Is Housing Quality Associated with Malaria Incidence among Young Children and Mosquito Vector Numbers? Evidence from Korogwe, Tanzania

Background Several studies conducted in Northeast Tanzania have documented declines in malaria transmission even before interventions were scaled up. One explanation for these reductions may be the changes in socio-environmental conditions associated with economic development, and in particular improvements in housing construction. Objective This analysis seeks to identify (1) risk factors for malaria incidence among young children and (2) household and environmental factors associated with mosquito vector numbers collected in the child’s sleeping area. Both analyses focus on housing construction quality as a key determinant. Methodology For 435 children enrolled in a larger trial of intermittent preventive treatment for malaria in infants in the Korogwe District in Tanga, Northeastern Tanzania, detailed information on their dwelling characteristics were collected in the last year of the trial. Principal components analysis was used to construct an index of housing structure quality and converted to quintile units for regression analysis. Univariate and multivariate random effects negative binomial regressions were used to predict risk factors for child malaria incidence and the mean total number of indoor female Anopheles gambiae and funestus mosquitoes collected per household across three occasions. Findings Building materials have substantially improved in Korogwe over time. Multivariate regressions showed that residing in rural areas (versus urban) increased malaria incidence rates by over three-fold and mean indoor female A. gambiae and funestus numbers by nearly two-fold. Compared to those residing in the lowest quality houses, children residing in the highest quality houses had one-third lower malaria incidence rates, even when wealth and rural residence were controlled for. Living in the highest quality houses reduced vector numbers while having cattle near the house significantly increased them. Conclusions Results corroborate findings from other studies that show associations between malaria incidence and housing quality; associations were concentrated amongst the highest quality houses

Global fund financing to the 34 malaria-eliminating countries under the new funding model 2014-2017 : an analysis of national allocations and regional grants

The Global Fund to Fight AIDS, Tuberculosis, and Malaria (GFATM) has been the largest financial supporter of malaria since 2002. In 2011, the GFATM transitioned to a new funding model (NFM), which prioritizes grants to high burden, lower income countries. This shift raises concerns that some low endemic countries, dependent on GFATM financing to achieve their malaria elimination goals, would receive less funding under the NFM. This study aims to understand the projected increase or decrease in national and regional funding from the GFATM's NFM to the 34 malaria-eliminating countries.; Average annual disbursements under the old funding model were compared to average annual national allocations for all eligible 34 malaria-eliminating countries for the period of 2014-2017. Regional grant funding to countries that are due to receive additional support was then included in the comparison and analysed. Estimated funding ranges for the countries under the NFM were calculated using the proposed national allocation plus the possible adjustments and additional funding. Finally, the minimum and maximum funding estimates were compared to average annual disbursements under the old funding model.; A cumulative 31 % decrease in national financing from the GFATM is expected for the countries included in this analysis. Regional grants augment funding for almost half of the eliminating countries, and increase the cumulative percent change in GTFAM funding to 32 %, though proposed activities may not be funded directly through national malaria programmes. However, if countries receive the maximum possible funding, 46 % of the countries included in this analysis would receive less than they received under the previous funding model.; Many malaria-eliminating countries have projected national declines in funding from the GFATM under the NFM. While regional grants enhance funding for eliminating countries, they may not be able to fill country-level funding gaps for local commodities and implementation. If the GFATM is able to nuance its allocation methodology to mitigate drastic funding declines for malaria investments in low transmission countries, the GFATM can ensure previous investments are not lost. By aligning with WHO's Global Technical Strategy for Malaria and investing in both high- and low-endemic countries, the Global Fund can tip the scale on a global health threat and contribute toward the goal of eventual malaria eradication

Malaria risk factor assessment using active and passive surveillance data from Aceh Besar, Indonesia, a low endemic, malaria elimination setting with Plasmodium knowlesi, Plasmodium vivax, and Plasmodium falciparum

Background: As malaria transmission declines, it becomes more geographically focused and more likely due to asymptomatic and non-falciparum infections. To inform malaria elimination planning in the context of this changing epidemiology, local assessments on the risk factors for malaria infection are necessary, yet challenging due to the low number of malaria cases. Methods: A population-based, cross-sectional study was performed using passive and active surveillance data collected in Aceh Besar District, Indonesia from 2014 to 2015. Malaria infection was defined as symptomatic polymerase chain reaction (PCR)-confirmed infection in index cases reported from health facilities, and asymptomatic or symptomatic PCR-confirmed infection identified in reactive case detection (RACD). Potential risk factors for any infection, species-specific infection, or secondary-case detection in RACD were assessed through questionnaires and evaluated for associations. Results: Nineteen Plasmodium knowlesi, 12 Plasmodium vivax and six Plasmodium falciparum cases were identified passively, and 1495 community members screened in RACD, of which six secondary cases were detected (one P. knowlesi, three P. vivax, and two P. falciparum, with four being asymptomatic). Compared to non-infected subjects screened in RACD, cases identified through passive or active surveillance were more likely to be male (AOR 12.5, 95 % CI 3.0–52.1), adult (AOR 14.0, 95 % CI 2.2–89.6 for age 16–45 years compared to <15 years), have visited the forest in the previous month for any reason (AOR 5.6, 95 % CI 1.3–24.2), and have a workplace near or in the forest and requiring overnight stays (AOR 7.9, 95 % CI 1.6–39.7 compared to workplace not near or in the forest). Comparing subjects with infections of different species, differences were observed in sub-district of residence and other demographic and behavioural factors. Among subjects screened in RACD, cases compared to non-cases were more likely to be febrile and reside within 100 m of the index case. Conclusion: In this setting, risk of malaria infection in index and RACD identified cases was associated with forest exposure, particularly overnights in the forest for work. In low-transmission settings, utilization of data available through routine passive and active surveillance can support efforts to target individuals at high ris

Malaria elimination transmission and costing in the Asia-Pacific: developing an investment case

Background:; The Asia-Pacific region has made significant progress against malaria, reducing cases and deaths by over 50% between 2010 and 2015. These gains have been facilitated in part, by strong political and financial commitment of governments and donors. However, funding gaps and persistent health system challenges threaten further progress. Achieving the regional goal of malaria elimination by 2030 will require an intensification of efforts and a plan for sustainable financing. This article presents an investment case for malaria elimination to facilitate these efforts.; Methods:; A transmission model was developed to project rates of decline of; Plasmodium falciparum; and; Plasmodium vivax; malaria and the output was used to determine the cost of the interventions that would be needed for elimination by 2030. In total, 80 scenarios were modelled under various assumptions of resistance and intervention coverage. The mortality and morbidity averted were estimated and health benefits were monetized by calculating the averted cost to the health system, individual households, and society. The full-income approach was used to estimate the economic impact of lost productivity due to premature death and illness, and a return on investment was computed.; Results; : The study estimated that malaria elimination in the region by 2030 could be achieved at a cost of USD 29.02 billion (range: USD 23.65-36.23 billion) between 2017 and 2030. Elimination would save over 400,000 lives and avert 123 million malaria cases, translating to almost USD 90 billion in economic benefits. Discontinuing vector control interventions and reducing treatment coverage rates to 50% will result in an additional 845 million cases, 3.5 million deaths, and excess costs of USD 7 billion. Malaria elimination provides a 6:1 return on investment.; Conclusion:; This investment case provides compelling evidence for the benefits of continued prioritization of funding for malaria and can be used to develop an advocacy strategy

Costs of Eliminating Malaria and the Impact of the Global Fund in 34 Countries

<div><p>Background</p><p>International financing for malaria increased more than 18-fold between 2000 and 2011; the largest source came from The Global Fund to Fight AIDS, Tuberculosis and Malaria (Global Fund). Countries have made substantial progress, but achieving elimination requires sustained finances to interrupt transmission and prevent reintroduction. Since 2011, global financing for malaria has declined, fueling concerns that further progress will be impeded, especially for current malaria-eliminating countries that may face resurgent malaria if programs are disrupted.</p><p>Objectives</p><p>This study aims to 1) assess past total and Global Fund funding to the 34 current malaria-eliminating countries, and 2) estimate their future funding needs to achieve malaria elimination and prevent reintroduction through 2030.</p><p>Methods</p><p>Historical funding is assessed against trends in country-level malaria annual parasite incidences (APIs) and income per capita. Following Kizewski et al. (2007), program costs to eliminate malaria and prevent reintroduction through 2030 are estimated using a deterministic model. The cost parameters are tailored to a package of interventions aimed at malaria elimination and prevention of reintroduction.</p><p>Results</p><p>The majority of Global Fund-supported countries experiencing increases in total funding from 2005 to 2010 coincided with reductions in malaria APIs and also overall GNI per capita average annual growth. The total amount of projected funding needed for the current malaria-eliminating countries to achieve elimination and prevent reintroduction through 2030 is approximately US$8.5 billion, or about$1.84 per person at risk per year (PPY) (ranging from $2.51 PPY in 2014 to$1.43 PPY in 2030).</p><p>Conclusions</p><p>Although external donor funding, particularly from the Global Fund, has been key for many malaria-eliminating countries, sustained and sufficient financing is critical for furthering global malaria elimination. Projected cost estimates for elimination provide policymakers with an indication of the level of financial resources that should be mobilized to achieve malaria elimination goals.</p></div

Average annual growth rate in Gross National Income per capita between 2000 and 2010 by Gross National Income per capita for 2011.

<p>Notes: The Global Fund income categories are based on the World Bank (Atlas Method) Income Classifications. Lower middle income countries are further divided into two groups: lower-lower middle income countries and upper-lower middle income countries based on the midpoint of the GNI per capita range of the lower middle income category. Classifications are as follows: low income, $1,025 or less; lower-lower middle income,$1,026–$2,530; upper-lower middle income$2,531–$4,035; upper middle income,$4,036–$12,475. GNI per capita average annual growth data for the Democratic People's Republic of Korea was unavailable. Data for China is unreliable, reporting 100% of malaria funding from the Global Fund, and therefore removed. High income countries—Korea, Saudi Arabia, and Turkey—are not shown. ¹Data obtained from the World Bank. If information was not available for 2010, data from the most recent year available was used. ²Data taken from the World Health Organization's 2011 World Malaria Report Annex 2 for the period of 2005-2010, not including contributions reported by donors. Bubble legend to scale.</p

34 malaria-eliminating countries, sorted by identified national target malaria elimination goals.

<p>*Provincial goals, therefore 2030 is assumed for national goal.</p><p>NNG: No National Goal. If no national elimination goal is identified, 2030 is assumed unless otherwise noted in methodology.</p>1<p>Azerbaijan's national goal for elimination was by 2013; however authors have assumed 2014 since no declaration of elimination has been reported.</p>2<p>Elimination goal of 2020 declared under the EMMIE regional initiative.</p><p>34 malaria-eliminating countries, sorted by identified national target malaria elimination goals.</p

LLIN adjusted costs to eliminate malaria in 34 countries, 2014–2030.

<p>Notes: Higher LLIN estimates occur from 2014 to 2020 are associated with higher coverage rates for countries further from elimination years in both scenarios (80% for “high coverage scenario” and 50% for “low coverage scenario”). As elimination year nears, countries move into the next coverage tier of both scenarios (50% for “high coverage scenario” and 30% for “low coverage scenario”). For all endemic countries between the years of 2026 and 2030, coverage rates are at their lowest (30% for high coverage scenario and 15% for low coverage scenario).</p

Estimated costs for malaria elimination and prevention of reintroduction in the 34 malaria-eliminating countries, 2014–2030.

<p>Notes: The decrease in 2020 is due to a number of countries reaching their national target elimination years, at which time, based on our model assumptions, certain interventions cease (CHWs, LLINs). Other interventions, such as treatment continue, decline by reducing coverage levels per declining incidence. Estimated costs for 2021 to 2030 include all projected expenditures for maintaining elimination interventions thorough 2030 for DPRK, Iran, the Philippines, Thailand, Solomon Islands, Vanuatu, and Vietnam. This period also includes expenditures for prevention of reintroduction interventions among countries that have eliminated prior to 2021. Prices are in 2013 USD$.</p

Change in total malaria funding, percentage of Global Fund funding, and API between 2005 and 2010.

<p>Notes: Countries are clustered in the top right quadrant indicating higher reductions in API and an increase in total funding for malaria. Countries in the lower right quadrant have either stagnating or increasing APIs despite increases in funding, likely due to importation issues. In the upper left quadrant, countries have reduced APIs, with a commensurate decrease in funding.</p><p>Source: Data taken from the World Health Organization's 2011 World Malaria Report Annex 2 for total funding for malaria includes country reported government and external funding for the period of 2005–2010, and does not include “Contributions reported by donors”. Additionally, due to sparse data, the percent increase in total funding for malaria was calculated for the following countries during the respective time period: Algeria (2008–2010); Belize (2005–2009); Dominican Republic (2007–2010); El Salvador (2005–2009); Malaysia (2007–2010); Nicaragua (2006–2010); South Africa 2007–2010; Swaziland (2007–2010); Uzbekistan (2005–2009).</p><p>Change in total malaria funding, percentage of Global Fund funding, and API between 2005 and 2010.</p