Trop Med Int Health , 9 : Declining efficacy of Artesunate plus sulphadoxine-pyrimethamine in Northeastern India. Malar J ; 13 : Malaria parasite burden and treatment seeking behavior in ethnic communities of Assam, northeastern India. J Infect , 52 : Persistent transmission of malaria in Garo hills of Meghalaya bordering Bangladesh, north-east India.
Malar J ; 9 : Spatial distribution and r-DNA second internal transcribed spacer characterization of Anopheles dirus Diptera: Culicidae complex species in north-east India. Continuing challenge of infectious diseases in India. Lancet ; : Sharma VP. About us. Editorial board.
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Ahead of print. Current issue. Submit article. Figure 1. Malaria-attributable morbidity and transmission trends during in Tripura, Northeast India. Figure 2. Monthly distribution of malaria cases in Tripura during Source : State Health Directorate of Tripura unpublished data, personal communication Click here to view. Table I. Distribution of malaria cases in different districts of Tripura during , Northeast India Click here to view.
Table II. Meteorological data and monthly distribution of malaria cases for data based on in Tripura, Northeast India Click here to view. Table III. Results of cross-sectional malaria prevalence surveys in malaria endemic blocks of South Tripura district, Tripura, Northeast India Click here to view. Table IV. Relative abundance of anopheline mosquito species in South Tripura district, Tripura, Northeast India Click here to view. Your Account Logout.
The Contextual Determinants of Malaria.
Edited By Elizabeth A. Professor Casman, Hadi Professor Dowlatabadi. Edition 1st Edition. First Published Multilevel analysis is a statistical tool applied to data with nested sources of variability, which involve units at lower level nested within units at a higher level [ 32 , 33 ].
A three-level model was applied as follows Model 0 null model which only contained a random intercept and overall variation in malaria at the village-level. Model 1 contained individual-level covariates, Model 2 contained individual and household-level covariates and full model Model 3 included individual, household and village-level covariates.
In this present paper, only the final model is presented. Variation of the outcomes at the village-level was described by village-level random effect variance and standard error. The percentage of proportional change in variance PCV was calculated between the null model and each subsequent model to examine the extent to which the covariates explained the variation in malaria across villages [ 32 ]. Of all participants in all provinces, gender distribution was equally-represented and the majority of respondents Small proportion of participants in Maluku Bed net ownership at the time of the surveys ranged from Household density was relatively high in all provinces.
Majority of respondents were living in a village with good access to drinking water sources Most of respondents in Maluku and Papua were lived in lower highland zone areas Among all surveyed adults in Maluku, West Papua and Papua, 4.
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Of which, the majority of respondents in Maluku While in both West Papua and Papua, larger proportion of respondents High self-reported malaria prevalence was observed in five districts in Papua province including Jayapura In West Papua province, the highest self-reported malaria prevalence was found in Fakfak.
In Maluku, the prevalence was ranged from 2. Occupation, education and slept under a net last night before the survey were statistically associated with self-reporting malaria in all study sites. Self-reported malaria defined when respondent had malaria within the last months and diagnosed by local health providers.
Italic value indicates a statistically significant association at p -value less than 0. NA variable had p -value more than 0.
The contextual determinants of malaria, edited by Elizabeth A. Casman and Hadi Dowlatabadi
OR odds ratio, CI confidence interval, s. In the bivariate analysis, household-level covariates include bed net ownership, house density, access to public health centers PHCs and wealth index were found to be associated with self-reporting malaria. The final multivariate analysis indicated that odds of reporting malaria were associated with access to safe drinking water, place of residence, community education, bed net coverage and zone.
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No significant association between access to improved drinking water source and self-report malaria in both West Papua and Papua. No association was apparent between self-reporting malaria and zone in both Maluku and West Papua. In all province, the village-level variance decreased as village-level factor included in the model Additional file 1 : Tables S1—S3. The percent change in variance PCV was varied among provinces ranged from 5. This study has revealed individual, household and village-level factors associated with self-reported malaria in Maluku, West Papua and Papua province, Indonesia.
This study demonstrated notable differences in factors associated with self-reported malaria at every level among provinces, suggesting the needs of further site-specific and targeted intervention programmes. This is the first study that provide evidence concerning self-reported malaria prevalence in adults and its associated factors in multi-locations in Indonesia. When examining associations between individual-level risk factors and self-reported malaria, we identified that self-reporting malaria was associated with gender, occupation and education although there were differences between locations.
The odds of reporting malaria tended to be higher in adults who worked in the primary industries Maluku and Papua and tertiary industries West Papua. The finding that people who work in the primary sectors such as farmers, fishermen and miners were more likely to report malaria might be relevant with the ecological setting that exists in Maluku and Papua.
Coastal ecosystems, lowland rice fields and highland forests are common in these islands, which provide an ideal habitat for several malaria vectors, such as Anopheles farauti , Anopheles bancroftii , Anopheles karwari , Anopheles koliensis and Anopheles punctulatus [ 34 ]. Improving knowledge and awareness on malaria risk among rural communities should be the primary goal for these areas.
Therefore, preventive interventions need a strong multi-sectoral collaboration between health and agriculture authorities. Differently, intervention strategies in West Papua should be directed to scaling-up malaria preventive campaigns toward tertiary workforces e. It is also worth noting that Papuan adults who had better educational background tend to self-reporting malaria, but not in Maluku. This difference could be explained by the fact that people who have better education would normally be more knowledgeable and aware of malaria and thus could be more likely to either over-report of having malaria Papua or be aware of malaria prevention practices Maluku.
Other possible reasons could be explained by the fact that in such high transmission area with a considerable well-educated migrant population like in Papua, it was logical that adults with higher education status are more likely to report malaria. In both West Papua and Papua, the association of gender and self-reported malaria was negligible which suggest that in highly endemic areas both men and women could have even probability of acquiring malaria. Variation in evidence regarding associations between gender, occupation and education with malaria however have also been reported in several studies from Congo [ 12 ], India [ 35 ], Malawi [ 36 ] and Cambodia [ 37 ].
These findings suggest that different strategies for malaria prevention and control should be targeted towards different socio-demographical groups in each province, such as approaches to promote awareness on malaria in Maluku could be differ from that in Papua. The use of ITN a night before the survey is associated with self-reporting malaria among Papuan adults; which those who used ITN were less likely to report malaria at the time of the survey.
This finding is inconsistent with the previous study in Papua [ 19 ], which found that ITN has a positive correlation with acquiring malaria; those who slept under a net was likely to have higher odds of malaria. The current study also revealed intriguing evidence on the association between self-reported malaria and bed net ownership. A report from North Maluku could partly explain this finding that there was a common behaviour among adults in this region to regularly go outside at night or sleep outside for several reasons e.
In addition to human behaviour, local vector behaviour could be also other important factor affecting the effectiveness of bed net. For instance, An. Additionally, there is other malaria vector such as An. Indeed, this finding suggests that whilst Indonesian government has massively distributed bed nets towards these high transmission provinces, its utilization and factors associated with the effectiveness and compliance on using bed nets are far from clear and thus further studies should measure bed net utilization and retreatment among these populations to better inform health authorities on designing effective interventions.
Targeted measures have to be applied towards these communities through sensitizing the community on re-treating their nets regularly to improve the effectiveness of bed nets and promoting the use of topical repellents. The association between household wealth status and reporting malaria was observed in West Papua and Papua province.
The study demonstrated that people lived in more affluent richer households more likely to reporting malaria compared to those individuals who lived in the poorest households. When self-report malaria is used as the measure of malaria infection, these findings may be contradicting with general agreements given that poor communities is prone to contracting malaria [ 12 , 40 — 43 ] although the relationship between malaria and poverty at the micro-level e.
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However, the results of this study are supported by the findings of other study in Nigeria where most malaria contracted by better-off socioeconomic status and urban populations [ 45 ]. Whereas others [ 46 , 47 ] also found no clear association between household socioeconomic status and malaria. The fact that the odds of reporting malaria was higher among wealthy and well-educated populations in Papua and West Papua could perhaps partly explained by a great number of migrants come from outside Papua with better socioeconomic status that came to works i.
It is known that there is the largest gold mining in Papua. It is worth noting that illness awareness and perception among wealthier and well-educated people would normally better than those poor people. These richer groups have better capability in recognizing symptoms and access in seeking treatment immediately and hence they are likely to report malaria. However, further epidemiological studies are needed to understand the relationships between socioeconomic status and actual malaria illness since using self-report malaria could be over under report infection [ 47 ].
The results of this study also indicated that distance in terms of time spent to travel from home to the nearest health facility was significantly associated with reporting malaria in all sites. The result was consistent with other observations in Tanzania [ 48 ] and India [ 35 ]. If those people who self-reported malaria were assumed of having true positive malaria, this finding explain that indeed delays in seeking health services to obtain appropriate and timely treatments is one of the factors that may exacerbate the risk of malaria.
Extreme geographical conditions and poor infrastructures e. However, different responses were observed in both West Papua and Papua, which the odds of reporting malaria was lower among those who perceived that there was no health facility available in their neighbourhood.
Such findings could be explained by the fact that this self-reported might not be correlated with malaria infection and it might be that people who live in the area where there is no point-of-care available or even those who live further away are less likely to seek treatment and hence less likely to reporting malaria.
Taken together, limited health services and supporting physical infrastructures could affect their behaviour in seeking treatments which could either lead to greater risk of infection or under-reporting malaria illness. These findings highlight the importance of both strengthening active community-based interventions and strong inter-sectoral collaboration i. This study also highlights the influence of factors operating at the contextual level regard to self-reporting malaria in adults. At the village level, in all provinces, the study showed that self-reporting malaria was associated with the type of residential.
Adults in rural Maluku and West Papua were likely to report malaria compared to those who resided in urban areas.
The Contextual Determinants of Malaria (2002, Paperback)
In addition, those participants in whose village had better water infrastructure seem to less likely to reporting malaria. These findings are consistent with a typical condition where malaria transmission commonly found in such disadvantaged and deprived rural communities [ 19 ]. If self-report malaria is measured as malaria incidence, one reasonable explanation for this is that rural people in Maluku and West Papua who have no adequate access to improved drinking water sources may likely to frequently travel to collect water in the river or spring in the forest, which may increase the likelihood of exposure to mosquito biting such as An.
These findings highlight needs of interventions to positively improve access on drinking water sources in rural areas. In addition, other explanation for these may be due to the fact that rural households in Maluku and West Papua are tend to keep and raise livestock e. However, data for the presence or livestock ownership were not collected in the survey. Different response was observed in Papua; Papuan people who lived in rural areas appears to less likely to report malaria compared to those who lived in the cities.
It is possible that this self-reported malaria may be influenced by factors including the availability of health facilities and diagnostic services in the village. Community education level was associated with self-reporting malaria in both West Papua and Papua. Notably, in both West Papua and Papua, people who living in the community where the majority of residents are well-educated more likely to report malaria.
That said, these findings suggest that self-reported malaria may be due to the fact that people were more aware of malaria, hence people were more likely to report malaria. Community bed net ownership was found to have different effects on self-reporting malaria in West Papua and Papua. Community bed net ownership decreased the odds of self-reporting malaria in West Papua but contrarily it increased the odds of self-reporting malaria in Papua. The findings in West Papua highlight that the community bed net ownership at the community level more greatly decreases of having malaria that at the household level; suggesting that the effect of community on malaria transmission is important.
In contrast, in Papua, community bed net ownership may less effective relative to individual level bed net ownership. Possible explanation for this observed association includes herd immunity that may be exist within communities. The existence of individuals with asymptomatic and sub-microscopic infections within household or community may facilitate malaria transmission and reduce the mass effect of bed net [ 50 ]. A recent study in Timika city Papua have showed a considerable proportion of asymptomatic infections among older Papuans, females and those who did not own bed net [ 51 ].
It is possible that this self-reported malaria in Papua may not correlated with malaria infection and it may be influenced by factors include awareness, socioeconomic condition and access to healthcare services. While if self-reporting malaria is used as measure of malaria infection, the evidence from West Papua could be explained due to the fact the existence of malaria vectors and their habitats in all elevation level. Three major malaria vectors in Papua include An.
The present study has several limitations thus the findings should be carefully interpreted. However, this study did not able to compare self-reported malaria estimates with the actual malaria prevalence, especially in these three provinces as there are no recent malaria prevalence data available and no comparable large-scale population-based surveys representative of district- or province-level have been conducted so far. Hence, there is a need to verify the recent prevalence of malaria with a more comprehensive population-based standalone malariometric survey to provide guidance for policymaking and elimination malaria efforts in these three provinces.
This indicates that self-reported questionnaire approach has been able to adequately capture the variation in magnitude of malaria transmission at province-level and hence it is still appropriate and reasonable for use in studies on investigating risk factors associated with malaria.
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On top of that, the diagnostic capacity to perform unified microscopic procedure in every public health centres PHCs and active case finding have been improved in the past decade especially in these hyperendemic areas studied [ 2 ], so it is possible that this may eliminate potential bias associated with self-reporting malaria e. It is important to note that the data used in this study were a subset of National Basic Health Research which conducted by the Ministry of Health of Indonesia. Given the nature of this survey, there were logistical and financial constraints which limited the data collection e.
Thus, self-report of malaria is the easy and feasible way to capture general malaria status across Indonesia archipelago. This study suggests that malaria diagnostic testing should be performed in the future RISKESDAS surveys to provide better information on malaria prevalence and minimise the biases. Despite its limitations, self-reported malaria however has also been used in several African studies [ 45 , 55 — 57 ] as well as Indonesian studies [ 19 , 49 ] and it has helped provide important insights on the epidemiology of malaria in the region.
Secondly, as this study used data from the cross-sectional survey, this study did not able to explain causal relationships underlying the malaria infection in the areas studied; however the present study was not aimed to make any causal claims. In addition, the results of this study might be also influenced by the effects of recall biases as we utilized self-reported data. Moreover, the multilevel models also indicated that according to the PCV there might be some unmeasured contextual factors at the village-level that could better explain the variation in the village-level in association with the probability of malaria.
Despite these shortcomings, this information would have been useful in further understanding the aetiology of malaria in area studied.