| Literature DB >> 30235205 |
Derick Nii Mensah Osakunor1, David Moinina Sengeh2, Francisca Mutapi1,3.
Abstract
There is a disease epidemiological transition occurring in Africa, with increasing incidence of noninfectious diseases, superimposed on a health system historically geared more toward the management of communicable diseases. The persistence and sometimes emergence of new pathogens allows for the occurrence of coinfections and comorbidities due to both infectious and noninfectious diseases. There is therefore a need to rethink and restructure African health systems to successfully address this transition. The historical focus of more health resources on infectious diseases requires revision. We hypothesise that the growing burden of noninfectious diseases may be linked directly and indirectly to or further exacerbated by the existence of neglected tropical diseases (NTDs) and other infectious diseases within the population. Herein, we discuss the health burden of coinfections and comorbidities and the challenges to implementing effective and sustainable healthcare in Africa. We also discuss how existing NTD and infectious disease intervention programs in Africa can be leveraged for noninfectious disease intervention. Furthermore, we explore the potential for new technologies-including artificial intelligence and multiplex approaches-for diagnosis and management of chronic diseases for improved health provision in Africa.Entities:
Mesh:
Year: 2018 PMID: 30235205 PMCID: PMC6147336 DOI: 10.1371/journal.pntd.0006711
Source DB: PubMed Journal: PLoS Negl Trop Dis ISSN: 1935-2727
Fig 1Adapted maps of Africa showing the overlap of neglected tropical diseases (NTDs), infectious, and noninfectious diseases.
The figure shows (A) pathogeographic patterns of 187 global human infectious diseases [3], (B) patterns of the six most common neglected tropical diseases [4], (C) burden of the most frequently diagnosed cancer among males [10], and (D) probability of dying from the four main noninfectious diseases between the ages of 30 and 70 years [11]. Infectious diseases show distinct spatial patterns (A), which overlap with the most common neglected tropical diseases (B), commonly diagnosed cancers (C), and the mortality rates from major noninfectious diseases including cardiovascular diseases, cancer, chronic respiratory diseases, and diabetes (D).
Fig 2Host infectome analysis based on IgM reactivity to multiple infections in a Zimbabwean cohort.
Results indicate variable responses to infections across all age groups. IgM, immunoglobulin (Ig) M.
Summary of publications on health impacts of coinfections in Africa within the last 10 years.
| Year | Source | Disease dynamics | Health impacts |
|---|---|---|---|
| 2007 | Hoffmann and Thio 2007 [ | Hepatitis B virus–HIV | Liver enzyme alterations, reducing antiretroviral tolerance and increasing its toxic effects. Blunt immune recovery from antiretroviral therapy. |
| 2009 | Hadley and Naude 2009 [ | HIV–Tuberculosis–Malignant tumours | Increased mortality. |
| Degarege, Animut et al., 2009 [ | Malaria–Soil-transmitted helminths | Impact on malaria severity, although small. | |
| 2010 | lsa, Gwamzhi et al., 2010 [ | Hepatitis B and C viruses–HIV/AIDS | Impact on causing hepatotoxicity. |
| Sangweme, Midzi et al., 2010 [ | Schistosomiasis–Malaria | Higher peripheral blood malaria parasite density, promoting transmission. | |
| Modjarrad and Vermund 2010 [ | HIV–Tuberculosis–Syphilis | Tuberculosis and syphilis may increase HIV viral load, increasing disease progression. | |
| 2012 | Ntusi, Badri et al., 2012 [ | Increased mortality. | |
| Faurholt-Jepsen, Range et al., 2012 [ | Tuberculosis–Diabetes | Poor treatment outcomes including delayed recovery of body mass and haemoglobin levels, hence poor recovery from disease. | |
| Webb, Barrett et al., 2012 [ | Chronic myeloid leukaemia–HIV | Poor cytogenic response to leukaemia treatment. | |
| van den Bogaart, Berkhout et al., 2012 [ | Visceral leishmaniasis–Malaria | Early detection results in good prognosis, but patients stand a high risk of severe symptoms of leishmaniasis. | |
| 2013 | Ladep, Agbaji et al., 2013 [ | Hepatitis B virus–HIV | Reduced survival. With the appropriate treatment Tenofovir, this impact may be annulled |
| Taye, Alemayehu et al., 2013 [ | Podoconiosis–Soil-transmitted helminths | Increased blood losses/anaemia. | |
| 2014 | Baldassarre, Mdodo et al., 2014 [ | HIV/AIDS–Cryptococcal meningitis | Increased mortality. |
| Knight, Muloiwa et al., 2014 [ | HIV–Stevens Johnson syndrome–Toxic epidermal necrolysis | Increased risk of systemic bacterial infection and mortality. | |
| Biraro, Egesa et al., 2014 [ | Helminths, malaria, or HIV coinfection in household contacts of Tuberculosis patients | No evidence of increased risk to latent Tuberculosis. Th1 cytokine responses in those with prior BCG vaccination was reduced. | |
| Degarege, Animut et al., 2014 [ | Malaria–Helminths | Undernutrition; severity is comparable to those with single infections. | |
| 2015 | Umanah, Ncayiyana et al., 2015 [ | HIV–Tuberculosis | Treatment failures and increased mortality. |
| 2017 | Morawski, Yunus et al., 2017 [ | HIV–Hookworm | Decreased CD4+ T cell counts during antiretroviral therapy. |
Systematic review of literature (PubMed); electronic search terms were (a) [(Co-infection* OR Coinfection*) AND (Co-morbid* OR Comorbid*) AND (Africa) AND (Health impact*)] (b) [(Co-infection* OR Coinfection*) AND (Co-morbid* OR Comorbid*) AND (Africa) AND (Health impact*) AND Helminth*)]. Selection criteria: human studies, original articles, and studies that relate coinfection or comorbidity to a secondary health impact published in the last 10 years.
Abbreviations: AIDS, acquired immune deficiency syndrome; BCG, Bacillus Calmette–Guérin; CD4+, cluster of differentiation 4.
Fig 3Summary of infections and the types of cancers they cause, via direct or indirect links.
Each coloured line/alphabet represents a pathological pattern. Information adapted from aCrosbie, Einstein, and colleagues, 2013 [16]; bAhmadi Ghezeldasht, Shirdel, and colleagues, 2013 [55]; cMarra, Sordelli, and colleagues, 2011 [56]; dMostafa, Sheweita, and colleagues, 1999 [57]; eDittmer and Damania, 2016 [58]; fPolk and Peek, 2010 [59]; and gBower, Nelson, and colleagues, 2005 [17]. HHV8, human herpes virus 8; KSHV, Kaposi sarcoma-associated herpesvirus; HIV, human immunodeficiency virus.
Health systems in Africa: Structure and challenges.
| Region | Model country | System structure | Challenges | Source |
|---|---|---|---|---|
| Anglophone | Tanzania | Bottom–up approach. Village health services for remote areas at level 1. Level 2 consists of dispensary services for localities with larger populations. Level 3 offers services to even larger populations, up to 50,000 people. | Lack of access for the poor due to the copayment system, insurance requirements, and the insurgence of private physician practices. Absenteeism, low morale, inadequate qualified work force, lack of equipment and supplies. Centralisation at the high level of care. | [ |
| Kenya | Well organised and pyramidal, with dispensaries, health centres, subdistrict hospitals/private clinics, provincial and national hospitals. | Recurrent strikes by doctors, problems with financing health systems, high cost of health services, HIV/AIDS and malaria alone consumes the greatest part of resources. | [ | |
| Uganda | Village health teams and community medicine distributors at level 1. Higher up is the health centre II in parishes, health centre III in sub-country, health centre IV, the regional referral hospitals, and three national referral and teaching hospitals. | Village volunteers can be unreliable, lower levels are quick to refer cases. Inadequate infrastructure, inequity in health services, lack of sustenance, low remuneration for staff, paucity of specialised physicians, poor training, high rates of staff layoffs. Poor data collection and utilisation. | [ | |
| Francophone | Cote d’Ivoire | Follows the 1996 health system organisation with three-tier pyramidal structure. Level 1: health, urban medical, school and university health centres. Level 2: general, regional and specialised hospitals. Level 3: specialised health institutes. | Low level of qualified personnel (one doctor per 10,000). High cost of universal healthcare led to its abandonment, hence lots of out of pocket care. | [ |
| Senegal | Similar structure to that of Cote d’Ivoire. Pyramidal with three levels. Central level: Ministry of Health. Regional level: local health systems. Peripheral level: health districts. | Disparities in distribution of facilities across the country. Sustained by government budget and relies a lot on donor support. Inadequate workforce, inadequate training, poor infrastructure and communication machinery. Social and religious barriers with disparities in quality of care. | [ | |
| Lusophone | Angola | Has three levels. Primary level: referral health centres or district hospitals, health posts. Secondary care: specialised facilities and general hospitals. Tertiary care: specialised health facilities and central hospitals | Lack of proper remunerations, inadequate allocation of resources by leadership, lack of decentralisation, persistent shortage of essential drugs, lack of data collection and availability. | [ |
| Mozambique | Has four levels. Primary level: health posts (the least equipped) and health centres. Secondary level: rural hospitals and urban hospitals. Tertiary level: five general and seven provincial and district hospitals. Quaternary level: three central hospitals. | Shortage of qualified staff to brain drain, and the system has some of the lowest salaries in Africa. Over reliance on foreign donor support makes it unsustainable. Poor infrastructure and absence of diagnostic tools. Inequitable distribution of health facilities. | [ | |
| Hispanophone | Equatorial Guinea | Similar structure to that of other countries with a national Ministry of health, Tertiary, Secondary, and Primary healthcare facilities. | Poor leadership and governance, low health financing (93.5% of health cost is out of pocket). Poor service delivery, lack of skilled physicians, and poor management of medical resources. Lack of available health data countrywide. | [ |