7.88 100.0

0.0

25.0

Female. control households

I. Animal husbandry 2. Agriculture 3. Hunting/gathering 4. Fetching fuel 5. Manufacturing 6. Food processing 7. Construction 8. Domestic work 9. Child care IO. Trading I I. Agriculture wage labour 12. Non-agricultural work 13. Study Total household maintenance (4, 6, 8, 9) Total directly productive (I, 2, 3, 5, 7, IO, II, 12) Total all work (includes 13) Percentage not working/studying

12.6

2.7

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Table 2 Mean hours per day per person working or studying by age group and sex, Nawal Parasi Age group and sample size IO-19 (a = 201) 20-44 (n = 251) Hours % Hours %

45-54 (n = 59) Hours %

55+ (n = 47) Hours %

Males, control households

1.Animal husbandry 2. Agriculture 3. Hunting/gathering 4. Fetching fuel 5. Manufacturing 6. Food processing 7. Construction 8. Domestic work 9. Child care 10. Trading Il. Agriculture wage labour 12. Non-agricultural work 13. Study Total household maintenance (4, 6, 8, 9) Total directly productive (1, 2, 3, 5, 7, 10, II, 12) Total all work (includes 13) Percentage not working/studying

2.37 1.55 0.17 0.30 0.07 0.12 0.02 0.55 0.10 0.28 0.28 0.68 2.42 1.07

26.6 17.4 1.9 3.4 0.7 1.3 0.2 6.2 I.1 3.2 3.2 7.7 27.2 12.0

2.10 3.17 0.03 0.40 0.17 0.12 0.08 0.88 0.03 0.93 0.72 1.53 0.08 1.22

20.5 30.9 0.3 3.9 1.6 I.1 0.8 8.6 0.3 9.1 7.0 15.0 0.8 14.0

1.88 3.27 0.00 0.82 0.58 0.00 0.23 0.45 0.28 0.47 0.87 0.47 0.00 1.55

20.2 35.1 0.0 8.8 6.3 0.0 2.5 4.8 3.0 5.0 9.3 5.0 0.0 16.6

2.38 3.17 0.00 0.02 0.38 0.05 0.18 1.65 0.17 0.38 0.08 0.72 0.05 1.88

25.8 34.3 0.0 0.2 4.2 0.5 2.0 17.9 1.8 4.2 0.9 7.8 0.5 20.4

5.42

60.9

6.93

67.6

7.77

83.4

7.30

79.1

8.90

100.0

10.25 100.0

9.32

100.0

9.23 100.0

11.5

7.6

16.0

27.0

Age group and sample size IO-19 (n = 172) 20-44 (n = 242) Hours % Hours %

45-54 (n = 45) Hours %

55+ (n = 44) Hours %

Females, control households

1. Animal husbandry 2. Agriculture 3. Hunting/gathering 4. Fetching fuel 5. Manufacturing 6. Food processing 7. Construction 8. Domestic work 9. Child care 10. Trading 11. Agriculture wage labour 12. Non-agricultural work 13. Study Total household maintenance (4, 6, 8, 9) Total directly productive (1, 2, 3, 5, 7, 10, 11, 12) Total all work (includes 13) Percentage not working/studying

1.57 0.78 0.02 0.62 0.05 0.15 0.07 5.12 1.02 0.03 0.25 0.12 0.82 6.90

14.8 7.4 0.2 5.8 0.5 1.4 0.6 48.3 9.6 0.3 2.4 1.1 7.7 65.1

1.20 I .07 0.03 0.37 0.15 0.30 0.07 6.68 1.25 0.27 0.28 0.05 0.00 8.60

10.2 9.1 0.3 3.1 1.3 2.6 0.6 57.0 10.7 2.3 2.4 0.4 0.0 73.4

1.88 0.75 0.00 0.13 0.00 0.28 0.00 5.22 2.00 0.05 0.48 0.00 0.00 7.63

17.4 6.9 0.0 1.2 0.0 2.6 0.0 48.3 18.5 0.5 4.5 0.0 0.0 70.7

1.88 0.38 0.00 0.20 0.00 0.43 0.00 4.48 2.38 0.00 0.00 0.27 0.00 7.50

2.88

27.2

3.12

26.6

3.17

29.3

2.53 25.2

10.60

100.0

10.80 100.0

10.03 100.0

5.6

24.2

4.6

11.72 100.0 3.3

18.8 3.8 0.0 2.0 0.0 4.3 0.0 44.7 23.8 0.0 0.0 2.7 0.0 74.8

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40

30

Es? 20

IO

0

_

u-4

5-14

15-24

25-34

35-44

45-54

55+

_

50

Nawal

15-24

25-34

35-44

45-54

Parasi

55+

Age group Male Female -t_ - _F___ Fig. 1. Age distribution by sex.

3.2. Patient characteristics

The age and sex distribution of patients by area is shown in Fig. 1. Cases were concentrated in teenagers and young adults, with a slightly different age profile between areas and between males and females. Mean age was 25.64 (SD. 11.23) for males and 23.65 (SD. 14.96) for females in Dhanusa, and 22.20 (S.D. 12.39) for males and 18.51 (SD. 13.92) for females in Nawal Parasi.

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The great majority of infections were of P. vivax, with only 1% in Dhanusa and 20% in Nawal Parasi being infected with P. falciparum. 79% of cases in Dhanusa were indigenous (i.e. classified as being infected within Nepal) and 2% imported, in contrast to 61% and 34% in Nawal Parasi (the latter area borders districts in India where malaria was a major problem). 41% of cases in Dhanusa and 39% in Nawal Parasi reported having had a previous malaria episode in the last 12 months. 88% of these episodes were said to have been treated. Less than 1% of patients in Dhanusa and 8% of patients in Nawal Parasi used a mosquito net. Very few patients in either district lived in houses with both doors and windows screened; 18% in Dhanusa and 6% in Nawal Parasi had doors screened. Comparison of case-control pairs indicated that in Dhanusa, cases were more likely than controls to have wage labour as their main or secondary occupation (CMH

Table 3 Use of treatment services

Sought treatment outside the home (n) Mean days before seeking treatment (median) Percentage making 1 visit 2 visits 3 visits Place of first visit: Hospital Health post Malaria office Malaria volunteer Faith healer Drug seller Private doctor Other (a) Mean (median) minutes travelled on first visit to Hospital/health post Malaria offtce Malaria volunteer Faith healer Drug seller Private doctor Reason for not seeking treatment outside the home: MFW visited Waited for MFW No need Other (n)

Dhanusa

Nawal Parasi

61.0% (249) 3.1 (3.0)

84.5% (239) 5.0 (4.0)

95.2% 4.8 0.0

46.1% 42.1 11.2

0.0%

2.4 21.3 63.9 2.0 2.4 1.6 0.4 (249)

1.3% 7.1 10.0 29.1 4.6 13.8 33.1 0.4 (239)

I30 (60) 58 (60) 23 (5) 44 (60) 86 (60) 84 (45)

149 (165) 86 (75) 52 (15) 37 (5) 72 (15) 68 (5)

12.3% 14.5 4.4 8.8 (159)

77.3% 6.8 0.0 15.9 (44)

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test, P < 0.05). Cases also had a significantly less valuable house than controls (paired t test, P = 0.0006), and produced significantly less paddy (P = 0.0001) and maize (P = 0.0043). While cases had less registered and cultivated land, and a lower value of livestock, these differences were not significant. In Nawal Parasi the relationships were consistently the other way: cases were wealthier according to various socio-economic indicators, but none of the differences were significant at even the 10% level, except house value (P = 0.0678). Cases were more likely than controls to give wage labour as the secondary occupation (CMH test, P < 0.05). 3.3. Use 0s treatment services Individuals with a fever had the option of waiting for the monthly visit of the malaria field worker (MFW) or visiting private or public sources of treatment. Table 3 summarizes the behaviour of those who sought treatment. Of note is the high proportion seeking treatment, and in Nawal Parasi the multiple visits and use of private sources. Information on travel time suggests that the latter were on average closer in the Nawal Parasi area than public treatment sources. Patients in Nawal Parasi delayed longer before seeking treatment and taking first visits only, private sources (defined as faith healer, drug seller and private doctor) were visited sooner than public sources (P = 0.0019). The second and third visits in Nawal Parasi were most frequently to public malaria-specific treatment sources, suggesting that some visits to private sources had not resulted in cure. Of those who made a second visit and had visited a private source at their first visit, only 18% made their second visit to a private source, in contrast to 53% to a malaria office or volunteer. However, 38% of all second visits and 31% of all third visits were to private sources. A few of these had already been given presumptive treatment at a public source. It was not possible to generalize about all cases visiting private facilities, because only those who became known to public services were included in the sample. There seemed to be differences between the sexes in the use of different treatment sources. A greater proportion of women than men used malaria volunteers in both districts. A greater proportion of men than women in Dhanusa used malaria offices, and in Nawal Parasi private doctors. 3.4. Expenditure on treatment and travel Table 4 summarizes information on the cost of treatment. Of those who sought treatment, 94% did not pay in Dhanusa, primarily because they attended malaria control services; in contrast only 33% did not pay in Nawal Parasi. The mean cost per person paying was Rs56 ($3.40) in Dhanusa and Rs36 ($2.19) in Nawal Parasi.* The table shows also the median, since it represents better the ‘average’ experience; it tended to be considerably lower than the mean. Although private services were obviously more expensive than the free malaria control services, they seemed fairly moderately priced and in fact were as cheap as or cheaper than the health post. However, the health post cost in the table represents the cost to those who paid ‘$1 = Rs16.46.

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Table 4 Cost of treatment Dhanusa Number paying of those self-treating

6141

215

Mean cost/person self-treating (Rs)

I6

Number paying of those seeking treatment outside the home

I51249

Mean (median) total cost per person paying (Rs)

56

Mean (median) cost in Rs per person paying” for visit to Health post Malaria offtce Malaria volunteer Faith healer Drug seller Private doctor Mean (median) treatment cost per person paying with PV infection PF infection sFor private practitioners, prices were a small minority of users paid, generally provided by the malaria control staff or does not necessarily imply professional

Nawal Parasi

II

1591239

(24)

36 (15)

100 (100) Y I8 (18) 2 II (5) 4 9 (4) 5 186 (35) 3

56 (24) -

31 54 15 36 11 31

(25) (54) (12) (15) (5) (15)

“9 2 IO 7 32 76

27 (15) 63 (20)

paid by all users. For malaria offices and malaria volunteers, for laboratory investigations. It is not known whether these were volunteers, or by an adjacent provider. The term ‘private doctor’ medical qualifications.

something: it was reported to be for either fees or laboratory investigations. Half of those using the health post paid nothing. In Nawal Parasi, 10 patients attending a malaria volunteer paid for laboratory investigations, suggesting that the volunteers may also have been practising on their own account, and in both districts a few patients attending malaria offices paid for certain services. It is possible, however, that patients had reported expenditure made on the trip to the public services but not actually made to the service provider. Those with P. falciparum infections spent considerably more than those with P. vivax infections. 3.5. Impact on household’s productive time: adult malaria episode Time lost from work can be calculated on the basis of self-reported data, or on the basis of a comparison of the work patterns of sick and healthy people. Both approaches were used in the study, and results relating to the former are presented here.

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Table 5 Consequences for household productive time: reported time lost because of adult malaria episode Dhanusa Number of days unable to work: 0 l-5 6-10 II-20 21-30 >30 Mean days disabled per worker ill (SD.) n Number of days partially disabled: 0 l-5 6-10 11-20 21-30 Mean (SD.) days partially disabled per worker ill n

19.8% 56.6 21.4 1.6 0.5 0.0 3.8 (3.2) 364

70.3 26.1 2.5 I.1 0.0 1.0 (2.1) 364

Nawal Parasi

10.3% 25.8 25.4 30.6 7.5 0.4 9.5 (7.5) 252

59.5 22.6 Il.5 5.6 0.8 2.6 (4.7) 252

Assistance with household work provided: Yes No

38.5% 61.5

88.1 11.9

Number of helpers: 1 2 3

83.1 14.0 2.9

73.4 23.0 3.6

Source of help (all helpers): Household member Hired labour Other

82.2 9.8 8.0

87.5 4.8 7.6

Net days lost (days lost minus helpers’ time): Mean (SD.) Median

2.9 (4.2) 3.0

6.0 (11.1) 5.0

Proportion (O/b)of days lost replaced by helpers: Mean (SD.) Median

33 (56) 0

32 20

Reason for no extra help: No-one available Not needed Others can’t do work Missing

39.2 42.9 17.0 0.9

26.7 66.7 6.7 0.0

Did extra work cause problems? Yes No

25.2 74.8

50.5 49.5

(86)

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Table 5 summarizes information on the consequences of a malaria episode of a working-age person for productive time. The period of incapacity caused by malaria differed considerably between the districts. Mean days disabled per person ill were 3.8 in Dhanusa and 9.5 in Nawal Parasi. A further 1 day (Dhanusa) and 2.6 days (Nawal Parasi) were spent partially disabled. It is notable that 20% of cases in Dhanusa lost no days of work, and 10% in Nawal Parasi. There was little difference between the sexes or between different age groups in days of work lost, except that adult women in Nawal Parasi had significantly fewer mean days disabled than men (P = 0.055) and significantly more days partially disabled (P = 0.0002). The same trend was evident in Dhanusa, though only the difference between the sexes for days partially disabled was significant (P = 0.019). Mean days of work lost differed significantly depending on whether those who sought treatment outside the home went first to a public or private facility: mean days of work lost in Dhanusa for those visiting a public facility were 3.9 days and for those visiting a private facility 5.7 days (P < 0.00005) and in Nawal Parasi 9.1 and 10.8 days (P = 0.008). It was not possible to check whether these two groups of patients were similar in terms of disease severity. Days of work lost were also strongly influenced in Nawal Parasi by parasite species: mean days of work lost per person with P. vivax were 8.9 days and with P.falciparum 12.2 days (P= < 0.00005). Assistance was provided with household tasks because of the illness in 39% of Dhanusa households and 88% of Nawal Parasi households. In most cases only one helper was provided, and this person was usually a household member. Only 10% of helpers in Dhanusa, and 5% in Nawal Parasi, were wage labourers. In order to assess how completely the days lost through illness were replaced, total hours of work lost were estimated, based on reported days of total disability and reported hours worked when partially disabled, and converted to hours lost using as a standard daily hours worked by the case’s control. Total hours of help were then calculated, and net time loss and percentage of lost time replaced by helpers calculated. On average around a third of hours lost were replaced by helpers, reducing the days lost (including an allowance for partial disability) to 2.9 in Dhanusa and 6.0 in Nawal Parasi. These means disguise an extremely wide range, partly because data on helpers’ time were inevitably approximate and in some instances exceeded reported time lost. Only 25% of households in Dhanusa where helpers were provided reported that the increased work load for the healthy household members presented problems. This figure was 51% in Nawal Parasi, presumably because of the longer illness episode. Overall, therefore, 10% (39% x 25%) of Dhanusa households faced difficulties with work activities because of additional labour demands, and 45% of Nawal Parasi households. In addition, lack of people available was given as a reason for not providing assistance in 24% of households in Dhanusa and 3% in Nawal Parasi. Very few of the helpers were children: most were adults, the mean age of helpers being 30 years (SD. 14) in Dhanusa and 37 years (S.D. 14) in Nawal Parasi. The most common helpers were husbands or wives, and fathers or mothers. Those households that stated that they faced problems because of increased work loads

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were asked which activities were neglected in order to help with the patient’s work. Common responses were agriculture and domestic work (Dhanusa) and these together with animal husbandry (Nawal Parasi). Only five helpers in Nawal Parasi, and none in Dhanusa, said they neglected educational activities. To assess the significance of the period of incapacity caused by malaria, total available labour days per month per household were calculated and compared with total days disabled due to illness in the last month for all household members in productive age groups and with days disabled due to malaria. On average, 4.2% (Dhanusa) and 7.4% (Nawal Parasi) of available workdays were lost because of illness, with malaria accounting for 2.0% and 4.7%, respectively, of total workdays.

Table 6 Consequences for households’ productive time: reported time lost because of child malaria episode Dhanusa

Nawal Parasi

4.0 (3.6) (n=44)

9.0 (9.6) (n=31)

19.5 20.5

93.5 6.5

3.9 (3.5)

10.9 (10.1)

Number of hours/day spent looking after child: <2 2-4 4-6 6-8 All day

23.5 52.9 Il.8 0.0 II.8

27.6 21.6 13.8 10.3 20.7

Relationship of carer to child: Mother Other

88.6 II.4

82.8 Il.2

Carer able to do normal activities as well: Yes No

68.6 31.4

69.0 31.0

Mean (S.D.) days child not active/child ill

Extra caring time required: Yes No Mean (SD.) days of extra care per child ill

Help with normal activities: Yes No Don’t know

4/ll 7111

519 319 l/9

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3.6. Impact on household’s productive time: child malaria episode

In the case of a child malaria episode, the impact on productive time is felt through the consequences of the time required for caring. Relevant information is presented in Table 6. Since children are drawn into productive activities early in Nepal, questions related to carers’ time were asked only for children aged O-9 years. The mean days a child was not active per child ill was 4 days in Dhanusa and 9 in Nawal Parasi. Extra time was required for caring in the great majority of cases, the amount of time being very similar to the length of the illness. However, only a minority of children needed care throughout the day, and in 69% of cases (in both districts) the carer could do her normal activities at the same time as caring for the child. In those cases where this was not possible, only about half received help. The great majority of carers were mothers. 3.7. Consequences for household cash availability Apart from expenditure on treatment, households lost cash income either because they had to hire wage labour to help with farming or because there was a period when they could not undertake normal income-generating activities, such as wage labour, fetching wood or other trading activities. 2% of patient households in Dhanusa hired wage labour, at a mean cost per household of Rs73 ($4.43) (median Rs56), and 5% in Nawal Parasi, at a mean cost per household of Rs78 ($4.74) (median Rs61). 9% of households in Dhanusa had other reasons for loss of income, mainly through inability to do wage labour, causing a mean loss of Rs169 ($10.27) (median RslOO). In Nawal Parasi these figures were 20% and Rs139 ($8.44) (median R&39), and again the majority of losses resulted from inability to do wage labour. Around 60% of households in both districts reported no other problems caused by the illness. Because households were not followed over a crop cycle, an objective measure of the impact of malaria on production was not available. However, households were asked whether or not they thought that malaria would affect household production. 28% of households in Dhanusa and 29% in Nawal Parasi thought that it would, the main reasons in Dhanusa being inability to cultivate crops (7% of households) or produce items for sale/sell labour (12%). In Nawal Parasi, the main reason was inability to cultivate crops (23% of households). 4. Discussion In Nepal, malaria is primarily a disease affecting adults, and more often men than women. The main aim of this study was not to consider the socio-economic determinants of who gets malaria, and the evidence on variations in socio-economic circumstances between cases and controls should be treated with caution. However, it did seem in Dhanusa that cases ranked worse on a range of socio-economic measures than controls. In particular, they were more likely to be dependent on wage labour. In the Terai, wage labourers are likely to be the poorest [9]; they are also more vulnerable to illness, since it results in immediate cash loss. In Nawal Parasi, the proportion of imported cases was high, and thus the characteristics of those who engag-

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ed in cross-border activity (e.g. migrating to find work, trading) is likely to have strongly influenced who became infected. Nepal, like many other South-East Asian countries, has a considerable network of private practitioners and drug sellers. Rarely do malaria control programmes pay any attention to the activities of the private sector, believing that if free public treatment services are offered, they will be taken up. However, it is common knowledge that many cases spend money on private sources of treatment. For example, in a study of users of two malaria clinics in Thailand, 32% and 18% of patients reported prior treatment, mainly purchase of drugs [lo]. The study reported here has shown that in one area in particular, frequent recourse was made to the private sector. The fact that many patients went first to the private sector and subsequently attended public treatment outlets, and that these patients had a longer than average period of disability, implies that they were spending money on treatment that was inadequate to cure the malaria episode. However, the survey would not have picked up those who were successfully treated by the private sector and never appeared at public facilities. Further investigation of the cost, clientele and treatment practices of private practitioners would be valuable. This study, in common with other time allocation studies [7,11], has found that subsistence farmers work a relatively long day, of 8-l 1 h, with little unused time. Women worked slightly longer hours than men, and had markedly different duties. However, as might be expected with relatively large households, there was sufficient flexibility within most households to cope with a relatively brief reduction in labour supply such as that caused by malaria, except where others could not stand in for the sick person in activities such as wage labour and trading. There has been little attention paid previously to reallocation of tasks within the household to cope with the labour reduction implications of illness, though it has been recognized as an important component of the ‘coping process’ [ 11. If the value to the household of the usual activities of the sick person is higher than that of those undertaken by other household members, it is likely that reallocation will occur. Bonilla and Rodriguez found in an area of Colombia that the work of family members with malaria was virtually always taken over by non-salaried family members rather than by hired workers or neighbours [12]. Family members continued at the same time their normal activities, thus sacrificing leisure [ 131. Nur and Mahran [14] found in the Gezira in Sudan that 62% of the loss of work hours due to malaria and schistosomiasis were compensated for by family members. In the case of malaria, labour hours lost to agriculture were completely compensated for, though primarily by women and children, whose household activities and schooling suffered as a result [ 151. This study found a lower rate of replacement but found little to suggest that major difficulties would be caused to most households by any labour deficit resulting from malaria. Interestingly, helpers were usually adults, few children acting as helpers. Thus adult illness did not adversely affect children’s schooling (though school attendance was very low, especially amongst girls). Since the focus of the study was on short-term effects, it was not possible to examine potential long-term consequences that might result from the impact of illness on savings and investment behaviour. Particularly in areas where immunity reduces the impact of malaria on adults, at-

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tention has been focused on the time implications for mothers of caring for sick children. For example, Shepard et al. assumed that the loss of carers’ time equalled 30% of the length of a child malaria episode [ 161,and Ettling et al. [ 171and Leighton [18] assumed that the time loss for the mother was equal to the whole period of the child’s illness. Time allocation studies face particular difficulties in quantifying time spent in child care, because it is frequently carried out at the same time as other activities, but it is generally argued that child illness places a major additional burden on mothers [l 11. This study was primarily concerned with the additional child care burden imposed by child illness, rather than with the normal child care demands on mothers’ time. It is of note that the mothers in this study did not state that caring for a child with malaria was a major burden for them, that extra care was generally required only for part of the day, and that in the majority of cases mothers could combine child care with their normal activities. 31% could not. Because cases were concentrated at certain periods of the year, it was not possible to check whether the above generalizations held over the whole year or whether there were some times of the year when illness was more costly than at other times. When asked which were the busy months of the year, the majority quoted a period of 5-6 months, which overlapped with the main malaria transmission season. Thus the bulk of the episodes analysed here occurred during the busy agricultural period. However, there may still have been times (for example during planting paddy) when all labour available was required, and illness could mean a reduction in output. The difference in days of disability found here between the two areas cautions against crude comparisons with similar information from elsewhere, particularly if circumstances such as the availability of malaria treatment services and the species of parasite differ. A different survey in Nepal, using malaria workers to collect information on period of total incapacity in six districts [6], found mean days of work lost that were slightly higher than those found here for Dhanusa but reasonably consistent with those for Nawal Parasi. Differences in mean days of work lost were clearly associated with the speed of treatment and species of parasite. The particularly low mean days of work lost found here for Dhanusa may be explained by the fact that the area was one with persistent problems of malaria transmission, where control services were particularly active and the local population well informed about sources of treatment, and where P. vivax predominated. The reliance of this study on following up diagnosed cases and on recall inevitably meant that reports of time loss must be considered approximate. However, the methodology adopted had the virtue that all cases had confirmed malaria. The extent to which the cases included were representative of all cases in the two areas is unknown, though monthly active case detection was conducted throughout the areas. Around 40% of cases in both districts reported having had a previous malaria episode within the last year, suggesting that repeated attacks were not uncommon possibly conferring some degree of immunity. If so, those with only minor symptoms may not have reported themselves to the malaria field workers, and the mean period of disability found here may be an overestimate. It may also be an overestimate because of the reliance on recall: respondents may have reported the whole period of illness as lost work time when in fact not all the time would have been productively spent.

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Evidence of days of disability caused by malaria in other countries has been reviewed elsewhere [19]. The range of days of disability is extremely wide, and there seems to be a tendency for exaggeration, with studies that assume a period of incapacity using a higher figure than those where the estimate originated in an empirical study. In this context the finding of this study, that 10% (Nawal Parasi) and 20% (Dhanusa) of adult patients lost no work time, is of particular note. Detailed analysis of the time allocation data of this study [S] identified the behavioural response of the sick person to the illness as being of importance. This was also apparent in this analysis, in the suggestion that women were completely disabled for fewer days than men, but partially disabled for slightly longer.

5. Conclusions It is important to emphasize that this study was done in a context where the predominant parasite species was P. vivax, where there was an active control programme which in the areas studied helped to maintain estimated incidence at around S/1000 in Dhanusa and 4/1000 in Nawal Parasi, where treatment sources (both public and private) were relatively accessible, where there was a relatively short delay between the start of the illness and treatment and where cases were geographically scattered rather than concentrated in households (multiple cases in households within a relatively short period of time were rare). In this context, the economic consequences of malaria for households appeared to be relatively modest. Free sources of treatment were available fairly close at hand (especially through the volunteer system) and were used by the great majority of patients in one district and by a substantial proportion in the other. Households appeared largely able to cope with the labour supply problems caused by malaria. Substitution of household members for the sick person minimized the consequences for household production and maintenance activities. The most adverse consequences were probably experienced by those households dependent for their living on wage labour or cutting wood for sale, where illness could mean immediate loss of cash income. These results should not be taken to imply that malaria control was unimportant. The situation with control is likely to be a poor reflection of the situation without control. The data presented here were used to help project the resource savings resulting from control, and compared with the cost of control [20]. In areas such as those studied here (i.e. with a relatively high risk of malaria in the absence of control, and with less costly control than in the more hilly areas of Nepal), control appeared well worth while. Two implications for control programmes can be drawn from the analysis here. Firstly, the considerable use made of private treatment sources in one area suggested that more attention should be paid to their activities: to the appropriateness and quality of treatment and, if there were deficiencies, to how to improve their service. Secondly, the evidence here and from elsewhere in Nepal [21] suggests that the introduction of malaria volunteers, who take slides and give presumptive treatment, has been very successful. Treatment has been provided close at hand, thus encourag-

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ing speedy treatment, reducing the period of incapacity and particularly providing a service to women. The survey reported here was originally commissioned to feed into national and donor decisions on the level of investment appropriate for malaria control in Nepal. It was felt by national policy-makers that a demonstration of the economic consequences of malaria for households would support their case for continued investment in malaria control. As with other vector-borne diseases, control programmes can fall victim to their own success: once cases have been reduced to a relatively low level, the disease appears to be of lower priority and its ability to rapidly re-emerge can be overlooked. The results demonstrate many of the difficulties of obtaining evidence on the association of disease and production, and of interpreting the data. What constitutes a major or minor effect is hard to judge on the basis of evidence of loss of work days (even if converted to monetary terms). For example, does the 2% and 5% reduction found to be caused by malaria in household work days in the month in which the malaria attack occurred represent a serious problem or not? The conclusion of this study, based largely on perceptions of affected households, was that on balance and for most households it probably did not, but researchers in other situations might conclude to the contrary on the basis of an effect of a similar magnitude. Does this suggest that investigations such as this one are not worth while? Based on the value of the results, the answer is clearly no. Data on the costs of an illness are of limited use for making decisions on investment priorities on their own, but when the data were fed into a cost-effectiveness analysis that compared the costs of control with the consequences in terms of health effects and resource savings, malaria control appeared to be a priority for Nepal. Moreover, the survey highlighted the use of the private sector for treatment, and the importance of allowing for its role in treatment policy. Finally, the survey results have added to the existing sketchy international knowledge of the impact of malaria on households and their response to it, which hopefully will eventually develop to the point where it is easier to generalize about the impact of malaria in particular locations and the desirable responses of control programmes. The use of household surveys is frequently dismissed on the grounds of cost. In developing countries, however, and particularly in South-East Asia, where skilled interviewers can fairly readily be found and salaries are relatively low, household surveys are not excessively expensive and can provide important information for policy-makers. The study reported here emphasizes the value for health policy of using such surveys to obtain household-based evidence on treatment patterns and health expenditure, and to look in detail at how households respond to and cope with illness. Acknowledgements The survey was financed by the Overseas Development Administration of the British government and carried out under contract by New Era. I am particularly grateful to Dr M.B. Parajuli, formerly Chief, and S.L. Shresta, formerly Deputy Chief, National Malaria Eradication Organization, who greatly facilitated the field work; R.P. Shresta and T.N. Rajbansi of New Era, who supervized the implementa-

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tion of the survey; H. Dimond, who cleaned and edited the data set and did prehminary analyses; J. Picard, who did further analytical work on the data set; and Prof. D. Bradley and Dr M. Colbourne of the London School of Hygiene and Tropical Medicine, who provided advice on the design of the investigation. The author is head of the Health Economics and Financing Programme at the LSHTM, which is supported by ODA. References [l] [2] [3] [4] [5] [6] [7]

[8] [9] [IO]

[l I] [l2] [I31

[I41

[15] [16] 1171

[18] [19] [20] [21]

Feachem, R.G.A., Kjellstrom, T., Murray, C.J.L., Over, M. and Phillips, M.A., The Health 01 Adults in the Developing World, Oxford University Press, Oxford, 1992. Shepard, D.S. (Ed.), Economic impact of malaria in Africa, Tropical Medicine and Parasitology. 42 (3) Supplement 1, 197-224. HMG/WHO/USAID/ODA, Report of an Analysis of NMEO’s Activities for 1984 and 1985. Kathmandu, 1986. Conly, G.N., The Impact of Malaria on Economic Development: a Case-Study (Scientific Publication no. 297) Pan-American Health Organization, Washington, 1975. Audibert, M., Agricultural non-wage production and health status: a case-study in a tropical environment, Journal of Development Studies, 24 (1986) 275-291. Mills, A., The household costs of malaria in Nepal, Tropical Medicine and Parasitology, 44 (1993) 9-13. Acharya, M. and Bennett, L., The Rural Women of Nepal: an Aggregate Analysis and Summary of 8 Village Studies, Centre for Economic Development and Adminstration, Tribhuvan University, Kathmandu, 1981. Picard, J. and Mills, A., The effect of malaria on work time: analysis of data from two Nepali districts, Journal of Tropical Medicine and Hygiene, 95 (1992) 382-389. Seddon, D., Blaikie, P. and Cameron, J., Peasants and workers in Nepal. Vikas Publishing House, New Delhi, 1981. Ettling, M.B., Thimasarn, K., Krachaiklin, S. and Bualombai, P., Malaria clinics in Mae Sot. Thailand: factors affecting clinic attendance, Southeast Asian Journal of Tropical Medicine. 20 (3) 331-330. Leslie, J., Women’s time: a factor in the use of child survival techniologies, Health Policy and Planning, 4 (I) (1989) l-16. Bonilla, E. and Rodriguez, A., Determining malaria effects in rural Colombia, Social Science and Medicine, 37 (9) (1993) 1109-l 114. Bonilla de Castro, E., Development of Research Training Project in Socio-Economics of Malaria Eradication in Colombia: Executive Summary (unpublished report to Special Programme for Research and Training in Tropical Diseases), WHO, Geneva, 1985. Nur, E.T.M. and Mahran, H.A., The effect of health on agricultural labour supply: a theoretical and empirical investigation. In A.N. Herrin and P.L. Rosenfield (Eds.), Economics, Health and Tropical Diseases, School of Economics, University of the Philippines, 1988. Nur, E.T.M.. The impact of malaria on labour use and efficiency in the Sudan. Social Science and Medicine. 37 (9) (1993) 1115-l 120. Shepard, D.S., Ettling, M.B., Brinkmann, U. and Sauerborn, R.. The economic cost of malaria in Africa, Tropical Medicine and Parasitology, 42 (3) 199-203. Ettling, M.B., Thimasarn, K., Shepard, D.S. and Krachaiklin, S.. Econ nit analysis of several types of malaria clinics in Thailand, Bulletin of the World Health Organization, 69 (4) (1991) 467-476. Leighton, C. and Foster, R., Economic impacts of malaria in Kenya and Nigeria (HFS major applied research paper no. 6), Maryland, 1993. Mills. A., The economics of malaria control. In G.A.T. Targett (Ed.), Malaria: Waiting for the Vaccine. Wiley, Chichester, 1990. Mills, A., Is malaria control a priority? Evidence from Nepal. Health Economics. 2 (1993) 333-347. Mills, A., The economic evaluation of malaria control technologies: the case of Nepal, Social Science and Medicine, 34 (9) (1992) 965-972.