Thus viewed, the security hypothesis passes all tests in relation to car theft. Its main limitation is a lack of evidence relating to other crime types, though it is conceivable that different security measures impacted various types of crime at different times. It is also conceivable that, since many crimes are inter-linked, that a version of the keystone crime hypothesis (Farrell et al. 20082011a) occurred in some instances. Car theft plays a key role in facilitating many other types of crime and so its removal, like that of the keystone in an archway, causes those around it to tumble. In addition, the security hypothesis does not contradict other empirical evidence relating to the nature of the declines in crime such as the fact that falls in repeat victimization and crime at hot spots play important roles (Weisburd et al. 2004Thorpe 2007; Britton et al. 2012). The steeper declines in more concentrated crime that these studies found is consistent with crime falling more in places (such as New York City) where it was previously at a higher rate.
You can create a hot spot in which clickingor mousing over an object displays another object. To do this, createtwo buttons, hide one of the buttons, and use the Show/Hide Buttonaction to show and hide the target button.
8. After creating a hotspot question, you can customize its options in the menu on the right. You can adjust the question type, decide if your learners will get feedback, and how scoring will work. You can also specify the number of attempts your learners can take to answer correctly, add a time limit to each question, and select whether or not you will accept partially correct answers.
The Human Immunodeficiency Virus(HIV) infection prevalence in Cameroon has decreased from \(5.28\%\) in 2004 to \(2.8\%\) in 2018. However, this decrease in prevalence does not show disparities especially in terms of spatial or geographical pattern. Efficient control and fight against HIV infection may require targeting hotspot areas. This study aims at presenting a cartography of HIV infection situation in Cameroon using the 2004, 2011 and 2018 Demographic and Health Survey data, and investigating whether there exist spatial patterns of the disease, may help to detect hot-spots.
Despite the decrease in HIV epidemiology in Cameroon, this study has shown that there are spatial patterns for HIV infection in Cameroon and possible hot-spots have been identified. In its effort to eliminate HIV infection by 2030 in Cameroon, the public health policies may consider these detected HIV hot-spots, while maintaining effective control in other parts of the country.
Cameroon had also experienced a bright situation in terms of declination of HIV infection. In fact, from 2011 to 2018, the prevalence of HIV infection in Cameroon declined from \(5.55\%\) to \(2.8\%\) [4, 5]. However, there were persistent disparities in HIV infection in Cameroon in terms of age, place of residence, region and gender. Prevalence was higher in the female population (\(3.5\%\), versus \(1.9\%\) in male population), urban areas (\(2.9\%\), versus \(2.4\%\) in rural), and in adults aged 35-39 years. In terms of regional disparities, it was found that HIV infection was more prevalent in the South (\(5.8\%\)), East (\(5.6\%\)), Adamawa (\(4.1\%\)), North-West (\(4.0\%\)), and Center (\(3.5\%\)) regions. These regions were defined in the 2018-2022 National Strategic Plan for HIV/AIDS and STIs as priority areas of intervention. Moreover, these regions were to be targeted more in the fighting against HIV infection in Cameroon . However, regions are the first level of geographical and administrative division in Cameroon. They are generally very large with heterogeneous populations. For efficient interventions for HIV elimination, it would therefore be relevant to target hot-spots. These are accurate spots areas where the infection gets to spread the most. This study mainly aimed to identify the HIV infection hot-spots clusters in Cameroun for the periods, 2004, 2011, and 2018. Identifying hot-spots for diseases is important for public health authorities who should adopt them for better-targeted interventions. This has been done in other settings such as in Mongolia , Ethiopia , Brazil , Shanghai , Malawi , Nigeria  and India [13, 14].
After an approved request from the DHS program, the GPS and HIV biomarkers data were downloaded from their website ( ). For the periods of 1991 and 1998, GPS and HIV biomarkers data were not collected. Therefore, in this study, only the periods of 2004, 2011 and 2018 were considered for the spatial analysis of HIV infection in Cameroon. Blood samples were screened and double-checked for positive cases by the Pasteur Center of Cameroon. Then, for quality assessment, screened samples were re-screened externally by the Chantal Biya International Reference Center. A concordance of 98.96% was found between the outcomes of both centers. The primary endpoint of this study was confirmed HIV positive cases in the 15-64 age group. Table 1 shows the HIV prevalence for the respective periods.
Moran I test : Introduced by , the Moran I test is the global test most commonly used for assessing spatial autocorrelation. The null hypothesis is that the spatial distribution of the studied phenomenom is random, versus the alternative hypothesis in which the studied phenomenom is not random (there is a spatial autocorrelation). Moran I test is based on a neighborhood matrix that specifies the link between spatial units. In this study, the k nearest neighbor matrix, which for each spatial unit determines the k-nearest neighbors based on the distance between them (the default number of neighbors adopted during the analysis was 8). A significant Moran I test indicates that there is a presence of spatial autocorrelation, but could not identify the hot or cold spots areas. The Arcgis Pro software was further used to present a cartography of HIV infection in Cameroon for the respective periods of 2004, 2011, and 2018.
Hot-spots identification : The determination of hot-spot clusters was based on Getis-Ord statistics introduced by . The \(z-score\) of the prevalence of each feature were estimated, the idea was to compare each \(z-score\) to all others. To be a statistically significant hot spot, a feature will have a high value and be surrounded by other features with high values as well. This method is implemented in the Arcgis Pro software through the Spatial Statistics Tools. In this work, the optimzed hot-spot analysis was used, as it automatically aggregates prevalence data, identifies an appropriate scale of analysis, and corrects for both multiple testing and spatial dependence.
Table 2 displays the subdivisions of HIV infection hot-spots in Cameroon for the periods 2004, 2011 and 2018 respectively, while, Figs. 3, 4, and 5 display the maps of hot-spots of HIV infection in Cameroon for the respective periods.
This study has provided a spatial analysis of HIV infection situation in Cameroon for the periods of 2004, 2011 and 2018, while determining the hot-spots. These analyses go beyond the regional analysis which is commonly adopted by public health policies in Cameroon. In fact, in the recent document of the National Strategic Plan for HIV/AIDS, 6 regions; Adamaoua, East, North-West, Center, South-West and Littoral were identified as priority areas of intervention . A study by  revealed that HIV risk among pregnant women in Cameroon was higher in the East, North-West, and South-West regions. However, the exact localities or subdivisions in these regions were not identified. In such a way, the interventions on a large scale such as regions may be less efficient. The spatial analysis in this study aims at predicting hot-spots of HIV infection for the periods of 2004, 2011 and 2018, with the included localities. Based on the predicted hot-spots, the current regions which needed to be prioritize should be : Adamaoua, East, Center, South and North-West. We remark that the predicted hot-spots overlapp with the targeted regions in the strategic document for the fight against AIDS in Cameroon, our study identified the subdivisions with the hot-spots of HIV infection in Cameroon.
It has been found that the subdivisions: Nyong et Mfoumou, Haute Sanaga, Lom et Djerem are in the mid-way of the corridor Douala-Bangui. This stretch is among the longest and the most densely populated in the country which goes up to the capital city of Central Africa Republic (CAR). Cities and towns within these subdivisions have truck stops and rest-spots for truck drivers. Therefore these subdivisions on the corridor are hot-spots clusters of HIV infection as found in other settings [8, 11, 12]. In fact, truck drivers generally have high sexual risk behaviors, they practice unprotected sexual intercourse with multiple partners living in rest-spots cities [20,21,22,23]. Another characteristic of some hot-spots was that most of them were found on the cross-borders of Cameroon with CAR (Mbere, Lom et Djerem, Kadey, Boumba et Ngoko) and Congo (Haut-Nyong, Océan, Dja et Lobo and Mvila) respectively. Some studies revealed that border areas are generally subjected to a high rate of unsafe sex practice and a low level of HIV-related knowledge, attitudes, and practices [24,25,26]. This may explain the high risk of HIV infection in these subdivisions on the borders where there is high human mobility in and out. Other hot-spot subdivisions, may particularly experience sexual risk behaviors especially in terms of condom non-use. That may be the case in the Sanaga-Maritime subdivision where a study on adolescents in Edéa (Capital city of Sanaga-Maritime) found that adolescents both males and females had poor condom use perception .
One finds that scientific works on contextual and sociocultural factors of HIV infection in Cameroon are limited. Particularly, studies at the subdivisions levels are almost inexistent. The investigation of HIV infection with the associated contextual factors at the subdivision level could be relevant for the characterization of identified hot-spots of HIV infection in Cameroon. This could constitute a topic for further studies. 2b1af7f3a8