Delays in seeking and receiving health care services for pneumonia in children under five in the Peruvian Amazon: a mixed-methods study on caregivers’ perceptions

Abstract Background Delays in receiving adequate care for children suffering from pneumonia can be life threatening and have been described associated with parents’ limited education and their difficulties in recognizing the severity of the illness. The “three delays” was a model originally proposed to describe the most common determinants of maternal mortality, but has been adapted to describe delays in the health seeking process for caregivers of children under five. This study aims to explore the caregivers’ perceived barriers for seeking and receiving health care services in children under five years old admitted to a referral hospital for community-acquired pneumonia in the Peruvian Amazon Region using the three-delays model framework. Methods There were two parts to this mixed-method, cross-sectional, hospital-based study. First, medical charts of 61 children (1 to 60 months old) admitted for pneumonia were reviewed, and clinical characteristics were noted. Second, to examine health care-seeking decisions and actions, as well as associated delays in the process of obtaining health care services, we interviewed 10 of the children’s caregivers. Results Half of the children in our study were 9 months old or less. Main reasons for seeking care at the hospital were cough (93%) and fever (92%). Difficulty breathing and fast breathing were also reported in more than 60% of cases. In the interviews, caregivers reported delays of 1 to 14 days to go to the closest health facility. Factors perceived as causes for delays in deciding to seek care were apparent lack of skills to recognize signs and symptoms and of confidence in the health system, and practicing self-medication. No delays in reaching a health facility were reported. Once the caregivers reached a health facility, they perceived lack of competence of medical staff and inadequate treatment provided by the primary care physicians. Conclusion According to caregivers, the main delays to get health care services for pneumonia among young children were identified in the initial decision of caregivers to seek healthcare and in the health system to provide it. Specific interventions targeted to main barriers may be useful for reducing delays in providing appropriate health care for children with pneumonia

Genetic variability of <i>Taenia solium</i> cysticerci recovered from experimentally infected pigs and from naturally infected pigs using microsatellite markers

<div><p>The adult <i>Taenia solium</i>, the pork tapeworm, usually lives as a single worm in the small intestine of humans, its only known definitive host. Mechanisms of genetic variation in <i>T</i>. <i>solium</i> are poorly understood. Using three microsatellite markers previously reported [<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0006087#pntd.0006087.ref001" target="_blank">1</a>], this study explored the genetic variability of <i>T</i>. <i>solium</i> from cysts recovered from experimentally infected pigs. It then explored the genetic epidemiology and transmission in naturally infected pigs and adult tapeworms recovered from human carriers from an endemic rural community in Peru. In an initial study on experimental infection, two groups of three piglets were each infected with proglottids from one of two genetically different tapeworms for each of the microsatellites. After 7 weeks, pigs were slaughtered and necropsy performed. Thirty-six (92.3%) out of 39 cysts originated from one tapeworm, and 27 (100%) out of 27 cysts from the other had exactly the same genotype as the parental tapeworm. This suggests that the microsatellite markers may be a useful tool for studying the transmission of <i>T</i>. <i>solium</i>. In the second study, we analyzed the genetic variation of <i>T</i>. <i>solium</i> in cysts recovered from eight naturally infected pigs, and from adult tapeworms recovered from four human carriers; they showed genetic variability. Four pigs had cysts with only one genotype, and four pigs had cysts with two different genotypes, suggesting that multiple infections of genetically distinct parental tapeworms are possible. Six pigs harbored cysts with a genotype corresponding to one of the identified tapeworms from the human carriers. In the dendrogram, cysts appeared to cluster within the corresponding pigs as well as with the geographical origin, but this association was not statistically significant. We conclude that genotyping of microsatellite size polymorphisms is a potentially important tool to trace the spread of infection and pinpoint sources of infection as pigs spread cysts with a shared parental genotype.</p></div

Genotypes of cysts excised from naturally infected pigs based on sequencing.

<p>Genotypes of cysts excised from naturally infected pigs based on sequencing.</p

Map of the community showing the location of tapeworm carriers and cysticercosis-positive pigs.

<p>This figure was created using ArcGIS and <a href="http://escale.minedu.gob.pe/descargas/mapa.aspx" target="_blank">http://escale.minedu.gob.pe/descargas/mapa.aspx</a> was used also for base layer. Colors represent the genotype of tapeworms and cysts in pigs.</p

Genotype of cysts from experimental infection based on sequencing.

<p>Genotype of cysts from experimental infection based on sequencing.</p

Genotypes of cysts excised from naturally infected pigs based on sequencing.

<p>Genotypes of cysts excised from naturally infected pigs based on sequencing.</p

Characteristics of pigs with cysticercosis used in this study.

<p>Characteristics of pigs with cysticercosis used in this study.</p

Genotype of tapeworms found in Pampa Elera community.

<p>Genotype of tapeworms found in Pampa Elera community.</p

Automatic classification of pediatric pneumonia based on lung ultrasound pattern recognition.

Pneumonia is one of the major causes of child mortality, yet with a timely diagnosis, it is usually curable with antibiotic therapy. In many developing regions, diagnosing pneumonia remains a challenge, due to shortages of medical resources. Lung ultrasound has proved to be a useful tool to detect lung consolidation as evidence of pneumonia. However, diagnosis of pneumonia by ultrasound has limitations: it is operator-dependent, and it needs to be carried out and interpreted by trained personnel. Pattern recognition and image analysis is a potential tool to enable automatic diagnosis of pneumonia consolidation without requiring an expert analyst. This paper presents a method for automatic classification of pneumonia using ultrasound imaging of the lungs and pattern recognition. The approach presented here is based on the analysis of brightness distribution patterns present in rectangular segments (here called "characteristic vectors") from the ultrasound digital images. In a first step we identified and eliminated the skin and subcutaneous tissue (fat and muscle) in lung ultrasound frames, and the "characteristic vectors"were analyzed using standard neural networks using artificial intelligence methods. We analyzed 60 lung ultrasound frames corresponding to 21 children under age 5 years (15 children with confirmed pneumonia by clinical examination and X-rays, and 6 children with no pulmonary disease) from a hospital based population in Lima, Peru. Lung ultrasound images were obtained using an Ultrasonix ultrasound device. A total of 1450 positive (pneumonia) and 1605 negative (normal lung) vectors were analyzed with standard neural networks, and used to create an algorithm to differentiate lung infiltrates from healthy lung. A neural network was trained using the algorithm and it was able to correctly identify pneumonia infiltrates, with 90.9% sensitivity and 100% specificity. This approach may be used to develop operator-independent computer algorithms for pneumonia diagnosis using ultrasound in young children

UPGMA dendrogram depicting Dc genetic distances between 96 cysts based on three polymorphic nuclear microsatellite loci.

<p>1–10 (Pig P4), 11–20 (Pig P5), 21–30 (Pig P6), 31–43 (Pig P2), 44–58 (Pig P1), 59–70 (Pig P3), 71–82 (Pig P7), 83–96 (Pig P8). Two main groups are shown.</p