Improving equity in malaria treatment: Relationship of socio-economic status with health seeking as well as with perceptions of ease of using the services of different providers for the treatment of malaria in Nigeria

The study areas were four malaria-endemic communities (towns) in Enugu State, Southeast Nigeria, namely Udi and Nachi in Udi Local government area (LGA), and Inyi and Oji in Oji-River LGA. Udi and Oji are the LGA headquarters, while Nachi and Inyi are not. Each town has a population of at least 20,000 people, while majority of the residents are either subsistence farmers or small time traders. Each town is comprised of at least seven component villages and is an autonomous unit headed by a traditional ruler. Udi and Oji have a minimum of a government owned general hospital and a primary health care centre, together with private hospitals/clinics to complement the public providers. There is a comprehensive health centre and a primary health centre in Inyi, while Nachi is devoid of the presence of any public healthcare provider. Patent medicine stores, itinerant drug providers and herbalists can be found in these towns. Plasmodium falciparum causes more than 90% of all malaria cases in the study area [18].

The framework of the study is based on the premise that in order to finally consume malaria treatment services, three stages are involved. Stage 1 is concerned with the patient deciding on where to receive treatment in terms of geographic access, which is usually linked to geographic proximity of the healthcare provider. It also depends on the severity of illness. In stage 2, which occurs after the patient has visited the provider the next consideration is how easy it is to consult/see a healthcare provider in the facility visited. Stage 3 deals with issues of actually receiving definitive treatment in terms of collection of drugs. Hence, the manner that different SES groups perceive the three stages has a direct bearing of their level of accessibility of the different providers of malaria treatment services. Based on the contextual framework, operational definitions for the three stages as applied in the study are: (1) Near (geographic proximity): It refers to geographic access; (2) Ease of accessing or attending: It refers to the processes whereby patients get to a health facility, get registered and allowed to see a provider that would diagnose and prescribe treatment; and (3) Ease of receiving treatment: It refers to processes of collection of drugs.

A cross-sectional design was used and data was collected using a household survey. In each community four villages were selected by simple random sampling from a sample frame of a list of the villages. A listing of households in each selected village was undertaken to produce the sampling frame. Using the sampling frame, 370 households out of approximately 1,100 households per community were selected from the villages within each community using simple random sampling, with each village contributing equal numbers of households. In each selected household, one woman (primary care giver) or in her absence, male head of household was interviewed using a pre-tested questionnaire. The sample size for the study was a maximum of 300 respondents in each community which was based on an average malaria incidence rate of 10 – 15% in Enugu state [18], 95% confidence level, and 80% power. However, in order to control for refusals and incomplete questionnaires, 370 respondents were selected and approached for interview in each community.

The questionnaire was divided into different sections. The first section was used to collect socio-demographic data about the respondent and his/her household. The second section was used to collect data on actual malaria treatment-seeking behaviour, using one month recall period. In examining treatment-seeking behaviour, the questionnaire explored: How the respondents knew that they had malaria; no of days they were sick with malaria; whether they sought treatment; number of days that elapsed between the time they noticed that they were ill and the time they sought treatment; where they sought treatment and the reasons for doing so; amount of money that they paid to receive treatment; and cost of transportation to receive the treatment. The third section collected data about respondents' perceptions of ease of accessing and utilizing the services of different providers using a series of three questions to ask about the different attributes. The respondents were first presented with the wide choice of the different malaria treatment providers and were first asked how near to them the providers were. They state either yes or no to each provider and were allowed multiple answers. Then, in a similar manner, the respondents were asked how easy it was for them to actually attend the different providers for the treatment of malaria and lastly how easy it was for them to receive malaria treatment services from the providers. The last section of the questionnaire was used to collect data on household asset holdings as well as food expenditure.

Tabulations were used to analyse the quantitative data. The cost of treatment was computed as treatment cost plus transportation cost. Principal components analysis (PCA) was used to generate an asset-based household socio-economic status (SES) index [14, 19] that was used to investigate the equity implications of the findings. Information on ownership of a radio, bicycle, motorcycle, motorcar, refrigerator, together with the weekly household cost of food was used to generate the index.

The SES index was used to divide the households into SES terciles, which were then used to determine the equity implications of some of the key variables. The three SES groups were: the highest SES group (Q3) or least poor; middle SES group (Q2) or average; and lowest SES group (Q1) or most poor. Three SES groups were used instead of the more widely used five groups (quintiles) and four groups (quartiles) because the socio-economic class differences in the rural communities are narrow because of similar income generation activities at that level. Hence, it is more realistic to use two or three SES groups to differentiate the households rather than quintiles or quartiles. Chi-square analysis for trend was used to determine the statistical significance of the differentiation of the dependent variables into SES terciles. The measure of inequity was the concentration index [20, 21]. The concentration index varies from -1 and +1 and a negative sign shows that the variable of interest is higher among the poorest and if positive, it means that it is more among the richest (or least poor).

The number of questionnaires that were completed and acceptable for data analysis in the four groups of respondents was 356 in Inyi, 326 in Udi, 346 in Oji-river (Oji), 323 in Nachi (Table 1). Most of the respondents were females and they were either the wives or representatives of the household heads. Majority of the respondents from the local government headquarters (Oji and Udi) had some level of formal education but majority of respondents were not educated in Nachi while in Inyi it was 50%. Most of the households had approximately four residents, with the highest number of residents per household were from Inyi. The household food costs were highest in Inyi. Radio sets were the commonest movable asset owned by households while motorcar was the least common asset by households. Most households from Oji owned refrigerators.

While a slight majority (56.5%) of the respondents in Inyi had malaria within a month to the date of the interview, minority of the respondents in the other three communities had malaria (Table 2). Most of the people that had malaria sought one form of treatment or the other for the illness, although the lowest proportion that sought treatment was found in Oji community (77.14%). The longest delays before seeking treatment were found in Nachi and the shortest delays in Oji. The longest duration of illness was found in Udi and Inyi at approximately nine days respectively.

Overall, the patent medicine dealers (PMDs) were the providers that were perceived to be geographically most accessible to the people in the communities, with the exception of Oji where it was public hospital (Table 3). The next nearest set of providers to the people were private hospital in Inyi and public hospital in Udi, while it was traditional healers in Nachi and patent medicine dealers in Oji. The public hospitals were not near to people from Nachi and the community-health workers (CHWs) were not near to the people at all they were non-existent in the study areas. The patent medicine dealers were the providers that people perceived most easily accessed for services for the treatment of malaria in all the groups, except for Oji, where it was public hospital. Table 3 also shows that the majority of respondents generally found that it was easy to receive treatment from patent medicine dealers, except in Nachi, where the majority of the respondents found it easy to receive treatment from herbalists.

There were some SES differences in perceptions of proximity of the different healthcare providers were to the people in some of the study areas (Table 4). From the statistically significantly differences, the results show that apart from healthcare centre in Nachi, the most poor SES groups did not perceive all other healthcare providers to be near to them when compared to the average least poor SES groups. As was seen in the case of geographic access, there were statistically significant SES differences in perceptions of ease of accessing services, with average and least poor SES perceiving more ease of access to the providers compared to most poor SES (Table 5). In the instances where the results were statistically significant (in Inyi and Oji), the most poor respondents found it most difficult to access the services of the various healthcare facilities/providers. These were in cases of visits to herbalists in Inyi and Oji, private hospitals, public hospitals and patent medicine dealers in Oji, and community health workers and health center in Inyi.

There were evidences of socioeconomic inequity in perceived ease of receiving treatment for malaria from the various healthcare providers (Table 6). As in the case of perceptions of geographic accessibility and ease of receiving treatment, the average and least poor SES groups stated that it was easier for them to receive treatment from healthcare providers compared to most-poor SES group, with the exception of traditional healers and health centers in Nachi. In Inyi the highest proportion of respondents who perceived that it was easy to receive treatment from public hospitals belonged to the average SES group (p < 0.05). A similar result was found in the use of herbalists, private hospital, and public hospital in Oji. Statistically significant inequities were found in the use of community health workers in Inyi and use of patent medicine dealers in Oji. In Nachi, the highest proportion of people that found that it was easy to receive malaria treatment services from herbalists and health centre were from the most poor SES followed by the average SES group (p < 0.05).

There was generally statistically insignificant differences in incidence of malaria across the three SES groups in three groups, with the only exception been in Udi where the most poor SES had the lowest incidence of malaria (p = 0.05). Self-diagnosis was the procedure that was used by most of the respondents that had malaria to diagnose the illness. Laboratory tests, though the second most common method of diagnosis was not commonly used by the respondents. It is possible that some respondents used more than one procedure to diagnose their illnesses. Home treatment (treatment at home with already existing/stored drugs without recourse to a health provider) was not a common source of first treatment (Table 7). Traditional medicines were very common sources of treatment in Nachi (27.59%) and they were the third most common source of treatment in Inyi (12.94%).

Distance of the healthcare provider to the consumers was a strong determinant of where people first sought treatment for malaria. Readily availability of drugs was the second overall most important reason that people gave for seeking for care from various providers. The quality of services was also an important determinant of where people first sought treatment, though it had the highest proportion of people in only Inyi.

While the most poor SES were most likely to seek treatment in Oji group (p < 0.05), the least poor SES in Udi had the least delay before seeking care for malaria (p = 0.07). There were statistically insignificant differences in the number of days the ill people had malaria. Concentration index shows that the rich had malaria more than the poor except for Oji where more of the respondents are from the poor group. The results also show that the least poor sought for treatment more than the most poor in all the study areas.

There was some evidence of socio-economic differentials with regards to the providers where treatment was first sought, although some of the directions of inequity were not uniform (Table 8). For instance, while the least poor SES respondents used home treatment more than the most poor in Inyi, the reverse was found in Udi (p < 0.05). However, the most poor SES used more of traditional medicines and least of private hospitals and clinics in Udi (p < 0.05). The remaining statistically significant evidences on socio-economic inequity were found in Nachi, where the most poor SES were most likely to use services of patent medicine dealers and in Udi, where the least poor SES were most likely to use the services of laboratories for the treatment of malaria (p < 0.05). The findings also show that there was no socio-economic difference with regards to the number of people that recovered after the first treatment action that was taken. The study indicates that the poor had more treatment of malaria in Udi while reverse is the case in Nachi and Inyi. The result also shows that in Inyi, the poor will go for traditional medicine, private hospital/clinic and patent medicine dealers more than the rich at concentration index of -0.08, -0.04 and -0.05 respectively.

The least poor SES generally spent more money to treat malaria, although the finding was only statistically significant in Nachi (p < 0.05) and slightly significant in Inyi (p < 0.10) (Table 9). Similarly, the least poor SES spent more on transportation to treat malaria and the finding was statistically significant in Udi and Nachi (p < 0.05). However, the opposite was found in Oji where the most poor SES actually spent the highest amount of money on transportation (p < 0.05). In Nachi, there was statistically significant SES differentials in total financial cost to treat malaria, with a progressive increase in costs and one moves from the most to the least poor SES (p < 0.05). The time costs to the least poor households were more as seen in Inyi and Nachi.