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Trends in malaria indicators after scale-up of community-based malaria management in Afghanistan

Abstract

Background

The Community-Based Malaria Management (CBMM) strategy, introduced in 2013 and expanded to all health facilities and health posts in Afghanistan by 2016, aimed to deliver rapid diagnostic testing and more timely treatment to all communities nationwide. In this study, trends for several malaria outcome indicators were compared before and after the expansion of the CBMM strategy, using cross-sectional analysis of surveillance data.

Methods

Generalized estimating equation (GEE) models with a Poisson distribution were used to assess trends of three key outcomes before (2012–2015) and after (2016–2019) CBMM expansion. These outcomes were annual malaria incidence rate (both all and confirmed malaria incidence), malaria death rate, and malaria test positivity rate. Additional variables assessed included annual blood examination rates (ABER) and malaria confirmation rate.

Results

Average malaria incidence rates decreased from 13.1 before CBMM expansion to 10.0 per 1000 persons per year after CBMM expansion (P < 0.001). The time period after CBMM was expanded witnessed a 339% increase in confirmed malaria incidence as compared to the period before (IRR 3.39, 95% CI 2.18, 5.27; P < 0.001). In the period since the expansion of CBMM (2016–2019), overall malaria incidence rate declined by 19% each year (IRR 0.81, 95% CI 0.71,0.92; P = 0.001) and the malaria death rate declined by 85% each year (IRR 0.15, 95% CI 0.12, 0.20; P < 0.001). In comparing the before period to the after period, the ABER increased from 2.3 to 3.5 per 100 person/year, the malaria test positivity rate increased from 12.2 to 20.5%, and the confirmation rate increased from 21% before to 71% after CBMM.

Conclusions

Afghanistan’s CBMM expansion to introduce rapid diagnostic tests and provide more timely treatment for malaria through all levels of care temporally correlates with significant improvement in multiple indicators of malaria control.

Background

Worldwide, malaria is a major public health problem with 241 million new infections and 627,000 deaths annually [1]. Afghanistan, a country in the World Health Organization (WHO) Eastern Mediterranean Region, has relatively low transmission of malaria [2]. The Afghanistan National Malaria and Leishmania Control Programme reported 174,893 malaria cases and zero deaths in 2019, the lowest number that has ever been reported for the country. The two main species of malaria parasites in Afghanistan are Plasmodium vivax (98% of all cases) and Plasmodium falciparum (2%) [2].

In Afghanistan, malaria incidence rates vary by location. The variation results from differences in parasites, vectors, human population density, behaviours, ecological, high temperature, humidity and agriculture (rice cultivation), socio-economic conditions, and access to health services for detection and treatment of malaria. Nationally, 27% of the Afghan population lives in areas at high risk for malaria. Areas at high risk are defined as provinces and districts with annual parasite incidence (API) rate per 1000 persons at risk of 1 or above and test positivity rate (TPR) at 9% and above. Half (50%) of the population lives in areas at medium risk (API < 1, TPR < 9%), and the remaining 23% live in areas with low and very low risk of malaria transmission or its absence in malaria free areas [3]. In 2019, more than 93% of total malaria cases were reported from six provinces that border with Pakistan (Nangarhar, Laghman, Kunar, Nooristan, Khost, and Paktika) and one district of Kabul. Nangarhar is one highest endemic province in the country and accounted for more than 45% of total malaria cases and 35% of total P. falciparum cases [2].

Malaria diagnosis either by microscopy or rapid diagnostic tests is recommended by the WHO for all suspected malaria cases before starting the treatment. Early and accurate diagnosis is essential both for effective management of the disease, and for malaria surveillance and elimination strategies. In Afghanistan, the Community-Based Management of Malaria (CBMM) strategy was designed to progressively expand access to malaria diagnosis and effective anti-malarial treatment at non-diagnostic health facilities and community including health posts [4]. Malaria diagnosis using microscopy has been available in all hospitals and Comprehensive Health Centres (CHCs) of Afghanistan. Since 2013, the focus of the CBMM in Afghanistan has changed to specifically increase access to rapid diagnostic testing (RDT) and timely treatment at the community level in all malaria endemic and non-endemic areas of Afghanistan. The programme consists of two key modules; case management, vector control; CBMM was scaled up nationwide in 2016 with the support of the Global Fund. A main pillar of this revised strategy is introducing RDT in all health facilities, not only those providing diagnosis and treatment for malaria, and expanding screening of malaria to health posts to run community-based screening programs. In addition, the CBMM expanded the community-based malaria case management program using networks of community health workers (CHW) to reach all patients with suspected malaria at a level closer to the home. Since 2016, more than 30,000 CHWs were trained on malaria case management, RDT use and distribution of long-lasting insecticidal net (LLIN) to community through mass campaign. Other malaria commodities, including medicines, were supplied to health posts and health facilities without laboratory services. As a result, in 2017 more than 90% of CHW reported screening and referral of newly identified cases of malaria, and more than 50% reported providing counselling, chloroquine treatment for vivax malaria, and artemisinin-based combination therapy for suspected and confirmed falciparum malaria cases [5].

While the magnitude of the scale-up and shift in focus of the CBMM are encouraging, the effectiveness of the programme in Afghanistan has not yet been evaluated. In this study, trends in annual malaria incidence and death rates were assessed during two time periods, 4 years before the expansion of CBMM (2012–2015), and 4 years after expansion the CBMM program (2016–2019). Additional indicators of programme impact were also tracked. The scope of analysis included both national and subnational trends in Afghanistan.

Methods

Data were extracted from the Malaria Leishmania Information System (MLIS) of the National Malaria Control Programme (NMCP) and Health Management Information System (HMIS). Data included clinical (diagnosed without a diagnostic test) and confirmed (diagnosed with a diagnostic test) malaria cases reported by approximately 2800 health facilities on a monthly basis. Patients were those with symptoms or diagnosis of malaria who visited health facilities, health posts and community member reached through outreach or mobile services. Data were initially collected on paper forms. The HMIS officers of non-governmental organizations (NGOs) and provincial malaria case managers checked the quality and completeness of the forms and entered them into the HMIS database. Hard and soft copies of collected data were shared with the provincial health directorate HMIS team on a monthly basis. The provincial HMIS and malaria officers reviewed and compiled the data and reported to the NMCP on a quarterly basis. Data were analysed and feedback provided to implementers on a quarterly basis. For this analysis, all data reported from 2012 to 2019 were used.

Analysis

To assess trends in malaria before and after the expansion of the CBMM programme in Afghanistan, seven indicators were measured (Table 1). The descriptive analysis included the following indicators: the malaria incidence rate (both all and confirmed malaria) per 1000 persons per year, malaria death rates per 100,000 persons per year, malaria test positivity rate, annual blood examination rate per 100 per year (ABER) and the malaria confirmation rate. Reporting completeness during this time period was assessed to understand the reliability of the data.

Table 1 Indicators of malaria, Afghanistan, 2012–2019

Generalized estimating equation (GEE) models with a Poisson distribution were used to assess the differences in these indicator rates before (2012–2015) and after (2016–2019) CBMM was expanded (a binary predictor variable of before and after was used). Temporal trends during the before and after years were conducted by using GEE models by stratifying on time period and using year as a predictor variable. Analyses were conducted at the provincial level with all the provinces of Afghanistan included. Stata v.15. was used for statistical analysis and ArcGIS v.10.3.1 was used to create maps of average annual malaria incidence and average annual incidence of death due to malaria during the before and after periods.

Results

Between 2012 and 2019, the total number of malaria cases (including clinical and confirmed) fell from 391,365 to 174,893. The overall malaria incidence rate declined from 15.4 to 5.5 per 1,000 per year and the malaria confirmation rate increased from 14 to 99% (Fig. 1A). The number of malaria cases that were confirmed by testing rose from 54,840 to 173,859; clinical cases declined 336,525 to 1034 (Fig. 1B). The malaria death rate fell from 0.1416 to 0 per 100,000 per year.

Fig. 1
figure 1

Several Malaria indicators in Afghanistan before (2012–15) and after (2016–2019) expansion the Rapid Testing for Malaria

Table 2 presents annual malaria data and combined data for the two time periods based on the start of CBMM expansion (2012–15 vs. 2016–19). Between 2012 and 2015, the total number of tests conducted was 2,365,753. After the expansion of CBMM (2016–2019), the total number of tests conducted was 4,097,900 (Table 2; Fig. 1B). Meanwhile, average malaria incidence rates decreased from 13.1 before CBMM expansion to 10.1 per 1000 persons per year after CBMM expansion. The malaria death rates per 100,000 decreased from 0.1345 to 0.0493 for the years after CBMM expansion. The malaria test positivity rate increased 12.2–20.5%. The ABER increased from 2.3 to 3.5 per 100 per year. The malaria confirmation rate increased from 14% to 2012 to 99% in 2019. Annual malaria testing, incidence, and deaths are presented in Appendix 1 by province from 2012 to 2019 (Fig. 2). The average annual Malaria incidence and death rates in Afghanistan before (2012–15) and after (2016–2019) the expansion of CBMM are presented in Fig. 2

Table 2 Annual malaria data and indicators in Afghanistan from 2012 to 2019
Fig. 2
figure 2

Malaria incidence and death rates due to malaria in Afghanistan before (2012–15; average of annual incidence) and after (2016–2019; average of annual incidence) expansion of CBMM

In the time period after CBMM expansion there was an 8% decrease in the malaria incidence rate as compared to the period before CBMM expansion (IRR 0.92, P = 0.692) (Table 3). For the time period after CBMM expansion, the confirmed malaria incidence rate increased 339% as compared to the period before CBMM was expanded (IRR 3.39, P < 0.001). There was a 65% decrease in the malaria death rate in the period after the expansion of CBMM compared to the period before (IRR 0.35, P < 0.001).

Table 3 Comparison of the period before CBMM and the period after the expansion of CBMM for malaria in Afghanistan

In examining only, the period since the expansion of CBMM (2016–2019), the overall malaria incidence rate declined by 19% each year (IRR 0.81, P = 0.001). The confirmed malaria incidence rate declined by 2% each year (IRR:0.98, P = 0.840). Malaria death incidence declined by 85% each year (IRR 0.15, P < 0.001) (Table 4).

Table 4 Average annual change in malaria outcomes before (2012-15) and after (2016–2019) expansion of CBMM for Malaria in Afghanistan

Discussion

The malaria trend analysis revealed several encouraging outcomes for malaria control in Afghanistan following the scale-up of the CBMM strategy. In line with the expansion of RDT, there was an increase in the number of suspected cases that received parasitological testing in a health facility and at community levels. During the period since this expansion, the malaria incidence rate and malaria death rate declined. The magnitude of the decline in incidence is remarkable - from 15.5 to 5.5 per 1000 persons/year between 2012 and 2019. The malaria deaths rate declined from 0.1416 to 0 per 100,000 persons per year for the same periods. Additionally, number of confirmed malaria cases increased following the expansion of RDT and the number of clinical cases decreased during the period. The ABER have increased, leading to a confirmation rate of nearly 100%.

The study results are similar to positive outcomes of other community-based malaria control models. A systematic review conducted in 2019 investigated the impact of community-delivered models (namely, Integrated Community Case Management and Home Management of Malaria) on coverage and malaria outcomes compared to non-community-delivered models [6]. The result of meta-analysis indicated that the implementation of community-delivered models improved malaria-attributed mortality. Community-delivered models also reduced the risk of parasitaemia from 25 to 70% compared to non-community-delivered models [6].

There were four limitations in the study and analysis. First, surveillance and health system data were used which meant authors were not fully able to assess quality (however, there was very high reporting completeness throughout the study period). Second, data were reported as aggregated and individual characteristics such as gender, age, and other personal and behaviour data were not available. Studying the potential associations between malaria and these characteristics will help target future interventions towards malaria elimination. Third, the surveillance data did not include most of the cases, which were diagnosed or received treatment in private health sectors. It is also unclear how use of the private health sector changed over time. Lastly, treatment data were not reported to the surveillance system and, therefore, it was not possible to assess trends in this important indicator.

There are also potential confounders that may explain or partially explain the differences witnessed in malaria indicators during the before versus after CBMM scale-up. These include vector control measures, the Basic Package of Health Services (BPHS), the Essential Package of Health Services (EPHS) and strengthening of malaria surveillance, Malaria Leishmania Information System (MLIS). The diagnosis and treatment of malaria has been integrated into BPHS and EPHS services, with malaria diagnosis and treatment (including microscopy and anti-malarial therapy) provided from health post level up to regional hospitals and provided malaria reports on monthly basis. Additionally, since expansion of CBMM after 2016, approximately 6,015,826 long-lasting insecticide nets (LLIN) have been distributed to targeted provinces. The LLIN distribution programme ensured 100% operational coverage (i.e., all target provinces and districts were covered through mass distribution campaigns and through continuous distribution at antenatal clinics). The programme sought to improve coverage and accessibility for at-risk populations, including pregnant women and children. Ecological factors such as changes in temperature or rainfall, variables that could influence malaria transmission in Afghanistan were not assessed.

The trend analysis for the period after CBMM expansion shows that most of the targets of Afghanistan’s National Strategic Plan for Malaria 2018–22 are on track to being met. The plan aims to reduce malaria incidence by 73% at the national level compared with 2016. Between 2016 and 2019, the number of reported malaria cases were reduced from 385,015 to 174,893 (55%). The proportion of confirmed malaria cases increased to 99% in 2019 compared to the baseline 49% in 2016. Nonetheless, 12 provinces remain at high risk for malaria with reported annual parasite incidence rates per 1000 persons at 1 and above and test positivity rate at 9% and above.

Conclusions

In summary, the CBMM expansion which introduced rapid diagnostic tests for malaria to many primary care settings correlated with significant increase in the number of confirmed cases, while also being correlated with significant reduction in annual malaria incidence and death rates. Use of RDTs for the diagnosis of malaria could be best applied as a tool at the community level to facilitate the early treatment of malaria in settings where microscopy services are not available. The data and the study results corroborate similar studies that recommend community-based interventions as best practices for malaria control, especially in resource-limited settings.

Availability of data and materials

All details of data (case numbers) that we used for our analysis are presented in Table 2 and Appendix 1.

References

  1. WHO. World malaria report 2021. Geneva: World Health Organization. https://www.mmv.org/sites/default/files/uploads/docs/publications/World%20Malaria%20Report_0.pdf. 2020.

  2. National Malaria Leishmania Control Programme, Ministry of Public Health. Malaria Annual Report. https://www.ecdc.europa.eu/en/publications-data/malaria-annual-epidemiological-report-2019 2019.

  3. National Malaria Leishmania Control Programme, Ministry of Public Health. National Strategic Plan " From Malaria Control to Elimination in Afghanistan”. https://moph.gov.af/sites/default/files/2019-07/NMSP%202018-2020%20Final.pdf. 2017.

  4. National Malaria Leishmania Control Programme. Ministry of Public Health. National Strategy for Community-based Management of Malaria (CBMM) in Afghanistan (2016–2020). 2016.

  5. National Malaria Leishmania Control Programme, Ministry of Public Health. Afghanistan CBHC “Community Based Health Care Evaluation Report” 2017.

  6. Oo WH, Gold L, Moore K, Agius PA, Fowkes FJI. The impact of community-delivered models of malaria control and elimination: a systematic review. Malar J. 2019;18:269.

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Acknowledgements

We would like to thank staff and directors of the Afghanistan Malaria programme for their efforts, and technical support.

Funding

This work was supported by UNDP Global Fund and the support from the UCSF’s International Traineeships in AIDS Prevention Studies (ITAPS), U.S. NIMH, R25 MH064712. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The views and opinions expressed herein are those of the individual authors and not necessarily those of UNDP, UCSF, MOH, or funders.

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Authors and Affiliations

Authors

Contributions

SDM (study design, implementation, data cleaning and analysis, reporting, manuscript writing), AAA, AWS, WM, TBA, MSN, HH, GQQ, ST (study design, interpretation of results, critical review of the manuscript), and SG, AM (study design, data analysis, reporting, critical review, manuscript writing, funding). All authors read and approved the final manuscript.

Corresponding author

Correspondence to Ali Mirzazadeh.

Ethics declarations

Ethics approval and consent to participate

We used de-identified public health surveillance data which does not require participant consent.

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All co-authors have reviewed the final draft of the paper and approved it before submission to the journal.

Competing interests

Nothing to declare.

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Appendix 1

Appendix 1

Province wise annual malaria testing, incidence and deaths in Afghanistan from 2012 to 2019.

Province Population Total microscopy and rapid tests for Malaria diagnosis Plasmodium vivax (PV) Malaria cases PV incidence rate per 1000 persons per year Plasmodium falciparum (PF) Malaria cases PF incidence rate per 1000 persons per year Total Confirmed Malaria cases Total clinical malaria cases Reported malaria cases (clinical and confirmed) Malaria test positivity rate (per 100 malaria tests per year) Malaria confirmation rate (per 100 reported cases per year) Malaria incidence rate (per 1000 persons per year) Annual blood examination rate (per 100 population per year) Total malaria deaths
Badakhshan               
2012 889,700 17,646 1154 1.30 4 0.004 1158 13,573 14,731 6.6% 7.9% 16.56 1.98% 0
2013 919,900 14,408 1140 1.24 4 0.004 1144 10,592 11,736 7.9% 9.7% 12.76 1.57% 0
2014 935,327 23,665 2261 2.42 239 0.256 2500 5769 8269 10.6% 30.2% 8.84 2.53% 0
2015 950,953 20,646 1810 1.90 77 0.081 1887 5636 7523 9.1% 25.1% 7.91 2.17% 0
2016 966,789 12,104 1201 1.24 22 0.023 1223 4733 5956 10.1% 20.5% 6.16 1.25% 0
2017 982,835 17,078 1110 1.13 10 0.010 1120 526 1646 6.6% 68.0% 1.67 1.74% 0
2018 1,017,499 15,114 731 0.72 6 0.006 737 95 832 4.9% 88.6% 0.82 1.49% 0
2019 1,035,658 13,435 407 0.39 23 0.022 430 0 430 3.2% 100.0% 0.42 1.30% 0
Badghis               
2012 464,100 1682 27 0.06 0 0.000 27 6666 6693 1.6% 0.4% 14.42 0.36% 0
2013 479,800 1997 28 0.06 4 0.008 32 3095 3127 1.6% 1.0% 6.52 0.42% 0
2014 487,838 955 13 0.03 2 0.004 15 3150 3165 1.6% 0.5% 6.49 0.20% 0
2015 495,958 1042 18 0.04 2 0.004 20 3739 3759 1.9% 0.5% 7.58 0.21% 0
2016 504,185 10,356 3228 6.40 197 0.391 3425 2225 5650 33.1% 60.6% 11.21 2.05% 0
2017 512,518 5476 418 0.82 12 0.023 430 1590 2020 7.9% 21.3% 3.94 1.07% 0
2018 530,574 6545 23 0.04 2 0.004 25 195 220 0.4% 11.4% 0.41 1.23% 0
2019 540,009 4518 12 0.02 1 0.002 13 0 13 0.3% 100.0% 0.02 0.84% 0
Baghlan               
2012 848,500 5567 18 0.02 0 0.000 18 646 664 0.3% 2.7% 0.78 0.66% 0
2013 855,400 3246 21 0.02 3 0.004 24 347 371 0.7% 6.5% 0.43 0.38% 0
2014 894,838 7906 8 0.01 1 0.001 9 543 552 0.1% 1.6% 0.62 0.88% 0
2015 910,784 5451 31 0.03 5 0.005 36 95 131 0.7% 27.5% 0.14 0.60% 0
2016 926,969 5297 69 0.07 10 0.011 79 25 104 1.5% 76.0% 0.11 0.57% 0
2017 946,394 10,799 122 0.13 8 0.008 130 19 149 1.2% 87.2% 0.16 1.14% 0
2018 977,297 10,147 70 0.07 1 0.001 71 17 88 0.7% 80.7% 0.09 1.04% 0
2019 995,814 9218 52 0.05 3 0.003 55 0 55 0.6% 100.0% 0.06 0.93% 0
Balkh               
2012 1,219,200 3431 154 0.13 0 0.000 154 3583 3737 4.5% 4.1% 3.07 0.28% 0
2013 1,318,000 3680 138 0.10 4 0.003 142 3734 3876 3.9% 3.7% 2.94 0.28% 0
2014 1,298,247 11,520 153 0.12 65 0.050 218 1187 1405 1.9% 15.5% 1.08 0.89% 0
2015 1,325,659 11,261 155 0.12 25 0.019 180 1457 1637 1.6% 11.0% 1.23 0.85% 0
2016 1,353,626 3867 12 0.01 82 0.061 94 1137 1231 2.4% 7.6% 0.91 0.29% 0
2017 1,382,155 5942 67 0.05 5 0.004 72 559 631 1.2% 11.4% 0.46 0.43% 0
2018 1,442,847 6412 56 0.04 10 0.007 66 843 909 1.0% 7.3% 0.63 0.44% 0
2019 1,475,649 8627 59 0.04 1 0.001 60 0 60 0.7% 100.0% 0.04 0.58% 0
Bamyan               
2012 418,500 680 27 0.06 0 0.000 27 833 860 4.0% 3.1% 2.05 0.16% 0
2013 432,700 804 34 0.08 7 0.016 41 691 732 5.1% 5.6% 1.69 0.19% 0
2014 439,899 952 58 0.13 9 0.020 67 578 645 7.0% 10.4% 1.47 0.22% 0
2015 447,218 2281 27 0.06 16 0.036 43 673 716 1.9% 6.0% 1.60 0.51% 0
2016 427,067 2299 167 0.39 357 0.836 524 312 836 22.8% 62.7% 1.96 0.54% 0
2017 462,144 1940 9 0.02 0 0.000 9 121 130 0.5% 6.9% 0.28 0.42% 0
2018 478,424 1660 49 0.10 4 0.008 53 5 58 3.2% 91.4% 0.12 0.35% 0
2019 486,928 707 19 0.04 1 0.002 20 0 20 2.8% 100.0% 0.04 0.15% 0
Daykundi               
2012 431,300 2097 82 0.19 14 0.032 96 2913 3009 4.6% 3.2% 6.98 0.49% 1
2013 378,900 2594 47 0.12 4 0.011 51 1956 2007 2.0% 2.5% 5.30 0.68% 0
2014 417,476 1798 40 0.10 10 0.024 50 2029 2079 2.8% 2.4% 4.98 0.43% 0
2015 424,339 1633 34 0.08 10 0.024 44 1756 1800 2.7% 2.4% 4.24 0.38% 0
2016 468,178 2818 132 0.28 66 0.141 198 2493 2691 7.0% 7.4% 5.75 0.60% 0
2017 493,634 4376 52 0.11 19 0.038 71 1374 1445 1.6% 4.9% 2.93 0.89% 0
2018 544,788 3434 124 0.23 5 0.009 129 853 982 3.8% 13.1% 1.80 0.63% 0
2019 507,610 4845 15 0.03 1 0.002 16 0 16 0.3% 100.0% 0.03 0.95% 0
Farah               
2012 480,500 1221 40 0.08 2 0.004 42 438 480 3.4% 8.8% 1.00 0.25% 0
2013 490,600 1186 15 0.03 5 0.010 20 337 357 1.7% 5.6% 0.73 0.24% 0
2014 498,951 658 17 0.03 1 0.002 18 310 328 2.7% 5.5% 0.66 0.13% 0
2015 507,405 815 13 0.03 7 0.014 20 381 401 2.5% 5.0% 0.79 0.16% 0
2016 515,973 3143 33 0.06 5 0.010 38 258 296 1.2% 12.8% 0.57 0.61% 0
2017 524,657 4942 110 0.21 2 0.004 112 311 423 2.3% 26.5% 0.81 0.94% 0
2018 543,237 5096 84 0.15 0 0.000 84 23 107 1.6% 78.5% 0.20 0.94% 0
2019 553,058 1899 25 0.05 1 0.002 26 0 26 1.4% 100.0% 0.05 0.34% 0
Faryab               
2012 931,800 2989 31 0.03 1 0.001 32 4471 4503 1.1% 0.7% 4.83 0.32% 0
2013 964,600 3527 26 0.03 3 0.003 29 3901 3930 0.8% 0.7% 4.07 0.37% 0
2014 981,197 5136 914 0.93 98 0.100 1012 2898 3910 19.7% 25.9% 3.98 0.52% 0
2015 998,147 3808 0 0.00 1 0.001 1 2461 2462 0.0% 0.0% 2.47 0.38% 0
2016 1,015,335 3966 14 0.01 0 0.000 14 2559 2573 0.4% 0.5% 2.53 0.39% 0
2017 1,032,765 7098 3 0.00 7 0.007 10 1099 1109 0.1% 0.9% 1.07 0.69% 0
2018 1,069,540 3747 30 0.03 0 0.000 30 472 502 0.8% 6.0% 0.47 0.35% 0
2019 1,089,228 3799 12 0.01 1 0.001 13 1 14 0.3% 92.9% 0.01 0.35% 0
Ghazni               
2012 1,149,400 10,086 1179 1.03 61 0.053 1240 4109 5349 12.3% 23.2% 4.65 0.88% 0
2013 1,188,600 14,258 1307 1.10 41 0.034 1348 4001 5349 9.5% 25.2% 4.50 1.20% 1
2014 1,240,437 16,606 1329 1.07 83 0.067 1412 3241 4653 8.5% 30.3% 3.75 1.34% 0
2015 1,228,831 17,392 832 0.68 80 0.065 912 2723 3635 5.2% 25.1% 2.96 1.42% 0
2016 1,249,376 22,488 841 0.67 67 0.054 908 2297 3205 4.0% 28.3% 2.57 1.80% 0
2017 1,270,192 17,949 959 0.76 85 0.067 1044 961 2005 5.8% 52.1% 1.58 1.41% 0
2018 1,315,041 8154 610 0.46 23 0.017 633 359 992 7.8% 63.8% 0.75 0.62% 0
2019 1,338,597 9905 750 0.56 129 0.096 879 1 880 8.9% 99.9% 0.66 0.74% 0
Ghor               
2012 646,300 361 19 0.03 34 0.053 53 1817 1870 14.7% 2.8% 2.89 0.06% 1
2013 668,000 585 15 0.02 29 0.043 44 1586 1630 7.5% 2.7% 2.44 0.09% 0
2014 679,085 415 5 0.01 22 0.032 27 1407 1434 6.5% 1.9% 2.11 0.06% 0
2015 690,296 339 7 0.01 29 0.042 36 1004 1040 10.6% 3.5% 1.51 0.05% 0
2016 701,653 1332 23 0.03 5 0.007 28 540 568 2.1% 4.9% 0.81 0.19% 0
2017 713,158 4755 39 0.05 11 0.015 50 485 535 1.1% 9.3% 0.75 0.67% 0
2018 738,224 3360 43 0.06 4 0.005 47 291 338 1.4% 13.9% 0.46 0.46% 0
2019 751,254 4637 13 0.02 0 0.000 13 0 13 0.3% 100.0% 0.02 0.62% 0
Hilmand               
2012 864,600 8643 314 0.36 32 0.037 346 11,296 11,642 4.0% 3.0% 13.47 1.00% 0
2013 867,600 10,776 375 0.43 63 0.073 438 12,939 13,377 4.1% 3.3% 15.42 1.24% 0
2014 909,395 14,840 204 0.22 104 0.114 308 12,845 13,153 2.1% 2.3% 14.46 1.63% 0
2015 924,711 13,146 106 0.11 11 0.012 117 12,631 12,748 0.9% 0.9% 13.79 1.42% 0
2016 894,805 11,746 106 0.12 13 0.015 119 7611 7730 1.0% 1.5% 8.64 1.31% 1
2017 938,184 21,474 192 0.20 32 0.034 224 8922 9146 1.0% 2.4% 9.75 2.29% 0
2018 1,395,514 24,637 101 0.07 10 0.007 111 978 1089 0.5% 10.2% 0.78 1.77% 0
2019 1,420,682 15,466 51 0.04 3 0.002 54 0 54 0.3% 100.0% 0.04 1.09% 0
Hirat               
2012 1,744,700 1927 9 0.01 0 0.000 9 5373 5382 0.5% 0.2% 3.08 0.11% 0
2013 1,816,100 2285 3 0.00 0 0.000 3 2791 2794 0.1% 0.1% 1.54 0.13% 0
2014 1,852,790 5567 24 0.01 1 0.001 25 1713 1738 0.4% 1.4% 0.94 0.30% 0
2015 1,890,202 6593 11 0.01 1 0.001 12 1325 1337 0.2% 0.9% 0.71 0.35% 0
2016 1,928,327 3295 12 0.01 3 0.002 15 947 962 0.5% 1.6% 0.50 0.17% 0
2017 1,967,180 9607 4 0.00 17 0.009 21 196 217 0.2% 9.7% 0.11 0.49% 0
2018 2,050,514 7736 12 0.01 0 0.000 12 117 129 0.2% 9.3% 0.06 0.38% 0
2019 2,095,117 4681 19 0.01 2 0.001 21 0 21 0.4% 100.0% 0.01 0.22% 0
Jawzjan               
2012 503,100 2263 20 0.04 0 0.000 20 3264 3284 0.9% 0.6% 6.53 0.45% 0
2013 521,400 2462 6 0.01 15 0.029 21 3967 3988 0.9% 0.5% 7.65 0.47% 0
2014 530,751 1957 11 0.02 4 0.008 15 1675 1690 0.8% 0.9% 3.18 0.37% 0
2015 540,255 2790 24 0.04 8 0.015 32 3001 3033 1.1% 1.1% 5.61 0.52% 0
2016 549,900 1738 3 0.01 5 0.009 8 1169 1177 0.5% 0.7% 2.14 0.32% 0
2017 559,691 2243 3 0.01 17 0.030 20 447 467 0.9% 4.3% 0.83 0.40% 0
2018 579,833 3196 17 0.03 0 0.000 17 21 38 0.5% 44.7% 0.07 0.55% 0
2019 590,866 6700 13 0.02 0 0.000 13 0 13 0.2% 100.0% 0.02 1.13% 0
Kabul               
2012 3,818,700 23,300 2013 0.53 20 0.005 2033 10,352 12,385 8.7% 16.4% 3.24 0.61% 1
2013 4,086,500 23,568 1133 0.28 74 0.018 1207 7441 8648 5.1% 14.0% 2.12 0.58% 1
2014 4,227,261 28,007 2013 0.48 37 0.009 2050 7971 10,021 7.3% 20.5% 2.37 0.66% 0
2015 4,372,977 30,343 3394 0.78 81 0.019 3475 9769 13,244 11.5% 26.2% 3.03 0.69% 1
2016 4,523,718 53,469 10,844 2.40 257 0.057 11,101 11,628 22,729 20.8% 48.8% 5.02 1.18% 5
2017 4,679,648 58,254 13,468 2.88 269 0.057 13,737 4678 18,415 23.6% 74.6% 3.94 1.24% 2
2018 4,860,880 63,631 11,208 2.31 180 0.037 11,388 3799 15,187 17.9% 75.0% 3.12 1.31% 1
2019 5,029,850 43,034 5906 1.17 126 0.025 6032 832 6864 14.0% 87.9% 1.36 0.86% 0
Kandahar               
2012 1,127,000 6510 134 0.12 10 0.009 144 12,154 12,298 2.2% 1.2% 10.91 0.58% 0
2013 1,119,000 6215 99 0.09 5 0.004 104 7935 8039 1.7% 1.3% 7.18 0.56% 0
2014 1,200,929 5613 70 0.06 1 0.001 71 6548 6619 1.3% 1.1% 5.51 0.47% 0
2015 1,226,593 7088 150 0.12 1 0.001 151 4859 5010 2.1% 3.0% 4.08 0.58% 0
2016 1,193,020 12,086 88 0.07 2 0.002 90 2908 2998 0.7% 3.0% 2.51 1.01% 0
2017 1,279,520 11,921 101 0.08 63 0.049 164 242 406 1.4% 40.4% 0.32 0.93% 0
2018 1,351,169 12,122 125 0.09 18 0.013 143 379 522 1.2% 27.4% 0.39 0.90% 0
2019 1,368,036 17,490 338 0.25 19 0.014 357 1 358 2.0% 99.7% 0.26 1.28% 0
Kapisa               
2012 413,000 5369 388 0.94 1 0.002 389 1594 1983 7.2% 19.6% 4.80 1.30% 0
2013 426,800 7527 275 0.64 1 0.002 276 1120 1396 3.7% 19.8% 3.27 1.76% 0
2014 433,867 5148 109 0.25 0 0.000 109 720 829 2.1% 13.1% 1.91 1.19% 0
2015 441,010 5855 140 0.32 0 0.000 140 653 793 2.4% 17.7% 1.80 1.33% 0
2016 448,245 8768 763 1.70 39 0.087 802 2403 3205 9.1% 25.0% 7.15 1.96% 0
2017 455,574 12,061 2091 4.59 49 0.108 2140 1152 3292 17.7% 65.0% 7.23 2.65% 0
2018 471,574 14,229 1608 3.41 12 0.025 1620 75 1695 11.4% 95.6% 3.59 3.02% 0
2019 479,875 11,687 1098 2.29 8 0.017 1106 0 1106 9.5% 100.0% 2.30 2.44% 0
Khost               
2012 537,800 10,861 1610 2.99 61 0.113 1671 10,461 12,132 15.4% 13.8% 22.56 2.02% 0
2013 556,000 14,161 1298 2.33 121 0.218 1419 9018 10,437 10.0% 13.6% 18.77 2.55% 1
2014 565,211 26,224 1560 2.76 286 0.506 1846 5207 7053 7.0% 26.2% 12.48 4.64% 1
2015 574,582 19,670 1435 2.50 492 0.856 1927 6445 8372 9.8% 23.0% 14.57 3.42% 0
2016 584,075 23,215 2473 4.23 349 0.598 2822 4292 7114 12.2% 39.7% 12.18 3.97% 1
2017 593,691 24,731 3600 6.06 407 0.686 4007 1675 5682 16.2% 70.5% 9.57 4.17% 0
2018 614,584 23,897 2864 4.66 106 0.172 2970 406 3376 12.4% 88.0% 5.49 3.89% 0
2019 625,473 20,294 1778 2.84 21 0.034 1799 1 1800 8.9% 99.9% 2.88 3.24% 0
Kunar               
2012 421,700 40,847 7464 17.70 200 0.474 7664 33,376 41,040 18.8% 18.7% 97.32 9.69% 0
2013 436,000 39,298 5354 12.28 173 0.397 5527 39,103 44,630 14.1% 12.4% 102.36 9.01% 0
2014 443,272 65,356 12,534 28.28 1277 2.881 13,811 23,198 37,009 21.1% 37.3% 83.49 14.74% 3
2015 450,652 55,648 12,150 26.96 308 0.683 12,458 32,670 45,128 22.4% 27.6% 100.14 12.35% 4
2016 440,231 67,782 19,235 43.69 914 2.076 20,149 28,522 48,671 29.7% 41.4% 110.56 15.40% 3
2017 465,706 115,289 37,373 80.25 1235 2.652 38,608 13,023 51,631 33.5% 74.8% 110.87 24.76% 1
2018 482,115 132,366 40,427 83.85 1119 2.321 41,546 6348 47,894 31.4% 86.7% 99.34 27.46% 0
2019 490,690 97,434 30,015 61.17 141 0.287 30,156 0 30,156 31.0% 100.0% 61.46 19.86% 0
Kunduz               
2012 935,600 11,155 123 0.13 0 0.000 123 5028 5151 1.1% 2.4% 5.51 1.19% 0
2013 972,200 9260 53 0.05 0 0.000 53 3480 3533 0.6% 1.5% 3.63 0.95% 0
2014 990,937 9437 46 0.05 6 0.006 52 1001 1053 0.6% 4.9% 1.06 0.95% 1
2015 1,010,037 6895 21 0.02 0 0.000 21 494 515 0.3% 4.1% 0.51 0.68% 0
2016 961,309 7123 54 0.06 4 0.004 58 344 402 0.8% 14.4% 0.42 0.74% 0
2017 1,049,249 8284 123 0.12 6 0.006 129 377 506 1.6% 25.5% 0.48 0.79% 0
2018 1,091,116 11,793 120 0.11 0 0.000 120 29 149 1.0% 80.5% 0.14 1.08% 0
2019 1,113,676 8881 93 0.08 0 0.000 93 0 93 1.0% 100.0% 0.08 0.80% 0
Laghman               
2012 417,200 37,619 4129 9.90 70 0.168 4199 30,394 34,593 11.2% 12.1% 82.92 9.02% 0
2013 431,200 37,810 2142 4.97 49 0.114 2191 24,334 26,525 5.8% 8.3% 61.51 8.77% 0
2014 438,346 50,427 8171 18.64 800 1.825 8971 23,292 32,263 17.8% 27.8% 73.60 11.50% 2
2015 445,588 78,359 17,784 39.91 902 2.024 18,686 37,723 56,409 23.8% 33.1% 126.59 17.59% 0
2016 445,238 169,476 64,194 144.18 3240 7.277 67,434 34,473 101,907 39.8% 66.2% 228.88 38.06% 1
2017 460,352 130,626 45,363 98.54 2789 6.058 48,152 21,184 69,336 36.9% 69.4% 150.62 28.38% 0
2018 476,537 167,947 53,724 112.74 3923 8.232 57,647 11,940 69,587 34.3% 82.8% 146.03 35.24% 0
2019 484,952 159,817 38,792 79.99 1173 2.419 39,965 177 40,142 25.0% 99.6% 82.78 32.96% 0
Logar               
2012 367,000 3008 285 0.78 6 0.016 291 1638 1929 9.7% 15.1% 5.26 0.82% 0
2013 379,400 2701 157 0.41 18 0.047 175 1213 1388 6.5% 12.6% 3.66 0.71% 0
2014 385,638 5128 851 2.21 27 0.070 878 1056 1934 17.1% 45.4% 5.02 1.33% 0
2015 392,045 4528 542 1.38 35 0.089 577 1896 2473 12.7% 23.3% 6.31 1.15% 0
2016 398,535 8510 1242 3.12 62 0.156 1304 1435 2739 15.3% 47.6% 6.87 2.14% 0
2017 405,109 11,846 1822 4.50 19 0.047 1841 580 2421 15.5% 76.0% 5.98 2.92% 0
2018 419,377 13,952 1627 3.88 26 0.062 1653 108 1761 11.8% 93.9% 4.20 3.33% 0
2019 426,821 12,110 694 1.63 6 0.014 700 0 700 5.8% 100.0% 1.64 2.84% 0
Nangarhar               
2012 1,409,600 244,604 29,108 20.65 517 0.367 29,625 116,035 145,660 12.1% 20.3% 103.33 17.35% 30
2013 1,462,600 236,080 25,217 17.24 1441 0.985 26,658 80,157 106,815 11.3% 25.0% 73.03 16.14% 18
2014 1,489,787 279,057 41,554 27.89 2542 1.706 44,096 63,032 107,128 15.8% 41.2% 71.91 18.73% 24
2015 1,517,388 274,610 53,087 34.99 2582 1.702 55,669 97,207 152,876 20.3% 36.4% 100.75 18.10% 45
2016 1,545,448 315,613 67,114 43.43 3187 2.062 70,301 64,644 134,945 22.3% 52.1% 87.32 20.42% 35
2017 1,573,973 416,333 92,948 59.05 3951 2.510 96,899 25,208 122,107 23.3% 79.4% 77.58 26.45% 7
2018 1,635,872 495,795 105,650 64.58 2405 1.470 108,055 12,731 120,786 21.8% 89.5% 73.84 30.31% 0
2019 1,668,481 423,073 74,825 44.85 1093 0.655 75,918 8 75,926 17.9% 100.0% 45.51 25.36% 0
Nimroz               
2012 147,700 83 1 0.01 0 0.000 1 382 383 1.2% 0.3% 2.59 0.06% 0
2013 152,800 82 2 0.01 0 0.000 2 307 309 2.4% 0.6% 2.02 0.05% 0
2014 162,135 81 1 0.01 0 0.000 1 241 242 1.2% 0.4% 1.49 0.05% 0
2015 164,978 197 1 0.01 5 0.030 6 246 252 3.0% 2.4% 1.53 0.12% 0
2016 161,033 770 5 0.03 8 0.050 13 105 118 1.7% 11.0% 0.73 0.48% 0
2017 170,790 1048 6 0.04 0 0.000 6 94 100 0.6% 6.0% 0.59 0.61% 0
2018 176,898 1054 4 0.02 0 0.000 4 68 72 0.4% 5.6% 0.41 0.60% 0
2019 180,200 1664 9 0.05 0 0.000 9 0 9 0.5% 100.0% 0.05 0.92% 0
Nuristan               
2012 138,600 5109 423 3.05 23 0.166 446 3220 3666 8.7% 12.2% 26.45 3.69% 0
2013 143,200 4797 355 2.48 26 0.182 381 3412 3793 7.9% 10.0% 26.49 3.35% 0
2014 145,574 6290 647 4.44 19 0.131 666 3040 3706 10.6% 18.0% 25.46 4.32% 1
2015 147,967 10,134 1725 11.66 43 0.291 1768 4332 6100 17.4% 29.0% 41.23 6.85% 0
2016 150,391 14,529 3732 24.82 134 0.891 3866 2714 6580 26.6% 58.8% 43.75 9.66% 0
2017 152,845 22,795 7033 46.01 247 1.616 7280 4891 12,171 31.9% 59.8% 79.63 14.91% 0
2018 158,211 31,952 11,687 73.87 432 2.731 12,119 5100 17,219 37.9% 70.4% 108.84 20.20% 0
2019 160,993 30,266 10,343 64.25 108 0.671 10,451 0 10,451 34.5% 100.0% 64.92 18.80% 0
Paktika               
2012 407,100 10,362 1025 2.52 69 0.169 1094 12,980 14,074 10.6% 7.8% 34.57 2.55% 2
2013 420,700 16,120 1351 3.21 59 0.140 1410 14,105 15,515 8.7% 9.1% 36.88 3.83% 2
2014 427,692 22,779 2251 5.26 131 0.306 2382 18,568 20,950 10.5% 11.4% 48.98 5.33% 0
2015 434,742 24,769 2040 4.69 177 0.407 2217 15,052 17,269 9.0% 12.8% 39.72 5.70% 0
2016 441,883 27,688 2046 4.63 261 0.591 2307 5690 7997 8.3% 28.8% 18.10 6.27% 1
2017 449,116 37,823 5471 12.18 651 1.450 6122 6379 12,501 16.2% 49.0% 27.83 8.42% 0
2018 748,910 39,002 5621 7.51 550 0.734 6171 3469 9640 15.8% 64.0% 12.87 5.21% 0
2019 762,108 33,888 3063 4.02 164 0.215 3227 3 3230 9.5% 99.9% 4.24 4.45% 0
Paktya               
2012 516,300 13,839 1060 2.05 23 0.045 1083 7823 8906 7.8% 12.2% 17.25 2.68% 0
2013 525,500 10,437 916 1.74 53 0.101 969 5551 6520 9.3% 14.9% 12.41 1.99% 0
2014 542,896 14,974 1405 2.59 110 0.203 1515 3751 5266 10.1% 28.8% 9.70 2.76% 0
2015 551,987 13,276 1380 2.50 38 0.069 1418 3022 4440 10.7% 31.9% 8.04 2.41% 0
2016 532,780 13,108 1288 2.42 93 0.175 1381 1131 2512 10.5% 55.0% 4.71 2.46% 0
2017 570,534 20,717 1678 2.94 117 0.205 1795 1112 2907 8.7% 61.7% 5.10 3.63% 0
2018 590,668 17,274 897 1.52 25 0.042 922 506 1428 5.3% 64.6% 2.42 2.92% 0
2019 601,230 12,690 476 0.79 14 0.023 490 0 490 3.9% 100.0% 0.81 2.11% 0
Panjsher               
2012 143,700 4242 65 0.45 0 0.000 65 349 414 1.5% 15.7% 2.88 2.95% 0
2013 137,700 3895 20 0.15 0 0.000 20 239 259 0.5% 7.7% 1.88 2.83% 0
2014 151,004 2565 21 0.14 0 0.000 21 110 131 0.8% 16.0% 0.87 1.70% 0
2015 153,487 2106 18 0.12 0 0.000 18 149 167 0.9% 10.8% 1.09 1.37% 0
2016 144,535 1227 10 0.07 3 0.021 13 68 81 1.1% 16.0% 0.56 0.85% 0
2017 158,548 1290 47 0.30 1 0.006 48 46 94 3.7% 51.1% 0.59 0.81% 0
2018 164,115 1273 72 0.44 18 0.110 90 15 105 7.1% 85.7% 0.64 0.78% 0
2019 167,000 928 72 0.43 2 0.012 74 0 74 8.0% 100.0% 0.44 0.56% 0
Parwan               
2012 620,900 3276 12 0.02 8 0.013 20 1316 1336 0.6% 1.5% 2.15 0.53% 1
2013 642,300 2803 36 0.06 0 0.000 36 497 533 1.3% 6.8% 0.83 0.44% 0
2014 653,362 2518 33 0.05 0 0.000 33 519 552 1.3% 6.0% 0.84 0.39% 0
2015 664,502 2986 54 0.08 0 0.000 54 510 564 1.8% 9.6% 0.85 0.45% 0
2016 675,795 2516 132 0.20 1 0.001 133 588 721 5.3% 18.4% 1.07 0.37% 0
2017 687,243 2883 440 0.64 20 0.029 460 304 764 16.0% 60.2% 1.11 0.42% 0
2018 711,621 3705 597 0.84 1 0.001 598 35 633 16.1% 94.5% 0.89 0.52% 0
2019 724,561 2407 191 0.26 2 0.003 193 0 193 8.0% 100.0% 0.27 0.33% 0
Samangan               
2012 362,500 299 22 0.06 2 0.006 24 540 564 8.0% 4.3% 1.56 0.08% 0
2013 335,700 242 10 0.03 0 0.000 10 424 434 4.1% 2.3% 1.29 0.07% 0
2014 381,459 1697 27 0.07 0 0.000 27 835 862 1.6% 3.1% 2.26 0.44% 0
2015 387,928 2049 4 0.01 0 0.000 4 681 685 0.2% 0.6% 1.77 0.53% 0
2016 394,487 1195 14 0.04 26 0.066 40 885 925 3.3% 4.3% 2.34 0.30% 0
2017 401,134 2066 9 0.02 0 0.000 9 108 117 0.4% 7.7% 0.29 0.52% 0
2018 415,343 1058 2 0.00 1 0.002 3 184 187 0.3% 1.6% 0.45 0.25% 0
2019 422,859 883 4 0.01 0 0.000 4 0 4 0.5% 100.0% 0.01 0.21% 0
Sar-e-Pul               
2012 522,900 1341 58 0.11 1 0.002 59 4056 4115 4.4% 1.4% 7.87 0.26% 0
2013 451,000 1591 92 0.20 3 0.007 95 2875 2970 6.0% 3.2% 6.59 0.35% 0
2014 550,238 1442 49 0.09 6 0.011 55 1922 1977 3.8% 2.8% 3.59 0.26% 0
2015 559,577 6391 105 0.19 13 0.023 118 1367 1485 1.8% 7.9% 2.65 1.14% 0
2016 569,043 1708 8 0.01 5 0.009 13 919 932 0.8% 1.4% 1.64 0.30% 0
2017 578,639 3392 8 0.01 1 0.002 9 288 297 0.3% 3.0% 0.51 0.59% 0
2018 599,137 2027 4 0.01 2 0.003 6 143 149 0.3% 4.0% 0.25 0.34% 0
2019 609,986 4546 6 0.01 0 0.000 6 0 6 0.1% 100.0% 0.01 0.75% 0
Takhar               
2012 917,700 9009 140 0.15 1 0.001 141 16,297 16,438 1.6% 0.9% 17.91 0.98% 0
2013 950,100 8109 178 0.19 4 0.004 182 11,950 12,132 2.2% 1.5% 12.77 0.85% 0
2014 966,576 22,810 592 0.61 37 0.038 629 4789 5418 2.8% 11.6% 5.61 2.36% 0
2015 983,336 19,671 394 0.40 36 0.037 430 3011 3441 2.2% 12.5% 3.50 2.00% 0
2016 1,000,336 12,812 482 0.48 25 0.025 507 3400 3907 4.0% 13.0% 3.91 1.28% 0
2017 1,017,575 21,014 562 0.55 11 0.011 573 316 889 2.7% 64.5% 0.87 2.07% 0
2018 1,053,852 24,713 410 0.39 2 0.002 412 17 429 1.7% 96.0% 0.41 2.35% 0
2019 1,073,319 21,326 471 0.44 1 0.001 472 0 472 2.2% 100.0% 0.44 1.99% 0
Uruzgan               
2012 328,000 4564 110 0.34 10 0.030 120 2109 2229 2.6% 5.4% 6.80 1.39% 0
2013 339,200 4534 162 0.48 34 0.100 196 3746 3942 4.3% 5.0% 11.62 1.34% 0
2014 380,469 3170 83 0.22 6 0.016 89 3115 3204 2.8% 2.8% 8.42 0.83% 0
2015 386,818 4394 95 0.25 15 0.039 110 2414 2524 2.5% 4.4% 6.53 1.14% 0
2016 343,069 4440 98 0.29 5 0.015 103 832 935 2.3% 11.0% 2.73 1.29% 0
2017 362,253 5275 53 0.15 5 0.014 58 889 947 1.1% 6.1% 2.61 1.46% 0
2018 361,030 7134 111 0.31 22 0.061 133 966 1099 1.9% 12.1% 3.04 1.98% 0
2019 428,466 7264 69 0.16 9 0.021 78 0 78 1.1% 100.0% 0.18 1.70% 0
Wardak               
2012 558,400 3907 232 0.42 4 0.007 236 979 1215 6.0% 19.4% 2.18 0.70% 0
2013 577,100 3660 160 0.28 4 0.007 164 543 707 4.5% 23.2% 1.23 0.63% 0
2014 586,623 3788 329 0.56 13 0.022 342 753 1095 9.0% 31.2% 1.87 0.65% 0
2015 596,287 5311 449 0.75 17 0.029 466 873 1339 8.8% 34.8% 2.25 0.89% 0
2016 606,077 5052 536 0.88 12 0.020 548 364 912 10.8% 60.1% 1.50 0.83% 0
2017 615,992 6294 589 0.96 31 0.050 620 252 872 9.9% 71.1% 1.42 1.02% 0
2018 637,634 4622 776 1.22 14 0.022 790 31 821 17.1% 96.2% 1.29 0.72% 0
2019 648,866 4035 359 0.55 20 0.031 379 8 387 9.4% 97.9% 0.60 0.62% 0
Zabul               
2012 284,600 13,511 2133 7.49 57 0.200 2190 6460 8650 16.2% 25.3% 30.39 4.75% 0
2013 294,100 12,447 1677 5.70 25 0.085 1702 6241 7943 13.7% 21.4% 27.01 4.23% 0
2014 299,125 21,899 554 1.85 46 0.154 600 4117 4717 2.7% 12.7% 15.77 7.32% 0
2015 304,126 15,338 321 1.06 3 0.010 324 2894 3218 2.1% 10.1% 10.58 5.04% 0
2016 309,192 25,039 530 1.71 43 0.139 573 1133 1706 2.3% 33.6% 5.52 8.10% 0
2017 314,325 15,440 191 0.61 14 0.045 205 1042 1247 1.3% 16.4% 3.97 4.91% 0
2018 371,043 15,776 278 0.75 6 0.016 284 556 840 1.8% 33.8% 2.26 4.25% 0
2019 377,648 10,405 697 1.85 40 0.106 737 2 739 7.1% 99.7% 1.96 2.76% 0

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Mahmoodi, S.D., Atarud, A.A., Sediqi, A.W. et al. Trends in malaria indicators after scale-up of community-based malaria management in Afghanistan. Malar J 21, 165 (2022). https://doi.org/10.1186/s12936-022-04174-x

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Keywords

  • Malaria
  • Community-based
  • Afghanistan
  • Diagnostic tests